| Abstract: | ABSTRACT: Algorithms for Landsat MSS digital data are needed to reduce the necessity of calibrating each Landsat scene if these data are to be useful in monitoring programs for surface suspended sediments. In this study digital data were extracted from 16 Landsat Multispectral Scanner (MSS) scenes collected between March 1987 and August 1988 over Enid Reservoir in North Central Mississippi. These data were converted to radiance and reflectance data for comparison with field measurements of surface suspended sediment concentrations. Concentrations ranged from 2 to 168 mg/1 during the study with only four greater than 100 mg/l. Linear and polynomial regression analyses were used to relate the surface suspended sediment concentrations with radiance and reflectance. Reflectance in MSS band 2 (0.6 to 0.7 μm) and MSS band 3 (0.7 to 0.8 μm) were best related to the surface suspended sediment concentrations with coefficients of determination accounting for 71 percent and 68 percent of the variation in the data, respectively. Regressions with radiance data accounted for 36 percent (band 2) or less of the variation. Logarithmic transformations of either reflectance or sediment concentrations increase the coefficients of determination for MSS band 2 reflectance data to 81 percent. Regressions between the ratio of MSS band 1 to MSS band 2 reflectances and concentrations also accounted for 80 percent of the variation. An equation Loge SS (mg/l) = 9.21R½+ 2.71R½2 + 8.45, where S is surface suspended sediment concentrations and R1/2 is the ratio of MSS band 1 to MSS band 2 reflectances, provided the best fit to the data with a coefficient of determination of 0.82. This equation is essentially the same as an algorithm proposed by Topliss et at. (1990), for estimating surface suspended sediment concentrations in Canadian coastal waters. These equations for Enid Reservoir and Canadian waters suggest that it may be possible to develop an algorithm for widespread use for estimating surface suspended sediments. |
