Digital soil mapping using remote sensing indices, terrain attributes, and vegetation features in the rangelands of northeastern Iran

Digital soil mapping has been introduced as a viable alternative to the traditional mapping methods due to being fast and cost-effective. The objective of the present study was to investigate the capability of the vegetation features and spectral indices as auxiliary variables in digital soil mapping models to predict soil properties. A region with an area of 1225 ha located in Bajgiran rangelands, Khorasan Razavi province, northeastern Iran, was chosen. A total of 137 sampling sites, each containing 3–5 plots with 10-m interval distance along a transect established based on randomized-systematic method, were investigated. In each plot, plant species names and numbers as well as vegetation cover percentage (VCP) were recorded, and finally one composite soil sample was taken from each transect at each site (137 soil samples in total). Terrain attributes were derived from a digital elevation model, different bands and spectral indices were obtained from the Landsat7 ETM+ images, and vegetation features were calculated in the plots, all of which were used as auxiliary variables to predict soil properties using artificial neural network, gene expression programming, and multivariate linear regression models. According to R2 RMSE and MBE values, artificial neutral network was obtained as the most accurate soil properties prediction function used in scorpan model. Vegetation features and indices were more effective than remotely sensed data and terrain attributes in predicting soil properties including calcium carbonate equivalent, clay, bulk density, total nitrogen, carbon, sand, silt, and saturated moisture capacity. It was also shown that vegetation indices including NDVI, SAVI, MSAVI, SARVI, RDVI, and DVI were more effective in estimating the majority of soil properties compared to separate bands and even some soil spectral indices.

[1]  Zohreh Mosleh,et al.  The effectiveness of digital soil mapping to predict soil properties over low-relief areas , 2016, Environmental Monitoring and Assessment.

[2]  Jingfeng Huang,et al.  Comparison of Vegetation Indices and Red‐edge Parameters for Estimating Grassland Cover from Canopy Reflectance Data , 2007 .

[3]  Frédérique Seyler,et al.  Mapping soil organic carbon on a national scale: Towards an improved and updated map of Madagascar , 2017 .

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

[5]  Philippe Lagacherie,et al.  The utility of remotely-sensed vegetative and terrain covariates at different spatial resolutions in modelling soil and watertable depth (for digital soil mapping) , 2013 .

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

[7]  A. Huete.  A soil-adjusted vegetation index (SAVI) , 1988 .

[8]  Ghanim A. Abbadi,et al.  Vegetation analysis of Failaka Island (Kuwait) , 2002 .

[9]  S. Lakshmi,et al.  A Comparison of Soil Texture Distribution and Soil Moisture Mapping of Chennai Coast using Landsat ETM+ and IKONOS Data , 2015 .

[10]  Priyabrata Santra,et al.  Digital soil mapping of sand content in arid western India through geostatistical approaches , 2017 .

[11]  C. Siebe,et al.  Mapping soil salinity using a combined spectral response index for bare soil and vegetation: A case study in the former lake Texcoco, Mexico , 2006 .

[12]  Budiman Minasny,et al.  The neuro-m method for fitting neural network parametric pedotransfer functions , 2002 .

[13]  Budiman Minasny,et al.  The neuro-m method for fitting neural network parametric pedotransfer functions , 2002 .

[14]  The interaction between poisonous plants and soil quality in response to grassland degradation in the alpine region of the Qinghai-Tibetan Plateau , 2014, Plant Ecology.

[15]  Wei Wei,et al.  Effects of vegetation restoration on the spatial distribution of soil moisture at the hillslope scale in semi-arid regions , 2015 .

[16]  Graciela Metternicht,et al.  Spatial discrimination of salt- and sodium-affected soil surfaces , 1997 .

[17]  D. T. Lewis,et al.  Soil Properties Associated with Landscape Position , 1993 .

[18]  Fawzi H. Masoud,et al.  Archives of Agronomy and Soil Science , 2007 .

[19]  A. E. Tercan,et al.  The effects of land use changes on some soil properties in İndağı Mountain Pass – Çankırı, Turkey , 2007 .

[20]  Y. Huanga,et al.  Development of soft computing and applications in agricultural and biological engineering , 2010 .

[21]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[22]  A. Cerda,et al.  Soil aggregate stability in three Mediterranean environments , 1996 .

[23]  Jan M. H. Hendrickx,et al.  Environmental factors of spatial distribution of soil salinity on flat irrigated terrain , 2011 .

[24]  K. McGwire,et al.  Vegetation canopy cover effects on sediment erosion processes in the Upper Colorado River Basin Mancos Shale formation, Price, Utah, USA , 2016 .

[25]  José A. M. Demattê,et al.  Prediction of soil properties using imaging spectroscopy: Considering fractional vegetation cover to improve accuracy , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[26]  P. Curran.  Remote sensing of foliar chemistry , 1989 .

[27]  Yubin Lan,et al.  Review: Development of soft computing and applications in agricultural and biological engineering , 2010 .

[28]  R. D. Ramsey,et al.  Landsat Spectral Data for Digital Soil Mapping , 2008 .

[29]  Saleh M. Al-Alawi,et al.  Principal component and multiple regression analysis in modelling of ground-level ozone and factors affecting its concentrations , 2005, Environ. Model. Softw..

[30]  N. Toomanian,et al.  Digital Soil Mapping Using Artificial Neural Networks and Terrain-Related Attributes , 2015 .

[31]  H. Khademi,et al.  COMPARISON OF ARTIFICIAL NEURAL NETWORK (ANN) AND MULTIVARIATE LINEAR REGRESSION (MLR) MODELS TO PREDICT SOIL ORGANIC CARBON USING DIGITAL TERRAIN ANALYSIS (CASE STUDY: ZARGHAM ABAD SEMIROM, ISFAHAN PROVIANCE) , 2011 .

[32]  Rodnei Rizzo,et al.  Digital soil mapping at local scale using a multi-depth Vis–NIR spectral library and terrain attributes , 2016 .

[33]  Yan Li,et al.  Spatial distribution of the herbaceous layer and its relationship to soil physical–chemical properties in the southern margin of the Gurbantonggut Desert, northwestern China , 2016 .

[34]  C. Walter,et al.  Digital assessment of soil-salinity dynamics after a major flood in the Niger River valley , 2013 .

[35]  E. Mahmoudabadi,et al.  Spatial distribution of soil heavy metals in different land uses of an industrial area of Tehran (Iran) , 2015, International Journal of Environmental Science and Technology.

[36]  J. Campbell.  Introduction to remote sensing , 1987 .

[37]  D. P. Franzmeier,et al.  Use of Saran Resin to Coat Natural Soil Clods for Bulk-Density and Water-Retention Measurements , 1966 .

[38]  John P. Wilson,et al.  Terrain analysis : principles and applications , 2000 .

[39]  Nader Fathianpour,et al.  Neural network models to predict cation exchange capacity in arid regions of Iran , 2005 .

[40]  P. Tueller,et al.  Vegetation-soil relationships on arid and semiarid rangelands , 1988 .

[41]  Edoardo A.C. Costantini,et al.  Can γ-radiometrics predict soil textural data and stoniness in different parent materials? A comparison of two machine-learning methods , 2014 .

[42]  S. Somaratne,et al.  Prediction of Soil Organic Carbon across Different Land‐use Patterns , 2005 .

[43]  S. M. Bateni,et al.  Estimation of soil cation exchange capacity using Genetic Expression Programming (GEP) and Multivariate Adaptive Regression Splines (MARS) , 2015 .

[44]  M. Omid,et al.  Determination of Soil Organic Carbon Variability of Rainfed Crop Land in Semi-arid Region (Neural Network Approach) , 2010 .

[45]  M. Babel,et al.  Principal Component and Multiple Regression Analyses for the Estimation of Suspended Sediment Yield in Ungauged Basins of Northern Thailand , 2014 .

[46]  A. Gitelson,et al.  Remote estimation of chlorophyll content in higher plant leaves , 1997 .

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

[48]  Yuxin Miao,et al.  Identifying important factors influencing corn yield and grain quality variability using artificial neural networks , 2006, Precision Agriculture.

[49]  Budiman Minasny,et al.  Digital mapping of soil salinity in Ardakan region, central Iran , 2014 .

[50]  Richard Gloaguen,et al.  Improved remote sensing detection of soil salinity from a semi-arid climate in Northeast Brazil , 2011 .

[51]  H. Medina,et al.  Regional-scale variability of soil properties in Western Cuba , 2017 .

[52]  Alfred Stein,et al.  Surface modelling of soil properties based on land use information , 2011 .

[53]  N. Coops,et al.  Prediction of soil properties using a process-based forest growth model to match satellite-derived estimates of leaf area index , 2012 .

[54]  G. Bollero,et al.  Soil Quality Assessment of Tillage Impacts in Illinois , 1999 .

[55]  Peter Keith Woodward,et al.  Artificial neural network for stress-strain behavior of sandy soils: Knowledge based verification , 2005 .

[56]  Hamidreza Azemati,et al.  Effective Environmental Factors on Designing Productive Learning Environments , 2018 .

[57]  Hossein Asadi,et al.  Spatial variability of soil organic matter using remote sensing data , 2016 .

[58]  D. Mulla,et al.  Aggregate Stability in the Palouse Region of Washington: Effect of Landscape Position , 1990 .

[59]  T. Skaggs,et al.  Regional-scale soil salinity assessment using Landsat ETM + canopy reflectance , 2015 .

[60]  W. Tan,et al.  Effect of different vegetation cover on the vertical distribution of soil organic and inorganic carbon in the Zhifanggou Watershed on the loess plateau , 2016 .

[61]  R. Taghizadeh‐Mehrjardi,et al.  Prediction of soil surface salinity in arid region of central Iran using auxiliary variables and genetic programming , 2016 .

[62]  L. Wilding,et al.  Spatial variability: its documentation, accommodation and implication to soil surveys , 1985 .

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

[64]  Ruhollah Taghizadeh-Mehrjardi,et al.  Digital mapping of cation exchange capacity using genetic programming and soil depth functions in Baneh region, Iran , 2016 .

[65]  B. Fu,et al.  Differential responses of shrubs and herbs present at the Upper Minjiang River basin (Tibetan Plateau) to several soil variables , 2006 .

[66]  William R. Horwath,et al.  On-Farm Assessment of Soil Quality in California ’ s Central Valley , 2001 .

[67]  André Vervoort,et al.  Spatial structures of soil organic carbon in tropical forests—A case study of Southeastern Tanzania , 2009 .

[68]  Zhongchen Wang,et al.  Relationship of spatial heterogeneity for vegetation and aeolian sand soil properties on longitudinal dunes in Gurbantunggut Desert, China , 2013, Environmental Earth Sciences.

[69]  Cristiano Ballabio,et al.  A Plant ecology approach to digital soil mapping, improving the prediction of soil organic carbon content in alpine grasslands , 2012 .

[70]  M. Gevrey,et al.  Two-way interaction of input variables in the sensitivity analysis of neural network models , 2006 .

[71]  Claudionor Ribeiro da Silva,et al.  Soil prediction using artificial neural networks and topographic attributes , 2013 .

[72]  Malcolm Coull,et al.  Mapping soil carbon stocks across Scotland using a neural network model , 2016 .

[73]  Marc Pansu,et al.  Handbook of Soil Analysis: Mineralogical, Organic and Inorganic Methods , 2006 .

[74]  A. Magurran,et al.  Measuring Biological Diversity , 2004 .

[75]  Sabine Grunwald,et al.  Integrating spectral indices into prediction models of soil phosphorus in a subtropical wetland , 2009 .

[76]  Yoshua Bengio,et al.  Pattern Recognition and Neural Networks , 1995 .

[77]  M. Varni,et al.  Suspended sediment concentration controlling factors: an analysis for the Argentine Pampas region , 2016 .

[78]  A. Bilgili,et al.  Spatial assessment of soil salinity in the Harran Plain using multiple kriging techniques , 2012, Environmental Monitoring and Assessment.

[79]  David Clifford,et al.  Putting regional digital soil mapping into practice in Tropical Northern Australia , 2015 .

[80]  B. Fu,et al.  Relationships between vegetation and soil and topography in a dry warm river valley, SW China , 2008 .

[81]  D G Rossiter,et al.  Digital soil mapping : Towards a multiple-use Soil Information System , 2005 .

[82]  Zhu,et al.  Prediction of Soil Properties Using Fuzzy Membership , 2007 .

[83]  Zhanhuan Shang,et al.  The relationship of vegetation and soil differentiation during the formation of black-soil-type degraded meadows in the headwater of the Qinghai-Tibetan Plateau, China , 2013, Environmental Earth Sciences.

[84]  B. Minasny,et al.  journal homepage: www.elsevier.com/locate/geoderma Global pedodiversity, taxonomic distance, and the World Reference Base , 2022 .

[85]  A. Huete,et al.  A review of vegetation indices , 1995 .

[86]  K. Höllig,et al.  Matlab® , 2019, Aufgaben und Lösungen zur Höheren Mathematik 1.

[87]  F. J. García-Haro,et al.  A generalized soil-adjusted vegetation index , 2002 .

[88]  R. Lal,et al.  Assessing land cover and soil quality by remote sensing and geographical information systems (GIS) , 2013 .

[89]  A. Tavili,et al.  Effective environmental factors in the distribution of vegetation types in Poshtkouh rangelands of Yazd Province (Iran) , 2004 .

[90]  M. El-Bana,et al.  Changes in vegetation composition and diversity in relation to morphometry, soil and grazing on a hyper-arid watershed in the central Saudi Arabia , 2012 .