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.

几十年来，土壤科学家使用统计和非统计绘图模型对土壤特性进行了空间估计。通常在土壤制图研究中，地图质量是通过土壤特性的观测值和预测值的成对比较来评估的，从中计算总结整个地图质量的统计指数。通常，这些指数基于平均误差和相关性统计。在这项研究中，我们推荐了一种更合适、更有效的利用泰勒图和太阳图进行地图评估的方法。泰勒图和太阳图是汇总图，利用统计指数之间的关系将地图质量的不同方面可视化为单个图。与当前地图质量评估相比的一个重要优势是，地图质量可以通过几个统计量的组合效果来评估，而不仅仅是基于单个索引或索引列表。我们通过模拟研究和土壤数据的两种应用来说明常用统计指数的使用及其组合到汇总图中。在模拟研究中，生成了九张具有已知统计特性的地图，并用表格和汇总图对其进行了评估。在使用土壤数据的第一个案例研究中，根据绘图模型参数中的多个排列跟踪了大比例尺表土有机碳图的质量变化，而在第二个案例研究中，多个表土有机碳含量图用于对由各种统计和非统计模型制作的相同区域进行比较和评估。我们认为在所有情况下，通过汇总图获得更好的地图质量洞察，而不是使用单个索引或广泛的索引列表。这强调了使用集成摘要图形就定量地图质量进行交流的重要性，以避免单一地图质量指数可能暗示的过度信任。