A spatial downscaling method for SMAP soil moisture considering vegetation memory and spatiotemporal fusion

References

• Abowarda, A. S., L. L. Bai, C. J. Zhang, D. Long, X. Y. Li, Q. Huang, and Z. L. Sun. 2021. “Generating Surface Soil Moisture at 30 m Spatial Resolution Using Both Data Fusion and Machine Learning Toward Better Water Resources Management at the Field Scale.” Remote Sensing of Environment 255: 112301. https://doi.org/10.1016/j.rse.2021.112301.
   Web of Science ®Google Scholar
• Amazirh, A., O. Merlin, and S. Er-Raki. 2019. “Including Sentinel-1 Radar Data to Improve the Disaggregation of MODIS Land Surface Temperature Data.” ISPRS Journal of Photogrammetry and Remote Sensing 150:11–26. https://doi.org/10.1016/j.isprsjprs.2019.02.004.
   Web of Science ®Google Scholar
• Bai, Jueying, Qian Cui, Wen Zhang, and Lingkui Meng. 2019. “An Approach for Downscaling SMAP Soil Moisture by Combining Sentinel-1 SAR and MODIS Data.” Remote Sensing 11 (23): 2736. https://doi.org/10.3390/rs11232736.
   Web of Science ®Google Scholar
• Bhuiyan, H. A. K. M., H. McNairn, J. Powers, M. Friesen, A. Pacheco, T. J. Jackson, M. H. Cosh, et al. 2018. “Assessing SMAP Soil Moisture Scaling and Retrieval in the Carman (Canada) Study Site.” Vadose Zone Journal 17 (1): 1–14. https://doi.org/10.2136/vzj2018.07.0132.
   Web of Science ®Google Scholar
• Bolten, J. D., and W. T. Crow. 2012. “Improved Prediction of Quasi-Global Vegetation Conditions Using Remotely-Sensed Surface Soil Moisture.” Geophysical Research Letters 39 (19): 39. https://doi.org/10.1029/2012gl053470.
   Google Scholar
• Breiman, L. 2001. “Random Forests.” Machine Learning 45 (1): 5–32. https://doi.org/10.1023/A:1010933404324.
   Web of Science ®Google Scholar
• Brocca, L., W. Zhao, and H. Lu. 2023. “High-Resolution Observations from Space to Address New Applications in Hydrology.” Innovation (Camb) 4 (3): 100437. https://doi.org/10.1016/j.xinn.2023.100437.
   PubMedGoogle Scholar
• Chanasyk, D. S., and M. A. Naeth. 1996. “Field Measurement of Soil Moisture Using Neutron Probes.” Canadian Journal of Soil Science 76: 317–323. https://doi.org/10.4141/cjss96-038.
   Web of Science ®Google Scholar
• Chang, Li-Ling, Ravindra Dwivedi, John F. Knowles, Yuan-Hao Fang, Guo-Yue Niu, Jon D. Pelletier, Craig Rasmussen, Matej Durcik, Greg A. Barron-Gafford, and Thomas Meixner. 2018. “Why Do Large-Scale Land Surface Models Produce a Low Ratio of Transpiration to Evapotranspiration?” Journal of Geophysical Research: Atmospheres 123 (17): 9109–9130. https://doi.org/10.1029/2018JD029159.
   Web of Science ®Google Scholar
• Chen, T., R. A. M. de Jeu, Y. Y. Liu, G. R. van der Werf, and A. J. Dolman. 2014. “Using Satellite Based Soil Moisture to Quantify the Water Driven Variability in NDVI: A Case Study Over Mainland Australia.” Remote Sensing of Environment 140:330–338. https://doi.org/10.1016/j.rse.2013.08.022.
   Web of Science ®Google Scholar
• Chen, S. D., D. X. She, L. P. Zhang, M. Y. Guo, and X. Liu. 2019. “Spatial Downscaling Methods of Soil Moisture Based on Multisource Remote Sensing Data and Its Application.” Water 11 (7): 1401. https://doi.org/10.3390/w11071401.
   Web of Science ®Google Scholar
• Chen, Yong, and Huiling Yuan. 2020. “Evaluation of Nine Sub-Daily Soil Moisture Model Products Over China Using High-Resolution in Situ Observations.” Journal of Hydrology 588: 125054. https://doi.org/10.1016/j.jhydrol.2020.125054.
   Web of Science ®Google Scholar
• Chen, Yi, Zhao Zhang, and Fulu Tao. 2018. “Improving Regional Winter Wheat Yield Estimation Through Assimilation of Phenology and Leaf Area Index from Remote Sensing Data.” European Journal of Agronomy 101:163–173. https://doi.org/10.1016/j.eja.2018.09.006.
   Web of Science ®Google Scholar
• Coenders-Gerrits, A. M., R. J. van der Ent, T. A. Bogaard, L. Wang-Erlandsson, M. Hrachowitz, and H. H. Savenije. 2014. “Uncertainties in Transpiration Estimates.” Nature 506 (7487): E1–E2. https://doi.org/10.1038/nature12925.
   PubMed Web of Science ®Google Scholar
• Dai, Yongjiu, Wei Shangguan, Nan Wei, Qinchuan Xin, Hua Yuan, Shupeng Zhang, Shaofeng Liu, Xingjie Lu, Dagang Wang, and Fapeng Yan. 2019. “A Review of the Global Soil Property Maps for Earth System Models.” Soil 5 (2): 137–158. https://doi.org/10.5194/soil-5-137-2019.
   Web of Science ®Google Scholar
• Djamai, N., R. Magagi, K. Goita, O. Merlin, Y. Kerr, and A. Roy. 2016. “A Combination of DISPATCH Downscaling Algorithm with CLASS Land Surface Scheme for Soil Moisture Estimation at Fine Scale During Cloudy Days.” Remote Sensing of Environment 184:1–14. https://doi.org/10.1016/j.rse.2016.06.010.
   Web of Science ®Google Scholar
• Ebrahimi-Khusfi, M., S. K. Alavipanah, S. Hamzeh, F. Amiraslani, N. N. Samany, and J. P. Wigneron. 2018. “Comparison of Soil Moisture Retrieval Algorithms Based on the Synergy Between SMAP and SMOS-IC.” International Journal of Applied Earth Observation and Geoinformation 67:148–160. https://doi.org/10.1016/j.jag.2017.12.005.
   Web of Science ®Google Scholar
• El Hajj, M., N. Baghdadi, M. Zribi, and H. Bazzi. 2017. “Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution Over Agricultural Areas.” Remote Sensing 9 (12): 1292. https://doi.org/10.3390/rs9121292.
   Web of Science ®Google Scholar
• Engstrom, Ryan, Allen Hope, Hyojung Kwon, and Douglas Stow. 2008. “The Relationship Between Soil Moisture and NDVI Near Barrow, Alaska.” Physical Geography 29 (1): 38–53. https://doi.org/10.2747/0272-3646.29.1.38.
   Web of Science ®Google Scholar
• Famiglietti, James S., Dongryeol Ryu, Aaron A. Berg, Matthew Rodell, and Thomas J. Jackson. 2008. “Field Observations of Soil Moisture Variability Across Scales.” Water Resources Research 44 (1): W01423. https://doi.org/10.1029/2006wr005804.
   Web of Science ®Google Scholar
• Fan, Xinyi, Peng Gao, Biqing Tian, Changxue Wu, and Xingmin Mu. 2023. “Spatio-Temporal Patterns of NDVI and Its Influencing Factors Based on the ESTARFM in the Loess Plateau of China.” Remote Sensing 15 (10): 2553. https://doi.org/10.3390/rs15102553.
   Web of Science ®Google Scholar
• Fisher, R. A., and C. D. Koven. 2020. “Perspectives on the Future of Land Surface Models and the Challenges of Representing Complex Terrestrial Systems.” Journal of Advances in Modeling Earth Systems 12 (4): e2018MS001453. https://doi.org/10.1029/2018MS001453.
   Web of Science ®Google Scholar
• Gessner, U., V. Naeimi, I. Klein, C. Kuenzer, D. Klein, and S. Dech. 2013. “The Relationship Between Precipitation Anomalies and Satellite-Derived Vegetation Activity in Central Asia.” Global and Planetary Change 110:74–87. https://doi.org/10.1016/j.gloplacha.2012.09.007.
   Web of Science ®Google Scholar
• Han, Eunjin, Wade T. Crow, Thomas Holmes, and John Bolten. 2014. “Benchmarking a Soil Moisture Data Assimilation System for Agricultural Drought Monitoring.” Journal of Hydrometeorology 15 (3): 1117–1134. https://doi.org/10.1175/JHM-D-13-0125.1.
   Web of Science ®Google Scholar
• Hu, Fengmin, Zushuai Wei, Wen Zhang, Donyu Dorjee, and Lingkui Meng. 2020. “A Spatial Downscaling Method for SMAP Soil Moisture Through Visible and Shortwave-Infrared Remote Sensing Data.” Journal of Hydrology 590: 125360. https://doi.org/10.1016/j.jhydrol.2020.125360.
   Web of Science ®Google Scholar
• Kwon, Moonhyuk, Hyun-Han Kwon, and Dawei Han. 2018. “A Spatial Downscaling of Soil Moisture from Rainfall, Temperature, and AMSR2 Using a Gaussian-Mixture Nonstationary Hidden Markov Model.” Journal of Hydrology 564:1194–1207. https://doi.org/10.1016/j.jhydrol.2017.12.015.
   Web of Science ®Google Scholar
• Lacava, T., L. Brocca, M. Faruolo, P. Matgen, T. Moramarco, N. Pergola, and V. Tramutoli. 2012. “A Multi-Sensor (SMOS, AMSR-E and ASCAT) Satellite-Based Soil Moisture Products Inter-Comparison.” Paper presented at the 2012 IEEE International Geoscience and Remote Sensing Symposium, 22-27 July 2012.
   Google Scholar
• Lievens, H., S. K. Tomer, A. Al Bitar, G. J. M. De Lannoy, M. Drusch, G. Dumedah, H. J. H. Franssen, et al. 2015. “SMOS Soil Moisture Assimilation for Improved Hydrologic Simulation in the Murray Darling Basin, Australia.” Remote Sensing of Environment 168:146–162. https://doi.org/10.1016/j.rse.2015.06.025.
   Web of Science ®Google Scholar
• Lindell, D. B., and D. G. Long. 2016. “High-Resolution Soil Moisture Retrieval with ASCAT.” IEEE Geoscience and Remote Sensing Letters 13 (7): 972–976. https://doi.org/10.1109/LGRS.2016.2557321.
   Web of Science ®Google Scholar
• Liu, L. B., L. Gudmundsson, M. Hauser, D. H. Qin, S. C. Li, and S. I. Seneviratne. 2020. “Soil Moisture Dominates Dryness Stress on Ecosystem Production Globally.” Nature Communications 11 (1): 4892. https://doi.org/10.1038/s41467-020-18631-1.
   PubMed Web of Science ®Google Scholar
• Long, Di, Liangliang Bai, La Yan, Caijin Zhang, Wenting Yang, Huimin Lei, Jinling Quan, Xianyong Meng, and Chunxiang Shi. 2019. “Generation of Spatially Complete and Daily Continuous Surface Soil Moisture of High Spatial Resolution.” Remote Sensing of Environment 233: 111364. https://doi.org/10.1016/j.rse.2019.111364.
   Web of Science ®Google Scholar
• Lopatin, J., K. Dolos, H. J. Hernandez, M. Galleguillos, and F. E. Fassnacht. 2016. “Comparing Generalized Linear Models and Random Forest to Model Vascular Plant Species Richness Using LiDAR Data in a Natural Forest in Central Chile.” Remote Sensing of Environment 173: 200–210. https://doi.org/10.1016/j.rse.2015.11.029.
   Web of Science ®Google Scholar
• Mao, T. N., W. Shangguan, Q. L. Li, L. Li, Y. Zhang, F. N. Huang, J. D. Li, W. Liu, and R. Q. Zhang. 2022. “A Spatial Downscaling Method for Remote Sensing Soil Moisture Based on Random Forest Considering Soil Moisture Memory and Mass Conservation.” Remote Sensing 14 (16): 3858. https://doi.org/10.3390/rs14163858.
   Web of Science ®Google Scholar
• McNally, A., S. Shukla, K. R. Arsenault, S. Wang, C. D. Peters-Lidard, and J. P. Verdin. 2016. “Evaluating ESA CCI Soil Moisture in East Africa.” International Journal of Applied Earth Observation and Geoinformation 48:96–109. https://doi.org/10.1016/j.jag.2016.01.001.
   PubMed Web of Science ®Google Scholar
• Meng, S. S., X. H. Xie, and S. L. Liang. 2017. “Assimilation of Soil Moisture and Streamflow Observations to Improve Flood Forecasting with Considering Runoff Routing Lags.” Journal of Hydrology 550:568–579. https://doi.org/10.1016/j.jhydrol.2017.05.024.
   Web of Science ®Google Scholar
• Merlin, O., M. J. Escorihuela, M. A. Mayoral, O. Hagolle, A. Al Bitar, and Y. Kerr. 2013. “Self-Calibrated Evaporation-Based Disaggregation of SMOS Soil Moisture: An Evaluation Study at 3 km and 100 m Resolution in Catalunya, Spain” Remote Sensing of Environment 130:25–38. https://doi.org/10.1016/j.rse.2012.11.008.
   Web of Science ®Google Scholar
• Mimeau, L., Y. Tramblay, L. Brocca, C. Massari, S. Camici, and P. Finaud-Guyot. 2021. “Modeling the Response of Soil Moisture to Climate Variability in the Mediterranean Region.” Hydrology and Earth System Sciences 25 (2): 653–669. https://doi.org/10.5194/hess-25-653-2021.
   Web of Science ®Google Scholar
• Mishra, Vikalp, W. Lee Ellenburg, Robert E. Griffin, John R. Mecikalski, James F. Cruise, Christopher R. Hain, and Martha C. Anderson. 2018. “An Initial Assessment of a SMAP Soil Moisture Disaggregation Scheme Using TIR Surface Evaporation Data Over the Continental United States.” International Journal of Applied Earth Observation and Geoinformation 68:92–104. https://doi.org/10.1016/j.jag.2018.02.005.
   Web of Science ®Google Scholar
• Mladenova, I. E., J. D. Bolten, W. Crow, N. Sazib, and C. Reynolds. 2020. “Agricultural Drought Monitoring via the Assimilation of SMAP Soil Moisture Retrievals into a Global Soil Water Balance Model.” Frontiers in Big Data 3: 10. https://doi.org/10.3389/fdata.2020.00010.
   PubMedGoogle Scholar
• Mladenova, I. E., T. J. Jackson, E. Njoku, R. Bindlish, S. Chan, M. H. Cosh, T. R. H. Holmes, et al. 2014. “Remote Monitoring of Soil Moisture Using Passive Microwave-Based Techniques – Theoretical Basis and Overview of Selected Algorithms for AMSR-E.” Remote Sensing of Environment 144:197–213. https://doi.org/10.1016/j.rse.2014.01.013.
   Web of Science ®Google Scholar
• Muñoz-Sabater, J., E. Dutra, A. Agustí-Panareda, C. Albergel, G. Arduini, G. Balsamo, S. Boussetta, et al. 2021. “ERA5-Land: A State-of-the-art Global Reanalysis Dataset for Land Applications.” Earth System Science Data 13 (9): 4349–4383. https://doi.org/10.5194/essd-13-4349-2021.
   Web of Science ®Google Scholar
• Musyimi, Z. 2011. “Temporal Relationships Between Remotely-Sensed Soil Moisture and NDVI Over Africa: Potential for Drought Early Warning.” Master's Thesis. Enschede, Netherlands: Dept. of Geo-Information Science and Technology, University of Twenty.
   Google Scholar
• Peng, Jian, Alexander Loew, Olivier Merlin, and Niko E. C. Verhoest. 2017. “A Review of Spatial Downscaling of Satellite Remotely Sensed Soil Moisture.” Reviews of Geophysics 55 (2): 341–366. https://doi.org/10.1002/2016RG000543.
   Web of Science ®Google Scholar
• Peng, J., J. Niesel, A. Loew, S. Q. Zhang, and J. Wang. 2015. “Evaluation of Satellite and Reanalysis Soil Moisture Products Over Southwest China Using Ground-Based Measurements.” Remote Sensing 7 (11): 15729–15747. https://doi.org/10.3390/rs71115729.
   Web of Science ®Google Scholar
• Qin, J., K. Yang, N. Lu, Y. Y. Chen, L. Zhao, and M. L. Han. 2013. “Spatial Upscaling of in-Situ Soil Moisture Measurements Based on MODIS-Derived Apparent Thermal Inertia.” Remote Sensing of Environment 138:1–9. https://doi.org/10.1016/j.rse.2013.07.003.
   Web of Science ®Google Scholar
• Robock, A., K. Y. Vinnikov, G. Srinivasan, J. K. Entin, S. E. Hollinger, N. A. Speranskaya, S. X. Liu, and A. Namkhai. 2000. “The Global Soil Moisture Data Bank.” Bulletin of the American Meteorological Society 81 (6): 1281–1299. https://doi.org/10.1175/1520-0477(2000)081<1281:Tgsmdb>2.3.Co;2.
   Web of Science ®Google Scholar
• Sahoo, A. K., G. J. M. De Lannoy, R. H. Reichle, and P. R. Houser. 2013. “Assimilation and Downscaling of Satellite Observed Soil Moisture Over the Little River Experimental Watershed in Georgia, USA.” Advances in Water Resources 52:19–33. https://doi.org/10.1016/j.advwatres.2012.08.007.
   Web of Science ®Google Scholar
• Schmugge, T., T. J. Jackson, and H. L. McKim. 1980. “Survey of Methods for Soil Moisture Determination.” Water Resources Research 16 (6): 961–979. https://doi.org/10.1029/WR016i006p00961.
   Web of Science ®Google Scholar
• Shi, Benlin, Xinyu Zhu, Yunchuan Hu, and Yanyan Yang. 2017. “Drought Characteristics of Henan Province in 1961–2013 Based on Standardized Precipitation Evapotranspiration Index.” Journal of Geographical Sciences 27 (3): 311–325. https://doi.org/10.1007/s11442-017-1378-4.
   Web of Science ®Google Scholar
• Sun, Y. Y., S. F. Huang, J. W. Ma, J. R. Li, X. T. Li, H. Wang, S. Chen, and W. B. Zang. 2017. “Preliminary Evaluation of the SMAP Radiometer Soil Moisture Product Over China Using In Situ Data.” Remote Sensing 9 (3): 292. https://doi.org/10.3390/rs9030292.
   Web of Science ®Google Scholar
• Sur, Chanyang, Do-Hyuk Kang, Kyoung Jae Lim, Jae E. Yang, Yongchul Shin, and Younghun Jung. 2020. “Soil Moisture–Vegetation–Carbon Flux Relationship Under Agricultural Drought Condition Using Optical Multispectral Sensor.” Remote Sensing 12 (9): 1359. https://doi.org/10.3390/rs12091359.
   Web of Science ®Google Scholar
• Tao, Guofeng, Kun Jia, Xiangqin Wei, Mu Xia, Bing Wang, Xianhong Xie, Bo Jiang, Yunjun Yao, and Xiaotong Zhang. 2021. “Improving the Spatiotemporal Fusion Accuracy of Fractional Vegetation Cover in Agricultural Regions by Combining Vegetation Growth Models.” International Journal of Applied Earth Observation and Geoinformation 101: 102362. https://doi.org/10.1016/j.jag.2021.102362.
   Web of Science ®Google Scholar
• van Hateren, Theresa C., Marco Chini, Patrick Matgen, and Adriaan J. Teuling. 2021. “Ambiguous Agricultural Drought: Characterising Soil Moisture and Vegetation Droughts in Europe from Earth Observation.” Remote Sensing 13 (10): 1990. https://doi.org/10.3390/rs13101990.
   Web of Science ®Google Scholar
• Wang, Y. A., X. Fu, D. M. Wu, M. D. Wang, K. D. Lu, Y. J. Mu, Z. G. Liu, Y. H. Zhang, and T. Wang. 2021. “Agricultural Fertilization Aggravates Air Pollution by Stimulating Soil Nitrous Acid Emissions at High Soil Moisture.” Environmental Science & Technology 55 (21): 14556–14566. https://doi.org/10.1021/acs.est.1c04134.
   PubMed Web of Science ®Google Scholar
• Wang, Aihui, and Xueli Shi. 2019. “A Multilayer Soil Moisture Dataset Based on the Gravimetric Method in China and Its Characteristics.” Journal of Hydrometeorology 20 (8): 1721–1736. https://doi.org/10.1175/JHM-D-19-0035.1.
   Web of Science ®Google Scholar
• Wang, Shuai, Chaozi Wang, Chenglong Zhang, Jingyuan Xue, Pu Wang, Xingwang Wang, Weishu Wang, et al. 2022. “A Classification-Based Spatiotemporal Adaptive Fusion Model for the Evaluation of Remotely Sensed Evapotranspiration in Heterogeneous Irrigated Agricultural Area.” Remote Sensing of Environment 273: 112962. https://doi.org/10.1016/j.rse.2022.112962.
   Web of Science ®Google Scholar
• West, H., N. Quinn, M. Horswell, and P. White. 2018. “Assessing Vegetation Response to Soil Moisture Fluctuation under Extreme Drought Using Sentinel-2.” Water 10 (7): 838. https://doi.org/10.3390/w10070838.
   Web of Science ®Google Scholar
• Wigneron, J. P., X. J. Li, F. Frappart, L. Fan, A. Al-Yaari, G. De Lannoy, X. Z. Liu, M. J. Wang, E. Le Masson, and C. Moisy. 2021. “SMOS-IC Data Record of Soil Moisture and L-VOD: Historical Development, Applications and Perspectives.” Remote Sensing of Environment 254: 112238. https://doi.org/10.1016/j.rse.2020.112238.
   Web of Science ®Google Scholar
• Wu, R. J., and Q. Li. 2021. “Assessing the Soil Moisture Drought Index for Agricultural Drought Monitoring Based on Green Vegetation Fraction Retrieval Methods.” Natural Hazards 108 (1): 499–518. https://doi.org/10.1007/s11069-021-04693-x.
   Web of Science ®Google Scholar
• Xu, Mengyuan, Ning Yao, Haoxuan Yang, Jia Xu, Annan Hu, Luis Gustavo Goncalves de Goncalves, and Gang Liu. 2022. “Downscaling SMAP Soil Moisture Using a Wide & Deep Learning Method Over the Continental United States.” Journal of Hydrology 609: 127784. https://doi.org/10.1016/j.jhydrol.2022.127784.
   Web of Science ®Google Scholar
• Yu, X., and V. P. Drnevich. 2004. “Soil Water Content and dry Density by Time Domain Reflectometry.” Journal of Geotechnical and Geoenvironmental Engineering 130 (9): 922–934. https://doi.org/10.1061/(ASCE)1090-0241(2004)130:9(922).
   Web of Science ®Google Scholar
• Zeng, Chao, Di Long, Huanfeng Shen, Penghai Wu, Yaokui Cui, and Yang Hong. 2018. “A two-Step Framework for Reconstructing Remotely Sensed Land Surface Temperatures Contaminated by Cloud.” ISPRS Journal of Photogrammetry and Remote Sensing 141:30–45. https://doi.org/10.1016/j.isprsjprs.2018.04.005.
   Web of Science ®Google Scholar
• Zhang, Hankui K., Bo Huang, Ming Zhang, Kai Cao, and Le Yu. 2015. “A Generalization of Spatial and Temporal Fusion Methods for Remotely Sensed Surface Parameters.” International Journal of Remote Sensing 36 (17): 4411–4445. https://doi.org/10.1080/01431161.2015.1083633.
   Web of Science ®Google Scholar
• Zhang, R. Z., X. J. Jia, and Q. F. Qian. 2022a. “Seasonal Forecasts of Eurasian Summer Heat Wave Frequency.” Environmental Research Communications 4 (2): 025007. https://doi.org/10.1088/2515-7620/ac5364.
   Google Scholar
• Zhang, Li, Lei Ji, and Bruce K. Wylie. 2011. “Response of Spectral Vegetation Indices to Soil Moisture in Grasslands and Shrublands.” International Journal of Remote Sensing 32 (18): 5267–5286. https://doi.org/10.1080/01431161.2010.496471.
   Web of Science ®Google Scholar
• Zhang, Yufang, Shunlin Liang, Zhiliang Zhu, Han Ma, and Tao He. 2022b. “Soil Moisture Content Retrieval from Landsat 8 Data Using Ensemble Learning.” ISPRS Journal of Photogrammetry and Remote Sensing 185:32–47. https://doi.org/10.1016/j.isprsjprs.2022.01.005.
   Web of Science ®Google Scholar
• Zhang, Q., Q. Q. Yuan, J. Li, Y. Wang, F. J. Sun, and L. P. Zhang. 2021. “Generating Seamless Global Daily AMSR2 Soil Moisture (SGD-SM) Long-Term Products for the Years 2013–2019.” Earth System Science Data 13 (3): 1385–1401. https://doi.org/10.5194/essd-13-1385-2021.
   Web of Science ®Google Scholar
• Zhao, Wei, and Si-Bo Duan. 2020. “Reconstruction of Daytime Land Surface Temperatures under Cloud-Covered Conditions Using Integrated MODIS/Terra Land Products and MSG Geostationary Satellite Data.” Remote Sensing of Environment 247: 111931. https://doi.org/10.1016/j.rse.2020.111931.
   Web of Science ®Google Scholar
• Zhao, W., and A. N. Li. 2015. “A Comparison Study on Empirical Microwave Soil Moisture Downscaling Methods Based on the Integration of Microwave-Optical/IR Data on the Tibetan Plateau.” International Journal of Remote Sensing 36 (19–20): 4986–5002. https://doi.org/10.1080/01431161.2015.1041178.
   Web of Science ®Google Scholar
• Zhao, Hongfei, Jie Li, Qiangqiang Yuan, Liupeng Lin, Linwei Yue, and Hongzhang Xu. 2022. “Downscaling of Soil Moisture Products Using Deep Learning: Comparison and Analysis on Tibetan Plateau.” Journal of Hydrology 607: 127570. https://doi.org/10.1016/j.jhydrol.2022.127570.
   Web of Science ®Google Scholar
• Zhao, Wei, Nilda Sánchez, Hui Lu, and Ainong Li. 2018. “A Spatial Downscaling Approach for the SMAP Passive Surface Soil Moisture Product Using Random Forest Regression.” Journal of Hydrology 563:1009–1024. https://doi.org/10.1016/j.jhydrol.2018.06.081.
   Web of Science ®Google Scholar
• Zhu, X. L., J. Chen, F. Gao, X. H. Chen, and J. G. Masek. 2010. “An Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model for Complex Heterogeneous Regions.” Remote Sensing of Environment 114 (11): 2610–2623. https://doi.org/10.1016/j.rse.2010.05.032.
   Web of Science ®Google Scholar
• Zhu, Xiaolin, Si-Bo Duan, Zhao-Liang Li, Penghai Wu, Hua Wu, Wei Zhao, and Yonggang Qian. 2022. “Reconstruction of Land Surface Temperature Under Cloudy Conditions from Landsat 8 Data Using Annual Temperature Cycle Model.” Remote Sensing of Environment 281: 113261. https://doi.org/10.1016/j.rse.2022.113261.
   Web of Science ®Google Scholar
• Zhuo, L., and D. W. Han. 2016. “The Relevance of Soil Moisture by Remote Sensing and Hydrological Modelling.” 12th International Conference on Hydroinformatics (Hic 2016) – Smart Water for the Future 154:1368–1375. https://doi.org/10.1016/j.proeng.2016.07.499.
   Google Scholar