Research And Application Of Vegetation Parameters Retrieval Based On "Satellite-Site" Remote Sensing Data

Hyperspectral remote sensing can detect the successive spectral curve of ground objects with hyperspectral resolution in some spectral region and form the unique multi-dimensional spectrum space. Remote sensing application focuses on spreading spatial information on spectrum dimension, thereby obtaining more spectral information of object. Multispectral remote sensing notes land speetra by non-eontinual wide wave band fashion in wave width region,which use one data of wide wave band represents radiation trend of a relative wide spectral region.By comparison with multispectral remote sensing,hyperspectra can completely note everything by a integrated speetral curve in its watching region and can receive continual spectral information.The application of hyperspectral remote sensing can strengthen the monitoring ability of physiological ecological parameters and improve the monitoring accuracy of crop growth. Based on field experiments and regional sampling of peanut, this study analyzed the hyperspectral characteristics of peanut canopy by comprehensive application of hyperspectral remote sensing, growth analysis, physiological ecology test, and mathematical statistics, for different species and nitrogen treaments. Then the hyperspectral estimation models of chlorophyll content, Leaf biomass, and leaf area index(LAI) were established on the basis of vegetation index, spectral characteristic parameters, at all growth stages.Finally, the estimation model of vegetation index was used to the GF satellite images of remote sensing. Then the actual application probability was tested, which provided the theory basis and key technology for dynamic monitoring of growth change and precise management. The main results are as follows:(1) The data including canopy spectrum of peanut, leaf biomass, chlorophyll content and LAI, were used to establish the estimation models based on NDVI,RVI, original spectral characteristic parametersred edge parameters.(2) The application of estimation model was evaluated based on physiological ecological parameters of peanut in remote sensing data of GF. The result suggested that there was a significant correlationship between the predicted and measured values when NDVI or RVI was used to decode the remote sensing information of GF.