Generally, the characterization of land surface roughness is obtained from the analysis of height variations observed along transects (e.g., root mean square (RMS) height, correlation length, and autocorrelation function). These surface roughness measurements are then used as inputs for surface dynamics modeling, e.g., for soil erosion modeling, runoff estimation, and microwave remote sensing scattering modeling and calibration. In the past, researchers have suggested various methods for estimating roughness parameters based on ground measurements, e.g., using a pin profilometer, but these methods require physical contact with the land and can be time-consuming to conduct. The target of this research is to develop a technique for deriving surface roughness characteristics from digital camera images by applying photogrammetric and geographical information systems (GIS) analysis techniques. First, ground photos acquired by a digital camera in the field were used to create a point cloud and 3D digital terrain model (DTM). Then, the DTM was imported to a GIS environment to calculate the surface roughness parameter for each field site. The results of the roughness derivation can be integrated with soil moisture for backscattering simulation, e.g., for inversion modeling to retrieve the backscattering coefficient. The results show that the proposed method has a high potential for retrieving surface roughness parameters in a time- and cost-efficient manner. The selection of homogeneous fields and the increased spatial distribution of sites in the study area will show a better result for microwave backscattering modeling.
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