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7 October 2011 Goodness-of-fit measures: what do they tell about vegetation variable retrieval performance from Earth observation data
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Abstract
The capability of models to predict vegetation biophysical variables is usually evaluated by means of one or several goodness-of-fit measures, ranging from absolute error indices (e.g. the root mean square error, RMSE) over correlation based measures (e.g. coefficient of determination, R2) to a group of dimensionless evaluation indices (e.g. relative RMSE). Hence, the greatest difficulty for the readers is the lack of comparability between the different models' accuracies. Therefore, the objective of our study was to provide an overview about the quantitative assessment of biophysical variable retrieval performance. Furthermore, we aimed to suggest an optimal set of statistical measures. This optimum set of statistics should be insensitive to the magnitude of values, range and outliers. For this purpose, a literature review was carried out, summarizing the statistical measures that have been used to evaluate model performances. Followed by this literature review and supported by some exemplary datasets, a range of statistical measures was calculated and their interrelationships analyzed. From the results of the literature review and the test analyses, we recommend an optimum statistic set, including RMSE, R², the normalized RMSE and some other indicators. Using at least the recommended statistics, comparability of model prediction accuracies is guaranteed. If applied, this will enable a better intercomparison of scientific results urgently needed in times of increasing data availability for current and upcoming EO missions.
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Katja Richter, Tobias B. Hank, Clement Atzberger, and Wolfram Mauser "Goodness-of-fit measures: what do they tell about vegetation variable retrieval performance from Earth observation data", Proc. SPIE 8174, Remote Sensing for Agriculture, Ecosystems, and Hydrology XIII, 81740R (7 October 2011); https://doi.org/10.1117/12.897980
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