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This article in JEQ

  1. Vol. 39 No. 1, p. 1-4
     
    Received: Aug 24, 2009


    * Corresponding author(s): dlobell@stanford.edu
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doi:10.2134/jeq2009.0326

Remote Sensing of Soil Degradation: Introduction

  1. David B. Lobell *
  1. Dep. of Environmental Earth System Science and Program on Food Security and the Environment, Stanford Univ., 473 Via Ortega, Stanford, CA 94305

Abstract

In the 21st century, mapping and monitoring the occurrence of soil degradation will be an important component of successful land management. Remote sensing, with its unique ability to measure across space and time, will be an increasingly indispensible tool for assessing degradation. However, much of the recent experience and progress in using remote sensing and other geospatial technologies to map soil degradation is reported outside of the peer-reviewed literature. This motivated the organization of a special collection of papers focused on remote sensing of soil degradation, to highlight recent successes, common challenges, and promising new approaches. This introductory paper provides an overview of the papers, gaps in knowledge, and future research directions. Across several regions and types of degradation, many assessments to date have relied heavily on data from the Landsat satellite sensor. Many approaches have also relied at some point on subjective visual interpretation, either of the satellite imagery itself or to provide field data used to train models that use satellite data. While subjectivity is not necessarily bad, it precludes repeatability and makes it even more important to rigorously test model estimates with independent data. Overall, it remains quite challenging to find robust relationships between remote sensing measures and soil degradation, particularly for slight to moderate levels of degradation. There have nonetheless been some clear successes, and there remains great potential for progress. Promising directions outlined in the papers include using multi-year measures of vegetation condition, combining different sensor systems including optical and radar data, and using advanced statistical techniques such as Bayesian networks and decision trees.

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Copyright © 2010. American Society of Agronomy, Crop Science Society of America, Soil Science SocietyAmerican Society of Agronomy, Crop Science Society of America, and Soil Science Society of America