A methodology for digital soil mapping in poorly-accessible areas

A. Cambule, D.G. Rossiter, J.J. Stoorvogel

Research output: Contribution to journalArticleAcademicpeer-review

33 Citations (Scopus)

Abstract

Effective soil management requires knowledge of the spatial patterns of soil variation within the landscape to enable wise land use decisions. This is typically obtained through time-consuming and costly surveys. The aim of this study was to develop a cost-efficient methodology for digital soil mapping in poorly-accessible areas. The methodology uses a spatial model calibrated on the basis of limited soil sampling and explanatory covariables related to soil-forming factors, developed from readily available secondary information from accessible areas. The model is subsequently applied in the poorly-accessible areas. This can only be done if the environmental conditions in the poorly-accessible areas are also found in the accessible areas in which the model is developed. This study illustrates the methodology in an exercise to predict soil organic carbon (SOC) concentration in the Limpopo National Park, Mozambique. Readily-available secondary data was used as explanatory variables representing the soil-forming factors. Conditions in the accessible and poorly-accessible areas corresponded sufficiently to allow the extrapolation of the spatial model into the latter. The spatial variation of SOC in the accessible area was mostly described by the sampling cluster (71.5%) and the landscape unit (46.3%). Therefore ordinary (punctual) kriging (OK) and kriging with external drift (KED) based on the landscape unit were used to predict SOC. A linear regression (LM) model using only landscape stratification was used as control. All models were independently validated with test sets collected in both accessible and poorly-accessible areas. In the former the root mean squared error of prediction (RMSEP) was 0.42–0.50% SOC. The ratio between the RMSEP in the poorly-accessible and accessible areas was 0.67–0.72, showing that the methodology can be applied to predict SOC in poorly-accessible areas as successful as in accessible areas. The methodology is thus recommended for areas with similar access problems, especially for baseline studies and for sample design in two-stage surveys
Original languageEnglish
Pages (from-to)341-353
JournalGeoderma
Volume192
DOIs
Publication statusPublished - 2013

Keywords

  • residual maximum-likelihood
  • reflectance spectroscopy
  • shifting cultivation
  • terrain attributes
  • spatial prediction
  • organic-matter
  • carbon
  • variogram
  • scales
  • geostatistics

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