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

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 2K, 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. •A LST algorithm is developed for the forthcoming ESA's Sentinel-3 satellite.•The LST algorithm is based on a synergistic approach between OLCI and SLSTR data.•ENVISAT MERIS and AASTR pairs are used as a proxy for the algorithm.•The algorithm is validated against in situ data over five different test sites.•The explicit dependency on emissivity is a key advantage of the proposed algorithm.