文献类型：期刊文献

英文题名：A One-Source Approach for Estimating Land Surface Heat Fluxes Using Remotely Sensed Land Surface Temperature

作者：Yang, Yongmin[1,2] Qiu, Jianxiu[3] Su, Hongbo[4,5] Bai, Qingmei[6] Liu, Suhua[4,7] Li, Lu[4,7] Yu, Yilei[8,9] Huang, Yaoxian[3]

第一作者：Yang, Yongmin

通信作者：Qiu, JX[1];Su, HB[2];Su, HB[3]

机构：[1]State Key Lab Simulat & Regulat Water Cycle River, Beijing 100038, Peoples R China;[2]China Inst Water Resources & Hydropower Res, Res Ctr Flood & Drought Disaster Reduct, Minist Water Resources, Beijing 100038, Peoples R China;[3]Sun Yat Sen Univ, Sch Geog & Planning, Guangdong Prov Key Lab Urbanizat & Geosimulat, Guangzhou 510275, Guangdong, Peoples R China;[4]Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China;[5]Florida Atlantic Univ, Dept Civil Environm & Geomat Engn, Boca Raton, FL 33431 USA;[6]Xian Meteorol Bur, Xian 710016, Peoples R China;[7]Chinese Acad Sci, Grad Univ, Beijing 100049, Peoples R China;[8]Chinese Acad Forestry, Inst Wetland Res, Beijing 100091, Peoples R China;[9]Beijing Key Lab Wetland Serv & Restorat, Beijing 100091, Peoples R China

年份：2017

卷号：9

期号：1

外文期刊名：REMOTE SENSING

收录：;EI(收录号：20170503299362);Scopus(收录号：2-s2.0-85010699937);WOS:【SCI-EXPANDED(收录号:WOS:000395492600043)】；

基金：This work is supported by the National Science Foundation of China (Grant Nos. 41501415, 41501450, 41571356 and 51420105014), the Natural Science Foundation of Guangdong Province, China (No. 2016A030310154), the Fundamental Research Funds for the Central Universities (No. 16lgpy06), Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (No. 2015A006), IWHR Research & Development Support Program: "Study on regional drought monitoring and early warning based on satellite remote sensing and land surface hydrological model", the National High Technology Research and Development Program (2012AA12A309) and IWHR Research & Development Support Program (No. JZ0145B612016). We greatly thank the National Snow and Ice Center for providing the SMACEX data set. The HiWATER-MUSOEXE data set is provided by Cold and Arid Regions Sciences Data Center at Lanzhou (http://westdc.westgis.ac.cn). All people involved in the field campaign are greatly acknowledged.

语种：英文

外文关键词：One-Source Model for Land (OSML); evapotranspiration; land surface heat flux; land surface temperature; SMACEX; MUSOEXE

摘要：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 (T-aero) 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 (r(ae)) 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/m(2) and 46.5 W/m(2) at SMACEX site and 50.1 W/m(2) and 67.0 W/m(2) 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.