Spatio-Temporal Analysis of Water Surface Temperature in a Reservoir and its Relation with Water Quality in a Climate Change Context

Remote sensing community is making enormous efforts to implement early warning systems capable for following spatio-temporal patterns of water quality and climate change risk indicators, being Horizon 2030 EOXPOSURE project one of them. This work presents first results of surface temperature Landsat 8 Level 2 Collection 2 products analysis for a reservoir and compare them with field data measurements. A Root Mean Square Error (RMSE) of 1.7°C and a Mean Absolute Percentage Error (MAPE) of 7% were obtained for these products but validation curve resulted not confident at a 95% level. A semiempirical linear model with 94% accuracy, RMSE of 1.1°C and a MAPE of 5% is presented. It was successfully validated with a control group data set obtaining 94% accuracy. A Water Surface Temperature temporal series is shown for the 2013–2020 period and spatio temporal patterns are analyzed and discussed. Water surface temperature behavior in zones with algal bloom occurrence present greater significant values, up to 3°C, than those with clearer water, indicating that water emissitiviy must be revised for these cases.