{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T19:49:17Z","timestamp":1772740157910,"version":"3.50.1"},"reference-count":84,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,3,21]],"date-time":"2022-03-21T00:00:00Z","timestamp":1647820800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDA19030203"],"award-info":[{"award-number":["XDA19030203"]}]},{"name":"National Natural Science Foundation of China (NSFC)","award":["41661144022"],"award-info":[{"award-number":["41661144022"]}]},{"name":"National Natural Science Foundation of China (NSFC)","award":["42090014"],"award-info":[{"award-number":["42090014"]}]},{"name":"Chinese Academy of Sciences President\u2019s International Fellowship Initiative","award":["2020VTA0001"],"award-info":[{"award-number":["2020VTA0001"]}]},{"name":"Most High-Level Foreign Expert Program","award":["GL20200161002"],"award-info":[{"award-number":["GL20200161002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Model calibration and validation are challenging in poorly gauged basins. We developed and applied a new approach to calibrate hydrological models using distributed geospatial remote sensing data. The Soil and Water Assessment Tool (SWAT) model was calibrated using only twelve months of remote sensing data on actual evapotranspiration (ETa) geospatially distributed in the 37 sub-basins of the Lake Chad Basin in Africa. Global sensitivity analysis was conducted to identify influential model parameters by applying the Sequential Uncertainty Fitting Algorithm\u2013version 2 (SUFI-2), included in the SWAT-Calibration and Uncertainty Program (SWAT-CUP). This procedure is designed to deal with spatially variable parameters and estimates either multiplicative or additive corrections applicable to the entire model domain, which limits the number of unknowns while preserving spatial variability. The sensitivity analysis led us to identify fifteen influential parameters, which were selected for calibration. The optimized parameters gave the best model performance on the basis of the high Nash\u2013Sutcliffe Efficiency (NSE), Kling\u2013Gupta Efficiency (KGE), and determination coefficient (R2). Four sets of remote sensing ETa data products were applied in model calibration, i.e., ETMonitor, GLEAM, SSEBop, and WaPOR. Overall, the new approach of using remote sensing ETa for a limited period of time was robust and gave a very good performance, with R2 &gt; 0.9, NSE &gt; 0.8, and KGE &gt; 0.75 applying to the SWAT ETa vs. the ETMonitor ETa and GLEAM ETa. The ETMonitor ETa was finally adopted for further model applications. The calibrated SWAT model was then validated during 2010\u20132015 against remote sensing data on total water storage change (TWSC) with acceptable performance, i.e., R2 = 0.57 and NSE = 0.55, and remote sensing soil moisture data with R2 and NSE greater than 0.85.<\/jats:p>","DOI":"10.3390\/rs14061511","type":"journal-article","created":{"date-parts":[[2022,3,21]],"date-time":"2022-03-21T21:48:42Z","timestamp":1647899322000},"page":"1511","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":63,"title":["Calibration and Validation of SWAT Model by Using Hydrological Remote Sensing Observables in the Lake Chad Basin"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6413-7300","authenticated-orcid":false,"given":"Ali","family":"Bennour","sequence":"first","affiliation":[{"name":"State Key Laboratory of Remote Sensing Sciences, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100045, China"},{"name":"Water Resources Department, Commissariat Regional au Developpement Agricole, Medenine 4100, Tunisia"}]},{"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 Sciences, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9176-4556","authenticated-orcid":false,"given":"Massimo","family":"Menenti","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Sciences, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"Faculty of Civil Engineering and Geosciences, Delft University of Technology, Stevinweg 1, 2825 CN Delft, The Netherlands"}]},{"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 Sciences, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4294-2453","authenticated-orcid":false,"given":"Yelong","family":"Zeng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Sciences, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100045, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1314-2556","authenticated-orcid":false,"given":"Beatrice","family":"Asenso Barnieh","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Sciences, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"Earth Observation Research and Innovation Centre (EORIC), University of Energy and Natural Resources, Sunyani P.O. Box 214, Ghana"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3510-9829","authenticated-orcid":false,"given":"Min","family":"Jiang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Sciences, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1038\/s41893-019-0220-7","article-title":"China and India lead in greening of the world through land-use management","volume":"2","author":"Chen","year":"2019","journal-title":"Nat. Sustain."},{"key":"ref_2","unstructured":"Odada, E.O., Oyebande, L., and Oguntola, A.J. (2006, January 27). Lake Chad: Experience and lessons learned Brief. 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