{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T07:58:04Z","timestamp":1770883084044,"version":"3.50.1"},"reference-count":79,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,4,17]],"date-time":"2023-04-17T00:00:00Z","timestamp":1681689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China (NSFC)","award":["42090014"],"award-info":[{"award-number":["42090014"]}]},{"name":"National Natural Science Foundation of China (NSFC)","award":["42171039"],"award-info":[{"award-number":["42171039"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Global, long-term, gap-free, high quality soil moisture products are extremely important for hydrological monitoring and climate change research. However, soil moisture products produced from satellite observations have data gaps due to the limited capabilities of satellite orbit\/swath and retrieval algorithms, which limit the regional and global applications of soil moisture data in hydrology and agriculture studies. To solve this problem, we proposed a gap-filling method to reconstruct a global gap-free surface soil moisture product by applying the machine learning (Random Forest) algorithm on a pixel-by-pixel basis, taking into account the nonlinear relationship between surface soil moisture and the related surface environmental variables. The gap-filling method was applied to the NN-SM surface soil moisture product, which has a fraction of data gaps of around 50% globally on a multi-year average. A global daily gap-free surface soil moisture dataset from 2002 to 2020 was then generated. The reconstructed values of several sub-regions after manually eliminating the original values were cross-verified with the original data, and this clearly demonstrated the reliability of the reconstruction method with the correlation coefficient (R) ranging between 0.770 and 0.918, the Root Mean Square Error (RMSE) between 0.057 and 0.082 m3\/m3, the unbiased Root Mean Square Error (ubRMSE) between 0.053 and 0.081 m3\/m3, and Bias between \u22120.012 and 0.008 m3\/m3. The accuracy of the reconstructed surface soil moisture dataset was evaluated using in situ observations of surface soil moisture at 12 sites from the International Soil Moisture Network (ISMN) and the Long-Term Agroecosystem Research (LTAR) network, and the results showed good accuracy in terms of R (0.610), RMSE (0.067 m3\/m3), ubRMSE (0.045 m3\/m3) and Bias (0.031 m3\/m3). Overall, the reconstructed surface soil moisture dataset retained the characteristics of the NN-SM product, such as high accuracy and good spatiotemporal pattern. However, with the advantage of continuous spatiotemporal coverage, it is more suitable for further applications in the analysis of global surface soil moisture trends, land surface hydrological processes, and land-atmosphere energy and water exchanges, etc.<\/jats:p>","DOI":"10.3390\/rs15082116","type":"journal-article","created":{"date-parts":[[2023,4,17]],"date-time":"2023-04-17T04:45:32Z","timestamp":1681706732000},"page":"2116","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Reconstruction of Global Long-Term Gap-Free Daily Surface Soil Moisture from 2002 to 2020 Based on a Pixel-Wise Machine Learning Method"],"prefix":"10.3390","volume":"15","author":[{"given":"Pei","family":"Mi","sequence":"first","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6085-8274","authenticated-orcid":false,"given":"Chaolei","family":"Zheng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3108-8645","authenticated-orcid":false,"given":"Li","family":"Jia","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Yu","family":"Bai","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e2019GL086498","DOI":"10.1029\/2019GL086498","article-title":"Terrestrial Evaporation and Moisture Drainage in a Warmer Climate","volume":"47","author":"Gianotti","year":"2020","journal-title":"Geophys. Res. Lett."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"206","DOI":"10.5360\/membrane.28.206","article-title":"Global hydrological cycle and world water resources","volume":"28","author":"Oki","year":"2003","journal-title":"Membrane"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"135373","DOI":"10.1016\/j.scitotenv.2019.135373","article-title":"Flash droughts characterization over China: From a perspective of the rapid intensification rate","volume":"704","author":"Liu","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Wei, L.Y., Jiang, S.H., Ren, L.L., Yuan, F., and Zhang, L.Q. (2019). Performance of Two Long-Term Satellite-Based and GPCC 8.0 Precipitation Products for Drought Monitoring over the Yellow River Basin in China. Sustainability, 11.","DOI":"10.3390\/su11184969"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3376","DOI":"10.1109\/JSTARS.2019.2934732","article-title":"Drought Monitoring and Evaluation by ESA CCI Soil Moisture Products Over the Yellow River Basin","volume":"12","author":"Zhang","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1038\/s41558-018-0154-5","article-title":"CLIMATE HYDROLOGY A hot future for European droughts","volume":"8","author":"Teuling","year":"2018","journal-title":"Nat. Clim. Chang."},{"key":"ref_7","first-page":"131","article-title":"Impact of different satellite soil moisture products on the predictions of a continuous distributed hydrological model","volume":"48","author":"Laiolo","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"9338","DOI":"10.1002\/2014JD021454","article-title":"Influences of soil moisture and vegetation on convective precipitation forecasts over the United States Great Plains","volume":"119","author":"Collow","year":"2014","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1175\/JHM-388.1","article-title":"Comparison, validation, and transferability of eight multiyear global soil wetness products","volume":"5","author":"Dirmeyer","year":"2004","journal-title":"J. Hydrometeorol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1109\/TGRS.2002.808243","article-title":"Soil moisture retrieval from AMSR-E","volume":"41","author":"Njoku","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1281","DOI":"10.1175\/1520-0477(2000)081<1281:TGSMDB>2.3.CO;2","article-title":"The Global Soil Moisture Data Bank","volume":"81","author":"Robock","year":"2000","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.rse.2014.04.006","article-title":"Global-scale evaluation of two satellite-based passive microwave soil moisture datasets (SMOS and AMSR-E) with respect to Land Data Assimilation System estimates","volume":"149","author":"Wigneron","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Raoult, N., Delorme, B., Ottle, C., Peylin, P., Bastrikov, V., Maugis, P., and Polcher, J. (2018). Confronting Soil Moisture Dynamics from the ORCHIDEE Land Surface Model with the ESA-CCI Product: Perspectives for Data Assimilation. Remote Sens., 10.","DOI":"10.3390\/rs10111786"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"889","DOI":"10.1109\/TGRS.2005.863858","article-title":"Derivation of surface soil moisture from ENVISAT ASAR wide swath and image mode data in agricultural areas","volume":"44","author":"Loew","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.rse.2015.03.008","article-title":"Evaluation of remotely sensed and reanalysis soil moisture products over the Tibetan Plateau using in-situ observations","volume":"163","author":"Zeng","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"791","DOI":"10.5194\/essd-9-791-2017","article-title":"A global satellite environmental data record derived from AMSR-E and AMSR2 microwave Earth observations","volume":"9","author":"Du","year":"2017","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"8594","DOI":"10.3390\/rs6098594","article-title":"Inter-Calibration of Satellite Passive Microwave Land Observations from AMSR-E and AMSR2 Using Overlapping FY3B-MWRI Sensor Measurements","volume":"6","author":"Du","year":"2014","journal-title":"Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.rse.2017.05.012","article-title":"Evaluation of AMSR-E retrieval by detecting soil moisture decrease following massive dryland re-vegetation in the Loess Plateau, China","volume":"196","author":"Feng","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.rse.2017.10.026","article-title":"Global-scale assessment and combination of SMAP with ASCAT (active) and AMSR2 (passive) soil moisture products","volume":"204","author":"Kim","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.rse.2015.02.002","article-title":"A global comparison of alternate AMSR2 soil moisture products: Why do they differ?","volume":"161","author":"Kim","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.rse.2013.12.002","article-title":"A new approach for validating satellite estimates of soil moisture using large-scale precipitation: Comparing AMSR-E products","volume":"142","author":"Tuttle","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.rse.2011.05.029","article-title":"Soil moisture mapping over the central part of the Tibetan Plateau using a series of ASAR WS images","volume":"120","author":"Su","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4552","DOI":"10.1109\/TGRS.2011.2148200","article-title":"The FengYun-3 Microwave Radiation Imager On-Orbit Verification","volume":"49","author":"Yang","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"4994","DOI":"10.1109\/TGRS.2016.2561938","article-title":"Assessment of the SMAP Passive Soil Moisture Product","volume":"54","author":"Chan","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.rse.2017.01.021","article-title":"Validation of SMAP surface soil moisture products with core validation sites","volume":"191","author":"Colliander","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1038\/s41597-022-01772-x","article-title":"Global soil moisture data fusion by Triple Collocation Analysis from 2011 to 2018","volume":"9","author":"Xie","year":"2022","journal-title":"Sci. Data"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Yao, P., Shi, J., Zhao, T., Lu, H., and Al-Yaari, A. (2017). Rebuilding long time series global soil moisture products using the neural network adopting the microwave vegetation index. Remote Sens., 9.","DOI":"10.3390\/rs9010035"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1038\/s41597-021-00925-8","article-title":"A long term global daily soil moisture dataset derived from AMSR-E and AMSR2 (2002\u20132019)","volume":"8","author":"Yao","year":"2021","journal-title":"Sci. Data"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.rse.2016.10.050","article-title":"Does AMSR2 produce better soil moisture retrievals than AMSR-E over Australia?","volume":"188","author":"Cho","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"111364","DOI":"10.1016\/j.rse.2019.111364","article-title":"Generation of spatially complete and daily continuous surface soil moisture of high spatial resolution","volume":"233","author":"Long","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_31","first-page":"219","article-title":"Monitoring soil moisture content with modis data","volume":"36","author":"Guo","year":"2004","journal-title":"Soil"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Llamas, R.M., Guevara, M., Rorabaugh, D., Taufer, M., and Vargas, R. (2020). Spatial Gap-Filling of ESA CCI Satellite-Derived Soil Moisture Based on Geostatistical Techniques and Multiple Regression. Remote Sens., 12.","DOI":"10.3390\/rs12040665"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/S0034-4257(01)00274-7","article-title":"A simple interpretation of the surface temperature\/vegetation index space for assessment of surface moisture status","volume":"79","author":"Sandholt","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.envsoft.2011.10.015","article-title":"A three-dimensional gap filling method for large geophysical datasets: Application to global satellite soil moisture observations","volume":"30","author":"Wang","year":"2012","journal-title":"Environ. Model. Softw."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.advwatres.2009.10.008","article-title":"Estimating soil moisture using remote sensing data: A machine learning approach","volume":"33","author":"Ahmad","year":"2010","journal-title":"Adv. Water Resour."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Liu, Y.X.Y., Yang, Y.P., Jing, W.L., and Yue, X.F. (2018). Comparison of Different Machine Learning Approaches for Monthly Satellite-Based Soil Moisture Downscaling over Northeast China. Remote Sens., 10.","DOI":"10.3390\/rs10010031"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1385","DOI":"10.5194\/essd-13-1385-2021","article-title":"Generating seamless global daily AMSR2 soil moisture (SGD-SM) long-term products for the years 2013\u20132019","volume":"13","author":"Zhang","year":"2021","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2999","DOI":"10.1016\/j.rse.2008.02.011","article-title":"Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery","volume":"112","author":"Chan","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.isprsjprs.2011.11.002","article-title":"An assessment of the effectiveness of a random forest classifier for land-cover classification","volume":"67","author":"Ghimire","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.rse.2016.03.006","article-title":"Downscaling land surface temperatures at regional scales with random forest regression","volume":"178","author":"Hutengs","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Sun, H., and Xu, Q. (2021). Evaluating Machine Learning and Geostatistical Methods for Spatial Gap-Filling of Monthly ESA CCI Soil Moisture in China. Remote Sens., 13.","DOI":"10.3390\/rs13142848"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"134230","DOI":"10.1016\/j.scitotenv.2019.134230","article-title":"Machine learning approaches for spatial modeling of agricultural droughts in the south-east region of Queensland Australia","volume":"699","author":"Rahmati","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_43","unstructured":"Didan, K. (2023, February 05). MOD13C1 MODIS\/Terra Vegetation Indices 16-Day L3 Global 0.05Deg CMG V006. Distributed by NASA EOSDIS Land Processes DAAC. Available online: https:\/\/doi.org\/10.5067\/MODIS\/MOD13C1.006."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1038\/s41597-022-01214-8","article-title":"Global spatiotemporally continuous MODIS land surface temperature dataset","volume":"9","author":"Yu","year":"2022","journal-title":"Sci. Data"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1999","DOI":"10.1002\/qj.3803","article-title":"The ERA5 global reanalysis","volume":"146","author":"Hersbach","year":"2020","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_46","unstructured":"Friedl, M., and Sulla-Menashe, D. (2023, February 05). MCD12C1 MODIS\/Terra+Aqua Land Cover Type Yearly L3 Global 0.05Deg CMG V006. distributed by NASA EOSDIS Land Processes DAAC. Available online: https:\/\/doi.org\/10.5067\/MODIS\/MCD12C1.006."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Zhou, J., Jia, L., Menenti, M., and Liu, X. (2021). Optimal Estimate of Global Biome\u2014Specific Parameter Settings to Reconstruct NDVI Time Series with the Harmonic ANalysis of Time Series (HANTS) Method. Remote Sens., 13.","DOI":"10.3390\/rs13214251"},{"key":"ref_48","unstructured":"Menenti, M., Azzali, S., Verhoef, W., and Vanswol, R. (September, January 28). Mapping agroecological zones and time lag in vegetation growth by means of fourier analysis of time series of NDVI images. Proceedings of the Symp on Remote Sensing for Oceanography, Hydrology and Agriculture, at the Cospar 29th Plenary Meeting, Washington, DC, USA."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Zhou, J., Jia, L., van Hoek, M., Menenti, M., Lu, J., Hu, G., and Ieee (2016, January 10\u201315). An optimization of parameter settings in HANTS for global NDVI time series reconstruction. Proceedings of the 36th IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729884"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1911","DOI":"10.1080\/014311600209814","article-title":"Reconstructing cloudfree NDVI composites using Fourier analysis of time series","volume":"21","author":"Roerink","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_51","unstructured":"Verhoef, W. (1996). Fourier Analysis of Temporal NDVI in the Southern African and American Continents, DLOWinand Staring Centre."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1675","DOI":"10.5194\/hess-15-1675-2011","article-title":"The International Soil Moisture Network: A data hosting facility for global in situ soil moisture measurements","volume":"15","author":"Dorigo","year":"2011","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Smith, A.B., Walker, J.P., Western, A.W., Young, R.I., Ellett, K.M., Pipunic, R.C., Grayson, R.B., Siriwardena, L., Chiew, F.H.S., and Richter, H. (2012). The Murrumbidgee soil moisture monitoring network data set. Water Resour. Res., 48.","DOI":"10.1029\/2012WR011976"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Dorigo, W.A., Xaver, A., Vreugdenhil, M., Gruber, A., Hegyiova, A., Sanchis-Dufau, A.D., Zamojski, D., Cordes, C., Wagner, W., and Drusch, M. (2013). Global Automated Quality Control of In Situ Soil Moisture Data from the International Soil Moisture Network. Vadose Zone J., 12.","DOI":"10.2136\/vzj2012.0097"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1907","DOI":"10.1175\/BAMS-D-12-00203.1","article-title":"A multiscale soil moisture and freeze-thaw monitoring network on the third pole","volume":"94","author":"Yang","year":"2013","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Rudiger, C., Hancock, G., Hemakumara, H.M., Jacobs, B., Kalma, J.D., Martinez, C., Thyer, M., Walker, J.P., Wells, T., and Willgoose, G.R. (2007). Goulburn River experimental catchment data set. Water Resour. Res., 43.","DOI":"10.1029\/2006WR005837"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1602","DOI":"10.1109\/TGRS.2012.2186971","article-title":"Validation of the SMOS L2 Soil Moisture Data in the REMEDHUS Network (Spain)","volume":"50","author":"Sanchez","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1016\/j.jhydrol.2008.12.003","article-title":"Hydrological modelling and associated microwave emission of a semi-arid region in South-western Niger","volume":"375","author":"Pellarin","year":"2009","journal-title":"J. Hydrol."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.jhydrol.2009.06.021","article-title":"The AMMA-CATCH experiment in the cultivated Sahelian area of south-west Niger\u2014Investigating water cycle response to a fluctuating climate and changing environment","volume":"375","author":"Cappelaere","year":"2009","journal-title":"J. Hydrol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.jhydrol.2009.01.015","article-title":"Multi-scale soil moisture measurements at the Gourma meso-scale site in Mali","volume":"375","author":"Gruhier","year":"2009","journal-title":"J. Hydrol."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.jhydrol.2009.06.045","article-title":"The AMMA-CATCH Gourma observatory site in Mali: Relating climatic variations to changes in vegetation, surface hydrology, fluxes and natural resources","volume":"375","author":"Mougin","year":"2009","journal-title":"J. Hydrol."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Bosch, D.D., Sheridan, J.M., Lowrance, R.R., Hubbard, R.K., Strickland, T.C., Feyereisen, G.W., and Sullivan, D.G. (2007). Little river experimental watershed database. Water Resour. Res., 43.","DOI":"10.1029\/2006WR005844"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.jhydrol.2005.08.020","article-title":"Temporal stability of surface soil moisture in the Little Washita River watershed and its applications in satellite soil moisture product validation","volume":"323","author":"Cosh","year":"2006","journal-title":"J. Hydrol."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"W05S01","DOI":"10.1029\/2007WR006083","article-title":"Preface to special section on fifty years of research and data collection: US Department of Agriculture Walnut Gulch Experimental Watershed","volume":"44","author":"Moran","year":"2008","journal-title":"Water Resour. Res."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"2847","DOI":"10.1029\/2001WR000419","article-title":"Long-term soil water content database, Reynolds Creek Experimental Watershed, Idaho, United States","volume":"37","author":"Seyfried","year":"2001","journal-title":"Water Resour. Res."},{"key":"ref_66","first-page":"W09475","article-title":"Little river experimental watershed, Tifton, Georgia, United States: A geographic database","volume":"43","author":"Sullivan","year":"2007","journal-title":"Water Resour. Res."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1016\/j.rse.2013.08.022","article-title":"Using satellite based soil moisture to quantify the water driven variability in NDVI: A case study over mainland Australia","volume":"140","author":"Chen","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_68","first-page":"332","article-title":"Soil Moisture Inversion Based on Environmental Variables and Machine Learning","volume":"53","author":"Wang","year":"2022","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1038\/s41597-023-01991-w","article-title":"A 21-year dataset (2000\u20132020) of gap-free global daily surface soil moisture at 1-km grid resolution","volume":"10","author":"Zheng","year":"2023","journal-title":"Sci. Data"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.rse.2017.07.001","article-title":"ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions","volume":"203","author":"Dorigo","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Xiao, Z.Q., Jiang, L.M., Zhu, Z.L., Wang, J.D., and Du, J.Y. (2016). Spatially and Temporally Complete Satellite Soil Moisture Data Based on a Data Assimilation Method. Remote Sens., 8.","DOI":"10.3390\/rs8010049"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"4248","DOI":"10.1109\/TGRS.2010.2051158","article-title":"Estimate of Phase Transition Water Content in Freeze-Thaw Process Using Microwave Radiometer","volume":"48","author":"Zhang","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1704","DOI":"10.1002\/hyp.7930","article-title":"A new soil freeze\/thaw discriminant algorithm using AMSR-E passive microwave imagery","volume":"25","author":"Zhao","year":"2011","journal-title":"Hydrol. Process."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"e2021JF006104","DOI":"10.1029\/2021JF006104","article-title":"The Biophysical Role of Water and Ice Within Permafrost Nearing Collapse: Insights from Novel Geophysical Observations","volume":"126","author":"James","year":"2021","journal-title":"J. Geophys. Res. Earth Surf."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"2303","DOI":"10.5194\/hess-15-2303-2011","article-title":"The Tibetan Plateau observatory of plateau scale soil moisture and soil temperature (Tibet-Obs) for quantifying uncertainties in coarse resolution satellite and model products","volume":"15","author":"Su","year":"2011","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Van der Vliet, M., van der Schalie, R., Rodriguez-Fernandez, N., Colliander, A., de Jeu, R., Preimesberger, W., Scanlon, T., and Dorigo, W. (2020). Reconciling Flagging Strategies for Multi-Sensor Satellite Soil Moisture Climate Data Records. Remote Sens., 12.","DOI":"10.3390\/rs12203439"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/S0034-4257(02)00087-1","article-title":"The MODIS Land product quality assessment approach","volume":"83","author":"Roy","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Wu, X.D., Wen, J.G., Xiao, Q., You, D.Q., Dou, B., Lin, X., and Hueni, A. (2018). Accuracy Assessment on MODIS (V006), GLASS and MuSyQ Land-Surface Albedo Products: A Case Study in the Heihe River Basin, China. Remote Sens., 10.","DOI":"10.3390\/rs10122045"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"1336","DOI":"10.1016\/j.advwatres.2005.03.013","article-title":"Numerical investigation of the impact of uncertainties in satellite rainfall estimation and land surface model parameters on simulation of soil moisture","volume":"28","author":"Hossain","year":"2005","journal-title":"Adv. Water Resour."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/8\/2116\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:17:16Z","timestamp":1760123836000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/8\/2116"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,17]]},"references-count":79,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["rs15082116"],"URL":"https:\/\/doi.org\/10.3390\/rs15082116","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,17]]}}}