Multispectral Remote Sensing Data Are Effective and Robust in Mapping Regional Forest Soil Organic Carbon Stocks in a Northeast Forest Region in China

doi:10.3390/rs12030393

	Multispectral Remote Sensing Data Are Effective and Robust in Mapping Regional Forest Soil Organic Carbon Stocks in a Northeast Forest Region in China
	Wang, Shuai 1,3,4; Gao, Jinhu 5; Zhuang, Qianlai 4; Lu, Yuanyuan 2; Gu, Hanlong 3; Jin, Xinxin 3
刊名	REMOTE SENSING
	2020-02-01
卷号	12 期号:3 页码:18
关键词	soil organic carbon stocks multispectral remote sensing forestry ecology spatial variation
DOI	10.3390/rs12030393
通讯作者	Gu, Hanlong(guhanlong@syau.edu.cn)
英文摘要	Accurately mapping the spatial distribution information of soil organic carbon (SOC) stocks is a key premise for soil resource management and environment protection. Rapid development of satellite remote sensing provides a great opportunity for monitoring SOC stocks at a large scale. In this study, based on 12 environmental variables of multispectral remote sensing, topography and climate and 236 soil sampling data, three different boosted regression tree (BRT) models were compared to obtain the most accurate map of SOC stocks covering the forest area of Lvshun District in the Northeast China. Four validation indexes, including mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R-2), and Lin's concordance correlation coefficient (LCCC) were calculated to evaluate the performance of the three models. The results showed that the full variable model performed the best, except the model using multispectral remote sensing variables. In the full variable model, the regional SOC stocks are primarily determined by multispectral remote sensing variables, followed by topographic and climatic variables, with the relative importance of variables in the model being 63%, 28%, and 9%, respectively. The average prediction results of full variables model and only multispectral remote sensing variables model were 8.99 and 9.32 kg m(-2), respectively. Our results indicated that there is a strong dependence of SOC stocks on multispectral remote sensing data when forest ecosystems have dense natural vegetation. Our study suggests that the multispectral remote sensing variables should be used to map SOC stocks of forest ecosystems in our study region.
资助项目	China Postdoctoral Science Foundation[2019M660782] ; China Postdoctoral Science Foundation[2018M631824] ; Young scientific and Technological Talents Project of Liaoning Province[LSNQN201910] ; Young scientific and Technological Talents Project of Liaoning Province[LSNQN201914] ; National Natural Science Foundation of China[71603171]
WOS关键词	REGRESSION ; VEGETATION ; ECOSYSTEM ; COVER
WOS研究方向	Remote Sensing
语种	英语
出版者	MDPI
WOS记录号	WOS:000515393800052
资助机构	China Postdoctoral Science Foundation ; Young scientific and Technological Talents Project of Liaoning Province ; National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/132702]
专题	中国科学院地理科学与资源研究所
通讯作者	Gu, Hanlong
作者单位	1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China 2.Minist Ecol & Environm, Nanjing Inst Environm Sci, Nanjing 210042, Peoples R China 3.Shenyang Agr Univ, Coll Land & Environm, Shenyang 110866, Peoples R China 4.Purdue Univ, Dept Earth Atmospher & Planetary Sci, W Lafayette, IN 47907 USA 5.Shanxi Acad Agr Sci, Inst Cash Crops, Taiyuan 030031, Peoples R China
推荐引用方式 GB/T 7714	Wang, Shuai,Gao, Jinhu,Zhuang, Qianlai,et al. Multispectral Remote Sensing Data Are Effective and Robust in Mapping Regional Forest Soil Organic Carbon Stocks in a Northeast Forest Region in China[J]. REMOTE SENSING,2020,12(3):18.
APA	Wang, Shuai,Gao, Jinhu,Zhuang, Qianlai,Lu, Yuanyuan,Gu, Hanlong,&Jin, Xinxin.(2020).Multispectral Remote Sensing Data Are Effective and Robust in Mapping Regional Forest Soil Organic Carbon Stocks in a Northeast Forest Region in China.REMOTE SENSING,12(3),18.
MLA	Wang, Shuai,et al."Multispectral Remote Sensing Data Are Effective and Robust in Mapping Regional Forest Soil Organic Carbon Stocks in a Northeast Forest Region in China".REMOTE SENSING 12.3(2020):18.