A regional-scale hyperspectral prediction model of soil organic carbon considering geomorphic features - Bao, Yilin; Ustin, Susan; Meng, Xiangtian; Zhang, Xinle; Guan, Haixiang; Qi, Beisong; Liu, Huanjun (Geoderma, 2021)
[Display omitted] •Proposed a framework for detecting and quantifying surface SOC content in complex terrain.•The effects of different ancillary variables in modelling were determined.•Recursive feature elimination ensures the integrity and robustness of the inputs.•Texture features were efficient and flexible for enriching the RF regression model.•Regional soil organic carbon was mapped with GF-5 satellite hyperspectral data. The prediction of soil organic carbon (SOC) from hyperspectral data often lacks geographic and environmental information related to soil genesis, which would improve the accuracy of the predicted SOC. The main purpose of this study was to improve the accuracy of SOC prediction and the mapping of SOC spatial distributions. We employed satellite hyperspectral image (HSI) data combined with ancillary variables (spectral indexes (SIs), terrain attributes (TAs) and spectral texture features (TFs)) by first stratifying the soil at the great group level. The central part of the Songnen Plain in Northeast China was selected as a region for a case study, because the region attracts considerable research interest as major grain production area in China. In different prediction models, recursive feature elimination (RFE) was applied to optimize input variables to reflect the soil-landscape relationships of different soil classes. The results showed that when the soil stratification strategy and ancillary variables were comprehensively considered, the accuracy of the model was significantly improved (with a coefficient of determination (R2) of 0.76, root mean square error (RMSE) of 3.16 g kg−1, and ratio of performance to interquartile distance (RPIQ) of 2.28). The introduction of SIs, TAs and TFs improved the R2 values by 6.15%, 6.15%, and 13.85%, respectively, compared to those achieved with the original reflectance (OR) bands alone. Moreover, the introduction of ancillary variables improved the accuracies of the SOC models, yielding R2 values of Phaeozems, Chernozems, Arenosols and Cambisols of 0.79, 0.53, 0.76, and 0.81, respectively. Compared with the prediction model, which is based on only the OR, the proposed model can better explain SOC spatial variations. The performance comparison highlights the advantage of the considering geomorphic features when utilized for SOC prediction in regional-scale; this model covers the elimination and expression of optimal ancillary variables for different soil classes, which are closely related to the formation of various soil types and the geomorphic evolution of the region. The SOC map that we obtained shows detailed soil information and effectively expresses the soil factors associated with the environment. The map can support planners in establishing efficient SOC monitoring methods and assessments and prioritizing inputs for future exploitation and research.