Estimation and Mapping of Soil Organic Matter Content Using a Stacking Ensemble Learning Model Based on Hyperspectral Images

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2023-10, Vol.15 (19), p.4713
• Main Authors: Wu, Menghong, Dou, Sen, Lin, Nan, Jiang, Ranzhe, Zhu, Bingxue
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Carbon content; Correlation coefficient; Correlation coefficients; Crop growth; Cultivated lands; Data mining; Datasets; Efficiency; Ensemble learning; Environmental monitoring; Estimation; estimation model; Hyperspectral imaging; Hypotheses; Image quality; Information management; Learning; Machine learning; Mapping; Neural networks; Optimization; Organic matter; Organic soils; Predictions; Remote sensing; Soil organic matter; soil organic matter (SOM); Soil surfaces; Soils; Spatial distribution; Stacking; stacking ensemble learning framework; ZY1-02D
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15194713

Fast and accurate SOM estimation and spatial mapping are significant for cultivated land planning and management, crop growth monitoring, and soil carbon pool estimation. It is a key problem to construct a fast and efficient estimation model based on hyperspectral remote sensing image data to realize the inversion mapping of SOM in large areas. In order to solve the problem that the estimation accuracy is not high due to the influence of hyperspectral image quality and soil sample quantity during the estimation model construction, this study explored a method for constructing an estimation model of SOM contents based on a new stacking ensemble learning algorithm and hyperspectral images. Surface soil samples in Huangzhong County of Qinghai Province were collected, and their ZY1-02D hyperspectral remote sensing images were investigated. As input data, a feature band dataset was constructed using the Pearson correlation coefficient and successive projections algorithm. Based on the dataset, a new SOM estimation model under the stacking ensemble learning framework combined with heterogeneous models was developed by optimizing the combination of base and meta-learners. Finally, the spatial distribution map of SOM was plotted based on the result of the model over the study area. The result suggested that the input data quality of the estimation model is improved by constructing a feature band dataset. The multi-class ensemble learning estimation model with the combination strategy of the base and meta-learners has better predictive effects and stability than the single-algorithm and single-level ensemble models with homogeneous learners. The coefficient of determination is 0.829, the residual prediction deviation is 2.85, and the predictive set root mean square error is 1.953. The results can provide new ideas for estimating SOM content using hyperspectral images and ensemble learning algorithms, and serve as a reference for mapping large-scale SOM spatial distribution using space-borne hyperspectral images.