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Synergistic use of MERIS and AATSR as a proxy for estimating Land Surface Temperature from Sentinel-3 data

Sobrino, J. A., Jiménez-Muñoz, J. C., Sòria, G., Ruescas, A. B., Danne, O., Brockmann, C., Ghent, D., Remedios, J., North, P., Merchant, C., Berger, M., Mathieu, P. P. and Göttsche, F.-M. (2016) Synergistic use of MERIS and AATSR as a proxy for estimating Land Surface Temperature from Sentinel-3 data. Remote Sensing of Environment, 179. pp. 149-161. ISSN 0034-4257

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To link to this item DOI: 10.1016/j.rse.2016.03.035

Abstract/Summary

Land Surface Temperature (LST) is one of the key parameters in the physics of land-surface processes on regional and global scales, combining the results of all surface-atmosphere interactions and energy fluxes between the surface and the atmosphere. With the advent of the ESA's Sentinel 3 (S3) satellite, accurate LST retrieval methodologies exploiting the synergy between OLCI and SLSTR instruments can be developed. In this paper we propose a candidate methodology for retrieving LST from data acquired with the forthcoming S3 instruments. The LST algorithm is based on the Split-Window (SW) technique with an explicit dependence on surface emissivity, in contrast to the AATSR level 2 algorithm with emissivity dependence embedded in the algorithm coefficients. Performance of the methodology is assessed by using MERIS/AATSR pairs (instruments with similar characteristics to OLCI and SLSTR, respectively). LST retrievals using different datasets of input emissivity are validated against in situ data measured along one year (2011) in five test sites and intercompared to the standard AATSR level 2 products. Validation results show that LST is retrieved with the proposed SW algorithm typically with RMSE below 2 K, providing slightly better results than the AATSR level 2 product. The main advantage of the proposed algorithm is that it allows for improvements in input emissivities to be directly translated into improved LST retrievals.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > National Centre for Earth Observation (NCEO)
Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:65883
Publisher:Elsevier

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