Abstract
The satellite derived data is essential to remotely access the material surface temperature (MST) on Earth’s facade. The present study aims to predict MST at higher resolutions from MODIS satellite low resolution (1000 m) data. The vegetation proportion (VP) based regression models has been used in the case study of Jaipur city, India. Three fundamental VP parameters [Soil adjusted vegetation index (SAVI), Normalized difference vegetation index (NDVI), and fractional vegetation cover (fc)] has been analyzed by the linear and polynomial regression model connecting MST and VP. The MST prediction results showed that linear regression model achieved better accuracy as compared to polynomial models. The NDVI has shown the highest correlation coefficient (R2) and outperformed other VP, in all the seasons. The input data within pixel changeability coefficient (Cv) of (15% to 25%) had shown higher accuracy in MST prediction. The regression models were able to predict MST up to 200 m resolution from 1000 m spatial resolution data. The outcome of this research would facilitate the prediction of MST for numerous Earth surface materials.
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