Spatial variability of soil texture fractions and pH in a flood plain (case study from eastern Iran)

Abstract Flood plain ecosystems show significant soil spatial variability. Understanding spatial variations of soil texture fractions and pH in flood plains is necessary for ensuring proper management of these plains, because these properties influence soil structure, fertility, hydraulic conductivity, infiltration, and erosion. In the present study, the distribution of soil texture fractions and pH was investigated in a flood plain with intensive wind erosion for an area of ~ 41,000 ha in Zahak county of Sistan and Baluchestan province in eastern Iran. A random forest technique was used to link environmental variables and the studied properties. 460 soil samples were collected from 0 to 30 cm depth across a 750 m grid. 361 samples were used for training and 99 for independent validation. Results showed that the distance from the river was the most important environmental variable for predicting soil texture fractions and pH in the study area. Natural channel networks, elevation, valley depth, LS factor, NDSI, vertical distance to channel networks, slope, wind effect, NDVI, and brightness were other important variables. The maps produced indicated a higher sand content near Sistan River. Clay, silt, and pH contents increased with distance from Sistan River. Results showed that clay and pH had a similar distribution in the study area. The values of RMSE for the maps of estimated sand, silt, clay, and pH in validation data were respectively 21.40, 17.45, 6.06 and 0.45. These values of RMSE for sand, silt, clay, and pH were respectively 10.3, 10.7, 15.1, and 13.3% lower than a simple model (mean model). Results indicated that using the distance from the river and channel networks as a variable in digital soil mapping can increase the accuracy of the predictive maps of soil properties in flood plains.

[1]  John H. Grove,et al.  Soil-landscape modeling across a physiographic region : Topographic patterns and model transportability , 2006 .

[2]  J. D. Rhoades,et al.  Effects of Clay Type and Content, Exchangeable Sodium Percentage, and Electrolyte Concentration on Clay Dispersion and Soil Hydraulic Conductivity , 1978 .

[3]  J. Whitney.  Geology, Water, and Wind in the Lower Helmand Basin, Southern Afghanistan , 2006 .

[4]  Mario Chica-Olmo,et al.  An assessment of the effectiveness of a random forest classifier for land-cover classification , 2012 .

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

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

[7]  Baoguo Li,et al.  Three-Dimensional Mapping of Soil Organic Carbon by Combining Kriging Method with Profile Depth Function , 2015, PloS one.

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

[9]  张静,et al.  Banana Ovate family protein MaOFP1 and MADS-box protein MuMADS1 antagonistically regulated banana fruit ripening , 2015 .

[10]  Bernd Schilling,et al.  Estimation of total organic carbon storage and its driving factors in soils of Bavaria (southeast Germany) , 2014 .

[11]  Thomas Gumbricht,et al.  Mapping of soil properties and land degradation risk in Africa using MODIS reflectance , 2016 .

[12]  Vinay Kumar Dadhwal,et al.  Digital mapping of soil organic and inorganic carbon status in India , 2016 .

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

[14]  J. S. Lessels,et al.  Digital soil mapping of organic carbon concentration in paddy growing soils of Northern Sri Lanka , 2016 .

[15]  M. Wiesmeier,et al.  Digital mapping of soil organic matter stocks using Random Forest modeling in a semi-arid steppe ecosystem , 2011, Plant and Soil.

[16]  Adriana Bruggeman,et al.  A high resolution map of soil types and physical properties for Cyprus: A digital soil mapping optimization , 2017 .

[17]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[18]  L. Montanarella,et al.  A map of the topsoil organic carbon content of Europe generated by a generalized additive model , 2015 .

[19]  J. Rinklebe,et al.  Estimation of soil properties with geostatistical methods in floodplains , 2008 .

[20]  O. Chadwick,et al.  Modeling deep soil properties on California grassland hillslopes using LiDAR digital elevation models , 2016 .

[21]  G. Heuvelink,et al.  SoilGrids1km — Global Soil Information Based on Automated Mapping , 2014, PloS one.

[22]  David G. Rossiter,et al.  Mapping soil organic matter in low-relief areas based on land surface diurnal temperature difference and a vegetation index , 2014 .

[23]  N. Toomanian,et al.  Legacy soil maps as a covariate in digital soil mapping: A case study from Northern Iran , 2016 .

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

[25]  Patrick Bogaert,et al.  Updating soil survey maps using random forest and conditioned Latin hypercube sampling in the loess derived soils of northern Iran , 2014 .

[26]  Alfred E. Hartemink,et al.  Digital mapping of soil carbon in a viticultural region of Southern Brazil , 2016 .

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

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

[29]  R. Kerry,et al.  Digital mapping of soil organic carbon at multiple depths using different data mining techniques in Baneh region, Iran , 2016 .

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

[31]  Z. Vekerdy,et al.  History of environmental change in the Sistan Basin based on satellite image analysis: 1976-2005 , 2006 .

[32]  Thomas C. Edwards,et al.  Machine learning for predicting soil classes in three semi-arid landscapes , 2015 .

[33]  Budiman Minasny,et al.  Mapping soil organic carbon content over New South Wales, Australia using local regression kriging , 2016 .

[34]  G. Gee,et al.  Particle-size Analysis , 2018, SSSA Book Series.

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

[36]  De-Cheng Li,et al.  Retrieval and Mapping of Soil Texture Based on Land Surface Diurnal Temperature Range Data from MODIS , 2015, PloS one.

[37]  Budi Setiawan,et al.  Digital mapping for cost-effective and accurate prediction of the depth and carbon stocks in Indonesian peatlands , 2016 .