Adab H , Morbidelli R , Saltalippi C , et al. Machine learning to estimate surface soil moisture from remote sensing data. Water, 2020, 12 (11): 3223 https://doi.org/10.3390/w12113223 Cited in this article [2]

Ahmad S , Kalra A , Stephen H . Estimating soil moisture using remote sensing data: A machine learning approach. Advances in Water Resources, 2010, 33 (1): 69- 80. https://doi.org/10.1016/j.advwatres.2009.10.008 Cited in this article [2]

Ahmed N U . Estimating soil moisture from 6.6 GHz dual polarization, and/or satellite derived vegetation index. International Journal of Remote Sensing, 1995, 16 (4): 687- 708. https://doi.org/10.1080/01431169508954434 Cited in this article [1]

Ali I , Greifeneder F , Stamenkovic J , et al. Review of machine learning approaches for biomass and soil moisture retrievals from remote sensing data. Remote Sensing, 2015, 7 (12): 16398- 16421. https://doi.org/10.3390/rs71215841 Cited in this article [2]

Altese E , Bolognani O , Mancini M , et al. Retrieving soil moisture over bare soil from ERS 1 synthetic aperture radar data: Sensitivity analysis based on a theoretical surface scattering model and field data. Water Resources Research, 1996, 32 (3): 653- 661. https://doi.org/10.1029/95WR03638 Cited in this article [1]

Attema E P W , Ulaby F T . Vegetation modeled as a water cloud. Radio Science, 1978, 13 (2): 357- 364. https://doi.org/10.1029/RS013i002p00357 Cited in this article [2]

Baghdadi N , King C , Chanzy A , et al. An empirical calibration of the integral equation model based on SAR data, soil moisture and surface roughness measurement over bare soils. International Journal of Remote Sensing, 2002, 23 (20): 4325- 4340. https://doi.org/10.1080/01431160110107671 Cited in this article [1]

Baghdadi N , Gherboudj I , Zribi M , et al. Semi-empirical calibration of the IEM backscattering model using radar images and moisture and roughness field measurements. International Journal of Remote Sensing, 2004, 25 (18): 3593- 3623. https://doi.org/10.1080/01431160310001654392 Cited in this article [1]

Bahar E . Full-wave solutions for the depolarization of the scattered radiation fields by rough surfaces of arbitrary slope. IEEE Transactions on Antennas and Propagation, 1981, 29 (3): 443- 454. https://doi.org/10.1109/TAP.1981.1142604 Cited in this article [2]

Bai J Y. 2020. Applicability of SMAP passive microwave data in agricultural drought monitoring in China[Ph. D. thesis] (in Chinese). Wuhan: Wuhan University.

Bai X J , He B B , Xing M F , et al. Method for soil moisture retrieval in arid prairie using TerraSAR-X data. Journal of Applied Remote Sensing, 2015, 9 (1): 096062 https://doi.org/10.1117/1.JRS.9.096062 Cited in this article [2]

Bai X J , He B B . Potential of Dubois model for soil moisture retrieval in prairie areas using SAR and optical data. International Journal of Remote Sensing, 2015, 36 (22): 5737- 5753. https://doi.org/10.1080/01431161.2015.1103920 Cited in this article [1]

Bai X J , He B B , Li X W . Optimum surface roughness to parameterize advanced integral equation model for soil moisture retrieval in prairie area using Radarsat-2 data. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54 (4): 2437- 2449. https://doi.org/10.1109/TGRS.2015.2501372 Cited in this article [1]

Bai X J. 2017. Research on methods for soil moisture retrieval in prairies areas based on multi-frequency and multi-polarization SAR data[Ph. D. thesis] (in Chinese). Chengdu: University of Electronic Science and Technology of China.

Behari J . Microwave Dielectric Behaviour of Wet Soils. Dordrecht: Springer, 2005, Cited in this article [1]

Bindlish R , Barros A P . Parameterization of vegetation backscatter in radar-based, soil moisture estimation. Remote Sensing of Environment, 2001, 76 (1): 130- 137. https://doi.org/10.1016/S0034-4257(00)00200-5 Cited in this article [2]

Bono A , Alvarez R . Use of surface soil moisture to estimate profile water storage by polynomial regression and artificial neural networks. Agronomy Journal, 2012, 104 (4): 934- 938. https://doi.org/10.2134/agronj2012.0011 Cited in this article [2]

Cao Z T , Gao H X , Nan Z T , et al. A semi-physical approach for downscaling satellite soil moisture data in a typical cold alpine area, northwest China. Remote Sensing, 2021, 13 (3): 509 https://doi.org/10.3390/rs13030509 Cited in this article [4]

Chai S S , Walker J P , Makarynskyy O , et al. Use of soil moisture variability in artificial neural network retrieval of soil moisture. Remote Sensing, 2009, 2 (1): 166- 190. https://doi.org/10.3390/rs2010166 Cited in this article [3]

Chan S K , Bindlish R , O'Neill P , et al. Development and assessment of the SMAP enhanced passive soil moisture product. Remote Sensing of Environment, 2018, 204: 931- 941. https://doi.org/10.1016/j.rse.2017.08.025 Cited in this article [2]

Chen K S , Wu T D , Tsang L , et al. Emission of rough surfaces calculated by the integral equation method with comparison to three-dimensional moment method simulations. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41 (1): 90- 101. https://doi.org/10.1109/TGRS.2002.807587 Cited in this article [2]

Chen S L , Liu Y B , Wen Z M . Satellite retrieval of soil moisture: An overview. Advances in Earth Science, 2012, 27 (11): 1192- 1203.

Choudhury B J , Schmugge T J , Chang A , et al. Effect of surface roughness on the microwave emission from soils. Journal of Geophysical Research: Oceans, 1979, 84 (C9): 5699- 5706. https://doi.org/10.1029/JC084iC09p05699 Cited in this article [2]

Collow T W , Robock A , Basara J B , et al. Evaluation of SMOS retrievals of soil moisture over the central United States with currently available in situ observations. Journal of Geophysical Research: Atmospheres, 2012, 117 (D9): D09113 https://doi.org/10.1029/2011JD017095 Cited in this article [1]

Crow W T , Berg A A , Cosh M H , et al. Upscaling sparse ground-based soil moisture observations for the validation of coarse-resolution satellite soil moisture products. Reviews of Geophysics, 2012, 50 (2): RG2002 https://doi.org/10.1029/2011RG000372 Cited in this article [1]

De Roo R D , Du Y , Ulaby F T , et al. 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, 2001, 39 (4): 864- 872. https://doi.org/10.1109/36.917912 Cited in this article [1]

Deng X D , Wang H Q . Recent advances on algorithms and applications of soil moisture retrieval from microwave remote sensing. Journal of Zhejiang University (Agriculture and Life Sciences), 2022, 48 (3): 289- 302. https://doi.org/10.3785/j.issn.1008-9209.2021.04.281

Deng Y C , Xu Y H . Introduction to the methods of soil moisture content measuring. Journal of China Hydrology, 2007, 27 (4): 20- 24. https://doi.org/10.3969/j.issn.1000-0852.2007.04.005

Ding Q , Liang Y J , Xu N H , et al. Inversion of soil moisture by BDS dual-frequency multi-star combination under low vegetation cover environment. Progress in Geophysics, 2022, 37 (6): 2242- 2250. https://doi.org/10.6038/pg2022FF0525

Djamai N , Magagi R , Goita K , et al. Disaggregation of SMOS soil moisture over the Canadian Prairies. Remote Sensing of Environment, 2015, 170: 255- 268. https://doi.org/10.1016/j.rse.2015.09.013 Cited in this article [2]

Dobriyal P , Qureshi A , Badola R , et al. A review of the methods available for estimating soil moisture and its implications for water resource management. Journal of Hydrology, 2012, 458-459: 110- 117. https://doi.org/10.1016/j.jhydrol.2012.06.021 Cited in this article [3]

Dobson M C , Ulaby F T , Hallikainen M , et al. Microwave dielectric behavior of wet soil-Part Ⅱ: Dielectric mixing models. IEEE Transactions on Geoscience and Remote Sensing, 1985, GE-23 (1): 35- 46. https://doi.org/10.1109/TGRS.1985.289498 Cited in this article [2]

Dong J Z , Crow W T , Tobin K J , et al. Comparison of microwave remote sensing and land surface modeling for surface soil moisture climatology estimation. Remote Sensing of Environment, 2020, 242: 111756 https://doi.org/10.1016/j.rse.2020.111756 Cited in this article [1]

Dubois P C , Van Zyl J , Engman T . Measuring soil moisture with imaging radars. IEEE Transactions on Geoscience and Remote Sensing, 1995, 33 (4): 915- 926. https://doi.org/10.1109/36.406677 Cited in this article [2]

Evans D L , Farr T G , Van Zyl J J . Estimates of surface roughness derived from synthetic aperture radar (SAR) data. IEEE Transactions on Geoscience and Remote Sensing, 1992, 30 (2): 382- 389. https://doi.org/10.1109/36.134087 Cited in this article [1]

Fitzgerald R M , Maradudin A A . A reciprocal phase-perturbation theory for rough-surface scattering. Waves in Random Media, 1994, 4 (3): 275- 296. https://doi.org/10.1088/0959-7174/4/3/004 Cited in this article [2]

Ford T W , Quiring S M . Comparison of contemporary in situ, model, and satellite remote sensing soil moisture with a focus on drought monitoring. Water Resources Research, 2019, 55 (2): 1565- 1582. https://doi.org/10.1029/2018WR024039 Cited in this article [1]

Fung A K , Li Z , Chen K S . Backscattering from a randomly rough dielectric surface. IEEE Transactions on Geoscience and Remote Sensing, 1992, 30 (2): 356- 369. https://doi.org/10.1109/36.134085 Cited in this article [2]

Fung A K. 1994. Microwave Scattering and Emission Models and Their Applications. Norwood: Artech House. Cited in this article [2]

Fung A K , Chen K S . An update on the IEM surface backscattering model. IEEE Geoscience and Remote Sensing Letters, 2004, 1 (2): 75- 77. https://doi.org/10.1109/LGRS.2004.826564 Cited in this article [2]

Gao Y , Walker J P , Allahmoradi M , et al. Optical sensing of vegetation water content: A synthesis study. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8 (4): 1456- 1464. https://doi.org/10.1109/JSTARS.2015.2398034 Cited in this article [1]

Gherboudj I , Magagi R , Berg A A , et al. Soil moisture retrieval over agricultural fields from multi-polarized and multi-angular RADARSAT-2 SAR data. Remote Sensing of Environment, 2011, 115 (1): 33- 43. https://doi.org/10.1016/j.rse.2010.07.011 Cited in this article [2]

Hu Z , Xu L , Yu B . Soil moisture retrieval using convolutional neural networks: Application to passive microwave remote sensing. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2018, XLⅡ-3: 583- 586. https://doi.org/10.5194/isprs-archives-XLⅡ-3-583-2018 Cited in this article [2]

Im J , Park S , Rhee J , et al. Downscaling of AMSR-E soil moisture with MODIS products using machine learning approaches. Environmental Earth Sciences, 2016, 75 (15): 1120 https://doi.org/10.1007/s12665-016-5917-6 Cited in this article [3]

Jackson T J . Ⅲ. Measuring surface soil moisture using passive microwave remote sensing. Hydrological Processes, 1993, 7 (2): 139- 152. https://doi.org/10.1002/hyp.3360070205 Cited in this article [2]

Jackson T J , Hsu A Y . Soil moisture and TRMM microwave imager relationships in the Southern Great Plains 1999 (SGP99) experiment. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39 (8): 1632- 1642. https://doi.org/10.1109/36.942541 Cited in this article [1]

Jiang L M , Cui H Z , Wang G X , et al. Progress on remote sensing of snow, surface soil frozen/thaw state and soil moisture. Remote Sensing Technology and Application, 2020, 35 (6): 1237- 1262. https://doi.org/10.11873/j.issn.1004-0323.2020.6.1237

Jiang R Z , Sui Y , Zhang X , et al. Estimation of soil organic carbon by combining hyperspectral and radar remote sensing to reduce coupling effects of soil surface moisture and roughness. Geoderma, 2024, 444: 116874 https://doi.org/10.1016/j.geoderma.2024.116874 Cited in this article [1]

Joseph A T , Van Der Velde R , O'Neill P E , et al. Soil moisture retrieval during a corn growth cycle using L-Band (1.6 GHz) radar observations. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46 (8): 2365- 2374. https://doi.org/10.1109/TGRS.2008.917214 Cited in this article [2]

Joseph A T , Van Der Velde R , O'Neill P E , et al. Effects of corn on C- and L-band radar backscatter: A correction method for soil moisture retrieval. Remote Sensing of Environment, 2010, 114 (11): 2417- 2430. https://doi.org/10.1016/j.rse.2010.05.017 Cited in this article [2]

Kang Y , Wen J , Zhang T T , et al. Assessment of the land surface wetness by using satellite remote sensing data over the Loess Plateau. Chinese Journal of Geophysics, 2014, 57 (8): 2473- 2483. https://doi.org/10.6038/cjg20140809

Kara A , Pekel E , Ozcetin E , et al. Genetic algorithm optimized a deep learning method with attention mechanism for soil moisture prediction. Neural Computing and Applications, 2024, 36 (4): 1761- 1772. https://doi.org/10.1007/s00521-023-09168-7 Cited in this article [2]

Karthikeyan L , Pan M , Wanders N , et al. Four decades of microwave satellite soil moisture observations: Part 1. A review of retrieval algorithms. Advances in Water Resources, 2017, 109: 106- 120. https://doi.org/10.1016/j.advwatres.2017.09.006 Cited in this article [1]

Kerr Y H , Njoku E G . A semiempirical model for interpreting microwave emission from semiarid land surfaces as seen from space. IEEE Transactions on Geoscience and Remote Sensing, 1990, 28 (3): 384- 393. https://doi.org/10.1109/36.54364 Cited in this article [2]

Kerr Y H , Waldteufel P , Wigneron J P , et al. The SMOS mission: new tool for monitoring key elements of the global water cycle. Proceedings of the IEEE, 2010, 98 (5): 666- 687. https://doi.org/10.1109/JPROC.2010.2043032 Cited in this article [1]

Kim J , Hogue T S . Improving spatial soil moisture representation through integration of AMSR-E and MODIS products. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50 (2): 446- 460. https://doi.org/10.1109/TGRS.2011.2161318 Cited in this article [2]

Kong J L , Li J J , Zhen P P , et al. Inversion of soil moisture in arid area based on microwave and optical remote sensing data. Journal of Geo-Information Science, 2016a, 18 (6): 857- 863.

Kong J L , Zhen P P , Li J J , et al. Retrieval for soil moisture using microwave remote sensing data based on a new combined roughness parameter. Geography and Geo-Information Science, 2016b, 32 (3): 34- 38. https://doi.org/10.3969/j.issn.1672-0504.2016.03.007

Kornelsen K C , Coulibaly P . Advances in soil moisture retrieval from synthetic aperture radar and hydrological applications. Journal of Hydrology, 2013, 476: 460- 489. https://doi.org/10.1016/j.jhydrol.2012.10.044 Cited in this article [1]

Kseneman M , Gleich D , Cucej Ž . Soil moisture estimation using high-resolution spotlight TerraSAR-X data. IEEE Geoscience and Remote Sensing Letters, 2011, 8 (4): 686- 690. https://doi.org/10.1109/LGRS.2010.2099641 Cited in this article [2]

Kweon S K , Oh Y . A modified water-cloud model with leaf angle parameters for microwave backscattering from agricultural fields. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53 (5): 2802- 2809. https://doi.org/10.1109/TGRS.2014.2364914 Cited in this article [2]

Li J C , Zhang H , Ma W C , et al. Spaceborne GNSS-R soil moisture inversion method based on support vector machine. Progress in Geophysics, 2023, 38 (5): 1960- 1966. https://doi.org/10.6038/pg2023GG0610

Li J H , Wang S S , Gunn G , et al. A model for downscaling SMOS soil moisture using Sentinel-1 SAR data. International Journal of Applied Earth Observation and Geoinformation, 2018, 72: 109- 121. https://doi.org/10.1016/j.jag.2018.07.012 Cited in this article [3]

Li P X , Liu Z Q , Yang J , et al. Soil moisture retrieval of winter wheat fields based on random forest regression using quad-polarimetric SAR images. Geomatics and Information Science of Wuhan University, 2019, 44 (3): 405- 412. https://doi.org/10.13203/j.whugis20160531

Li Q , Shi J C , Chen K S . A generalized power law spectrum and its applications to the backscattering of soil surfaces based on the integral equation model. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40 (2): 271- 280. https://doi.org/10.1109/36.992784 Cited in this article [2]

Li Z L , Leng P , Zhou C H , et al. Soil moisture retrieval from remote sensing measurements: Current knowledge and directions for the future. Earth-Science Reviews, 2021, 218: 103673 https://doi.org/10.1016/j.earscirev.2021.103673 Cited in this article [1]

Ma H L , Zeng J Y , Zhang X , et al. Surface soil moisture from combined active and passive microwave observations: Integrating ASCAT and SMAP observations based on machine learning approaches. Remote Sensing of Environment, 2024, 308: 114197 https://doi.org/10.1016/j.rse.2024.114197 Cited in this article [2]

McColl K A , Alemohammad S H , Akbar R , et al. The global distribution and dynamics of surface soil moisture. Nature Geoscience, 2017, 10 (2): 100- 104. https://doi.org/10.1038/ngeo2868 Cited in this article [2]

Merlin O , Chehbouni A , Kerr Y H , et al. A downscaling method for distributing surface soil moisture within a microwave pixel: Application to the Monsoon '90 data. Remote Sensing of Environment, 2006, 101 (3): 379- 389. https://doi.org/10.1016/j.rse.2006.01.004 Cited in this article [1]

Merlin O , Rudiger C , Al Bitar A , et al. Disaggregation of SMOS soil moisture in Southeastern Australia. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50 (5): 1556- 1571. https://doi.org/10.1109/TGRS.2011.2175000 Cited in this article [3]

Merlin O , Malbéteau Y , Notfi Y , et al. Performance metrics for soil moisture downscaling methods: Application to DISPATCH data in central Morocco. Remote Sensing, 2015, 7 (4): 3783- 3807. https://doi.org/10.3390/rs70403783 Cited in this article [3]

Moeremans B , Dautrebande S . Soil moisture evaluation by means of multi-temporal ERS SAR PRI images and interferometric coherence. Journal of Hydrology, 2000, 234 (3-4): 162- 169. https://doi.org/10.1016/S0022-1694(00)00251-1 Cited in this article [2]

Molero B , Merlin O , Malbéteau Y , et al. SMOS disaggregated soil moisture product at 1 km resolution: Processor overview and first validation results. Remote Sensing of Environment, 2016, 180: 361- 376. https://doi.org/10.1016/j.rse.2016.02.045 Cited in this article [2]

Oh Y , Sarabandi K , Ulaby F T . An empirical model and an inversion technique for radar scattering from bare soil surfaces. IEEE Transactions on Geoscience and Remote Sensing, 1992, 30 (2): 370- 381. https://doi.org/10.1109/36.134086 Cited in this article [2]

Oh Y , Sarabandi K , Ulaby F T . Semi-empirical model of the ensemble-averaged differential Mueller matrix for microwave backscattering from bare soil surfaces. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40 (6): 1348- 1355. https://doi.org/10.1109/TGRS.2002.800232 Cited in this article [2]

Oh Y . Quantitative retrieval of soil moisture content and surface roughness from multipolarized radar observations of bare soil surfaces. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42 (3): 596- 601. https://doi.org/10.1109/TGRS.2003.821065 Cited in this article [2]

Owe M , De Jeu R , Holmes T . Multisensor historical climatology of satellite-derived global land surface moisture. Journal of Geophysical Research: Earth Surface, 2008, 113 (F1): F01002 https://doi.org/10.1029/2007JF000769 Cited in this article [1]

Pan M , Sahoo A K , Wood E F . Improving soil moisture retrievals from a physically-based radiative transfer model. Remote Sensing of Environment, 2014, 140: 130- 140. https://doi.org/10.1016/j.rse.2013.08.020 Cited in this article [2]

Peng J , Loew A , Merlin O , et al. A review of spatial downscaling of satellite remotely sensed soil moisture. Reviews of Geophysics, 2017, 55 (2): 341- 366. https://doi.org/10.1002/2016RG000543 Cited in this article [1]

Petropoulos G P , Ireland G , Barrett B . Surface soil moisture retrievals from remote sensing: Current status, products & future trends. Physics and Chemistry of the Earth, Parts A/B/C, 2015, 83- 84. https://doi.org/10.1016/j.pce.2015.02.009 Cited in this article [1]

Piles M , Camps A , Vall-Llossera M , et al. Downscaling SMOS-derived soil moisture using MODIS visible/infrared data. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49 (9): 3156- 3166. https://doi.org/10.1109/TGRS.2011.2120615 Cited in this article [2]

Piles M , Sanchez N , Vall-Llossera M , et al. A downscaling approach for SMOS land observations: Evaluation of high-resolution soil moisture maps over the Iberian Peninsula. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7 (9): 3845- 3857. https://doi.org/10.1109/JSTARS.2014.2325398 Cited in this article [2]

Plant W J . A two-scale model of short wind-generated waves and scatterometry. Journal of Geophysical Research: Oceans, 1986, 91 (C9): 10735- 10749. https://doi.org/10.1029/JC091iC09p10735 Cited in this article [2]

Portal G , Vall-Llossera M , Piles M , et al. A spatially consistent downscaling approach for SMOS using an adaptive moving window. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11 (6): 1883- 1894. https://doi.org/10.1109/JSTARS.2018.2832447 Cited in this article [4]

Prévot L , Champion I , Guyot G . Estimating surface soil moisture and leaf area index of a wheat canopy using a dual-frequency (C and X Bands) scatterometer. Remote Sensing of Environment, 1993, 46 (3): 331- 339. https://doi.org/10.1016/0034-4257(93)90053-Z Cited in this article [2]

Qin X D , Pang Z G , Jiang W , et al. Progress and development trend of soil moisture microwave remote sensing retrieval method. Journal of Geo-Information Science, 2021, 23 (10): 1728- 1742. https://doi.org/10.12082/dqxxkx.2021.210104

Sabaghy S , Walker J P , Renzullo L J , et al. Spatially enhanced passive microwave derived soil moisture: Capabilities and opportunities. Remote Sensing of Environment, 2018, 209: 551- 580. https://doi.org/10.1016/j.rse.2018.02.065 Cited in this article [1]

Sandholt I , Rasmussen K , Andersen J . A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. Remote Sensing of Environment, 2002, 79 (2-3): 213- 224. https://doi.org/10.1016/S0034-4257(01)00274-7 Cited in this article [1]

Schmugge T , O'Neill P E , Wang J R . Passive microwave soil moisture research. IEEE Transactions on Geoscience and Remote Sensing, 1986, GE-24 (1): 12- 22. https://doi.org/10.1109/TGRS.1986.289584 Cited in this article [1]

Shi J C , Wang J , Hsu A Y , et al. Estimation of bare surface soil moisture and surface roughness parameter using L-Band SAR image data. IEEE Transactions on Geoscience and Remote Sensing, 1997, 35 (5): 1254- 1266. https://doi.org/10.1109/36.628792 Cited in this article [2]

Shi J C , Chen K S , Li Q , et al. A parameterized surface reflectivity model and estimation of bare-surface soil moisture with L-band radiometer. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40 (12): 2674- 2686. https://doi.org/10.1109/TGRS.2002.807003 Cited in this article [2]

Shi J C , Jiang L M , Zhang L X , et al. A parameterized multifrequency-polarization surface emission model. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43 (12): 2831- 2841. https://doi.org/10.1109/TGRS.2005.857902 Cited in this article [2]

Shi J C , Du Y , Du J Y , et al. Progresses on microwave remote sensing of land surface parameters. Science China Earth Sciences, 2012, 55 (7): 1052- 1078. https://doi.org/10.1007/s11430-012-4444-x

Song P L , Huang J F , Mansaray L R . An improved surface soil moisture downscaling approach over cloudy areas based on geographically weighted regression. Agricultural and Forest Meteorology, 2019, 275: 146- 158. https://doi.org/10.1016/j.agrformet.2019.05.022 Cited in this article [2]

Tian H , Wang C H , Wen J , et al. Soil moisture estimation over an arid environment in Mongolia from passive microwave remote sensing based on a simplified parameterization method. Chinese Journal of Geophysics, 2012, 55 (2): 415- 427. https://doi.org/10.6038/j.issn.0001-5733.2012.02.006

Ulaby F T , Batlivala P P , Dobson M C . Microwave backscatter dependence on surface roughness, soil moisture, and soil texture: Part Ⅰ-bare soil. IEEE Transactions on Geoscience Electronics, 1978, 16 (4): 286- 295. https://doi.org/10.1109/TGE.1978.294586 Cited in this article [5]

Ulaby F T, Moore R K, Fung A K. 1982. Microwave Remote Sensing: Active and Passive. Volume 2-Radar Remote Sensing and Surface Scattering and Emission Theory. Norwood: Artech House. Cited in this article [2]

Ulaby F T , Sarabandi K , Mcdonald K , et al. Michigan microwave canopy scattering model. International Journal of Remote Sensing, 1990, 11 (7): 1223- 1253. https://doi.org/10.1080/01431169008955090 Cited in this article [2]

Vergopolan N , Chaney N W , Beck H E , et al. Combining hyper-resolution land surface modeling with SMAP brightness temperatures to obtain 30-m soil moisture estimates. Remote Sensing of Environment, 2020, 242: 111740 https://doi.org/10.1016/j.rse.2020.111740 Cited in this article [1]

Voronovich A . Small-slope approximation for electromagnetic wave scattering at a rough interface of two dielectric half-spaces. Waves in Random Media, 1994, 4 (3): 337- 367. https://doi.org/10.1088/0959-7174/4/3/008 Cited in this article [2]

Wang J R , Choudhury B J . Remote sensing of soil moisture content, over bare field at 1.4 GHz frequency. Journal of Geophysical Research: Oceans, 1981, 86 (C6): 5277- 5282. https://doi.org/10.1029/JC086iC06p05277 Cited in this article [2]

Wang J R , Mcmurtrey J E , Engman E T , et al. Radiometric measurements over bare and vegetated fields at 1.4-GHz and 5-GHz frequencies. Remote Sensing of Environment, 1982, 12 (4): 295- 311. https://doi.org/10.1016/0034-4257(82)90042-6 Cited in this article [1]

Wang L , Gong H L , Pan Y , et al. Retrieval of soil moisture in typical steppe of Xilinhot based on Sentinel-1 SAR data. Journal of Arid Meteorology, 2019, 37 (6): 979- 986. https://doi.org/10.11755/j.issn.1006-7639(2019)-06-0979

Wang L G , Gao Y . Soil moisture retrieval from sentinel-1 and sentinel-2 data using ensemble learning over vegetated fields. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023, 16: 1802- 1814. https://doi.org/10.1109/JSTARS.2023.3242264 Cited in this article [2]

Wang S G , Ma C F , Zhao Z B , et al. Estimation of soil moisture of agriculture field in the middle reaches of the Heihe River Basin based on Sentinel-1 and Landsat 8 imagery. Remote Sensing Technology and Application, 2020, 35 (1): 13- 22. 13-22, 47 https://doi.org/10.11873/j.issn.1004-0323.2020.1.0013

Wang X , Zhang Z X , Zhao X L , et al. A review of researches on monitoring of soil moisture by remote sensing. Acta Pedologica Sinica, 2007, 44 (1): 157- 163. https://doi.org/10.3321/j.issn:0564-3929.2007.01.023

Wang X Y , She Y L , Zhang S C , et al. Soil moisture inversion in highland areas based on Sentinel-1 and Landsat 8 remote sensing data. Bulletin of Surveying and Mapping, 2024, (7): 140- 146. https://doi.org/10.13474/j.cnki.11-2246.2024.0725

Wang Z Z , Wang W Y , Tong X L , et al. Progress in spaceborne passive microwave remote sensing technology and its application. Chinese Journal of Space Science, 2023, 43 (6): 986- 1015. https://doi.org/10.11728/cjss2023.06.yg15

Weimann A , Von Schonermark M , Schumann A , et al. Soil moisture estimation with ERS-1 SAR data in the East-German loess soil area. International Journal of Remote Sensing, 1998, 19 (2): 237- 243. https://doi.org/10.1080/014311698216224 Cited in this article [2]

Weisberg S . Applied Linear Regression. New York: John Wiley & Sons, 2005 Cited in this article [1]

Weiß T , Jagdhuber T , Ramsauer T , et al. RTM-based downscaling of medium resolution soil moisture using Sentinel-1 data over agricultural fields. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2024, 17: 15463- 15479. https://doi.org/10.1109/JSTARS.2024.3448625 Cited in this article [1]

Wigneron J P , Kerr Y , Waldteufel P , et al. L-band Microwave Emission of the Biosphere (L-MEB) Model: Description and calibration against experimental data sets over crop fields. Remote Sensing of Environment, 2007, 107 (4): 639- 655. https://doi.org/10.1016/j.rse.2006.10.014 Cited in this article [2]

Wigneron J P , Jackson T J , O'Neill P , et al. Modelling the passive microwave signature from land surfaces: A review of recent results and application to the L-Band SMOS & SMAP soil moisture retrieval algorithms. Remote Sensing of Environment, 2017, 192: 238- 262. https://doi.org/10.1016/j.rse.2017.01.024 Cited in this article [1]

Wu T D , Chen K S , Shi J C , et al. A transition model for the reflection coefficient in surface scattering. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39 (9): 2040- 2050. https://doi.org/10.1109/36.951094 Cited in this article [2]

Wu X L , Walker J P , Das N N , et al. Evaluation of the SMAP brightness temperature downscaling algorithm using active-passive microwave observations. Remote Sensing of Environment, 2014, 155: 210- 221. https://doi.org/10.1016/j.rse.2014.08.021 Cited in this article [1]

Xie Q X , Jia L , Menenti M , et al. Global soil moisture data fusion by triple collocation analysis from 2011 to 2018. Scientific Data, 2022, 9 (1): 687 https://doi.org/10.1038/s41597-022-01772-x Cited in this article [1]

Xu Y P , Wang L , Ma Z Q , et al. Spatially explicit model for statistical downscaling of satellite passive microwave soil moisture. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58 (2): 1182- 1191. https://doi.org/10.1109/TGRS.2019.2944421 Cited in this article [1]

Yang T , Chen X W , Wan W , et al. Soil moisture retrieval in the Qinghai-Xizang Plateau using optical and passive microwave remote sensing data. Chinese Journal of Geophysics, 2017, 60 (7): 2556- 2567. https://doi.org/10.6038/cjg20170705

Yu F , Li H T , Zhang C M , et al. A new approach for surface soil moisture retrieving using two-polarized microwave remote sensing data. Geomatics and Information Science of Wuhan University, 2014, 39 (2): 225- 228. https://doi.org/10.13203/j.whugis20120527

Zhang M M , Zhang L H , Li F , et al. A comparison of DISPATCH, stepwise regression soil moisture downscaling methods and SMAP soil moisture products in Qilian Mountain. Journal of Beijing Normal University (Natural Science), 2020, 56 (1): 110- 121. https://doi.org/10.16360/j.cnki.jbnuns.2020.01.016

Zhang X L , Hu Z Q , Chu S L . Methods for measuring soil water content: A review. Chinese Journal of Soil Science, 2005, 36 (1): 118- 123. https://doi.org/10.19336/j.cnki.trtb.2005.01.030

Zhang Z G , Liang Y J , Ren C , et al. Research on the preferred method of wavelet base based on GPS-IR inversion of soil moisture. Progress in Geophysics, 2020, 35 (1): 72- 78. https://doi.org/10.6038/pg2020CC0550

Zhao H F , Li J , Yuan Q Q , et al. Downscaling of soil moisture products using deep learning: Comparison and analysis on Qinghai-Xizang Plateau. Journal of Hydrology, 2022, 607: 127570 https://doi.org/10.1016/j.jhydrol.2022.127570 Cited in this article [1]

Zhao T J , Zhang L X , Shi J C , et al. A physically based statistical methodology for surface soil moisture retrieval in the Qinghai-Xizang Plateau using microwave vegetation indices. Journal of Geophysical Research, 2011, 116 (D8): D08116 https://doi.org/10.1029/2010JD015229 Cited in this article [1]

Zhao T J , Shi J C , Entekhabi D , et al. Retrievals of soil moisture and vegetation optical depth using a multi-channel collaborative algorithm. Remote Sensing of Environment, 2021, 257: 112321 https://doi.org/10.1016/j.rse.2021.112321 Cited in this article [1]

Zhao W , Wen F P , Cai J F . Methods, progresses, and challenges of passive microwave soil moisture spatial downscaling. National Remote Sensing Bulletin, 2022, 26 (9): 1699- 1722. https://doi.org/10.11834/jrs.20210515 Cited in this article [1]

Zribi M , Dechambre M . A new empirical model to retrieve soil moisture and roughness from C-Band radar data. Remote Sensing of Environment, 2003, 84 (1): 42- 52. https://doi.org/10.1016/S0034-4257(02)00069-X Cited in this article [1]

白珏莹. 2020. SMAP被动微波数据在我国农业干旱监测中的适用性研究[博士论文]. 武汉: 武汉大学. Cited in this article [1]

白晓静. 2017. 基于多波段多极化SAR数据的草原地表土壤水分反演方法研究[博士论文]. 成都: 电子科技大学. Cited in this article [4]

陈 书林 , 刘 元波 , 温 作民 . 卫星遥感反演土壤水分研究综述. 地球科学进展, 2012, 27 (11): 1192- 1203. Cited in this article [1]

邓 小东 , 王 宏全 . 土壤水分微波遥感反演算法及应用研究进展. 浙江大学学报(农业与生命科学版), 2022, 48 (3): 289- 302. https://doi.org/10.3785/j.issn.1008-9209.2021.04.281 Cited in this article [2]

邓 英春 , 许 永辉 . 土壤水分测量方法研究综述. 水文, 2007, 27 (4): 20- 24. https://doi.org/10.3969/j.issn.1000-0852.2007.04.005 Cited in this article [1]

丁 琴 , 梁 月吉 , 徐 宁辉 , 等. 低植被覆盖环境下BDS双频多星组合反演土壤水分研究. 地球物理学进展, 2022, 37 (6): 2242- 2250. https://doi.org/10.6038/pg2022FF0525 Cited in this article [1]

蒋 玲梅 , 崔 慧珍 , 王 功雪 , 等. 积雪、土壤冻融与土壤水分遥感监测研究进展. 遥感技术与应用, 2020, 35 (6): 1237- 1262. https://doi.org/10.11873/j.issn.1004-0323.2020.6.1237 Cited in this article [2]

康 悦 , 文 军 , 张 堂堂 , 等. 卫星遥感数据评估黄土高原陆面干湿程度研究. 地球物理学报, 2014, 57 (8): 2473- 2483. https://doi.org/10.6038/cjg20140809 Cited in this article [1]

孔 金玲 , 李 菁菁 , 甄 珮珮 , 等. 微波与光学遥感协同反演旱区地表土壤水分研究. 地球信息科学学报, 2016a, 18 (6): 857- 863. Cited in this article [1]

孔 金玲 , 甄 珮珮 , 李 菁菁 , 等. 基于新的组合粗糙度参数的土壤水分微波遥感反演. 地理与地理信息科学, 2016b, 32 (3): 34- 38. https://doi.org/10.3969/j.issn.1672-0504.2016.03.007 Cited in this article [1]

李 金超 , 张 晖 , 马 卫春 , 等. 基于支持向量机的星载GNSS-R土壤湿度反演方法. 地球物理学进展, 2023, 38 (5): 1960- 1966. https://doi.org/10.6038/pg2023GG0610 Cited in this article [1]

李 平湘 , 刘 致曲 , 杨 杰 , 等. 利用随机森林回归进行极化SAR土壤水分反演. 武汉大学学报(信息科学版), 2019, 44 (3): 405- 412. https://doi.org/10.13203/j.whugis20160531 Cited in this article [2]

覃 湘栋 , 庞 治国 , 江 威 , 等. 土壤水分微波反演方法进展和发展趋势. 地球信息科学学报, 2021, 23 (10): 1728- 1742. https://doi.org/10.12082/dqxxkx.2021.210104 Cited in this article [2]

施 建成 , 杜 阳 , 杜 今阳 , 等. 微波遥感地表参数反演进展. 中国科学: 地球科学, 2012, 42 (6): 814- 842. Cited in this article [2]

田 辉 , 王 澄海 , 文 军 , 等. 基于简化参数方法的蒙古干旱区土壤湿度被动微波遥感. 地球物理学报, 2012, 55 (2): 415- 427. https://doi.org/10.6038/j.issn.0001-5733.2012.02.006 Cited in this article [1]

王 龙 , 宫 辉力 , 潘 云 , 等. 基于Sentinel-1 SAR数据的锡林浩特市典型草原土壤水分反演. 干旱气象, 2019, 37 (6): 979- 986. https://doi.org/10.11755/j.issn.1006-7639(2019)-06-0979 Cited in this article [1]

王 树果 , 马 春锋 , 赵 泽斌 , 等. 基于Sentinel-1及Landsat 8数据的黑河中游农田土壤水分估算. 遥感技术与应用, 2020, 35 (1): 13- 22. 13-22, 47 https://doi.org/10.11873/j.issn.1004-0323.2020.1.0013 Cited in this article [1]

汪 潇 , 张 增祥 , 赵 晓丽 , 等. 遥感监测土壤水分研究综述. 土壤学报, 2007, 44 (1): 157- 163. https://doi.org/10.3321/j.issn:0564-3929.2007.01.023 Cited in this article [1]

王 霞迎 , 折 育霖 , 张 双成 , 等. 基于Sentinel-1和Landsat 8遥感数据的高原地区土壤水分反演. 测绘通报, 2024, (7): 140- 146. https://doi.org/10.13474/j.cnki.11-2246.2024.0725 Cited in this article [1]

王 振占 , 王 文煜 , 佟 晓林 , 等. 星载被动微波遥感技术及其应用进展. 空间科学学报, 2023, 43 (6): 986- 1015. https://doi.org/10.11728/cjss2023.06.yg15 Cited in this article [1]

杨 婷 , 陈 秀万 , 万 玮 , 等. 基于光学与被动微波遥感的青藏高原地区土壤水分反演. 地球物理学报, 2017, 60 (7): 2556- 2567. https://doi.org/10.6038/cjg20170705 Cited in this article [1]

余 凡 , 李 海涛 , 张 承明 , 等. 利用双极化微波遥感数据反演土壤水分的新方法. 武汉大学学报(信息科学版), 2014, 39 (2): 225- 228. https://doi.org/10.13203/j.whugis20120527 Cited in this article [1]

张 明敏 , 张 兰慧 , 李 峰 , 等. 祁连山区DISPATCH、多元回归降尺度方法及SMAP产品的应用对比. 北京师范大学学报(自然科学版), 2020, 56 (1): 110- 121. https://doi.org/10.16360/j.cnki.jbnuns.2020.01.016 Cited in this article [2]

张 学礼 , 胡 振琪 , 初 士立 . 土壤含水量测定方法研究进展. 土壤通报, 2005, 36 (1): 118- 123. https://doi.org/10.19336/j.cnki.trtb.2005.01.030 Cited in this article [1]

张 志刚 , 梁 月吉 , 任 超 , 等. 小波基在GPS-IR反演土壤湿度中优选方法的研究. 地球物理学进展, 2020, 35 (1): 72- 78. https://doi.org/10.6038/pg2020CC0550 Cited in this article [1]

赵 伟 , 文 凤平 , 蔡 俊飞 . 被动微波土壤水分遥感产品空间降尺度研究: 方法、进展及挑战. 遥感学报, 2022, 26 (9): 1699- 1722. https://doi.org/10.11834/jrs.20210515 Cited in this article [4]