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Publications of year 2022

Articles in journal or book chapters

  1. Aboveground forest biomass varies across continents, ecological zones and successional stages: Refined IPCC default values for tropical and subtropical forests. Environmental Research Letters, 17(1), 2022. 
    @Article{Rozendaal2022,
author = {Rozendaal, D.M.A. and Requena Suarez, D. and De Sy, V. and Avitabile, V. and Carter, S. and Adou Yao, C.Y. and Alvarez-Davila, E. and Anderson-Teixeira, K. and Araujo-Murakami, A. and Arroyo, L. and Barca, B. and Baker, T.R. and Birigazzi, L. and Bongers, F. and Branthomme, A. and Brienen, R.J.W. and Carreiras, J.M.B. and Cazzolla Gatti, R. and Cook-Patton, S.C. and Decuyper, M. and Devries, B. and Espejo, A.B. and Feldpausch, T.R. and Fox, J. and G P Gamarra, J. and Griscom, B.W. and Harris, N. and H\"arault, B. and Honorio Coronado, E.N. and Jonckheere, I. and Konan, E. and Leavitt, S.M. and Lewis, S.L. and Lindsell, J.A. and N'Dja, J.K. and N'Guessan, A.E. and Marimon, B. and Mitchard, E.T.A. and Monteagudo, A. and Morel, A. and Pekkarinen, A. and Phillips, O.L. and Poorter, L. and Qie, L. and Rutishauser, E. and Ryan, C.M. and Santoro, M. and Silayo, D.S. and Sist, P. and Slik, J.W.F. and Sonk\"a, B. and Sullivan, M.J.P. and Vaglio Laurin, G. and Vilanova, E. and Wang, M.M.H. and Zahabu, E. and Herold, M.},
journal = {Environmental Research Letters},
title = {Aboveground forest biomass varies across continents, ecological zones and successional stages: Refined IPCC default values for tropical and subtropical forests},
year = {2022},
number = {1},
volume = {17},
art_number = {014047},
doi = {10.1088/1748-9326/ac45b3},
owner = {ofrey},

}


  2. A. Araza, S. de Bruin, M. Herold, S. Quegan, N. Labriere, P. Rodriguez-Veiga, V. Avitabile, M. Santoro, E.T.A. Mitchard, C.M. Ryan, O.L. Phillips, S. Willcock, H. Verbeeck, J. Carreiras, L. Hein, M.-J. Schelhaas, A.M. Pacheco-Pascagaza, P. da Conceicao Bispo, G.V. Laurin, G. Vieilledent, F. Slik, A. Wijaya, S.L. Lewis, A. Morel, J. Liang, H. Sukhdeo, D. Schepaschenko, J. Cavlovic, H. Gilani, and R. Lucas. A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps. Remote Sensing of Environment, 272(112917), 2022. 
    @Article{Araza2022,
author = {Araza, A. and de Bruin, S. and Herold, M. and Quegan, S. and Labriere, N. and Rodriguez-Veiga, P. and Avitabile, V. and Santoro, M. and Mitchard, E.T.A. and Ryan, C.M. and Phillips, O.L. and Willcock, S. and Verbeeck, H. and Carreiras, J. and Hein, L. and Schelhaas, M.-J. and Pacheco-Pascagaza, A.M. and da Conceicao Bispo, P. and Laurin, G.V. and Vieilledent, G. and Slik, F. and Wijaya, A. and Lewis, S.L. and Morel, A. and Liang, J. and Sukhdeo, H. and Schepaschenko, D. and Cavlovic, J. and Gilani, H. and Lucas, R.},
journal = {Remote Sensing of Environment},
title = {A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps},
year = {2022},
number = {112917},
volume = {272},
doi = {10.1016/j.rse.2022.112917},
owner = {ofrey},

}


  3. A. Bertone, C. Barboux, X. Bodin, T. Bolch, F. Brardinoni, R. Caduff, H.H. Christiansen, M.M. Darrow, R. Delaloye, B. Etzelmüller, O. Humlum, C. Lambiel, K.S. Lilleoren, V. Mair, G. Pellegrinon, L. Rouyet, L. Ruiz, and T. Strozzi. Incorporating InSAR kinematics into rock glacier inventories: insights from 11 regions worldwide. Cryosphere, 16(7):2769-2792, 2022. 
    @Article{Bertone2022,
author = {Bertone, A. and Barboux, C. and Bodin, X. and Bolch, T. and Brardinoni, F. and Caduff, R. and Christiansen, H.H. and Darrow, M.M. and Delaloye, R. and Etzelm\"uller, B. and Humlum, O. and Lambiel, C. and Lilleoren, K.S. and Mair, V. and Pellegrinon, G. and Rouyet, L. and Ruiz, L. and Strozzi, T.},
journal = {Cryosphere},
title = {Incorporating {InSAR} kinematics into rock glacier inventories: insights from 11 regions worldwide},
year = {2022},
number = {7},
pages = {2769-2792},
volume = {16},
doi = {10.5194/tc-16-2769-2022},
owner = {ofrey},

}


  4. Achille Capelli, Franziska Koch, Patrick Henkel, Markus Lamm, Florian Appel, Christoph Marty, and Jürg Schweizer. GNSS signal-based snow water equivalent determination for different snowpack conditions along a steep elevation gradient. The Cryosphere, 16(2):505-531, September 2022. Keyword(s): GNSS, Snow water equivalent, SWE, Liquid water content (LWC), snow height, Davos, Laret, Weissfluhjoch, Küblis, snow, SLF, WSL.
    Abstract: Snow water equivalent (SWE) can be measured using low-cost Global Navigation Satellite System (GNSS) sensorswith one antenna placed below the snowpack and another one serving as a reference above the snow. The underlying GNSSsignal-based algorithm for SWE determination for dry- and wet-snow conditions processes the 5 carrier phases and signalstrengths and derives additionally liquid water content (LWC) and snow depth (HS). So far, the algorithm was testedintensively for high-alpine conditions with distinct seasonal accumulation and ablation phases. In general, snow occurrence,snow amount, snow density and LWC can vary considerably with climatic conditions and elevation. Regarding alpine regions,lower elevations mean generally earlier and faster melting, more rain-on-snow events and shallower snowpack. Therefore, we10 assessed the applicability of the GNSS-based SWE measurement at four stations along a steep elevation gradient (820, 1185,1510 and 2540 m a.s.l.) in the eastern Swiss Alps during two winter seasons (2018-2020). Reference data of SWE, LWC andHS were collected manually and with additional automated sensors at all locations. The GNSS-derived SWE estimates agreedvery well with manual reference measurements along the elevation gradient and the accuracy (RMSE = 34 mm,RMSRE = 11 %) was similar under wet- and dry-snow conditions, although significant differences in snow density and15 meteorological conditions existed between the locations. The GNSS-derived SWE was more accurate than measured withother automated SWE sensors. However, with the current version of the GNSS algorithm, the determination of daily changesof SWE was found to be less suitable compared to manual measurements or pluviometer recordings and needs furtherrefinement. The values of the GNSS-derived LWC were robust and within the precision of the manual and radar measurements.The additionally derived HS correlated well with the validation data. We conclude that SWE can reliably be determined using low-cost GNSS-sensors under a broad range of climatic conditions and LWC and HS are valuable add-ons. 

    @Article{capelliKochHenkelLammAppelMartySchweizerCRYOSPHERE2022GNSSsignalBasedSWEForDifferentSnowPackConditionsSLFSites,
author = {Achille Capelli and Franziska Koch and Patrick Henkel and Markus Lamm and Florian Appel and Christoph Marty and J\"urg Schweizer},
journal = {The Cryosphere},
title = {{GNSS} signal-based snow water equivalent determination for different snowpack conditions along a steep elevation gradient},
year = {2022},
month = {sep},
number = {2},
pages = {505--531},
volume = {16},
abstract = {Snow water equivalent (SWE) can be measured using low-cost Global Navigation Satellite System (GNSS) sensorswith one antenna placed below the snowpack and another one serving as a reference above the snow. The underlying GNSSsignal-based algorithm for SWE determination for dry- and wet-snow conditions processes the 5 carrier phases and signalstrengths and derives additionally liquid water content (LWC) and snow depth (HS). So far, the algorithm was testedintensively for high-alpine conditions with distinct seasonal accumulation and ablation phases. In general, snow occurrence,snow amount, snow density and LWC can vary considerably with climatic conditions and elevation. Regarding alpine regions,lower elevations mean generally earlier and faster melting, more rain-on-snow events and shallower snowpack. Therefore, we10 assessed the applicability of the GNSS-based SWE measurement at four stations along a steep elevation gradient (820, 1185,1510 and 2540 m a.s.l.) in the eastern Swiss Alps during two winter seasons (2018-2020). Reference data of SWE, LWC andHS were collected manually and with additional automated sensors at all locations. The GNSS-derived SWE estimates agreedvery well with manual reference measurements along the elevation gradient and the accuracy (RMSE = 34 mm,RMSRE = 11 %) was similar under wet- and dry-snow conditions, although significant differences in snow density and15 meteorological conditions existed between the locations. The GNSS-derived SWE was more accurate than measured withother automated SWE sensors. However, with the current version of the GNSS algorithm, the determination of daily changesof SWE was found to be less suitable compared to manual measurements or pluviometer recordings and needs furtherrefinement. The values of the GNSS-derived LWC were robust and within the precision of the manual and radar measurements.The additionally derived HS correlated well with the validation data. We conclude that SWE can reliably be determined using low-cost GNSS-sensors under a broad range of climatic conditions and LWC and HS are valuable add-ons.},
doi = {10.5194/tc-16-505-2022},
file = {:capelliKochHenkelLammAppelMartySchweizerCRYOSPHERE2022GNSSsignalBasedSWEForDifferentSnowPackConditionsSLFSites.pdf:PDF},
keywords = {GNSS, Snow water equivalent, SWE, Liquid water content (LWC), snow height, Davos, Laret, Weissfluhjoch, K\"ublis, snow, SLF, WSL},
owner = {ofrey},
publisher = {Copernicus {GmbH}},

}


  5. O. Cartus, M. Santoro, U. Wegmuller, N. Labriere, and J. Chave. Sentinel-1 Coherence for Mapping Above-Ground Biomass in Semiarid Forest Areas. IEEE Geoscience and Remote Sensing Letters, 19, 2022. 
    @Article{Cartus2022,
author = {Cartus, O. and Santoro, M. and Wegmuller, U. and Labriere, N. and Chave, J.},
journal = {IEEE Geoscience and Remote Sensing Letters},
title = {Sentinel-1 Coherence for Mapping Above-Ground Biomass in Semiarid Forest Areas},
year = {2022},
volume = {19},
doi = {10.1109/LGRS.2021.3071949},
owner = {ofrey},

}


  6. A. Cicoira, S. Weber, A. Biri, B. Buchli, R. Delaloye, R. Da Forno, I. Gärtner-Roer, S. Gruber, T. Gsell, A. Hasler, R. Lim, P. Limpach, R. Mayoraz, M. Meyer, J. Noetzli, M. Phillips, E. Pointner, H. Raetzo, C. Scapozza, T. Strozzi, L. Thiele, A. Vieli, D. Vonder Mühll, V. Wirz, and J. Beutel. In situ observations of the Swiss periglacial environment using GNSS instruments. Earth System Science Data, 14(11):5061-5091, 2022. 
    @Article{Cicoira2022,
author = {Cicoira, A. and Weber, S. and Biri, A. and Buchli, B. and Delaloye, R. and Da Forno, R. and G\"artner-Roer, I. and Gruber, S. and Gsell, T. and Hasler, A. and Lim, R. and Limpach, P. and Mayoraz, R. and Meyer, M. and Noetzli, J. and Phillips, M. and Pointner, E. and Raetzo, H. and Scapozza, C. and Strozzi, T. and Thiele, L. and Vieli, A. and Vonder M\"uhll, D. and Wirz, V. and Beutel, J.},
journal = {Earth System Science Data},
title = {In situ observations of the {Swiss} periglacial environment using {GNSS} instruments},
year = {2022},
number = {11},
pages = {5061-5091},
volume = {14},
doi = {10.5194/essd-14-5061-2022},
owner = {ofrey},
url = {https://essd.copernicus.org/articles/14/5061/2022/},

}


  7. Roberto Coscione, Irena Hajnsek, Charles Werner, and Othmar Frey. Assessing the impact of positioning errors in car-borne repeat-pass SAR interferometry with a controlled rail-based experiment. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15:8402-8415, 2022. Keyword(s): SAR Processing, SAR Interferometry, Car-borne SAR, positioning errors, rail-based SAR, experiment, terrestrial radar interferometry, TRI, Gamma L-band SAR, ground-based SAR, GB-SAR.
    Abstract: Agile synthetic aperture radar (SAR) platforms such as car-borne and UAV-borne SAR systems require combined inertial navigation systems (INS) and global navigation satellite systems (GNSS) to measure the radar sensor trajectories used for focusing and interferometric processing. Measurement inaccuracies from INS/GNSS systems lead to residual phase errors in the SAR products whose minimisation is crucial to derive accurate topographic and deformation information. In this work, we analyse the impact of residual positioning errors on car-borne repeat-pass SAR interferometry at L-band for different INS/GNSS measurement configurations and for the typical car-borne acquisition geometry. The positioning errors are evaluated both during single SAR acquisitions with long integration times and between different acquisitions as a function of the distance of the radar platform from the GNSS reference stations. We show the reduction of interferometric phase errors achievable by additionally using a GNSS receiver mounted in the vicinity of the SAR platform as compared to remote reference stations of the national network of permanent GNSS receivers. Test results obtained in a controlled setup with a rail-based SAR system equipped with a navigation-grade INS/GNSS system show maximum repeat-pass trajectory errors on the order of 1-2 cm using a local GNSS reference station and up to 10-15 cm using the remote reference stations, leading to azimuth and range phase trends in the interferometric products. 

    @Article{coscioneWernerHajnsekFrey2022ImpactOfPositioningErrorsInCarborneRepeatpassINSAR,
author = {Coscione, Roberto and Hajnsek, Irena and Werner, Charles and Frey, Othmar},
journal = {{IEEE} Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
title = {Assessing the impact of positioning errors in car-borne repeat-pass {SAR} interferometry with a controlled rail-based experiment},
year = {2022},
issn = {1939-1404},
pages = {8402--8415},
volume = {15},
abstract = {Agile synthetic aperture radar (SAR) platforms such as car-borne and UAV-borne SAR systems require combined inertial navigation systems (INS) and global navigation satellite systems (GNSS) to measure the radar sensor trajectories used for focusing and interferometric processing. Measurement inaccuracies from INS/GNSS systems lead to residual phase errors in the SAR products whose minimisation is crucial to derive accurate topographic and deformation information. In this work, we analyse the impact of residual positioning errors on car-borne repeat-pass SAR interferometry at L-band for different INS/GNSS measurement configurations and for the typical car-borne acquisition geometry. The positioning errors are evaluated both during single SAR acquisitions with long integration times and between different acquisitions as a function of the distance of the radar platform from the GNSS reference stations. We show the reduction of interferometric phase errors achievable by additionally using a GNSS receiver mounted in the vicinity of the SAR platform as compared to remote reference stations of the national network of permanent GNSS receivers. Test results obtained in a controlled setup with a rail-based SAR system equipped with a navigation-grade INS/GNSS system show maximum repeat-pass trajectory errors on the order of 1-2 cm using a local GNSS reference station and up to 10-15 cm using the remote reference stations, leading to azimuth and range phase trends in the interferometric products.},
doi = {10.1109/jstars.2022.3193053},
file = {:coscioneWernerHajnsekFrey2022ImpactOfPositioningErrorsInCarborneRepeatpassINSAR.pdf:PDF},
keywords = {SAR Processing, SAR Interferometry, Car-borne SAR, positioning errors, rail-based SAR, experiment, terrestrial radar interferometry, TRI, Gamma L-band SAR, ground-based SAR, GB-SAR},
owner = {ofrey},
publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},

}


  8. Richard Czikhardt, Hans van der Marel, Juraj Papco, and Ramon F. Hanssen. On the Efficacy of Compact Radar Transponders for InSAR Geodesy: Results of Multiyear Field Tests. IEEE Transactions on Geoscience and Remote Sensing, 60:1-13, 2022. Keyword(s): SAR Processing, Corner Reflectors, InSAR, SAR Interferometry.
    Abstract: Compact and low-cost radar transponders are an attractive alternative to corner reflectors (CRs) for interferometric synthetic aperture radar (InSAR) deformation monitoring, datum connection, and geodetic data integration. Recently, such transponders have become commercially available for C-band sensors, which poses relevant questions on their characteristics in terms of radiometric, geometric, and phase stability. Especially for extended time series and for high-precision geodetic applications, the impact of secular or seasonal effects, such as variations in temperature and humidity, has yet to be proven. In this article, we address these challenges using a multitude of short baseline experiments with four transponders and six CRs deployed at test sites in The Netherlands and Slovakia. Combined together, we analyzed 980 transponder measurements in Sentinel-1 time series to a maximum extent of 21 months. We find an average radar cross section (RCS) of over 42 dBm2 within a range of up to 15 deg of elevation misalignment, which is comparable to a triangular trihedral CR with a leg length of 2.0 m. Its RCS shows the temporal variations of 0.3-0.7 dBm2 (standard deviation), which is partially correlated with surface temperature changes. The precision of the InSAR phase double differences over short baselines between a transponder and a stable reference CRs is found to be 0.5-1.2 mm (one sigma). We observe a correlation with surface temperature, leading to seasonal variations of up to +/-3 mm, which should be modeled and corrected for in high-precision InSAR applications. For precise SAR positioning, we observe antenna-specific constant internal electronic delays of 1.2-2.1 m in slant range, i.e., within the range resolution of the Sentinel-1 interferometric wide (IW) product, with a temporal variability of less than 20 cm. Comparing similar transponders from the same series, we observe distinct differences in performance. Our main conclusion is that these characteristics are favorable for a wide range of geodetic applications. For particular demanding applications, individual calibration of single devices is strongly recommended. 

    @Article{czikhardtvanDerMarelPapcoHanssenTGRS2022CompactRadarTranspondersforInSARGeodesy,
author = {Czikhardt, Richard and van der Marel, Hans and Papco, Juraj and Hanssen, Ramon F.},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
title = {On the Efficacy of Compact Radar Transponders for InSAR Geodesy: Results of Multiyear Field Tests},
year = {2022},
issn = {1558-0644},
pages = {1-13},
volume = {60},
abstract = {Compact and low-cost radar transponders are an attractive alternative to corner reflectors (CRs) for interferometric synthetic aperture radar (InSAR) deformation monitoring, datum connection, and geodetic data integration. Recently, such transponders have become commercially available for C-band sensors, which poses relevant questions on their characteristics in terms of radiometric, geometric, and phase stability. Especially for extended time series and for high-precision geodetic applications, the impact of secular or seasonal effects, such as variations in temperature and humidity, has yet to be proven. In this article, we address these challenges using a multitude of short baseline experiments with four transponders and six CRs deployed at test sites in The Netherlands and Slovakia. Combined together, we analyzed 980 transponder measurements in Sentinel-1 time series to a maximum extent of 21 months. We find an average radar cross section (RCS) of over 42 dBm2 within a range of up to 15 deg of elevation misalignment, which is comparable to a triangular trihedral CR with a leg length of 2.0 m. Its RCS shows the temporal variations of 0.3-0.7 dBm2 (standard deviation), which is partially correlated with surface temperature changes. The precision of the InSAR phase double differences over short baselines between a transponder and a stable reference CRs is found to be 0.5-1.2 mm (one sigma). We observe a correlation with surface temperature, leading to seasonal variations of up to +/-3 mm, which should be modeled and corrected for in high-precision InSAR applications. For precise SAR positioning, we observe antenna-specific constant internal electronic delays of 1.2-2.1 m in slant range, i.e., within the range resolution of the Sentinel-1 interferometric wide (IW) product, with a temporal variability of less than 20 cm. Comparing similar transponders from the same series, we observe distinct differences in performance. Our main conclusion is that these characteristics are favorable for a wide range of geodetic applications. For particular demanding applications, individual calibration of single devices is strongly recommended.},
doi = {10.1109/TGRS.2021.3119917},
file = {:czikhardtvanDerMarelPapcoHanssenTGRS2022CompactRadarTranspondersforInSARGeodesy.pdf:PDF},
keywords = {SAR Processing, Corner Reflectors, InSAR, SAR Interferometry},

}


  9. Claudio De Luca, Francesco Casu, Michele Manunta, Giovanni Onorato, and Riccardo Lanari. Comments on ``Study of Systematic Bias in Measuring Surface Deformation With SAR Interferometry''. IEEE Transactions on Geoscience and Remote Sensing, 60:1-5, 2022.
    Abstract: In a recent publication, Ansari et al. (2021) claimed (see, in particular, the Discussion and Recommendation Section in their article) that the advanced differential SAR interferometry (InSAR) algorithms for surface deformation retrieval, based on the small baseline approach, are affected by systematic biases in the generated InSAR products. Therefore, to avoid such biases, they recommended a strategy primarily focused on excluding "the short temporal baseline interferograms and using long baselines to decrease the overall phase errors." In particular, among various techniques, Ansari et al. (2021) identified the solution presented by Manunta et al. (2019) as a small baseline advanced InSAR processing approach where the presence of the above-mentioned biases (referred to as a fading signal) compromises the accuracy of the retrieved InSAR deformation products. We show that the claim of Ansari et al. (2021) is not correct (at least) for what concerns the mentioned approach discussed by Manunta et al. (2019). In particular, by processing the Sentinel-1 dataset relevant to the same area in Sicily (southern Italy) investigated by Ansari et al. (2021), we demonstrate that the generated InSAR products do not show any significant bias. 

    @Article{deLucaCasuManuntaOnoratoLanariTGRS2022CommentsOnSudyOfSystematicBiasInDInSARDeformation,
author = {De Luca, Claudio and Casu, Francesco and Manunta, Michele and Onorato, Giovanni and Lanari, Riccardo},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
title = {Comments on ``Study of Systematic Bias in Measuring Surface Deformation With SAR Interferometry''},
year = {2022},
issn = {1558-0644},
pages = {1-5},
volume = {60},
abstract = {In a recent publication, Ansari et al. (2021) claimed (see, in particular, the Discussion and Recommendation Section in their article) that the advanced differential SAR interferometry (InSAR) algorithms for surface deformation retrieval, based on the small baseline approach, are affected by systematic biases in the generated InSAR products. Therefore, to avoid such biases, they recommended a strategy primarily focused on excluding "the short temporal baseline interferograms and using long baselines to decrease the overall phase errors." In particular, among various techniques, Ansari et al. (2021) identified the solution presented by Manunta et al. (2019) as a small baseline advanced InSAR processing approach where the presence of the above-mentioned biases (referred to as a fading signal) compromises the accuracy of the retrieved InSAR deformation products. We show that the claim of Ansari et al. (2021) is not correct (at least) for what concerns the mentioned approach discussed by Manunta et al. (2019). In particular, by processing the Sentinel-1 dataset relevant to the same area in Sicily (southern Italy) investigated by Ansari et al. (2021), we demonstrate that the generated InSAR products do not show any significant bias.},
doi = {10.1109/TGRS.2021.3103037},
file = {:deLucaCasuManuntaOnoratoLanariTGRS2022CommentsOnSudyOfSystematicBiasInDInSARDeformation.pdf:PDF},
owner = {ofrey},

}


  10. Davide Giudici, Pietro Guccione, Marco Manzoni, Andrea Monti Guarnieri, and Fabio Rocca. Compact and Free-Floating Satellite MIMO SAR Formations. IEEE Transactions on Geoscience and Remote Sensing, 60:1-12, 2022. Keyword(s): SAR Formation, MIMO, Spaceborne SAR, Multistatic SAR.
    Abstract: We discuss a coherent synthetic aperture radar (SAR) formation where N identical sensors transmit at the same time, code, and frequency. This is a particular multiple-input-multiple-output (MIMO) configuration, where the transmitted waveforms interfere together, resulting in an illumination pattern that randomly changes in space and time. Similar to the single-input-multiple-output (SIMO) formations, the diversity provided by the N receiver phase centers can be used to mitigate this interference and reduce the pulse repetition frequency (PRF) for achieving large swath coverage. The good point, in the MIMO case, is that the signal-to-noise ratio (SNR) gain of the system increases, theoretically, with the square of the number of elements. However, residual spurious sidelobes may appear as ghosts of the multiple illuminators. In practice, the power gain is to be optimized, together with ambiguity rejection, sidelobes, and azimuth resolution. The actual performances achievable by these formations in terms of impulse response function (IRF), SNR, and sensitivity to the precise positioning of the sensors are discussed theoretically and based on simulations. 

    @Article{giudiciGuccioneManzoniGuarnieriRoccaTGRS2022CompactAndFreeFloatingSatelliteMIMOSARFormations,
author = {Giudici, Davide and Guccione, Pietro and Manzoni, Marco and Guarnieri, Andrea Monti and Rocca, Fabio},
journal = {{IEEE} Transactions on Geoscience and Remote Sensing},
title = {Compact and Free-Floating Satellite {MIMO} {SAR} Formations},
year = {2022},
issn = {1558-0644},
pages = {1-12},
volume = {60},
abstract = {We discuss a coherent synthetic aperture radar (SAR) formation where N identical sensors transmit at the same time, code, and frequency. This is a particular multiple-input-multiple-output (MIMO) configuration, where the transmitted waveforms interfere together, resulting in an illumination pattern that randomly changes in space and time. Similar to the single-input-multiple-output (SIMO) formations, the diversity provided by the N receiver phase centers can be used to mitigate this interference and reduce the pulse repetition frequency (PRF) for achieving large swath coverage. The good point, in the MIMO case, is that the signal-to-noise ratio (SNR) gain of the system increases, theoretically, with the square of the number of elements. However, residual spurious sidelobes may appear as ghosts of the multiple illuminators. In practice, the power gain is to be optimized, together with ambiguity rejection, sidelobes, and azimuth resolution. The actual performances achievable by these formations in terms of impulse response function (IRF), SNR, and sensitivity to the precise positioning of the sensors are discussed theoretically and based on simulations.},
doi = {10.1109/TGRS.2021.3062973},
file = {:giudiciGuccioneManzoniGuarnieriRoccaTGRS2022CompactAndFreeFloatingSatelliteMIMOSARFormations.pdf:PDF},
keywords = {SAR Formation, MIMO, Spaceborne SAR, Multistatic SAR},
owner = {ofrey},
publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},

}


  11. Timo Grebner, Pirmin Schoeder, Vinzenz Janoudi, and Christian Waldschmidt. Radar-Based Mapping of the Environment: Occupancy Grid-Map Versus SAR. IEEE Microwave and Wireless Components Letters, pp 1-4, 2022. Keyword(s): SAR Processing, automotive radar, automotive SAR, synthetic aperture radar, SAR, caborne SAR, Frequency-modulated continuous-wave radar, multiple-input multiple-output (MIMO) radar, occupancy grid map, OGM.
    Abstract: For autonomous driving vehicles, highly accurate representations of the environment are essential for both trajectory planning and self-localization. Different possibilities allow to generate detailed maps of the environment based on chirp-sequence radar sensors for advanced driver assistance systems (ADASs). For the first time, this letter shows a qualitative comparison between synthetic aperture radar (SAR)- and occupancy grid map (OGM)-based environment representation using identical measurement data. The differences of existing signal processing chains as well as a visual measurement-based comparison of the resulting environmental maps is presented. 

    @Article{grebnerSchoederJanoudiWaldschmidtIEEEMWCL2022CarborneMappingAt77GhzOccupancyGridVsSAR,
author = {Grebner, Timo and Schoeder, Pirmin and Janoudi, Vinzenz and Waldschmidt, Christian},
journal = {IEEE Microwave and Wireless Components Letters},
title = {Radar-Based Mapping of the Environment: Occupancy Grid-Map Versus {SAR}},
year = {2022},
issn = {1558-1764},
pages = {1-4},
abstract = {For autonomous driving vehicles, highly accurate representations of the environment are essential for both trajectory planning and self-localization. Different possibilities allow to generate detailed maps of the environment based on chirp-sequence radar sensors for advanced driver assistance systems (ADASs). For the first time, this letter shows a qualitative comparison between synthetic aperture radar (SAR)- and occupancy grid map (OGM)-based environment representation using identical measurement data. The differences of existing signal processing chains as well as a visual measurement-based comparison of the resulting environmental maps is presented.},
doi = {10.1109/LMWC.2022.3145661},
file = {:grebnerSchoederJanoudiWaldschmidtIEEEMWCL2022CarborneMappingAt77GhzOccupancyGridVsSAR.pdf:PDF},
keywords = {SAR Processing, automotive radar, automotive SAR, synthetic aperture radar, SAR, caborne SAR, Frequency-modulated continuous-wave radar;multiple-input multiple-output (MIMO) radar;occupancy grid map, OGM},
owner = {ofrey},

}


  12. Fengming Hu, Freek J. van Leijen, Ling Chang, Jicang Wu, and Ramon F. Hanssen. Combined Detection of Surface Changes and Deformation Anomalies Using Amplitude-Augmented Recursive InSAR Time Series. IEEE Transactions on Geoscience and Remote Sensing, 60:1-16, 2022. Keyword(s): PSI, Deformation, Persistent Scatterer Interferometry.
    Abstract: Synthetic aperture radar (SAR) missions with short repeat times enable opportunities for near real-time deformation monitoring. Traditional multitemporal interferometric SAR (MT-InSAR) is able to monitor long-term and periodic deformation with high precision by time-series analysis. However, as time series lengthen, it is time-consuming to update the current results by reprocessing the whole dataset. Additionally, the number of coherent scatterers varies over time due to disappearing and emerging scatterers due to inevitable changes in surface scattering, and potential deformation anomalies require changes in the prevailing deformation model. Here, we propose a novel method to analyze InSAR time series recursively and detect both significant changes in scattering as well as deformation anomalies based on the new acquisitions. Sequential change detection is developed to identify temporary coherent scatterers (TCSs) using amplitude time series. Based on the predicted phase residuals, scatterers with abnormal deformation displacements are identified by a generalized ratio test, while the parameters of stable scatterers are updated using Kalman filtering. The quality of the anomaly detection is assessed based on the detectability power and the minimum detectable deformation. This facilitates (near) real-time data processing and decreases the false alarm likelihood. Experimental results show that the technique can be used for the real-time evaluation of deformation risks. 

    @Article{huVanLeijenChangWuHanssenTGRS2022CombinedDetectionOfSurfaceChangesAndDeformatioUsingAmplitudeAugmentedRecursiveInSARTimeSeries,
author = {Hu, Fengming and van Leijen, Freek J. and Chang, Ling and Wu, Jicang and Hanssen, Ramon F.},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
title = {Combined Detection of Surface Changes and Deformation Anomalies Using Amplitude-Augmented Recursive InSAR Time Series},
year = {2022},
issn = {1558-0644},
pages = {1-16},
volume = {60},
abstract = {Synthetic aperture radar (SAR) missions with short repeat times enable opportunities for near real-time deformation monitoring. Traditional multitemporal interferometric SAR (MT-InSAR) is able to monitor long-term and periodic deformation with high precision by time-series analysis. However, as time series lengthen, it is time-consuming to update the current results by reprocessing the whole dataset. Additionally, the number of coherent scatterers varies over time due to disappearing and emerging scatterers due to inevitable changes in surface scattering, and potential deformation anomalies require changes in the prevailing deformation model. Here, we propose a novel method to analyze InSAR time series recursively and detect both significant changes in scattering as well as deformation anomalies based on the new acquisitions. Sequential change detection is developed to identify temporary coherent scatterers (TCSs) using amplitude time series. Based on the predicted phase residuals, scatterers with abnormal deformation displacements are identified by a generalized ratio test, while the parameters of stable scatterers are updated using Kalman filtering. The quality of the anomaly detection is assessed based on the detectability power and the minimum detectable deformation. This facilitates (near) real-time data processing and decreases the false alarm likelihood. Experimental results show that the technique can be used for the real-time evaluation of deformation risks.},
doi = {10.1109/TGRS.2021.3093108},
file = {:huVanLeijenChangWuHanssenTGRS2022CombinedDetectionOfSurfaceChangesAndDeformatioUsingAmplitudeAugmentedRecursiveInSARTimeSeries.pdf:PDF},
keywords = {PSI, Deformation, Persistent Scatterer Interferometry},
owner = {ofrey},

}


  13. V. Humphrey and C. Frankenberg. Continuous ground monitoring of vegetation optical depth and water content with GPS signals. Biogeosciences Discussions, 2022:1-44, 2022. Keyword(s): GNSS, forest, monitoring, microwave, radar, L-band, vegetation. 
    @Article{humphreyFrankenberg2022VegetationOpticalDepthAndWaterContentWithGPSSignals,
author = {Humphrey, V. and Frankenberg, C.},
journal = {Biogeosciences Discussions},
title = {Continuous ground monitoring of vegetation optical depth and water content with GPS signals},
year = {2022},
pages = {1--44},
volume = {2022},
doi = {10.5194/bg-2022-84},
file = {:humphreyFrankenberg2022VegetationOpticalDepthAndWaterContentWithGPSSignals.pdf:PDF},
keywords = {GNSS, forest, monitoring, microwave, radar, L-band, vegetation},
owner = {ofrey},
url = {https://bg.copernicus.org/preprints/bg-2022-84/},

}


  14. Guodong Jin, Wei Wang, Yunkai Deng, He Yan, and Robert Wang. A Novel Range-Azimuth Joint Modulation Scheme for Range Ambiguity Suppression. IEEE Transactions on Geoscience and Remote Sensing, 60:1-10, 2022. Keyword(s): Azimuth, Doppler effect, Synthetic aperture radar, Modulation, Bandwidth, Radar imaging, Frequency modulation, Azimuth phase coding (APC), nonlinear frequency modulation (NLFM) waveforms, range ambiguity suppression.
    Abstract: Range ambiguity is a technical challenge for current spaceborne synthetic aperture radar (SAR) systems. To this end, a novel range-azimuth joint modulation transmission scheme is proposed, and the corresponding imaging processing and performance analysis are detailed. Compared with the azimuth phase coding (APC) technique, this scheme fully exploits the sampling margins of the range and azimuth dimensions, resulting in the range ambiguities experiencing a double suppression effect. Starting from the range-azimuth joint modulation scheme, to obtain the best ambiguity suppression performance, the design of a nonlinear frequency modulation (NLFM) waveform with a continuous piecewise linear instantaneous frequency is formulated and tackled via a MATLAB optimization toolbox. The detailed simulation results based on LuTan-1 (LT-1) parameters illustrate that the proposed methodologies outperform the APC method and provide considerable ambiguity suppression. 

    @Article{Jin2022,
author = {Jin, Guodong and Wang, Wei and Deng, Yunkai and Yan, He and Wang, Robert},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
title = {A Novel Range-Azimuth Joint Modulation Scheme for Range Ambiguity Suppression},
year = {2022},
issn = {1558-0644},
pages = {1-10},
volume = {60},
abstract = {Range ambiguity is a technical challenge for current spaceborne synthetic aperture radar (SAR) systems. To this end, a novel range-azimuth joint modulation transmission scheme is proposed, and the corresponding imaging processing and performance analysis are detailed. Compared with the azimuth phase coding (APC) technique, this scheme fully exploits the sampling margins of the range and azimuth dimensions, resulting in the range ambiguities experiencing a double suppression effect. Starting from the range-azimuth joint modulation scheme, to obtain the best ambiguity suppression performance, the design of a nonlinear frequency modulation (NLFM) waveform with a continuous piecewise linear instantaneous frequency is formulated and tackled via a MATLAB optimization toolbox. The detailed simulation results based on LuTan-1 (LT-1) parameters illustrate that the proposed methodologies outperform the APC method and provide considerable ambiguity suppression.},
doi = {10.1109/TGRS.2021.3075233},
keywords = {Azimuth;Doppler effect;Synthetic aperture radar;Modulation;Bandwidth;Radar imaging;Frequency modulation;Azimuth phase coding (APC);nonlinear frequency modulation (NLFM) waveforms;range ambiguity suppression},
owner = {ofrey},

}


  15. Josef Kellndorfer, Oliver Cartus, Marco Lavalle, Christophe Magnard, Pietro Milillo, Shadi Oveisgharan, Batu Osmanoglu, Paul A. Rosen, and Urs Wegmuller. Global seasonal Sentinel-1 interferometric coherence and backscatter data set. Scientific Data, 9(1):73, 2022.
    Abstract: This data set is the first-of-its-kind spatial representation of multi-seasonal, global C-band Synthetic Aperture Radar (SAR) interferometric repeat-pass coherence and backscatter signatures. Coverage comprises land masses and ice sheets from 82° Northern to 79° Southern latitudes. The data set is derived from multi-temporal repeat-pass interferometric processing of about 205,000 Sentinel-1 C-band SAR images acquired in Interferometric Wide-Swath Mode from 1-Dec-2019 to 30-Nov-2020. The data set encompasses three sets of seasonal (December-February, March-May, June-August, September-November) metrics produced with a pixel spacing of three arcseconds: 1) Median 6-, 12-, 18-, 24-, 36-, and 48-days repeat-pass coherence at VV or HH polarizations, 2) Mean radiometrically terrain corrected backscatter (γ0) at VV and VH, or HH and HV polarizations, and 3) Estimated parameters of an exponential coherence decay model. The data set has been produced to obtain global, spatially detailed information on how decorrelation affects interferometric measurements of surface displacement and is rich in spatial and temporal information for a variety of mapping applications. 

    @Article{kellndorferEtAl2022GlobalSeasonalSentinel1InterferometricCoherenceAndBackScatter,
author = {Kellndorfer, Josef and Cartus, Oliver and Lavalle, Marco and Magnard, Christophe and Milillo, Pietro and Oveisgharan, Shadi and Osmanoglu, Batu and Rosen, Paul A. and Wegmuller, Urs},
journal = {Scientific Data},
title = {Global seasonal Sentinel-1 interferometric coherence and backscatter data set},
year = {2022},
issn = {2052-4463},
number = {1},
pages = {73},
volume = {9},
abstract = {This data set is the first-of-its-kind spatial representation of multi-seasonal, global C-band Synthetic Aperture Radar (SAR) interferometric repeat-pass coherence and backscatter signatures. Coverage comprises land masses and ice sheets from 82° Northern to 79° Southern latitudes. The data set is derived from multi-temporal repeat-pass interferometric processing of about 205,000 Sentinel-1 C-band SAR images acquired in Interferometric Wide-Swath Mode from 1-Dec-2019 to 30-Nov-2020. The data set encompasses three sets of seasonal (December-February, March-May, June-August, September-November) metrics produced with a pixel spacing of three arcseconds: 1) Median 6-, 12-, 18-, 24-, 36-, and 48-days repeat-pass coherence at VV or HH polarizations, 2) Mean radiometrically terrain corrected backscatter (γ0) at VV and VH, or HH and HV polarizations, and 3) Estimated parameters of an exponential coherence decay model. The data set has been produced to obtain global, spatially detailed information on how decorrelation affects interferometric measurements of surface displacement and is rich in spatial and temporal information for a variety of mapping applications.},
doi = {10.1038/s41597-022-01189-6},
file = {:kellndorferEtAl2022GlobalSeasonalSentinel1InterferometricCoherenceAndBackScatter.pdf:PDF},
owner = {ofrey},
refid = {Kellndorfer2022},
url = {https://doi.org/10.1038/s41597-022-01189-6},

}


  16. W. Kochtitzky, L. Copland, W. Van Wychen, R. Hugonnet, R. Hock, J.A. Dowdeswell, T. Benham, T. Strozzi, A. Glazovsky, I. Lavrentiev, D.R. Rounce, R. Millan, A. Cook, A. Dalton, H. Jiskoot, J. Cooley, J. Jania, and F. Navarro. The unquantified mass loss of Northern Hemisphere marine-terminating glaciers from 2000-2020. Nature Communications, 13(1), 2022. 
    @Article{Kochtitzky2022,
author = {Kochtitzky, W. and Copland, L. and Van Wychen, W. and Hugonnet, R. and Hock, R. and Dowdeswell, J.A. and Benham, T. and Strozzi, T. and Glazovsky, A. and Lavrentiev, I. and Rounce, D.R. and Millan, R. and Cook, A. and Dalton, A. and Jiskoot, H. and Cooley, J. and Jania, J. and Navarro, F.},
journal = {Nature Communications},
title = {The unquantified mass loss of Northern Hemisphere marine-terminating glaciers from 2000-2020},
year = {2022},
number = {1},
volume = {13},
art_number = {5835},
doi = {10.1038/s41467-022-33231-x},
owner = {ofrey},

}


  17. Juha Lemmetyinen, Juval Cohen, Anna Kontu, Juho Vehvil�inen, Henna-Reetta Hannula, Ioanna Merkouriadi, Stefan Scheiblauer, Helmut Rott, Thomas Nagler, Elisabeth Ripper, Kelly Elder, Hans-Peter Marshall, Reinhard Fromm, Marc Adams, Chris Derksen, Joshua King, Adriano Meta, Alex Coccia, Nick Rutter, Melody Sandells, Giovanni Macelloni, Emanuele Santi, Marion Leduc-Leballeur, Richard Essery, Cecile Menard, and Michael Kern. Airborne SnowSAR data at X and Ku bands over boreal forest, alpine and tundra snow cover. Earth System Science Data, 14(9):3915-3945, September 2022. 
    @Article{lemmetyinenEtAlESSD2022AirborneSARDataAtXandKuBandOverBorealForestAlpineAndTundraSnowCover,
author = {Juha Lemmetyinen and Juval Cohen and Anna Kontu and Juho Vehvil�inen and Henna-Reetta Hannula and Ioanna Merkouriadi and Stefan Scheiblauer and Helmut Rott and Thomas Nagler and Elisabeth Ripper and Kelly Elder and Hans-Peter Marshall and Reinhard Fromm and Marc Adams and Chris Derksen and Joshua King and Adriano Meta and Alex Coccia and Nick Rutter and Melody Sandells and Giovanni Macelloni and Emanuele Santi and Marion Leduc-Leballeur and Richard Essery and Cecile Menard and Michael Kern},
journal = {Earth System Science Data},
title = {Airborne {SnowSAR} data at {X} and {Ku} bands over boreal forest, alpine and tundra snow cover},
year = {2022},
month = {sep},
number = {9},
pages = {3915--3945},
volume = {14},
doi = {10.5194/essd-14-3915-2022},
owner = {ofrey},
publisher = {Copernicus {GmbH}},

}


  18. Haoyu Lin, Yunkai Deng, Heng Zhang, Jili Wang, Da Liang, Tingzhu Fang, and Robert Wang. Estimating and Removing Ionospheric Effects for L-Band Spaceborne Bistatic SAR. IEEE Transactions on Geoscience and Remote Sensing, 60:1-16, 2022. Keyword(s): Ionosphere, Synthetic aperture radar, Imaging, Spaceborne radar, L-band, Focusing, Dispersion, Bistatic synthetic aperture radar (BiSAR), ionospheric effects correction, L-band, LuTan-1 (LT-1).
    Abstract: One of the challenges of the low-frequency spaceborne synthetic aperture radar (SAR) is that propagation through the ionosphere will introduce nonnegligible errors in the final SAR product. In the low-frequency bistatic SAR (BiSAR) system, the ionosphere will degrade the imaging performance and cause nonnegligible phase errors in the single-pass SAR interferometry application, which results in undesired errors of digital elevation model (DEM). In this article, a method that embedded into the focusing procedure is proposed, which aims to estimate and remove ionospheric effects on L-band spaceborne BiSAR system. First, the impacts of ionospheric effects on the BiSAR system are demonstrated, including the deterioration of imaging performance and the geometric distortion. Then, a method is proposed to correct ionospheric effects. Afterward, the simulations, including point targets and distributed targets, are carried out to verify the effectiveness of the proposed method. The imaging results and the DEM reconstruction results show that the proposed method can effectively estimate and remove ionospheric effects on spaceborne BiSAR systems. 

    @Article{Lin2022,
author = {Lin, Haoyu and Deng, Yunkai and Zhang, Heng and Wang, Jili and Liang, Da and Fang, Tingzhu and Wang, Robert},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
title = {Estimating and Removing Ionospheric Effects for L-Band Spaceborne Bistatic SAR},
year = {2022},
issn = {1558-0644},
pages = {1-16},
volume = {60},
abstract = {One of the challenges of the low-frequency spaceborne synthetic aperture radar (SAR) is that propagation through the ionosphere will introduce nonnegligible errors in the final SAR product. In the low-frequency bistatic SAR (BiSAR) system, the ionosphere will degrade the imaging performance and cause nonnegligible phase errors in the single-pass SAR interferometry application, which results in undesired errors of digital elevation model (DEM). In this article, a method that embedded into the focusing procedure is proposed, which aims to estimate and remove ionospheric effects on L-band spaceborne BiSAR system. First, the impacts of ionospheric effects on the BiSAR system are demonstrated, including the deterioration of imaging performance and the geometric distortion. Then, a method is proposed to correct ionospheric effects. Afterward, the simulations, including point targets and distributed targets, are carried out to verify the effectiveness of the proposed method. The imaging results and the DEM reconstruction results show that the proposed method can effectively estimate and remove ionospheric effects on spaceborne BiSAR systems.},
doi = {10.1109/TGRS.2021.3137860},
keywords = {Ionosphere;Synthetic aperture radar;Imaging;Spaceborne radar;L-band;Focusing;Dispersion;Bistatic synthetic aperture radar (BiSAR);ionospheric effects correction;L-band;LuTan-1 (LT-1)},
owner = {ofrey},

}


  19. Haoyu Lin, Yunkai Deng, Heng Zhang, Jili Wang, and Yongwei Zhang. An Extended Model of Ionospheric Dispersion Effects for Nonlinear Frequency Modulation Signal and Correction Method. IEEE Geoscience and Remote Sensing Letters, 19:1-5, 2022. Keyword(s): Dispersion, Synthetic aperture radar, Time-frequency analysis, Focusing, Ionosphere, Frequency modulation, Spaceborne radar, Ionospheric dispersion effects, LuTan-1 (LT-1), nonlinear frequency modulation (NLFM) signal, synthetic aperture radar (SAR).
    Abstract: Nonlinear frequency modulation (NLFM) signal can construct the signal's power spectral density (PSD) to reduce sidelobes without loss of signal-to-noise ratio. LuTan-1 (LT-1) is an L-band spaceborne synthetic aperture radar (SAR) mission which is launched in the beginning of 2022, and a high-precision NLFM signal generator is developed in LT-1. However, the existing model, i.e., the traditional frozen ionosphere model, cannot accurately describe ionospheric dispersion effects faced by the NLFM signal due to the nonlinear characteristic of the instantaneous frequency. Thus, an extended model is established in this letter to describe ionospheric dispersion effects of the NLFM signal. Then, the differences of ionospheric dispersion effects on the NLFM and linear frequency modulation (LFM) signals are compared. Afterward, a method that embedded into the focusing procedure is proposed, which aims to eliminate ionospheric dispersion effects for the NLFM signal. Finally, the hardware-in-the-loop simulations of point targets and distributed targets are performed to verify the proposed method. The method proposed in this letter is used in the ground processing system of LT-1. 

    @Article{Lin2022a,
author = {Lin, Haoyu and Deng, Yunkai and Zhang, Heng and Wang, Jili and Zhang, Yongwei},
journal = {IEEE Geoscience and Remote Sensing Letters},
title = {An Extended Model of Ionospheric Dispersion Effects for Nonlinear Frequency Modulation Signal and Correction Method},
year = {2022},
issn = {1558-0571},
pages = {1-5},
volume = {19},
abstract = {Nonlinear frequency modulation (NLFM) signal can construct the signal's power spectral density (PSD) to reduce sidelobes without loss of signal-to-noise ratio. LuTan-1 (LT-1) is an L-band spaceborne synthetic aperture radar (SAR) mission which is launched in the beginning of 2022, and a high-precision NLFM signal generator is developed in LT-1. However, the existing model, i.e., the traditional frozen ionosphere model, cannot accurately describe ionospheric dispersion effects faced by the NLFM signal due to the nonlinear characteristic of the instantaneous frequency. Thus, an extended model is established in this letter to describe ionospheric dispersion effects of the NLFM signal. Then, the differences of ionospheric dispersion effects on the NLFM and linear frequency modulation (LFM) signals are compared. Afterward, a method that embedded into the focusing procedure is proposed, which aims to eliminate ionospheric dispersion effects for the NLFM signal. Finally, the hardware-in-the-loop simulations of point targets and distributed targets are performed to verify the proposed method. The method proposed in this letter is used in the ground processing system of LT-1.},
doi = {10.1109/LGRS.2022.3184471},
keywords = {Dispersion;Synthetic aperture radar;Time-frequency analysis;Focusing;Ionosphere;Frequency modulation;Spaceborne radar;Ionospheric dispersion effects;LuTan-1 (LT-1);nonlinear frequency modulation (NLFM) signal;synthetic aperture radar (SAR)},
owner = {ofrey},

}


  20. Guido Luzi, Anna Barra, Qi Gao, Pedro F.Espin-Lopez, Riccardo Palama, Oriol Monserrat, Michele Crosetto, and Xavier Colell. A low-cost active reflector and a passive corner reflector network for assisting landslide monitoring using multi-temporal InSAR. Remote Sensing Letters, 13(11):1080-1089, 2022.
    Abstract: A C-band Low-cost Active Reflector (AR)has been tested in a real experimental campaign aimed at monitoring through multi-temporal InSAR an area threatened by a landslide that occurred in 2019. To monitor and characterize the movement, a network of eight Passive Corner Reflectors and one Active Reflector were installed along a forested slope. A set of 285 interferograms obtained combining 60 Sentinel-1 SAR images were processed to evaluate the stability of the area. The AR, installed in a stable location close to the landslide, was used to provide a reference point in this low coherence area. Despite the high sensitivity of the phase response of such devices to temperature changes, the device operates with a stability of +/-2mm in deformation retrieval, a value acceptable for monitoring purposes, with a moderate range of temperature values. 

    @Article{luziEtAlRSL2022TransponderAndReflectorNetworkForLandslideMonitoring,
author = {Guido Luzi and Anna Barra and Qi Gao and Pedro F.Espin-Lopez and Riccardo Palama and Oriol Monserrat and Michele Crosetto and Xavier Colell},
journal = {Remote Sensing Letters},
title = {A low-cost active reflector and a passive corner reflector network for assisting landslide monitoring using multi-temporal {InSAR}},
year = {2022},
number = {11},
pages = {1080-1089},
volume = {13},
abstract = {A C-band Low-cost Active Reflector (AR)has been tested in a real experimental campaign aimed at monitoring through multi-temporal InSAR an area threatened by a landslide that occurred in 2019. To monitor and characterize the movement, a network of eight Passive Corner Reflectors and one Active Reflector were installed along a forested slope. A set of 285 interferograms obtained combining 60 Sentinel-1 SAR images were processed to evaluate the stability of the area. The AR, installed in a stable location close to the landslide, was used to provide a reference point in this low coherence area. Despite the high sensitivity of the phase response of such devices to temperature changes, the device operates with a stability of +/-2mm in deformation retrieval, a value acceptable for monitoring purposes, with a moderate range of temperature values.},
doi = {10.1080/2150704X.2022.2122891},
owner = {ofrey},
publisher = {Taylor \& Francis},

}


  21. Yasser Maghsoudi, Andrew J. Hooper, Tim J. Wright, Milan Lazecky, and Homa Ansari. Characterizing and correcting phase biases in short-term, multilooked interferograms. Remote Sensing of Environment, 275:113022, 2022. Keyword(s): InSAR, Phase bias, Fading signal, Correction, Closure Phase, SAR Interferometry, Radar interferometry, Time Series, Interferometric Stack, ground motion, deformation, subsidence, displacement, monitoring.
    Abstract: Interferometric Synthetic Aperture Radar (InSAR) is widely used to measure deformation of the Earth's surface over large areas and long time periods. A common strategy to overcome coherence loss in long-term interferograms is to use multiple multilooked shorter interferograms, which can cover the same time period but maintain coherence. However, it has recently been shown that using this strategy can introduce a bias (also referred to as a ``fading signal'') in the interferometric phase. We isolate the signature of the phase bias by constructing ``daisy chain'' sums of short-term interferograms of different length covering identical 1-year time intervals. This shows that the shorter interferograms are more affected by this phenomenon and the degree of the effect depends on ground cover types; cropland and forested pixels have significantly larger bias than urban pixels and the bias for cropland mimics subsidence throughout the year, whereas forests mimics subsidence in the spring and heave in the autumn. We, propose a method for correcting the phase bias, based on the assumption, borne out by our observations, that the bias in an interferogram is linearly related to the sum of the bias in shorter interferograms spanning the same time. We tested the algorithm over a study area in western Turkey by comparing average velocities against results from a phase linking approach, which estimates the single primary phases from all the interferometric pairs, and has been shown to be almost insensitive to the phase bias. Our corrected velocities agree well with those from a phase linking approach. Our approach can be applied to global compilations of short-term interferograms and provides accurate long-term velocity estimation without a requirement for coherence in long-term interferograms. 

    @Article{maghsoudiHooperWrightLazeckyAnsariRSE2022CorrectionPhaseBiasesInShortTermMultilookedInterferogramsClosurePhase,
author = {Yasser Maghsoudi and Andrew J. Hooper and Tim J. Wright and Milan Lazecky and Homa Ansari},
journal = {Remote Sensing of Environment},
title = {Characterizing and correcting phase biases in short-term, multilooked interferograms},
year = {2022},
issn = {0034-4257},
pages = {113022},
volume = {275},
abstract = {Interferometric Synthetic Aperture Radar (InSAR) is widely used to measure deformation of the Earth's surface over large areas and long time periods. A common strategy to overcome coherence loss in long-term interferograms is to use multiple multilooked shorter interferograms, which can cover the same time period but maintain coherence. However, it has recently been shown that using this strategy can introduce a bias (also referred to as a ``fading signal'') in the interferometric phase. We isolate the signature of the phase bias by constructing ``daisy chain'' sums of short-term interferograms of different length covering identical 1-year time intervals. This shows that the shorter interferograms are more affected by this phenomenon and the degree of the effect depends on ground cover types; cropland and forested pixels have significantly larger bias than urban pixels and the bias for cropland mimics subsidence throughout the year, whereas forests mimics subsidence in the spring and heave in the autumn. We, propose a method for correcting the phase bias, based on the assumption, borne out by our observations, that the bias in an interferogram is linearly related to the sum of the bias in shorter interferograms spanning the same time. We tested the algorithm over a study area in western Turkey by comparing average velocities against results from a phase linking approach, which estimates the single primary phases from all the interferometric pairs, and has been shown to be almost insensitive to the phase bias. Our corrected velocities agree well with those from a phase linking approach. Our approach can be applied to global compilations of short-term interferograms and provides accurate long-term velocity estimation without a requirement for coherence in long-term interferograms.},
doi = {https://doi.org/10.1016/j.rse.2022.113022},
file = {:maghsoudiHooperWrightLazeckyAnsariRSE2022CorrectionPhaseBiasesInShortTermMultilookedInterferogramsClosurePhase.pdf:PDF},
keywords = {InSAR, Phase bias, Fading signal, Correction, Closure Phase, SAR Interferometry, Radar interferometry, Time Series, Interferometric Stack, ground motion, deformation, subsidence, displacement, monitoring},
owner = {ofrey},
priority = {prio2},
url = {https://www.sciencedirect.com/science/article/pii/S0034425722001365},

}


  22. Michele Manunta and Yasir Muhammad. A Novel Algorithm Based on Compressive Sensing to Mitigate Phase Unwrapping Errors in Multitemporal DInSAR Approaches. IEEE Transactions on Geoscience and Remote Sensing, 60:1-20, 2022.
    Abstract: In this work, we present a new method based on the compressive sensing (CS) theory to correct phase unwrapping (PhU) errors in the multitemporal sequence of interferograms exploited by advanced differential interferometric synthetic aperture radar (DInSAR) techniques to generate deformation time series. The developed algorithm estimates the PhU errors by using a modified $L_{1}$ -norm estimator applied to the interferometric network built in the temporal/spatial baseline plane. Indeed, in order to search the minimum $L_{1}$ -norm sparse solution, we apply the iterative reweighted least-squares method with an improved weight function that takes account of the baseline characteristics of the interferometric pairs. Moreover, we also introduce a quality function to identify those solutions that have no physical meaning. Although the proposed approach can be applied to different multitemporal DInSAR approaches, our analysis is tailored to the full-resolution small baseline subset (SBAS) processing chain that we properly modify to implement the proposed CS-based algorithm. To assess the performance of the developed technique, we carry out an extended experimental analysis based on simulated and real SAR data. In particular, we process two wide SAR datasets acquired by Sentinel-1 and COSMO-SkyMed constellations over central Italy between 2011 and 2019. The achieved experimental results clearly demonstrate the effectiveness of the developed approach in retrieving PhU errors and generating displacement time series related to strongly nonlinear deformation phenomena. Indeed, the developed CS-based technique significantly increases the number of detected coherent points and improves the accuracy of the retrieved deformation time series. 

    @Article{manuntaMuhammadTGRS2022CompressiveSensingBasedMitigationOfPhaseUnwrappingErrorsInDInSAR,
author = {Manunta, Michele and Muhammad, Yasir},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
title = {A Novel Algorithm Based on Compressive Sensing to Mitigate Phase Unwrapping Errors in Multitemporal DInSAR Approaches},
year = {2022},
issn = {1558-0644},
pages = {1-20},
volume = {60},
abstract = {In this work, we present a new method based on the compressive sensing (CS) theory to correct phase unwrapping (PhU) errors in the multitemporal sequence of interferograms exploited by advanced differential interferometric synthetic aperture radar (DInSAR) techniques to generate deformation time series. The developed algorithm estimates the PhU errors by using a modified $L_{1}$ -norm estimator applied to the interferometric network built in the temporal/spatial baseline plane. Indeed, in order to search the minimum $L_{1}$ -norm sparse solution, we apply the iterative reweighted least-squares method with an improved weight function that takes account of the baseline characteristics of the interferometric pairs. Moreover, we also introduce a quality function to identify those solutions that have no physical meaning. Although the proposed approach can be applied to different multitemporal DInSAR approaches, our analysis is tailored to the full-resolution small baseline subset (SBAS) processing chain that we properly modify to implement the proposed CS-based algorithm. To assess the performance of the developed technique, we carry out an extended experimental analysis based on simulated and real SAR data. In particular, we process two wide SAR datasets acquired by Sentinel-1 and COSMO-SkyMed constellations over central Italy between 2011 and 2019. The achieved experimental results clearly demonstrate the effectiveness of the developed approach in retrieving PhU errors and generating displacement time series related to strongly nonlinear deformation phenomena. Indeed, the developed CS-based technique significantly increases the number of detected coherent points and improves the accuracy of the retrieved deformation time series.},
doi = {10.1109/TGRS.2021.3079158},
owner = {ofrey},

}


  23. R. Naderpour, M. Schwank, D. Houtz, and C. Matzler. L-Band Radiometry of Alpine Seasonal Snow Cover: 4 Years at the Davos-Laret Remote Sensing Field Laboratory. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15:8199-8220, 2022. 
    @Article{Naderpour2022,
author = {Naderpour, R. and Schwank, M. and Houtz, D. and Matzler, C.},
journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
title = {L-Band Radiometry of Alpine Seasonal Snow Cover: 4 Years at the Davos-Laret Remote Sensing Field Laboratory},
year = {2022},
pages = {8199-8220},
volume = {15},
doi = {10.1109/JSTARS.2022.3195614},
owner = {ofrey},

}


  24. Reza Naderpour, Mike Schwank, Derek Houtz, Charles Werner, and Christian Mätzler. Wideband Backscattering From Alpine Snow Cover: A Full-Season Study. IEEE Transactions on Geoscience and Remote Sensing, 60(4302215):1-15, 2022. Keyword(s): Radar, remote sensing, snow, WBSCAT, wide-band scatterometer, ESA SnowLab.
    Abstract: This article experimentally investigates relationships between copol backscattering at a wide range of frequencies (L- to Ka-bands) and snow-ground state parameters (SPs) in different evolution phases during the full winter cycle of 2019/2020. Backscattering coefficients from 1 to 40 GHz, in situ snow-ground SPs, and meteorological data are measured at the Davos-Laret Remote Sensing Field Laboratory (Switzerland). Relative strengths of the snow-ground system's three primary scattering elements (air-snow interface, snow volume, and snow-ground interface) on backscattering are assessed. An anticorrelation between reasonably high snow wetness and backscattering coefficient is found, especially at higher microwave frequencies. For small amounts of snow wetness, backscatter coefficients at L- and S-bands are intensified via increasing snow volume and snow surface scattering. Snow-ground SPs influence backscattering according to their characteristic time scales of temporal evolution. Under dry snow conditions and at low and intermediate frequencies, ground permittivity is the major influencer of backscatter at a time scale of roughly two weeks. Snowfall is the major influencer of backscatter at a time scale of a few hours to a few days. The findings of this article are valuable to the development of retrieval algorithms using machine learning while maintaining a grasp on the ongoing physical processes. Another key message is that multifrequency active microwave measurements are critical to maximize the number of retrievable SPs and their estimation accuracy. For example, while Ka-band performs well in the detection of snow cover, L-band measurements are more responsive to changes of snow water equivalent (SWE) under moist or wet snow conditions. 

    @Article{naderpourSchwankWernerMatzlerTGRS2022WBSCATandELBARATimeSeries,
author = {Naderpour, Reza and Schwank, Mike and Houtz, Derek and Werner, Charles and M\"atzler, Christian},
journal = {{IEEE} Transactions on Geoscience and Remote Sensing},
title = {Wideband Backscattering From Alpine Snow Cover: A Full-Season Study},
year = {2022},
issn = {1558-0644},
number = {4302215},
pages = {1-15},
volume = {60},
abstract = {This article experimentally investigates relationships between copol backscattering at a wide range of frequencies (L- to Ka-bands) and snow-ground state parameters (SPs) in different evolution phases during the full winter cycle of 2019/2020. Backscattering coefficients from 1 to 40 GHz, in situ snow-ground SPs, and meteorological data are measured at the Davos-Laret Remote Sensing Field Laboratory (Switzerland). Relative strengths of the snow-ground system's three primary scattering elements (air-snow interface, snow volume, and snow-ground interface) on backscattering are assessed. An anticorrelation between reasonably high snow wetness and backscattering coefficient is found, especially at higher microwave frequencies. For small amounts of snow wetness, backscatter coefficients at L- and S-bands are intensified via increasing snow volume and snow surface scattering. Snow-ground SPs influence backscattering according to their characteristic time scales of temporal evolution. Under dry snow conditions and at low and intermediate frequencies, ground permittivity is the major influencer of backscatter at a time scale of roughly two weeks. Snowfall is the major influencer of backscatter at a time scale of a few hours to a few days. The findings of this article are valuable to the development of retrieval algorithms using machine learning while maintaining a grasp on the ongoing physical processes. Another key message is that multifrequency active microwave measurements are critical to maximize the number of retrievable SPs and their estimation accuracy. For example, while Ka-band performs well in the detection of snow cover, L-band measurements are more responsive to changes of snow water equivalent (SWE) under moist or wet snow conditions.},
doi = {10.1109/TGRS.2021.3112772},
file = {:naderpourSchwankWernerMatzlerTGRS2022WBSCATandELBARATimeSeries.pdf:PDF},
keywords = {Radar, remote sensing, snow, WBSCAT, wide-band scatterometer, ESA SnowLab},
owner = {ofrey},
publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},

}


  25. Ahmad Naghavi, Mohammad Sadegh Fazel, Mojtaba Beheshti, and Ehsan Yazdian. A sequential MUSIC algorithm for scatterers detection in SAR tomography enhanced by a robust covariance estimator. Digital Signal Processing, pp 103621, 2022. Keyword(s): SAR Processing, SAR Tomography, TomoSAR, Tomography, MUSIC, DOA, Multiple Signal Classification, Synthetic aperture radar tomography (TomoSAR), Scatterers detection, Sequential MUSIC algorithm, Covariance matrix estimation.
    Abstract: Synthetic aperture radar (SAR) tomography (TomoSAR) is an appealing tool for the extraction of height information of urban infrastructures. Due to the widespread applications of the MUSIC algorithm in source localization, it is a suitable solution in TomoSAR when multiple snapshots (looks) are available. While the classical MUSIC algorithm aims to estimate the whole reflectivity profile of scatterers, sequential MUSIC algorithms are suited for the detection of sparse point-like scatterers. In this class of methods, successive cancellation is performed through orthogonal complement projections on the MUSIC power spectrum. In this work, a new sequential MUSIC algorithm named recursive covariance cancelled MUSIC (RCC-MUSIC), is proposed. This method brings higher accuracy in comparison with the previous sequential methods at the cost of a negligible increase in computational cost. Furthermore, to improve the performance of RCC-MUSIC, it is combined with the recent method of covariance matrix estimation called correlation subspace. Utilizing the correlation subspace method results in a denoised covariance matrix which in turn, increases the accuracy of subspace-based methods. Several numerical examples are presented to compare the performance of the proposed method with the relevant state-of-the-art methods. As a subspace method, simulation results demonstrate the efficiency of the proposed method in terms of estimation accuracy and computational load. 

    @Article{naghaviFazelBeheshtiYazdianDSP2022SequentialMUSICforSARTomographyEnhancedByRobustCovarianceEstimator,
author = {Ahmad Naghavi and Mohammad Sadegh Fazel and Mojtaba Beheshti and Ehsan Yazdian},
journal = {Digital Signal Processing},
title = {A sequential MUSIC algorithm for scatterers detection in SAR tomography enhanced by a robust covariance estimator},
year = {2022},
issn = {1051-2004},
pages = {103621},
abstract = {Synthetic aperture radar (SAR) tomography (TomoSAR) is an appealing tool for the extraction of height information of urban infrastructures. Due to the widespread applications of the MUSIC algorithm in source localization, it is a suitable solution in TomoSAR when multiple snapshots (looks) are available. While the classical MUSIC algorithm aims to estimate the whole reflectivity profile of scatterers, sequential MUSIC algorithms are suited for the detection of sparse point-like scatterers. In this class of methods, successive cancellation is performed through orthogonal complement projections on the MUSIC power spectrum. In this work, a new sequential MUSIC algorithm named recursive covariance cancelled MUSIC (RCC-MUSIC), is proposed. This method brings higher accuracy in comparison with the previous sequential methods at the cost of a negligible increase in computational cost. Furthermore, to improve the performance of RCC-MUSIC, it is combined with the recent method of covariance matrix estimation called correlation subspace. Utilizing the correlation subspace method results in a denoised covariance matrix which in turn, increases the accuracy of subspace-based methods. Several numerical examples are presented to compare the performance of the proposed method with the relevant state-of-the-art methods. As a subspace method, simulation results demonstrate the efficiency of the proposed method in terms of estimation accuracy and computational load.},
doi = {https://doi.org/10.1016/j.dsp.2022.103621},
file = {:naghaviFazelBeheshtiYazdianDSP2022SequentialMUSICforSARTomographyEnhancedByRobustCovarianceEstimator.pdf:PDF},
keywords = {SAR Processing, SAR Tomography, TomoSAR, Tomography, MUSIC, DOA, Multiple Signal Classification, Synthetic aperture radar tomography (TomoSAR), Scatterers detection, Sequential MUSIC algorithm, Covariance matrix estimation},
owner = {ofrey},
url = {https://www.sciencedirect.com/science/article/pii/S105120042200238X},

}


  26. Maria I. Navarro-Hernandez, Javier Valdes-Abellan, Roberto Tomas, Juan M. Lopez-Sanchez, Pablo Ezquerro, Guadalupe Bru, Roberta Boni, Claudia Meisina, and Gerardo Herrera. ValInSAR: A Systematic Approach for the Validation of Differential SAR Interferometry in Land Subsidence Areas. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15:3650-3671, 2022. Keyword(s): SAR Processing, Interferometry, SAR Interferometry, Persistent Scatterer Interferometry, PSI, Validation, reflector, deformation, displacement, ground motion.
    Abstract: Land subsidence is a natural or anthropogenic process triggering the settlement of the Earth's surface. When this phenomenon is induced by groundwater withdrawal, compaction of unconsolidated sediments causes land displacement. Differential interferometric synthetic aperture radar (DInSAR) is widely used nowadays to monitor subsidence over extensive areas. However, validation of DInSAR measurements with in-situ techniques is lacking in many case studies, reducing the reliability of further analyses. The aim of this article is to propose a systematic methodology to validate DInSAR measurements with in-situ techniques to obtain reliable subsidence measurements. The article provides a literature review of the most common approaches to validate DInSAR measurements and a description of the proposed systematic methodology, which is supported by a MATLAB open-source code. The methodology allows the analysis of both DInSAR-based velocity and displacement time series. We propose a set of statistics to assess the accuracy of the DInSAR estimates. For this purpose, RMSE parameters have been normalized with the range and the average of the in-situ deformation values. Moreover, combining these normalized parameters with the Pearson correlation coefficient (R2), a classification scheme is recommended for accepting/rejecting the DInSAR data for further analyses. This methodology has been applied in three study areas characterized by very well-documented subsidence processes: The Alto Guadalent�n Valley and Murcia City in Spain, and San Luis Potosi in Mexico. 

    @Article{navarroHernandezEtAlJSTARS2022ValidationOfDInSARInSubsidenceAreas,
author = {Navarro-Hernandez, Maria I. and Valdes-Abellan, Javier and Tomas, Roberto and Lopez-Sanchez, Juan M. and Ezquerro, Pablo and Bru, Guadalupe and Boni, Roberta and Meisina, Claudia and Herrera, Gerardo},
journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
title = {{ValInSAR}: A Systematic Approach for the Validation of Differential {SAR} Interferometry in Land Subsidence Areas},
year = {2022},
issn = {2151-1535},
pages = {3650-3671},
volume = {15},
abstract = {Land subsidence is a natural or anthropogenic process triggering the settlement of the Earth's surface. When this phenomenon is induced by groundwater withdrawal, compaction of unconsolidated sediments causes land displacement. Differential interferometric synthetic aperture radar (DInSAR) is widely used nowadays to monitor subsidence over extensive areas. However, validation of DInSAR measurements with in-situ techniques is lacking in many case studies, reducing the reliability of further analyses. The aim of this article is to propose a systematic methodology to validate DInSAR measurements with in-situ techniques to obtain reliable subsidence measurements. The article provides a literature review of the most common approaches to validate DInSAR measurements and a description of the proposed systematic methodology, which is supported by a MATLAB open-source code. The methodology allows the analysis of both DInSAR-based velocity and displacement time series. We propose a set of statistics to assess the accuracy of the DInSAR estimates. For this purpose, RMSE parameters have been normalized with the range and the average of the in-situ deformation values. Moreover, combining these normalized parameters with the Pearson correlation coefficient (R2), a classification scheme is recommended for accepting/rejecting the DInSAR data for further analyses. This methodology has been applied in three study areas characterized by very well-documented subsidence processes: The Alto Guadalent�n Valley and Murcia City in Spain, and San Luis Potosi in Mexico.},
doi = {10.1109/JSTARS.2022.3171517},
file = {:navarroHernandezEtAlJSTARS2022ValidationOfDInSARInSubsidenceAreas.pdf:PDF},
keywords = {SAR Processing, Interferometry, SAR Interferometry, Persistent Scatterer Interferometry, PSI, Validation, reflector, deformation, displacement, ground motion},
owner = {ofrey},

}


  27. F. Paul, L. Piermattei, D. Treichler, L. Gilbert, L. Girod, A. Kääb, L. Libert, T. Nagler, T. Strozzi, and J. Wuite. Three different glacier surges at a spot: What satellites observe and what not. Cryosphere, 16(6):2505-2526, 2022. 
    @Article{Paul2022,
author = {Paul, F. and Piermattei, L. and Treichler, D. and Gilbert, L. and Girod, L. and K\"a\"ab, A. and Libert, L. and Nagler, T. and Strozzi, T. and Wuite, J.},
journal = {Cryosphere},
title = {Three different glacier surges at a spot: What satellites observe and what not},
year = {2022},
number = {6},
pages = {2505-2526},
volume = {16},
doi = {10.5194/tc-16-2505-2022},
owner = {ofrey},

}


  28. G. Picard, H. Läwe, and C. Mätzler. Brief communication: A continuous formulation of microwave scattering from fresh snow to bubbly ice from first principles. Cryosphere, 16(9):3861-3866, 2022. 
    @Article{Picard2022,
author = {Picard, G. and L\"awe, H. and M\"atzler, C.},
journal = {Cryosphere},
title = {Brief communication: A continuous formulation of microwave scattering from fresh snow to bubbly ice from first principles},
year = {2022},
number = {9},
pages = {3861-3866},
volume = {16},
doi = {10.5194/tc-16-3861-2022},
owner = {ofrey},

}


  29. Melody Sandells, Henning Loewe, Ghislain Picard, Marie Dumont, Richard Essery, Nicolas Floury, Anna Kontu, Juha Lemmetyinen, William Maslanka, Samuel Morin, Andreas Wiesmann, and Christian Matzler. X-Ray Tomography-Based Microstructure Representation in the Snow Microwave Radiative Transfer Model. IEEE Transactions on Geoscience and Remote Sensing, 60(4301115):1-15, 2022. Keyword(s): Snow, Microstructure, Snow microstructure, X-ray, tomography, X-ray tomography, microwave, microwave scattering, SMRT, SMRTmodel, snow, snow microwave radiative transfer (SMRT), microwave remote sensing, radar, radar remote sensing, Nordic Snow Radar Experiment, NoSREx.
    Abstract: The modular Snow Microwave Radiative Transfer (SMRT) model simulates microwave scattering behavior in snow via different selectable theories and snow microstructure representations, which is well suited to intercomparisons analyses. Here, five microstructure models were parameterized from X-ray tomography and thin-section images of snow samples and evaluated with SMRT. Three field experiments provided observations of scattering and absorption coefficients, brightness temperature, and/or backscatter with the increasing complexity of snowpack. These took place in Sodankyla, Finland, and Weissfluhjoch, Switzerland. Simulations of scattering and absorption coefficients agreed well with observations, with higher errors for snow with predominantly vertical structures. For simulation of brightness temperature, difficulty in retrieving stickiness with the Sticky Hard Sphere microstructure model resulted in relatively poor performance for two experiments, but good agreement for the third. Exponential microstructure gave generally good results, near to the best performing models for two field experiments. The Independent Sphere model gave intermediate results. New Teubner-Strey and Gaussian Random Field models demonstrated the advantages of SMRT over microwave models with restricted microstructural geometry. Relative model performance is assessed by the quality of the microstructure model fit to micro-computed tomography (CT) data and further improvements may be possible with different fitting techniques. Careful consideration of simulation stratigraphy is required in this new era of high-resolution microstructure measurement as layers thinner than the wavelength introduce artificial scattering boundaries not seen by the instrument. 

    @Article{sandellsEtAlTGRS2022XRayTomographyBasedMicrosctructureInTheSnowMicrowaveRadiativeTransferModel,
author = {Sandells, Melody and Loewe, Henning and Picard, Ghislain and Dumont, Marie and Essery, Richard and Floury, Nicolas and Kontu, Anna and Lemmetyinen, Juha and Maslanka, William and Morin, Samuel and Wiesmann, Andreas and Matzler, Christian},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
title = {{X}-Ray Tomography-Based Microstructure Representation in the Snow Microwave Radiative Transfer Model},
year = {2022},
issn = {1558-0644},
number = {4301115},
pages = {1-15},
volume = {60},
abstract = {The modular Snow Microwave Radiative Transfer (SMRT) model simulates microwave scattering behavior in snow via different selectable theories and snow microstructure representations, which is well suited to intercomparisons analyses. Here, five microstructure models were parameterized from X-ray tomography and thin-section images of snow samples and evaluated with SMRT. Three field experiments provided observations of scattering and absorption coefficients, brightness temperature, and/or backscatter with the increasing complexity of snowpack. These took place in Sodankyla, Finland, and Weissfluhjoch, Switzerland. Simulations of scattering and absorption coefficients agreed well with observations, with higher errors for snow with predominantly vertical structures. For simulation of brightness temperature, difficulty in retrieving stickiness with the Sticky Hard Sphere microstructure model resulted in relatively poor performance for two experiments, but good agreement for the third. Exponential microstructure gave generally good results, near to the best performing models for two field experiments. The Independent Sphere model gave intermediate results. New Teubner-Strey and Gaussian Random Field models demonstrated the advantages of SMRT over microwave models with restricted microstructural geometry. Relative model performance is assessed by the quality of the microstructure model fit to micro-computed tomography (CT) data and further improvements may be possible with different fitting techniques. Careful consideration of simulation stratigraphy is required in this new era of high-resolution microstructure measurement as layers thinner than the wavelength introduce artificial scattering boundaries not seen by the instrument.},
doi = {10.1109/TGRS.2021.3086412},
file = {:sandellsEtAlTGRS2022XRayTomographyBasedMicrosctructureInTheSnowMicrowaveRadiativeTransferModel.pdf:PDF},
keywords = {Snow, Microstructure, Snow microstructure, X-ray, tomography, X-ray tomography, microwave, microwave scattering, SMRT, SMRTmodel, snow, snow microwave radiative transfer (SMRT), microwave remote sensing, radar, radar remote sensing, Nordic Snow Radar Experiment, NoSREx},

}


  30. M. Santoro, O. Cartus, and J.E.S. Fransson. Dynamics of the Swedish forest carbon pool between 2010 and 2015 estimated from satellite L-band SAR observations. Remote Sensing of Environment, 270(112846), 2022. 
    @Article{Santoro2022,
author = {Santoro, M. and Cartus, O. and Fransson, J.E.S.},
journal = {Remote Sensing of Environment},
title = {Dynamics of the Swedish forest carbon pool between 2010 and 2015 estimated from satellite {L}-band {SAR} observations},
year = {2022},
number = {112846},
volume = {270},
art_number = {112846},
doi = {10.1016/j.rse.2021.112846},
owner = {ofrey},

}


  31. M. Santoro, O. Cartus, U. Wegmüller, S. Besnard, N. Carvalhais, A. Araza, M. Herold, J. Liang, J. Cavlovic, and M.E. Engdahl. Global estimation of above-ground biomass from spaceborne C-band scatterometer observations aided by LiDAR metrics of vegetation structure. Remote Sensing of Environment, 279(113114), 2022. 
    @Article{Santoro2022,
author = {Santoro, M. and Cartus, O. and Wegm\"uller, U. and Besnard, S. and Carvalhais, N. and Araza, A. and Herold, M. and Liang, J. and Cavlovic, J. and Engdahl, M.E.},
journal = {Remote Sensing of Environment},
title = {Global estimation of above-ground biomass from spaceborne C-band scatterometer observations aided by LiDAR metrics of vegetation structure},
year = {2022},
number = {113114},
volume = {279},
doi = {10.1016/j.rse.2022.113114},
owner = {ofrey},

}


  32. Gustavo H. X. Shiroma, Marco Lavalle, and Sean M. Buckley. An Area-Based Projection Algorithm for SAR Radiometric Terrain Correction and Geocoding. IEEE Transactions on Geoscience and Remote Sensing, 60:1-23, 2022.
    Abstract: This article describes a projection algorithm between radar and map coordinates based on the representation of radar samples as area elements (AEs) rather than point elements. Each AE on the map grid (geographic grid) is associated with a number of radar grid samples that intersect completely or partially the AE. The association enables the geocoding (i.e., the map projection of radar imagery) with adaptive multilooking, accurately accounting for all radar samples contributing to the geocoded elements according to topography and radar geometry. By using averaging rather than interpolation, the proposed projection does not suffer from interpolation overfitting. The area-based geocoding also enables the generation of the geocoded polarimetric covariance matrix (GCOV) and geocoded synthetic aperture radar (SAR) interferograms with adaptive multilooking. Analogously, the slant-range projection of geocoded data is improved by projecting geographic grid pixels onto the radar grid according to their corresponding location based on the radar geometry without leaving gaps. This approach is used to reduce the computation time of previously published radiometric terrain correction (RTC) algorithms, performing 3.6-6.5 times faster over multilooked data and up to 26.3 times faster over single-look data. We demonstrate the strengths of the proposed area projection (AP) algorithm for RTC and geocoding using Uninhabited Aerial Vehicle SAR (UAVSAR), Sentinel-1B, and ALOS-2/PALSAR-2 data, and evaluate the results in the context of the upcoming NASA-ISRO SAR (NISAR) mission. 

    @Article{shiromaLavalleBuckleyTGRS2022AreaBasedProjectionAlgorithmRTCandGeocoding,
author = {Shiroma, Gustavo H. X. and Lavalle, Marco and Buckley, Sean M.},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
title = {An Area-Based Projection Algorithm for SAR Radiometric Terrain Correction and Geocoding},
year = {2022},
issn = {1558-0644},
pages = {1-23},
volume = {60},
abstract = {This article describes a projection algorithm between radar and map coordinates based on the representation of radar samples as area elements (AEs) rather than point elements. Each AE on the map grid (geographic grid) is associated with a number of radar grid samples that intersect completely or partially the AE. The association enables the geocoding (i.e., the map projection of radar imagery) with adaptive multilooking, accurately accounting for all radar samples contributing to the geocoded elements according to topography and radar geometry. By using averaging rather than interpolation, the proposed projection does not suffer from interpolation overfitting. The area-based geocoding also enables the generation of the geocoded polarimetric covariance matrix (GCOV) and geocoded synthetic aperture radar (SAR) interferograms with adaptive multilooking. Analogously, the slant-range projection of geocoded data is improved by projecting geographic grid pixels onto the radar grid according to their corresponding location based on the radar geometry without leaving gaps. This approach is used to reduce the computation time of previously published radiometric terrain correction (RTC) algorithms, performing 3.6-6.5 times faster over multilooked data and up to 26.3 times faster over single-look data. We demonstrate the strengths of the proposed area projection (AP) algorithm for RTC and geocoding using Uninhabited Aerial Vehicle SAR (UAVSAR), Sentinel-1B, and ALOS-2/PALSAR-2 data, and evaluate the results in the context of the upcoming NASA-ISRO SAR (NISAR) mission.},
doi = {10.1109/TGRS.2022.3147472},
owner = {ofrey},

}


  33. Marcel Stefko, Othmar Frey, Charles Werner, and Irena Hajnsek. Calibration and Operation of a Bistatic Real-Aperture Polarimetric-Interferometric Ku-Band Radar. IEEE Transactions on Geoscience and Remote Sensing, 60(5106719):1-19, 2022.
    Abstract: This article presents the bistatic operation mode and the performance analysis of KAPRI, a terrestrial frequency-modulated continuous wave (FMCW) Ku-band polarimetric radar interferometer capable of acquiring bistatic full-polarimetric datasets with high spatial and temporal resolution. In the bistatic configuration, the system is composed of two independently-operating KAPRI devices, one serving as a primary transmitter-receiver, and the other as a secondary receiver. The secondary bistatic dataset is affected by possible offsets between the two devices' reference clocks, as well as distortions arising from geometry. To correct for this, we present a two-chirp bistatic FMCW signal model which accounts for the distortions, and a reference chirp transmission procedure which allows correcting the clock offsets in the deramped signal time domain. The second challenge of operation of a bistatic polarimetric system is polarimetric calibration, since it is not possible to employ purely monostatic targets such as corner reflectors. For this purpose we developed a novel active calibration device VSPARC (Variable-Signature Polarimetric Active Radar Calibrator), designed for monostatic and bistatic calibration of all polarimetric channels. VSPARC and its associated novel polarimetric calibration method were then used to achieve full calibration of both KAPRI devices with polarimetric phase calibration accuracy of 20 deg and 30 dB polarization purity in field conditions. This article thus presents a complete measurement configuration and data processing pipeline necessary for synchronization, coregistration, and polarimetric calibration of bistatic and monostatic datasets acquired by a real-aperture FMCW radar. 

    @Article{stefkoFreyWernerHajnsekTGRS2022CalibrationAndOperationOfBistaticPolGPRI,
author = {Stefko, Marcel and Frey, Othmar and Werner, Charles and Hajnsek, Irena},
journal = {{IEEE} Transactions on Geoscience and Remote Sensing},
title = {Calibration and Operation of a Bistatic Real-Aperture Polarimetric-Interferometric Ku-Band Radar},
year = {2022},
issn = {1558-0644},
number = {5106719},
pages = {1--19},
volume = {60},
abstract = {This article presents the bistatic operation mode and the performance analysis of KAPRI, a terrestrial frequency-modulated continuous wave (FMCW) Ku-band polarimetric radar interferometer capable of acquiring bistatic full-polarimetric datasets with high spatial and temporal resolution. In the bistatic configuration, the system is composed of two independently-operating KAPRI devices, one serving as a primary transmitter-receiver, and the other as a secondary receiver. The secondary bistatic dataset is affected by possible offsets between the two devices' reference clocks, as well as distortions arising from geometry. To correct for this, we present a two-chirp bistatic FMCW signal model which accounts for the distortions, and a reference chirp transmission procedure which allows correcting the clock offsets in the deramped signal time domain. The second challenge of operation of a bistatic polarimetric system is polarimetric calibration, since it is not possible to employ purely monostatic targets such as corner reflectors. For this purpose we developed a novel active calibration device VSPARC (Variable-Signature Polarimetric Active Radar Calibrator), designed for monostatic and bistatic calibration of all polarimetric channels. VSPARC and its associated novel polarimetric calibration method were then used to achieve full calibration of both KAPRI devices with polarimetric phase calibration accuracy of 20 deg and 30 dB polarization purity in field conditions. This article thus presents a complete measurement configuration and data processing pipeline necessary for synchronization, coregistration, and polarimetric calibration of bistatic and monostatic datasets acquired by a real-aperture FMCW radar.},
doi = {10.1109/TGRS.2021.3121466},
file = {:stefkoFreyWernerHajnsekTGRS2022CalibrationAndOperationOfBistaticPolGPRI.pdf:PDF},
owner = {ofrey},
publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},

}


  34. Marcel Stefko, Silvan Leinss, Othmar Frey, and Irena Hajnsek. Coherent backscatter enhancement in bistatic Ku-/X-band radar observations of dry snow. The Cryosphere, 16(7):2859-2879, 2022. Keyword(s): SAR Processing, Snow, Coherent backscatter enhancement, bistatic, bistatic SAR, Ku-band, X-band, radar, dry snow.
    Abstract: The coherent backscatter opposition effect (CBOE) enhances the backscatter intensity of electromagnetic waves by up to a factor of two in a very narrow cone around the direct return direction when multiple scattering occurs in a weakly absorbing, disordered medium. So far, this effect has not been investigated in terrestrial snow in the microwave spectrum. It has also received little attention in scattering models. We present the first characterization of the CBOE in dry snow using ground-based and space-borne bistatic radar systems. For a seasonal snow pack in Ku-band (17.2 GHz),we found backscatter enhancement of 50-60% (+1.8-2.0 dB) at zero bistatic angle and a peak half-width-at-half-maximum (HWHM) of 0.25 deg. In X-band (9.65 GHz), we found backscatter enhancement of at least 35% (+1.3 dB) and an estimated HWHM of 0.12 deg in the accumulation areas of glaciers in the Jungfrau-Aletsch region, Switzerland. Sampling of the peak shape at different bistatic angles allows estimating the scattering and absorption mean free paths, Lambda_T and Lambda_A. In the VV polarization, we obtained Lambda_T = 0.4 +/- 0.1 m and Lambda_A = 19 +/- 12 m at Ku-band, and Lambda_T = 2.1 +/- 0.4 m, Lambda_A = 21.8 +/- 2.7 m at X-band. The HH polarization yielded similar results. The observed backscatter enhancement is thus significant enough to require consideration in backscatter models describing monostatic and bistatic radar experiments. Enhanced backscattering beyond the Earth, on the surface of solar system bodies, has been interpreted as being caused by the presence of water ice. In agreement with this interpretation, our results confirm the presence of the CBOE at X- and Ku-band frequencies in terrestrial snow. 

    @Article{stefkoLeinssFreyHanjsek2022CoherentBackscatterEnhancementInBistaticRadarObservationsOfSnow,
author = {Marcel Stefko and Silvan Leinss and Othmar Frey and Irena Hajnsek},
journal = {The Cryosphere},
title = {Coherent backscatter enhancement in bistatic {Ku}-/{X}-band radar observations of dry snow},
year = {2022},
number = {7},
pages = {2859--2879},
volume = {16},
abstract = {The coherent backscatter opposition effect (CBOE) enhances the backscatter intensity of electromagnetic waves by up to a factor of two in a very narrow cone around the direct return direction when multiple scattering occurs in a weakly absorbing, disordered medium. So far, this effect has not been investigated in terrestrial snow in the microwave spectrum. It has also received little attention in scattering models. We present the first characterization of the CBOE in dry snow using ground-based and space-borne bistatic radar systems. For a seasonal snow pack in Ku-band (17.2 GHz),we found backscatter enhancement of 50-60% (+1.8-2.0 dB) at zero bistatic angle and a peak half-width-at-half-maximum (HWHM) of 0.25 deg. In X-band (9.65 GHz), we found backscatter enhancement of at least 35% (+1.3 dB) and an estimated HWHM of 0.12 deg in the accumulation areas of glaciers in the Jungfrau-Aletsch region, Switzerland. Sampling of the peak shape at different bistatic angles allows estimating the scattering and absorption mean free paths, Lambda_T and Lambda_A. In the VV polarization, we obtained Lambda_T = 0.4 +/- 0.1 m and Lambda_A = 19 +/- 12 m at Ku-band, and Lambda_T = 2.1 +/- 0.4 m, Lambda_A = 21.8 +/- 2.7 m at X-band. The HH polarization yielded similar results. The observed backscatter enhancement is thus significant enough to require consideration in backscatter models describing monostatic and bistatic radar experiments. Enhanced backscattering beyond the Earth, on the surface of solar system bodies, has been interpreted as being caused by the presence of water ice. In agreement with this interpretation, our results confirm the presence of the CBOE at X- and Ku-band frequencies in terrestrial snow.},
doi = {10.5194/tc-16-2859-2022},
file = {:stefkoLeinssFreyHanjsek2022CoherentBackscatterEnhancementInBistaticRadarObservationsOfSnow.pdf:PDF},
keywords = {SAR Processing, Snow, Coherent backscatter enhancement, bistatic, bistatic SAR, Ku-band, X-band, radar, dry snow},
owner = {ofrey},
publisher = {Copernicus {GmbH}},

}


  35. T. Strozzi, A. Wiesmann, A. Kääb, T. Schellenberger, and F. Paul. Ice Surface Velocity in the Eastern Arctic from Historical Satellite SAR Data. Earth System Science Data Discussions, 2022:1-42, 2022. 
    @Article{Strozzi2022,
author = {Strozzi, T. and Wiesmann, A. and K\"a\"ab, A. and Schellenberger, T. and Paul, F.},
journal = {Earth System Science Data Discussions},
title = {Ice Surface Velocity in the Eastern Arctic from Historical Satellite {SAR} Data},
year = {2022},
pages = {1-42},
volume = {2022},
doi = {10.5194/essd-2022-44},
owner = {ofrey},
url = {https://essd.copernicus.org/preprints/essd-2022-44/},

}


  36. S. Tao, J. Chave, P.-L. Frison, T. Le Toan, P. Ciais, J. Fang, J.-P. Wigneron, M. Santoro, H. Yang, X. Li, N. Labriere, and S. Saatchi. Increasing and widespread vulnerability of intact tropical rainforests to repeated droughts. Proceedings of the National Academy of Sciences of the United States of America, 119(37)(e2116626119), 2022. 
    @Article{Tao2022,
author = {Tao, S. and Chave, J. and Frison, P.-L. and Le Toan, T. and Ciais, P. and Fang, J. and Wigneron, J.-P. and Santoro, M. and Yang, H. and Li, X. and Labriere, N. and Saatchi, S.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
title = {Increasing and widespread vulnerability of intact tropical rainforests to repeated droughts},
year = {2022},
number = {e2116626119},
volume = {119(37)},
doi = {10.1073/pnas.2116626119},
owner = {ofrey},

}


  37. Leung Tsang, Michael Durand, Chris Derksen, Ana P. Barros, Do-Hyuk Kang, Hans Lievens, Hans-Peter Marshall, Jiyue Zhu, Joel Johnson, Joshua King, Juha Lemmetyinen, Melody Sandells, Nick Rutter, Paul Siqueira, Anne Nolin, Batu Osmanoglu, Carrie Vuyovich, Edward J. Kim, Drew Taylor, Ioanna Merkouriadi, Ludovic Brucker, Mahdi Navari, Marie Dumont, Richard Kelly, Rhae Sung Kim, Tien-Hao Liao, and Xiaolan Xu. Review Article: Global Monitoring of Snow Water Equivalent using High Frequency Radar Remote Sensing. The Cryosphere, 16(9):3531-3573, September 2022. Keyword(s): Snow Water Equivalent, SWE, Synthetic Aperture Radar, SAR, Review Paper. 
    @Article{tsangEtalCryosphere2022ReviewArticleGlobalMonitoringOfSWEUsingHighFrequencyRadar,
author = {Leung Tsang and Michael Durand and Chris Derksen and Ana P. Barros and Do-Hyuk Kang and Hans Lievens and Hans-Peter Marshall and Jiyue Zhu and Joel Johnson and Joshua King and Juha Lemmetyinen and Melody Sandells and Nick Rutter and Paul Siqueira and Anne Nolin and Batu Osmanoglu and Carrie Vuyovich and Edward J. Kim and Drew Taylor and Ioanna Merkouriadi and Ludovic Brucker and Mahdi Navari and Marie Dumont and Richard Kelly and Rhae Sung Kim and Tien-Hao Liao and Xiaolan Xu},
journal = {The Cryosphere},
title = {Review Article: Global Monitoring of Snow Water Equivalent using High Frequency Radar Remote Sensing},
year = {2022},
month = {sep},
number = {9},
pages = {3531--3573},
volume = {16},
doi = {10.5194/tc-2021-295},
file = {:tsangEtalCryosphere2022ReviewArticleGlobalMonitoringOfSWEUsingHighFrequencyRadar.pdf:PDF},
keywords = {Snow Water Equivalent, SWE, Synthetic Aperture Radar, SAR, Review Paper},
owner = {ofrey},
publisher = {Copernicus {GmbH}},
url = {https://tc.copernicus.org/articles/16/3531/2022/},

}


  38. Jianbing Xiang, Xiaolei Lv, Xikai Fu, and Ye Yun. Detection and Estimation Algorithm for Marine Target With Micromotion Based on Adaptive Sparse Modified-LV's Transform. IEEE Transactions on Geoscience and Remote Sensing, 60:1-17, 2022. Keyword(s): Clutter, Radar, Radar detection, Radar clutter, Frequency modulation, Fractals, Estimation, Adaptive sparse Fourier transform (ASFT), adaptive sparse-modified Lv's transform (ASMLVT), Mairne targets, micro-Doppler (m-D), modified-Lv's transform (MLVT), radar target detection and estimation.
    Abstract: Due to the complex marine environment and high-order frequency modulation (FM) on radar echo from the micromotion of the target, the effective and robust detection of a marine target with micromotion under heavy sea clutters' background is a challenging task. In this article, we propose a novel detection and estimation algorithm based on adaptive sparse modified-Lv's transform (ASMLVT). First, the micro-Doppler (m-D) characteristics of marine targets are employed and modeled as quadratic frequency-modulated (QFM) signals. Second, we modify the 2-D robust sparse Fourier transform (2-D-RSFT) and make it adaptive to the sea clutters' background, namely, 2-D adaptive sparse Fourier transform (2-D-ASFT). Then, we substitute the 2-D Fast Fourier transform (2-D-FFT) operation with 2-D-ASFT in the modified-Lv's transform (MLVT). The proposed algorithm can not only achieve good energy accumulation and accurate parametric estimation for marine targets with micromotion but is also robust to the heavy sea clutters and can greatly reduce false alarms. Besides, it has a good cross-term suppression ability to detect multitargets. Experiments with simulated and real radar datasets show that the proposed algorithm can effectively detect and estimate multitargets with micromotion under heavy sea clutter and low signal-to-clutter ratio (SCR) background. 

    @Article{Xiang2022,
author = {Xiang, Jianbing and Lv, Xiaolei and Fu, Xikai and Yun, Ye},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
title = {Detection and Estimation Algorithm for Marine Target With Micromotion Based on Adaptive Sparse Modified-LV's Transform},
year = {2022},
issn = {1558-0644},
pages = {1-17},
volume = {60},
abstract = {Due to the complex marine environment and high-order frequency modulation (FM) on radar echo from the micromotion of the target, the effective and robust detection of a marine target with micromotion under heavy sea clutters' background is a challenging task. In this article, we propose a novel detection and estimation algorithm based on adaptive sparse modified-Lv's transform (ASMLVT). First, the micro-Doppler (m-D) characteristics of marine targets are employed and modeled as quadratic frequency-modulated (QFM) signals. Second, we modify the 2-D robust sparse Fourier transform (2-D-RSFT) and make it adaptive to the sea clutters' background, namely, 2-D adaptive sparse Fourier transform (2-D-ASFT). Then, we substitute the 2-D Fast Fourier transform (2-D-FFT) operation with 2-D-ASFT in the modified-Lv's transform (MLVT). The proposed algorithm can not only achieve good energy accumulation and accurate parametric estimation for marine targets with micromotion but is also robust to the heavy sea clutters and can greatly reduce false alarms. Besides, it has a good cross-term suppression ability to detect multitargets. Experiments with simulated and real radar datasets show that the proposed algorithm can effectively detect and estimate multitargets with micromotion under heavy sea clutter and low signal-to-clutter ratio (SCR) background.},
doi = {10.1109/TGRS.2021.3138188},
keywords = {Clutter;Radar;Radar detection;Radar clutter;Frequency modulation;Fractals;Estimation;Adaptive sparse Fourier transform (ASFT);adaptive sparse-modified Lv's transform (ASMLVT);Mairne targets;micro-Doppler (m-D);modified-Lv's transform (MLVT);radar target detection and estimation},
owner = {ofrey},

}


  39. Feng Xiao, Andrea Monti Guarnieri, Zegang Ding, and Marco Manzoni. Improving the Split-Spectrum Method for Sentinel-1 Differential TOPSAR Interferometry. IEEE Geoscience and Remote Sensing Letters, 19:1-5, 2022. Keyword(s): Interferometry, Ionosphere, Synthetic aperture radar, Dispersion, Fitting, Estimation, Filtering, Differential SAR Interferometry (DInSAR), ionosphere estimation, split-spectrum method.
    Abstract: Differential SAR interferometry (DInSAR) is a useful technique used to measure small movements and surface deformation. However, ionospheric phase screens are a major error source in multipass terrain observation by progressive scans sar (TOPSAR) interferograms. In this letter, an improved split-spectrum method is proposed. First, the burst used for ionospheric phase estimation is selected through coherence, and then, the ionospheric phase of the burst is estimated based on the split-spectrum method. Finally, the TOPSAR ionospheric space-variable phase in a large scene is obtained through 2-D space-variable fitting, which avoids the complicated processing of splicing between bursts of different periods and reduces the number of unwrapping calculations for large scenes after splicing. The method can ensure that the number of calculations is reduced without loss of accuracy. Sentinel-1 TOPSAR real data processing verifies the correctness of the proposed method. 

    @Article{xiaoMontiGuarnieriDingManzoniGRSL2022ImprovedSplitSpectrumMethodForSentinel1DINSARwithTOPSAR,
author = {Xiao, Feng and Guarnieri, Andrea Monti and Ding, Zegang and Manzoni, Marco},
journal = {IEEE Geoscience and Remote Sensing Letters},
title = {Improving the Split-Spectrum Method for Sentinel-1 Differential TOPSAR Interferometry},
year = {2022},
issn = {1558-0571},
pages = {1-5},
volume = {19},
abstract = {Differential SAR interferometry (DInSAR) is a useful technique used to measure small movements and surface deformation. However, ionospheric phase screens are a major error source in multipass terrain observation by progressive scans sar (TOPSAR) interferograms. In this letter, an improved split-spectrum method is proposed. First, the burst used for ionospheric phase estimation is selected through coherence, and then, the ionospheric phase of the burst is estimated based on the split-spectrum method. Finally, the TOPSAR ionospheric space-variable phase in a large scene is obtained through 2-D space-variable fitting, which avoids the complicated processing of splicing between bursts of different periods and reduces the number of unwrapping calculations for large scenes after splicing. The method can ensure that the number of calculations is reduced without loss of accuracy. Sentinel-1 TOPSAR real data processing verifies the correctness of the proposed method.},
doi = {10.1109/LGRS.2022.3145371},
file = {:xiaoMontiGuarnieriDingManzoniGRSL2022ImprovedSplitSpectrumMethodForSentinel1DINSARwithTOPSAR.pdf:PDF},
keywords = {Interferometry;Ionosphere;Synthetic aperture radar;Dispersion;Fitting;Estimation;Filtering;Differential SAR Interferometry (DInSAR);ionosphere estimation;split-spectrum method},
owner = {ofrey},

}


  40. Chuan Xiong, Jiancheng Shi, Jinmei Pan, Haokui Xu, Tao Che, Tianjie Zhao, Yan Ren, Deyuan Geng, Tao Chen, Kaiwen Jiang, and Peng Feng. Time Series X- and Ku-Band Ground-Based Synthetic Aperture Radar Observation of Snow-Covered Soil and Its Electromagnetic Modeling. IEEE Transactions on Geoscience and Remote Sensing, 60:1-13, 2022. Keyword(s): Snow, X-band, Ku-band, times series.
    Abstract: The snow water equivalent (SWE, a measurement of the amount of water contained in snow packs) is an important variable in earth systems. Microwave remote sensing provides a possible solution for estimating the SWE globally. To support radar SWE retrieval, the snow backscattering theory needs to be studied; the forward simulation model needs to be validated against natural snow observations. In this study, a one-winter experiment to observe the time series backscattering coefficient of snow-covered bare soil is reported. This is the first long time series snow-covered soil backscattering experiment that was measured by an imaging radar. The backscattering coefficient was observed at three frequencies covering the X-band and dual-Ku bands, which are of great interest to the snow remote sensing community and are used for SWE estimation in mountains. The calibration of the synthetic aperture radar (SAR) system was conducted manually and carefully to ensure high-quality radar observation data. The observations from our experiment show that in general, the time series backscattering signature of snow-covered terrain is mainly driven by soil freezing, snow grain size growth, and snow accumulation processes. The time series observations for dry snow are modeled by backscattering models with model inputs directly calculated from field measurements. Our simulation results indicate that the time series radar backscattering at three frequencies and four polarizations can be simulated with high accuracy, including the cross-polarization channels. This study provides some key understanding of the time series signature of radar backscattering from snow and provides some key implications for SWE retrieval from radar observations. 

    @Article{xiongEtAlTGRS2022TimeSeriesOfXandKuBandGBSARObservations,
author = {Xiong, Chuan and Shi, Jiancheng and Pan, Jinmei and Xu, Haokui and Che, Tao and Zhao, Tianjie and Ren, Yan and Geng, Deyuan and Chen, Tao and Jiang, Kaiwen and Feng, Peng},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
title = {Time Series X- and Ku-Band Ground-Based Synthetic Aperture Radar Observation of Snow-Covered Soil and Its Electromagnetic Modeling},
year = {2022},
issn = {1558-0644},
pages = {1-13},
volume = {60},
abstract = {The snow water equivalent (SWE, a measurement of the amount of water contained in snow packs) is an important variable in earth systems. Microwave remote sensing provides a possible solution for estimating the SWE globally. To support radar SWE retrieval, the snow backscattering theory needs to be studied; the forward simulation model needs to be validated against natural snow observations. In this study, a one-winter experiment to observe the time series backscattering coefficient of snow-covered bare soil is reported. This is the first long time series snow-covered soil backscattering experiment that was measured by an imaging radar. The backscattering coefficient was observed at three frequencies covering the X-band and dual-Ku bands, which are of great interest to the snow remote sensing community and are used for SWE estimation in mountains. The calibration of the synthetic aperture radar (SAR) system was conducted manually and carefully to ensure high-quality radar observation data. The observations from our experiment show that in general, the time series backscattering signature of snow-covered terrain is mainly driven by soil freezing, snow grain size growth, and snow accumulation processes. The time series observations for dry snow are modeled by backscattering models with model inputs directly calculated from field measurements. Our simulation results indicate that the time series radar backscattering at three frequencies and four polarizations can be simulated with high accuracy, including the cross-polarization channels. This study provides some key understanding of the time series signature of radar backscattering from snow and provides some key implications for SWE retrieval from radar observations.},
doi = {10.1109/TGRS.2021.3071373},
file = {:xiongEtAlTGRS2022TimeSeriesOfXandKuBandGBSARObservations.pdf:PDF},
keywords = {Snow, X-band, Ku-band, times series},
owner = {ofrey},

}


  41. Y. Xu, L. Yu, P. Ciais, W. Li, M. Santoro, H. Yang, and P. Gong. Recent expansion of oil palm plantations into carbon-rich forests. Nature Sustainability, 5(7):574-577, 2022. 
    @Article{Xu2022,
author = {Xu, Y. and Yu, L. and Ciais, P. and Li, W. and Santoro, M. and Yang, H. and Gong, P.},
journal = {Nature Sustainability},
title = {Recent expansion of oil palm plantations into carbon-rich forests},
year = {2022},
number = {7},
pages = {574-577},
volume = {5},
doi = {10.1038/s41893-022-00872-1},
owner = {ofrey},

}


  42. H. Yang, P. Ciais, J.-P. Wigneron, J. Chave, O. Cartus, X. Chen, L. Fan, J.K. Green, Y. Huang, E. Joetzjer, H. Kay, D. Makowski, F. Maignan, M. Santoro, S. Tao, L. Liu, and Y. Yao. Climatic and biotic factors influencing regional declines and recovery of tropical forest biomass from the 2015/16 El Niño. Proceedings of the National Academy of Sciences of the United States of America, 119(26), 2022. 
    @Article{Yang2022,
author = {Yang, H. and Ciais, P. and Wigneron, J.-P. and Chave, J. and Cartus, O. and Chen, X. and Fan, L. and Green, J.K. and Huang, Y. and Joetzjer, E. and Kay, H. and Makowski, D. and Maignan, F. and Santoro, M. and Tao, S. and Liu, L. and Yao, Y.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
title = {Climatic and biotic factors influencing regional declines and recovery of tropical forest biomass from the 2015/16 El Ni\~no},
year = {2022},
number = {26},
volume = {119},
art_number = {e2101388119},
doi = {10.1073/pnas.2101388119},
owner = {ofrey},

}


  43. Ce Yang, Naiming Ou, Yunkai Deng, Dacheng Liu, Yanyan Zhang, Nan Wang, and Robert Wang. Pattern Synthesis Algorithm for Range Ambiguity Suppression in the LT-1 Mission via Sequential Convex Optimizations. IEEE Transactions on Geoscience and Remote Sensing, 60:1-13, 2022. Keyword(s): Synthetic aperture radar, Optimization, Antenna measurements, Phased arrays, Microwave antennas, Convex functions, Backscatter, Antenna pattern synthesis, array antenna, convex optimization, quadrature polarimetric (quad-pol), range ambiguity, synthetic aperture radar (SAR).
    Abstract: The innovative spaceborne Earth observation mission LuTan-1 (LT-1) deploys advanced full polarimetric L-band synthetic aperture radar (SAR) to obtain high-precision, multidimensional ground feature information. As the quad-pol SAR system, LT-1 suffers from strong ambiguities that degrade the quality of the observation products. Antenna pattern synthesis algorithm can suppress the range ambiguities without the increase of the azimuth ambiguities and the system complexity and therefore has great potential to improve the overall ambiguity performance of the quad-pol SAR system. However, the previous research on this kind of method is generally limited to specific situations and lacks of the analysis of actual measurement results. Based on the application requirements of LT-1, this article proposes a novel pattern synthesis algorithm that suppresses the range ambiguities via sequential convex optimizations. In simulation and comparison, the proposed algorithm effectively suppresses the strong co-polarized range ambiguities and shows the flexibility, efficiency, and stability that are significantly better than the previous algorithms. What is more, the analysis of practical performance loss is performed based on the measurement result of the LT-1 antenna, and the practicality and validity of the algorithm are strongly verified. 

    @Article{Yang2022,
author = {Yang, Ce and Ou, Naiming and Deng, Yunkai and Liu, Dacheng and Zhang, Yanyan and Wang, Nan and Wang, Robert},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
title = {Pattern Synthesis Algorithm for Range Ambiguity Suppression in the LT-1 Mission via Sequential Convex Optimizations},
year = {2022},
issn = {1558-0644},
pages = {1-13},
volume = {60},
abstract = {The innovative spaceborne Earth observation mission LuTan-1 (LT-1) deploys advanced full polarimetric L-band synthetic aperture radar (SAR) to obtain high-precision, multidimensional ground feature information. As the quad-pol SAR system, LT-1 suffers from strong ambiguities that degrade the quality of the observation products. Antenna pattern synthesis algorithm can suppress the range ambiguities without the increase of the azimuth ambiguities and the system complexity and therefore has great potential to improve the overall ambiguity performance of the quad-pol SAR system. However, the previous research on this kind of method is generally limited to specific situations and lacks of the analysis of actual measurement results. Based on the application requirements of LT-1, this article proposes a novel pattern synthesis algorithm that suppresses the range ambiguities via sequential convex optimizations. In simulation and comparison, the proposed algorithm effectively suppresses the strong co-polarized range ambiguities and shows the flexibility, efficiency, and stability that are significantly better than the previous algorithms. What is more, the analysis of practical performance loss is performed based on the measurement result of the LT-1 antenna, and the practicality and validity of the algorithm are strongly verified.},
doi = {10.1109/TGRS.2021.3099132},
keywords = {Synthetic aperture radar;Optimization;Antenna measurements;Phased arrays;Microwave antennas;Convex functions;Backscatter;Antenna pattern synthesis;array antenna;convex optimization;quadrature polarimetric (quad-pol);range ambiguity;synthetic aperture radar (SAR)},
owner = {ofrey},

}


  44. Kaiyu Zhang, Xiaolei Lv, Huiming Chai, and Jingchuan Yao. Unsupervised SAR Image Change Detection for Few Changed Area Based on Histogram Fitting Error Minimization. IEEE Transactions on Geoscience and Remote Sensing, 60:1-19, 2022. Keyword(s): Synthetic aperture radar, Remote sensing, Radar polarimetry, Change detection algorithms, Histograms, Rail transportation, Radiometry, Change detection, conditional random fields (CRFs), half-normal distribution, image thresholding, synthetic aperture radar (SAR) images, unsupervised change detection.
    Abstract: Change detection in synthetic aperture radar (SAR) images is an essential task of remote sensing image analysis. However, the thresholding procedure is the main difficulty in change detection for a few changed areas for traditional change detection methods. In this article, we propose a novel change detection method for very few changed or even none changed areas. The proposed method contains three procedures: difference image (DI) generation, thresholding, and spatial analysis. In the second procedure, a new thresholding method called histogram fitting error minimization (HFEM) is proposed for a few changed areas. HFEM is derived under the assumption that the unchanged class in the absolute-valued DI follows the half-normal distribution, and the changed class follows the Gaussian distribution. In the spatial analysis procedure, a new conditional random fields (CRF) method based on half-normal distribution is proposed to model the mutual influences among image pixels. The proposed CRF method is called half-normal CRF (HNCRF). Experiments carried out on both synthetic datasets and four real SAR datasets demonstrate the superiority of our method. Not only a few changed datasets but datasets with lots of changes are used in the experiments. The kappa coefficients of the proposed method can reach up to ten times that of the traditional method under extreme conditions. The results prove that the proposed method outperforms the traditional methods in the case of a few changed areas. Meanwhile, the proposed method can get similar results compared with traditional methods under normal conditions. 

    @Article{Zhang2022,
author = {Zhang, Kaiyu and Lv, Xiaolei and Chai, Huiming and Yao, Jingchuan},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
title = {Unsupervised SAR Image Change Detection for Few Changed Area Based on Histogram Fitting Error Minimization},
year = {2022},
issn = {1558-0644},
pages = {1-19},
volume = {60},
abstract = {Change detection in synthetic aperture radar (SAR) images is an essential task of remote sensing image analysis. However, the thresholding procedure is the main difficulty in change detection for a few changed areas for traditional change detection methods. In this article, we propose a novel change detection method for very few changed or even none changed areas. The proposed method contains three procedures: difference image (DI) generation, thresholding, and spatial analysis. In the second procedure, a new thresholding method called histogram fitting error minimization (HFEM) is proposed for a few changed areas. HFEM is derived under the assumption that the unchanged class in the absolute-valued DI follows the half-normal distribution, and the changed class follows the Gaussian distribution. In the spatial analysis procedure, a new conditional random fields (CRF) method based on half-normal distribution is proposed to model the mutual influences among image pixels. The proposed CRF method is called half-normal CRF (HNCRF). Experiments carried out on both synthetic datasets and four real SAR datasets demonstrate the superiority of our method. Not only a few changed datasets but datasets with lots of changes are used in the experiments. The kappa coefficients of the proposed method can reach up to ten times that of the traditional method under extreme conditions. The results prove that the proposed method outperforms the traditional methods in the case of a few changed areas. Meanwhile, the proposed method can get similar results compared with traditional methods under normal conditions.},
doi = {10.1109/TGRS.2022.3190977},
keywords = {Synthetic aperture radar;Remote sensing;Radar polarimetry;Change detection algorithms;Histograms;Rail transportation;Radiometry;Change detection;conditional random fields (CRFs);half-normal distribution;image thresholding;synthetic aperture radar (SAR) images;unsupervised change detection},
owner = {ofrey},

}


  45. Yujie Zheng, Heresh Fattahi, Piyush Agram, Mark Simons, and Paul Rosen. On Closure Phase and Systematic Bias in Multilooked SAR Interferometry. IEEE Transactions on Geoscience and Remote Sensing, 60:1-11, 2022. Keyword(s): SAR Processing, SAR Interferometry, Interferometry, InSAR, time-series analysis, closure phase, non-zero closure phase, phase consistency, systematic bias, deformation, displacement, multilooking, SBAS, PSI, persistent scatterer intereferometry, SqueeSAR.
    Abstract: In this article, we investigate the link between the closure phase and the observed systematic bias in deformation modeling with multilooked SAR interferometry. Multilooking or spatial averaging is commonly used to reduce stochastic noise over a neighborhood of distributed scatterers in interferometric synthetic aperture radar (InSAR) measurements. However, multilooking may break consistency among a triplet of interferometric phases formed from three acquisitions leading to a residual phase error called closure phase. Understanding the cause of closure phase in multilooked InSAR measurements and the impact of closure phase errors on the performance of InSAR time-series algorithms is crucial for quantifying the uncertainty of ground displacement time series derived from InSAR measurements. We develop a model that consistently explains both closure phase and systematic bias in multilooked interferometric measurements. We show that nonzero closure phase can be an indicator of temporally inconsistent physical processes that alter both phase and amplitude of interferometric measurements. We propose a method to estimate the systematic bias in the InSAR time series with generalized closure phase measurements. We validate our model with a case study in Barstow-Bristol Trough, CA, USA. We find systematic differences on the order of cm/year between InSAR time-series results using subsets of varying maximum temporal baselines. We show that these biases can be identified and accounted for. 

    @Article{zhengFattahiAgramSimonsRosenTGRS2022OnClosurePhaseAndSystematicBiasInMultilookedSARInterferometry,
author = {Zheng, Yujie and Fattahi, Heresh and Agram, Piyush and Simons, Mark and Rosen, Paul},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
title = {On Closure Phase and Systematic Bias in Multilooked {SAR} Interferometry},
year = {2022},
issn = {1558-0644},
pages = {1-11},
volume = {60},
abstract = {In this article, we investigate the link between the closure phase and the observed systematic bias in deformation modeling with multilooked SAR interferometry. Multilooking or spatial averaging is commonly used to reduce stochastic noise over a neighborhood of distributed scatterers in interferometric synthetic aperture radar (InSAR) measurements. However, multilooking may break consistency among a triplet of interferometric phases formed from three acquisitions leading to a residual phase error called closure phase. Understanding the cause of closure phase in multilooked InSAR measurements and the impact of closure phase errors on the performance of InSAR time-series algorithms is crucial for quantifying the uncertainty of ground displacement time series derived from InSAR measurements. We develop a model that consistently explains both closure phase and systematic bias in multilooked interferometric measurements. We show that nonzero closure phase can be an indicator of temporally inconsistent physical processes that alter both phase and amplitude of interferometric measurements. We propose a method to estimate the systematic bias in the InSAR time series with generalized closure phase measurements. We validate our model with a case study in Barstow-Bristol Trough, CA, USA. We find systematic differences on the order of cm/year between InSAR time-series results using subsets of varying maximum temporal baselines. We show that these biases can be identified and accounted for.},
doi = {10.1109/TGRS.2022.3167648},
file = {:zhengFattahiAgramSimonsRosenTGRS2022OnClosurePhaseAndSystematicBiasInMultilookedSARInterferometry.pdf:PDF},
keywords = {SAR Processing, SAR Interferometry, Interferometry, InSAR, time-series analysis, closure phase, non-zero closure phase, phase consistency, systematic bias, deformation, displacement, multilooking, SBAS, PSI, persistent scatterer intereferometry, SqueeSAR},
owner = {ofrey},

}


Conference articles

  1. O. Antropov, J. Miettinen, T. Hame, R. Yrjo, L. Seitsonen, R.E. McRoberts, M. Santoro, O. Cartus, N.M. Duran, M. Herold, M. Pardini, K. Papathanassiou, and I. Hajnsek. Intercomparison of Earth Observation Data and Methods for Forest Mapping in the Context of Forest Carbon Monitoring. In Proc. IEEE Int. Geosci. Remote Sens. Symp., pages 5777-5780, July 2022. 
    @InProceedings{Antropov2022,
author = {Antropov, O. and Miettinen, J. and Hame, T. and Yrjo, R. and Seitsonen, L. and McRoberts, R.E. and Santoro, M. and Cartus, O. and Duran, N.M. and Herold, M. and Pardini, M. and Papathanassiou, K. and Hajnsek, I.},
booktitle = {Proc. IEEE Int. Geosci. Remote Sens. Symp.},
title = {Intercomparison of Earth Observation Data and Methods for Forest Mapping in the Context of Forest Carbon Monitoring},
year = {2022},
month = jul,
pages = {5777-5780},
doi = {10.1109/IGARSS46834.2022.9884618},
owner = {ofrey},

}


  2. D. R. Boyd, A. M. Alam, M. Kurum, A. C. Gurbuz, and B. Osmanoglu. Preliminary Snow Water Equivalent Retrieval of SnowEX20 SWESARR Data. In Proc. IEEE Int. Geosci. Remote Sens. Symp., pages 3927-3930, July 2022.
    Abstract: This paper explores the retrieval of snow water equivalent (SWE) through the use of machine learning techniques and active radar data collected over the 2020 SnowEx campaign. The retrieval makes use of active radar measurements provided by NASA's SWESARR instrument for direct sensing of snowpack sensitivity to SWE. The example results show that an RMSE of 1.93 cm can be obtained through a combined use of SAR data with sufficient ancillary data. Such results may indicate successful SWE estimation by means of pairing spaceborne SAR measurements with sufficient auxiliary information. 

    @INPROCEEDINGS{boydAlamKurumGurbuzOsmanogluIGARSS2022PreliminarySWERetrievalOfSnowEX20SweSARRData,
author={Boyd, D. R. and Alam, A. M. and Kurum, M. and Gurbuz, A. C. and Osmanoglu, B.},
booktitle={Proc. IEEE Int. Geosci. Remote Sens. Symp.},
title={Preliminary Snow Water Equivalent Retrieval of {SnowEX20} {SWESARR} Data},
year={2022},
volume={},
number={},
pages={3927-3930},
abstract={This paper explores the retrieval of snow water equivalent (SWE) through the use of machine learning techniques and active radar data collected over the 2020 SnowEx campaign. The retrieval makes use of active radar measurements provided by NASA's SWESARR instrument for direct sensing of snowpack sensitivity to SWE. The example results show that an RMSE of 1.93 cm can be obtained through a combined use of SAR data with sufficient ancillary data. Such results may indicate successful SWE estimation by means of pairing spaceborne SAR measurements with sufficient auxiliary information.},
keywords={},
doi={10.1109/IGARSS46834.2022.9883412},
ISSN={2153-7003},
month={July},

}


  3. Othmar Frey, Charles Werner, and Rafael Caduff. Dual-frequency car-borne DInSAR at L-band and Ku-band for mobile mapping of surface displacements. In Proc. of EUSAR 2022 - 14th European Conference on Synthetic Aperture Radar, pages 489-492, July 2022. VDE Verlag GmbH. Keyword(s): SAR Processing, mobile mapping, surface displacements, mobile mapping of surface displacements, landslide, geohazard mapping, car-borne SAR, Interferometry, SAR Interferometry, repeat-pass Interferometry, differential interferometry, DInSAR, Gamma L-band SAR, L-band, Ku-band, Gamma Portable Radar Interferometer, GPRI, INS, GNSS, Honeywell, Honeywell HGuide n580, deformation, displacement, monitoring, UAV, Time-Domain Back-projection, TDBP, GPU, NVIDIA, CUDA.
    Abstract: We present our recent developments and experimental results on car-borne mobile mapping of ground-surface displacementswith our in-house-developed SAR systems. Recently, we have successfully demonstrated car-borne andUAV-borne DInSAR with the Gamma L-band SAR system. Meanwhile we have upgraded our car-borne measurementconfiguration that now permits acquiring simultaneously at L-band and at Ku-band. We show first interferometricresults with short temporal baselines from simultaneous acquisitions at both frequencies and in particular we discussthe complementary aspects of the two frequencies in terms of sensitivity to line-of-sight displacements and temporaldecorrelation in typical measurement scenarios. 

    @InProceedings{freyWernerCaduffEUSAR2022DualFrequencyCarborneDInSARatLBandAndKuBandForMobileMappingOfSurfaceDisplacements,
author = {Frey, Othmar and Werner, Charles and Caduff, Rafael},
booktitle = {Proc. of EUSAR 2022 - 14th European Conference on Synthetic Aperture Radar},
title = {Dual-frequency car-borne {DInSAR} at {L-band} and {Ku-band} for mobile mapping of surface displacements},
year = {2022},
month = jul,
pages = {489-492},
publisher = {VDE Verlag GmbH},
abstract = {We present our recent developments and experimental results on car-borne mobile mapping of ground-surface displacementswith our in-house-developed SAR systems. Recently, we have successfully demonstrated car-borne andUAV-borne DInSAR with the Gamma L-band SAR system. Meanwhile we have upgraded our car-borne measurementconfiguration that now permits acquiring simultaneously at L-band and at Ku-band. We show first interferometricresults with short temporal baselines from simultaneous acquisitions at both frequencies and in particular we discussthe complementary aspects of the two frequencies in terms of sensitivity to line-of-sight displacements and temporaldecorrelation in typical measurement scenarios.},
file = {:freyWernerCaduffEUSAR2022DualFrequencyCarborneDInSARatLBandAndKuBandForMobileMappingOfSurfaceDisplacements.pdf:PDF},
keywords = {SAR Processing, mobile mapping, surface displacements, mobile mapping of surface displacements, landslide, geohazard mapping, car-borne SAR, Interferometry, SAR Interferometry, repeat-pass Interferometry, differential interferometry, DInSAR, Gamma L-band SAR, L-band, Ku-band, Gamma Portable Radar Interferometer, GPRI, INS, GNSS, Honeywell, Honeywell HGuide n580, deformation, displacement, monitoring, UAV, Time-Domain Back-projection, TDBP, GPU, NVIDIA, CUDA},
owner = {ofrey},

}


  4. M. Lavalle, C. Telli, N. Pierdicca, U. Khati, O. Cartus, and J. Kellndorfer. Global Sentinel-1 InSAR Coherence: Opportunities for Model-Based Estimation of Land Parameters. In Proc. IEEE Int. Geosci. Remote Sens. Symp., pages 1133-1136, July 2022. 
    @InProceedings{Lavalle2022,
author = {Lavalle, M. and Telli, C. and Pierdicca, N. and Khati, U. and Cartus, O. and Kellndorfer, J.},
booktitle = {Proc. IEEE Int. Geosci. Remote Sens. Symp.},
title = {Global {S}entinel-1 {InSAR} Coherence: Opportunities for Model-Based Estimation of Land Parameters},
year = {2022},
month = jul,
pages = {1133-1136},
doi = {10.1109/IGARSS46834.2022.9883082},
owner = {ofrey},

}


  5. Kaiyu Liu, Robert Wang, Heng Zhang, Dacheng Liu, Naiming Ou, Yafeng Chen, Haixia Yue, Weidong Yu, Yunkai Deng, Da Liang, Yuanbo Jiao, Jili Wanga, and Wei Yu. LuTan-1: An Innovative L-band Spaceborne SAR Mission. In Proc. of EUSAR 2022 - 14th European Conference on Synthetic Aperture Radar, pages 614-618, 2022. VDE. Keyword(s): SAR Mission, LuTan-1, LT-1, L-band, Tandem, formation flying, bistatic, bistatic SAR, spaceborne SAR, synchronization link.
    Abstract: The LuTan-1 (referred as LT-1) mission is the first bistatic spaceborne SAR mission for civil applications in China, which contains two full-polarimetric L-band SAR satellites operating with flexible flight configurations. The life time of the individual satellites is specified as 8 years, and the two data acquisition stages are planned. In phase I, two satellites fly in a formation with a variable baseline, and the bistatic InSAR stripmap mode is utilized to acquire the global digital elevation and terrain models with high accuracy and spatial resolution. In phase II, two satellites shall share the common reference orbit with a 180-degree orbital phasing difference. The repeat cycle will be decreased to 4 days and topographic variations with millimetre accuracy at large scale can be measured using the differential InSAR technique. Many meth-odologies and technologies of high degree of innovation are proposed and employed. Both satellites have been success-fully launched in Jan.26, and Feb. 28, 2022, respectively. In the following lifetime, LuTan-1 will continually provide observation data of high-quality which could provide new possibilities, advances and relevant information for the land dynamics monitoring. 

    @InProceedings{liuaEtAlEUSAR2022LuTan1AnInnovativeLBandSpaceborneSARMission,
author = {Kaiyu Liu and Robert Wang and Heng Zhang and Dacheng Liu and Naiming Ou and Yafeng Chen and Haixia Yue and Weidong Yu and Yunkai Deng and Da Liang and Yuanbo Jiao and Jili Wanga and Wei Yu},
booktitle = {Proc. of EUSAR 2022 - 14th European Conference on Synthetic Aperture Radar},
title = {{LuTan}-1: An Innovative {L-}band Spaceborne {SAR} Mission},
year = {2022},
pages = {614-618},
publisher = {VDE},
abstract = {The LuTan-1 (referred as LT-1) mission is the first bistatic spaceborne SAR mission for civil applications in China, which contains two full-polarimetric L-band SAR satellites operating with flexible flight configurations. The life time of the individual satellites is specified as 8 years, and the two data acquisition stages are planned. In phase I, two satellites fly in a formation with a variable baseline, and the bistatic InSAR stripmap mode is utilized to acquire the global digital elevation and terrain models with high accuracy and spatial resolution. In phase II, two satellites shall share the common reference orbit with a 180-degree orbital phasing difference. The repeat cycle will be decreased to 4 days and topographic variations with millimetre accuracy at large scale can be measured using the differential InSAR technique. Many meth-odologies and technologies of high degree of innovation are proposed and employed. Both satellites have been success-fully launched in Jan.26, and Feb. 28, 2022, respectively. In the following lifetime, LuTan-1 will continually provide observation data of high-quality which could provide new possibilities, advances and relevant information for the land dynamics monitoring.},
file = {:liuaEtAlEUSAR2022LuTan1AnInnovativeLBandSpaceborneSARMission.pdf:PDF},
keywords = {SAR Mission, LuTan-1, LT-1, L-band, Tandem, formation flying, bistatic, bistatic SAR, spaceborne SAR, synchronization link},
owner = {ofrey},

}


  6. Robert Siegmund, Ramon Brcic, Paul Kotzerke, and Michael Eineder. The European Ground Motion Service EGMS - Processing Central Europe with First Results on Quality and Point Densities. In Proc. IEEE Int. Geosci. Remote Sens. Symp., pages 5105-5108, July 2022.
    Abstract: We report on first results of the European Ground Motion Service from the perspective of GAF AG and DLR who are responsible for specific tasks and areas of the overall project. The EGMS products and processing chain are shortly described and first results on point density and data quality are presented. 

    @InProceedings{siegmundBrcicKotzerkeEinederIGARSS2022EGMSFirstResultsOnQualityAndPointDensities,
author = {Siegmund, Robert and Brcic, Ramon and Kotzerke, Paul and Eineder, Michael},
booktitle = {Proc. IEEE Int. Geosci. Remote Sens. Symp.},
title = {The European Ground Motion Service {EGMS} - Processing Central Europe with First Results on Quality and Point Densities},
year = {2022},
month = {July},
pages = {5105-5108},
abstract = {We report on first results of the European Ground Motion Service from the perspective of GAF AG and DLR who are responsible for specific tasks and areas of the overall project. The EGMS products and processing chain are shortly described and first results on point density and data quality are presented.},
doi = {10.1109/IGARSS46834.2022.9883234},
issn = {2153-7003},
owner = {ofrey},

}


  7. Marcel Stefko, Othmar Frey, and Irena Hajnsek. Snow Characterization at Ku-Band with a Bistatic Polarimetric Ground-Based Radar. In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, pages 4256-4259, July 2022.
    Abstract: The Ku-band provides opportunities for investigations of snow morphology through radar observations, since it exhibits a relatively high amount of scattering even from snow layers of limited depth, while maintaining low absorption. Due to technological and practical challenges, the bistatic parameter space of Ku-band radar observations of natural media such as snow, has been relatively unexplored. We present radar measurements of snow cover obtained with KAPRI, a bistatic polarimetric Ku-band radar system. In August 2021 and March 2022, we carried out time series observations of the Aletsch glacier in the Swiss Alps, acquiring a fully-polarimetric interferometric time series of both monostatic and simultaneous bistatic observations of the glacier's accumulation zone. This dataset will serve as a test-bed to investigate new snow parameter inversion methods based on bistatic Ku-band radar data. The bistatic polarimetric measurement configuration, as well as preliminary results of the analysis of radar backscatter, are presented. 

    @InProceedings{stefkoFreyHajnsekIGARSS2022SnowCharacterizationAtKuBandWithABistaticPolarimetricGroundBasedRadar,
author = {Stefko, Marcel and Frey, Othmar and Hajnsek, Irena},
booktitle = {IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium},
title = {Snow Characterization at Ku-Band with a Bistatic Polarimetric Ground-Based Radar},
year = {2022},
month = {July},
pages = {4256-4259},
abstract = {The Ku-band provides opportunities for investigations of snow morphology through radar observations, since it exhibits a relatively high amount of scattering even from snow layers of limited depth, while maintaining low absorption. Due to technological and practical challenges, the bistatic parameter space of Ku-band radar observations of natural media such as snow, has been relatively unexplored. We present radar measurements of snow cover obtained with KAPRI, a bistatic polarimetric Ku-band radar system. In August 2021 and March 2022, we carried out time series observations of the Aletsch glacier in the Swiss Alps, acquiring a fully-polarimetric interferometric time series of both monostatic and simultaneous bistatic observations of the glacier's accumulation zone. This dataset will serve as a test-bed to investigate new snow parameter inversion methods based on bistatic Ku-band radar data. The bistatic polarimetric measurement configuration, as well as preliminary results of the analysis of radar backscatter, are presented.},
doi = {10.1109/IGARSS46834.2022.9884442},
file = {:stefkoFreyHajnsekIGARSS2022SnowCharacterizationAtKuBandWithABistaticPolarimetricGroundBasedRadar.pdf:PDF},
issn = {2153-7003},
owner = {ofrey},

}


  8. Tazio Strozzi, Rafael Caduff, Nina Jones, Andrea Manconi, and Urs Wegm�ller. L-Band StripMap-ScanSAR Persistent Scatterer Interferometry in Alpine Environments with ALOS-2 PALSAR-2. In Proc. IEEE Int. Geosci. Remote Sens. Symp., pages 1644-1647, 2022. 
    @InProceedings{Strozzi2022a,
author = {Strozzi, Tazio and Caduff, Rafael and Jones, Nina and Manconi, Andrea and Wegm�ller, Urs},
booktitle = {Proc. IEEE Int. Geosci. Remote Sens. Symp.},
title = {{L}-Band {StripMap}-{ScanSAR} Persistent Scatterer Interferometry in Alpine Environments with {ALOS-2} {PALSAR}-2},
year = {2022},
pages = {1644-1647},
doi = {10.1109/IGARSS46834.2022.9884743},
owner = {ofrey},

}


  9. Haokui Xu, Leung Tsang, and Xiaolan Xu. Tomography imaging of terrestrial snow for SWE retrieval using frequency-angular correlation functions and asymmetrical distorted Born's approximation. In Proc. IEEE Int. Geosci. Remote Sens. Symp., pages 4557-4558, July 2022.
    Abstract: Stratification in terrestrial snow is a key factor in the retrieval of snow water equivalence (SWE) due to the different snow volume fractions and particle sizes. In studying the layered structure of snow, radar tomography has been used and multiple ground based experiments are performed. The conventional back projection method has been used to construct the image based on radar measurements at different incident angles and frequencies. However, the conventional back projection method based on Born's approximation would show deformation in the final snow image. In this paper, we use the asymmetrical distorted Born's approximation to correct the deformation in the image. 

    @INPROCEEDINGS{xuTsangXuIGARSS2022TomographyImagingOfTerrestrialSnowForSWERetrieval,
author={Xu, Haokui and Tsang, Leung and Xu, Xiaolan},
booktitle={Proc. IEEE Int. Geosci. Remote Sens. Symp.},
title={Tomography imaging of terrestrial snow for {SWE} retrieval using frequency-angular correlation functions and asymmetrical distorted Born's approximation},
year={2022},
volume={},
number={},
pages={4557-4558},
abstract={Stratification in terrestrial snow is a key factor in the retrieval of snow water equivalence (SWE) due to the different snow volume fractions and particle sizes. In studying the layered structure of snow, radar tomography has been used and multiple ground based experiments are performed. The conventional back projection method has been used to construct the image based on radar measurements at different incident angles and frequencies. However, the conventional back projection method based on Born's approximation would show deformation in the final snow image. In this paper, we use the asymmetrical distorted Born's approximation to correct the deformation in the image.},
keywords={},
doi={10.1109/IGARSS46834.2022.9884932},
ISSN={2153-7003},
month={July},

}


Miscellaneous

  1. Othmar Frey, Charles Werner, Andrea Manconi, and Roberto Coscione. High-resolution mobile mapping of slope stability with car- and UAV-borne InSAR systems, 2022. Note: EGU General Assembly 2022; Conference Location: Vienna, Austria; Conference Date: May 23-27, 2022; Conference lecture held on May 25, 2022.
    Abstract: Terrestrial radar interferometry (TRI) has become an operational tool to measure slope surface displacements [1,2]. The day-and-night and all-weather capability of TRI together with the ability to measure line-of-sight displacements in the range of sub-centimeter to sub-millimeter precision are strong assets that complement other geodetic measurement techniques and devices such as total stations, GNSS, terrestrial laser scanning, and close/mid-range photogrammetric techniques.(Quasi-)stationary TRI systems are bound to relatively high frequencies (X- to Ku-band or even higher) to obtain reasonable spatial resolution in azimuth and yet the azimuth resolution is typically only in the order of tens of meters for range distances beyond a few kilometers. These aspects are limiting factors to obtain surface displacement maps at high spatial resolution for areas of interest at several kilometers distance and also for (slightly) vegetated slopes due to the fast temporal decorrelation at high frequencies. Recently, we have implemented and demonstrated car-borne and UAV-borne repeat-pass interferometry-based mobile mapping of surface displacements with an in-house-developed compact L-band FMCW SAR system which we have deployed 1) on a car and 2) on VTOL UAVs (Scout B1-100 and Scout B-330) by Aeroscout GmbH [3,4]. The SAR imaging and interferometric data processing is performed directly in map coordinates using a time-domain back-projection (TDBP) approach [5,6] which precisely takes into account the 3-D acquisition geometry.We have meanwhile further consolidated our experience with the repeat-pass SAR interferometry data acquisition, SAR imaging, interferometricprocessing, and surface displacement mapping using the car-borne and UAV-borne implementations of our InSAR system based on a number of repeat-pass interferometry campaigns. In our contribution, we present the capabilities of this new InSAR-based mobile mapping system and we discuss the lessons learned from our measurement campaigns. References: [1] Caduff, R., Schlunegger, F., Kos, A. & Wiesmann, A. A review of terrestrial radar interferometry for measuring surface change in the geosciences. Earth Surface Processes and Landforms 40, 208-228 (2015). [2] Monserrat, O., Crosetto, M. & Luzi, G. A review of ground-based SAR interferometry for deformation measurement. ISPRS Journal of Photogrammetry and Remote Sensing 93, 40-48 (2014). [3] O. Frey, C. L. Werner, and R. Coscione, Car-borne and UAV-borne mobile mapping of surface displacements with a compact repeat-pass interferometric SAR system at L-band, in Proc. IEEE Int. Geosci. Remote Sens. Symp., 2019, pp. 274-277. [4] O. Frey, C. L. Werner, A. Manconi, and R. Coscione, Measurement of surface displacements with a UAV-borne/car-borne L-band DInSAR system: system performance and use cases, in Proc. IEEE Int. Geosci. Remote Sens. Symp.IEEE, 2021, pp.628-631. [5] O. Frey, C. Magnard, M. R\"uegg, and E. Meier, Focusing of airborne synthetic aperture radar data from highly nonlinear flight tracks, IEEE Trans. Geosci. Remote Sens., vol. 47, no. 6, pp. 1844-1858, June 2009. [6] O. Frey, C. L. Werner, and U. Wegmuller, GPU-based parallelized time-domain back-projection processing for agile SAR platforms, in Proc. IEEE Int. Geosci. Remote Sens. Symp., July 2014, pp. 1132-113. 

    @Misc{freyWernerManconiCoscioneEGU2022Abstract,
author = {Frey, Othmar and Werner, Charles and Manconi, Andrea and Coscione, Roberto},
note = {EGU General Assembly 2022; Conference Location: Vienna, Austria; Conference Date: May 23-27, 2022; Conference lecture held on May 25, 2022.},
title = {High-resolution mobile mapping of slope stability with car- and {UAV}-borne {InSAR} systems},
year = {2022},
abstract = {Terrestrial radar interferometry (TRI) has become an operational tool to measure slope surface displacements [1,2]. The day-and-night and all-weather capability of TRI together with the ability to measure line-of-sight displacements in the range of sub-centimeter to sub-millimeter precision are strong assets that complement other geodetic measurement techniques and devices such as total stations, GNSS, terrestrial laser scanning, and close/mid-range photogrammetric techniques.(Quasi-)stationary TRI systems are bound to relatively high frequencies (X- to Ku-band or even higher) to obtain reasonable spatial resolution in azimuth and yet the azimuth resolution is typically only in the order of tens of meters for range distances beyond a few kilometers. These aspects are limiting factors to obtain surface displacement maps at high spatial resolution for areas of interest at several kilometers distance and also for (slightly) vegetated slopes due to the fast temporal decorrelation at high frequencies. Recently, we have implemented and demonstrated car-borne and UAV-borne repeat-pass interferometry-based mobile mapping of surface displacements with an in-house-developed compact L-band FMCW SAR system which we have deployed 1) on a car and 2) on VTOL UAVs (Scout B1-100 and Scout B-330) by Aeroscout GmbH [3,4]. The SAR imaging and interferometric data processing is performed directly in map coordinates using a time-domain back-projection (TDBP) approach [5,6] which precisely takes into account the 3-D acquisition geometry.We have meanwhile further consolidated our experience with the repeat-pass SAR interferometry data acquisition, SAR imaging, interferometricprocessing, and surface displacement mapping using the car-borne and UAV-borne implementations of our InSAR system based on a number of repeat-pass interferometry campaigns. In our contribution, we present the capabilities of this new InSAR-based mobile mapping system and we discuss the lessons learned from our measurement campaigns. References: [1] Caduff, R., Schlunegger, F., Kos, A. & Wiesmann, A. A review of terrestrial radar interferometry for measuring surface change in the geosciences. Earth Surface Processes and Landforms 40, 208-228 (2015). [2] Monserrat, O., Crosetto, M. & Luzi, G. A review of ground-based SAR interferometry for deformation measurement. ISPRS Journal of Photogrammetry and Remote Sensing 93, 40-48 (2014). [3] O. Frey, C. L. Werner, and R. Coscione, Car-borne and UAV-borne mobile mapping of surface displacements with a compact repeat-pass interferometric SAR system at L-band, in Proc. IEEE Int. Geosci. Remote Sens. Symp., 2019, pp. 274-277. [4] O. Frey, C. L. Werner, A. Manconi, and R. Coscione, Measurement of surface displacements with a UAV-borne/car-borne L-band DInSAR system: system performance and use cases, in Proc. IEEE Int. Geosci. Remote Sens. Symp.IEEE, 2021, pp.628-631. [5] O. Frey, C. Magnard, M. R\"uegg, and E. Meier, Focusing of airborne synthetic aperture radar data from highly nonlinear flight tracks, IEEE Trans. Geosci. Remote Sens., vol. 47, no. 6, pp. 1844-1858, June 2009. [6] O. Frey, C. L. Werner, and U. Wegmuller, GPU-based parallelized time-domain back-projection processing for agile SAR platforms, in Proc. IEEE Int. Geosci. Remote Sens. Symp., July 2014, pp. 1132-113.},
copyright = {Creative Commons Attribution 4.0 International},
doi = {10.3929/ethz-b-000574748},
journal = {EGUsphere},
pages = {EGU22-8587},
publisher = {Copernicus},
type = {Other Conference Item},

}


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Last modified: Fri Feb 24 14:22:26 2023
Author: Othmar Frey, Earth Observation and Remote Sensing, Institute of Environmental Engineering, Swiss Federal Institute of Technology - ETH Zurich .

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