Estimation model of soil organic matter in desert mining area based on multispectral image data

Soil is related closely to human living and vegetation grow.The quality of soil organic matter (SOM) influences plant development.Scientists take a variety of researcheson soil.Many findings focus on the estimation of SOM using remote sensing data, which are usually hyperspectral and multispectral.The former has a detailed result of band information, while the latter provides a macroscopical and convenient way to get in whole area.In addition, processing hyperspectral data needs a strong mathematical background and software technology, while processing multispectraldata needs less.To apply the multispectral method to make decisions on buildings and planning is of great significance.In order to save resources, increase efficiency and accuracy, in May 2014, we collected soil samples in the various layers of 0~10, 10~20 and 20~30 cm, and there were totally 45 points marked by GPS(global positioning system) on Google Earth.The weighed aluminum box was used to hold some soil in each layer.The collections were taken back and dried for 24 h.Then the dried soil was weighed and the soil moisture was calculated.Meanwhile, the image needed pretreatment.The atmospheric correction should be taken to remove bands′ noises to get clear data.Then the pixels of the image for each sample point were used to establish models.And at the same time, other soil was crushed and sieved in 2 mm, and the SOM was measured by the potassium dichromate volumetric method.The final work was to combine the reflectance data of multispectral image and the measured SOM data.We used the reflectance(R), the reflectance reciprocal(1/R), the reflectance reciprocal′s logarithm (ln(1/R)), the reflectance′s first derivative(FDR) and the measured SOM to build multiple linear regression models, and then, it was found that the FDR model had a better precision with the R2 of 0.963 between the predicted and the measured.This meant that the more effective approach could be applied to express the regional SOM if needed.By the FDR model, we predicted the SOM content in study area.It showed that the area with SOM content of 0~5 g/kg was 84.065% of the whole area and that with SOM content of greater than 10 g/kg was 0.001 5%.The greatest SOM value was 13.065 g/kg,and the inferior was closed to 0.355 g/kg, which was very low.The SOM content in the Wu caiwan area was lower than that in Qitai County, for the former′s SOM was less than 1% in the most area.It also indicated that the highest average SOM content in the mining area was 7.35 g/kg, which was influenced by the organic matter in coal.The bare land's area was 2 674.44 km2, accounting for 63% of all area, and the mean SOM content was 6.12 g/kg.The saline land and desert woodland had lower SOM content because of the development of water soil loss, salinization and desertification.The low SOM content and less precipitation made the area a desert increasingly.Further more, we found that in the arid area, the soil moisture content was extremely low, so it was not only influenced weakly by moisture to using remote sensing means to estimate SOM, but also formed an advantageous method which provided a higher simulation precision.All in all, it is imperative to restore the ecologic environment in the study area.Measures should be taken immediately.Choosing appropriate vegetation to plant in desert will be the key to the restoring works, while enhancing supervision of the Kalamaili Nature Reserve and controlling grazing will contribute to slow down those negative phenomena above.