Mapping total soil nitrogen from a site in northeastern China

Shuai Wang, Xinxin Jin, Kabindra Adhikari, WenWen Li, Miao Yu, Zhenxing Bian, Qiubing Wang

Research output: Contribution to journalArticlepeer-review

47 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)134-146
Number of pages13
StatePublished - Jul 2018


  • Digital soil mapping
  • Random forest
  • Remote sensing

ASJC Scopus subject areas

  • Earth-Surface Processes


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