Digital soil class mapping in Brazil: a systematic review

ABSTRACT: In Brazil several digital soil class mapping studies were carried out from 2006 onwards to maximize the use of existing maps and information and to provide estimates for wider areas. However, there is no consensus on which methods have produced superior results in the predictive value of soil maps. This study conducts a systematic review of digital soil class mapping in Brazil and aims to analyze the factors which can improve the accuracy of digital soil class maps. Data from 334 digital soil class mapping studies were grouped and analyzed by Student's t-test, Wilcoxon-Mann-Whitney test and Kruskal-Wallis test. When conventional maps were used for validation, the studies showed average values of 63 % and when field samples were used, 56 % for Overall Accuracy. Studies compatible with the Planimetric Cartographic Accuracy Standard for Digital Cartographic Products (PEC-PCD) averaged between 4 % and 15 % higher accuracy than those of the incompatible group. There seems to be no evidence that increasing the number of variables and samples results in more accurate soil map prediction, but studies using variables related to four soil-forming factors enhanced accuracy. From a density of 0.08 MU km–2 and upwards, it became more difficult for studies to obtain greater accuracy. Artificial neural network classifiers and Decision Tree models seem to be producing more accurate digital soil class maps.

摘要：自2006年起，巴西已开展多项数字土壤类别制图研究，旨在最大化利用现有地图与信息，并为更大范围区域提供土壤信息估算结果。然而，针对何种方法可提升土壤地图的预测性能，学界尚未达成共识。本研究对巴西数字土壤类别制图领域开展系统综述，旨在剖析可提升数字土壤类别制图精度的各类影响因素。本研究归集了334项数字土壤类别制图研究的相关数据，采用学生t检验（Student's t-test）、威尔科克森-曼-惠特尼检验（Wilcoxon-Mann-Whitney test）与克鲁斯卡尔-沃利斯检验（Kruskal-Wallis test）开展分组与统计分析。若以传统地图作为验证基准，相关研究的总体准确率（Overall Accuracy）平均值为63%；若以野外采样数据作为验证基准时，总体准确率平均值为56%。符合《数字制图产品平面制图精度标准》（Planimetric Cartographic Accuracy Standard for Digital Cartographic Products，简称PEC-PCD）的研究，其制图精度平均较不符合该标准的研究高出4%至15%。目前尚无证据表明，增加变量与采样数量可提升土壤地图预测精度；但采用与四大土壤形成因子相关的变量开展的研究，其制图精度显著提升。当制图单元（Mapping Unit，缩写MU）密度达到0.08个/km²及以上时，研究进一步提升制图精度的难度显著增大。人工神经网络分类器（Artificial Neural Network Classifier）与决策树（Decision Tree）模型似乎可生成精度更优的数字土壤类别制图成果。