| Remote sensing information acquisition of land surface and marine ecosystem is a key part of the global environment change research, which more and more depends on the long-term continuous observation data collection and analysis. Among them, soil moisture is the control factor in crop growth, and it has been very urgent for us to detect soil moisture of lager region. The traditional methods can only catch the information of a spot and is inefficient and time-consuming. It is difficult to meet scientific research and large area monitoring and comprehensive analysis for relevant departments. With the blastoff of TERRA-MODIS and MODIS data have high spectral resolution and we can get them on free of charge, it has practical value to extract land surface information using MODIS data.With the support of China-German technology cooperation and exchange projects (2007DFB70200) and Shandong province natural science fund (Y2008E10),the mainly content of this paper includes: 1. This paper systematically introduced historical background of EOS plan, TERRA satellite and AQUA satellite, the characteristics of MODIS sensor, the technical indexes and band application of MODIS data, MODIS standard data products and potential applications.2.This paper systematically summarized the domestic and foreign drought monitoring principle and method, including traditional and sensing drought monitoring methods.3. This paper systematically studied monitoring data fine treatment, including geometric correction, physical calibration, temperature calculation and the albedo computation.4.This paper researched and established a model of soil moisture monitoring for Zhangqiu city and verified the model in middle October.This paper mainly studied soil moisture of the wheat seeding and sprout in October in Zhangqiu when the surface vegetation coverage is quite low. This paper selected thermal inertia model which is suitable for retrieving soil moisture in low vegetation coverage region. Based on energy balance theory, this paper used the band 1, 2, 3, 4, 5, 7, 31 of MODIS data to retrieve the key parameters including surface temperature and albedo which are needed in this model. Then we use the experience linear model to establish the relationship between the apparent thermal inertia data and the measured data to achieve the large area soil moisture information in Zhangqiu. Last we based on the measured data to validate the reliability and precision of the methods.This paper can obtain the following conclusions:1. Due to the high spectral resolution and continuity, real-time of MODIS data, it can retrieve the parameters of soil moisture, including surface temperature and the albedo. MOIDS data is by far the best widespread monitoring data source.2.In the retrieving of the remote sensing parameters, based on the energy balance theory, it used the Plunk function to retrieve surface temperature. It used wide band instead of the whole wavelength to retrieve the albedo and it reduce the difficulty of retrieving the parameters.3.For low vegetation coverage area, using the measured soil moisture of 10cm depth to fit the ATI, the result show that it is feasible to use the established model to monitor the 10cm depth soil moisture in Zhangqiu in middle October.4.Based on the thermal inertia method, the precision of the model can reach 85%.The thermal inertia method can be used to establish the moisture sensing information model in low vegetation, for example, the period of the winter wheat seeding. Among them, the traditional linear experience model is feasible . The involving parameters are less and easy to debug. So the model is easy to spread. The soil moisture monitoring model established by this paper improves the practicality and operability of soil moisture monitoring . |
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