Training pedologist for soil mapping: Contextualizing methods and its accuracy using the project pedagogy approach

Costa, Elias Mendes; Ceddia, Marcos Bacis; Santos, Felipe Nascimento dos; Silva, Laiz de Oliveira; Rezende, Igor Prata Terra de; Fernandes, Douglath Alves Corrêa

doi:10.36783/18069657rbcs20200130

Cited by 1 publication

(1 citation statement)

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“…Our results reveal the potential of an effective method to depict the soil spatial variability using computational systems and EO data by producing a synthetic soil image (SYSI). In this sense, capacitation courses (e.g., Universidade PronaSolos), free and open access datasets and products (e.g., febr) and software (e.g., Python, R), collaborative work and others, will be necessary to increase the assimilation of these technologies in future works (Dalmolin et al, 2020;Samuel-Rosa et al, 2020;Costa et al, 2021).…”

Section: Some Considerations Regarding the Pronasolosmentioning

confidence: 99%

Fine-scale soil mapping with Earth Observation data: a multiple geographic level comparison

Safanelli¹,

Demattê²,

Santos³

et al. 2021

Revista Brasileira De Ciência Do Solo

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Multitemporal collections of satellite images and their products have recently been explored in digital soil mapping. This study aimed to produce a bare soil image (BSI) for the São Paulo State (Brazil) to perform a pedometric analysis for different geographical levels. First, we assessed the potential of the BSI for predicting the surface (0.00-0.20 m) and subsurface (0.80-1.00 m) clay, iron oxides (Fe 2 O 3 ), aluminum (m%) and bases saturation (V%) contents at the state level, which are important properties for soil classification. In this task, legacy soil samples, the BSI and terrain attributes were employed in machine learning. In a second moment, we evaluated the capacity of the BSI for clustering the landscape at the regional level, comparing the predicted patterns with a legacy semi-detailed soil map from a smaller reference site. In the final stage, the predicted soil maps from the state level were investigated at the farm level considering several sites distributed across the São Paulo state. Our results demonstrated that clay and Fe 2 O 3 reached the best prediction performance for both depths at the state level, reaching a RMSE of less than 10 %, RPIQ higher than 1.6 and R 2 of at least 0.41. Additionally, the predicted landscape clusters had a significant association with the main pedological classes, subsurface color, soil mineralogy and texture from the legacy semi-detailed soil map. Illustrative examples at the farm level indicated great capacity of BSI in detecting the variations of soils, which were linked to several soil properties, such as texture, iron content, drainage network, among others. Therefore, this study demonstrates that BSI is valuable information derived from optical Earth Observation data that can contribute to the future of soil survey and mapping in Brazil (PronaSolos).

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Section: Some Considerations Regarding the Pronasolosmentioning

confidence: 99%

Fine-scale soil mapping with Earth Observation data: a multiple geographic level comparison

Safanelli¹,

Demattê²,

Santos³

et al. 2021

Revista Brasileira De Ciência Do Solo

View full text Add to dashboard Cite

show abstract

Training pedologist for soil mapping: Contextualizing methods and its accuracy using the project pedagogy approach

Abstract: Training pedologist for soil mapping: Contextualizing methods and its accuracy using the project pedagogy approach. Rev Bras Cienc Solo. 2021;45:e0200130.

Cited by 1 publication

References 32 publications

Fine-scale soil mapping with Earth Observation data: a multiple geographic level comparison

Fine-scale soil mapping with Earth Observation data: a multiple geographic level comparison

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