Protocol for the use of legacy data and magnetic signature on soil mapping of São Paulo Central West, Brazil

Silvero, Nélida Elizabet Quiñonez; Siqueira, Diego Silva; Coelho, Ricardo Marques; Ferreira, Domingos Da Costa; Marques, José Carlos

doi:10.1016/j.scitotenv.2019.07.269

Cited by 7 publications

(6 citation statements)

References 52 publications

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“…Magnetic susceptibility (χ) at soil surface showed readings from 2.1 to 3,689 with a CV, mean value, and standard deviation of 2.32%, 239, and 555.9, respectively. Studies carried out using samples from soil surface in Brazil presented χ values varying from 48 to 9,670 and higher CV values compared to our data (Godinho Silva et al, 2016;Silvero et al, 2019;Teixeira et al, 2018). In layer C, χ had a CV, mean value, and standard deviation of 2.53%, 164.3, and 416.4, respectively.…”

Section: Soil Datasetcontrasting

confidence: 62%

“…Soil attributes, such as clay and sand contents, soluble aluminium (Al 3+ ), aluminium (Alsat) and base saturation (Bsat), cation exchange capacity (CEC), soil organic matter (SOM), sum of bases (SB), and the pH play a crucial role to identify climate changes (Gray and Bishop, 2019;, soil degradation (Chen and Rao, 2008;Lal, 2015;Nampak et al, 2018), soil classification systems Rizzo et al, 2020), soil fertility , and soil security (Bennett et al, 2019;McBratney et al, 2014). Likewise, soil property, such as the magnetic susceptibility (χ), which stems mainly from soil maghemite and magnetite contents, helps to understand pedogenesis (De Jong et al, 2000;de Souza Bahia et al, 2017;Jordanova, 2016;Lourenço et al, 2014;Maher, 1998;Torrent et al, 2010) and map soil classes (Ramos et al, 2017;Silvero et al, 2019;Teixeira et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Geotechnologies applied in digital soil mapping

Mendes¹

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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.

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Section: Soil Datasetcontrasting

confidence: 62%

Section: Introductionmentioning

confidence: 99%

Geotechnologies applied in digital soil mapping

Mendes¹

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“…The study areas varied from 1.75 km 2 (175 ha) (Pelegrino et al, 2016) to approximately 120,000 km 2 , which represents 48 % of the state of São Paulo (Silvero et al, 2019). Approximately 50 % of the studies were conducted in areas up to 120 km 2 .…”

Section: Descriptive Statistics Of the Data Extracted From The Studiesmentioning

confidence: 99%

“…The most detailed cartographic scale was 1:10,000 used in 49 studies (Giasson et 2017). The number of MU varied, using a simplified legend, (Figueiredo et al, 2008), between 3 and 34 (Vasques et al, 2015;Silvero et al, 2019) with an average of 9.5 MU per study. About 75 % of the studies had a ratio of up to approximately 0.4 MU km -2 but one study carried out by Pelegrino et al (2016) stands out for its high ratio of 2.8 MU km -2 , with a study area of 1.75 km 2 and five soil classes.…”

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%

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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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Soil mineralogical attributes estimated by color as accessed by proximal sensors and machine learning

Baldo

Marques

Fernandes

et al. 2021

Soil Science Soc of Amer J

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Detailed mapping is essential for land use and management planning. The mappings require a robust database. Costs and time associated with obtaining the database are high and, therefore, it is not always possbile to obtain it. Soil color is a pedoindicator attribute that can be easily characterized. This study aimed to use soil color, based on the RGB (red–green–blue) system and obtained by diffuse reflectance spectroscopy (DRS) and mobile proximal sensor (MPS) to estimate mineralogical attributes using machine learning techniques for the Western Plateau of São Paulo. A total of 600 samples were collected throughout the study area. The samples were analyzed by DRS and then photographed. The color data were obtained by the RGB system after analysis in a computer program. The samples were subjected to laboratory analysis to quantify the contents of crystalline and noncrystalline Fe, hematite, goethite, kaolinite, and gibbsite. The database was subjected to the random forest machine learning algorithm and geostatistics. The use of random forest allowed estimating soil mineralogical attributes based on the RGB system by DRS and MPS. Detailed maps of mineralogical attributes could be constructed using the RGB system by the DRS and MPS techniques. The MPS technique can be used to characterize soil color, reducing the costs associated with analysis and the time required for data collection.

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Protocol for the use of legacy data and magnetic signature on soil mapping of São Paulo Central West, Brazil

Cited by 7 publications

References 52 publications

Geotechnologies applied in digital soil mapping

Geotechnologies applied in digital soil mapping

Digital soil class mapping in Brazil: a systematic review

Soil mineralogical attributes estimated by color as accessed by proximal sensors and machine learning

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