{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T12:11:10Z","timestamp":1774440670914,"version":"3.50.1"},"reference-count":76,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2017,12,25]],"date-time":"2017-12-25T00:00:00Z","timestamp":1514160000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Although numerous satellite-based soil moisture (SM) products can provide spatiotemporally continuous worldwide datasets, they can hardly be employed in characterizing fine-grained regional land surface processes, owing to their coarse spatial resolution. In this study, we proposed a machine-learning-based method to enhance SM spatial accuracy and improve the availability of SM data. Four machine learning algorithms, including classification and regression trees (CART), K-nearest neighbors (KNN), Bayesian (BAYE), and random forests (RF), were implemented to downscale the monthly European Space Agency Climate Change Initiative (ESA CCI) SM product from 25-km to 1-km spatial resolution. During the regression, the land surface temperature (including daytime temperature, nighttime temperature, and diurnal fluctuation temperature), normalized difference vegetation index, surface reflections (red band, blue band, NIR band and MIR band), and digital elevation model were taken as explanatory variables to produce fine spatial resolution SM. We chose Northeast China as the study area and acquired corresponding SM data from 2003 to 2012 in unfrozen seasons. The reconstructed SM datasets were validated against in-situ measurements. The results showed that the RF-downscaled results had superior matching performance to both ESA CCI SM and in-situ measurements, and can positively respond to precipitation variation. Additionally, the RF was less affected by parameters, which revealed its robustness. Both CART and KNN ranked second. Compared to KNN, CART had a relatively close correlation with the validation data, but KNN showed preferable precision. Moreover, BAYE ranked last with significantly abnormal regression values.<\/jats:p>","DOI":"10.3390\/rs10010031","type":"journal-article","created":{"date-parts":[[2017,12,26]],"date-time":"2017-12-26T03:06:38Z","timestamp":1514257598000},"page":"31","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":70,"title":["Comparison of Different Machine Learning Approaches for Monthly Satellite-Based Soil Moisture Downscaling over Northeast China"],"prefix":"10.3390","volume":"10","author":[{"given":"Yangxiaoyue","family":"Liu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Yaping","family":"Yang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8021-3943","authenticated-orcid":false,"given":"Wenlong","family":"Jing","sequence":"additional","affiliation":[{"name":"Guangzhou Institute of Geography, Guangzhou 510070, China"},{"name":"Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangzhou 510070, China"},{"name":"Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou 510070, China"}]},{"given":"Xiafang","family":"Yue","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China"}]}],"member":"1968","published-online":{"date-parts":[[2017,12,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"798","DOI":"10.1175\/1520-0442(1992)005<0798:VILSWB>2.0.CO;2","article-title":"Variability in large-scale water balance with land surface-atmosphere interaction","volume":"5","author":"Entekhabi","year":"1992","journal-title":"J. Clim."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Drusch, M. (2007). Initializing numerical weather prediction models with satellite-derived surface soil moisture: Data assimilation experiments with ECMWF\u2019s Integrated Forecast System and the TMI soil moisture data set. J. Geophys. Res. Atmos., 112.","DOI":"10.1029\/2006JD007478"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.earscirev.2010.02.004","article-title":"Investigating soil moisture\u2014Climate interactions in a changing climate: A review","volume":"99","author":"Seneviratne","year":"2010","journal-title":"Earth Sci. Rev."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1038\/nature05095","article-title":"Land-atmosphere coupling and climate change in Europe","volume":"443","author":"Seneviratne","year":"2006","journal-title":"Nature"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/0034-4257(91)90013-V","article-title":"Applications of microwave remote sensing of soil moisture for water resources and agriculture","volume":"35","author":"Engman","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"385","DOI":"10.2134\/agronj1962.00021962005400050005x","article-title":"Availability of soil water to plants as affected by soil moisture content and meteorological conditions","volume":"54","author":"Denmead","year":"1962","journal-title":"Agron. J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1080\/15481603.2016.1258971","article-title":"Estimation of hourly and daily evapotranspiration and soil moisture using downscaled LST over various urban surfaces","volume":"54","author":"Jiang","year":"2017","journal-title":"GISci. Remote Sens."},{"key":"ref_8","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_9","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_10","doi-asserted-by":"crossref","first-page":"1572","DOI":"10.1109\/TGRS.2012.2186581","article-title":"Evaluation of SMOS Soil Moisture Products Over Continental U.S. Using the SCAN\/SNOTEL Network","volume":"50","author":"Bitar","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Xu, B., and Li, J. (2014, January 27\u201331). A methodology to estimate representativeness of LAI station observation for validation: A case study with Chinese Ecosystem Research Network (CERN) in situ data. Proceedings of the SPIE Asia Pacific Remote Sensing, Beijing, China.","DOI":"10.1117\/12.2068845"},{"key":"ref_12","first-page":"1","article-title":"Validation of the ASCAT Soil Water Index using in situ data from the International Soil Moisture Network","volume":"30","author":"Paulik","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"91","DOI":"10.5194\/hess-15-91-2011","article-title":"Evaluation of global continental hydrology as simulated by the Land-surface processes and exchanges dynamic global vegetation model","volume":"15","author":"Murray","year":"2011","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.ecocom.2010.02.007","article-title":"Chinese ecosystem research network: Progress and perspectives","volume":"7","author":"Fu","year":"2010","journal-title":"Ecol. Complex."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Dorigo, W., Xaver, A., Vreugdenhil, M., Gruber, A., Hegyiov\u00e1, A., Sanchis-Dufau, A., 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_16","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_17","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 Trans. Am. Geophys. Union"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Gruber, A., Dorigo, W., Zwieback, S., Xaver, A., and Wagner, W. (2013). Characterizing coarse-scale representativeness of in situ soil moisture measurements from the International Soil Moisture Network. Vadose Zone J., 12.","DOI":"10.2136\/vzj2012.0170"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.geoderma.2010.07.001","article-title":"Large-scale spatial variability of dried soil layers and related factors across the entire Loess Plateau of China","volume":"159","author":"Wang","year":"2010","journal-title":"Geoderma"},{"key":"ref_20","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_21","first-page":"3","article-title":"Joint advanced microwave scanning radiometer (AMSR) science team meeting","volume":"13","author":"Lobl","year":"2001","journal-title":"Earth Obs."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1109\/TGRS.2003.817195","article-title":"A preliminary survey of radio-frequency interference over the US in Aqua AMSR-E data","volume":"42","author":"Li","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1729","DOI":"10.1109\/36.942551","article-title":"Soil moisture retrieval from space: The Soil Moisture and Ocean Salinity (SMOS) mission","volume":"39","author":"Kerr","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2347","DOI":"10.1109\/TGRS.2004.836867","article-title":"The WindSat spaceborne polarimetric microwave radiometer: Sensor description and early orbit performance","volume":"42","author":"Gaiser","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"805","DOI":"10.5589\/m04-043","article-title":"Estimating soil moisture at the watershed scale with satellite-based radar and land surface models","volume":"30","author":"Moran","year":"2004","journal-title":"Can. J. Remote Sens."},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.rse.2013.02.027","article-title":"Soil moisture mapping using Sentinel-1 images: Algorithm and preliminary validation","volume":"134","author":"Paloscia","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"704","DOI":"10.1109\/JPROC.2010.2043918","article-title":"The soil moisture active passive (SMAP) mission","volume":"98","author":"Entekhabi","year":"2010","journal-title":"Proc. IEEE"},{"key":"ref_29","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_30","doi-asserted-by":"crossref","first-page":"3205","DOI":"10.5194\/hess-17-3205-2013","article-title":"The COsmic-ray Soil Moisture Interaction Code (COSMIC) for use in data assimilation","volume":"17","author":"Shuttleworth","year":"2013","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2016.06.010","article-title":"A combination of DISPATCH downscaling algorithm with CLASS land surface scheme for soil moisture estimation at fine scale during cloudy days","volume":"184","author":"Djamai","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1327","DOI":"10.1016\/j.agrformet.2009.03.004","article-title":"Estimating volumetric surface moisture content for cropped soils using a soil wetness index based on surface temperature and NDVI","volume":"149","author":"Mallick","year":"2009","journal-title":"Agric. For. Meteorol."},{"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","unstructured":"Fang, B., Lakshmi, V., Bindlish, R., Jackson, T.J., Cosh, M., and Basara, J. (2013). Passive microwave soil moisture downscaling using vegetation index and skin surface temperature. Vadose Zone J., 12.","DOI":"10.2136\/vzj2013.05.0089er"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3156","DOI":"10.1109\/TGRS.2011.2120615","article-title":"Downscaling SMOS-derived soil moisture using MODIS visible\/infrared data","volume":"49","author":"Piles","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1228","DOI":"10.1109\/36.701075","article-title":"The Moderate Resolution Imaging Spectroradiometer (MODIS): Land remote sensing for global change research","volume":"36","author":"Justice","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3127","DOI":"10.1007\/s11269-013-0337-9","article-title":"Machine Learning Techniques for Downscaling SMOS Satellite Soil Moisture Using MODIS Land Surface Temperature for Hydrological Application","volume":"27","author":"Srivastava","year":"2013","journal-title":"Water Resour. Manag."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"558","DOI":"10.1109\/TGRS.2015.2462074","article-title":"Spatial downscaling of satellite soil moisture data using a vegetation temperature condition index","volume":"54","author":"Peng","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Jing, W., Yang, Y., Yue, X., and Zhao, X. (2016). A Comparison of Different Regression Algorithms for Downscaling Monthly Satellite-Based Precipitation over North China. Remote Sens., 8.","DOI":"10.3390\/rs8100835"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A soil-adjusted vegetation index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"3935","DOI":"10.1016\/j.rse.2008.06.012","article-title":"Towards deterministic downscaling of SMOS soil moisture using MODIS derived soil evaporative efficiency","volume":"112","author":"Merlin","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2305","DOI":"10.1016\/j.rse.2010.05.007","article-title":"An improved algorithm for disaggregating microwave-derived soil moisture based on red, near-infrared and thermal-infrared data","volume":"114","author":"Merlin","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1098","DOI":"10.13031\/2013.30829","article-title":"Topographic effects on the distribution of surface soil water and the location of ephemeral gullies","volume":"31","author":"Moore","year":"1988","journal-title":"Trans. ASAE"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2733","DOI":"10.1214\/12-AOS1049","article-title":"Optimal weighted nearest neighbour classifiers","volume":"40","author":"Samworth","year":"2012","journal-title":"Ann. Stat."},{"key":"ref_45","first-page":"233","article-title":"On assessing prior distributions and Bayesian regression analysis with G-prior distributions","volume":"6","author":"Zellner","year":"1986","journal-title":"Bayesian Inference Decis. Tech."},{"key":"ref_46","first-page":"358","article-title":"Classification and Regression Trees (CART)","volume":"40","author":"Breiman","year":"1984","journal-title":"Biometrics"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1947","DOI":"10.1021\/ci034160g","article-title":"Random Forest:\u2009 A Classification and Regression Tool for Compound Classification and QSAR Modeling","volume":"43","author":"Svetnik","year":"2003","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1080\/02757259409532220","article-title":"A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water content and fractional vegetation cover","volume":"9","author":"Carlson","year":"1994","journal-title":"Remote Sens. Rev."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1016\/S2095-3119(15)61159-5","article-title":"Vegetation changes in the agricultural-pastoral areas of northern China from 2001 to 2013","volume":"15","author":"Wei","year":"2016","journal-title":"J. Integr. Agric."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1175\/JHM-D-12-0149.1","article-title":"Development of a China Dataset of Soil Hydraulic Parameters Using Pedotransfer Functions for Land Surface Modeling","volume":"14","author":"Dai","year":"2013","journal-title":"J. Hydrometeorol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1002\/jame.20026","article-title":"A China data set of soil properties for land surface modeling","volume":"5","author":"Shangguan","year":"2013","journal-title":"J. Adv. Model. Earth Syst."},{"key":"ref_52","unstructured":"Bontemps, S., Defourny, P., Radoux, J., van Bogaert, E., Lamarche, C., Achard, F., Mayaux, P., Boettcher, M., Brockmann, C., and Kirches, G. (2013, January 9\u201313). Consistent global land cover maps for climate modelling communities: Current achievements of the ESA\u2019s land cover CCI. Proceedings of the ESA Living Planet Symposium, Edimburgh, UK."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1541","DOI":"10.1175\/BAMS-D-11-00254.1","article-title":"The ESA climate change initiative: Satellite data records for essential climate variables","volume":"94","author":"Hollmann","year":"2013","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_54","first-page":"96","article-title":"Evaluating ESA CCI soil moisture in East Africa","volume":"48","author":"McNally","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/S0034-4257(02)00084-6","article-title":"An overview of MODIS Land data processing and product status","volume":"83","author":"Justice","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/S0924-2716(02)00124-7","article-title":"The shuttle radar topography mission\u2014A new class of digital elevation models acquired by spaceborne radar","volume":"57","author":"Rabus","year":"2003","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_57","first-page":"237","article-title":"The TRMM Multi-Satellite Precipitation Analysis (TMPA)","volume":"90","author":"Huffman","year":"2007","journal-title":"J. Hydrometeorol."},{"key":"ref_58","unstructured":"Western, A., and Grayson, R. (2000). Soil moisture and runoff processes at Tarrawarra. Spat. Patterns Catchment Hydrol. Obs. Model., 209\u2013246."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1146\/annurev.earth.30.091201.140434","article-title":"Scaling of soil moisture: A hydrologic perspective","volume":"8","author":"Western","year":"2002","journal-title":"Annu. Rev. Earth Planet. Sci."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1848","DOI":"10.1175\/1520-0450(1995)034<1848:CDTSAA>2.0.CO;2","article-title":"CART decision-tree statistical analysis and prediction of summer season maximum surface ozone for the Vancouver, Montreal, and Atlantic regions of Canada","volume":"34","author":"Burrows","year":"1995","journal-title":"J. Appl. Meteorol."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"826","DOI":"10.1016\/j.jclinepi.2009.11.020","article-title":"Propensity score estimation: Neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression","volume":"63","author":"Westreich","year":"2010","journal-title":"J. Clin. Epidemiol."},{"key":"ref_62","unstructured":"Yu, C., Ooi, B.C., Tan, K.-L., and Jagadish, H. (2001, January 11\u201314). Indexing the distance: An efficient method to knn processing. Proceedings of the 27th International Conference on Very Large Data Bases, Roma, Italy."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Drummond, A.J., and Rambaut, A. (2007). BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol. Biol., 7.","DOI":"10.1186\/1471-2148-7-214"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_65","first-page":"18","article-title":"Classification and regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1080\/01431160412331269698","article-title":"Random forest classifier for remote sensing classification","volume":"26","author":"Pal","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_67","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"14","DOI":"10.3389\/fninf.2014.00014","article-title":"Machine learning for neuroimaging with scikit-learn","volume":"8","author":"Abraham","year":"2014","journal-title":"Front. Neuroinform."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"2319","DOI":"10.1029\/98WR01469","article-title":"Comparison of soil moisture penetration depths for several bare soils at two microwave frequencies and implications for remote sensing","volume":"34","author":"Owe","year":"1998","journal-title":"Water Resour. Res."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.geoderma.2012.12.016","article-title":"A comparison of sensor resolution and calibration strategies for soil texture estimation from hyperspectral remote sensing","volume":"197\u2013198","author":"Casa","year":"2013","journal-title":"Geoderma"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.geoderma.2011.01.013","article-title":"A soil particle-size distribution dataset for regional land and climate modelling in China","volume":"171\u2013172","author":"Shangguan","year":"2012","journal-title":"Geoderma"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"5003","DOI":"10.1175\/2009JCLI2604.1","article-title":"The soil moisture\u2014Precipitation feedback in simulations with explicit and parameterized convection","volume":"22","author":"Hohenegger","year":"2009","journal-title":"J. Clim."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Wagner, W., Scipal, K., Pathe, C., Gerten, D., Lucht, W., and Rudolf, B. (2003). Evaluation of the agreement between the first global remotely sensed soil moisture data with model and precipitation data. J. Geophys. Res. Atmos., 108.","DOI":"10.1029\/2003JD003663"},{"key":"ref_74","first-page":"203","article-title":"Estimating soil sand content using thermal infrared spectra in arid lands","volume":"33","author":"Sawut","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_75","first-page":"223","article-title":"Downscaling Global Weather Forecast Outputs Using ANN for Flood Prediction","volume":"2011","author":"Hoai","year":"2011","journal-title":"J. Appl. Math."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"2517","DOI":"10.1029\/95WR01955","article-title":"Artificial Neural Network Modeling of the Rainfall-Runoff Process","volume":"31","author":"Hsu","year":"2010","journal-title":"Water Resour. Res."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/1\/31\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:55:30Z","timestamp":1760208930000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/1\/31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,12,25]]},"references-count":76,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2018,1]]}},"alternative-id":["rs10010031"],"URL":"https:\/\/doi.org\/10.3390\/rs10010031","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,12,25]]}}}