Vegetation Effects Modeling in Soil Moisture Retrieval Using MSVI

Abstract

Brightness temperature (BT) measured by passive microwave sensors is usually affected by soil moisture, vegetation cover, and soil roughness. Soil moisture estimates have been limited to regions that had either bare soil or low to moderate amounts of vegetation cover.In this study, Simultaneous Land Parameters Retrieval Model (SLPRM) as an iterative least-squares minimization method has been used. This algorithm retrieves surface soil moisture, land surface temperature, and canopy temperature simultaneously using brightness temperature data in bare soil, low to moderate and higher amounts of vegetation cover.Furthermore, a new index called MSVI (Multi Sensor Vegetation Index) has been introduced to approximate vegetation effects on properly observed brightness temperatures. The algorithm includes model construction, calibration, and validation using observations carried out for the SMEX03 (Soil Moisture Experiment 2003) region in the South and North of Oklahoma. The results indicated about 0.9 percent improvement on soil moisture estimation accuracy using the MSVI.