ABSTRACT
The growing demand of world food and energy supply increases the threat of global warming due to higher greenhouse gas emissions by agricultural activity. Therefore, it is widely admitted that agriculture must establish a new paradigm in terms of environmental sustainability that incorporate techniques for mitigation of greenhouse gas emissions. This article addresses to the scientific demand to estimate in a fast and inexpensive manner current and potential soil organic carbon (SOC) stocks in degraded pastures, using remote sensing techniques. Four pastures on sandy soils under Brazilian Cerrado vegetation in São Paulo state were chosen due to their SOC sequestration potential, which was characterized for the soil depth 0-50 cm. Subsequently, a linear regression analysis was performed between SOC and Leaf Area Index (LAI) measured in the field (LAIfield) and derived by satellite (LAIsatellite) as well as SOC and pasture reflectance in six spectra from 450 nm - 2350 nm, using the Enhanced Thematic Mapper (ETM+) sensor of satellite Landsat 7. A high correlation between SOC and LAIfield (R² = 0.9804) and LAIsatellite (R² = 0.9812) was verified. The suitability of satellite derived LAI for SOC determination leads to the assumption, that orbital remote sensing is a very promising SOC estimation technique from regional to global scale.
ABSTRACT
The growing demand of world food and energy supply increases the threat of global warming due to higher greenhouse gas emissions by agricultural activity. Therefore, it is widely admitted that agriculture must establish a new paradigm in terms of environmental sustainability that incorporate techniques for mitigation of greenhouse gas emissions. This article addresses to the scientific demand to estimate in a fast and inexpensive manner current and potential soil organic carbon (SOC) stocks in degraded pastures, using remote sensing techniques. Four pastures on sandy soils under Brazilian Cerrado vegetation in São Paulo state were chosen due to their SOC sequestration potential, which was characterized for the soil depth 0-50 cm. Subsequently, a linear regression analysis was performed between SOC and Leaf Area Index (LAI) measured in the field (LAIfield) and derived by satellite (LAIsatellite) as well as SOC and pasture reflectance in six spectra from 450 nm - 2350 nm, using the Enhanced Thematic Mapper (ETM+) sensor of satellite Landsat 7. A high correlation between SOC and LAIfield (R² = 0.9804) and LAIsatellite (R² = 0.9812) was verified. The suitability of satellite derived LAI for SOC determination leads to the assumption, that orbital remote sensing is a very promising SOC estimation technique from regional to global scale.