基于地统计与遥感反演相结合的有机质预测制图研究

土壤水分对土壤光谱反射率有显著影响,而以往有机质遥感反演制图中却很少将水分作为预测建模的变量。为了使遥感制图更加符合野外实际环境,提高有机质预测制图精度,在充分考虑土壤样点空间自相关、异相关与野外复杂环境特点的基础上,通过地统计获得研究区水分的空间分布数据,结合遥感反射率,建立多因子预测模型,得到了吉林省黑土区土壤有机质空间分布图。结果表明,有机质遥感制图中,水分因素的加入,使模型的建立更加符合野外实际情况,显著提高了有机质预测制图的精度。

Prediction and Mapping of Soil Organic Matter Based on Geostatistics and Remote Sensing Inversion

Soil moisture has a significant impact on soil spectral reflectance, while it was rarely involved in modeling for remote-sensing-inversion-based mapping of soil organic matter in the past. In order to improve the accuracy of spatial prediction of soil organic matter, by taking into full account the characteristics of soil sampling sites, such as spatial autocorrelation, independence and complex field environment, the paper gathered via geostatistis soil moisture spatial distribution data in the study area, based on which in combination of remote sensing reflectance a multivariable prediction model was built up and a soil organic matter spatial distribution map of the black soil region in Jilin Province was plotted. Results show that in remote-sensing mapping of soil organic matter, the involvement of soil moisture as a variable, made the model more consistent with the field reality, and improved significantly the prediction accuracy of the mapping, which fully reflected the variation of soil organic matter in the black soil region of Jilin Province.