Pedology and soil class mapping from proximal and remote sensed data

Poppiel, Raúl Roberto; Lacerda, Marilusa Pinto Coelho; Demattê, José Alexandre Melo; Oliveira, Manuel P.; Gallo, Bruna Cristina; Safanelli, José Lucas

doi:10.1016/j.geoderma.2019.04.028

Cited by 37 publications

(43 citation statements)

References 69 publications

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“…We obtained bare soil images by applying the Normalized Difference Vegetation Index – NDVI [10] to suppress vegetated areas (dense, moderate and sparse), and the Middle Infrared Index-MIDII [11] to mask areas with straw or burned. Then, the bare soil reflectance was retrieved in a bare soil composite (single image), denominated Synthetic Soil Image (SYSI) by selecting the lowest value of the MIDII which is correlated with low moisture content using the R programing language [12] , according to [8] . We applied the Multiple Endmember Spectral Mixture Analysis (MESMA) [13] to address the inter-class and intra-class soil spectral variability using as inputs data the SYSI and the endmembers to select the mixture model (a soil class) that best fits each pixel.…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

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Soil class map of the Rio Jardim watershed in Central Brazil at 30 meter spatial resolution based on proximal and remote sensed data and MESMA method

et al. 2019

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Geospatial soil information is critical for agricultural policy formulation and decision making, land-use suitability analysis, sustainable soil management, environmental assessment, and other research topics that are of vital importance to agriculture and economy. Proximal and Remote sensing technologies enables us to collect, process, and analyze spectral data and to retrieve, synthesize, visualize valuable geospatial information for multidisciplinary uses. We obtained the soil class map provided in this article by processing and analyzing proximal and remote sensed data from soil samples collected in toposequences based on pedomorphogeological relashionships. The soils were classified up to the second categorical level (suborder) of the Brazilian Soil Classification System (SiBCS), as well as in the World Reference Base (WRB) and United States Soil Taxonomy (ST) systems. The raster map has 30 m resolution and its accuracy is 73% (Kappa coefficient of 0.73). The soil legend represents a soil class followed by its topsoil color.

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Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

“…Poppiel et al. [8] presented a flowchart to illustrate the complete methodology. We showed summary quantifications of the mapped soil classes in Fig.…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Soil class map of the Rio Jardim watershed in Central Brazil at 30 meter spatial resolution based on proximal and remote sensed data and MESMA method

et al. 2019

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“…That was possible because the spectral patterns of SySI can provide valuable information on pedogenic processes, which are useful for understanding and predicting soil variation [56]. The SySI also can indicate the soil weathering products, which cause spatial variations in the soil color and temporally stable soil attributes, such as mineralogy and texture [10,62]. Thus, complementary RS data can improved prediction models, as reported by Loiseau et al [16] where adding RS covariates increased the R 2 and decreased the bias of the clay content estimation on bare topsoil layers (e.g., 0-30 cm).…”

Section: Interpretation Of Covariate Layersmentioning

confidence: 99%

“…Within a landform, there exists slight differences in local edaphic conditions, such as soil texture and mineralogy, and soil moisture and temperature regimes [39]. These local conditions provides the most significant alterations of the soil reflectance patterns [10,62] and segregation of the plant communities [39], which could be captured and measured by the SySI, SyVI w and SyVI d at the finest (local) resolution. Generally, the Keys to Soil Taxonomy [68] uses the same differing criteria to define families of soils.…”

Section: Interpretation Of Covariate Layersmentioning

confidence: 99%

Mapping at 30 m Resolution of Soil Attributes at Multiple Depths in Midwest Brazil

et al. 2019

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The Midwest region in Brazil has the largest and most recent agricultural frontier in the country where there is no currently detailed soil information to support the agricultural intensification. Producing large-extent digital soil maps demands a huge volume of data and high computing capacity. This paper proposed mapping surface and subsurface key soil attributes with 30 m-resolution in a large area of Midwest Brazil. These soil maps at multiple depth increments will provide adequate information to guide land use throughout the region. The study area comprises about 851,000 km2 in the Cerrado biome (savannah) in the Brazilian Midwest. We used soil data from 7908 sites of the Brazilian Soil Spectral Library and 231 of the Free Brazilian Repository for Open Soil Data. We selected nine key soil attributes for mapping and aggregated them into three depth intervals: 0–20, 20–60 and 60–100 cm. A total of 33 soil predictors were prepared using Google Earth Engine (GEE), such as climate and geologic features with 1 km-resolution, terrain and two new covariates with 30 m-resolution, based on satellite measurements of the topsoil reflectance and the seasonal variability in vegetation spectra. The scorpan model was adopted for mapping of soil variables using random forest regression (RF). We used the model-based optimization by tuning RF hyperparameters and calculated the scaled permutation importance of covariates in R software. Our results were promising, with a satisfactory model performance for physical and chemical attributes at all depth intervals. Elevation, climate and topsoil reflectance were the most important covariates in predicting sand, clay and silt. In general, for predicting soil chemical attributes, climatic variables, elevation and vegetation reflectance provided to be the most important of predictive components, while for organic matter it was a combination of climatic dynamics and reflectance bands from vegetation and topsoil. The multiple depth maps showed that soil attributes largely varied across the study area, from clayey to sandy, suggesting that less than 44% of the studied soils had good natural fertility. We concluded that key soil attributes from multiple depth increments can be mapped using Earth observations data and machine learning methods with good performance.

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“…Soils of tropical Midwest Brazil usually present high weathering degree and tend to have relatively homogenous profiles [5,7]. Some studies has been shown that topsoil spectral patterns are related to the subsoil pattern variations and dynamic processes which occur within the soil profile [35][36][37]. In addition, bare topsoil reflectance composites produced from Landsat time series [38][39][40] were considered as reliable proxies of topsoil spatial patterns, which can be integrated with other datasets by machine learning to better capture information from deeper layers of the Earth's crust [30].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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Pedology and soil class mapping from proximal and remote sensed data

Cited by 37 publications

References 69 publications

Soil class map of the Rio Jardim watershed in Central Brazil at 30 meter spatial resolution based on proximal and remote sensed data and MESMA method

Soil class map of the Rio Jardim watershed in Central Brazil at 30 meter spatial resolution based on proximal and remote sensed data and MESMA method

Mapping at 30 m Resolution of Soil Attributes at Multiple Depths in Midwest Brazil

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

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