TY - JOUR
T1 - An integrated approach for the evaluation of quantitative soil maps through Taylor and solar diagrams
AU - Wadoux, Alexandre M.J.C.
AU - Walvoort, Dennis J.J.
AU - Brus, Dick J.
PY - 2022/1/1
Y1 - 2022/1/1
N2 - For many decades, soil scientists have produced spatial estimates of soil properties using statistical and non-statistical mapping models. Commonly in soil mapping studies the map quality is assessed through pairwise comparison of observed and predicted values of a soil property, from which statistical indices summarizing the quality of the entire map are computed. Often these indices are based on average error and correlation statistics. In this study, we recommend a more appropriate and effective method of map evaluation by means of Taylor and solar diagrams. Taylor and solar diagrams are summary diagrams exploiting the relationship between statistical indices to visualize differentiable aspects of map quality into a single plot. An important advantage over current map quality evaluation is that map quality can be assessed from the combined effect of a few statistical quantities, not just on the basis of a single index or list of indices. We illustrate the use of common statistical indices and their combination into summary diagrams with a simulation study and two applications on soil data. In the simulation study nine maps with known statistical properties are produced and evaluated with tables and summary diagrams. In the first case study with soil data, change in the quality of a large-scale topsoil organic carbon map is tracked for a number of permutations in the mapping model parameters, whereas in the second case study several maps of topsoil organic carbon content for the same area, made by various statistical and non-statistical models, are compared and evaluated. We consider that in all cases better insights in map quality are obtained with summary diagrams, instead of using a single index or an extensive list of indices. This underpins the importance of using integrated summary graphics to communicate on quantitative map quality so as to avoid excessive trust that a single map quality index may suggest.
AB - For many decades, soil scientists have produced spatial estimates of soil properties using statistical and non-statistical mapping models. Commonly in soil mapping studies the map quality is assessed through pairwise comparison of observed and predicted values of a soil property, from which statistical indices summarizing the quality of the entire map are computed. Often these indices are based on average error and correlation statistics. In this study, we recommend a more appropriate and effective method of map evaluation by means of Taylor and solar diagrams. Taylor and solar diagrams are summary diagrams exploiting the relationship between statistical indices to visualize differentiable aspects of map quality into a single plot. An important advantage over current map quality evaluation is that map quality can be assessed from the combined effect of a few statistical quantities, not just on the basis of a single index or list of indices. We illustrate the use of common statistical indices and their combination into summary diagrams with a simulation study and two applications on soil data. In the simulation study nine maps with known statistical properties are produced and evaluated with tables and summary diagrams. In the first case study with soil data, change in the quality of a large-scale topsoil organic carbon map is tracked for a number of permutations in the mapping model parameters, whereas in the second case study several maps of topsoil organic carbon content for the same area, made by various statistical and non-statistical models, are compared and evaluated. We consider that in all cases better insights in map quality are obtained with summary diagrams, instead of using a single index or an extensive list of indices. This underpins the importance of using integrated summary graphics to communicate on quantitative map quality so as to avoid excessive trust that a single map quality index may suggest.
KW - Accuracy
KW - Digital soil mapping
KW - Error
KW - Goodness-of-fit
KW - Pedometrics
KW - Soil science
U2 - 10.1016/j.geoderma.2021.115332
DO - 10.1016/j.geoderma.2021.115332
M3 - Article
AN - SCOPUS:85115195413
SN - 0016-7061
VL - 405
JO - Geoderma
JF - Geoderma
M1 - 115332
ER -