干旱区生态环境知识资源中心(Arid): Remote Sensing of Soil Organic Carbon at Regional Scale Based on Deep Learning: A Case Study of Agro-Pastoral Ecotone in Northern China

Arid

DOI	10.3390/rs15153846
	Remote Sensing of Soil Organic Carbon at Regional Scale Based on Deep Learning: A Case Study of Agro-Pastoral Ecotone in Northern China
	Guo, Zichen; Li, Yuqiang; Wang, Xuyang; Gong, Xiangwen; Chen, Yun; Cao, Wenjie
通讯作者	Li, YQ
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2023
卷号	15 期号:15
英文摘要	The North China agro-pastoral zone is a large, ecologically fragile zone in the arid and semi-arid regions. Quantitative remote sensing inversion of soil organic carbon (SOC) in this region can facilitate understanding of the current status of degraded land restoration and provide data support for carbon cycling research in the region. Deep learning (DNN) for SOC inversion has been W.a hot topic over the past decade, but there have been few studies at the regional scale in the arid and semi-arid zones. In this study, a DNN model with five hidden layers and five skip connections was established using 644 spatially distributed SOC samples and Landsat 8 OLI imagery. The model was compared with the random forest algorithm in terms of generalization ability. The main conclusions were as follows: 1. The DNN algorithm can establish a high-precision SOC inversion model (R-2 = 0.52, RMSE = 0.7), with 90% of errors concentrated in the range of -2.5 to 2.5 kg & BULL;C/m(2); 2. the Boruta variable-screening algorithm can effectively improve the model accuracy of the random forest algorithm, but due to the DNN's better ability to mine hidden information in the data, the improvement effect on the DNN model accuracy is limited; 3. the SOC samples in arid and semi-arid areas are highly positively skewed, with a significant impact on the modeling accuracy of DNN, and conversion is required to obtain a model with better generalization ability; and 4. in arid and semi-arid regions, SOC has a weak correlation with vegetation indices but a stronger correlation with temperature, elevation, and aridity. This study established a reliable deep learning model for SOC density in a large arid and semi-arid region, providing a reference and framework for the establishment of SOC inversion models in other regions.
英文关键词	deep learning random forest digital soil mapping agro-pastoral ecotone
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:001045629100001
WOS关键词	DIFFUSE-REFLECTANCE SPECTROSCOPY ; MATTER DECOMPOSITION ; VEGETATION COVER ; NITROGEN ; STORAGE ; TEMPERATURE ; POTENTIALS ; PREDICTION ; REGRESSION ; INDEXES
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/398303
推荐引用方式 GB/T 7714	Guo, Zichen,Li, Yuqiang,Wang, Xuyang,et al. Remote Sensing of Soil Organic Carbon at Regional Scale Based on Deep Learning: A Case Study of Agro-Pastoral Ecotone in Northern China[J],2023,15(15).
APA	Guo, Zichen,Li, Yuqiang,Wang, Xuyang,Gong, Xiangwen,Chen, Yun,&Cao, Wenjie.(2023).Remote Sensing of Soil Organic Carbon at Regional Scale Based on Deep Learning: A Case Study of Agro-Pastoral Ecotone in Northern China.REMOTE SENSING,15(15).
MLA	Guo, Zichen,et al."Remote Sensing of Soil Organic Carbon at Regional Scale Based on Deep Learning: A Case Study of Agro-Pastoral Ecotone in Northern China".REMOTE SENSING 15.15(2023).