Remotely Sensing Retrieval Model Of Vegetation Moisture Content Of Eco-Water Parameters

Eco-water can not only intercept and retain a part of precipitation, but also has an important role in maintaining ecological balance in small basin. The studies of water resource are more at home and aboard which is essentially different from the eco-water. Vegetation moisture content which is one index of eco-water is very profound. Many scholars had processed digital images and extracted moisture content information from TM, ETM, SPOT, MODIS and Radar images by the features of vegetation with spectral information. Many achievements had gain that vegetation moisture content and soil moisture content were retrieved by the measured spectral data. But the vegetation moisture content was different from the vegetation moisture content of eco-water, and the vegetation moisture content above mentioned was concentrated in the study of precision agriculture whose object was specific, unitary and limited. So, the study of vegetation moisture content which is an important index of eco-water is very significant. It can supply quantitative foundation for retrieving eco-water resource.In this paper, the study area was Maoergai which is located the upper Minjiang River. Based on the research group pre-studies, the vegetation moisture content which is one index of eco-water was retrieved, and the achievements and innovations were as follows:(1) Designed measurement program, vegetation spectral data and vegetation samples were collected. Average spectrum values processed were the final spectral values and the relative water content extracted by traditional method. (2) Using statistics method, vegetation moisture content was retrieved by moisture Index and mathematical conversion model was established. Analyzing the deviations of models, the model whose deviation is least and spectrum is matched with remotely sensing data was selected. The results show that the SR was the best moisture index which was to extracted vegetation moisture content, and the deviation of regression model was least. In addition, linear regression function was the best and most reasonable than logarithmic regression function and quadratic regression function. So, the function (y=-46.4326x+84.0291) was used to retrieved vegetation moisture content, and the correlation coefficient was 0.8102.(3) By TM/ETM remotely sensing images in 1999 and 2007, different vegetations information had been extracted, and remotely sensing information database had been built. With the regression model, vegetation moisture content had been retrieved. Overlapping the retrieved result and vegetation types, two years'were highly coincide, which illustrated that SR can better regress FMC, and the retrieved result had high precision.(4) Based on the remote information and other material, the retrieved result had been analyzed and evaluated. Overall, the vegetation moisture content in 2007 was higher than one's in 1999, and the reason was the change of evergreen forest and natural grassland.