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

• Published in: Revista Brasileira de Ciência do Solo Vol. 45
• Main Authors: José Lucas Safanelli, José Alexandre Melo Demattê, Natasha Valadares dos Santos, Jorge Tadeu Fim Rosas, Nélida Elizabet Quiñonez Silvero, Benito Roberto Bonfatti, Wanderson de Sousa Mendes
• Format: Journal Article
• Language: English
• Published: Sociedade Brasileira de Ciência do Solo 01.01.2021
• Subjects: pedometrics; PronaSolos; remote sensing; soil cartography; soil mapping
• ISSN: 1806-9657
• DOI: 10.36783/18069657rbcs20210080

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).