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

Detalhes bibliográficos
Autor(a) principal: Safanelli,José Lucas
Data de Publicação: 2021
Outros Autores: Demattê,José Alexandre Melo, Santos,Natasha Valadares dos, Rosas,Jorge Tadeu Fim, Silvero,Nélida Elizabet Quiñonez, Bonfatti,Benito Roberto, Mendes,Wanderson de Sousa
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Revista Brasileira de Ciência do Solo (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832021000100307
Resumo: ABSTRACT 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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spelling Fine-scale soil mapping with Earth Observation data: a multiple geographic level comparisonremote sensingsoil cartographysoil mappingpedometricsPronaSolosABSTRACT 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).Sociedade Brasileira de Ciência do Solo2021-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832021000100307Revista Brasileira de Ciência do Solo v.45 2021reponame:Revista Brasileira de Ciência do Solo (Online)instname:Sociedade Brasileira de Ciência do Solo (SBCS)instacron:SBCS10.36783/18069657rbcs20210080info:eu-repo/semantics/openAccessSafanelli,José LucasDemattê,José Alexandre MeloSantos,Natasha Valadares dosRosas,Jorge Tadeu FimSilvero,Nélida Elizabet QuiñonezBonfatti,Benito RobertoMendes,Wanderson de Sousaeng2021-12-06T00:00:00Zoai:scielo:S0100-06832021000100307Revistahttp://www.scielo.br/scielo.php?script=sci_serial&pid=0100-0683&lng=es&nrm=isohttps://old.scielo.br/oai/scielo-oai.php||sbcs@ufv.br1806-96570100-0683opendoar:2021-12-06T00:00Revista Brasileira de Ciência do Solo (Online) - Sociedade Brasileira de Ciência do Solo (SBCS)false
dc.title.none.fl_str_mv Fine-scale soil mapping with Earth Observation data: a multiple geographic level comparison
title Fine-scale soil mapping with Earth Observation data: a multiple geographic level comparison
spellingShingle Fine-scale soil mapping with Earth Observation data: a multiple geographic level comparison
Safanelli,José Lucas
remote sensing
soil cartography
soil mapping
pedometrics
PronaSolos
title_short Fine-scale soil mapping with Earth Observation data: a multiple geographic level comparison
title_full Fine-scale soil mapping with Earth Observation data: a multiple geographic level comparison
title_fullStr Fine-scale soil mapping with Earth Observation data: a multiple geographic level comparison
title_full_unstemmed Fine-scale soil mapping with Earth Observation data: a multiple geographic level comparison
title_sort Fine-scale soil mapping with Earth Observation data: a multiple geographic level comparison
author Safanelli,José Lucas
author_facet Safanelli,José Lucas
Demattê,José Alexandre Melo
Santos,Natasha Valadares dos
Rosas,Jorge Tadeu Fim
Silvero,Nélida Elizabet Quiñonez
Bonfatti,Benito Roberto
Mendes,Wanderson de Sousa
author_role author
author2 Demattê,José Alexandre Melo
Santos,Natasha Valadares dos
Rosas,Jorge Tadeu Fim
Silvero,Nélida Elizabet Quiñonez
Bonfatti,Benito Roberto
Mendes,Wanderson de Sousa
author2_role author
author
author
author
author
author
dc.contributor.author.fl_str_mv Safanelli,José Lucas
Demattê,José Alexandre Melo
Santos,Natasha Valadares dos
Rosas,Jorge Tadeu Fim
Silvero,Nélida Elizabet Quiñonez
Bonfatti,Benito Roberto
Mendes,Wanderson de Sousa
dc.subject.por.fl_str_mv remote sensing
soil cartography
soil mapping
pedometrics
PronaSolos
topic remote sensing
soil cartography
soil mapping
pedometrics
PronaSolos
description ABSTRACT 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).
publishDate 2021
dc.date.none.fl_str_mv 2021-01-01
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publisher.none.fl_str_mv Sociedade Brasileira de Ciência do Solo
dc.source.none.fl_str_mv Revista Brasileira de Ciência do Solo v.45 2021
reponame:Revista Brasileira de Ciência do Solo (Online)
instname:Sociedade Brasileira de Ciência do Solo (SBCS)
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