Digital soil mapping including additional point sampling in Posses ecosystem services pilot watershed, southeastern Brazil

Silva, Bárbara Pereira Christófaro; Silva, Marx Leandro Naves; Avalos, Fabio Arnaldo Pomar; Menezes, Michele Duarte de; Curi, Nilton

doi:10.1038/s41598-019-50376-w

Cited by 31 publications

(21 citation statements)

References 56 publications

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“…The median number of samples was 2,463. The lowest number of samples (74) was found in studies that used field observations for spatial soil class prediction models based on Decision Trees and Logistic Regression algorithms (Silva et al, 2019). Nevertheless, in this article the authors conducted other studies with additional points that improved the prediction performance of each model.…”

Section: Descriptive Statistics Of the Data Extracted From The Studiesmentioning

confidence: 94%

“…A mean of nine predictor variables were used per study; i.e., the variables selected and used in the predictive models per study. The maximum number of variables used in the same study was 43 (Silva et al, 2019); on the other hand, in one of the studies (Pelegrino et al, 2016) only two variables were used (aspect and wetness index) obtaining overall accuracy of 50 %.…”

Section: Descriptive Statistics Of the Data Extracted From The Studiesmentioning

confidence: 99%

“…Of the 334 studies, 263 presented cartographic scale and spatial resolution (pixel size) information used (Table 3); 38 studies were found incompatible with the planimetric PEC-PCD, since their pixel size is higher than that indicated at the cartographic scale. (Giasson et al, 2006) 2008 (Figueiredo et al, 2008) 2009 (Crivelenti et al, 2009) 2010 (Chagas et al, 2010); (Coelho and Giasson, 2010) (Höfig et al, 2014); (Teske et al, 2014) 2015 (Bagatini et al, 2015); (Giasson et al, 2015); (Teske et al, 2015a); (Teske et al, 2015b); (Vasques et al, 2015) 2016 (Arruda et al, 2016); (Bagatini et al, 2016); (Demattê et al, 2016); (Dias et al, 2016); (Henrique et al, 2016); (Pelegrino et al, 2016) 2017 (Chagas et al, 2017); (Wolski et al, 2017) 2018 (Costa et al, 2018); (Meier et al, 2018) 2019 (Campos et al, 2019a); (Campos et al, 2019b); (Moura-Bueno et al, 2019); (Silva et al, 2019); (Silvero et al, 2019) All learning algorithms were assigned to a type of classifier such as Artificial Neural Network (ANN), Bayes classifiers, Decision Tree (DT), Logistic Regression (LR) and Support Vector Machine (SVM). Approximately 95 % of the studies used DT, ANN and LR classifiers (Table 4).…”

Section: Descriptive Statistics Of the Data Extracted From The Studiesmentioning

confidence: 99%

See 2 more Smart Citations

Digital soil class mapping in Brazil: a systematic review

Coelho

Giasson

Campos

et al. 2021

Sci. agric. (Piracicaba, Braz.)

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In Brazil several digital soil class mapping studies were carried out from 2006 onwards to maximize the use of existing maps and information and to provide estimates for wider areas. However, there is no consensus on which methods have produced superior results in the predictive value of soil maps. This study conducts a systematic review of digital soil class mapping in Brazil and aims to analyze the factors which can improve the accuracy of digital soil class maps. Data from 334 digital soil class mapping studies were grouped and analyzed by Student's t-test, Wilcoxon-Mann-Whitney test and Kruskal-Wallis test. When conventional maps were used for validation, the studies showed average values of 63 % and when field samples were used, 56 % for Overall Accuracy. Studies compatible with the Planimetric Cartographic Accuracy Standard for Digital Cartographic Products (PEC-PCD) averaged between 4 % and 15 % higher accuracy than those of the incompatible group. There seems to be no evidence that increasing the number of variables and samples results in more accurate soil map prediction, but studies using variables related to four soil-forming factors enhanced accuracy. From a density of 0.08 MU km-2 and upwards, it became more difficult for studies to obtain greater accuracy. Artificial neural network classifiers and Decision Tree models seem to be producing more accurate digital soil class maps.

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Section: Descriptive Statistics Of the Data Extracted From The Studiesmentioning

confidence: 94%

Section: Descriptive Statistics Of the Data Extracted From The Studiesmentioning

confidence: 99%

Section: Descriptive Statistics Of the Data Extracted From The Studiesmentioning

confidence: 99%

See 1 more Smart Citation

Digital soil class mapping in Brazil: a systematic review

Coelho

Giasson

Campos

et al. 2021

Sci. agric. (Piracicaba, Braz.)

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“…In DSM studies [68][69][70][71][72][73][74][75], random forests [42] is increasingly being used to infer relationships between diverse soil attributes (at single and multiple depths) and several covariates (from multiple sources and resolutions) across landscapes. This fact relies on that RF can handle both linear and nonlinear relationships in data.…”

Section: Soil Modelling By Random Forest (Rf)mentioning

confidence: 99%

“…RF models can be interpreted by providing measures for variable importance [68][69][70][71][72][73][74][75], based on the increase in mean square error when a covariate is randomly permuted. Thus, we used the folds estimates to calculate the mean frequency of use for the covariates in the models and reported as a measure of the scaled permutation importance for each soil attribute prediction [42], using the ranger package version 0.11.1 [77] in R [49].…”

Section: Covariates' Importancementioning

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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Hydrological response of a headwater catchment in Southeast Brazil—Threshold patterns of stormflow response

Chittolina

Rocha

Domingues

et al. 2023

Hydrological Processes

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Headwater catchments are essential water sources for sustaining low flow in downstream rivers. The montane region of Serra da Mantiqueira in Southeast Brazil is the largest water supplier for the densely populated area of São Paulo, where we investigated the hydrological response of a subtropical catchment and four of its sub‐catchments. We discussed the relationships between rainfall, streamflow, baseflow, stormflow, soil moisture, and water table depth using high‐frequency field observations over 4 years. Baseflow accounted for most of the streamflow during the dry season and for half of streamflow in the wet season. In contrast, the variations in soil moisture were dampened, with increases in the early wet season preceding the increase in streamflow and decreases in the late wet season following the decrease in streamflow. The mean runoff coefficient and baseflow index varied from 23% to 37% and from 62% to 75% across the catchments, respectively. At the event scale, all catchments had a pronounced threshold behaviour, where stormflow increased sharply when event precipitation reached ≃10 mm, soil moisture reached ≃51% and water table depth decreased to ≃135 cm. The event stormflow coefficient was highly variable, with a maximum of 25%. The temporally integrated stormflow coefficient was low for conditions below a soil moisture threshold (≃3%–10%) and high above a soil moisture threshold (≃8%–15%). Our estimates of flows and threshold behaviours were consistent across the various study catchments, improving our understanding of runoff generation at a catchment scale. This information should help improve the management of water resources focused on water and food security in a context of climate change.

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Digital soil mapping including additional point sampling in Posses ecosystem services pilot watershed, southeastern Brazil

Cited by 31 publications

References 56 publications

Digital soil class mapping in Brazil: a systematic review

Digital soil class mapping in Brazil: a systematic review

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

Hydrological response of a headwater catchment in Southeast Brazil—Threshold patterns of stormflow response

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