Search

• We used three different satellite multi-temporal bare soil images. • Clay prediction models were generated from five different sensors data. • Best prediction models were fitted using hyperspectral and ultraspectral data. • Regional clay prediction models were applied in study area data. • Only the ultraspectral regional model obtained a good prediction result. Proximal and remote sensors are emerging as powerful sources of soil spectral information at an array of temporal and spatial resolutions. This study investigated clay content prediction at three spectral acquisition levels: laboratory, airborne, and spaceborne. Two approaches were tested, the use of prediction models developed with local and regional spectral libraries (52 samples for local scale and 950, 200 e 224 samples for regional scale), termed internal and external models respectively. Local soil samples (52), were collected in a smallholder area, 83 ha, located in southeastern Brazil. Spectral data in the visible (Vis), near-infrared (NIR), and shortwave infrared (SWIR) regions were acquired in the laboratory using FieldSpec 3 sensor, and the clay content was determined by sedimentation technique. Afterward, bare soil images from AISA-FENIX, Planetscope, Sentinel-2 MultiSpectral Instrument (MSI) and Landsat-8 Operational Land Imager (OLI) were obtained. The clay content determined in the laboratory was related to the soil spectra acquired by each of the sensors and was predicted using the Cubist regression tree algorithm. The results obtained from local spectral libraries showed good predictions using FieldSpec 3 and AISA-FENIX sensors. Landsat-8 OLI and Sentinel-2 MSI provided satisfactory results, while PlanetScope gave poor results. For the prediction using regional spectral libraries, only lab-based FieldSpec 3 sensor provided a fair prediction, while other sensors gave poor results. This study demonstrated that soil sensing is possible at any level taking into account its advantages and limitations. This approach paves the way for acquiring soil spectra for smallholder farms. [ABSTRACT FROM AUTHOR]

[Display omitted] • Carbon sequestration potential of the clay fraction. • Determination of the carbon saturation deficit of each mineral of the clay fraction. • Identification of the equation for the prediction of carbon sequestration potential. • Identification of land uses with the greatest carbon sequestration potential. The capacity of soil to sequester carbon (C) is a key process that promotes the reduction of CO 2 in the atmosphere. Soils can absorb as much as 20% of anthropogenic carbon emissions, which can contribute to mitigate climate change. This capacity relies on the organo-mineral association, which includes different minerals, Fe and Al oxides, which have a critical soil organic carbon (SOC) sorption surface. Based on an equation of the potential C saturation deficit of fine soil particles (<20 μm/silt and clay fractions) for tropical regions, this study investigated the SOC sequestration potential of the clay fraction for soils in Piracicaba region, São Paulo State, Brazil as influenced by the clay minerals. This potential was fitted to a spatial regression model for soil depths 0–20 cm and 80 to 100 cm. In the surface layer, the sequestration potential was mostly explained by the relative abundance of soil minerals (Kaolinite, Hematite, Goethite and Gibbsite) determined using vis-NIR-SWIR spectroscopy. A direct relationship was observed with Goethite and Gibbsite, indicating that low concentrations would reduce the sequestration potential. At 80 to 100 cm depth, Kaolinite and Hematite explained most variation in SOC sequestration potential. Additionally, the C associated with the mineral fraction and the C saturation potential as a function of minerals were modeled and a strong importance of hematite in the C sequestration and stabilization cycle was identified at both depths. The individual mineral contribution to SOC sequestration potential was also mapped, which identified high contributions of goethite and gibbsite for deep soil layers. The influence of land use on the carbon sequestration potential of minerals was observed, with the greatest potential being found in areas with pasture and cropping mosaics and grassland and forest mosaics, with a high presence of kaolinite and hematite. These minerals have a greater potential for carbon sequestration at greater depths and, therefore, could be critical in climate change mitigation strategies. [ABSTRACT FROM AUTHOR]

[Display omitted] • Goethite, haematite, kaolinite and gibbsite abundances releveled by Vis-NIR-SWIR data; • Novelty framework to reach a continuums bare soil reflectance image; • Major soil mineralogical components mapping for the whole Brazilian territory; • Soil mineralogy mapping for large area by spectroscopy and Digital Soil Mapping; 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. [ABSTRACT FROM AUTHOR]

The free iron content is a vital indicator of pedogenic processes in tropical soils and can be used to understand the soil's weathering history and aid in classification. Despite its importance in agriculture and pedology, laboratory analyses of soil iron content are not widely used because they are costly and time-consuming. Remote sensing data combined with digital soil mapping are effective tools to regionalise soil iron content. They can reduce the number of soil samples needed to characterise soil variability and, consequently, laboratory analysis costs. This study aimed to create a strategy for mapping free iron content using a 35-year time series of Landsat images combined with topographic parameters at two spatial resolutions (5 and 30 m) in a region with high variability in soils and geology in the state of São Paulo, Brazil. The dataset comprised 344 observations of free iron content at 0–20 cm depth over a 2574 km2 area. The dataset was split into calibration and a validation set (85:15%), and the environmental variables were chosen based on the scorpan factors. Spatial prediction functions for free iron were developed using several machine learning algorithms linking soil observations with the environmental variables. We found that the temporal bare soil image improved model performance. Although 5 and 30 m resolution terrain data differed slightly, the best-fit model was obtained at 5 m resolution (root mean square error, 25.09 g kg−1; adjusted R2, 0.84). Among the evaluated machine learning algorithms, Random Forest was the most accurate method for predicting free iron distribution in the study area. The free iron content map can identify soil types in more detail and should be prioritised in future pedological studies. [Display omitted] • Four out of eight machine learning fit well total free iron predictions. • Hybrid method has a high performance as well as the Random Forest. • Predictions using 5 m or 30 m resolution terrain data have a slight difference. • Integrating approaches improve free iron content predictions. [ABSTRACT FROM AUTHOR]

• Hydrological data contributed more than relief for prediction of sand, clay, and OM. • Confluence angle and channel sinuosity have distinct patterns over different soil types. • Hydrology, relief, and SySI data resulted in R2 of 0.71 for clay prediction. • Drainage morphometry changes across the landscape according to the soil class. Drainage network (DN) is the representation of all the stream channels developed over the landscape. The morphometry of DN describes the relationship between channel characteristics and basin geometry, which is regulated by a series of processes, such as weathering, geomorphology, sediment erosion/deposition. The interaction between these factors impacts soil formation, resulting in a relationship between DN morphometry and soil characteristics. Unstable surfaces produced shallow soils, enhancing surface runoff and high drainage density, while stable landscapes favor vertical infiltration through old and weathered soils. Digital soil mapping has benefited from multiple environmental variables, such as relief and satellite data, but DN information can offer great contributions for the prediction of soil attributes. In this work, we applied a set of complex DN variables to perform digital maps of clay, sand, and soil organic carbon (SOC), for a 1378 km2 site in the São Paulo state, Brazil. We analyzed the relationship between the drainage density (DD), drainage frequency (DF), channel sinuosity, and confluence angle with soil classes and attributes. The Cubist and Random Forest algorithms were tested to predict the soil information, and to evaluate the impact of the new hydrological variables. The results showed that landscapes predominated by clayey soils favor surface runoff and increase channel formation, with higher channel sinuosity and acute confluence angles. These new drainage variables contributed 35 to 55% for SOC prediction. DD and DF were the most important drainage variables on the models, ranging from 65 to 70%. The external validation reached R2 of 0.72 and 0.56 for the prediction of clay and sand, respectively. The impact of DN information on the model performance suggests that more work is needed to better explore and understand the relationship between DN and soil information. [ABSTRACT FROM AUTHOR]