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]

• Multi-temporal SYSI S2-MSI image provided the best model performances. • PlanetScope image did not provide any improvements in model performances. • Satellite images in different resolutions affect soil property maps and strategies related to classification and management. Soil maps at appropriate scales can aid decision-making in agriculture and the environment. In this sense, remote sensing products have shown their power to investigate soil properties, but the assessment of its spatial information can be hampered by the presence of other objects than soil. To overcome this issue, soil scientists have been studying spatial patterns from multi-temporal satellite images aiming at at improving soil property maps. In this work, we applied the cubist algorithm to predict topsoil properties (clay, sand, organic matter and iron contents, and soil color components) in south-eastern Brazil using multi-temporal (Landsat8-OLI, and Sentinel2-MSI) and single-date images (PlanetScope, Landsat8-OLI, and Sentinel2-MSI). We aimed to evaluate the influence of satellite's spatial, spectral and temporal resolutions on soil mapping. Predictive models were constructed with 120 soil samples and using four (vis-NIR) and six (vis-NIR-SWIR) spectral bands as predictors in a 10-fold cross-validation procedure. The multi-temporal image obtained from the Sentinel2-MSI satellite (with 10 m pixel size and six spectral bands), showed the best model performances in cross-validation (R2 between 0.48 and 0.78). The PlanetScope image, which has only four bands in the vis-NIR region and spatial resolution of 3 m did not improve model performances, although the R2 values were higher for soil color components (R2 > 0.5). Satellite images in different spatial, spectral and temporal resolution provides slightly different soil property maps which may promote different strategies regarding soil classification and management. However, satellite images should be used with caution, as they provide only surficial information about the soil variability and confirmation with field surveys is required. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) is a crucial metric for mitigating greenhouse gas emissions and developing climate-smart agriculture. DayCent is widely used to simulate SOC dynamics and soil trace gas fluxes in various ecosystems. In this study, we developed DayCent-CUTE (auto-Calibration, sensitivity, and Uncertainty analysis ToolSet) for conducting global sensitivity analysis (GSA), auto-calibration, and uncertainty analysis for the model. The tool encompassed a pair of GSA methods and two distinct parameter optimization methods. A collection of 30 field experiments, encompassing 212 combinations of management treatments and 581 SOC measurements, was divided into 18 sites for calibration and 12 sites for independent model evaluation. The posterior parameter distribution obtained from the auto-calibration process reduces the model bias and RMSE values, while the Nash-Sutcliffe efficiency and R2 values showed improvements. The DayCent-CUTE proves to be an efficient and flexible tool that enhances the applications of the DayCent model. • Graphical user interface for DayCent's sensitivity analysis and auto-calibration. • Incorporate exponential curve and model parameters for soil C pools initialization. • SCE-UA and DDS algorithm efficiently search parameter solutions. [ABSTRACT FROM AUTHOR]

• Lithology, pedogenesis and relief affect soil magnetic susceptibility. • Pedogenesis strongly affect magnetic susceptibility on more evolved soil classes. • Lithology contribute more to magnetic susceptibility in less evolved soil classes. • Magnetic susceptibility had good correlation with soil texture, CEC and iron content. • Field sensor can be used in delimitation of soil classes and digital soil mapping. Soil magnetic susceptibility (κ) has potential to be used as a pedoenvironmental indicator from which mineralogy, pedogeochemical, pedogeomorphological and pedogenic processes can be inferred. It can be used in pedosphere studies, as an auxiliary information for appropriate and sustainable soil use and management. This research aimed to analyze how pedogenesis and geochemical processes affect the κ and some of its attributes, as well as its potential use in discriminating soil great groups, following the digital soil mapping approach. The study area is located in São Paulo State - Brazil. Soil samples were collected for physical–chemical analysis from 79 locations (0–20 cm depth). At these sites, magnetic susceptibility was measured with a portable field instrument and analyzed in terms of geology, relief and soil class. The results showed that geology strongly affects κ, mainly in diabase derived soils, followed by metamorphosed siltstone and siltstone. In fluvial sediments, the κ exhibits different behaviors due to different sediments deposited by the Capivari River. In less evolved soils, such as Cambisols, lithology is a more important contributor to κ than pedogenesis. In more evolved soils, pedogenesis increases κ, whereas argilluviation/ferralitization reduces it. The κ values did not decrease significantly or even increase downslope, due to the presence of diabase on the lower parts. Differences in κ where observed between diabase bedrock located in different parts of the study area, indicating more of an influence by geomorphic processes rather than lithology. With respect to soil attributes, positive correlations between κ and base saturation, cation exchange capacity, organic matter, and iron and clay content were found, whereas a negative correlation was found between κ and sand content. The κ correlates with changes in lithology and soil class demonstrating its application as a potential tool for the discrimination of soil great groups and digital soil mapping. [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]

• Spectral and terrain features for mapping cropland soils in Brazil. • Detailed soil spatial distribution and SOC stock was estimated in croplands. • Predicted maps revealed a high variability of topsoil among the cropped areas. • Earth Observation data is a valuable source for mapping and monitoring cropland soils. Despite the natural spatial variability, cropland soils are subject to many interventions that can lead to alterations of soil functioning. As the cropland expansion took place in Brazil the last decades, leading to significant land-use change and environmental impacts, detailed information about soils is fundamental for sustainable development. Thus, considering the lack of spatially explicit information about cropland soils in Brazil, we aimed at performing high-resolution mapping of key topsoil attributes using spectral and terrain features extracted from Earth observation data (EOD). With the resulting information, we also aimed at performing a general examination of the main agricultural regions and estimate the total organic carbon stocks on croplands soils. For this, we obtained environmental predictors from the historical collection of Landsat data and the digital elevation model from Shuttle Radar Topographic Mission at the cloud-based platform of Google Earth Engine. The environmental predictors (30 m spatial resolution) with georeferenced soil samples (n = 5097) were used for predicting the topsoil content (0–20 cm) of clay, sand, silt, cation exchange capacity, pH, soil organic carbon (SOC) and SOC stock. Prediction models of clay, sand, SOC content, and SOC stocks had the best performance metrics, achieving a R2 ranging from 0.44 to 0.74 and ratio of performance to the interquartile range higher than 1.5. The predicted maps revealed the variability of topsoil among the cropped areas, indicating that the agricultural expansion took place on sandy soils. The SOC stock map provided consistent estimates compared to previous datasets but revealed additional information at the local and regional scales. Thus, this study supports the proposition that EOD is a valuable source for extracting environmental features for mapping and monitoring cropland soils at finer resolutions, assisting the evaluation of soil spatial distribution and the historical agriculture expansion over large geographical areas. [ABSTRACT FROM AUTHOR]