Soil color and mineralogy are used as diagnostic criteria to distinguish different soil types. In the literature, 350–2500 nm spectra were successfully used to predict soil color and mineralogy, but these attributes currently are not mapped for most Brazilian soils. In this paper, we provided the first large-extent maps with 30 m resolution of soil color and mineralogy at three depth intervals for 850,000 km² of Midwest Brazil. We obtained soil 350–2500 nm spectra from 1397 sites of the Brazilian Soil Spectral Library at 0–20 cm, 20–60, and 60–100 cm depths. Spectra was used to derive Munsell hue, value, and chroma, and also second derivative spectra of the Kubelka–Munk function, where key spectral bands were identified and their amplitude measured for mineral quantification. Landsat composites of topsoil and vegetation reflectance, together with relief and climate data, were used as covariates to predict Munsell color and Fe–Al oxides, and 1:1 and 2:1 clay minerals of topsoil and subsoil. We used random forest for soil modeling and 10-fold cross-validation. Soil spectra and remote sensing data accurately mapped color and mineralogy at topsoil and subsoil in Midwest Brazil. Hematite showed high prediction accuracy (R² > 0.71), followed by Munsell value and hue. Satellite topsoil reflectance at blue spectral region was the most relevant predictor (25% global importance) for soil color and mineralogy. Our maps were consistent with pedological expert knowledge, legacy soil observations, and legacy soil class map of the study region.

中文翻译：

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巴西中西部近缘和遥感的土壤颜色和矿物学制图

土壤的颜色和矿物学被用作诊断标准，以区分不同的土壤类型。在文献中，350-2500 nm光谱已成功地用于预测土壤的颜色和矿物学，但是目前大多数巴西土壤未绘制这些属性。在本文中，我们提供了850,000 km ^2的三个深度间隔的具有30 m的土壤颜色和矿物学分辨率的第一张大范围地图巴西中西部地区。我们从0–20 cm，20–60和60–100 cm深度的巴西土壤光谱库的1397个站点获得了350–2500 nm的土壤光谱。光谱用于导出孟塞尔色相，值和色度，还用于导出Kubelka-Munk函数的二阶导数光谱，在其中识别关键光谱带并测量其振幅以进行矿物定量。表层土壤和植被反射率的Landsat复合材料，以及地形和气候数据，被用作协变量来预测孟塞尔颜色和Fe-Al氧化物以及表层土壤和下层土壤的1：1和2：1粘土矿物。我们使用随机森林进行土壤建模和10倍交叉验证。土壤光谱和遥感数据可准确绘制巴西中西部表层土壤和下层土壤的颜色和矿物学图。赤铁矿显示出较高的预测精度（R ²> 0.71），然后是Munsell值和色相。蓝色光谱区域的卫星表层土反射率是土壤颜色和矿物学最相关的预测因子（全球重要性的25％）。我们的地图与教育学专家知识，传统土壤观测以及研究区域的传统土壤分类图一致。