A5103157168- Soil Geostatistics and Mapping
- Soil Carbon and Nitrogen Dynamics
- Geochemistry and Geologic Mapping
- Remote Sensing in Agriculture
- Soil erosion and sediment transport
- Soil and Unsaturated Flow
- Land Use and Ecosystem Services
- Soil Moisture and Remote Sensing
- Geography and Environmental Studies
- Hydrology and Watershed Management Studies
- Mineral Processing and Grinding
- Landslides and related hazards
- Conservation, Biodiversity, and Resource Management
- Remote Sensing and LiDAR Applications
Universidade de São Paulo
2020-2023
University of Florida
2022-2023
Shahrekord University
2017
Soil organic carbon (SOC) stocks are a remarkable property for soil and environmental monitoring. The understanding of their dynamics in crop soils must go forward. objective this study was to determine the impact temporal controlling factors obtained by satellite images over SOC along depth, using machine learning algorithms. work carried out São Paulo state (Brazil) an area 2577 km2. We dataset boreholes with analyses from topsoil subsoil (0–100 cm). Additionally, remote sensing covariates...
The capacity of soil to sequester carbon (C) is a key process that promotes the reduction CO2 in atmosphere. Soils can absorb as much 20% anthropogenic emissions, which contribute mitigate climate change. This relies on organo-mineral association, includes different minerals, Fe and Al oxides, have critical organic (SOC) sorption surface. Based an equation potential C saturation deficit fine particles (<20 μm/silt clay fractions) for tropical regions, this study investigated SOC...
The capacity of soil to sequester carbon (C) is considered a method reduce the concentration CO2 in atmosphere, as soils can absorb about 20% anthropogenic emissions, helping mitigate climate change. This depends especially on clay fraction, which includes different minerals, such varying amounts pedogenic Fe and Al oxides, have critical organic sorption surface (SOC). Therefore, based equation determine potential C saturation deficit fine particles (<20 μm/silt fractions) for tropical...
Soil depth is one of the most critical factors which impact on culture productivity and makes difficult appropriate management decisions. However, assessing this parameter also challenging tasks in agronomic field. The objective work was to predict spatial distribution soil using remote sensing data machine learning techniques. 292 sites were allocated (based toposequence approach) perforated (from 0 2 m) at three different locations Brazil. Based these, in-situ traditional maps (denominated...