Evaluation of split-window methods for land surface temperature retrieval from the TIR observations of INSAT-3DS...

Land surface temperature (LST) is a vital environmental variable supporting quantitative assessment of surface thermal regimes and their relevance to large-scale environmental and climate processes. The Indian National Satellite 3D Second (INSAT-3DS), the sixth geostationary meteorological satellite in the INSAT-3 series, provides observation essential for diurnal LST retrieval across the Indian subcontinent and the broader South and Southeast Asian regions. This study aims to identify an optimal split-window (SW) approach for LST estimation from INSAT-3DS data. A total of 20 published SW methods were evaluated using 1,356,696 simulations generated with MODTRAN 5.3 radiative transfer model, covering diverse surface and atmospheric conditions as well as sensor geometries. The simulations dataset was partitioned into training and testing subsets in a 75:25 ratio. A three-stage evaluation framework was implemented, wherein method accuracy was assessed independently using training and testing data, alongside sensitivity analysis to quantify the influence of input parameter uncertainties on LST retrieval. Validation of the retrieved LSTs from the optimal SW approach, based on the Wan (2014) formulation, was performed using MODIS observations and in situ measurements across both winter and summer seasons. Comparisons with MODIS data over a 70-day period yielded average RMSE values between 1.932 and 2.702 K for daytime and nighttime observations. Further validation using 61 diurnal in situ measurements collected over 3 days confirmed the robustness of the algorithm with an RMSE of 1.454 K.