{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T05:57:29Z","timestamp":1769579849474,"version":"3.49.0"},"reference-count":46,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,18]],"date-time":"2022-12-18T00:00:00Z","timestamp":1671321600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2019YFE0127600"],"award-info":[{"award-number":["2019YFE0127600"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Satellite-based soil moisture products are suitable for large-scale regional monitoring due to the accessibility. Five soil moisture products including SMAP, ESA CCI, and AMSR2 (ascending, descending, and average) were selected in the continental United States (US) from 2016 to 2021. To evaluate the performance of the products and assess their applicability, ISMN (International Soil Moisture Network) data were used as the in situ measurement. PBIAS (Percentage of BIAS), R (Pearson correlation coefficient), RMSE (Root Mean Square Error), ubRMSE (unbiased RMSE), MAE (Mean Absolute Error), and MBE (Mean Bias Error) were selected for evaluation. The performance of five products over six observation networks and various land cover types was compared, and the differences were analyzed at monthly, seasonal, and annual scales. The results show that SMAP had the smallest deviation with the ISMN data because PBIAS was around \u22120.13, and MBE was around \u22120.02 m3\/m3. ESA CCI performed the best in almost all aspects; its R reached around 0.7, and RMSE was only around 0.07 m3\/m3 at the three time scales. The performance of the AMSR2 products varied greatly across the time scales, and increasing errors and deviations showed from 2016 to 2020. The PBO_H2O and USCRN networks could reflect soil moisture characteristics in the continental US, while iRON performed poorly. The evaluation of the networks was closely related to spatial distributions. All products performed better over grasslands and shrublands with R, which was greater than 0.52, and ubRMSE was around 0.1 m3\/m3, while products performed worse over forests, where PBIAS was less than \u22120.62, and RMSE was greater than 0.2 m3\/m3, except for ESA CCI. From the boxplot, SMAP was close to the ISMN data with differences less than 0.004 m3\/m3 between the median and lower quartiles.<\/jats:p>","DOI":"10.3390\/s22249977","type":"journal-article","created":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T09:31:01Z","timestamp":1671442261000},"page":"9977","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Evaluation of Several Satellite-Based Soil Moisture Products in the Continental US"],"prefix":"10.3390","volume":"22","author":[{"given":"Shouming","family":"Feng","sequence":"first","affiliation":[{"name":"Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8891-1153","authenticated-orcid":false,"given":"Xinyi","family":"Huang","sequence":"additional","affiliation":[{"name":"Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China"}]},{"given":"Shuaishuai","family":"Zhao","sequence":"additional","affiliation":[{"name":"Yellow River Lijin Bureau, Yellow River Conservancy Commission, Lijin 257400, China"}]},{"given":"Zhihao","family":"Qin","sequence":"additional","affiliation":[{"name":"MOA Key Laboratory of Agricultural Remote Sensing, Institute of Agro-Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}]},{"given":"Jinlong","family":"Fan","sequence":"additional","affiliation":[{"name":"National Satellite Meteorological Center, Beijing 100081, China"}]},{"given":"Shuhe","family":"Zhao","sequence":"additional","affiliation":[{"name":"Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,18]]},"reference":[{"key":"ref_1","first-page":"1027","article-title":"Spectral features of soil moisture","volume":"43","author":"He","year":"2006","journal-title":"Acta Pedol. Sin."},{"key":"ref_2","first-page":"4615","article-title":"Advances in soil moisture retrieval from remote sensing","volume":"39","author":"Pan","year":"2019","journal-title":"Acta Ecol. Sin."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1138","DOI":"10.1126\/science.1100217","article-title":"Regions of Strong Coupling Between Soil Moisture and Precipitation","volume":"305","author":"Koster","year":"2004","journal-title":"Science"},{"key":"ref_4","first-page":"1494","article-title":"Spatial-temporal characteristics of soil moisture in China","volume":"71","author":"Zhang","year":"2016","journal-title":"Acta Geogr. Sin."},{"key":"ref_5","first-page":"4432","article-title":"Application of temperature vegetation dryness index in the estimation of soil moisture of the Songnen Plain","volume":"39","author":"Wu","year":"2019","journal-title":"Acta Ecol. Sin."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhou, H., Geng, G., Yang, J., Hu, H., Sheng, L., and Lou, W. (2022). Improving Soil Moisture Estimation via Assimilation of Remote Sensing Product into the DSSAT Crop Model and Its Effect on Agricultural Drought Monitoring. Remote Sens., 14.","DOI":"10.3390\/rs14133187"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Farokhi, M., Faridani, F., Lasaponara, R., Ansari, H., and Faridhosseini, A. (2021). Enhanced Estimation of Root Zone Soil Moisture at 1 Km Resolution Using SMAR Model and MODIS-Based Downscaled AMSR2 Soil Moisture Data. Sensors, 21.","DOI":"10.3390\/s21155211"},{"key":"ref_8","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_9","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_10","first-page":"1778","article-title":"Evaluation of remote sensing and reanalysis soil moisture products on the Tibetan Plateau","volume":"73","author":"Fan","year":"2018","journal-title":"Acta Geogr. Sin."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Meng, X., Mao, K., Meng, F., Shen, X., Xu, T., and Cao, M. (2019). Long-Term Spatiotemporal Variations in Soil Moisture in North East China Based on 1-Km Resolution Downscaled Passive Microwave Soil Moisture Products. Sensors, 19.","DOI":"10.3390\/s19163527"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Lu, X., Zhao, H., Huang, Y., Liu, S., Ma, Z., Jiang, Y., Zhang, W., and Zhao, C. (2022). Generating Daily Soil Moisture at 16 m Spatial Resolution Using a Spatiotemporal Fusion Model and Modified Perpendicular Drought Index. Sensors, 22.","DOI":"10.3390\/s22145366"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhang, J., Becker-Reshef, I., and Justice, C. (2015, January 26\u201331). Evaluation of the ASCAT Surface Soil Moisture Product for Agricultural Drought Monitoring in USA. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7325852"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Suman, S., Srivastava, P.K., Petropoulos, G.P., Pandey, D.K., and O\u2019Neill, P.E. (2020). Appraisal of SMAP Operational Soil Moisture Product from a Global Perspective. Remote Sens., 12.","DOI":"10.3390\/rs12121977"},{"key":"ref_15","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_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2018.05.008","article-title":"Global-Scale Evaluation of SMAP, SMOS and ASCAT Soil Moisture Products Using Triple Collocation","volume":"214","author":"Chen","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2621","DOI":"10.1175\/JHM-D-17-0063.1","article-title":"Assessment of the SMAP Level-4 Surface and Root-Zone Soil Moisture Product Using in Situ Measurements","volume":"18","author":"Reichle","year":"2017","journal-title":"J. Hydrometeorol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"6180","DOI":"10.1109\/JSTARS.2022.3190438","article-title":"Satellite-Based Assessment of Meteorological and Agricultural Drought in Mainland Southeast Asia","volume":"15","author":"Li","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Peng, J., Muller, J.-P., Blessing, S., Giering, R., Danne, O., Gobron, N., Kharbouche, S., Ludwig, R., M\u00fcller, B., and Leng, G. (2019). Can We Use Satellite-Based FAPAR to Detect Drought?. Sensors, 19.","DOI":"10.3390\/s19173662"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1175\/JHM-D-12-0160.1","article-title":"A Nonparametric Multivariate Multi-Index Drought Monitoring Framework","volume":"15","author":"Hao","year":"2014","journal-title":"J. Hydrometeorol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3105","DOI":"10.5194\/hess-22-3105-2018","article-title":"Historical Drought Patterns over Canada and Their Teleconnections with Large-Scale Climate Signals","volume":"22","author":"Asong","year":"2018","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"620","DOI":"10.1016\/j.jhydrol.2017.07.033","article-title":"Drought Monitoring with Soil Moisture Active Passive (SMAP) Measurements","volume":"552","author":"Mishra","year":"2017","journal-title":"J. Hydrol."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Liu, Y., Yang, Y., and Yue, X. (2018). Evaluation of Satellite-Based Soil Moisture Products over Four Different Continental In-Situ Measurements. Remote Sens., 10.","DOI":"10.3390\/rs10071161"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Dorigo, W., Gruber, A., Van, O.P., Wagner, W., Drusch, M., Mecklenburg, S., Robock, A., and Jcakson, T. (2011, January 10\u201315). The International Soil Moisture Network\u2014An observational network for soil moisture product validations. Proceedings of the 34th International Symposium on Remote Sensing of Environment, Remote Sensing and Photogrammetry Society, Sydney, Australia.","DOI":"10.5194\/hess-15-1675-2011"},{"key":"ref_25","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_26","doi-asserted-by":"crossref","first-page":"5749","DOI":"10.5194\/hess-25-5749-2021","article-title":"The International Soil Moisture Network: Serving Earth System Science for over a Decade","volume":"25","author":"Dorigo","year":"2021","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2136\/vzj2012.0097","article-title":"Global Automated Quality Control of In Situ Soil Moisture Data from the International Soil Moisture Network","volume":"12","author":"Dorigo","year":"2013","journal-title":"Vadose Zone J."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"111380","DOI":"10.1016\/j.rse.2019.111380","article-title":"The SMAP and Copernicus Sentinel 1A\/B Microwave Active-Passive High Resolution Surface Soil Moisture Product","volume":"233","author":"Das","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2845","DOI":"10.1109\/TGRS.2020.3012896","article-title":"Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record","volume":"59","author":"Preimesberger","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","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_31","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_32","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_33","doi-asserted-by":"crossref","first-page":"F01002","DOI":"10.1029\/2007JF000769","article-title":"Multisensor Historical Climatology of Satellite-Derived Global Land Surface Moisture","volume":"113","author":"Owe","year":"2008","journal-title":"J. Geophys. Res."},{"key":"ref_34","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_35","doi-asserted-by":"crossref","first-page":"D04113","DOI":"10.1029\/2008JD010257","article-title":"Land Surface Temperature from Ka Band (37 GHz) Passive Microwave Observations","volume":"114","author":"Holmes","year":"2009","journal-title":"J. Geophys. Res."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.rse.2009.08.016","article-title":"MODIS Collection 5 Global Land Cover: Algorithm Refinements and Characterization of New Datasets","volume":"114","author":"Friedl","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_37","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_38","doi-asserted-by":"crossref","unstructured":"Cui, C., Xu, J., Zeng, J., Chen, K.-S., Bai, X., Lu, H., Chen, Q., and Zhao, T. (2017). Soil Moisture Mapping from Satellites: An Intercomparison of SMAP, SMOS, FY3B, AMSR2, and ESA CCI over Two Dense Network Regions at Different Spatial Scales. Remote Sens., 10.","DOI":"10.3390\/rs10010033"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Qu, Y., Zhu, Z., Chai, L., Liu, S., Montzka, C., Liu, J., Yang, X., Lu, Z., Jin, R., and Li, X. (2019). Rebuilding a Microwave Soil Moisture Product Using Random Forest Adopting AMSR-E\/AMSR2 Brightness Temperature and SMAP over the Qinghai\u2013Tibet Plateau, China. Remote Sens., 11.","DOI":"10.3390\/rs11060683"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1900","DOI":"10.2166\/wcc.2022.299","article-title":"Evaluation of Four Bias Correction Methods and Random Forest Model for Climate Change Projection in the Mara River Basin, East Africa","volume":"13","author":"Das","year":"2022","journal-title":"J. Water Clim. Change"},{"key":"ref_41","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_42","first-page":"1571","article-title":"Applicability evaluation of CFSR climate data for hydrologic simulation: A case study in the Bahe River Basin","volume":"71","author":"Hu","year":"2016","journal-title":"Acta Geogr. Sin."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Wu, X., Wang, G., Yao, R., Wang, L., Yu, D., and Gui, X. (2019). Investigating Surface Urban Heat Islands in South America Based on MODIS Data from 2003\u20132016. Remote Sens., 11.","DOI":"10.3390\/rs11101212"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Xu, X., Shew, B., Zaman, S., Lee, J., and Zhi, Y. (2020, January 26). Assessment of SMAP and ESA CCI Soil Moisture Over the Great Lakes Basin. Proceedings of the IGARSS 2020\u20142020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA.","DOI":"10.1109\/IGARSS39084.2020.9323638"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"10784","DOI":"10.1109\/JSTARS.2021.3122068","article-title":"Validation of SMOS, SMAP, and ESA CCI Soil Moisture Over a Humid Region","volume":"14","author":"Xu","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Wu, X., Lu, G., Wu, Z., He, H., Scanlon, T., and Dorigo, W. (2020). Triple Collocation-Based Assessment of Satellite Soil Moisture Products with in Situ Measurements in China: Understanding the Error Sources. Remote Sens., 12.","DOI":"10.3390\/rs12142275"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/24\/9977\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:43:36Z","timestamp":1760147016000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/24\/9977"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,18]]},"references-count":46,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["s22249977"],"URL":"https:\/\/doi.org\/10.3390\/s22249977","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,18]]}}}