A radiance-based split-window algorithm for land surface temperature retrieval: Theory and application to MODIS data

作     者：Wang, Mengmeng He, Guojin Zhang, Zhaoming Wang, Guizhou Wang, Zhihua Yin, Ranyu Cui, Shiai Wu, Zhijie Cao, Xiaojie 

作者机构：China Univ Geosci Wuhan Fac Informat Engn Wuhan Hubei Peoples R China Chinese Acad Sci Inst Remote Sensing & Digital Earth Beijing Peoples R China Key Lab Earth Observat Hainan Prov Sanya Peoples R China Chinese Acad Sci Inst Geog Sci & Nat Resources Res State Key Lab Resources & Environm Informat Syst Beijing Peoples R China Longyan Univ Coll Resources Engn Longyan Peoples R China 

出 版 物：《INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION》 (国际应用地球观测和地球信息杂志)

年 卷 期：2019年第76卷

页      面：204-217页

核心收录：

学科分类：070801[理学-固体地球物理学] 07[理学] 08[工学] 0708[理学-地球物理学] 0816[工学-测绘科学与技术] 

基　　金：Strategic Priority Research Program of the Chinese Academy of Sciences [XDA190090300] National Key Research and Development Programs of China [2016YFB0501502, 2016YFA0600302] National Natural Science Foundation of China [61801443, 61401461] 

主　　题：Land surface temperature Split-window algorithm Simplification of Planck's function MODIS 

摘      要：The split-window algorithm is the most commonly used method for land surface temperature (1ST) retrieval from satellite data. Simplification of the Planck s function, as an important step in developing the SWA, allows us to directly relate the radiance to the temperature toward solving the radiative transfer equation (RTE) set. In this study, Planck s radiance relationship between two adjacent thermal infrared channels was modeled to solve the RTE set instead of simplification of the Planck s function. A radiance-based split-window algorithm (RBSWA) was developed and applied to Moderate Resolution Imaging Spectroradiometer (MODIS) data. The performance of the RBSWA was assessed and compared with three most common brightness temperature-based split-window algorithms (BTBSWAs) by using the simulated data and satellite measurements. Simulation analysis showed that the 1ST retrieval using RBSWA had a Root Mean Square Error (RMSE) of 0.5 K and achieved an improvement of 0.3 K compared with three BTBSWAs, and the 1ST retrieval accuracy using RBSWA was better than 1.5 K considering uncertainties in input parameters based on the sensitivity analysis. For application of RBSWA to MODIS data, the results showed that: 1) comparison between 1ST from MODIS 1ST product and 1ST retrieved using RBSWA showed a mean RMSE of 1.33 K for 108 groups of MODIS image covering continental US, which indicates RBSWA is reliable and robust;2) when using the measurements from US surface radiation budget network as real values the RMSE of the RBSWA algorithm was 2.55 K and was slightly better than MODIS 1ST product;and 3) through the cross validation using Advanced Spaceborne Thermal Emission and Reflection Radiometer 1ST product, the RMSE of the RBSWA algorithm was 2.23 K and was 0.28 K less than that of MODIS 1ST product. We conclude that the RBSWA for 1ST retrieval from MODIS data can attain a better accuracy than the BTBSWA.