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Predicting Soil Organic Carbon and Soil Nitrogen Stocks in Topsoil of Forest Ecosystems in Northeastern China Using Remote Sensing Data
Remote Sensing ( IF 5 ) Pub Date : 2020-03-31 , DOI: 10.3390/rs12071115 Shuai Wang , Qianlai Zhuang , Xinxin Jin , Zijiao Yang , Hongbin Liu
Remote Sensing ( IF 5 ) Pub Date : 2020-03-31 , DOI: 10.3390/rs12071115 Shuai Wang , Qianlai Zhuang , Xinxin Jin , Zijiao Yang , Hongbin Liu
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, 1.21kg 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 R2= 0.56 and root mean square error (RMSE) = 00.85 kg m-2 for SOC stocks, R2= 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.
更新日期：2020-03-31
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