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The figures are reproduced from Ref. [31] Figure 8 Evolution of the correlation length through the direction (a) and of the estimated surface roughness W est (b) for the RP and LTR surface patterns at different fluences.
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The four methods are implemented in MATLAB™ to estimate surface roughness parameters – Ra, Rq, and Rz.
Estimating surface roughness or soil moisture by solving the IEM with two unknowns is a classic example of under-determination and is at the core of the problems associated with the use of radar imagery coupled with IEM-like models.
The comparison reveals that fusion reconstruction estimates surface roughness closer to stylus profilometer measurements as compared to GLCM, wavelet decomposition and photometric stereo based texture analysis.
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Estimating landscape surface roughness using radar helps in modeling of the land surface.
The ANN model estimates the surface roughness with high accuracy compared to the multiple regression model.
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Another dataset for wind field modeling is land use or land cover, which is used to estimate the surface roughness length.
The ones which we will consider in this paper are land surface temperature from thermal infrared data, surface soil moisture from passive microwave data, snow cover using both visible and microwave data, water quality using visible and near-infrared data and estimating landscape surface roughness using lidar.
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