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Introduction to Digital Soil Mapping using the SOC Mapping Cookbook

author: Guillermo Federico Olmedo date: 12th March 2018 autosize: false

DSM: definition

Digital Soil Mapping (DSM) is the creation and the population of a geographically referenced soil database, generated at a given resolution by using field and laboratory observation methods coupled with environmental data through quantitative relationships (see http://digitalsoilmapping.org/).

Digital Soil Mapping: Origin

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“Soils being a result of a very complicated interaction between local climate, plant and animal organisms, content and structure of parent rocks, topography, and, finally, age of the terrain” (Dokuchaev, 1883)

$$П = f(К,О,Г)В$$

where П is the soil, and К is the climate, О are the organism and Г represents the paretn material. В representes the age factor.

DSM: Conceptual basis

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  • Dokuchaev 1883
  • Jenny 1941
  • Troeh 1964
  • Walker 1968
  • Legros and Bonneric 1979
  • Moore et al 1993
  • Heuvelink and Webster 2001

The quantitative-digital model

$$S_a = f(s_{[x; y ~ t]},c_{[x; y ~ t]},o_{[x; y ~ t]},r_{[x; y ~ t]},p_{[x; y ~ t]},a_{[x; y ~ t]},n_{[x; y ~ t]})$$

  • where $S_a$ is the soil attribute of interest at a specific location; $s$ is the soil or other soil properties; $c$ is the climate or climatic properties of the environment; $o$ are the organisms, vegetation, fauna or human activity; $r$ is topography or landscape attributes; $p$ is parent material or lithology; $a$ is the substrate age or the time factor; and $n$ represents the space or spatial location. The spatial coordinates and time ($x, y ~ t$) must be known for the seven factors.

  • The left side of the equation is usually represented by the available geo-spatial soil observational data (e.g., from legacy soil profile collections) and the right side of the equation is represented by the soil prediction factors.

McBratney, Mendonça-Santos y Minasny, (2003) – Geoderma 117. pp 3 - 52

The soil prediction factors

  • S: Soil (maps, profiles, samples)
  • C: Climate (temperature...)
  • O: Landuse, NDVI, Biomass
  • R:
    • DEM + derivates
    • Aspect
    • Slope
    • Curvature
    • Topographic Wetness Index
    • Landscape/Terrain classification
  • P – Litology
  • A – Age
  • N – Spatial position

The functions

  • Multiple linear models
  • Geostatistical models
  • Decision trees
  • Neural networks
  • Fuzzy logic models
  • Kernel based models

Baseline datasets

  • Digital terrain parameters
  • Climate surfaces
  • Remote sensing
  • Legacy and polygon maps (i.e., soil, geology, land use/cover)
  • SoilGrids250m system

Pelletier et al. 2016, Shangguan et al., 2017, Hengl et al. 2017, Fick and Hijmans, 2017

Global-to-local and local-to-global SOC mapping

  • All models are wrong but some of they are useful
  • Models should not compete but inform each other
  • Different models (and modeling cultures) will capture different portions of SOC
  • There are no best method on digital soil mapping (no silver bullets)
  • Ensemble learning should be part of the pedometrics agenda
  • The eternal question is, how inform nationwide policy decision based on the best information available? Box 1976, Breiman 2001, Ho and Pepyne 2002,, Finke 2012, Qiao et al. 2015

Digital terrain analysis (DTA) in SAGA GIS

  • Topography is a major driver of soil variability
  • Topography can be represented by digital elevation models
  • We have digital elevation models globally available
  • Using DTA we can derive topographic attributes (e.g. Slope, aspect, wetness index).
  • Exercise 1. Derive terrain parameters in SAGA GIS using a digital elevation model of 1x1 km.

Workflow (Guevara et al, NACP, 2017)

title: no Guevara et al, NACP, 2017

Workflow using the SOC Mapping Cookbook