Mapping Brazilian soil mineralogy using proximal and remote sensing data

• Autor(a) principal: Rosin, Nícolas Augusto
• Data de Publicação: 2023
• Outros Autores: Demattê, José A.M., Poppiel, Raul Roberto, Silvero, Nélida E.Q., Rodriguez-Albarracin, Heidy S., Rosas, Jorge Tadeu Fim, Greschuk, Lucas Tadeu, Bellinaso, Henrique, Minasny, Budiman, Gomez, Cecile, Marques Júnior, José [UNESP], Fernandes, Kathleen [UNESP]
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1016/j.geoderma.2023.116413; http://hdl.handle.net/11449/249743
• topic: Bare soil image; Digital soil mapping; Pedometrics; Reflectance spectroscopy

Minerals control many soil functions and play a crucial role in addressing global existential issues. Measuring the abundance of soil minerals is a laborious, costly, and time-consuming task; however, soil spectroscopy can be a useful tool to overcome this issue. This work aimed to map the abundance of major mineralogical components of soils in Brazil from surface to 1 m deep and at a spatial resolution of 30 m. Spectral data of the Brazilian Soil Spectral Library with Vis-NIR-SWIR was used to estimate the abundance of haematite, goethite, kaolinite, and gibbsite. These minerals were spatialized using digital soil mapping techniques. We also developed a novel framework to obtain bare soil reflectance for areas without natural or anthropic soil exposure (continuous image) and used it as covariate. Soil minerals and their abundances were successfully estimated by Vis-NIR-SWIR reflectance. Haematite predictions presented the most accurate results with Random Forest models, followed by gibbsite, kaolinite, and goethite. The spatial validation with reference mineralogical data found R2 of 0.64 (haematite), 0.40 (goethite), 0.20 (kaolinite/Kt), 0.29 (gibbsite/Gbs), and 0.40 (Kt/Kt + Gbs). The resulting maps of soil minerals were in accordance with the geology, pedology, climate, and relief of Brazil and revealed the spatial distribution of mineral abundances at a finer resolution than existing geological and pedological maps, reaching a farm level detail.