Lake surface temperature retrieval from Landsat-8 and retrospective analysis in Karaoun Reservoir, Lebanon

International audience

Inglés. The importance of lake water surface temperature has long been highlighted for ecological and hydrological studies as well as for water quality management. In the absence of regular field observations, satellite remote sensing has been recognized as a cost-effective way to monitor water surface temperature on large spatial and temporal scales. The thermal infrared sensors (TIRS) onboard of Landsat satellites (since 1984) are adequate tools for monitoring surface temperature of small to medium sized lakes with a biweekly frequency, as well as for performing retrospective analysis. Nonetheless, the satellite data have to deal with effects due to the atmosphere so that several approaches to correct for atmospheric contributions have been proposed. Among these are: (i) the radiative transfer equation (RTE); (ii) a single-channel algorithm that depends on water vapor content and emissivity (SC1); (iii) its improved version including air temperature (SC2); and (iv) a mono-window (MW) algorithm that requires emissivity, atmospheric transmissivity, and effective mean atmospheric temperature. We aim to evaluate these four approaches in a river dammed reservoir with a size of 12 km² using data gathered from the band 10 of the TIRS onboard of Landsat 8. Satellite-derived temperatures were then compared to in situ data acquired from thermistors at the time of Landsat 8 overpasses. All approaches showed a good performance, with the SC1 algorithm yielding the lowest root mean square error (0.73 K), followed by the SC2 method (0.89 K), the RTE (0.94 K), and then the MW algorithm (1.23 K). Based on the validation results, we then applied the SC1 algorithm to Landsat 4, 5, and 8 thermal data (1984 to 2018) to extend data series to past years. These data do not reveal any warming trend of the reservoir surface temperature. The results of this study also confirm how the 100-m spatial resolution of TIRS is valuable as an additional source of data to field-based monitoring.