Impact Of Temperature And Its Effects In Hydrology In Yercaud Hill

Abstract Land surface temperature (LST) is an important variable in climate, hydrologic, ecological, and biophysical and biochemical studies. The most effective way to obtain LST measurements is through satellites. Presently, LST from moderate resolution imaging spectra radiometer (MODIS) sensor is applied in various fields due to its high spatial and temporal availability over the globe, but quite difficult to provide observations in cloudy conditions. In our project is evolves of prediction of LST under clear and cloudy conditions using microwave vegetation indices (MVIs), elevation, latitude, longitude and Julian day as inputs employing an NDVI model. MVIs can be obtained even under cloudy condition, since microwave radiation has an ability to penetrate through clouds. Separate NDVI models were trained and tested for the grid cells for which both LST and MVI were available. The performance of the models was evaluated based on standard evaluation measures. The best performing model was used to predict LST where MVIs were available. Results revealed that predictions of LST using NDVI are in good agreement with the observed values. The NDVI approach presented in this study promises to be useful for predicting LST using satellite observations even in cloudy conditions.