|Rahman, M. - UNIVERSITY OF ARIZONA|
|Bryant, R. - UNIVERSITY OF ARIZONA|
|Sano, E. - EMBRAPA, BRAZIL|
|Holifield Collins, Chandra|
|Kerschner, C. - ARMY TEC|
|Orr, B. - UNIVERSITY OF ARIZONA|
Submitted to: International Journal of Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: October 30, 2006
Publication Date: June 30, 2007
Citation: Rahman, M.M., Moran, M.S., Thoma, D., Bryant, R., Sano, E.E., Holifield Collins, C.D., Skirvin, S.M., Kerschner, C., Orr, B. 2007. A derivation of roughness correlation length for parameterizing radar backscatter models. International J. of Remote Sensing. 1-18. Interpretive Summary: Satellite remote sensing had made regional analysis of agricultural and natural resources problems very fast and less expensive. Radar satellite is a specific form of remote sensing that is capable of measuring the moisture content of the ground surface. The radar signal scattered from the surface and received by satellite antenna is not only sensitive to the moisture content of the targeted soil, but also the roughness of the ground. This makes retrieval of moisture content from the radar signal complicated. This study reports a practical method for determining regional surface roughness from a combination of imagery and theory with improved precision and reduced need for field measured ancillary information. It also develops a conceptual framework for measuring the surface roughness and surface moisture content without the use of ancillary data. Moisture information at a regional scale has a variety of agricultural application, such as, estimation of crop yield and monitoring of rangeland health.
Technical Abstract: Surface roughness is a key parameter in radar backscatter models designed to retrieve surface soil moisture information from radar images. This work offers a theory-based approach for estimating a key roughness parameter, termed the roughness correlation length (L). The L is the length in centimeters from a point on the ground to a short distance for which the heights of a rough surface are correlated with each other. The approach is based on the relation between L and h as theorized by the Integral Equation Model (IEM). The h is another roughness parameter, which is the root mean squared height variation of a rough surface. The relation is calibrated for a given site based on the radar backscatter of the site under dry soil conditions. When this relation is supplemented with the site-specific measurements of h, it is possible produce estimates of L. The approach was validated with several radar images of the Walnut Gulch Experimental Watershed in Southeast Arizona. Results showed that the IEM performed well in reproducing satellite-based radar backscatter when this new derivation of L was used as input. This was a substantial improvement over the use of field measurements of L. This new approach also has advantages over empirical formulations for estimation of L because it does not require field measurements of soil moisture for iterative calibration and it accounts for the very complex relation between L and h found in heterogeneous landscapes. Finally, this new approach opens up the possibility of determining both roughness parameters without ancillary data based on the radar backscatter difference measured for two different incident angles.