| The development of precision farming urgently requests that remote sensing technique offers to timely and accurate ground information. Soil water content, soil organic matter content, soil roughness and soil texture etc. are very important information in precision farming. As hot point and frontier in remote sensing, hyperspectral remote sensing technique not only has the advantages of traditional remote sensing that can timely and undisturbedly be used to detect large area crop, but also has special advantages. It has very high spectral resolution. More delicate spectral difference of crops can help us to precisely classify crops types and to monitor and analyze crops' vigor and the environment factors that affect crops' product. Hyperspectral remote sensing has great potential of quantitatively retrieving for objects' characteristics.This thesis focuses on extracting soil information from hyperspectral data, and puts great emphasis on the study of retrieving soil characteristics from laboratory spectra. The first chapter mainly introduced the background of hyperspectral remote sensing and precision farming, and then, introduced the applications and perspectives of hyperspectral remote sensing in precision farming. In the second chapter, we primarily introduced the measurement of soil characteristics and soil spectra in laboratory, and analyzed feature of soil spectra. The third chapter is the most important part of this thesis. We discussed soil spectral properties. It included: 1) The relationship between soil minerals and soil spectral reflectance; 2) The relationship between soil color and soil spectra as well as inversion of soil color from spectral reflectance; 3) The relationship between soil surface moisture and soil spectral reflectance as well as evaluation of several inversion method of soil surface moisture from reflectance; 4) The relationship between soil organic matter and soil spectral reflectance as well as inversion of soil organic matter and soil spectral reflectance; 5) The relationship between soil texture, soil ferric oxide and soil spectral reflectance. The fourth part studied the BRDF properties of soil and with two models inverse models' parameter of soils. The fifth part introduced the imaging mechanism of remote sensing and the spectra and radiance calibration methods for remote sensing images, as well as inversion of soil characteristics from airborne remote sensing image. The sixth chapter summarized the whole thesis and listed the achievement of this study, as same as, pointed out the difficulties in precise inversion of soil characteristics from hyperspectral image.Main development and conclusion as follows:(1) By analyzing a large number of soil spectra, we found except at the obvious absorption position, the line of these points' reflectance at the wavelengths 400, 600, 800, 1350, 1800, 2100 and 2400 nm are fitted well with spectral curve. This is useful for soil spectral data compressing and band selecting.(2) From the correlation between soil spectral reflectance and soil color, we utilized regression model to forecast soil Munsel properties.(3) The relationship between normalized soil reflectance and moisture was investigated.For all the wavelengths and all the soils, results show that for low soil moisture levels, the reflectance decreased when the moisture increased. Conversely, after a critical point, soil reflectance increased with soil moisture. For some soils, the reflectance of the wettest conditions can overpass that of the driest conditions. For both low and high soil moisture levels, and the seven wavelengths selected, the relative reflectance was strongly correlated with moisture. Adjustment of the relationships over individual soil types provides better soil moisture retrieval performances.(4) The normalization of reflectance approach, derivative approaches and the difference approaches were used to forecast soil surface moisture. And The best overall retrieval performances were achieved with the absorbance derivatives and...
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