Abstract
Multitemporal collections of satellite images and their products have recently been explored in digital soil mapping. This study aimed to produce a bare soil image (BSI) for the São Paulo State (Brazil) to perform a pedometric analysis for different geographical levels. First, we assessed the potential of the BSI for predicting the surface (0.00-0.20 m) and subsurface (0.80-1.00 m) clay, iron oxides (Fe 2 O 3 ), aluminum (m%) and bases saturation (V%) contents at the state level, which [...]
Highlights
Digital soil mapping (DSM) has been explored in Brazil to produce updated and higher resolution maps of soil properties and soil taxonomic classes (Chagas et al, 2010; ten Caten et al, 2012; Samuel-Rosa et al, 2013; Giasson et al, 2015; Arruda et al, 2016; Vasques et al, 2016; Moura-Bueno et al, 2019)
The Synthetic Soil Image (SYSI) with 30 m resolution produced over the São Paulo State reveals different patterns that are related to soil and rock types (Figure 2)
A direct representation of bare soil reflectance of São Paulo State was produced, representing most of the historical and recent cropped area. The relationship of this information was confirmed with clay and Fe2O3 spatial distribution by a satisfactory prediction performance when employed to machine learning with auxiliary spatial information
Summary
Digital soil mapping (DSM) has been explored in Brazil to produce updated and higher resolution maps of soil properties and soil taxonomic classes (Chagas et al, 2010; ten Caten et al, 2012; Samuel-Rosa et al, 2013; Giasson et al, 2015; Arruda et al, 2016; Vasques et al, 2016; Moura-Bueno et al, 2019). The DSM relies on the relationship of soil observations with environmental data employed in a model, which delivers a predicted value for a specific location usually coupled with an uncertainty estimate (McBratney et al, 2003; Lagacherie et al, 2006; Minasny and McBratney, 2016). In this regard, Earth Observation (EO) data are explored as model predictors due to their widespread availability and ability to cover large geographical areas. We can mention the practical and economical limitations of sampling strategies that seek to improve the representation of soil diversity, as well as the lack or poor relationship of some environmental information with soils when calibrating prediction models
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