Publications

Wang, MM; He, GJ; Zhang, ZM; Wang, GZ; Wang, ZH; Yin, RY; Cui, SA; Wu, ZJ; Cao, XJ (2019). A radiance-based split-window algorithm for land surface temperature retrieval: Theory and application to MODIS data. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 76, 204-217.

Abstract
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.

DOI:
10.1016/j.jag.2018.11.015

ISSN:
0303-2434