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Study on Land Surface Soil Moisture Retrieval Method Based on Hyper Spectral Measured Date

Author: JiBieKe·HaLiKeBaYi
Tutor: DingJianLi
School: Xinjiang University
Course: Cartography and Geographic Information Systems
Keywords: Hyperspectral soil moisture Retrieval Neural networks
CLC: S152.7
Type: Master's thesis
Year: 2011
Downloads: 161
Quote: 1
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Abstract


soil water content play an important role in Global Energy Exchange System, particularly in the surface water evaporation and seepage flow, latent heat and sensible heat. It is a General conditions of Plant Growth and Developments, but it is one of the key input parameters in monitoring drought and the output of crops. Therefore, monitor soil moisture in hydrology, agriculture and other fields of science has great significance. Hyperspectral remote sensing has resolution ratio of wave band width less than 10 nm, the wave bands are continuous, It contains a great of information within emissivity spectra, That has made the inversion of the land slight information possible. Object spectrum studies have shown that hyperspectral remote sensing can acquire more subtle, rich information of material to separate and identify ground targets in spatial dimension and spectral dimension. In Inversion of soil moisture, we can obtain more realistic continuous spectral curve of soil moisture by using hyperspectral measured data to indicate the spectral variation and weak changes between different bands, to provide timely, accurate and fast information about the soil moisture content.In order to indicate the Influencing Mechanism of moisture to soil spectra reflectance, and provide theoretical information for Remote Sensing Monitoring of other soil properties’spectral by finding out Quantitative Relationship Between soil moisture and soil spectral reflectance. This thesis has carried out study based on Weigan-Kuqa River delta oasis soil spectrum data.Firstly, by using spectral analysis method and statistical analysis to analysis The spectral characteristics of soil moisture and its variation to show that different soil moisture spectral curves are different, but the shape’s of curves are similar. Differences in soil moisture spectrum, first mainly in the soil water content in different intensity of the spectral reflectance, followed by in different locations and different absorption intensity of the every band.Secondly, make Mathematical transformation for the reflectance data to select the best optimal wavelengths for the estimation of soil moisture, and afford the input Parameters for regression model. Finally, make use of optimal wavelengths to estimate soil moisture with the BP neural network. and compared the accuracy of regression model to BP Neural Networks Model。The result show that Inverse, logarithmic and differential transformation of soil moisture spectral date can improve the accuracy of estimation, in varying degrees. The accuracy of BP Neural Networks Model is preferable to regression Model.

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CLC: > Agricultural Sciences > Agriculture as the foundation of science > Soil > Soil physics > Soil moisture
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