Spatial Variability prediction of soil erodibility index using digital soil mapping technique in Baneh city Kanisef region

Document Type : Original Article

Abstract

Soil erodibility (K) index is one of vital parameters in water erosion prediction. Therefore knowledge about spatial variability of this parameter (K) could efficacy help to model water erosion in area of interest. Our purpose is to predict spatial variability of soil erodibility index using digital soil mapping technique in Baneh region (Kanisef area), Kurdistan Province. In this study, based on hypercube sampling methods, 217 soil sampling sites were selected in area of 4000-ha and then samples collected from depth 0-30 cm and some soil analysis (i.e. calcium carbonates, clay, silt, sand, surface special weight and soil organic carbon) in the laboratory measured. Using RETC software soil infiltration values were obtained and then K factor calculated according to Vaezi equation (2008). The relationship between K factor and ancillary data covariates (derived from DEM and Landsat image) was obtained by land-soil models (Solim) and artificial neural network. Result showed that Solim model (R2 and RMSE 0/72 and 0/00013, respectively) have higher performance than artificial neural network (R2 and RMSE 0/67 and 0/00015, respectively) for soil erodibility index prediction. Our result also showed it is possible to map soil erodibility index continuously with reasonable accuracy. Finally digital map of K factor was prepared using Soilm model in the study area. The digital map of K factor obtained by Solim indicated ranging of soil erodibility from 0.0094 to 0.0095 ton.ha/Mj.mm. We recommend prediction of spatial variation of K factor by the other digital soil mapping techniques.

Keywords

References

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Applied Soil Research
Volume 6, Issue 2
August 2018
Pages 15-26

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History

  • Receive Date: 12 November 2016
  • Revise Date: 04 September 2018
  • Accept Date: 01 January 2017

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