Improving the Spatiotemporal Transferability of Hyperspectral Remote Sensing for Estimating Soil Organic Matter by Minimizing the Coupling Effect of Soil Physical Properties on the Spectrum: A Case Study in Northeast China

• Published in: Agronomy (Basel) 2024-05, Vol.14 (5), p.1067
• Main Authors: Sui, Yuanyuan, Jiang, Ranzhe, Lin, Nan, Yu, Haiye, Zhang, Xin
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Carbon; Carbon content; Carbon cycle; Case studies; Climate change; Climatic changes; Coupling; Estimation; Food security; hyperspectral imagery; Hyperspectral imaging; Methods; model migration; Organic matter; Organic soils; Parameter estimation; Physical properties; Polynomials; Precipitation; Prediction models; Remote sensing; Root-mean-square errors; Satellite imagery; Soil moisture; Soil organic matter; Soil physical properties; Soil properties; Soil surfaces; Soils; Spectral sensitivity; spectrum correction; Surface roughness; Sustainable agriculture
• ISSN: 2073-4395; 2073-4395
• DOI: 10.3390/agronomy14051067

Soil organic matter (SOM) is important for the global carbon cycle, and hyperspectral remote sensing has proven to be a promising method for fast SOM content estimation. However, because of the neglect of the spectral response of soil physical properties, the accuracy and spatiotemporal transferability of the SOM prediction model are poor. This study aims to improve the spatiotemporal transferability of the SOM prediction model by alleviating the coupling effect of soil physical properties on spectra. Based on satellite hyperspectral images and soil physical variables, including soil moisture (SM), soil surface roughness (root-mean-square height, RMSH), and soil bulk weight (SBW), a soil spectral correction model was established based on the information unmixing method. Two important grain-producing areas in Northeast China were selected as study areas to verify the performance and transferability of the spectral correction model and SOM content prediction model. The results showed that soil spectral corrections based on fourth-order polynomials and the XG-Boost algorithm had excellent accuracy and generalization ability, with residual predictive deviations (RPDs) exceeding 1.4 in almost all the bands. In addition, when the soil spectral correction strategy was adopted, the accuracy of the SOM prediction model and the generalization ability after the model migration were significantly improved. The SOM prediction accuracy based on the XG-Boost-corrected spectrum was the highest, with a coefficient of determination (R2) of 0.76, a root-mean-square error (RMSE) of 5.74 g/kg, and an RPD of 1.68. The prediction accuracy, R2 value, RMSE, and RPD of the model after the migration were 0.72, 6.71 g/kg, and 1.53, respectively. Compared with the direct migration prediction of the model, adopting the soil spectral correction model based on fourth-order polynomials and XG-Boost reduced the RMSE of the SOM prediction results by 57.90% and 60.27%, respectively. This performance comparison highlighted the advantages for considering soil physical properties in regional-scale SOM predictions.