基于多尺度特征增强的黑土有机质含量高光谱卫星遥感反演

陈伟涛; 徐佳辉; 王锐; 王瑞禛; 杨汉水

doi:10.3799/dqkx.2025.154

Volume 50 Issue 12

Dec. 2025

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Article Navigation > Earth Science > 2025 > 50(12): 4909-4918

Chen Weitao, Xu Jiahui, Wang Rui, Wang Ruizhen, Yang Hanshui, 2025. Hyperspectral Remote Sensing Inversion of Black Soil Organic Matter Content Based on Multi-Scale Feature Enhancement. Earth Science, 50(12): 4909-4918. doi: 10.3799/dqkx.2025.154

Citation:

Chen Weitao, Xu Jiahui, Wang Rui, Wang Ruizhen, Yang Hanshui, 2025. Hyperspectral Remote Sensing Inversion of Black Soil Organic Matter Content Based on Multi-Scale Feature Enhancement. Earth Science, 50(12): 4909-4918. doi: 10.3799/dqkx.2025.154

Citation:

Chen Weitao, Xu Jiahui, Wang Rui, Wang Ruizhen, Yang Hanshui, 2025. Hyperspectral Remote Sensing Inversion of Black Soil Organic Matter Content Based on Multi-Scale Feature Enhancement. Earth Science, 50(12): 4909-4918. doi: 10.3799/dqkx.2025.154

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Hyperspectral Remote Sensing Inversion of Black Soil Organic Matter Content Based on Multi-Scale Feature Enhancement

doi: 10.3799/dqkx.2025.154

Chen Weitao^{1
,
,},
Xu Jiahui^{2, 3},
Wang Rui³,
Wang Ruizhen³,
Yang Hanshui^{1, 4
,
,}

1.
School of Computer Science, China University of Geosciences (Wuhan), Wuhan 430078, China
2.
General Design Department, Hubei Aerospace Technology Research Institute, Wuhan 430040, China
3.
Geological Exploration and Evaluation Key Laboratory, Ministry of Education, China University of Geosciences (Wuhan), Wuhan 430074, China
4.
Heilongjiang Provincial Institute of Natural Resources Survey, Harbin 150036, China

Received Date: 2025-03-29
Publish Date: 2025-12-25

Abstract

Abstract

The northeastern black soil region, recognized as a critical grain-producing area in China, has experienced a continuous decline in soil organic matter (SOM) content in recent years. The application of hyperspectral remote sensing technology for SOM content retrieval plays a crucial role in assessing the current status of black soil and implementing conservation measures. To address the low retrieval accuracy resulting from scale discrepancies in spatial and spectral characteristics of black soil, this study developed a remote sensing retrieval model with multi-scale feature enhancement. By constructing a multi-scale feature enhancement structure, spectral features were systematically extracted at different scales. Furthermore, skip connections were incorporated to effectively integrate initial spectral features with deep hierarchical features derived from convolutional networks, thereby strengthening the model's spectral representation capacity. When benchmarked against traditional methods such as partial least squares regression and random forest models, the proposed model demonstrates enhanced capability in capturing multi-scale black soil features and establishing spectral relationships. This advancement enables accurate SOM content retrieval under complex conditions, providing both theoretical significance and practical value for advancing intelligent remote sensing retrieval and sustainable conservation of black soil resources.
- black soil,
- soil organic matter,
- deep learning,
- ZY-1 02D satellite,
- hyperspectral remote sensing

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References(33)

References

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