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In this study, the Random Forest (RF) model was trained to predict SSM from relevant land surface feature variables (i.e., land surface temperature, vegetation indices, soil texture, and geographical information) and precipitation, based on the in situ soil moisture data of the International Soil Moisture Network (ISMN.). The results of the RF model show an RMSE of 0.05 m3 m\u22123 and a correlation coefficient of 0.9. The calculated impurity-based feature importance indicates that the Antecedent Precipitation Index affects most of the predicted soil moisture. The geographical coordinates also significantly influence the prediction (i.e., RMSE was reduced to 0.03 m3 m\u22123 after considering geographical coordinates), followed by land surface temperature, vegetation indices, and soil texture. The spatio-temporal pattern of RF predicted SSM was compared with the European Space Agency Climate Change Initiative (ESA-CCI) soil moisture product, using both time-longitude and latitude diagrams. The results indicate that the RF SSM captures the spatial distribution and the daily, seasonal, and annual variabilities globally.<\/jats:p>","DOI":"10.3390\/rs13234893","type":"journal-article","created":{"date-parts":[[2021,12,6]],"date-time":"2021-12-06T03:10:38Z","timestamp":1638760238000},"page":"4893","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["In Situ Observation-Constrained Global Surface Soil Moisture Using Random Forest Model"],"prefix":"10.3390","volume":"13","author":[{"given":"Lijie","family":"Zhang","sequence":"first","affiliation":[{"name":"Faculty of Geo-Information and Earth Observation (ITC), University of Twente, 7514 AE Enschede, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2166-5314","authenticated-orcid":false,"given":"Yijian","family":"Zeng","sequence":"additional","affiliation":[{"name":"Faculty of Geo-Information and Earth Observation (ITC), University of Twente, 7514 AE Enschede, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0421-8245","authenticated-orcid":false,"given":"Ruodan","family":"Zhuang","sequence":"additional","affiliation":[{"name":"Faculty of Geo-Information and Earth Observation (ITC), University of Twente, 7514 AE Enschede, The Netherlands"},{"name":"Department of European and Mediterranean Cultures, Architecture, Environment, Cultural Heritage, University of Basilicata, 75100 Matera, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1485-8908","authenticated-orcid":false,"given":"Brigitta","family":"Szab\u00f3","sequence":"additional","affiliation":[{"name":"Institute for Soil Sciences, Centre for Agricultural Research, 1022 Budapest, Hungary"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0225-144X","authenticated-orcid":false,"given":"Salvatore","family":"Manfreda","sequence":"additional","affiliation":[{"name":"Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, 80125 Naples, Italy"}]},{"given":"Qianqian","family":"Han","sequence":"additional","affiliation":[{"name":"Faculty of Geo-Information and Earth Observation (ITC), University of Twente, 7514 AE Enschede, The Netherlands"}]},{"given":"Zhongbo","family":"Su","sequence":"additional","affiliation":[{"name":"Faculty of Geo-Information and Earth Observation (ITC), University of Twente, 7514 AE Enschede, The Netherlands"},{"name":"Key Laboratory of Subsurface Hydrology and Ecological Effect in Arid Region of Ministry of Education, School of Water and Environment, Chang\u2019an University, Xi\u2019an 710054, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,2]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Space-time modeling of soil moisture: Stochastic rainfall forcing with heterogeneous vegetation","volume":"42","author":"Isham","year":"2006","journal-title":"Water Resour. Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/S0034-4257(99)00036-X","article-title":"A method for estimating soil moisture from ERS Scatterometer and soil data","volume":"70","author":"Wagner","year":"1999","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"8289","DOI":"10.1175\/JCLI-D-14-00555.1","article-title":"The observed state of the water cycle in the early twenty-first century","volume":"28","author":"Rodell","year":"2015","journal-title":"J. Clim."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4198","DOI":"10.1175\/JCLI3856.1","article-title":"Soil moisture feedbacks to precipitation in Southern Africa","volume":"19","author":"Cook","year":"2006","journal-title":"J. Clim."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1002\/2013JD020890","article-title":"Impact of initial soil moisture anomalies on climate mean and extremes over Asia","volume":"119","author":"Liu","year":"2014","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_6","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_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2136\/vzj2012.0170","article-title":"Characterizing coarse-scale representativeness of in situ soil moisture measurements from the international soil moisture network","volume":"12","author":"Gruber","year":"2013","journal-title":"Vadose Zone J."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Dorigo, W., Himmelbauer, I., Aberer, D., Schremmer, L., Petrakovic, I., Zappa, L., Preimesberger, W., Xaver, A., Annor, F., and Ard\u00f6, J. (2021). The International Soil Moisture Network: Serving Earth system science for over a decade. Hydrol. Earth Syst. Sci., 1\u201383.","DOI":"10.5194\/hess-25-5749-2021"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.rse.2011.11.017","article-title":"Evaluation of remotely sensed and modelled soil moisture products using global ground-based in situ observations","volume":"118","author":"Albergel","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1016\/j.rse.2014.07.023","article-title":"Evaluation of the ESA CCI soil moisture product using ground-based observations","volume":"162","author":"Dorigo","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Bulut, B., Tugrul Yilmaz, M., Afshar, M.H., \u00fcnal \u015eorman, A., Y\u00fccel, I., Cosh, M.H., and \u015eim\u015fek, O. (2019). Evaluation of remotely-sensed and model-based soil moisture products according to different soil type, vegetation cover and climate regime using station-based observations over Turkey. Remote Sens., 11.","DOI":"10.3390\/rs11161875"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2009WR008016","article-title":"Spatial-temporal variability of soil moisture and its estimation across scales","volume":"46","author":"Brocca","year":"2010","journal-title":"Water Resour. Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4466","DOI":"10.1002\/jgrd.50301","article-title":"Evaluation of AMSR-E retrievals and GLDAS simulations against observations of a soil moisture network on the central Tibetan Plateau","volume":"118","author":"Chen","year":"2013","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Cheng, M., Zhong, L., Ma, Y., Zou, M., Ge, N., Wang, X., and Hu, Y. (2019). A study on the assessment of multi-source satellite soil moisture products and reanalysis data for the Tibetan Plateau. Remote Sens., 11.","DOI":"10.3390\/rs11101196"},{"key":"ref_15","first-page":"1009","article-title":"Evaluation of the ERA5 reanalysis as a potential reference dataset for hydrological modeling over North-America","volume":"10","author":"Tarek","year":"2019","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","unstructured":"Yin, J., Zhan, X., and Liu, J. (2020). Noaa satellite soil moisture operational product system (Smops) version 3.0 generates higher accuracy blended satellite soil moisture. Remote Sens., 12.","DOI":"10.3390\/rs12172861"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zeng, Y., Su, Z., Van Der Velde, R., Wang, L., Xu, K., Wang, X., and Wen, J. (2016). Blending satellite observed, model simulated, and in situ measured soil moisture over Tibetan Plateau. Remote Sens., 8.","DOI":"10.3390\/rs8030268"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4322","DOI":"10.1175\/2009JCLI2832.1","article-title":"On the nature of soil moisture in land surface models","volume":"22","author":"Koster","year":"2009","journal-title":"J. Clim."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1038\/s41597-020-0450-6","article-title":"A 3 km spatially and temporally consistent European daily soil moisture reanalysis from 2000 to 2015","volume":"7","author":"Naz","year":"2020","journal-title":"Sci. Data"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhuang, R., Zeng, Y., Manfreda, S., and Su, Z. (2020). Quantifying long-term land surface and root zone soil moisture over Tibetan plateau. Remote Sens., 12.","DOI":"10.3390\/rs12030509"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Su, Z., Zeng, Y., Romano, N., Manfreda, S., Franc\u00e9s, F., Ben Dor, E., Szab\u00f3, B., Vico, G., Nasta, P., and Zhuang, R. (2020). An integrative information aqueduct to close the gaps between satellite observation ofwater cycle and local sustainable management of water resources. Water, 12.","DOI":"10.3390\/w12051495"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1109\/MGRS.2015.2510084","article-title":"A survey on Gaussian processes for earth-observation data analysis: A comprehensive investigation","volume":"4","author":"Verrelst","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1038\/s41586-019-0912-1","article-title":"Prabhat Deep learning and process understanding for data-driven Earth system science","volume":"566","author":"Reichstein","year":"2019","journal-title":"Nature"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Cai, Y., Zheng, W., Zhang, X., Zhangzhong, L., and Xue, X. (2019). Research on soil moisture prediction model based on deep learning. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0214508"},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5194\/essd-13-1-2021","article-title":"An improved global remote-sensing-based surface soil moisture (RSSSM) dataset covering 2003\u20132018","volume":"13","author":"Chen","year":"2021","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_28","unstructured":"Nori, H., Jenkins, S., Koch, P., and Caruana, R. (2019). InterpretML: A Unified Framework for Machine Learning Interpretability. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2615","DOI":"10.5194\/hess-23-2615-2019","article-title":"Mapping soil hydraulic properties using random-forest-based pedotransfer functions and geostatistics","volume":"23","author":"Laborczi","year":"2019","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Menze, B.H., Kelm, B.M., Masuch, R., Himmelreich, U., Bachert, P., Petrich, W., and Hamprecht, F.A. (2009). A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data. BMC Bioinform., 10.","DOI":"10.1186\/1471-2105-10-213"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1029\/2011EO170001","article-title":"A new international network for in situ soil moisture data","volume":"92","author":"Dorigo","year":"2011","journal-title":"Eos"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.rse.2019.02.008","article-title":"Assessment and inter-comparison of recently developed\/reprocessed microwave satellite soil moisture products using ISMN ground-based measurements","volume":"224","author":"Wigneron","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_34","first-page":"2892","article-title":"Dead sea water level and surface area monitoring using spatial data extraction from remote sensing images","volume":"8","author":"Ghatasheh","year":"2013","journal-title":"Int. Rev. Comput. Softw."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3135","DOI":"10.5194\/hess-15-3135-2011","article-title":"The impact of land surface temperature on soil moisture anomaly detection from passive microwave observations","volume":"15","author":"Parinussa","year":"2011","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1109\/LGRS.2004.824749","article-title":"Case study of soil moisture effect on land surface temperature retrieval","volume":"1","author":"Sun","year":"2004","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Matsushima, D. (2019). Thermal Inertia-Based Method for Estimating Soil Moisture. Soil Moisture, IntechOpen. Available online: https:\/\/www.intechopen.com\/chapters\/62991.","DOI":"10.5772\/intechopen.80252"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.rse.2013.08.027","article-title":"New refinements and validation of the collection-6 MODIS land-surface temperature\/emissivity product","volume":"140","author":"Wan","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_39","unstructured":"Wan, Z., Hook, S., and Hulley, G. (2015). MOD11A1 MODIS\/Terra Land Surface Temperature\/Emissivity Daily L3 Global 1 km SIN Grid V006, NASA EOSDIS Land Processes DAAC."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Sobrino, A., Julien, Y., and Garc, S. (2020). Surface Temperature of the Planet Earth from Satellite Data. Remote Sens., 12.","DOI":"10.3390\/rs12020218"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/0034-4257(91)90017-Z","article-title":"Normalized difference vegetation index measurements from the advanced very high resolution radiometer","volume":"35","author":"Goward","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1080\/01431160802108497","article-title":"Assessing potential of MODIS derived temperature\/vegetation condition index (TVDI) to infer soil moisture status","volume":"30","author":"Patel","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zhao, W., Li, A., Huang, P., Juelin, H., and Xianming, M. (2017, January 23\u201328). Surface Soil Moisture Relationship Model Construction Based on Random Forest Method. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127378"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"3833","DOI":"10.1016\/j.rse.2008.06.006","article-title":"Development of a two-band enhanced vegetation index without a blue band","volume":"112","author":"Jiang","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2636","DOI":"10.3390\/s7112636","article-title":"Sensitivity of the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) to topographic effects: A case study in high-density cypress forest","volume":"7","author":"Matsushita","year":"2007","journal-title":"Sensors"},{"key":"ref_46","unstructured":"Didan, K. (2021, September 30). MOD13A1 MODIS\/Terra Vegetation Indices 16-Day L3 Global 500 m SIN Grid V006 Data Set, Available online: https:\/\/lpdaac.usgs.gov\/node\/838."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Montzka, C., R\u00f6tzer, K., Bogena, H.R., Sanchez, N., and Vereecken, H. (2018). A new soil moisture downscaling approach for SMAP, SMOS, and ASCAT by predicting sub-grid variability. Remote Sens., 10.","DOI":"10.3390\/rs10030427"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"180091","DOI":"10.1038\/sdata.2018.91","article-title":"HYSOGs250m, global gridded hydrologic soil groups for curve-number-based runoff modeling","volume":"5","author":"Ross","year":"2018","journal-title":"Sci. Data"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Hengl, T., De Jesus, J.M., MacMillan, R.A., Batjes, N.H., Heuvelink, G.B.M., Ribeiro, E., Samuel-Rosa, A., Kempen, B., Leenaars, J.G.B., and Walsh, M.G. (2014). SoilGrids1km\u2014Global soil information based on automated mapping. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0105992"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Hengl, T., Mendes de Jesus, J., Heuvelink, G.B.M., Ruiperez Gonzalez, M., Kilibarda, M., and Blagoti\u0107, A. (2017). SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0169748"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Pan, F., Peters-Lidard, C.D., and Sale, M.J. (2003). An analytical method for predicting surface soil moisture from rainfall observations. Water Resour. Res., 39.","DOI":"10.1029\/2003WR002142"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"024011","DOI":"10.1088\/1748-9326\/7\/2\/024011","article-title":"An underestimated role of precipitation frequency in regulating summer soil moisture","volume":"7","author":"Wu","year":"2012","journal-title":"Environ. Res. Lett."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1489","DOI":"10.1175\/1520-0493(1997)125<1489:ATDNSO>2.0.CO;2","article-title":"A three-dimensional numerical simulation of a great plains dryline","volume":"125","author":"Shaw","year":"1997","journal-title":"Mon. Weather Rev."},{"key":"ref_54","unstructured":"Mu\u00f1oz Sabater, J. (2021, September 30). ERA5-Land Hourly Data from 1981 to Present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). Available online: https:\/\/doi.org\/10.24381\/cds.e2161bac."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"3515","DOI":"10.5194\/hess-22-3515-2018","article-title":"ERA-5 and ERA-Interim driven ISBA land surface model simulations: Which one performs better","volume":"22","author":"Albergel","year":"2018","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"717","DOI":"10.5194\/essd-11-717-2019","article-title":"Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology","volume":"11","author":"Gruber","year":"2019","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"6780","DOI":"10.1109\/TGRS.2017.2734070","article-title":"Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals","volume":"55","author":"Gruber","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.rse.2004.03.014","article-title":"A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter","volume":"91","author":"Chen","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1175\/1520-0450(1986)025<0227:CBNSMM>2.0.CO;2","article-title":"Correlations between Nimbus-7 scanning multichannel microwave radiometer data and an antecedent precipitation index","volume":"25","author":"Wilke","year":"1986","journal-title":"J. Clim. Appl. Meteorol."},{"key":"ref_60","first-page":"81","article-title":"Influence of antecedent precipitation index on the hydrograph shape","volume":"1","author":"Benkhaled","year":"2004","journal-title":"Br. Hydrol. Soc."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1489","DOI":"10.1016\/j.proenv.2011.09.237","article-title":"Discussion on using antecedent precipitation index to supplement relative soil moisture data series","volume":"10","author":"Zhao","year":"2011","journal-title":"Procedia Environ. Sci."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1080\/02626660903546175","article-title":"Rainfall\u2014runoff simulation using a normalized antecedent precipitation index precipitation index","volume":"52","author":"Ali","year":"2010","journal-title":"Hydrol. Sci. J."},{"key":"ref_63","unstructured":"Hillel, D. (2004). Encyclopedia of Soils in the Environment, Elsevier."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"933","DOI":"10.1038\/nmeth.4438","article-title":"Points of Significance: Ensemble methods: Bagging and random forests","volume":"14","author":"Altman","year":"2017","journal-title":"Nat. Methods"},{"key":"ref_65","unstructured":"Varoquaux, G., Buitinck, L., Louppe, G., Grisel, O., Pedregosa, F., and Mueller, A. (2011). Scikit-learn: Machine Learning in Python. J. Mach. Learn. Res., 2825\u20132830."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1524","DOI":"10.1111\/1752-1688.12478","article-title":"Evaluation of the European Space Agency Climate Change Initiative Soil Moisture Product over China Using Variance Reduction Factor","volume":"52","author":"Shen","year":"2016","journal-title":"J. Am. Water Resour. Assoc."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Zhu, L., Wang, H., Tong, C., Liu, W., and Du, B. (2019). Evaluation of ESA Active, Passive and Combined Soil Moisture Products Using Upscaled Ground Measurements. Sensors, 19.","DOI":"10.3390\/s19122718"},{"key":"ref_68","unstructured":"Zhang, L., Zeng, Y., Zhuang, R., Manfreda, S., Han, Q., Su, Z., and Szab\u00f3, B. (2021, September 30). RF_global_SSM_2000-2019_0.25_degree 2021. Available online: https:\/\/doi.org\/10.6084\/m9.figshare.14932884.v3."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/23\/4893\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:38:54Z","timestamp":1760168334000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/23\/4893"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,2]]},"references-count":68,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["rs13234893"],"URL":"https:\/\/doi.org\/10.3390\/rs13234893","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,2]]}}}