{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T22:30:41Z","timestamp":1772231441042,"version":"3.50.1"},"reference-count":46,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,4,6]],"date-time":"2024-04-06T00:00:00Z","timestamp":1712361600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Mendenhall Postdoctoral Fellowship program of the U.S. Geological Survey"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The operational Simplified Surface Energy Balance (SSEBop) model has been utilized to generate gridded evapotranspiration data from Landsat images. These estimates are primarily driven by two sources of information: reference evapotranspiration and Landsat land surface temperature (LST) values. Hence, SSEBop is limited by the availability of Landsat data. Here, in this proof-of-concept paper, we utilize the Continuous Change Detection and Classification (CCDC) algorithm to generate synthetic Landsat data, which are then used as input for SSEBop to generate evapotranspiration estimates for six target areas in the continental United States, representing forests, shrublands, and irrigated agriculture. These synthetic land cover data are then used to generate the LST data required for SSEBop evapotranspiration estimates. The synthetic LST, evaporative fractions, and evapotranspiration data from CCDC closely mirror the phenological cycles in the observed Landsat data. Across the six sites, the median correlation in seasonal LST was 0.79, and the median correlation in seasonal evapotranspiration was 0.8. The median root mean squared error (RMSE) values were 2.82 \u00b0C for LST and 0.50 mm\/day for actual evapotranspiration. CCDC predictions typically underestimate the average evapotranspiration by less than 1 mm\/day. The average performance of the CCDC evaporative fractions, and corresponding evapotranspiration estimates, were much better than the initial LST estimates and, therefore, promising. Future work could include bias correction to improve CCDC\u2019s ability to accurately reproduce synthetic Landsat data during the summer, allowing for more accurate evapotranspiration estimates, and determining the ability of SSEBop to predict regional evapotranspiration at seasonal timescales based on projected land cover change from CCDC.<\/jats:p>","DOI":"10.3390\/rs16071297","type":"journal-article","created":{"date-parts":[[2024,4,8]],"date-time":"2024-04-08T03:11:33Z","timestamp":1712545893000},"page":"1297","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["SSEBop Evapotranspiration Estimates Using Synthetically Derived Landsat Data from the Continuous Change Detection and Classification Algorithm"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5141-026X","authenticated-orcid":false,"given":"Mikael P.","family":"Hiestand","sequence":"first","affiliation":[{"name":"U.S. Geological Survey (USGS), Earth Resources Observation and Science (EROS) Center, Sioux Falls, SD 57198, USA"},{"name":"Climate Hazards Center, University of California, Santa Barbara, CA 93106, USA"}]},{"given":"Heather J.","family":"Tollerud","sequence":"additional","affiliation":[{"name":"U.S. Geological Survey (USGS), Earth Resources Observation and Science (EROS) Center, Sioux Falls, SD 57198, USA"}]},{"given":"Chris","family":"Funk","sequence":"additional","affiliation":[{"name":"Climate Hazards Center, University of California, Santa Barbara, CA 93106, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8810-8539","authenticated-orcid":false,"given":"Gabriel B.","family":"Senay","sequence":"additional","affiliation":[{"name":"U.S. Geological Survey (USGS), Earth Resources Observation and Science (EROS) Center, North Central Climate Adaptation Science Center, Fort Collins, CO 80523, USA"}]},{"given":"Kate C.","family":"Fickas","sequence":"additional","affiliation":[{"name":"U.S. Geological Survey (USGS), Earth Resources Observation and Science (EROS) Center, Sioux Falls, SD 57198, USA"},{"name":"Climate Hazards Center, University of California, Santa Barbara, CA 93106, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9602-321X","authenticated-orcid":false,"given":"MacKenzie O.","family":"Friedrichs","sequence":"additional","affiliation":[{"name":"KBR, Contractor to the USGS, EROS Center, Sioux Falls, SD 57198, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2025","DOI":"10.1175\/2010JCLI3812.1","article-title":"Evaluation of Drought Indices Based on Thermal Remote Sensing of Evapotranspiration over the Continental United States","volume":"24","author":"Anderson","year":"2011","journal-title":"J. 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