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).

RESUMO Índices de vegetação na delimitação de zonas de manejo para a soja A delimitação de zonas para fins de manejo específico pode ser uma abordagem operacional e economicamente viável na agricultura de precisão. Objetivou-se investigar a relação entre índices espectrais amostrados durante diferentes estádios fenológicos da cultura da soja e as zonas de produtividade da lavoura. Atributos do solo estratificados em cada zona e a influência da altitude também foram estudados. Foram calculados os índices de razão simples, de vegetação por diferença normalizada e de vegetação ajustado ao solo, durante os estádios V6, R5 e R5.5 da soja. Procedeu-se à análise de dependência espacial via semivariogramas para os índices de vegetação, produtividade da soja e elevação do terreno. O mapa da produtividade foi tomado como referência para avaliar a concordância espacial com os diferentes mapas gerados a partir dos índices espectrais. Os valores médios de atributos químicos e granulométricos do solo foram calculados e analisados por teste de médias entre as zonas delineadas. A divisão do campo em duas zonas de manejo, em função da combinação da altitude, índice de razão simples do estádio V6 e índice de vegetação ajustado ao solo do estádio R5.5, foi a que apresentou maior concordância com o mapa de produtividade da soja. Foram identificadas diferenças nos teores de fósforo, argila e silte, nas zonas de manejo delineadas.

ABSTRACT The delimitation of site-specific management zones may be an operational and economically feasible approach in precision agriculture. This study aimed at investigating the spatial correlations between spectral indexes sampled during different growth stages of soybean and crop yield. Soil attributes stratified in each zone and the influence of altitude were also assessed. The simple ratio index, normalized difference vegetation index and soil-adjusted vegetation index were calculated for soybean at the V6, R5 and R5.5 stages. Spatial dependence analysis via semivariogram was performed for the vegetation indexes, soybean yield and terrain elevation. The crop yield map was taken as a reference to assess the spatial agreement with the different maps generated from the spectral indexes. The average values for chemical and granulometric soil attributes were calculated and analyzed by their means among the zones delineated. The field division into two management zones, due to the combination of altitude, simple ratio index of the V6 stage and soil-adjusted vegetation index of the R5.5 stage, showed the highest agreement with the soybean yield map. Differences between the delineated zones were identified for the phosphorus, clay and silt contents.