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ISSN: 2381-8719

Journal of Geology & Geophysics
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Research Article

Land Surface Temperature and Scaling Factors for Different SatellitesDatasets

Mukesh Singh Boori1, 4*, Heiko Balzter3, Komal Choudhary1 and Vit Vozenílek4
1Samara State Aerospace University, Russia
2American Sentinel University, Colorado, USA
3University of Leicester England, UK
4Palacky University Olomouc, Czech Republic
Corresponding Author : Mukesh Singh Boori
Department of Geo-informatics, Section of Earth
Sciences Palacký University, Czech Republic
Tel: +420585631111
E-mail: [email protected]
Received July 08, 2015; Accepted September 15, 2015; Published September 30, 2015
Citation: Boori MS, Balzter H, Choudhary K, Vozenílek V (2015) Land Surface Temperature and Scaling Factors for Different Satellites Datasets. J Geol Geophys 4:223. doi:10.4172/2381-8719.1000223
Copyright: © 2015 Boori MS, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Abstract

Land surface parameters are highly integrated and have a direct effect on water and energy balance and weather predictions. Due to the difficulties in correcting the influences of the atmosphere absorbability and the earth surface emissivity diversification, the retrieval of land surface temperature (LST) from satellite data is a challenging task. To retrieve microwave land emissivity, infrared surface skin temperatures have been used as surface physical temperature. However, passive microwave emissions originate from deeper layers with respect to the skin temperature. So, this inconsistency in sensitivity depths between skin temperatures and microwave temperatures may introduce a discrepancy in the determined emissivity. In this research, six sample sites were chosen on the earth for 2013 and 2014 and then land surface temperature from AMSR-2, Landsat and ASTER brightness temperature values have been derived. The algorithm has been developed from a surface brightness temperature dataset, which has used as inputs surface parameters and atmospheric quantities. The retrieved LST has been compared within AMSR-2, Landsat and ASTER for the same period and area. Maximum time ASTER has shown higher temperature than other data and AMSR-2 has lower temperature on same area. Landsat and ASTER is closer to ground measured temperature than AMSR-2 data. It will be interesting to see how the satellite-derived surface temperature will behave in an assimilation scheme in a follow-up study.

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