| dc.description.abstract | Retrieving physical parameters that characterize the surface of the soil (e.g. the surface roughness) is critical for environmental studies in hydrology and agriculture, as they seem to play a role in the prediction of runoff within a catchment basin (Baghdadi et al., 2012). Soil surface roughness greatly influences the hydrological and agricultural activities which may reduce or improve on crop productivity.
In this research, the potential of synthetic aperture radar (SAR) data using sentinel 1 C -band for monitoring soil surface roughness classes over bare agricultural areas in Kakira sugarcane estates has been investigated using an incidence angle of 38.5°. Landcover types were classified into two classes i.e., bare soil and others (sugar cane plantation areas, roads, buildings and sugarcane residuals). This was done inorder to exact only the bare soil areas because the model used for this research only applies to bare soil areas. The backscatter coefficients (σ°) to define the roughness classes based on soil roughness parameters (hrms, Lc) were processed using integral equation model (IEM) after collocating the sentinel 1 image onto classified sentinel 2 images showin g the exacted bare soil areas.
The results were validated using field insitu measurements classified as smooth, moderately rough and rough. | en_US |
