Research Projects

Land Surface Temperature (LST) from MODIS (MOD 11) has coarse spatial resolution (1km). As the agriculture lands in India are mostly of 200-300m, such images are not suitable for monitoring the conditions of individual fields. Hence drought related applications suffer due to lack of temperature data. MODIS-LST are received at daily and hence contains the potential variability about temperature conditions. To benefit from this high temporal information, the finer thermal resolution data (e.g. from LANDSAT) need to be embedded but they are available at about 16 days frequency. Therefore, it is important to have LST data at finer resolution more frequently. However, downscaling LST data at finer resolution will have a trade-off between accuracy and the level to the lower pixel size. Since existing techniques for disaggregation do not show superiority of the adjustment of the resolution ratio and are of linear nature. But the ground land cover composition inside a coarse spatial resolution is non-linear, and hence deep learning regression model and fusion algorithms using CNN and LSTM utilizing data at multiple resolutions from Sentinel-2, Landsat-TM/ETM+/OLI and MODIS will be investigated in this study for potentially enhancing relationship of LST and other variables.