Retrieving Vegetation Moisture Content with Remotely Sensed Data

This paper presents an inversion model for vegetation moisture content based on remotely sensed data. Vegetation moisture content is an important index characterizing eco-water,which refers to the water closely related to vegetation and which plays an important role in adjusting and supplying surface water and ground water during the hydrological cycle. It shows the capability of the vegetation to hold water and what the state of vegetation. Also it is crucial in predicting natural disasters, such as droughts, landslides and so on. Tasselled cap transformation and quantitative remote sensing technology are used to prepare the input data for the model. With the model, two temporal vegetation moisture content maps were created from ETM and ASTER images of the study area and the maps were verified using basic eco-environment data.