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Xinxin Jin
College of Land and Environment, Shenyang Agricultural University, Shenyang, Liaoning, China

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Journal article
Published: 26 May 2020 in PeerJ
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Soil organic carbon (SOC) and soil total nitrogen (STN) are major soil indicators for soil quality and fertility. Accurate mapping SOC and STN in soils would help both managed and natural soils and ecosystem management. This study developed an improved similarity-based approach (ISA) to predicting and mapping topsoil (0–20 cm soil depth) SOC and STN in a coastal region of northeastern China. Six environmental variables including elevation, slope gradient, topographic wetness index, the mean annual temperature, the mean annual temperature, and normalized difference vegetation index were used as predictors. Soil survey data in 2012 was designed based on the clustering of the study area into six climatic vegetation landscape units. In each landscape unit, 20–25 sampling points were determined at different landform positions considering local climate, soil type, elevation and other environmental factors, and finally 126 sampling points were obtained. Soil sampling from the depth of 0–20 cm were used for model prediction and validation. The ISA model performance was compared with the geographically weighted regression (GWR), regression kriging (RK), boosted regression trees (BRT) considering mean absolute prediction error (MAE), root mean square error (RMSE), coefficient of determination (R2), and maximum relative difference (RD) indices. We found that the ISA method performed best with the highest R2 and lowest MAE, RMSE compared to GWR, RK, and BRT methods. The ISA method could explain 76% and 83% of the total SOC and STN variability, respectively, 12–40% higher than other models in the study area. Elevation had the largest influence on SOC and STN distribution. We conclude that the developed ISA model is robust and effective in mapping SOC and STN, particularly in the areas with complex vegetation-landscape when limited samples are available. The method needs to be tested for other regions in our future research.

ACS Style

Shuai Wang; Kabindra Adhikari; Qianlai Zhuang; Zijiao Yang; Xinxin Jin; Qiubing Wang; Zhenxing Bian. An improved similarity-based approach to predicting and mapping soil organic carbon and soil total nitrogen in a coastal region of northeastern China. PeerJ 2020, 8, e9126 .

AMA Style

Shuai Wang, Kabindra Adhikari, Qianlai Zhuang, Zijiao Yang, Xinxin Jin, Qiubing Wang, Zhenxing Bian. An improved similarity-based approach to predicting and mapping soil organic carbon and soil total nitrogen in a coastal region of northeastern China. PeerJ. 2020; 8 ():e9126.

Chicago/Turabian Style

Shuai Wang; Kabindra Adhikari; Qianlai Zhuang; Zijiao Yang; Xinxin Jin; Qiubing Wang; Zhenxing Bian. 2020. "An improved similarity-based approach to predicting and mapping soil organic carbon and soil total nitrogen in a coastal region of northeastern China." PeerJ 8, no. : e9126.

Journal article
Published: 31 March 2020 in Remote Sensing
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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 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.

ACS Style

Shuai Wang; Qianlai Zhuang; Xinxin Jin; Zijiao Yang; HongBin Liu. Predicting Soil Organic Carbon and Soil Nitrogen Stocks in Topsoil of Forest Ecosystems in Northeastern China Using Remote Sensing Data. Remote Sensing 2020, 12, 1115 .

AMA Style

Shuai Wang, Qianlai Zhuang, Xinxin Jin, Zijiao Yang, HongBin Liu. Predicting Soil Organic Carbon and Soil Nitrogen Stocks in Topsoil of Forest Ecosystems in Northeastern China Using Remote Sensing Data. Remote Sensing. 2020; 12 (7):1115.

Chicago/Turabian Style

Shuai Wang; Qianlai Zhuang; Xinxin Jin; Zijiao Yang; HongBin Liu. 2020. "Predicting Soil Organic Carbon and Soil Nitrogen Stocks in Topsoil of Forest Ecosystems in Northeastern China Using Remote Sensing Data." Remote Sensing 12, no. 7: 1115.

Journal article
Published: 26 January 2020 in Remote Sensing
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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.

ACS Style

Shuai Wang; Jinhu Gao; Qianlai Zhuang; Yuanyuan Lu; Hanlong Gu; Xinxin Jin. 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 2020, 12, 393 .

AMA Style

Shuai Wang, Jinhu Gao, Qianlai Zhuang, Yuanyuan Lu, Hanlong Gu, Xinxin Jin. 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. 2020; 12 (3):393.

Chicago/Turabian Style

Shuai Wang; Jinhu Gao; Qianlai Zhuang; Yuanyuan Lu; Hanlong Gu; Xinxin Jin. 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, no. 3: 393.

Journal article
Published: 14 November 2019 in Forests
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Forest soil organic carbon (SOC) accounts for a large portion of global soil carbon stocks. Accurately mapping forest SOC stocks is a necessity for quantifying forest carbon cycling and forest soil sustainable management. In this study, we used a boosted regression trees (BRT) model to predict the spatial distribution of SOC stocks during two time periods (1990 and 2015) and calculated their spatiotemporal changes during 25 years in Liaoning Province, China. A total of 367 (1990) and 539 (2015) sampling sites and 9 environmental variables (climate, topography, remote sensing) were used in the BRT model. The ten-fold cross-validation technique was used to evaluate the prediction performance and uncertainty of the BRT model in two periods. It was found that the BRT model could account for 65% and 59% of SOC stocks, respectively for the two periods. MAP and NDVI were the main environmental variables controlling the spatial variability of SOC stocks. Over the 25-year period, the average SOC stocks increased from 5.66 to 6.61 kg m−2. In the whole study area, the SOC stocks were the highest in the northeast, followed by the southwest, and the lowest in the middle of the spatial distribution pattern in the two periods. Our accurate mapping of SOC stocks, their spatial distribution characteristics, influencing factors, and main controlling factors in forest areas will assist soil management and help assess environmental changes in the region.

ACS Style

Shuai Wang; Qianlai Zhuang; Zijiao Yang; Na Yu; Xinxin Jin. Temporal and Spatial Changes of Soil Organic Carbon Stocks in the Forest Area of Northeastern China. Forests 2019, 10, 1023 .

AMA Style

Shuai Wang, Qianlai Zhuang, Zijiao Yang, Na Yu, Xinxin Jin. Temporal and Spatial Changes of Soil Organic Carbon Stocks in the Forest Area of Northeastern China. Forests. 2019; 10 (11):1023.

Chicago/Turabian Style

Shuai Wang; Qianlai Zhuang; Zijiao Yang; Na Yu; Xinxin Jin. 2019. "Temporal and Spatial Changes of Soil Organic Carbon Stocks in the Forest Area of Northeastern China." Forests 10, no. 11: 1023.

Journal article
Published: 28 June 2019 in Sustainability
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Quantification of soil organic carbon (SOC) and pH, and their spatial variations at regional scales, is a foundation to adequately assess agriculture, pollution control, or environmental health and ecosystem functioning, so as to establish better practices for land use and land management. In this study, we used the random forest (RF) model to map the distribution of SOC and pH in the topsoil (0–20 cm) and estimate SOC and pH changes from 1982 to 2012 in Liaoning Province, Northeast China. A total of 10 covariates (elevation, slope gradient, topographic wetness index (TWI), mean annual temperature (MAT), mean annual precipitation (MAP), visible-red band 3 (B3), near-infrared band 4 (B4), short-wave infrared band 5 (B5), normalized difference vegetation index (NDVI), and land-use data) and a set of 806 (in 1982) and 973 (in 2012) soil samples were selected. Cross-validation technology was used to test the performance and uncertainty of the RF model. We found that the prediction R2 of SOC and pH was 0.69 and 0.54 for 1982, and 0.63 and 0.48 for 2012, respectively. Elevation, NDVI, and land use are the main environmental variables affecting the spatial variability of SOC in both periods. Correspondingly, the topographic wetness index and mean annual precipitation were the two most critical environmental variables affecting the spatial variation of pH. The mean SOC and pH decreased from 18.6 to 16.9 kg−1 and 6.9 to 6.6, respectively, over a 30-year period. SOC distribution generated using the RF model showed a decreasing SOC trend from east to west across the city in the two periods. In contrast, the spatial distribution of pH showed an opposite trend in both periods. This study provided important information of spatial variations in SOC and pH to agencies and communities in this region, to evaluate soil quality and make decisions on remediation and prevention of soil acidification and salinization.

ACS Style

Li Qi; Shuai Wang; Qianlai Zhuang; Zijiao Yang; Shubin Bai; Xinxin Jin; Guangyu Lei. Spatial-Temporal Changes in Soil Organic Carbon and pH in the Liaoning Province of China: A Modeling Analysis Based on Observational Data. Sustainability 2019, 11, 3569 .

AMA Style

Li Qi, Shuai Wang, Qianlai Zhuang, Zijiao Yang, Shubin Bai, Xinxin Jin, Guangyu Lei. Spatial-Temporal Changes in Soil Organic Carbon and pH in the Liaoning Province of China: A Modeling Analysis Based on Observational Data. Sustainability. 2019; 11 (13):3569.

Chicago/Turabian Style

Li Qi; Shuai Wang; Qianlai Zhuang; Zijiao Yang; Shubin Bai; Xinxin Jin; Guangyu Lei. 2019. "Spatial-Temporal Changes in Soil Organic Carbon and pH in the Liaoning Province of China: A Modeling Analysis Based on Observational Data." Sustainability 11, no. 13: 3569.