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Estimation of land surface temperature from atmospherically corrected LANDSAT TM image using 6S and NCEP global reanalysis product

Srivastava, Prashant K., Han, Dawei, Rico-Ramirez, Miguel A., Bray, Michaela, Islam, Tanvir, Gupta, Manika and Dai, Qiang 2014. Estimation of land surface temperature from atmospherically corrected LANDSAT TM image using 6S and NCEP global reanalysis product. Environmental Earth Sciences 72 (12) , pp. 5183-5196. 10.1007/s12665-014-3388-1

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Abstract

Water vapour is the most variable constituent in the atmosphere which is responsible for serious noise in the optical satellite images. This research is focused on the vertical distribution of water vapour and deducing its possible effects on the atmospheric correction process. The vertical distribution of precipitable water vapour, water vapour mixing ratio with geopotential height and pressure were estimated through the weather research and forecasting (WRF) model by downscaling the National Center for Environmental Prediction (NCEP) global reanalysis product. In addition, the most widely used LANDSAT TM satellite image has been used for this assessment. The WRF model was applied with three domains centred on a LANDSAT captured image over the area. The 6S atmospheric correction code was utilised for viewing the effect of precipitable water vapour on satellite image correction. The analysis was conducted on two pressure levels (1,000 and 100 hPa) representing the troposphere and stratosphere, respectively. The validation of the atmospheric correction has been performed by estimating the land surface temperature (LST) over the Walnut Creek region and its comparison with the Soil Moisture Experiments in 2002 (SMEX02) LST field validation datasets. The overall analyses indicate a higher accuracy of LST repossession with 100 hPa corrected image.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Subjects: Q Science > QC Physics
Q Science > QE Geology
Publisher: Springer Verlag
ISSN: 1866-6280
Last Modified: 21 Feb 2019 16:22
URI: http://orca.cf.ac.uk/id/eprint/72959

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