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Vis-NIR-SWIR Remote Sensing Products as New Soil Data for Digital Soil Mapping

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Pedometrics

Part of the book series: Progress in Soil Science ((PROSOIL))

Abstract

Since the early ages of soil surveys, air photographs have been intensively used by soil surveyors for depicting the soil variations across landscapes. The variations of soil surfaces, specifically color and ratio of vegetation cover, that were revealed by this early remote sensing product were a great help for interpolating the scarce soil observations and for delineating the soil class boundaries. This was further transposed in digital soil mapping (McBratney et al. 2003), thanks to the large availability of remote sensing images provided by the emerging spatial data infrastructures. Up to now, digital soil mappers have mainly used remote sensing images as spatial data inputs for representing the landscape variables that are related with soil, such as vegetation and parent material (the soil covariates). Boettinger et al. (2008) reviewed the main indicators that could be retrieved for estimating these soil covariates, using multispectral data acquired in the visible near-infrared and short-wave infrared (VIS, 400–700 nm; NIR, 700–1100 nm; SWIR, 1100–2500 nm) spectral domain. After a spatial overlay with the sparse sets of observed and measured sites collected in a given area, the indicators derived from remote sensing have been used as independent variables in regression-like models or as external drift in geostatistic models (McBratney et al. 2003, Chap. 12 of this book).

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Authors and Affiliations

  1. LISAH, Univ Montpellier, INRA, IRD, Montpellier SupAgro, 34060, Montpellier, France

    Philippe Lagacherie & Cécile Gomez

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  1. Philippe Lagacherie
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  2. Cécile Gomez
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Correspondence to Philippe Lagacherie .

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Editors and Affiliations

  1. Sydney Institute of Agriculture & School of Life and Environmental Sciences, The University of Sydney, Sydney, New South Wales, Australia

    Alex. B. McBratney

  2. Sydney Institute of Agriculture & School of Life and Environmental Sciences, The University of Sydney, Sydney, New South Wales, Australia

    Budiman Minasny

  3. Sydney Institute of Agriculture & School of Life and Environmental Sciences, The University of Sydney, Sydney, New South Wales, Australia

    Uta Stockmann

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Lagacherie, P., Gomez, C. (2018). Vis-NIR-SWIR Remote Sensing Products as New Soil Data for Digital Soil Mapping. In: McBratney, A., Minasny, B., Stockmann, U. (eds) Pedometrics. Progress in Soil Science. Springer, Cham. https://doi.org/10.1007/978-3-319-63439-5_13

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