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This article in SSSAJ

  1. Vol. 76 No. 6, p. 2116-2127
    Received: Sept 16, 2010
    Published: October 24, 2012

    * Corresponding author(s): MogensH.Greve@agrsci.dk


Using Digital Elevation Models as an Environmental Predictor for Soil Clay Contents

  1. Mogens H. Greve *a,
  2. Rania Bou Kheira,
  3. Mette B. Grevea and
  4. Peder K. Bøcherb
  1. a Dep. of Agroecology and Environment, Faculty of Agricultural Sciences (DJF), Aarhus Univ. Blichers Allé 20, P.O. Box 50, DK-8830 Tjele, Denmark
    b Ecoinformatics and Biodiversity Group, Dep. of Bioscience, Aarhus Univ. Ny Munkegade 114, DK-8000 Aarhus C, Denmark


The objective of this study was to evaluate the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) as an environmental predictor for soil clay content (SCC). It was based on the applicability of different DEMs, i.e., SRTM with 90-m resolution and airborne Light Detection and Ranging (LIDAR) (in 24- and 90-m resolution), using regression-tree analysis. Ten terrain parameters were generated from these DEMs. These terrain parameters were used along other environmental variables to statistically explain SCC content in Denmark. Results indicated that the SRTM tree model (T1: 90-m resolution) explained the variability of SCC measurements quasi-similarly (variance V = 60%) to the LIDAR tree models with 24-m (T2) or 90-m (T3) resolution (V = 60% for T2 and 61.5% for T3). The prediction performances (in terms of RMSE) of the produced maps (using these trees) compared with independent field observations from the validation data set (9000 sites) were estimated as follows: Map T1, RMSE = 3.57%; Map T2, RMSE = 3.25%; and Map T3, RMSE = 3.15%. The relative improvement of T2 compared with T1 or T3 varied between 8.96 and 11.76%, respectively. Independent validation data also reflected higher correlations between measured SCC and SCC predicted from T2 (R2 = 0.60) compared with the other tree models (T1, R2 = 0.56; T3, R2 = 0.54). The modeling results indicate that the SRTM (including derivatives) has less predictive power than the LIDAR DEMs (with different resolutions) for mapping SCC in a low-relief landscape in Denmark.

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Copyright © 2012. Copyright © by the Soil Science Society of America, Inc.