Submitted to: Canadian Symposium on Remote Sensing Proceedings
Publication Type: Proceedings
Publication Acceptance Date: June 12, 1995
Publication Date: N/A
Interpretive Summary: Several current and future satellite systems will provide remotely sensed radar data on a global basis. Radar data is responsive to the electrical and structural properties of the soil and vegetation. For soil moisture applications, soil surface roughness must be corrected for using independent estimates. There are two general approaches that might be used, ancillary data bases (i.e., land cover map) and multitemporal observations (i.e., weekly measurements). This paper examines the data base approach. If one could determine these parameters using existing data bases or other remote sensing techniques, it might be possible to develop global roughness data such as those available for soils and vegetation. An operational soil surface roughness correction procedure would allow scientists and other users to monitor soil moisture and vegetation.
The significance of soil surface roughness on the relationship between radar backscatter and surface soil moisture has been recognized for some time. Several recent advances have begun to isolate and quantify roughness effects. These results suggest that it may be possible to estimate roughness directly using multipolarization radar data, that classical solutions to modeling the roughness effects do apply, and that surface soil random roughness appears to be the dominant parameter needed in modeling. Since current and near future satellite radar systems operate at only a single frequency and polarization, it will be necessary to provide an independent estimate of surface roughness if soil moisture is to be derived from radar data. Extensive research conducted primarily in the field of soil erosion to relate soil roughness to other variables may provide a solution. In addition, data sets available from the years of previous ground and aircraft experimentation could be applied. If the information necessary for defining the categories and conditions described in these previous studies can be determined using readily available ancillary data, ie., NDVI, crop calendar, etc., it might be possible to develop global roughness data.