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This article in SSSAJ

  1. Vol. 75 No. 3, p. 1032-1043
     
    Received: July 20, 2010
    Published: May, 2011


    * Corresponding author(s): brendan.malone@sydney.edu.au
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doi:10.2136/sssaj2010.0280

Using Additional Criteria for Measuring the Quality of Predictions and Their Uncertainties in a Digital Soil Mapping Framework

  1. Brendan P. Malone *a,
  2. Jaap J. de Gruijterb,
  3. Alex B. McBratneyc,
  4. Budiman Minasnyd and
  5. Dick J. Bruse
  1. a Faculty of Agriculture Food & Natural Resources The Univ. of Sydney S207 John Woolley Building NSW 2006 Australia
    b Wageningen Univ. and Research Centre P.O. Box 47 6700 AA Wageningen The Netherlands
    c Faculty of Agriculture Food & Natural Resources The Univ. of Sydney S224 John Woolley Building NSW 2006 Australia
    d Faculty of Agriculture Food & Natural Resources The Univ. of Sydney S222 John Woolley Building NSW 2006 Australia
    e Wageningen Univ. and Research Centre P.O. Box 47 6700 AA Wageningen The Netherlands

Abstract

In this paper we introduce additional criteria to assess the quality of digital soil property maps. Soil map quality is estimated on the basis of validating both the accuracy of the predictions and their uncertainties (which are expressed as a prediction interval [PI]). The first criterion is an accuracy measure that is different in form to the usual mean square error (MSE) because it accounts also for the prediction uncertainties. This measure is the spatial average of the statistical expectation of the mean square error of a simulated random value (MSES). The second criterion addresses the quality of the uncertainties which is estimated as the total proportion of the study area where the (1−α)–PI covers the true value. Ideally, this areal proportion equals the nominal value (1 − α). In the Lower Hunter Valley, NSW, Australia, we used both criteria to validate a soil pH map using additional units collected from a probability sample at five depth intervals: 0 to 5, 5 to 15, 15 to 30, 30 to 60, and 60 to 100 cm. For the first depth interval (0–5 cm) in 96% of the area, the 95% PI of pH covered the true value. The root mean squared simulation error (RMSES) at this depth was 1.0 pH units. Generally, the discrepancy between the nominal value and the areal proportion in addition to the RMSES increased with soil depth, indicating largely a growing imprecision of the map and underestimation of the uncertainty with increasing soil depth. In exploring this result, conventional map quality indicators emphasized a combination of bias and imprecision particularly with increasing soil depth. There is great value in coupling conventional map quality indicators with those which we propose in this study as they target the decision making process for improving the precision of maps and their uncertainties. For our study area we discuss options for improving on our results in addition to determining the possibility of extending a similar sampling approach for which multiple soil property maps can be validated concurrently.

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Copyright © 2011. Copyright © by the Soil Science Society of America, Inc.