Comparative analysis of two parameter-dependent split window algorithms for the land surface temperature retrieval using MODIS TIR observations

Abstract

MODIS Land Surface Temperature (LST) product is extensively used in agricultural studies like crop health assessment, soil moisture estimation, irrigation management, land use land cover change, air-temperature retrieval and crop water stress detection. Numerous studies have used Split Window (SW) algorithms to retrieve LST from MODIS TIR bands. Among them, some utilize Sensor View Angle Dependent (SVAD) or Columnar Water Vapor Dependent (CWVD) SW algorithms. Present study aims to make use of SVAD and CWVD SW algorithms and compare them to evaluate the LST retrieval accuracy over various land surface type. Theoretical accuracy assessment of the CWVD and SVAD algorithms demonstrates a good accuracy with the RMSE of 1.09K and 1.42K, respectively. The experimental retrieval of LST achieves exceptionally good accuracy, with a RMSE of 1.45K in the CWVD algorithm and 1.80K in the SVAD algorithm, particularly in heterogeneous regions. In homogeneous regions, the RMSE values are 1.14K in CWVD and 1.10K in SVAD. Both algorithms exhibit satisfactory accuracy; nevertheless, the application of these algorithms may vary in agricultural contexts. Based on the obtained results and the inclusion of required parameters, we have arrived at a conclusion regarding the superior performance of the SVAD compared to the CWVD for LST retrieval.