The accurate estimation of Land Surface Temperature (LST) plays a vital role in various fields, including hydrology, meteorology, and surface energy balance analysis. This study focuses on the estimation of LST using data acquired from the Joint Polar-orbiting Satellite System (JPSS) missions, specifically JPSS-1/NOAA-20 and JPSS-2/NOAA-21. The methodology for this research centers on the utilization of the split window algorithm, a well-established and recognized technique renowned for its proficiency in extracting accurate Land Surface Temperature (LST) values from remotely sensed data. This algorithm leverages the differential behavior of thermal infrared (TIR) radiance measured in two adjacent spectral channels to estimate LST, effectively mitigating the influence of atmospheric distortions on the acquired measurements.

To establish the accuracy of the proposed approach, the coefficients of the split window algorithm were determined through linear regression analysis, utilizing a dataset generated via extensive radiative transfer modeling. The calculated LST values were subsequently compared with LST products provided by the National Oceanic and Atmospheric Administration (NOAA). The evaluation process encompassed the computation of root mean square error (RMSE) values, offering insights into the performance of the algorithm for both JPSS-1/NOAA-20 and JPSS-2/NOAA-21 missions.

The obtained results demonstrate the potential of the split window algorithm to effectively estimate LST from JPSS satellite data. The RMSE values, 1.4 and 1.5 for JPSS-1/NOAA-20 and JPSS-2/NOAA-21, respectively, highlight the algorithm's capability to provide accurate LST estimates for different mission datasets. This research contributes to enhancing our understanding of land surface temperature dynamics using remote sensing technology and showcases the valuable insights that can be gained from JPSS missions in monitoring and studying Earth's surface processes.