TY - JOUR
T1 - Mapping total soil nitrogen from a site in northeastern China
AU - Wang, Shuai
AU - Jin, Xinxin
AU - Adhikari, Kabindra
AU - Li, WenWen
AU - Yu, Miao
AU - Bian, Zhenxing
AU - Wang, Qiubing
N1 - Funding Information:
This research was supported by the National Natural Science Foundation of China (No. 41371223 and 41501226 ). We would like to thank LetPub ( www.letpub.com ) for providing linguistic assistance during the preparation of this manuscript.
Publisher Copyright:
© 2018 Elsevier B.V.
PY - 2018/7
Y1 - 2018/7
N2 - Precise mapping of the spatial distribution of total soil nitrogen (TSN) is essential for soil resource management, agronomic sustainability, and nitrogen sequestration potential. Present study used three random forest (RF) and three multiple linear stepwise regressions (MLSR) models to map the distribution of topsoil (0–20 cm) TSN content in Lushun City in China's northeastern Liaoning Province. A total of 12 covariates (including topography, climate, and remote sensing images) and 115 soil samples were employed. An independent validation set of 23 samples was used to verify the performance of the model based on mean absolute prediction error (MAE), root mean square error (RMSE), coefficient of determination (R2), and Lin's concordance correlation coefficient (LCCC). Accuracy assessments showed that the RF model, combined with all environmental variables, had the best prediction performance. The second best was produced by the use of remote sensing alone. The third was the model that used only topographic and climatic variables alone. Remote sensing was not significantly inferior to the use of the model with all variables and can be used to build a realistic model. In the model with all covariates, the distribution of TSN is mainly explained by remote sensing, followed by topography variables, and climatic variables; their relative importance (RI) was 48%, 36%, and 16%, respectively. The results of remote sensing on the robust dependence of TSN should provide a link to dense natural vegetation in this forested environment. Additionally, remote sensing and its derived environmental variables should be used as the main predictors when mapping TSN in forested areas and other areas with similar vegetation coverage.
AB - Precise mapping of the spatial distribution of total soil nitrogen (TSN) is essential for soil resource management, agronomic sustainability, and nitrogen sequestration potential. Present study used three random forest (RF) and three multiple linear stepwise regressions (MLSR) models to map the distribution of topsoil (0–20 cm) TSN content in Lushun City in China's northeastern Liaoning Province. A total of 12 covariates (including topography, climate, and remote sensing images) and 115 soil samples were employed. An independent validation set of 23 samples was used to verify the performance of the model based on mean absolute prediction error (MAE), root mean square error (RMSE), coefficient of determination (R2), and Lin's concordance correlation coefficient (LCCC). Accuracy assessments showed that the RF model, combined with all environmental variables, had the best prediction performance. The second best was produced by the use of remote sensing alone. The third was the model that used only topographic and climatic variables alone. Remote sensing was not significantly inferior to the use of the model with all variables and can be used to build a realistic model. In the model with all covariates, the distribution of TSN is mainly explained by remote sensing, followed by topography variables, and climatic variables; their relative importance (RI) was 48%, 36%, and 16%, respectively. The results of remote sensing on the robust dependence of TSN should provide a link to dense natural vegetation in this forested environment. Additionally, remote sensing and its derived environmental variables should be used as the main predictors when mapping TSN in forested areas and other areas with similar vegetation coverage.
KW - Digital soil mapping
KW - Random forest
KW - Remote sensing
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U2 - 10.1016/j.catena.2018.03.023
DO - 10.1016/j.catena.2018.03.023
M3 - Article
AN - SCOPUS:85044852872
SN - 0341-8162
VL - 166
SP - 134
EP - 146
JO - Catena
JF - Catena
ER -