{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T06:28:38Z","timestamp":1789626518352,"version":"build-2803163510"},"reference-count":86,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2017,1,6]],"date-time":"2017-01-06T00:00:00Z","timestamp":1483660800000},"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>The partitioning of available energy between sensible heat and latent heat is important for precise water resources planning and management in the context of global climate change. Land surface temperature (LST) is a key variable in energy balance process and remotely sensed LST is widely used for estimating surface heat fluxes at regional scale. However, the inequality between LST and aerodynamic surface temperature (Taero) poses a great challenge for regional heat fluxes estimation in one-source energy balance models. To address this issue, we proposed a One-Source Model for Land (OSML) to estimate regional surface heat fluxes without requirements for empirical extra resistance, roughness parameterization and wind velocity. The proposed OSML employs both conceptual VFC\/LST trapezoid model and the electrical analog formula of sensible heat flux (H) to analytically estimate the radiometric-convective resistance (rae) via a quartic equation. To evaluate the performance of OSML, the model was applied to the Soil Moisture-Atmosphere Coupling Experiment (SMACEX) in United States and the Multi-Scale Observation Experiment on Evapotranspiration (MUSOEXE) in China, using remotely sensed retrievals as auxiliary data sets at regional scale. Validated against tower-based surface fluxes observations, the root mean square deviation (RMSD) of H and latent heat flux (LE) from OSML are 34.5 W\/m2 and 46.5 W\/m2 at SMACEX site and 50.1 W\/m2 and 67.0 W\/m2 at MUSOEXE site. The performance of OSML is very comparable to other published studies. In addition, the proposed OSML model demonstrates similar skills of predicting surface heat fluxes in comparison to SEBS (Surface Energy Balance System). Since OSML does not require specification of aerodynamic surface characteristics, roughness parameterization and meteorological conditions with high spatial variation such as wind speed, this proposed method shows high potential for routinely acquisition of latent heat flux estimation over heterogeneous areas.<\/jats:p>","DOI":"10.3390\/rs9010043","type":"journal-article","created":{"date-parts":[[2017,1,6]],"date-time":"2017-01-06T10:08:12Z","timestamp":1483697292000},"page":"43","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["A One-Source Approach for Estimating Land Surface Heat Fluxes Using Remotely Sensed Land Surface Temperature"],"prefix":"10.3390","volume":"9","author":[{"given":"Yongmin","family":"Yang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, Beijing 100038, China"},{"name":"Research Center on Flood and Drought Disaster Reduction of the Ministry of Water Resources, China Institute of Water Resources and Hydropower Research, Beijing 100038, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1275-4171","authenticated-orcid":false,"given":"Jianxiu","family":"Qiu","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongbo","family":"Su","sequence":"additional","affiliation":[{"name":"Key Laboratory of Water Cycle &amp; Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Beijing 100101, China"},{"name":"Department of Civil, Environmental and Geomatics Engineering, Florida Atlantic University, Boca Raton, FL 33431, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingmei","family":"Bai","sequence":"additional","affiliation":[{"name":"Xi\u2019an Meteorological Bureau, Xi\u2019an 710016, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suhua","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Water Cycle &amp; Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Beijing 100101, China"},{"name":"Graduate University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lu","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Water Cycle &amp; Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Beijing 100101, China"},{"name":"Graduate University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yilei","family":"Yu","sequence":"additional","affiliation":[{"name":"Institute of Wetland Research, Chinese Academy of Forestry, Beijing 100091, China"},{"name":"Beijing Key Laboratory of Wetland Services and Restoration, Beijing 100091, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaoxian","family":"Huang","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,1,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Brutsaert, W. 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