The Study Of Applying Hyper-spectral Remote Sensing Technology In Land Quality Monitoring

Land resource is a key factor of environments system and a centralized reflection of many environments issues. Land resource utilization is the most important part of sustainable development. To manage land resource efficiently, we need to monitor land quality. With the development of land resource management transferred from quantity management to quality and ecology protection, it required more advanced approaches to dynamic land monitoring. Dynamic land monitoring will develop to land quality monitoring from land quantity monitoring. Access timely and reliable information of land quality by collecting, analyzing and interpreting of a serial temporary quantitive data, we can explore the change of land quality. These changes are exactly signals of land improvement or degradation. It is significant to land management decision, especially to achieve the goal of sustainable land use that we identify adverse trends on land quality changes in advance. High spectral resolution remote sensing integrated the spectrum which reflects material features and the image which decides geometric relationship innovatively. Obtaining the spectra distribution of each pixel while acquiring the image of ground object and quantitatively analyzing the process of bio-physical and chemistry and parameters, it provides a new technology means for land quality monitoring and makes it possible to quantitative retrieval many land quality monitoring indicators.This paper applied hyperspectral remote sensing technique to land quality monitoring to explore its application potential in this field. According to the characteristics of hyperspectral remote sensing technique and spectral features of land quality indicators, we use multivariate statistics methods and approaches based on spectral position variables, select several spectral indicators which directly or indirectly reflect land quality, set up the retrieval models of land quality indicator, study the potential of applying hyperspectral remote sensing technique in land quality monitoring. Attempt to ameliorate land quality monitoring techniques, expand monitoring extents, decrease the self cost of survey, shorten the survey period, and make the results more scientific, objective and stable by this technique.From the view of the production function of land, this paper mainly study the methods of monitoring the land quality indicators (such as SOM, moisture, Fe2O3) by hyperspectral remote sensing technique. The main content of this paper includes: chapter 2 mainly discusses field spectral collection and its influence factors. The collection of accurate spectra in the field requires an awareness of the influences of the various sources of illumination, atmospheric characteristics and stability, winds, instrument field of view, target viewing and illumination geometry, instrument scanning time, and the spatial and temporal variability of the characteristics. Chapter 1 discussed how these factors influence spectrum measure result; The third, forth and fifth chapters studied the relationship between soil spectralIVreflection feature and three land qulity indicators (SOM, moisture and Fe2O3), analyzed the sensitive degree of various albedo transformation to this three soil phys-chemica! indicators, and set up retrieval model; The sixth chapter studied geometric rectification and atmospheric radiation correction of OMIS data and realized land quality parameter mapping.The main conclusions and innovation are:1. the relationship between SOM and soil spectral reflection features and retrieval model of SOM.SOM has no absorption peak in the region from 350nm to 2500mm in consideration, but in this wavelength region, spectral reflectance is negative correlation with SOM, and has high correlation in region around 675nm. As a whole, the detection ability of SOM in visible band is more powerful than in near infrared band. The most sensitive band is around 675nm. The ability in near infrared band is more sensitive after spectral derivative transformaton. The correlation coefficient between order...