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  1. Alexander, Tómas Halldórsson and Luan, Haijun and Jin, Hongxiao and Duan, Zheng (2025). Spatial Downscaling of Gridded Soil Moisture Products Using Optical and Thermal Satellite Data: Effect of Using Different Vegetation Indices. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 18, 7728-7741. 10.1109/jstars.2025.3543012
  2. Azad, Nasrin and He, Hailong (2025). Generalization of multiple depths soil temperature estimation using LSTM and CNN. Journal of Hydrology, 661, 133687. 10.1016/j.jhydrol.2025.133687
  3. Azad, Nasrin and Sheikhbaglou, Amirreza and Zvomuya, Francis and He, Hailong (2025). Applying LSTM to Model Multi‐Depth Soil Moisture Under Various Land Covers, Climates and Soils. European Journal of Soil Science, 76, 4, e70142. 10.1111/ejss.70142
  4. Bai, Xuesong and Qin, Zhengkun and Li, Juan and Zhang, Shupeng and Wang, Lili (2025). The Impact of Spatial Dynamic Error on the Assimilation of Soil Moisture Retrieval Products. Remote Sensing, 17, 2, 239. 10.3390/rs17020239
  5. Cai, Junfei and Zhao, Wei and Ding, Tao and Yin, Gaofei (2025). Generation of High-Resolution Surface Soil Moisture over Mountain Areas by Spatially Downscaling Remote Sensing Products Based on Land Surface Temperature–Vegetation Index Feature Space. Journal of Remote Sensing, 5, 0437. 10.34133/remotesensing.0437
  6. Chen, F. D. and Ye, Y. L. and Liu, L. L. and Huang, L. K. and Guo, F. and Chen, Y. L. (2025). A Novel Sliding Window Algorithm-Based Approach for Global Bias Correction in CYGNSS Soil Moisture Retrievals. Ieee Transactions on Geoscience and Remote Sensing, 63. Artn 4414811 10.1109/Tgrs.2025.3593278
  7. Chen, J. H. and Wang, Z. and Wei, Z. R. and Huang, C. and Yang, Y. T. and Wei, P. and Li, H. and You, Y. H. and Zhang, S. Q. and Dong, Z. J. and Liu, H. (2025). An Interpretable Attention Decision Forest Model for Surface Soil Moisture Retrieval. Remote Sensing, 17, 20, 3468. ARTN 3468 10.3390/rs17203468
  8. Chuphal, Dipesh Singh and Malik, Iqura and Singh, Rajesh and Vangala, Gayathri and Niranjannaik, M. and Vegad, Urmin and Dilip K, Nandana and Mukhopadhyay, Parthasarathi and Selvan, J. P. and Kapadia, Vivek and Mishra, Vimal (2025). Multi‐Day Extreme Precipitation Caused Major Floods in India During Summer Monsoon of 2024. Earth's Future, 13, 5, e2024EF005497. 10.1029/2024ef005497
  9. Dastbala, G. and Dehghani, M. and Karimi-Jashni, A. (2025). Spatial downscaling of SMAP soil moisture product by using deep belief network. Advances in Space Research, 76, 4, 2019-2038. 10.1016/j.asr.2025.05.092
  10. Dong, Zhounan and Yan, Qingyun and Jin, Shuanggen and Li, Li and Chen, Guodong (2025). Refined semi-empirical models for soil moisture retrieval in spaceborne GNSS-Reflectometry: Evaluation across diverse land cover types. Measurement, 242, 115849. 10.1016/j.measurement.2024.115849
  11. Duan, Pan and Zhao, Tianjie and Lü, Haishen and Lang, Shuyan and Zheng, Jingyao and Bai, Yu and Peng, Zhiqing and Wagner, Wolfgang and Guo, Peng and Shi, Hongtao (2025). A New Addition to Global Soil Moisture Mapping: CFOSAT Scatterometer Algorithm Development and Validation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 19, 2439-2460. 10.1109/JSTARS.2025.3645399
  12. Fang, B. and Lakshmi, V. and Hain, C. and Mishra, V. (2025). A global 400-m high-resolution soil moisture dataset derived from multi-sensor remote sensing observations. Sci Data, 13, 1, 65. 10.1038/s41597-025-06356-z
  13. Faridani, Farid and Mataffo, Alessandro and Corrado, Giandomenico and Dente, Antonio and Rossi, Claudio and D’Urso, Guido and Basile, Boris (2025). Integration of Sentinel-1 and -2 imagery through advanced cloud computing improves hillside vineyard soil moisture analysis. Agricultural Water Management, 315, 109541. 10.1016/j.agwat.2025.109541
  14. Farmani, Mohammad A. and Behrangi, Ali and Gupta, Aniket and Tavakoly, Ahmad and Geheran, Matthew and Niu, Guo-Yue (2025). Do land models miss key soil hydrological processes controlling soil moisture memory?. Hydrology and Earth System Sciences, 29, 2, 547-566. 10.5194/hess-29-547-2025
  15. Gan, Yuan and Huang, Shuzhe and Wang, Chao and Wang, Wei and Chen, Nengcheng (2025). Mapping 100 m multi-depth soil moisture with WRF-Hydro over Tibetan Plateau. Journal of Hydrology, 654, 132884. 10.1016/j.jhydrol.2025.132884
  16. Gaona, J. and Bavera, D. and Fioravanti, G. and Hahn, S. and Stradiotti, P. and Filippucci, P. and Camici, S. and Ciabatta, L. and Mosaffa, H. and Puca, S. and Roberto, N. and Brocca, L. (2025). Soil moisture product consistency for operational drought monitoring in Europe. Hydrology and Earth System Sciences, 29, 16, 3865-3888. 10.5194/hess-29-3865-2025
  17. Guan, Y. S. and Gu, X. H. and Dai, A. G. and Zhou, T. J. and Yuan, X. and Mishra, A. K. and Zscheischler, J. and Pokhrel, Y. and Wang, L. C. and Li, J. F. and Huang, S. Z. and Luo, S. J. and Li, L. W. and Kong, D. D. and Zhang, X. (2025). Anthropogenic enhancement of subsurface soil moisture droughts. Nature Climate Change, 15, 12, 1355-1362. 10.1038/s41558-025-02458-z
  18. Hagan, D. F. T. and Kim, S. and Wang, G. and Ma, X. and Hu, Y. and Liu, Y. Y. and Barth, A. and Liu, H. and Ullah, W. and Nooni, I. K. and Bhatti, S. A. (2025). Seamless finer-resolution soil moisture from the synergistic merging of the FengYun-3 satellite series. Sci Data, 12, 1, 983. 10.1038/s41597-025-05263-7
  19. Hodges, E. and Chew, C. and Small, E. E. and Bai, D. A. and Al-Khaldi, M. and Ouellette, J. D. and Johnson, J. T. and Lei, F. N. and Kurum, M. and Gurbuz, A. C. and Senyurek, V. and Nabi, M. M. and Xu, X. L. and Shah, R. S. and Yueh, S. H. and Hayashi, A. and Setti, P. T. and Tabibi, S. and Santi, E. and Pettinato, S. and Ruf, C. S. and Moghaddam, M. (2025). A Merged CYGNSS Soil Moisture Product Using a Minimum Variance Estimator. Ieee Transactions on Geoscience and Remote Sensing, 63. Artn 4415715 10.1109/Tgrs.2025.3597532
  20. Hu, Chen and She, Dunxian and Wang, Gangsheng and Zhang, Liping and Jing, Zhaoxia and Song, Zhihong and Xia, Jun (2025). Unravelling spatiotemporal propagation processes among meteorological, soil, and evaporative flash droughts from a three-dimensional perspective. Agricultural Water Management, 308, 109294. 10.1016/j.agwat.2025.109294
  21. Hu, Y. and Lv, S. N. and Li, Z. J. and Zeng, Y. J. and Li, S. Y. and Wen, J. and Su, Z. B. (2025). Improving Soil Freeze-Thaw Retrieval from Spaceborne L-Band Measurements Based on Diurnal Amplitude Variation. Journal of Remote Sensing, 5, 0806. ARTN 0806 10.34133/remotesensing.0806
  22. Islam, M. B. and Guerrieri, A. and Gravina, R. and Delaney, D. T. and Fortino, G. (2025). From Traditional Machine Learning to Fine-Tuning Large Language Models: A Review for Sensors-Based Soil Moisture Forecasting. Sensors (Basel), 25, 22, 6903. 10.3390/s25226903
  23. Izadgoshasb, H. and Santi, E. and Cordari, F. and Guerriero, L. and Chiavini, L. and Ambrogioni, V. and Pierdicca, N. (2025). Comparison of a Semiempirical Algorithm and an Artificial Neural Network for Soil Moisture Retrieval Using CYGNSS Reflectometry Data. Remote Sensing, 17, 21, 3636. ARTN 3636 10.3390/rs17213636
  24. Jambhali, K. V. and Koirala, B. and Bnoulkacem, Z. and Scheunders, P. (2025). Soil Moisture Content Estimation From Hyperspectral Remote Sensing Data. Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 18, 22231-22240. 10.1109/Jstars.2025.3603841
  25. Jiang, Menghui and Qiu, Tian and Wang, Ting and Zeng, Chao and Zhang, Boxuan and Shen, Huanfeng (2025). Seamless global daily soil moisture mapping using deep learning based spatiotemporal fusion. International Journal of Applied Earth Observation and Geoinformation, 139, 104517. 10.1016/j.jag.2025.104517
  26. Jiao, M. L. and Li, X. Q. and Sun, X. and Wu, J. J. and Zhao, T. J. and Tang, R. Y. and Bai, Y. (2025). Multi-Layer Soil Moisture Profiling Based on BKA-CNN by Integrating Sentinel-1/2 SAR and Multispectral Data. Agronomy-Basel, 15, 11, 2542. ARTN 2542 10.3390/agronomy15112542
  27. Jin, Yu and Ji, Min and Zheng, Naiquan and Zhang, Zhihua and Ding, Penghui and Zhao, Qian (2025). Soil MoistureRetrieval from TM-1 GNSS-R Reflections with Auxiliary Geophysical Variables: A Multi-Cluster and Seasonal Evaluation. Land, 15, 1, 36. 10.3390/land15010036
  28. Kim, Kyung Y. and Rajaram, Harihar and Lakshmi, Venkataraman (2025). Observing decreasing snow cover and increasing surface temperature across the Andes with remotely sensed and reanalysis data. Environmental Research Communications, 7, 2, 021009. 10.1088/2515-7620/adb382
  29. Kurnool. Anusha, Devi (2025). A Hybrid Convlstm–Hierarchical Gnn Framework for Soil Moisture Prediction Using Iot and Sentinel-2. International Journal of Applied Mathematics, 38, 11, 2184-2199. 10.12732/ijam.v38i11s.1320
  30. Lal, Preet and Singh, Gurjeet and Das, Narendra N. and Lohman, Rowena B. (2025). Validation of the NISAR Multi-Scale Soil Moisture Retrieval Algorithm across Various Spatial Resolutions and Landcovers Using the ALOS-2 SAR Data. Journal of Remote Sensing, 5. 10.34133/remotesensing.0729
  31. Liang, Yueji and Zhao, Xingyu and Zhu, Binglin and Guo, Xi and Ren, Chao and Lu, Xianjian and Feng, Zhengzhou and Pan, Jinlong (2025). GNSS-IR retrieval of soil moisture at hourly resolution taking into account corrections for inter-orbit phase bias of satellites. Advances in Space Research. 10.1016/j.asr.2025.11.101
  32. Li, Fupeng and Springer, Anne and Kusche, Jürgen and Gutknecht, Benjamin D. and Ewerdwalbesloh, Yorck (2025). Reanalysis and Forecasting of Total Water Storage and Hydrological States by Combining Machine Learning With CLM Model Simulations and GRACE Data Assimilation. Water Resources Research, 61, 2, e2024WR037926. 10.1029/2024wr037926
  33. Li, L. and Lin, X. and Fang, Y. and Hou, Z. J. and Leung, L. R. and Wang, Y. and Mao, J. and Xu, Y. and Massoud, E. and Shi, M. (2025). A unified ensemble soil moisture dataset across the continental United States. Sci Data, 12, 1, 546. 10.1038/s41597-025-04657-x
  34. Li, Shulin and Xing, Minfeng and Dong, Taifeng (2025). An Apparent Thermal Inertia Based Trapezoid Model for Downscaling ESA CCI Soil Moisture Products. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 18, 4473-4486. 10.1109/jstars.2024.3525305
  35. Liu, Jinyue and Zhao, Jie and He, Junhao and Qu, Jianjia and Xing, Yushen and Du, Rui and Chen, Shichao and Tang, Xianhui and Wang, Liang and Yue, Chao (2025). Spatiotemporal Trend and Influencing Factors of Surface Soil Moisture in Eurasian Drylands over the Past Four Decades. Forests, 16, 4, 589. 10.3390/f16040589
  36. Liu, R. H. and Chang, C. and Zhong, R. S. and Lu, S. Y. (2025). Soil Moisture Monitoring Method and Data Products: Current Research Status and Future Development Trends. Remote Sensing, 17, 24, 3945. ARTN 3945 10.3390/rs17243945
  37. Liu, Xiangyu and Zhuang, Qianlai and Liao, Chang and Jorgenson, M. Torre (2025). Changes in soil water content and lateral flow exert large effects on soil thermal dynamics across Alaskan landscapes. Environmental Research Letters, 20, 2, 024056. 10.1088/1748-9326/adaa05
  38. Liu, Xu and Wang, Shitai and Yin, Min and Wei, Jialin and Xu, Zhengyang and Zhang, Xiaoyu and Lu, Zengyang (2025). Spaceborne GNSS-R combined with GNSS-IR for soil moisture retrieval. Advances in Space Research, 76, 1, 32-45. 10.1016/j.asr.2025.04.011
  39. Liu, Y. and Ren, C. and Shao, H. (2025). Study on soil moisture estimation using a three-frequency combination of observations integrated with robust estimation and machine learning. Sci Rep, 15, 1, 24015. 10.1038/s41598-025-09029-4
  40. Liu, Y. and Zha, Y. and Ran, G. and Zhang, Y. and Shi, L. (2025). SMRFR: A global multilayer soil moisture dataset generated using Random Forest from multi-source data. Sci Data, 12, 1, 1170. 10.1038/s41597-025-05511-w
  41. Liu, Yangxiaoyue and Xin, Ying and Yin, Cong (2025). A Transformer-based method to simulate multi-scale soil moisture. Journal of Hydrology, 655, 132900. 10.1016/j.jhydrol.2025.132900
  42. Liu, Y. J. and Fan, H. Y. and Jin, Y. and Zhu, S. A. (2025). Reconstruction of SMAP Soil Moisture Data Based on Residual Autoencoder Network with Convolutional Feature Extraction. Remote Sensing, 17, 22, 3729. ARTN 3729 10.3390/rs17223729
  43. Li, Yijie and Luo, Linyu and Guo, Fei and Yang, Furong and Wang, Tianyang and Gao, Hang and Bi, Xinyu and Zhang, Zhitao and Yao, Yifei (2025). Quality control and improvement of GNSS-IR soil moisture robust inversion model. Advances in Space Research, 75, 1, 91-107. 10.1016/j.asr.2024.07.069
  44. Li, Yu-Xuan and Leng, Pei and Kasim, Abba Aliyu and Li, Zhao-Liang (2025). Spatiotemporal variability and dominant driving factors of satellite observed global soil moisture from 2001 to 2020. Journal of Hydrology, 654, 132848. 10.1016/j.jhydrol.2025.132848
  45. Li, Z. H. and Yang, Q. Q. and Li, J. and Jin, T. Y. and Yuan, Q. Q. and Shen, H. F. and Zhang, L. P. (2025). Global multi-scale surface soil moisture retrieval coupling physical mechanisms and machine learning in the cloud environment. Remote Sensing of Environment, 329, 114928. ARTN 114928 10.1016/j.rse.2025.114928
  46. Lu, Z. X. and Fan, L. and Liu, X. Z. and Bai, X. J. and Baghdadi, N. and Frappart, F. and Zeng, J. Y. and De Lannoy, G. and Peng, J. and Xing, Z. P. and Li, X. J. and Feng, Z. Z. and Li, X. and Wigneron, J. P. (2025). Sentinel-1 vegetation optical depth retrievals over the international soil moisture network. International Journal of Digital Earth, 18, 2, 2555412. Artn 2555412 10.1080/17538947.2025.2555412
  47. Ma, Tian and Leng, Pei and Gao, Yu-Xin and Aliyu Kasim, Abba and Guo, Xiaonan and Zhang, Xia and Shang, Guo-Fei and Li, Zhao-Liang (2025). SPTS: Single Pixel in Time-Series Triangle Model for Estimating Surface Soil Moisture. IEEE Transactions on Geoscience and Remote Sensing, 63, 1-13. 10.1109/tgrs.2024.3520716
  48. Moghaddas, M. and Tajrishy, M. (2025). A novel random forest approach to downscale SMAP soil moisture products to 100 m resolution using temporally closest satellite observation data. Remote Sensing Applications-Society and Environment, 39, 101710. ARTN 101710 10.1016/j.rsase.2025.101710
  49. Moore, Kayla R. and Lychuk, Taras and McNairn, Heather and Geng, Xiaoyuan and Chipanshi, Aston and Ojo, E. Rotimi and Lapen, David and Powers, Jarrett and Halstead, Amanda and Gottfried, Kurt and Tetlock, Erica and Bence, Belinda and Champagne, Catherine and Pacheco, Anna and Glenn, Aaron and Crittenden, Stephen and Rollin, Patrick and Fitzmaurice, John and Bullock, Paul and Helgason, Warren and Johnson, Bruce and Jackson, Clayton and Waddell, Arnie and Kopeechuk, Andrew (2025). Collection: Datasets From Real-Time In-Situ Soil Monitoring for Agriculture 2025. IEEE Data Descriptions, 2, 433-440. 10.1109/ieeedata.2025.3612373
  50. Pataki, Angelika and Bertalan, László and Pásztor, László and Nagy, Loránd Attila and Abriha, Dávid and Liang, Shunlin and Singh, Sudhir Kumar and Szabó, Szilárd (2025). Soil Moisture Satellite Data Under Scrutiny: Assessing Accuracy Through Environmental Proxies and Extended Triple Collocation Analysis. Earth Systems and Environment, 9, 2, 801-824. 10.1007/s41748-025-00605-2
  51. Peng, Zhiqing and Zhao, Tianjie and Shi, Jiancheng and Hu, Lu and Jackson, Thomas J. and Cosh, Michael H. and Lu, Hui and Gao, Xiaodong and Zheng, Jingyao and Yao, Panpan and Cui, Qian and Guo, Peng and Song, Peilin and Wei, Zushuai and Wang, Mengjia and Fu, Anmin (2025). Understanding discrepancies in soil moisture from SMAP and AMSR2: insights into performance and dry-down behavior. GIScience & Remote Sensing, 62, 1, 2509404. 10.1080/15481603.2025.2509404
  52. Poehls, Jeran and Alonso, Lazaro and Koirala, Sujan and Reichstein, Markus and Carvalhais, Nuno (2025). Downscaling soil moisture to sub-km resolutions with simple machine learning ensembles. Journal of Hydrology, 652, 132624. 10.1016/j.jhydrol.2024.132624
  53. Rabiei, S. and Babaeian, E. and Grunwald, S. (2025). Surface and Subsurface Soil Moisture Estimation Using Fusion of SMAP, NLDAS-2, and SOLUS100 Data with Deep Learning. Remote Sensing, 17, 4, 659. ARTN 659 10.3390/rs17040659
  54. Şahin, Onur Güngör and Gündüz, Orhan (2025). Derivation of soil hydraulic properties (SHPs) using a Physics-Based inverse calibration method and International soil moisture network database. Journal of Hydrology, 660, 133445. 10.1016/j.jhydrol.2025.133445
  55. Sawada, Y. and Fujii, H. and Tsutsui, H. and Aida, K. and Shimada, R. and Kachi, M. and Koike, T. (2025). Ecohydrological Land Reanalysis: Vegetation Water Content and Soil Moisture Data by Land Data Assimilation. Geoscience Data Journal, 12, 4, e70025. ARTN e70025 10.1002/gdj3.70025
  56. Setti, Paulo and Tabibi, Sajad (2025). Comprehensive Analysis of CYGNSS GNSS-R Data for Enhanced Soil Moisture Retrieval. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 18, 663-679. 10.1109/jstars.2024.3498069
  57. Shangguan, Yulin and Tong, Cheng and Shi, Zhou and Wang, Hongquan and Deng, Xiaodong (2025). Comparison of different downscaling schemes for obtaining regional high-resolution soil moisture data. International Journal of Applied Earth Observation and Geoinformation, 141, 104652. 10.1016/j.jag.2025.104652
  58. Shen, C. Y. and Qin, C. R. and Lu, Z. and Ning, D. H. and Zang, Z. X. and Tang, H. L. and Pan, F. and Cheng, G. M. and Hu, J. M. and Meng, S. S. (2025). Assessing Impacts of Anthropogenic Modification on Surface Soil Moisture Dynamics: A Case Study over Southwest China. Hydrology, 12, 11, 275. ARTN 275 10.3390/hydrology12110275
  59. Shi, Y. J. and Dai, W. and Chen, G. S. and Zhang, X. and Li, N. and Fu, W. J. (2025). Improved soil moisture mapping using an integrated cyclic modeling and bias correction approach. Remote Sensing Applications-Society and Environment, 40, 101741. ARTN 101741 10.1016/j.rsase.2025.101741
  60. Sivaprasad, Visakh and Rahmati, Mehdi and Springer, Anne and Vereecken, Harry and Montzka, Carsten (2025). Development of Continuous AMSR-E/2 Soil Moisture Time Series by Hybrid Deep Learning Model (ConvLSTM2D and Conv2D) and Transfer Learning for Reanalyses. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 18, 11169-11182. 10.1109/jstars.2025.3557956
  61. Tai, S. L. and Yang, Z. and Gaudet, B. and Sakaguchi, K. and Berg, L. and Kaul, C. and Qian, Y. and Liu, Y. and Fast, J. (2025). A 1 km soil moisture dataset over eastern CONUS generated by assimilating SMAP data into the Noah-MP land surface model. Earth System Science Data, 17, 9, 4587-4611. 10.5194/essd-17-4587-2025
  62. Tu, Jinsheng and He, Xiufeng and Xu, Xueyong and Song, Minfeng and Xu, Xinzhe (2025). Assessment of Tianmu-1 multi-GNSS-R global soil moisture products. Advances in Space Research, 76, 3, 1476-1491. 10.1016/j.asr.2025.05.066
  63. Usman, M. and Ndehedehe, C. E. (2025). Leveraging Limited ISMN Soil Moisture Measurements to Develop the HYDRUS-1D Model and Explore the Potential of Remotely Sensed Precipitation for Soil Moisture Estimates in the Northern Territory, Australia. Remote Sensing, 17, 22, 3723. ARTN 3723 10.3390/rs17223723
  64. van Jaarsveld, Barry and Wanders, Niko and Sutanudjaja, Edwin H. and Hoch, Jannis and Droppers, Bram and Janzing, Joren and van Beek, Rens L. P. H. and Bierkens, Marc F. P. (2025). A first attempt to model global hydrology at hyper-resolution. Earth System Dynamics, 16, 1, 29-54. 10.5194/esd-16-29-2025
  65. Wagner, Paul D. and Duethmann, Doris and Kiesel, Jens and Pool, Sandra and Hrachowitz, Markus and Ceola, Serena and Herzog, Anna and Houska, Tobias and Loritz, Ralf and Spieler, Diana and Staudinger, Maria and Tarasova, Larisa and Thober, Stephan and Fohrer, Nicola and Tetzlaff, Doerthe and Wagener, Thorsten and Guse, Björn (2025). The Unexploited Treasures of Hydrological Observations Beyond Streamflow for Catchment Modeling. WIREs Water, 12, 2, e70018. 10.1002/wat2.70018
  66. Wang, Hui and Wei, Zushuai and Miao, Linguang and Tian, Feng and Zhao, Tianjie and Hu, Lu and Meng, Lingkui (2025). Spatio-Temporal reconstruction to fill spatial gaps in global satellite vegetation optical depth products. Remote Sensing of Environment, 325, 114787. 10.1016/j.rse.2025.114787
  67. Wang, Qingyun and Lu, Cuixian and Zheng, Yuxin and Wang, Zhuo and Li, Jiafeng and Tan, Yini (2025). In-situ and triple collocation-based evaluations of Tianmu-1 global soil moisture products. Remote Sensing of Environment, 328, 114892. 10.1016/j.rse.2025.114892
  68. Wang, Weixuan and Wei, Yating and Hao, Longfei and Wei, Zushuai and Zhao, Tianjie (2025). Soil moisture forecasting in wireless sensor networks via spatiotemporal graph convolutional networks. Vadose Zone Journal, 24, 1, e70000. 10.1002/vzj2.70000
  69. Wang, Y. K. and Zhang, Q. R. and Sun, S. K. and Yao, Y. F. and Hu, X. T. and Cai, S. B. and Wang, H. B. (2025). Estimation of soil moisture profiles in ungauged stations by hybridizing sequential data assimilation and machine learning. Journal of Hydrology, 663, 134292. ARTN 134292 10.1016/j.jhydrol.2025.134292
  70. Wang, Zihao and Gao, Qi and Crosetto, Michele and Escorihuela, Maria Jose (2025). High-resolution surface soil moisture retrieval: A hybrid machine learning framework integrating change detection and downscaling for precision water management. International Journal of Applied Earth Observation and Geoinformation, 142, 104702. 10.1016/j.jag.2025.104702
  71. Wang, Z. X. and Liu, S. X. (2025). Spatiotemporal variability of climate: A key driver shaping spatial pattern and dominating temporal evolution of root zone soil moisture in China. Journal of Hydrology, 663, 134147. ARTN 134147 10.1016/j.jhydrol.2025.134147
  72. Wei, Z. and Wei, L. and Wang, T. and Lu, Q. and Tian, S. and Zhang, F. and Zhong, Y. (2025). A global long-term daily multilayer soil moisture dataset derived from machine learning. Sci Data. 10.1038/s41597-025-06436-0
  73. Wei, Z. S. and Kou, J. X. and Miao, L. G. and Hu, F. M. and Li, L. Z. and Wu, X. C. and Li, S. T. and Meng, L. K. (2025). Exploring diurnal variation in soil moisture via sub-daily estimates reconstruction. Journal of Hydrology, 662, 134005. ARTN 134005 10.1016/j.jhydrol.2025.134005
  74. Wesselkamp, Marieke and Albrecht, Jakob and Pinnington, Ewan and Castillo, William J. and Pappenberger, Florian and Dormann, Carsten F. (2025). The ecological forecast limit revisited: Potential, absolute and relative system predictability. Methods in Ecology and Evolution, 16, 7, 1521-1541. 10.1111/2041-210x.70049
  75. Xia, Jiahui and Jin, Junliang and Yuan, Shanshui and Ren, Liliang and Ji, Fang and Jiang, Shanhu and Liu, Yi and Yang, Xiaoli (2025). Spatiotemporal Pattern of Soil Moisture and Its Association with Vegetation in the Yellow River Basin. Water, 17, 7, 1028. 10.3390/w17071028
  76. Xi, Xuan and Zhuang, Qianlai and Liu, Xinyu (2025). A Hybrid Physics‐Guided Deep Learning Modeling Framework for Predicting Surface Soil Moisture. Journal of Geophysical Research: Machine Learning and Computation, 2, 3, e2025JH000682. 10.1029/2025jh000682
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  78. Xu, Lei and Ye, Zhenni and Dai, Jin and Li, Qi and Hong, Youting and Tao, Yun and Yu, Hongchu and Zhang, Chong and Chen, Zeqiang and Chen, Nengcheng (2025). Spatiotemporal Seamless Estimation of Global Surface Soil Moisture Using Triple Collocation, Machine Learning, and Data Assimilation. IEEE Transactions on Geoscience and Remote Sensing, 63, 1-16. 10.1109/tgrs.2025.3568034
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