Performance of Hyperspectral Data in Predicting and Mapping Soil Organic Matter Content

As an important soil nutrient, soil organic matter (SOM) plays an important role in plant growth and development and is an important indicator of soil fertility. Compared with the traditional laboratory chemical analysis methods, reflectance spectroscopy and hyperspectral remote sensing satellite data provide a new way of thinking for obtaining soil nutrient content in a wide range, non-destructively and efficiently. In this paper, we investigated the prediction effect of the two data based on different transformation forms, and also proposed a method based on the integration of laboratory measured spectral and hyperspectral remote sensing satellite image data, and the results showed that the integration of the two data can better combine the advantages of each other, and the best transformation form is the logarithmic transformation (LG), with the R
²
of 0.783 and the RMSE of 4.162, which can help the wide range of spatial mapping of soil organic matter content.