{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T11:21:36Z","timestamp":1769599296699,"version":"3.49.0"},"reference-count":53,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,7,15]],"date-time":"2023-07-15T00:00:00Z","timestamp":1689379200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2021YFB3900603"],"award-info":[{"award-number":["2021YFB3900603"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The evaluation of satellite soil moisture is a big challenge owing to the large spatial mismatch between pixel-based satellite soil moisture products and point-based in situ measurements. Upscaling in situ measurements to obtain the \u201ctrue value\u201d of soil moisture content at the satellite grid\/footprint scale can make up for the scale difference and improve the validation. Many existing upscaling methods have strict requirements regarding the spatial distribution and quantity of soil moisture sensors. However, in reality, soil-moisture-monitoring networks are commonly sparse with low sensor density, which increases the difficulty of obtaining accurate upscaled soil moisture data and limits the validation of satellite products. For this reason, this paper proposes a scheme to upscale in situ measurements using five machine learning methods along with Landsat 8 datasets and DEM data to validate the accuracy of a SMAP-enhanced passive soil moisture product for a sparse network on the Qinghai\u2013Tibet Plateau. The proposed scheme realizes the upscaling of in situ soil moisture data to the pixel scale (30 m \u00d7 30 m) and then to the coarse grid scale (9 km \u00d7 9 km) by using multi-source remote sensing data as the bridge of scale conversion. The long-time SMAP SM products since April 2015 on the Qinghai\u2013Tibet Plateau were validated based on upscaled soil moisture data. The results show that (1) random forest regression performs the best, and the upscaled soil moisture data reflect the region-average soil moisture conditions that can be used for evaluating SMAP data; (2) the SMAP product meets its scientific measurement requirements; and (3) the SMAP product generally underestimates the soil moisture in the study area.<\/jats:p>","DOI":"10.3390\/ijgi12070281","type":"journal-article","created":{"date-parts":[[2023,7,17]],"date-time":"2023-07-17T00:41:05Z","timestamp":1689554465000},"page":"281","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Evaluation of SMAP-Enhanced Products Using Upscaled Soil Moisture Data Based on Random Forest Regression: A Case Study of the Qinghai\u2013Tibet Plateau, China"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-9215-7657","authenticated-orcid":false,"given":"Jia","family":"Chen","sequence":"first","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}]},{"given":"Fengmin","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou 450045, China"}]},{"given":"Junjie","family":"Li","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}]},{"given":"Yijia","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}]},{"given":"Wen","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}]},{"given":"Changqing","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}]},{"given":"Lingkui","family":"Meng","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/j.rse.2017.03.010","article-title":"Evaluating soil moisture retrievals from ESA\u2019s SMOS and NASA\u2019s SMAP brightness temperature datasets","volume":"193","author":"Wigneron","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"111680","DOI":"10.1016\/j.rse.2020.111680","article-title":"Soil moisture experiment in the Luan River supporting new satellite mission opportunities","volume":"240","author":"Zhao","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"101027","DOI":"10.1016\/j.ejrh.2022.101027","article-title":"Assessment of SMAP and SMOS soil moisture products using triple collocation method over Inner Mongolia","volume":"40","author":"Hu","year":"2022","journal-title":"J. Hydrol. Reg. Stud."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"112666","DOI":"10.1016\/j.rse.2021.112666","article-title":"A first assessment of satellite and reanalysis estimates of surface and root-zone soil moisture over the permafrost region of Qinghai-Tibet Plateau","volume":"265","author":"Xing","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_5","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_6","doi-asserted-by":"crossref","first-page":"1009","DOI":"10.1016\/j.jhydrol.2018.06.081","article-title":"A spatial downscaling approach for the SMAP passive surface soil moisture product using random forest regression","volume":"563","author":"Zhao","year":"2018","journal-title":"J. Hydrol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1002","DOI":"10.2136\/vzj2009.0173","article-title":"Potential of Wireless Sensor Networks for Measuring Soil Water Content Variability","volume":"9","author":"Bogena","year":"2010","journal-title":"Vadose Zone J."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Brocca, L., Ciabatta, L., Massari, C., Camici, S., and Tarpanelli, A. (2017). Soil Moisture for Hydrological Applications: Open Questions and New Opportunities. Water, 9.","DOI":"10.3390\/w9020140"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"112921","DOI":"10.1016\/j.rse.2022.112921","article-title":"A new SMAP soil moisture and vegetation optical depth product (SMAP-IB): Algorithm, assessment and inter-comparison","volume":"271","author":"Li","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2922","DOI":"10.1109\/TGRS.2020.3007371","article-title":"The Soil Moisture Active Passive Experiments: Validation of the SMAP Products in Australia","volume":"59","author":"Ye","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"111215","DOI":"10.1016\/j.rse.2019.111215","article-title":"Satellite surface soil moisture from SMAP, SMOS, AMSR2 and ESA CCI: A comprehensive assessment using global ground-based observations","volume":"231","author":"Ma","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ming, W., Ji, X., Zhang, M., Li, Y., Liu, C., Wang, Y., and Li, J. (2022). A Hybrid Triple Collocation-Deep Learning Approach for Improving Soil Moisture Estimation from Satellite and Model-Based Data. Remote Sens., 14.","DOI":"10.3390\/rs14071744"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.jhydrol.2015.01.061","article-title":"Inter-comparison of spatial upscaling methods for evaluation of satellite-based soil moisture","volume":"523","author":"Qin","year":"2015","journal-title":"J. Hydrol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"112891","DOI":"10.1016\/j.rse.2022.112891","article-title":"Assessment of 24 soil moisture datasets using a new in situ network in the Shandian River Basin of China","volume":"271","author":"Zheng","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1029\/2011RG000372","article-title":"Upscaling sparse ground-based soil moisture observations for the validation of coarse-resolution satellite soil moisture products","volume":"50","author":"Crow","year":"2012","journal-title":"Rev. Geophys."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4929","DOI":"10.1109\/TGRS.2016.2553085","article-title":"A Preliminary Evaluation of the SMAP Radiometer Soil Moisture Product Over United States and Europe Using Ground-Based Measurements","volume":"54","author":"Zeng","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ma, C., Li, X., Wei, L., and Wang, W. (2017). Multi-Scale Validation of SMAP Soil Moisture Products over Cold and Arid Regions in Northwestern China Using Distributed Ground Observation Data. Remote Sens., 9.","DOI":"10.3390\/rs9040327"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"11372","DOI":"10.3390\/rs70911372","article-title":"Upscaling In Situ Soil Moisture Observations to Pixel Averages with Spatio-Temporal Geostatistics","volume":"7","author":"Wang","year":"2015","journal-title":"Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhang, J., Li, X., Yang, R., Liu, Q., Zhao, L., and Dou, B. (2017). An Extended Kriging Method to Interpolate Near-Surface Soil Moisture Data Measured by Wireless Sensor Networks. Sensors, 17.","DOI":"10.3390\/s17061390"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3013","DOI":"10.1002\/2016WR019967","article-title":"Validation of SMAP soil moisture for the SMAPVEX15 field campaign using a hyper-resolution model","volume":"53","author":"Cai","year":"2017","journal-title":"Water Resour. Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"125054","DOI":"10.1016\/j.jhydrol.2020.125054","article-title":"Evaluation of nine sub-daily soil moisture model products over China using high-resolution in situ observations","volume":"588","author":"Chen","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.advwatres.2004.10.004","article-title":"Upscaling of field-scale soil moisture measurements using distributed land surface modeling","volume":"28","author":"Crow","year":"2005","journal-title":"Adv. Water Resour."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"9662","DOI":"10.1002\/2016GL069964","article-title":"An initial assessment of SMAP soil moisture retrievals using high-resolution model simulations and in situ observations","volume":"43","author":"Pan","year":"2016","journal-title":"Geophys. Res. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2013.07.003","article-title":"Spatial upscaling of in-situ soil moisture measurements based on MODIS-derived apparent thermal inertia","volume":"138","author":"Qin","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"100723","DOI":"10.1016\/j.ejrh.2020.100723","article-title":"Soil moisture evaluation over the Argentine Pampas using models, satellite estimations and in-situ measurements","volume":"31","author":"Spennemann","year":"2020","journal-title":"J. Hydrol. Reg. Stud."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Kang, J., Jin, R., Li, X., Zhang, Y., and Zhu, Z. (2018). Spatial Upscaling of Sparse Soil Moisture Observations Based on Ridge Regression. Remote Sens., 10.","DOI":"10.3390\/rs10020192"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1276","DOI":"10.1080\/15481603.2021.1974276","article-title":"Soil moisture retrieval over agricultural fields through integration of synthetic aperture radar and optical images","volume":"58","author":"Mardan","year":"2021","journal-title":"GIScience Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.isprsjprs.2022.01.005","article-title":"Soil moisture content retrieval from Landsat 8 data using ensemble learning","volume":"185","author":"Zhang","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhang, D., Zhang, W., Huang, W., Hong, Z., and Meng, L. (2017). Upscaling of Surface Soil Moisture Using a Deep Learning Model with VIIRS RDR. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6050130"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2663","DOI":"10.1109\/JSTARS.2017.2690220","article-title":"A Method for Upscaling In Situ Soil Moisture Measurements to Satellite Footprint Scale Using Random Forests","volume":"10","author":"Clewley","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_31","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_32","doi-asserted-by":"crossref","first-page":"931","DOI":"10.1016\/j.rse.2017.08.025","article-title":"Development and assessment of the SMAP enhanced passive soil moisture product","volume":"204","author":"Chan","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1394","DOI":"10.1109\/TGRS.2017.2762462","article-title":"Soil Moisture Retrieval From SMAP: A Validation and Error Analysis Study Using Ground-Based Observations Over the Little Washita Watershed","volume":"56","author":"Chen","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhang, T., Zhou, P., Shao, Y., and Gao, S. (2017). Validation Analysis of SMAP and AMSR2 Soil Moisture Products over the United States Using Ground-Based Measurements. Remote Sens., 9.","DOI":"10.3390\/rs9020104"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1109\/JSTARS.2021.3124743","article-title":"Validation of Soil Moisture Data Products from the NASA SMAP Mission","volume":"15","author":"Colliander","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"5780","DOI":"10.1002\/2016JD026388","article-title":"Evaluation of SMAP, SMOS, and AMSR2 soil moisture retrievals against observations from two networks on the Tibetan Plateau","volume":"122","author":"Chen","year":"2017","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_37","first-page":"1203","article-title":"Asian water tower change and its impacts","volume":"34","author":"Yao","year":"2019","journal-title":"Bull. Chin. Acad. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"7132","DOI":"10.1029\/2017JD027763","article-title":"Evaluation of Remotely Sensed and Reanalysis Soil Moisture Against In Situ Observations on the Himalayan-Tibetan Plateau","volume":"123","author":"Zhang","year":"2018","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Liu, J., Chai, L., Lu, Z., Qu, Y., Wang, J., and Yang, S. (2019\u20132, January 28). Validation of Five Passive Microwave Remotely Sensed Soil Moisture Products over the Qinghai-Tibet Plateau, China. Proceedings of the IGARSS 2019\u20142019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8899873"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.gloplacha.2013.12.001","article-title":"Recent climate changes over the Tibetan Plateau and their impacts on energy and water cycle: A review","volume":"112","author":"Yang","year":"2014","journal-title":"Glob. Planet. Chang."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"126468","DOI":"10.1016\/j.jhydrol.2021.126468","article-title":"Assessment of SMOS and SMAP soil moisture products against new estimates combining physical model, a statistical model, and in-situ observations: A case study over the Huai River Basin, China","volume":"598","author":"Wang","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.rse.2019.02.022","article-title":"Downscaling SMAP soil moisture estimation with gradient boosting decision tree regression over the Tibetan Plateau","volume":"225","author":"Wei","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_43","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_44","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1109\/LGRS.2014.2326775","article-title":"Regression Kriging-Based Upscaling of Soil Moisture Measurements from a Wireless Sensor Network and Multiresource Remote Sensing Information Over Heterogeneous Cropland","volume":"12","author":"Kang","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_45","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_46","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1006\/jcss.1997.1504","article-title":"A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting","volume":"55","author":"Freund","year":"1997","journal-title":"J. Comput. Syst. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1080\/00401706.1970.10488635","article-title":"Ridge Regression: Applications to Nonorthogonal Problems","volume":"12","author":"Hoerl","year":"1970","journal-title":"Technometrics"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1038\/s41586-019-0912-1","article-title":"Deep learning and process understanding for data-driven Earth system science","volume":"566","author":"Reichstein","year":"2019","journal-title":"Nature"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"111716","DOI":"10.1016\/j.rse.2020.111716","article-title":"Deep learning in environmental remote sensing: Achievements and challenges","volume":"241","author":"Yuan","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1175\/2010JHM1223.1","article-title":"Performance Metrics for Soil Moisture Retrievals and Application Requirements","volume":"11","author":"Entekhabi","year":"2010","journal-title":"J. Hydrometeorol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"125949","DOI":"10.1016\/j.jhydrol.2020.125949","article-title":"Comprehensive assessment of Fengyun-3 satellites derived soil moisture with in-situ measurements across the globe","volume":"594","author":"Liu","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"4546","DOI":"10.1109\/TGRS.2018.2825284","article-title":"Triangle Space-Based Surface Soil Moisture Estimation by the Synergistic Use of In Situ Measurements and Optical\/Thermal Infrared Remote Sensing: An Alternative to Conventional Validations","volume":"56","author":"Zhao","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/12\/7\/281\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:12:34Z","timestamp":1760127154000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/12\/7\/281"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,15]]},"references-count":53,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["ijgi12070281"],"URL":"https:\/\/doi.org\/10.3390\/ijgi12070281","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,15]]}}}