Inversion method of organic matter content of different types of soils in black soil area based on hyperspectral indices

• Published in: Open Geosciences 2024-12, Vol.16 (1)
• Main Authors: Lin, Nan, Liu, Yanlong, Liu, Qiang, Jiang, Ranzhe, Ma, Xunhu
• Format: Article
• Language: English
• Subjects: Accuracy; Agricultural land; Correlation coefficient; digital soil mapping; hyperspectral index; Organic matter; Remote sensing; Soil fertility; Soil organic matter; Soil properties; Soil types; Soils; SOMC; Spatial distribution; subsample regression of different soils; Vegetation
• ISSN: 2391-5447; 2391-5447
• DOI: 10.1515/geo-2022-0739

Soil organic matter content (SOMC) is a key factor in improving the soil fertility of arable land. Determining how to quickly and accurately grasp SOMC on a regional scale has become an important task for farmland quality monitoring. Hyperspectral imaging remote sensing technology can enable large-scale SOMC estimation, owing to its large-scale and fine spectral resolution. Enhancing the accuracy and reliability of SOM estimation models based on hyperspectral satellite remote sensing has emerged as a prominent topic of study. In this study, feature spectral indices such as difference indices (DI), ratio indices, and normalized indices were extracted using the correlation coefficient method and used as variables to construct a regression model for SOM, with a split-sample regression method employed to account for the complexity of soil types and map the corresponding spatial distribution of SOM. The results showed that the SOM estimation model, built using these feature spectral indices from hyperspectral satellite imagery, achieved high predictive accuracy, with ² values approaching 0.80 for most soil types. This demonstrates that the model effectively captures variations in SOM content across diverse soil backgrounds, highlighting its robustness and adaptability. The DI combinations, in particular, contributed significantly to prediction accuracy, demonstrating their importance as key spectral parameters for SOM estimation. Furthermore, among the three sets of feature model variables derived from the split-sample regression strategy, the enhanced vegetation indices and Soil-Adjusted Total Vegetation Index exhibited distinct contributions to different soil sample groups. This variation reveals the specific responsiveness of these indices to soil properties, which further enhances model performance in varied soil contexts. This study provides innovative methods for large-scale SOMC estimation, particularly by utilizing hyperspectral indices to enhance model accuracy across various soil types, demonstrating substantial practical significance.