Mehdi Ghlamnia - Assistance Professor, Department of surveying Engineering, Sanandaj Branch,Islamic Azad University
Salman Ahmadi - Assistant Professor of Eengineering Faculty, University of Kurdistan
Reza Khandan - Faculty of Geography, Department of Remote Sensing and GIS, University of Tehran, Iran
Seyed kazem Alavipanah - Professor, Faculty of Geography, Department of Remote Sensing and GIS, University of Tehran, Iran
Ali Darvishi Boloorani - Assistant Professor, Faculty of Geography, Department of Remote Sensing and GIS, University of Tehran, Iran
Saeid Hamzehe - Assistant Professor, Faculty of Geography, Department of Remote Sensing and GIS, University of Tehran, Iran

The accuracy of retrieved LST from satellites is of great importance. Among different LST validation methods, a cross-calibration procedure is highly cost-effective and applicable. The Indian National Satellite-۳D series (INSAT-۳D) and Meteosat Second Generation (MSG) are two geostationary satellites that which provide LST products with high temporal resolution. Considering MODIS as the reference (polar orbit that is onboard Aqua and Terra satellites), the comparison of the LST products of these geostationary satellites was evaluated from ۴th March to ۱st September ۲۰۱۵. For this purpose mean LST ratios were calculated for both MODIS-Imager (from INSAT-D) and MODIS-SEVIRI. Then the behavior of their mean LST ratio was analyzed for the exciting four major land covers and five elevation classes in the study area. The results showed that Imager data underestimated and overestimated the LST in comparison to MODIS data during the day and night time respectively. The SEVIRI LSTs underestimated the LST in both day and night time in comparison with MODIS products. In order to model the discrepancies between MODIS-Imager and MODIS-SEVIRI, for each land cover a multilinear regression model was fitted based on slope, aspect, azimuth, and View Zenith Angle (VZA). The results showed that barren, Shrub, grass, and cereal crops had low RMSEs in model fitting, respectively.

LST, Geostationary Satellite, Land cover, Elevation, remote sensing