Hyperspectral Characteristics Of Soil And Quantitative Remote Sensing Inversion On TM Data In Harbin

Organic matter is an important source of soil nutrition. It can not only provide almost all necessary nutrient elements with plants, but also takes determinant roles in the formation of soil structure, and the improvement of soil physical properties and increased capacity of soil fertility conservation and buffering properties. Quick and accurate acquirement of soil information is widely paid more attention to by researchers. Hyperspectral remote sensing technique has special advantages of high spectral resolution and strong band continuity and so on. It can monitor and analyze crops vigor and soil environmental factors that affect crops production in timely, accurate and dynamical way. These possess practical significance for agricultural product and soil quality monitoring, eco-environment management and maintenance.The content of soil organic matter and free ferric oxide, the characteristics of hyperspectral curve of soil composition were studied by methods of GPS positioning, field sampling and analysis, hyperspectral measurement. Correlation analysis between soil organic matter, ferric oxide and soil reflectance was made in their mathematical transformation forms by Matlab 7.1. Mathematical models were built among spectral reflectance as independent variables and organic matter and ferric oxide as the dependent variable by multivariate linear regression and BP neural network. Research results showed:(1) For the content of soil organic matter and free ferric oxide, there were greater differences between different types of land use (farmland, forest land, urban green land, Song Hua Jiang River, wetland). They ranged from 12.24 to 274.24 g/kg, and 0.65-10.59 mg/kg, respectively. Their average value was farmland>forest land> wetland>Song Hua Jiang River>urban green land, Song Hua Jiang River>farmland>urban green land>forest land>wetland, respectively.(2) The differences in soil color, soil mechanical composition, the content of soil organic matter and ferric oxide influenced soil spectra curve. Soil reflectance decreased with increased content of soil clay and soil organic matter. Slight loss of soil color, increased content of sand particles and decreased content of soil organic matter resulted in increased soil reflectance, comparatively.(3) Using reflectance logarithms, the countdown reflectance logarithms, reflectance of one-order differential, the relationship between soil spectral reflectance and the content of soil organic matter, and between soil spectral reflectance and the content of free ferric oxide were analyzed. It was found that logarithm transformation of soil organic matter content could increase their correlation. The models of multivariate linear regression and BP neural network were established in order to predict their content of soil organic matter and free ferric oxide. The prediction result from BP neural network model was better. The prediction accuracy for the content of free ferric oxide from BP neural network model was higher than that from multivariate linear regression model.(4) Spatial distribution maps in detail were drawn according to inversion models established between soil spectrum and characteristic parameters, and the mapping for soil organic matter and free ferric oxide by TM remote sensing image.