Retrieving microwave land surface emissivity and vegetation index using TRMM multi-sensor products

The microwave land surface emissivity (MLSE) is an important factor in monitoring soil moisture and vegetation properties. It is also crucial for developing physic-based rainfall retrieving algorithm for passive microwave sensors over land. In this study, we use TRMM multi-sensor products from VIRS, PR and TMI to retrieve the MLSE and the Emissivity Difference Vegetation Index (EDVI) in Amazon region. Our retrieval is applicable both day and night times under all-weather conditions which is particularly important for remote sensing since under cloudy conditions classic optical techniques are not applicable. Our goal is to provide a global EDVI dataset in high temporal resolution using multi-platform and multi-sensor measurements, such as TRMM, A-train, and the future GPM.