K. A. McColl, S. H. Alemohammad, R. Akbar, A. G. Konings, S. Yueh, and D. Entekhabi, “The global distribution and dynamics of surface soil moisture,” Nature Geoscience, vol. 10, no. 2, pp. 100–104, 2017, doi: 10.1038/ngeo2868.
T. E. Ochsner, M. H. Cosh, R. H. Cuenca, W. A. Dorigo, C. S. Draper, Y. Hagimoto, Y. H. Kerr, K. M. Larson, E. G. Njoku, E. E. Small, and M. Zreda, “State of the Art in Large-Scale Soil Moisture Monitoring,” Soil Science Society of America Journal, vol. 77, no. 6, pp. 1888–1919, 2013, doi: 10.2136/sssaj2013.03.0093.
E. T. Engman, “Applications of microwave remote sensing of soil moisture for water resources and agriculture,” Remote Sensing of Environment, vol. 35, no. 2-3, pp. 213–226, 1991, doi: 10.1016/0034-4257(91)90013-V.
L. Brocca, L. Ciabatta, C. Massari, S. Camici, and A. Tarpanelli, “Soil moisture for hydrological applications: Open questions and new opportunities,” Water, vol. 9, no. 2, p. 140, 2017, doi: 10.3390/w9020140.
D. Entekhabi, E. G. Njoku, P. E. O’Neill, K. H. Kellogg, W. T. Crow, W. N. Edelstein, J. K. Entin, S. D. Goodman, T. J. Jackson, J. Johnson et al., “The Soil Moisture Active Passive (SMAP) mission,” Proceedings of the IEEE, vol. 98, no. 5, pp. 704–716, 2010, doi: 10.1109/JPROC.2010.2043918.
Y. H. Kerr, P. Waldteufel, J.-P. Wigneron, J. Martinuzzi, J. Font, and M. Berger, “Soil moisture retrieval from space: The Soil Moisture and Ocean Salinity (SMOS) mission,” IEEE transactions on Geoscience and remote sensing, vol. 39, no. 8, pp. 1729–1735, 2001, doi: 10.1109/36.942551.
A. Balenzano, F. Mattia, G. Satalino, F. P. Lovergine, D. Palmisano, J. Peng, P. Marzahn, U. Wegmüller, O. Cartus, K. Dabrowska-Zielińska et al., “Sentinel-1 soil moisture at 1 km resolution: a validation study,” Remote Sensing of Environment, vol. 263, p. 112554, 2021, doi: 10.1016/j.rse.2021.112554.
J. Peng, C. Albergel, A. Balenzano, L. Brocca, O. Cartus, M. H. Cosh, W. T. Crow, K. Dabrowska-Zielinska, S. Dadson, M. W. Davidson, P. de Rosnay, W. Dorigo, A. Gruber, S. Hagemann, M. Hirschi, Y. H. Kerr, F. Lovergine, M. D. Mahecha, P. Marzahn, F. Mattia, J. P. Musial, S. Preuschmann, R. H. Reichle, G. Satalino, M. Silgram, P. M. van Bodegom, N. E. Verhoest, W. Wagner, J. P. Walker, U. Wegmüller, and A. Loew, “A roadmap for high-resolution satellite soil moisture applications – confronting product characteristics with user requirements,” Remote Sensing of Environment, vol. 252, p. 112162, 2021, doi: 10.1016/j.rse.2020.112162.
J. Garrison, V. U. Zavorotny, A. Egido, K. M. Larson, F. Nievinski, A. Mollfulleda, G. Ruffini, F. Martin, and C. Gommenginger, “GNSS Reflectometry for Earth Remote Sensing,” in Position, Navigation, and Timing Technologies in the 21st Century, 1st ed., Y. T. J. Morton, F. Diggelen, J. J. Spilker, B. W. Parkinson, S. Lo, and G. Gao, Eds. Wiley, 2020, pp. 1015–1114. ISBN 978-1-119-45841-8 978-1-119-45844-9
C. Chew, J. T. Reager, and E. Small, “CYGNSS data map flood inundation during the 2017 Atlantic hurricane season,” Scientific reports, vol. 8, no. 1, pp. 1–8, 2018, doi: 10.1038/s41598-018-27673-x.
S. Gleason, A. O’Brien, A. Russel, M. M. Al-Khaldi, and J. T. Johnson, “Geolocation, calibration and surface resolution of CYGNSS GNSS-R land observations,” Remote Sensing, vol. 12, no. 8, p. 1317, 2020, doi: 10.3390/rs12081317.
R. D. De Roo, Y. Du, F. T. Ulaby, and M. C. Dobson, “A semi-empirical backscattering model at L-band and C-band for a soybean canopy with soil moisture inversion,” IEEE Transactions on Geoscience and Remote Sensing, vol. 39, no. 4, pp. 864–872, 2001, doi: 10.1109/36.917912.
C. Chew and E. Small, “Soil moisture sensing using spaceborne GNSS reflections: Comparison of CYGNSS reflectivity to SMAP soil moisture,” Geophysical Research Letters, vol. 45, no. 9, pp. 4049–4057, 2018, doi: 10.1029/2018GL077905.
C. S. Ruf, R. Atlas, P. S. Chang, M. P. Clarizia, J. L. Garrison, S. Gleason, S. J. Katzberg, Z. Jelenak, J. T. Johnson, S. J. Majumdar et al., “New ocean winds satellite mission to probe hurricanes and tropical convection,” Bulletin of the American Meteorological Society, vol. 97, no. 3, pp. 385–395, 2016, doi: 10.1175/BAMS-D-14-00218.1.
P. d. T. Setti, S. Tabibi, and T. van Dam, “CYGNSS GNSS-R data for inundation monitoring in the Brazilian Pantanal wetland,” in IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, 2022, doi: 10.1109/IGARSS46834.2022.9883409.
C. Chew, E. Small, and H. Huelsing, “Flooding and inundation maps using interpolated CYGNSS reflectivity observations,” Remote Sensing of Environment, vol. 293, p. 113598, 2023, doi: 10.1016/j.rse.2023.113598.
P. T. Setti and S. Tabibi, “Spaceborne GNSS-Reflectometry for surface water mapping in the Amazon basin,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 17, pp. 6658–6670, 2024, doi: 10.1109/JSTARS.2024.3373899.
D. Stilla, M. Zribi, N. Pierdicca, N. Baghdadi, and M. Huc, “Desert roughness retrieval using CYGNSS GNSS-R data,” Remote Sensing, vol. 12, no. 4, p. 743, 2020, doi: 10.3390/rs12040743.
H. Carreno-Luengo, G. Luzi, and M. Crosetto, “Above-ground biomass retrieval over tropical forests: A novel GNSS-R approach with CyGNSS,” Remote Sensing, vol. 12, no. 9, p. 1368, 2020, doi: 10.3390/rs12091368.
C. Chew and E. Small, “Description of the UCAR/CU Soil Moisture Product,” Remote Sensing, vol. 12, no. 10, p. 1558, 2020, doi: 10.3390/rs12101558.
S. Chen, Q. Yan, S. Jin, W. Huang, T. Chen, Y. Jia, S. Liu, and Q. Cao, “Soil moisture retrieval from the CyGNSS data based on a bilinear regression,” Remote Sensing, vol. 14, no. 9, p. 1961, 2022, doi: 10.3390/rs14091961.
P. T. Setti Jr and S. Tabibi, “Evaluation of Spire GNSS-R reflectivity from multiple GNSS constellations for soil moisture estimation,” International Journal of Remote Sensing, vol. 44, no. 20, pp. 6422–6441, 2023, doi: 10.1080/01431161.2023.2270108.
P. T. Setti and S. Tabibi, “Concept and assessment of the University of Luxembourg CYGNSS-based soil moisture product,” in IGARSS 2024-2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2024, doi: 10.1109/IGARSS53475.2024.10642834. pp. 4461–4464.
M. M. Al-Khaldi, J. T. Johnson, A. J. O’Brien, A. Balenzano, and F. Mattia, “Time-series retrieval of soil moisture using CYGNSS,” IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 7, pp. 4322–4331, 2019, doi: 10.1109/TGRS.2018.2890646.
M. M. Al-Khaldi and J. T. Johnson, “Soil moisture retrievals using CYGNSS data in a time-series ratio method: progress update and error analysis,” IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1–5, 2021, doi: 10.1109/LGRS.2021.3086092.
S. H. Yueh, R. Shah, M. J. Chaubell, A. Hayashi, X. Xu, and A. Colliander, “A semiempirical modeling of soil moisture, vegetation, and surface roughness impact on CYGNSS reflectometry data,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–17, 2020, doi: 10.1109/TGRS.2020.3035989.
O. Eroglu, M. Kurum, D. Boyd, and A. C. Gurbuz, “High spatiotemporal resolution CYGNSS soil moisture estimates using artificial neural networks,” Remote sensing, vol. 11, no. 19, p. 2272, 2019, doi: 10.3390/rs11192272.
V. Senyurek, F. Lei, D. Boyd, A. C. Gurbuz, M. Kurum, and R. Moor-head, “Evaluations of machine learning-based CYGNSS soil moisture estimates against SMAP observations,” Remote Sensing, vol. 12, no. 21, p. 3503, 2020, doi: 10.3390/rs12213503.
T. M. Roberts, I. Colwell, C. Chew, S. Lowe, and R. Shah, “A deep-learning approach to soil moisture estimation with GNSS-R,” Remote Sensing, vol. 14, no. 14, p. 3299, 2022, doi: 10.3390/rs14143299.
E. Santi, M. Clarizia, D. Comite, L. Dente, L. Guerriero, N. Pierdicca, and N. Floury, “Combining CYGNSS and machine learning for soil moisture and forest biomass retrieval in view of the ESA scout HydroGNSS mission,” in IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2022, doi: 10.1109/IGARSS46834.2022.9884738. pp. 7433–7436.
M. Nabi, V. Senyurek, F. Lei, M. Kurum, and A. C. Gurbuz, “Quasi-global assessment of deep learning-based CYGNSS soil moisture retrieval,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023, doi: 10.1109/JSTARS.2023.3287591.
R. A. Perez, P. T. Setti Jr, and S. Tabibi, “Enhanced soil moisture estimation with GNSS-R and deep learning techniques,” AGU24.
E. Hodges, R. Akbar, C. Chew, E. Small, M. Al-Khaldi, J. Johnson, F. Lei, M. Kurum, A. Gurbuz, V. Senyurek, X. Xu, R. Shah, S. Yueh, A. Hayashi, P. Setti Jr, S. Tabibi, E. Santi, S. Pettinato, T. Max Roberts, I. Colwell, S. Lowe, and M. Moghaddam, “An assessment and intercomparison of CYGNSS soil moisture products,” in URSI National Radio Science Meeting, Boulder, CO., 2023.
E. Hodges, C. Chew, E. Small, M. Al-Khaldi, J. Ouellette, J. Johnson, F. Lei, M. Kurum, A. Gurbuz, V. Senyurek, X. Xu, R. Shah, S. Yueh, A. Hayashi, P. Setti Jr, S. Tabibi, E. Santi, S. Pettinato, T. Max Roberts, I. Colwell, S. Lowe, C. Ruf, and M. Moghaddam, “A blended CYGNSS soil moisture product partitioned with ancillary data,” in URSI National Radio Science Meeting, Boulder, CO., 2024, doi: 10.23919/USNC-URSINRSM60317.2024.10464722. pp. 174–174.
CYGNSS. 2018. CYGNSS Level 1 Science Data Record Version 2.1. Ver. 2.1. PO.DAAC, CA, USA. Dataset accessed [2023-11-09] at https://doi.org/10.5067/CYGNS-L1X21.
CYGNSS. 2020. CYGNSS Level 1 Science Data Record Version 3.0. Ver. 3.0. PO.DAAC, CA, USA. Dataset accessed [2023-11-09] at https://doi.org/10.5067/CYGNS-L1X30.
CYGNSS. 2021. CYGNSS Level 1 Science Data Record Version 3.1. Ver. 3.1. PO.DAAC, CA, USA. Dataset accessed [2023-11-15] at https://doi.org/10.5067/CYGNS-L1X31.
CYGNSS. 2024. CYGNSS Level 1 Science Data Record Version 3.2. Ver. 3.2. PO.DAAC, CA, USA. Dataset accessed [2024-02-15] at https: //doi.org/10.5067/CYGNS-L1X32.
T. Wang, C. S. Ruf, S. Gleason, A. J. O’Brien, D. S. McKague, B. P. Block, and A. Russel, “Dynamic calibration of GPS effective isotropic radiated power for GNSS-reflectometry Earth remote sensing,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–12, 2021, doi: 10.1109/TGRS.2021.3070238.
P. T. Setti and T. van Dam, “Comparison of the Effective Isotropic Radiated Power parameter in CYGNSS v2. 1 and v3. 0 level 1 data and its impact on soil moisture estimation,” in Geodesy for a Sustainable Earth: Proceedings of the 2021 Scientific Assembly of the International Association of Geodesy, Beijing, China, June 28–July 2, 2021. Springer, 2022, doi: 10.1007/1345_2022_176. pp. 417–422.
A. Colliander, R. H. Reichle, W. T. Crow, M. H. Cosh, F. Chen, S. Chan, N. N. Das, R. Bindlish, J. Chaubell, S. Kim et al., “Validation of soil moisture data products from the NASA SMAP mission,” IEEE Journal of selected topics in applied earth observations and remote sensing, vol. 15, pp. 364–392, 2021, doi: 10.1109/JSTARS.2021.3124743.
P. E. O’Neill, S. Chan, E. G. Njoku, T. Jackson, R. Bindlish, and J. Chaubell, “SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil Moisture. (SPL3SMP, Version 8),” Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center, 2021, doi: 10.5067/OMHVSRGFX38O.
P. E. O’Neill, S. Chan, E. G. Njoku, T. Jackson, R. Bindlish, J. Chaubell, and A. Colliander, “SMAP Enhanced L3 Radiometer Global and Polar Grid Daily 9 km EASE-Grid Soil Moisture. (SPL3SMP_E, Version 5),” Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center, 2021, doi: 10.5067/4DQ54OUIJ9DL.
D. Entekhabi, S. Yueh, P. O’Neill, K. Kellogg, A. Allen, R. Bindlish, M. Brown, S. Chan, A. Colliander, W. Crow, N. Das, G. De Lannoy, R. Dunbar, W. Edelstein, J. Entin et al., SMAP Handbook - Soil Moisture Active Passive - Mapping Soil Moisture and Freeze/Thaw from Space, 2014. [Online]. Available: https://smap.jpl.nasa.gov/system/internal_resources/details/original/178_SMAP_Handbook_FINAL_1_JULY_2014_Web.pdf
P. O’Neill, R. Bindlish, S. Chan, J. Chaubell, A. Colliander, E. Njoku, and T. Jackson, Soil Moisture Active Passive (SMAP) Algorithm theoretical basis document. Level 2 & 3 soil moisture (passive) data products, Jet Propulsion Laboratory, California Institute of Technology, 2021. [Online]. Available: https://nsidc.org/sites/default/files/l2_sm_p_ atbd_rev_g_final_oct2021_0.pdf
W. Dorigo, W. Wagner, C. Albergel, F. Albrecht, G. Balsamo, L. Brocca, D. Chung, M. Ertl, M. Forkel, A. Gruber et al., “ESA CCI Soil Moisture for improved earth system understanding: State-of-the art and future directions,” Remote Sensing of Environment, vol. 203, pp. 185–215, 2017, doi: 10.1016/j.rse.2017.07.001.
A. Gruber, T. Scanlon, R. van der Schalie, W. Wagner, and W. Dorigo, “Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology,” Earth System Science Data, vol. 11, no. 2, pp. 717–739, 2019, doi: 10.5194/essd-11-717-2019.
W. Preimesberger, T. Scanlon, C.-H. Su, A. Gruber, and W. Dorigo, “Homogenization of structural breaks in the global ESA CCI soil moisture multisatellite climate data record,” IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845–2862, 2020, doi: 10.1109/TGRS.2020.3012896.
W. Dorigo, W. Wagner, R. Hohensinn, S. Hahn, C. Paulik, A. Xaver, A. Gruber, M. Drusch, S. Mecklenburg, P. van Oevelen et al., “The International Soil Moisture Network: a data hosting facility for global in situ soil moisture measurements,” Hydrology and Earth System Sciences, vol. 15, no. 5, pp. 1675–1698, 2011, doi: 10.5194/hess-15-1675-2011.
W. Dorigo, A. Xaver, M. Vreugdenhil, A. Gruber, A. Hegyiova, A. Sanchis-Dufau, D. Zamojski, C. Cordes, W. Wagner, and M. Drusch, “Global automated quality control of in situ soil moisture data from the International Soil Moisture Network,” Vadose Zone Journal, vol. 12, no. 3, 2013, doi: 10.2136/vzj2012.0097.
D. R. Cook, “Soil temperature and moisture profile (STAMP) system handbook,” 11 2016, doi: 10.2172/1332724. Last accessed 01 November 2021. [Online]. Available: https://www.osti.gov/biblio/1332724
D. Cook, “Surface energy balance system (SEBS) instrument handbook,” 4 2018, doi: 10.2172/1004944. Last accessed 01 November 2021. [Online]. Available: https://www.osti.gov/biblio/1004944
A. Smith, J. Walker, A. Western, R. Young, K. Ellett, R. Pipunic, R. Grayson, L. Siriwardena, F. Chiew, and H. Richter, “The Murrumbidgee soil moisture monitoring network data set,” Water Resources Research, vol. 48, no. 7, Jul. 2012, doi: 10.1029/2012WR011976.
R. Young, J. Walker, N. Yeoh, A. Smith, K. Ellett, O. Merlin, and A. Western, “Soil moisture and meteorological observations from the Murrumbidgee catchment,” Department of Civil and Environmental Engineering, The University of Melbourne, 2008.
G. Schaefer, M. Cosh, and T. Jackson, “The USDA natural resources conservation service soil climate analysis network (SCAN),” Journal of Atmospheric and Oceanic Technology - J ATMOS OCEAN TECHNOL, vol. 24, no. 12, pp. 2073 – 2077, 12 2007, doi: 10.1175/2007JTECHA930.1.
G. H. Leavesley, O. David, D. C. Garen, J. Lea, J. K. Marron, T. C. Pagano, T. R. Perkins, and M. L. Strobel, “A Modeling Framework for Improved Agricultural Water Supply Forecasting,” pp. C21A–0497, Dec. 2008.
Leavesley, “A modelling framework for improved agricultural water-supply forecasting,” 2010.
J. Bell, M. Palecki, B. Baker, W. Collins, J. Lawrimore, R. Leeper, M. Hall, J. Kochendorfer, T. Meyers, T. Wilson, and H. Diamond, “U.S. climate reference network soil moisture and temperature observations,” Journal of Hydrometeorology, vol. 14, pp. 977–988, Jun. 2013, doi: 10.1175/JHM-D-12-0146.1.
D. I. Shuman, A. Nayyar, A. Mahajan, Y. Goykhman, K. Li, M. Liu, D. Teneketzis, M. Moghaddam, and D. Entekhabi, “Measurement scheduling for soil moisture sensing: From physical models to optimal control,” Proceedings of the IEEE, vol. 98, no. 11, pp. 1918–1933, 2010, doi: 10.1109/JPROC.2010.2052532.
R. Akbar, J. Campbell, A. R. Silva, R. Chen, A. Melebari, E. Hodges, D. Entekhabi, C. Ruf, and M. Moghaddam, “SoilSCAPE wireless in situ networks in support of CYGNSS land applications,” in IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2020, doi: 10.1109/IGARSS39084.2020.9324648. pp. 5042–5044.
M. Moghaddam, D. Entekhabi, Y. Goykhman, K. Li, M. Liu, A. Mahajan, A. Nayyar, D. Shuman, and D. Teneketzis, “A wireless soil moisture smart sensor web using physics-based optimal control: Concept and initial demonstrations,” Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of, vol. 3, pp. 522–535, Apr. 2011, doi: 10.1109/JSTARS.2010.2052918.
M. Moghaddam, A. Silva, D. Clewley, R. Akbar, S. Hussaini, J. Whit-comb, R. Devarakonda, R. Shrestha, R. Cook, G. Prakash, S. Santjana Vannan, and A. Boyer, “Soil moisture profiles and temperature data from soilSCAPE sites, USA,” 2016, doi: 10.3334/ORNLDAAC/1339.
A. Melebari, A. R. Silva, R. Akbar, E. Hodges, Y. Zhao, P. Nergis, D. S. McKague, C. Ruf, and M. Moghaddam, “CYGNSS SoilSCAPE sites: Sensor calibration and data analysis,” in IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2023, doi: 10.1109/IGARSS52108.2023.10282411. pp. 4628–4630.
J.-F. Pekel, A. Cottam, N. Gorelick, and A. S. Belward, “High-resolution mapping of global surface water and its long-term changes,” Nature, vol. 540, no. 7633, pp. 418–422, 2016, doi: 10.1038/nature20584.
P. Defourny, C. Lamarche, Q. Marissiaux, C. Brockmann, M. Boettcher, and G. Kirches, ICDR Land Cover 2016-2020 - Product User Guide and Specification, 2021. [Online]. Available: https://datastore.copernicus-climate.eu/documents/satellite-land-cover/D5.3.1_PUGS_ICDR_LC_v2.1.x_PRODUCTS_v1.1.pdf
M. A. Friedl, D. Sulla-Menashe, B. Tan, A. Schneider, N. Ramankutty, A. Sibley, and X. Huang, “MODIS collection 5 global land cover: Algorithm refinements and characterization of new datasets,” Remote sensing of Environment, vol. 114, no. 1, pp. 168–182, 2010, doi: 10.1016/j.rse.2009.08.016.
D. Entekhabi, R. H. Reichle, R. D. Koster, and W. T. Crow, “Performance metrics for soil moisture retrievals and application requirements,” Journal of Hydrometeorology, vol. 11, no. 3, pp. 832–840, 2010, doi: 10.1175/2010JHM1223.1.
A. Gruber, C.-H. Su, S. Zwieback, W. Crow, W. Dorigo, and W. Wagner, “Recent advances in (soil moisture) triple collocation analysis,” International Journal of Applied Earth Observation and Geoinformation, vol. 45, pp. 200–211, 2016, doi: 10.1016/j.jag.2015.09.002.
A. Stoffelen, “Toward the true near-surface wind speed: Error modeling and calibration using triple collocation,” Journal of geophysical research: oceans, vol. 103, no. C4, pp. 7755–7766, 1998, doi: 10.1029/97JC03180.
X. Deng, L. Zhu, H. Wang, X. Zhang, C. Tong, S. Li, and K. Wang, “Triple Collocation Analysis and in situ validation of the CYGNSS soil moisture product,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 16, pp. 1883–1899, 2023, doi: 10.1109/JSTARS.2023.3235111.
K. A. McColl, J. Vogelzang, A. G. Konings, D. Entekhabi, M. Piles, and A. Stoffelen, “Extended triple collocation: Estimating errors and correlation coefficients with respect to an unknown target,” Geophysical research letters, vol. 41, no. 17, pp. 6229–6236, 2014, doi: 10.1002/2014GL061322.
C. Tsamalis, “Clarifications on the equations and the sample number in triple collocation analysis using SST observations,” Remote Sensing of Environment, vol. 272, p. 112936, 2022, doi: 10.1016/j.rse.2022.112936.
R. D. De Roo and F. T. Ulaby, “Bistatic specular scattering from rough dielectric surfaces,” IEEE Transactions on Antennas and Propagation, vol. 42, no. 2, pp. 220–231, 1994, doi: 10.1109/8.277216.
C. Chew and E. Small, “Estimating inundation extent using CYGNSS data: A conceptual modeling study,” Remote Sensing of Environment, vol. 246, p. 111869, 2020, doi: 10.1016/j.rse.2020.111869.
F. Ulaby, R. Moore, and A. Fung, Microwave remote sensing: Active and passive. Volume 2-Radar remote sensing and surface scattering and emission theory. Artech House, 1982.
P. T. Setti Jr. and S. Tabibi, “Incidence angle normalization of spaceborne GNSS-R surface reflectivity for soil moisture retrieval,” in IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, 2023, doi: 10.1109/IGARSS52108.2023.10282074.
M. P. Clarizia, C. S. Ruf, P. Jales, and C. Gommenginger, “Spaceborne GNSS-R minimum variance wind speed estimator,” IEEE transactions on geoscience and remote sensing, vol. 52, no. 11, pp. 6829–6843, 2014, doi: 10.1109/TGRS.2014.2303831.
CYGNSS. 2020. UCAR-CU CYGNSS Level 3 Soil Moisture Version 1.0. Ver. 1.0. PO.DAAC, CA, USA. Dataset accessed [2024-03-10] at https://doi.org/10.5067/CYGNU-L3SM1.
CYGNSS. 2024. CYGNSS Level 3 Soil Moisture Version 3.2. Ver. 3.2. PO.DAAC, CA, USA. Dataset accessed [2024-10-31] at https://doi.org/10.5067/CYGNU-L3S32.
C. Forgotson, P. E. O’Neill, M. L. Carrera, S. Bélair, N. N. Das, I. E. Mladenova, J. D. Bolten, J. M. Jacobs, E. Cho, and V. M. Escobar, “How satellite soil moisture data can help to monitor the impacts of climate change: SMAP case studies,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 1590–1596, 2020, doi: 10.1109/JSTARS.2020.2982608.
