Soil Color and Mineralogy Mapping Using Proximal and Remote Sensing in Midwest Brazil

• Published in: Remote sensing (Basel, Switzerland) Vol. 12; no. 7; p. 1197
• Main Authors: Poppiel, Raúl Roberto, Lacerda, Marilusa Pinto Coelho, Rizzo, Rodnei, Safanelli, José Lucas, Bonfatti, Benito Roberto, Silvero, Nélida Elizabet Quiñonez, Demattê, José Alexandre Melo
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2020
• Subjects: Accuracy; aluminum oxide; Brazil; Cartography; Clay minerals; Climatic data; Color; data mining; derivative spectra; Diagnostic systems; expert opinion; Google Earth Engine; Hematite; Iron; Laboratories; Landsat; Landsat satellites; Machine learning; Mapping; meteorological data; Mineralogy; Minerals; Munsell color system; prediction; Reflectance; reflectance spectroscopy; Remote sensing; soil color; Soil mapping; Soil sciences; Soil types; Soils; spatial data; Spectra; Spectral bands; Spectrum analysis; subsoil; Subsoils; Topsoil; vegetation; Brazil; Cerrado Biome
• DOI: 10.3390/rs12071197
• ISSN: 2072-4292

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.