Building an exposed soil composite processor (SCMaP) for mapping spatial and temporal characteristics of soils with Landsat imagery (1984–2014)

Soil information with high spatial and temporal resolution is crucial to assess potential soil degradation and to achieve sustainable productivity and ultimately food security. The spatial resolution of existing soil maps can commonly be too coarse to account for local soil variations and owing to t...

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Bibliographic Details
Published inRemote sensing of environment Vol. 205; pp. 1 - 17
Main Authors Rogge, Derek, Bauer, Agnes, Zeidler, Julian, Mueller, Andreas, Esch, Thomas, Heiden, Uta
Format Journal Article
LanguageEnglish
Published New York Elsevier Inc 01.02.2018
Elsevier BV
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Summary:Soil information with high spatial and temporal resolution is crucial to assess potential soil degradation and to achieve sustainable productivity and ultimately food security. The spatial resolution of existing soil maps can commonly be too coarse to account for local soil variations and owing to the cost and resource needs required to update information these maps lack temporal information. With improved computational processing capabilities, increased data storage and most recently, the increasing amount of freely available data (e.g. Landsat, Sentinel-2A/B), remote sensing imagery can be integrated into existing soil mapping approaches to increase temporal and spatial resolution of soil information. Satellite multi-temporal data allows for generating cloud-free, radiometrically and phenologically consistent pixel based image composites of regional scale. Such data sets are of particular use for extracting soil information in areas of intermediate climate where soils are rarely exposed. The Soil Composite Mapping Processor (SCMaP) is a new approach designed to make use of per-pixel compositing to overcome the issue of limited soil exposure. The objective of this paper is to demonstrate the automated processors ability to handle large image databases to build multispectral reflectance composite base data layers that can support large scale top soil analyses. The functionality of the SCMaP is demonstrated using Landsat imagery over Germany from 1984 to 2014 applied over 5year periods. Three primary product levels are generated that will allow for a long term assessment and distribution of soils that include the distribution of exposed soils, a statistical information related to soil use and intensity and the generation of exposed soil reflectance image composites. The resulting composite maps provide useful value-added information on soils with the exposed soil reflectance composites showing high spatial coverage that correlate well with existing soil maps and the underlying geological structural regions. •Development of an automated Soil Composite Mapping Processor (SCMaP)•Extraction of large-scale temporal and spatial soil information•SCMaP handles large image databases to build multispectral reflectance composites.•Functionality of SCMaP demonstrated for Germany from 1984 to 2014•Products allow for large scale top-soil analyses in temperate zones
ISSN:0034-4257
1879-0704
DOI:10.1016/j.rse.2017.11.004