The farmland ecosystem is an important component of terrestrial ecosystems and has a fundamental role in the human
life. The wetland is an unique and versatile ecological system. It is important for rational development and sustainable
utilization of farmland and wetland resources to study on the measurement of valuation of farmland and wetland
ecosystem services. It also has important significance for improving productivity. With the rapid development of remote
sensing technology, it has become a powerful tool for evaluation of the value of ecosystem services. The land cover
types in Anxin County mainly was farmland and wetland, the indicator system for ecosystem services valuation was
brought up based on the remote sensing data of high spatial resolution ratio(Landsat-5 TM data and SPOT-5 data), the
technology system for measurement of ecosystem services value was established. The study results show that the total
ecosystem services value in 2009 in Anxin was 4.216 billion yuan, and the unit area value was between 8489 yuan/hm2
and 329535 yuan/hm2. The value of natural resources, water conservation value in farmland ecosystem and eco-tourism
value in wetland ecosystem were higher than the other, total of the three values reached 2.858 billion yuan, and the
percentage of the total ecosystem services values in Anxin was 67.79%. Through the statistics in the nine towns and
three villages of Anxin County, the juantou town has the highest services value, reached 0.736 billion yuan. Scientific
and comprehensive evaluation of the ecosystem services can conducive to promoting the understanding of the
importance of the ecosystem. The research results had significance to ensure the sustainable use of wetland resources and
the guidance of ecological construction in Anxin County.
Soil organic matter(SOM) is an important composition of soil. Soil mechanical composition
determines soil physical properties. Retrieving soil organic matter and mechanical components by
remote sensing is an important research content of soil remote sensing. In this study soil spectra data
are measured in Duolun county in the Inner Mongolia autonomous region, in China. Based on
statistical analysis of soil reflection spectra characteristics, 12 high spectral indexes, including soil
reflectance, reflectance ratio and normalized difference soil index（NDSI）, were constructed for
building inversion models of soil organic matter , total nitrogen (TN), soil mechanical composition
Firstly, calculating correlation coefficient between 12 high spectral indexes and soil organic matter,
total nitrogen, soil mechanical composition, then choosing high spectral indexes whose absolute
values of correlation coefficient greater than 0.5, finally by using linear regression analysis method
were biult inversion models of soil composition parameters. With the help of comprehensive analysis,
eliminating poor quality models, finally are established inversion model of part of soil composition
parameters, including SOM, TN, P(2-0.2), P(0.2-0.02), P(<0.002) etc. In view of total salt factor, P(>2)
factor and P(0.02-0.002) factor, this study failed to obtain a satisfactory inversion model. Limits on the
number of samples may be an important influence factors.
Research shows that in the study area there are 3 types of soil spectral curve. Between bow area of soil
reflection spectrum and soil organic matter, total nitrogen and soil mechanical composition there are a
deterministic relationship, and the deterministic relationship can be described. In this study, NDSI as
high spectral characteristic index is one of the most successful index for retrieving satisfactory model.
The position of 2 characteristic wavelength in NDSI is determined according to analyzing feature of
bow area of the soil reflection spectrum. The position of 2 characteristic wavelength just reflects
differences between different types of soil. Research shows that the NDSI in process of retrieving soil
compositions has great application potential. Can be expected application of similar model in
inversion model study by using hyperspectral remote sensing or multispectral remote sensing.
In order to research the indicator for sandy information, this paper conducts a study on soil organic matter (SOM) in sandy land. Taking the Otindag Sandy Land and its surrounding area as a test site, in Xilingol League, Inner Mongolia, the BJ-1 multispectral image as main data, the soil information parameters were analyzed firstly, and their difference between the sandy land and other land was distinguished. Secondly, the correlation between SOM and each band of multispectral image was analyzed, and the best inversion band was determined. Meanwhile, the quantitative retrieval model for SOM was established and validated. Finally, the soil organic matter was inversed quantitatively, and the whole distribution of SOM was obtained in Otindag Sandy Land. As the results showed that, with the development of land desertification, the content of soil organic matter declined obviously. The correlation between three bands of BJ-1 image and SOM was relatively good, correlation coefficient (r) was as high as 0.7. But the predicted accuracy of multiple regression retrieval model for SOM was higher, and it was more stable than the single band linear regression model. The reason is that three bands contain more effective information than a single band, it can reflected the difference of divergent soil types. The model was validated using independent samples, the standard error RMSE was 0.6445 and model accuracy was 62.65%.