Fusion of LST products of ASTER and MODIS Sensors Using STDFA Model

Land Surface Temperature (LST) is one of the most important physical and climatological  crucial yet variable parameter in environmental phenomena studies such as, soil moisture conditions, urban heat island, vegetation health, fire risk for forest areas and heats effects on human’s health. These studies need to land surface temperature with high spatial and temporal resolution. Remote sensing satellite sensors due to their technical constraints cannot take the high spatial and temporal land surface temperature data simultaneously. For example combining Advanced Space borne Thermal Emission and Reflection Radiometer (ASTER) LST products have spatial resolution of 90 m with repeat cycle of 16 days, whereas Moderate Resolution Imaging Spectro-radiometer (MODIS) LST products have spatial resolution of 1 km with daily repeat cycle. To address this shortage, this work used the Spatial and Temporal Data Fusion Approach (STDFA) to estimate the high spatial and temporal resolution LST by ASTER LST and MODIS LST products. This method was tested and validated in study areas located in Tehran, Iran. The MODIS daily 1-km LST product and the 16-day repeat cycle ASTER 90-m LST product are used to produce a synthetic “daily” LST product at ASTER spatial resolution. The actual ASTER LST products were used to evaluate the precision of the synthetic daily LST product. Here, the correlation coefficient was equal to 0.88, Root Mean Square Error (RMSE) reached about 3.38 K. The results showed that the algorithm can produce high-resolution temporal synthetic ASTER data that were similar to the actual observations.