Advancement of technology in the field of remote sensing (RS) and geographic information system (GIS) has introduced a significant amount of research challenges. Proper staging of the spatial data is necessary as geospatial repositories contain missing and erroneous information. Therefore, prediction of meteorological parameters with better accuracy is an indispensable task required for most of the applications related to weather/climatological analysis. The geostatistical interpolation methods are often considered to be the most preferred and appropriate methods for the prediction of meteorological parameters (such as land surface temperature (LST), normalized difference vegetation index (NDVI), moisture stress index (MSI), etc.), which yield minimal error. The methods based on regression exhibit better performance as the autocorrelation within the region of interest (RoI) is modeled and...
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