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Open AccessArticle

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

by Shuai Wang 1,2,3,†, Jinhu Gao 4,†, Qianlai Zhuang 2, Yuanyuan Lu 5, Hanlong Gu 1,* and Xinxin Jin 1
1
College of Land and Environment, Shenyang Agricultural University, Shenyang 110866, China
2
Department of Earth, Atmospheric, and Planetary Sciences, Purdue University, West Lafayette, IN 47907, USA
3
Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
4
Institute of Cash Crops, Shanxi Academy of Agricultural Sciences, Taiyuan 030031, China
5
Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing 210042, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this paper.
Remote Sens. 2020, 12(3), 393; https://doi.org/10.3390/rs12030393
Received: 31 December 2019 / Revised: 18 January 2020 / Accepted: 22 January 2020 / Published: 26 January 2020
(This article belongs to the Special Issue Remote Sensing Based Quantification of Soil Properties)
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 (R2), 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. View Full-Text
Keywords: soil organic carbon stocks; multispectral remote sensing; forestry ecology; spatial variation soil organic carbon stocks; multispectral remote sensing; forestry ecology; spatial variation
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Wang, S.; Gao, J.; Zhuang, Q.; Lu, Y.; Gu, H.; Jin, X. Multispectral Remote Sensing Data Are Effective and Robust in Mapping Regional Forest Soil Organic Carbon Stocks in a Northeast Forest Region in China. Remote Sens. 2020, 12, 393.

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