रिमोट सेंसिंग और जीआईएस जर्नल

अमूर्त

Land Surface Temperature with Land Cover Classes in ASTER and Landsat Data

Mukesh Singh Boori*, Vit Vozen?lek, Heiko Balzter and Komal Choudhary

Land surface temperature (LST) from satellite data is a challenging task due to the atmospheric absorption and diversification of earth surface emissivity. The analysis show that land surface temperature influenced by water content or vegetation condition. The combination of HDF Explorer and ArcGIS software was useful for automatically processing the pixel latitude, longitude and brightness temperature (BT) information from the ASTER HDF and Landsat imagery files. Forest areas with more moisture and water body have shown smaller temperature than settlements. The urban area refers to the phenomenon of higher atmospheric and surface temperatures occurring in urban areas than in the surrounding vegetation areas. A lookup table of effective temperature anomalies is constructed based on the BT to resolve the inconsistencies between infrared and BT variation. Here ASTER and Landsat data show similar behavior for all land cover classes temperature. The results can be referred to similar areas of the world for LST retrieval or land surface process research, in particular under extreme bad weather conditions. So mitigation of the high temperature effects via the configuration of green spaces and sustainable designs of urban environments have become an issue of increasing concern under changing climate.