Hyperspectral remote sensing for shallow waters: 2. Deriving bottom depthsand water properties by optimization

In earlier studies of passive remote sensing of shallow-water bathymetry, b ottom depths were usually derived by empirical regression. This approach pr ovides rapid data processing, but it requires knowledge of a few true depth s for the regression parameters to be determined, and it cannot reveal in-w ater constituents. In this study a newly developed hyperspectral, remote-se nsing reflectance model for shallow water is applied to data from computer simulations and field measurements. In the process, a remote-sensing reflec tance spectrum is modeled by a set of values of absorption, backscattering, bottom albedo, and bottom depth; then it is compared with the spectrum fro m measurements. The difference between the two spectral curves is minimized by adjusting the model Values in a predictor-corrector scheme. No informat ion in addition to the measured reflectance is required. When the differenc e reaches a minimum, or the set of variables is optimized, absorption coeff icients and bottom depths along with other properties are derived simultane ously For computer-simulated data at a wind speed of 5 m/s the retrieval er ror was 5.3% for depths ranging from 2.0 to 20.0 m and 7.0% for total absor ption coefficients at 440 nm ranging from 0.04 to 0.24 m(-1). At a wind spe ed of 10 m/s the errors were 5.1% for depth and 6.3% for total absorption a t 440 nm. For field data with depths ranging from 0.8 to 25.0 m the differe nce was 10.9% (R-2 = 0.96, N = 37) between inversion-derived and field-meas ured depth values and just 8.1% (N = 33) for depths greater than 2.0 m. The se results suggest that the model and the method used in this study, which do not require in situ calibration measurements, perform very well in retri eving in-water optical properties and bottom depths from above-surface hype rspectral measurements. (C) 1999 Optical Society of America.