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A radiance-based split-window algorithm for land surface temperature retrieval: Theory and application to MODIS data
Author(s) -
Mengmeng Wang,
Guojin He,
Zhaoming Zhang,
Guizhou Wang,
Zhihua Wang,
Ranyu Yin,
Shiai Cui,
Zhijie Wu,
Xiaojie Cao
Publication year - 2018
Publication title -
international journal of applied earth observation and geoinformation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.623
H-Index - 98
eISSN - 1872-826X
pISSN - 1569-8432
DOI - 10.1016/j.jag.2018.11.015
Subject(s) - radiance , mean squared error , moderate resolution imaging spectroradiometer , brightness temperature , remote sensing , radiative transfer , satellite , algorithm , data set , environmental science , atmospheric radiative transfer codes , root mean square , meteorology , mathematics , brightness , geography , physics , statistics , optics , quantum mechanics , astronomy
The split-window algorithm is the most commonly used method for land surface temperature (LST) 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 LST 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 LST 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 LST from MODIS LST product and LST 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 LST product; and 3) through the cross validation using Advanced Spaceborne Thermal Emission and Reflection Radiometer LST product, the RMSE of the RBSWA algorithm was 2.23 K and was 0.28 K less than that of MODIS LST product. We conclude that the RBSWA for LST retrieval from MODIS data can attain a better accuracy than the BTBSWA.

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