干旱区生态环境知识资源中心(Arid): Digital Mapping of Soil Organic Carbon Using Sentinel Series Data: A Case Study of the Ebinur Lake Watershed in Xinjiang

Arid

DOI	10.3390/rs13040769
	Digital Mapping of Soil Organic Carbon Using Sentinel Series Data: A Case Study of the Ebinur Lake Watershed in Xinjiang
	Li, Xiaohang; Ding, Jianli; Liu, Jie; Ge, Xiangyu; Zhang, Junyong
通讯作者	Ding, JL (corresponding author), Xinjiang Univ, Coll Resources & Environm Sci, Higher Educ Inst, Key Lab Smart City & Environm Modeling, Urumqi 800046, Peoples R China. ; Ding, JL (corresponding author), Xinjiang Univ, Key Lab Oasis Ecol, Urumqi 830046, Peoples R China.
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2021
卷号	13 期号:4
英文摘要	As an important evaluation index of soil quality, soil organic carbon (SOC) plays an important role in soil health, ecological security, soil material cycle and global climate cycle. The use of multi-source remote sensing on soil organic carbon distribution has a certain auxiliary effect on the study of soil organic carbon storage and the regional ecological cycle. However, the study on SOC distribution in Ebinur Lake Basin in arid and semi-arid regions is limited to the mapping of measured data, and the soil mapping of SOC using remote sensing data needs to be studied. Whether different machine learning methods can improve prediction accuracy in mapping process is less studied in arid areas. Based on that, combined with the proposed problems, this study selected the typical area of the Ebinur Lake Basin in the arid region as the study area, took the sentinel data as the main data source, and used the Sentinel-1A (radar data), the Sentinel-2A and the Sentinel-3A (multispectral data), combined with 16 kinds of DEM derivatives and climate data (annual average temperature MAT, annual average precipitation MAP) as analysis. The five different types of data are reconstructed by spatial data and divided into four spatial resolutions (10, 100, 300, and 500 m). Seven models are constructed and predicted by machine learning methods RF and Cubist. The results show that the prediction accuracy of RF model is better than that of Cubist model, indicating that RF model is more suitable for small areas in arid areas. Among the three data sources, Sentinel-1A has the highest SOC prediction accuracy of 0.391 at 10 m resolution under the RF model. The results of the importance of environmental variables show that the importance of Flow Accumulation is higher in the RF model and the importance of SLOP in the DEM derivative is higher in the Cubist model. In the prediction results, SOC is mainly distributed in oasis and regions with more human activities, while SOC is less distributed in other regions. This study provides a certain reference value for the prediction of small-scale soil organic carbon spatial distribution by means of remote sensing and environmental factors.
英文关键词	machine learning Sentinel-1A Sentinel-2A Sentinel-3A soil organic carbon digital soil mapping
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:000624440800001
WOS关键词	RANDOM FOREST ; TOTAL NITROGEN ; TERRAIN ATTRIBUTES ; SPATIAL-PATTERNS ; REGRESSION TREE ; CLIMATE-CHANGE ; PREDICTION ; STOCKS ; SEQUESTRATION ; TEMPERATURE
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
来源机构	新疆大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/351479
作者单位	[Li, Xiaohang; Ding, Jianli; Liu, Jie; Ge, Xiangyu; Zhang, Junyong] Xinjiang Univ, Coll Resources & Environm Sci, Higher Educ Inst, Key Lab Smart City & Environm Modeling, Urumqi 800046, Peoples R China; [Li, Xiaohang; Ding, Jianli; Liu, Jie; Ge, Xiangyu; Zhang, Junyong] Xinjiang Univ, Key Lab Oasis Ecol, Urumqi 830046, Peoples R China
推荐引用方式 GB/T 7714	Li, Xiaohang,Ding, Jianli,Liu, Jie,et al. Digital Mapping of Soil Organic Carbon Using Sentinel Series Data: A Case Study of the Ebinur Lake Watershed in Xinjiang[J]. 新疆大学,2021,13(4).
APA	Li, Xiaohang,Ding, Jianli,Liu, Jie,Ge, Xiangyu,&Zhang, Junyong.(2021).Digital Mapping of Soil Organic Carbon Using Sentinel Series Data: A Case Study of the Ebinur Lake Watershed in Xinjiang.REMOTE SENSING,13(4).
MLA	Li, Xiaohang,et al."Digital Mapping of Soil Organic Carbon Using Sentinel Series Data: A Case Study of the Ebinur Lake Watershed in Xinjiang".REMOTE SENSING 13.4(2021).