Analysis of random roughness and ridge height versus radar backscatter or the natural log of the NBRI did not return any discernible relationships. Grouping management units into tillage direction or residue cover type, factors that have shown significant effects in previous studies (McNairn et al., 1996; 1998), also did not return any discernible relationships.
Possible reasons for the differences between the positive results returned by previous studies are many. Previous studies have been conducted on what are described as “nearly flat” or “slight slope” agricultural fields (McNairn et al., 1996; 1998; Sahebi et al., 2002), however the management units evaluated in this study likely contained much larger variations in slope. Flat fields are the ideal situation for testing radar response to surface
roughness because radar responds to surface moisture variation (Major et al., 1994; Boisvert et al., 1995) and incidence angle. Flat surfaces decreases these impacts by limiting surface moisture variation and local incidence angle variation due to topography. Changes in surface moisture alter the dielectric properties of the soil and changes in local incidence angle
influence the back calculation of radar backscatter (σ0), confounding determination of surface roughness. No attempt was made to account for local incidence angle variations, due to the low accuracy of available digital elevation models.
The low dielectric of frozen soil water conditions in the early season imagery should limit soil moisture impacts and dry snow has been shown to be transparent to radar (Brown et al., 1993). However, from examination of Figure 4.2 to Figure 4.5, no difference is noted in the ability to determine surface roughness between December, February, and March versus May imagery dates. Another possible problem is the fact that surface roughness was indirectly estimated in this experiment and not directly measured. Differences between the random roughness created by the model tillage implements used by Alberts et al. (1995) and the actual tillage practices could contribute to a lack of correlation, however there does not appear to be any noticeable difference in radar backscatter or ln NBRI between no-till soybean and moldboard plow fields in the figures, even though random roughness between these two disparate, easily recognized, categories is large. Residue cover has also been found to cause differences in radar returns (McNairn et al., 1996; 1998) but separation of
management units into corn and soybean residue categories also failed to return a noticeable correlation.
4.5 Conclusions
Investigations into surface random roughness and ridge roughness and radar
backscatter failed to return any significant findings, precluding the opportunity to develop an enhanced tillage classification model. Possible confounding factors were investigated and
using new LiDAR digital elevation models, and more accurate determinations of surface random roughness using stereo-photography.
The development of a method for determining surface roughness of management units still remains, and the lack of surface roughness data causes problems with determining tillage practices and estimating hydrologic parameters. The launch of RADARSAT2 in summer 2007 will mark a new age in multi-polarimetric radar remote sensing capabilities and will hopefully enable the collection of quality surface roughness data.
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