Spatial variability of iron oxides in soils from Brazilian sandstone and basalt

Silva, Laércio Santos; Júnior, José Marques; Barrón, Vidal; Gomes, Romário Pimenta; Teixeira, Daniel De Bortoli; Siqueira, Diego Silva; Vasconcelos, Vinícius

doi:10.1016/j.catena.2019.104258

Cited by 46 publications

(31 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This criterion was determined from the degree of similarity between maps, such as the 20% error adopted by Silva et al. (2020) to compare Hm and Gt content maps obtained by diffuse reflectance spectroscopy and X‐ray diffraction techniques.…”

Section: Resultsmentioning

confidence: 99%

Magnetic susceptibility in the prediction of soil attributes in southern Brazil

Ramos

Inda

Barrón

et al. 2021

Soil Science Soc of Amer J

Self Cite

View full text Add to dashboard Cite

Global demand for soil information has led to investigations that have adopted ways to estimate soil attributes quickly and effectively. In this context, magnetic susceptibility (χ) has gained prominence because it is a technique capable of estimating other attributes that are more difficult to acquire. This study aimed to (a) evaluate the performance of χ for the prediction of sand, silt, clay, hue, hematite/(hematite + goethite) ratio, Fe content of pedogenic iron oxides, and remaining phosphorus and (b) develop maps of χ, soil attributes and attributes predicted by χ in the state of Rio Grande do Sul (RS), Brazil. Here, 198 soil samples under forest and native pasture were used for testing the potential of χ as a predictive technique, separating the data into calibration (nc = 149) and validation sets (nv = 49). Linear regression was used to obtain the pedotransfer equations according to soil classes and lithology. To visualize the distribution of the values of χ and other soil attributes in RS, maps were made with the real values of χ and the real and estimated values of soil attributes. The great range of the χ values and related attributes was associated with the lithological and pedological influence, allowing the construction of predictive models that encompass a large gradient of χ. In the predictions made in groups, the attributes of Oxisols and Ultisols were best estimated by χ; however, among the lithology groups, the extrusive igneous rocks stood out.

show abstract

Section: Resultsmentioning

confidence: 99%

Magnetic susceptibility in the prediction of soil attributes in southern Brazil

Ramos

Inda

Barrón

et al. 2021

Soil Science Soc of Amer J

Self Cite

View full text Add to dashboard Cite

show abstract

“…In addition, Liles et al [90] reported that soils developed over sedimentary rocks, as was most of our study area, showed an increasing in the coefficient of variation for Munsell chroma. Silva et al [10] found that the spatial variability of goethite was about twice higher than hematite in soils from the Western Paulista Plateau of Brazil, strongly influenced by the parent material. Thus, the lowest model performance for chroma may be related to effects of the density and locations of soil observations used for color predictions, combined with the high occurrence of sedimentary parent materials in the study area.…”

Section: Use Of Regression Models For Mapping Soil Propertiesmentioning

confidence: 99%

“…Therefore, to determine the positions of these bands, the resolution may be mathematically enhanced by calculating the derivatives of the spectra. The second derivative (SD) of the KM function is a promising method for spectral quantitative analysis [54], with sensibility for soil minerals' detection slightly smaller than X-ray diffraction [10,24]. Thus, we transformed the reflectance data of soils into the KM and then calculated the SD using Savitzky-Golay method (fitting 2nd polynomial order to 40-smoothing points), within The Unscrambler software [55].…”

Section: Spectral Processingmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2020

View full text Add to dashboard Cite

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.

show abstract

“…This method consists of using a set of contiguous data points to fit a polynomial curve as described in Torrent and Barron (2015). As pointed out by Scheinost et al (1998), andSilva et al (2020), the secondderivative of the K-M function curves has slightly smaller detection sensitivity than X-ray diffraction for quantifying minerals in soils.…”

Section: Processing Spectral Datamentioning

confidence: 99%

Geotechnologies applied in digital soil mapping

Mendes¹

View full text Add to dashboard Cite

Geotechnologies applied in digital soil mappingThe civilisation lives in a world of maps and soil maps are vital at regional and farm levels to achieve best management agricultural practices. Soil is the substrate for plant growth and vital to the fulfilment of the food demand. However, the cartographic scale of those soil maps, which for the best management agricultural practice (BMAP) have to be the most detailed as possible and they are scarce. The Digital Soil Mapping (DSM) became the easiest and feasible approach to achieve such demand. Despite previous studies have tried to better characterise soil depths, there is space for improvements on its dynamics and mapping. Looking at this goal, Remote Sensing (RS) technologies have proven to be a great power on this task. Nevertheless, some aspects of that approach still need to be tested using another hybrid, stochastic, and deterministic models for the predictions of magnetic susceptibility (MS) and soil attributes at surface and subsurface. Therefore, chapter 1 presents the evaluation of nine machine learning algorithms (MLAs) to predict the free iron content at the soil surface (e.g. 0 -20 cm) using the DSM framework. Based on the best performance of those nine MLAs, we selected five MLAs. Chapter 2 shows the use of those five MLAs with usual and new environmental variables (e.g. DEM, drainage network, and soil spectroscopy) to predict the MS and soil attributes up to 100 cm depth. Attempts on quantifying soil mineral consist of having an observation measured using traditional laboratory soil analysis. However, developments in interpreting and analysing the visible and near-infrared (VNIR) diffuse reflectance have allowed quantifying some soil minerals. In chapter 3, it implements a novel framework using VNIR spectroscopy to quantify the main soil minerals and evaluates the application of digital soil mapping framework to spatialise those soil minerals. Last but not least, the chapter 4 presents the novelty of using all predict soil components as predictors of the soil mapping units in the region of Piracicaba-SP at farm scale (1:20,000), generating the first detailed digital soil map of the region. Additionally in this chapter, it was created the digital yield environmental map for sugarcane production. Thus, this thesis presents a new integrative framework to achieve detail soil maps for the BMAP and serves as a guide for future soil surveys across the world.

show abstract

Spatial variability of iron oxides in soils from Brazilian sandstone and basalt

Cited by 46 publications

References 47 publications

Magnetic susceptibility in the prediction of soil attributes in southern Brazil

Magnetic susceptibility in the prediction of soil attributes in southern Brazil

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

Geotechnologies applied in digital soil mapping

Contact Info

Product

Resources

About