Predicting Soil Organic Carbon and Soil Nitrogen Stocks in Topsoil of Forest Ecosystems in Northeastern China Using Remote Sensing Data

doi:10.3390/rs12071115

	Predicting Soil Organic Carbon and Soil Nitrogen Stocks in Topsoil of Forest Ecosystems in Northeastern China Using Remote Sensing Data
	Wang, Shuai 1,2,3; Zhuang, Qianlai 3; Jin, Xinxin 1; Yang, Zijiao 1; Liu, Hongbin 1
刊名	REMOTE SENSING
	2020-04-01
卷号	12 期号:7 页码:21
关键词	soil organic carbon stocks soil total nitrogen stocks remote sensing data spatial variation digital soil mapping
DOI	10.3390/rs12071115
通讯作者	Liu, Hongbin(liuhongbinsy@syau.edu.cn)
英文摘要	Forest ecosystems play an important role in regional carbon and nitrogen cycling. Accurate and effective monitoring of their soil organic carbon (SOC) and soil total nitrogen (STN) stocks provides important information for soil quality assessment, sustainable forestry management and climate change policy making. In this study, a geographical weighted regression (GWR) model, a multiple stepwise regression (MLSR) model, and a boosted regression trees (BRT) model were compared to obtain the best prediction of SOC and STN stocks of the forest ecosystems in northeastern China. Five-hundred and thirteen topsoil (0-30 cm) samples (10.32 kg m(-2) (+/- 0.53) for SOC, 1.21 kg m(-2) (+/- 0.32) for STN), and 9 remotely-sensed environmental variables were collected and used for the model development and verification. By comparing with independent verification data, the best model (BRT) achieved R-2 = 0.56 and root mean square error (RMSE) = 00.85 kg m(-2) for SOC stocks, R-2 = 0.51 and RMSE = 0.22 kg m(-2) for STN stocks. Of all the remotely-sensed environment variables, soil adjusted vegetation index (SAVI) and normalized difference vegetation index (NDVI) are of the highest relative importance in predicting SOC and STN stocks. The spatial distribution of the predicted SOC and STN stocks gradually decreased from northeast to southwest. This study provides an attempt to rapidly predict SOC and STN stocks in the dense vegetation covered area. The results can help evaluate soil quality and facilitate land policy and regulation making by the government in the region.
资助项目	National Natural Science Foundation of China[71503174] ; China Postdoctoral Science Foundation[2019M660782] ; Young scientific and Technological Talents Project of Liaoning Province[LSNQN201910] ; Young scientific and Technological Talents Project of Liaoning Province[LSNQN201914] ; Planning Foundation of Liaoning Province[L18BJY006] ; Social and economic development of Liaoning Province[20201s1ktyb-077] ; Foundation for Young Scientific and Innovative Talents in Shenyang City[RC170180]
WOS关键词	GEOGRAPHICALLY WEIGHTED REGRESSION ; VEGETATION ; LANDSAT ; VARIABILITY ; CLIMATE ; COVER ; SCALE
WOS研究方向	Remote Sensing
语种	英语
出版者	MDPI
WOS记录号	WOS:000537709600065
资助机构	National Natural Science Foundation of China ; China Postdoctoral Science Foundation ; Young scientific and Technological Talents Project of Liaoning Province ; Planning Foundation of Liaoning Province ; Social and economic development of Liaoning Province ; Foundation for Young Scientific and Innovative Talents in Shenyang City
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/159498]
专题	中国科学院地理科学与资源研究所
通讯作者	Liu, Hongbin
作者单位	1.Shenyang Agr Univ, Coll Land & Environm, Shenyang 110866, Liaoning, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China 3.Purdue Univ, Dept Earth Atmospher & Planetary Sci, W Lafayette, IN 47907 USA
推荐引用方式 GB/T 7714	Wang, Shuai,Zhuang, Qianlai,Jin, Xinxin,et al. Predicting Soil Organic Carbon and Soil Nitrogen Stocks in Topsoil of Forest Ecosystems in Northeastern China Using Remote Sensing Data[J]. REMOTE SENSING,2020,12(7):21.
APA	Wang, Shuai,Zhuang, Qianlai,Jin, Xinxin,Yang, Zijiao,&Liu, Hongbin.(2020).Predicting Soil Organic Carbon and Soil Nitrogen Stocks in Topsoil of Forest Ecosystems in Northeastern China Using Remote Sensing Data.REMOTE SENSING,12(7),21.
MLA	Wang, Shuai,et al."Predicting Soil Organic Carbon and Soil Nitrogen Stocks in Topsoil of Forest Ecosystems in Northeastern China Using Remote Sensing Data".REMOTE SENSING 12.7(2020):21.