基于无人机高光谱遥感的黑土土壤有机碳含量反演方法研究

杨汉水; 马琳; 王瑞禛; 陈伟涛; 王力哲

doi:10.3799/dqkx.2025.061

Volume 50 Issue 8

Aug. 2025

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Article Navigation > Earth Science > 2025 > 50(8): 3144-3152

Yang Hanshui, Ma Lin, Wang Ruizhen, Chen Weitao, Wang Lizhe, 2025. Mapping Organic Carbon Content in Black Soil Using UAV Hyperspectral Remote Sensing and Deep Learning. Earth Science, 50(8): 3144-3152. doi: 10.3799/dqkx.2025.061

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Yang Hanshui, Ma Lin, Wang Ruizhen, Chen Weitao, Wang Lizhe, 2025. Mapping Organic Carbon Content in Black Soil Using UAV Hyperspectral Remote Sensing and Deep Learning. Earth Science, 50(8): 3144-3152. doi: 10.3799/dqkx.2025.061

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Mapping Organic Carbon Content in Black Soil Using UAV Hyperspectral Remote Sensing and Deep Learning

doi: 10.3799/dqkx.2025.061

Yang Hanshui^{1, 2
,
,},
Ma Lin¹,
Wang Ruizhen³,
Chen Weitao^{2
,
,
,},
Wang Lizhe²

1.
Heilongjiang Provincial Institute of Natural Resources Survey, Harbin 150036, China
2.
School of Computer Science, China University of Geosciences, Wuhan 430074, China
3.
Key Laboratory of Geological Survey and Evaluation, Ministry of Education, China University of Geosciences, Wuhan 430074, China

Received Date: 2025-04-02
Available Online: 2025-08-18
Publish Date: 2025-08-25

Abstract

Abstract

Black soil in northeastern China is an important agricultural resource but has been increasingly degraded due to long-term development. The use of satellite remote sensing technology to retrieve the organic carbon content in black soil offers technical support for the protection and sustainable use. However, satellite hyperspectral data suffer from low spatial resolution, and the retrieval accuracy for organic carbon content remains limited in fine-scaled study sites. To address these challenges, this study utilized UAV-based hyperspectral data and soil geochemical data instead. We proposed and compared four models based on one-dimensional convolutional neural networks (1DCNN)-MDS-1DCNN, LLE-1DCNN, PLSR-1DCNN, and KPCA-1DCNN, for organic carbon content retrieval using the Wudalianchi region in Heilongjiang Province as a case study. The results show that the LLE-1DCNN model outperforms the others, achieving an R² of 0.806 and an RMSE of 0.572% on the validation set. This approach offers promising potential for accurately retrieving organic carbon content in black soil and supporting its conservation and management.
- black soil,
- soil organic carbon,
- UAV hyperspectral,
- deep learning,
- remote sensing

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References(45)

References

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Mapping Organic Carbon Content in Black Soil Using UAV Hyperspectral Remote Sensing and Deep Learning

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