Geostatistical Kriging Interpolation for Spatial Enhancement of MODIS Land Surface Temperature Imagery

Thermal images play a crucial role in various applications, such as environmental monitoring, energy efficiency, and food safety. However, thermal images are often affected by low spatial resolution, limited accuracy, and noise, which reduce their usefulness and effectiveness. This research paper presents a novel approach for enhancing thermal images and optimizing using Kriging Interpolation KI. The proposed KI method combines a metaheuristic optimization algorithm, Particle Swarm Optimization (PSO), with Kriging, a geostatistical method for interpolation and prediction of spatially continuous variables. The proposed KI method has been evaluated on a set of low-resolution Land surface temperature (LST) images of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite and validated with higher resolution LandSat-8 LST. The use of PSO in combination with Kriging provides a powerful tool for efficient and accurate spatial enhancement of thermal images, allowing for the preservation of important thermal features and details while improving the overall quality of the images. The proposed KI algorithm demonstrated the effectiveness of the approach in enhancing the spatial resolution and accuracy of the MODIS thermal images. The results show that the proposed method outperforms traditional statistical LST image enhancement methods, such as DisTrad, TsHarp, and Regression Tree in terms of spatial resolution and accuracy. The proposed method has potential applications in agricultural, metrological, and environmental applications, where thermal images are used to continuously monitor and control temperature-sensitive data.