Estimating soil organic carbon from soil reflectance: a review

Estimating soil organic carbon from soil reflectance: a review 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

Estimating soil organic carbon from soil reflectance: a review

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Publisher
Springer US
Copyright
Copyright © 2009 by Springer Science+Business Media, LLC
Subject
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
ISSN
1385-2256
eISSN
1573-1618
D.O.I.
10.1007/s11119-009-9123-3
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

Precision AgricultureSpringer Journals

Published: Jun 11, 2009

References

  • On-the-go soil sensors for precision agriculture
    Adamchuk, VI; Hummel, JW; Morgan, MT; Upadhyaya, SK
  • Soil carbon and nitrogen changes as influenced by tillage and cropping systems in some Iowa soils
    Al-Kaisi, MM; Yin, XH; Licht, MA
  • A method for manual endmember selection and spectral unmixing
    Bateson, A; Curtiss, B
  • The reflectance spectra of organic matter in the visible near-infrared and short-wave infrared region (400–2500 nm) during a controlled decomposition process
    Ben-Dor, E; Inbar, Y; Chen, Y
  • Creating field extent digital elevation models for precision agriculture
    Bishop, TFA; McBratney, AB
  • Global soil characterization with VNIR diffuse reflectance spectroscopy
    Brown, DJ; Shepherd, KD; Walsh, MG; Dewayne Mays, M; Reinsch, TG

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