干旱区生态环境知识资源中心(Arid): Estimation of Soil Organic Carbon Content in the Ebinur Lake Wetland, Xinjiang, China, Based on Multisource Remote Sensing Data and Ensemble Learning Algorithms

Arid

DOI	10.3390/s22072685
	Estimation of Soil Organic Carbon Content in the Ebinur Lake Wetland, Xinjiang, China, Based on Multisource Remote Sensing Data and Ensemble Learning Algorithms
	Xie, Boqiang; Ding, Jianli; Ge, Xiangyu; Li, Xiaohang; Han, Lijing; Wang, Zheng
通讯作者	Ding, JL
来源期刊	SENSORS
EISSN	1424-8220
出版年	2022
卷号	22 期号:7
英文摘要	Soil organic carbon (SOC), as the largest carbon pool on the land surface, plays an important role in soil quality, ecological security and the global carbon cycle. Multisource remote sensing data-driven modeling strategies are not well understood for accurately mapping soil organic carbon. Here, we hypothesized that the Sentinel-2 Multispectral Sensor Instrument (MSI) data-driven modeling strategy produced superior outcomes compared to modeling based on Landsat 8 Operational Land Imager (OLI) data due to the finer spatial and spectral resolutions of the Sentinel-2A MSI data. To test this hypothesis, the Ebinur Lake wetland in Xinjiang was selected as the study area. In this study, SOC estimation was carried out using Sentinel-2A and Landsat 8 data, combining climatic variables, topographic factors, index variables and Sentinel-1A data to construct a common variable model for Sentinel-2A data and Landsat 8 data, and a full variable model for Sentinel-2A data, respectively. We utilized ensemble learning algorithms to assess the prediction performance of modeling strategies, including random forest (RF), gradient boosted decision tree (GBDT) and extreme gradient boosting (XGBoost) algorithms. The results show that: (1) The Sentinel-2A model outperformed the Landsat 8 model in the prediction of SOC contents, and the Sentinel-2A full variable model under the XGBoost algorithm achieved the best results R-2 = 0.804, RMSE = 1.771, RPIQ = 2.687). (2) The full variable model of Sentinel-2A with the addition of the red-edge band and red-edge index improved R-2 by 6% and 3.2% over the common variable Landsat 8 and Sentinel-2A models, respectively. (3) In the SOC mapping of the Ebinur Lake wetland, the areas with higher SOC content were mainly concentrated in the oasis, while the mountainous and lakeside areas had lower SOC contents. Our results provide a program to monitor the sustainability of terrestrial ecosystems through a satellite perspective.
英文关键词	ensemble learning algorithms Landsat 8 Sentinel-2A Sentinel-1A soil organic carbon digital soil mapping
类型	Article
语种	英语
开放获取类型	gold, Green Published
收录类别	SCI-E
WOS记录号	WOS:000780509200001
WOS关键词	HIGH-RESOLUTION MAP ; SPATIAL-DISTRIBUTION ; VEGETATION INDEX ; PREDICTION ; MATTER ; STOCKS ; SPECTROSCOPY ; VARIABLES ; CLIMATE ; STORAGE
WOS类目	Chemistry, Analytical ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS研究方向	Chemistry ; Engineering ; Instruments & Instrumentation
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/394481
推荐引用方式 GB/T 7714	Xie, Boqiang,Ding, Jianli,Ge, Xiangyu,et al. Estimation of Soil Organic Carbon Content in the Ebinur Lake Wetland, Xinjiang, China, Based on Multisource Remote Sensing Data and Ensemble Learning Algorithms[J],2022,22(7).
APA	Xie, Boqiang,Ding, Jianli,Ge, Xiangyu,Li, Xiaohang,Han, Lijing,&Wang, Zheng.(2022).Estimation of Soil Organic Carbon Content in the Ebinur Lake Wetland, Xinjiang, China, Based on Multisource Remote Sensing Data and Ensemble Learning Algorithms.SENSORS,22(7).
MLA	Xie, Boqiang,et al."Estimation of Soil Organic Carbon Content in the Ebinur Lake Wetland, Xinjiang, China, Based on Multisource Remote Sensing Data and Ensemble Learning Algorithms".SENSORS 22.7(2022).