Estimation Soil Organic Matter Using Airborne Hyperspectral Imagery

Soil organic matter (SOM) content plays an important part in soil environmental quality definition and should be estimated necessarily. The conventional methods for the SOM concentration assessment are mainly based on laboratory physicochemical analysis, which is costly and time consuming. Visible and near-infrared (Vis-NIR) spectroscopy offers the potential to quantify the SOM over large areas based on the soil spectral characteristics. Therefore, an innovative methodology using visible and near-infrared reflectance spectra are proposed in this work to monitoring the SOM rapidly and economically. A total of 91 soil samples and their spectral data collected in Yitong of China were utilized to characterize the relationship between the soil reflectance spectrum and SOM. First, continuum removal (CR) and competitive adaptive reweighted sampling (CARS) are introduced as the pretreatment method and wavebands selection method respectively, which can amplify the weak spectral characteristic. After the preprocessing phases, Partial Least Squares (PLS), Random Forest (RF) and XGBoost are carried out to estimate the SOM and the results show that XGBoost yields the best performance with R² of 0.9968 on training set and 0.6831 on testing set. Finally, the distribution trend of SOM in the whole study area is mapped using the optimal CR-CARS-XGBoost model.