Digital soil mapping: Predicting soil classes distribution in large areas based on existing soil maps from similar small areas

ABSTRACT There is an ever-growing need for soil maps, since detailed soil information is directly related to agricultural activities, urbanization and environmental protection. However, there is a lack of large-scale soil maps in developing tropical countries such as Brazil. Albeit there are soil maps for small areas, large regions usually have undetailed maps. Considering the importance of finding low-cost alternatives to overcome the lack of detailed soil information, the main objective of this work was to manually create a local soil map and extrapolate it to similar larger areas that lack detailed soil information. The Anhumas River Basin, in the municipality of Itajubá, southeast Brazil, was manually mapped and this map was used to predict soils distribution for the entire municipality. First, the prediction model was tested in the same basin and provided sufficient results, achieving 67% global accuracy and 0.62 Kappa coefficient. Second, the resulting map was used together with the soil map of the larger José Pereira Basin to map the entire municipality, achieving 54% global accuracy and 0.40 Kappa coefficient. Low resolution parent material information was found to confuse models; maps showed better results when this variable was removed. The Minas Gerais soil map presents general mapping units only for the Acrisol class and its associations with other soil classes in the area. The soil map predicted by this work identified more soil classes. Mapping representative areas and extrapolating these maps to larger similar areas constitute a promising alternative to overcome the lack of detailed soil maps.

摘要 对土壤图的需求与日俱增，详尽的土壤信息直接关联农业生产、城市化进程与环境保护工作。然而，巴西等热带发展中国家仍缺乏大尺度土壤图。尽管已有小区域尺度的土壤图，但大范围区域的土壤图往往较为简略、精度有限。鉴于寻找低成本解决方案以弥补详细土壤信息缺失的重要性，本研究的核心目标是手动绘制局部区域土壤图，并将其外推至其他缺乏详细土壤数据的相似更大区域。

研究以巴西东南部伊塔茹巴市（Itajubá）境内的安努马斯河流域（Anhumas River Basin）为对象开展手动制图，并将该图用于预测整个市域的土壤空间分布。首先，在同一流域内对该预测模型进行验证，结果表现优异，全局准确率达67%，Kappa系数（Kappa coefficient）为0.62。其次，将所得土壤图与面积更大的何塞·佩雷拉流域（José Pereira Basin）土壤图结合，完成整个市域的土壤制图，全局准确率达54%，Kappa系数为0.40。

研究发现，低分辨率母质信息会干扰模型预测；移除该变量后，土壤图的预测效果更佳。米纳斯吉拉斯州（Minas Gerais）现有土壤图仅针对强淋溶土（Acrisol）类及其与该区域内其他土壤类别的组合设置了通用制图单元。本研究预测得到的土壤图则识别出了更多土壤类别。通过绘制典型区域土壤图并将其外推至其他相似的更大区域，可为解决详细土壤图缺失的问题提供一条颇具前景的途径。