Soil texture has a great influence on the physical–hydric and chemical behavior of soils. In the Amazon regions, due to the presence of dense forest cover and limited access to roads, carrying out surveys and mapping of soils is challenging. When data exist, they are relatively sparse and the distribution is quite uneven. In this context, machine learning algorithms (ML) associated with remote sensor covariates offer a framework to derive digital maps of soil attributes. The objective of this study was to produce maps of surface and subsurface soil clay, silt, and sand contents in a 13.440 km² area in the Amazon. The specific objectives were to (a) evaluate the gain in prediction accuracy when using the P-band of airborne radar as a covariate; (b) evaluate two sampling approaches (Reference Area—RA and Total Area—TA); and (c) evaluate the transferability and performance of three ML algorithms: regression tree (RT), random forest (RF), and support vector machine (SVM). The study site was divided into three blocks, called Urucu, Araracanga, and Juruá, respectively. The soil dataset consisted of 151 surface and subsurface sand, silt, and clay observations and 21 covariates (20 relief variables and the backscattering coefficient from the P-band). Both the RA and TA sampling approach used 114 observations for training the prediction models (75%) and 37 for validation (25%). The RA approach was better for the development of sand and silt models. Overall, RF derived the most accurate predictions for all variables. The effect of introducing the P-band backscattering coefficient improved the sand prediction accuracy at the surface and subsurface in Araracanga, which had the highest sand content, with relative improvements (RI) of the R², root mean square error (RMSE), and mean absolute error (MAE) of 46%, 3%, and 4% at the surface, respectively, and 66.7%, 4.4%, and 5.2% at the subsurface, respectively. For silt, the P-band improved the predictions at the surface in Araracanga, which had the lowest silt contents among the blocks. For clay, adding the P-band improved the RF predictions at the subsurface, with RI of the R², RMSE, and MAE of 29%, 5%, and 5%, respectively. Despite the low observation density, inherently hindered by the low accessibility of the area and high costs of sampling thereof, the results showed the potential of ML algorithms boosted by airborne radar P-band to map soil clay, silt, and sand contents in the Amazon.

O Brasil é quarto maior emissor de gases de efeito estufa (GEE), em grande parte devido às mudanças no uso da terra e à agropecuária. No Cerrado, a conversão de áreas sob vegetação nativa para agropecuária impacta os ciclos biogeoquímicos do C e N no solo, necessitando a adoção de práticas agrícolas sustentáveis que aumentem o conteúdo de matéria orgânica do solo (MOS) e que promovam a estabilização do carbono (C) no solo. Este estudo teve como objetivo avaliar os estoques de C e N e a agregação do solo sob diferentes sistemas de manejo agrícolas sustentáveis, comparando-os com áreas de mata nativa do Cerrado, em Unaí (MG). O experimento foi conduzido em Unaí-MG, analisando os seguintes tratamentos: Mata Nativa (MN), Pasto Produtivo (PP) e Integração Lavoura-Pecuária (ILP). O delineamento foi inteiramente casualizado, com três repetições, e os dados analisados pelo teste de Tukey a 5% de significância. Os resultados indicam que o PP apresentou os maiores estoques de C e N, devido ao aporte contínuo de material vegetal via rebrota da liteira e renovação intensa do sistema radicular. O sistema ILP apresentou estoques de C maiores que a MN na camada superficial, que é a mais sensível ao manejo do solo. Porém, foi o tratamento que apresentou menores estoques quando se considera em profundidade (0-100 cm), possivelmente, pelo curto tempo de implantação desse sistema no campo. A MN e o PP apresentaram maior diâmetro médio ponderado de agregados do solo. Essa informação é importante porque um dos mecanismos importantes na estabilização do C no solo é a proteção física, isto é, a proteção da matéria orgânica dentro dos agregados do solo. Esse mecanismo deve estar atuando na estabilização do C nas áreas sob PP. Conclui-se que o PP é uma alternativa viável para a manutenção da qualidade do solo no Cerrado.

Introducion: Changes in land cover caused by open-pit mining have resulted in significant transformations in landscape structure and environmental balance. In the semiarid region of Paraíba, the extraction of ornamental rocks is a strategic economic activity, but it also generates direct impacts on local ecosystems, such as vegetation loss. Objective: The general objective of the study was to analyze the dynamics of land use and occupation in ornamental rock mining areas in the Sertão region of Paraíba, using NDVI in a time-space approach. Method: The Normalized Difference Vegetation Index (NDVI) was applied as a remote sensing tool to assess environmental changes that occurred between 2017 and 2024 in municipalities in the Sertão region of Paraíba, where mining operations are active. Data were obtained from a public database for the months of November 2017 and November 2024. For each year analyzed, two scenes were required to cover the entire study area. Results and Discussion: The results revealed a dual pattern: while part of the area showed a sharp decrease in NDVI, indicating environmental degradation associated with anthropogenic pressure, other sectors showed an increase in NDVI values, evidencing natural regeneration processes or sustainable management actions. NDVI was found to be an efficient and low-cost indicator for monitoring environmental changes over time, supporting impact assessments, public land use planning policies, and practices for restoring degraded areas. Research Implications: The application of geotechnologies proves essential to reconcile the development of ornamental rock mining with the conservation of natural resources in the Paraíba semiarid region. Originality/Value: The article addresses the use of geotechnologies for the control and monitoring of mining areas, specifically ornamental rock mining in the state of Paraíba, a topic that has been little explored and has scarcely been studied.