| Texto completo | |
| Autor(es): |
Safanelli, Jose Lucas
[1]
;
Melo Dematte, Jose Alexandre
[1]
;
dos Santos, Natasha Valadares
[1]
;
Rosas, Jorge Tadeu Fim
[1]
;
Quinonez Silvero, Nelida Elizabet
[1]
;
Bonfatti, Benito Roberto
[2]
;
Mendes, Wanderson de Sousa
[1]
Número total de Autores: 7
|
| Afiliação do(s) autor(es): | [1] Univ Sao Paulo, Dept Ciencia Solo, Escolar Super Agr Luiz de Queiroz, Piracicaba, SP - Brazil
[2] Univ Estado Minas Gerais, Unidade Passos, Passos, MG - Brazil
Número total de Afiliações: 2
|
| Tipo de documento: | Artigo Científico |
| Fonte: | Revista Brasileira de Ciência do Solo; v. 45, 2021. |
| Citações Web of Science: | 0 |
| 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). (AU) | |
| Processo FAPESP: | 16/01597-9 - Pedotransferência por geotecnologias associada à fotopedologia com vistas ao mapeamento pedológico de áreas agrícolas do estado de São Paulo |
| Beneficiário: | José Lucas Safanelli |
| Modalidade de apoio: | Bolsas no Brasil - Doutorado Direto |
| Processo FAPESP: | 14/22262-0 - Geotecnologias no mapeamento digital pedológico detalhado e biblioteca espectral de solos do Brasil: desenvolvimento e aplicações |
| Beneficiário: | José Alexandre Melo Demattê |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |
| Processo FAPESP: | 18/21356-1 - Geotecnologias para o mapeamento de áreas agrícolas do estado de São Paulo |
| Beneficiário: | José Lucas Safanelli |
| Modalidade de apoio: | Bolsas no Exterior - Estágio de Pesquisa - Doutorado Direto |