Enhanced Retrieve Land Surface Temperature from Modis Day-Time Mid-Infrared Data using Fuzzy Automatic Clustering Algorithm

Land surface temperature (LST) is a key variable in climatological and environmental studies. However, accurate measurements of LST over continents are not yet available for the whole globe. This thesis first reviews the state of the science of land surface temperature (LST) estimates from remote sensing platforms, models, and in situ approaches. Considering the uncertainties, we review the current LST validation and evaluation method. Then the requirements for LST products are specified, from the different user communities. Finally to identify the gaps between state of the science and the user community requirements, and discuss solutions to bridge these gaps.In this paper proposed clustering method is implememted to process subsequences of time series data and detect land cover change temperature measured as a function of time. Land cover change temperature measured is declared when consecutive subsequences that are extracted from one MODIS time series transitions from one cluster to another cluster and remains in the newly assigned cluster for the rest of the time series. The temporal sliding window designed to operate on a subsequence of the time series to extract information from two spectral bands from the MODIS product.