DAILY LAND SURFACE TEMPERATURE FROM MULTIPLE EARTH OBSERVATION DATA FUSION

Land surface temperature (LST) is one of the most important variables for the terrestrial ecosystem. It stands as a fundamental Essential Climate Variable (ECV) and hold paramount significance across different environmental and agricultural domains. Temperature estimation from satellites is increasingly widespread, which allows to obtain large-scale and almost real-time informations. However, estimating temperature using satellite data has some limitations: presence of cloud cover has an impact on remotely sensed observations. This paper presents a data fusion approach for enhancing Sentinel-3 LST products by replacing cloudy pixels with data from MODIS, GCOM-C and ERA5-Land. The model has been tested on a trial study area, but its versatility allows it to be applied worldwide. The resulting fused LST product is subjected to an evaluation against LANDSAT 8 and 9 LST data. The performance of the data fusion demonstrates the efficacy of the proposed fusion method, with Pearson correlation values ranging from 0.60 to 0.93. The study not only contributes to advancements in LST data quality but also establishes a benchmark for future research in satellite data fusion.