Mapping total soil nitrogen from a site in northeastern China

Abstract 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.

[1]  A. Zhu,et al.  Updating Conventional Soil Maps through Digital Soil Mapping , 2011 .

[2]  Feng Liu,et al.  Comparison of boosted regression tree and random forest models for mapping topsoil organic carbon concentration in an alpine ecosystem , 2016 .

[3]  Budiman Minasny,et al.  Mapping continuous depth functions of soil carbon storage and available water capacity , 2009 .

[4]  Heba Elbasiouny,et al.  Spatial variation of soil carbon and nitrogen pools by using ordinary Kriging method in an area of north Nile Delta, Egypt , 2014 .

[5]  R. Lal,et al.  Spatial variability of total soil carbon and nitrogen stocks for some reclaimed minesoils of southeastern Ohio , 2008 .

[6]  J. Tu.  Spatially varying relationships between land use and water quality across an urbanization gradient explored by geographically weighted regression. , 2011 .

[7]  Dominique Arrouays,et al.  Spatial distribution of soil organic carbon stocks in France , 2010 .

[8]  C. Hurburgh,et al.  Near-Infrared Reflectance Spectroscopy–Principal Components Regression Analyses of Soil Properties , 2001 .

[9]  C. Daughtry,et al.  Remote- and Ground-Based Sensor Techniques to Map Soil Properties , 2003 .

[10]  Karin Viergever,et al.  Knowledge discovery from models of soil properties developed through data mining , 2006 .

[11]  A-Xing Zhu,et al.  Identification of representative samples from existing samples for digital soil mapping , 2018 .

[12]  Dibyendu Dutta,et al.  Retrieval of tea polyphenol at leaf level using spectral transformation and multi-variate statistical approach , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[13]  D. W. Reeves.  The role of soil organic matter in maintaining soil quality in continuous cropping systems , 1997 .

[14]  Yiming Xu,et al.  Incorporation of satellite remote sensing pan-sharpened imagery into digital soil prediction and mapping models to characterize soil property variability in small agricultural fields , 2017 .

[15]  H. Jenny,et al.  Factors of Soil Formation , 1941 .

[16]  J. Bremner.  Nitrogen-Total , 2018, SSSA Book Series.

[17]  Chenghu Zhou,et al.  Purposive Sampling for Digital Soil Mapping for Areas with Limited Data , 2008 .

[18]  Rainer Duttmann,et al.  Prediction of soil property distribution in paddy soil landscapes using terrain data and satellite information as indicators , 2008 .

[19]  R. Henry,et al.  Simultaneous Determination of Moisture, Organic Carbon, and Total Nitrogen by Near Infrared Reflectance Spectrophotometry , 1986 .

[20]  Qiang Tang,et al.  Precipitation gradient determines the tradeoff between soil moisture and soil organic carbon, total nitrogen, and species richness in the Loess Plateau, China. , 2017, The Science of the total environment.

[21]  Budiman Minasny,et al.  Digital mapping of soil carbon , 2013 .

[22]  J. Huang,et al.  An error budget for digital soil mapping of cation exchange capacity using proximally sensed electromagnetic induction and remotely sensed γ‐ray spectrometer data , 2017 .

[23]  Chuanrong Zhang,et al.  Effect of Land Use Types on the Spatial Prediction of Soil Nitrogen , 2012 .

[24]  Rolf Nieder,et al.  Carbon and nitrogen in the terrestrial environment , 2008 .

[25]  Kabindra Adhikari,et al.  Role of environmental variables in the spatial distribution of soil carbon (C), nitrogen (N), and C:N ratio from the northeastern coastal agroecosystems in China , 2018 .

[26]  S. de Bruin,et al.  Implementation and evaluation of existing knowledge for digital soil mapping in Senegal , 2009 .

[27]  Z. Ganlin.  Soil Reference on the Bases of Chinese Soil Taxonomy , 2002 .

[28]  A-Xing Zhu,et al.  Construction of Membership Functions for Soil Mapping using the Partial Dependence of Soil on Environmental Covariates Calculated by Random Forest , 2017 .

[29]  Peter E. Thornton,et al.  Carbon-nitrogen interactions regulate climate-carbon cycle feedbacks: results from an atmosphere-ocean general circulation model , 2009 .

[30]  L. Lin,et al.  A concordance correlation coefficient to evaluate reproducibility. , 1989, Biometrics.

[31]  Budiman Minasny,et al.  On digital soil mapping , 2003 .

[32]  Chuanrong Zhang,et al.  Predictive mapping of soil total nitrogen at a regional scale: A comparison between geographically weighted regression and cokriging , 2013 .

[33]  Waldir de Carvalho Junior,et al.  Spatial prediction of soil surface texture in a semiarid region using random forest and multiple linear regressions , 2016 .

[34]  Xiang Yu,et al.  Evaluation of MLSR and PLSR for estimating soil element contents using visible/near-infrared spectroscopy in apple orchards on the Jiaodong peninsula , 2016 .

[35]  Richard D. Bardgett,et al.  SOIL MICROBES COMPETE EFFECTIVELY WITH PLANTS FOR ORGANIC‐NITROGEN INPUTS TO TEMPERATE GRASSLANDS , 2003 .

[36]  Jerome H Friedman,et al.  Multiple additive regression trees with application in epidemiology , 2003, Statistics in medicine.

[37]  S. Kaushal,et al.  Land use change and nitrogen enrichment of a Rocky Mountain watershed. , 2006, Ecological applications : a publication of the Ecological Society of America.

[38]  Budiman Minasny,et al.  High‐Resolution 3‐D Mapping of Soil Texture in Denmark , 2013 .

[39]  Impact of land-use types on soil nitrogen net mineralization in the sandstorm and water source area of Beijing, China , 2010 .

[40]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[41]  D. Sullivan,et al.  IKONOS Imagery to Estimate Surface Soil Property Variability in Two Alabama Physiographies , 2005 .

[42]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[43]  U. Schmidhalter,et al.  High resolution topsoil mapping using hyperspectral image and field data in multivariate regression modeling procedures , 2006 .

[44]  A. Prasad,et al.  Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction , 2006, Ecosystems.

[45]  B. J,et al.  Soil regionalisation by means of terrain analysis and process parameterisation , 2002 .

[46]  Suhas P. Wani,et al.  Effects of image pansharpening on soil total nitrogen prediction models in South India , 2018, Geoderma.

[47]  Fan Yang,et al.  Predictive Mapping of Topsoil Organic Carbon in an Alpine Environment Aided by Landsat TM , 2015, PloS one.

[48]  M. Nilsson,et al.  Ecosystem Feedbacks and Nitrogen Fixation in Boreal Forests , 2008, Science.

[49]  N. Batjes,et al.  Total carbon and nitrogen in the soils of the world , 1996 .

[50]  Shuhai Jia,et al.  Spatial-Temporal Changes of Soil Organic Carbon Content in Wafangdian, China , 2016 .

[51]  Dandan Wang,et al.  Synthesized use of VisNIR DRS and PXRF for soil characterization: Total carbon and total nitrogen☆ , 2015 .

[52]  G. Heuvelink,et al.  Mapping Soil Properties of Africa at 250 m Resolution: Random Forests Significantly Improve Current Predictions , 2015, PloS one.

[53]  Xinghui Xia,et al.  Temporal and Spatial Variations of Nutrients in Baiyangdian Lake, North China , 2011 .

[54]  Guillermo Chaminade.  Topography, soil carbon-nitrogen ratio and vegetation in boreal coniferous forests at the landscape level , 2006 .

[55]  B. Huwe,et al.  Uncertainty in the spatial prediction of soil texture: Comparison of regression tree and Random Forest models , 2012 .

[56]  Unai Pascual,et al.  Examining spatially varying relationships between coca crops and associated factors in Colombia, using geographically weight regression , 2013 .

[57]  Gary A. Peterson,et al.  Soil Attribute Prediction Using Terrain Analysis , 1993 .

[58]  Robert P. W. Duin,et al.  Bagging, Boosting and the Random Subspace Method for Linear Classifiers , 2002, Pattern Analysis & Applications.

[59]  Margaret A. Oliver,et al.  The spatial prediction of soil mineral N and potentially available N using elevation , 2005 .

[60]  M. Martina,et al.  Potential and limitations of using soil mapping information to understand landscape hydrology , 2011 .

[61]  Alfred E. Hartemink,et al.  Digital Mapping of Topsoil Carbon Content and Changes in the Driftless Area of Wisconsin, USA , 2015 .

[62]  Philippe Lagacherie,et al.  Using quantile regression forest to estimate uncertainty of digital soil mapping products , 2017 .

[63]  A. Zhu,et al.  Mapping soil organic matter concentration at different scales using a mixed geographically weighted regression method , 2016 .

[64]  Klara Dolos,et al.  Comparing Generalized Linear Models and random forest to model vascular plant species richness using LiDAR data in a natural forest in central Chile , 2016 .

[65]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[66]  Michael Thiel,et al.  High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models , 2017, PloS one.

[67]  Christopher J. Kucharik,et al.  Land-use effects on soil carbon and nitrogen on a U.S. Midwestern floodplain. , 2009 .

[68]  Deutsche Ausgabe.  World Reference Base for Soil Resources 2006 , 2007 .

[69]  B. De Baets,et al.  Wetland vegetation distribution modelling for the identification of constraining environmental variables , 2008, Landscape Ecology.

[70]  N. McKenzie,et al.  Spatial prediction of soil properties using environmental correlation , 1999 .

[71]  Qianlai Zhuang,et al.  Mapping stocks of soil organic carbon and soil total nitrogen in Liaoning Province of China , 2017 .

[72]  J. Peters,et al.  Random forests as a tool for ecohydrological distribution modelling , 2007 .