Synoptic surface moisture retrieval using special sensor microwave imager (SSM/I) and advanced very high resolution radiometer (AVHRR) data

An Integrated framework was developed, which provides a systematic approach for land surface moisture retrieval over large areas based on Special Sensor Microwave Imager (SSM/I) brightness temperatures. The higher spatial resolution Advanced Very High Resolution Radiometer (AVHRR) data in conjunction with the existing U.S. weather data were used to verify the methodology.; A fuzzy logic-based clustering method with a newly defined normalized fuzzy entropy was developed for land surface classification using SSM/I data. This method allows for the classification of nonuniform surface footprints and thereby increases the accuracy and completeness of land surface identification for a given scene.; Multiple data resources, including the AVHRR data, the U.S. soil classification map, and the Major Land Resource Area (MLRA) Handbook, were used to develop a parameter database that provided useful information such as the location and size of water bodies, soil properties, and vegetation properties used to improve the accuracy of the moisture retrieval modelling.; A new empirically-based model was developed to describe the relationship between the SSM/I brightness temperatures and surface moisture in terms of the antecedent precipitation index. The single footprint-based physical model developed in previous research was linked to the integrated framework to retrieve surface moisture and temperature over large areas at a practical level by using the dynamic database scheme.; The methodology was used to retrieve moisture resulting from several large storm systems in the Central Plain and Western Desert areas of the United States. The simulated results were compared to ground truth data visually and statistically.