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Bishop, Thomas; McBratney, Alex B.

doi:10.1023/a:1013322103518

Cited by 26 publications

(1 citation statement)

References 22 publications

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“…These methods differ in the assumptions about the form of relationships between primary and ancillary variables, and how the secondary information is used to estimate the primary variable at unsampled locations (Goovaerts 1997;McBratney et al 2003). Several ancillary variables are available at the field scale, for example digitized soil surveys, digital elevation models (DEM) and derived terrain indices, remotely sensed images of soil surface reflectance, apparent electrical conductivity (EC a ) and measurements of soil properties obtained with on-the-go sensors (Adamchuk et al 2004;Bishop and McBratney 2002;Hengl et al 2004;McBratney et al 2003). One of the most valuable sources of secondary information could be remote sensing because of the strong association between reflectances and SOC, and the low cost and real-time availability of imagery.…”

Section: Geostatistical Techniquesmentioning

confidence: 99%

Estimating soil organic carbon from soil reflectance: a review

et al. 2009

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Soil organic carbon (SOC) concentration is a useful soil property with which to guide agricultural applications of chemical inputs. To enable this, simple, accurate, rapid and inexpensive methods are needed to produce maps of surface SOC concentrations. Researchers have investigated estimates of soil surface properties from remotely sensed information as a means of rapidly quantifying and monitoring some surface soil properties, such as SOC. The objective of this paper is to review the potential and limitations of remotely sensed data for mapping and evaluating SOC. Several statistical methods including simple regression models, the 'soil line' approach, principal component analysis and geostatistics have been applied to data to investigate the accuracy of such estimates. A review of the literature shows that predictive equations are not universal and require new regression models for every scene. An important benefit of remotely sensed data is to suggest a sampling strategy that can lead to improved representation of spatial heterogeneity in SOC.

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