Hyperspectral remote sensing for shallow waters: 2 Deriving bottom depths and water properties by optimization

TLDR

Passive remote sensing of shallow-water bathymetry has traditionally relied on empirical regression, which requires a few known depths and cannot retrieve in‑water constituents. This study applies a newly developed hyperspectral reflectance model to both simulated and field data to estimate bottom depth and water properties. The model represents reflectance with absorption, backscattering, bottom albedo, and depth parameters, then iteratively minimizes the spectral difference with a predictor‑corrector scheme, requiring only the measured reflectance. Retrieval errors were 5–7 % for simulated data and 10.9 % overall (8.1 % for depths >2 m) in field data, demonstrating that the method accurately recovers optical properties and depths without in‑situ calibration.

Abstract

In earlier studies of passive remote sensing of shallow-water bathymetry, bottom depths were usually derived by empirical regression. This approach provides rapid data processing, but it requires knowledge of a few true depths for the regression parameters to be determined, and it cannot reveal in-water constituents. In this study a newly developed hyperspectral, remote-sensing reflectance model for shallow water is applied to data from computer simulations and field measurements. In the process, a remote-sensing reflectance spectrum is modeled by a set of values of absorption, backscattering, bottom albedo, and bottom depth; then it is compared with the spectrum from measurements. The difference between the two spectral curves is minimized by adjusting the model values in a predictor-corrector scheme. No information in addition to the measured reflectance is required. When the difference reaches a minimum, or the set of variables is optimized, absorption coefficients and bottom depths along with other properties are derived simultaneously. For computer-simulated data at a wind speed of 5 m/s the retrieval error was 5.3% for depths ranging from 2.0 to 20.0 m and 7.0% for total absorption coefficients at 440 nm ranging from 0.04 to 0.24 m(-1). At a wind speed of 10 m/s the errors were 5.1% for depth and 6.3% for total absorption at 440 nm. For field data with depths ranging from 0.8 to 25.0 m the difference was 10.9% (R2 = 0.96, N = 37) between inversion-derived and field-measured depth values and just 8.1% (N = 33) for depths greater than 2.0 m. These results suggest that the model and the method used in this study, which do not require in situ calibration measurements, perform very well in retrieving in-water optical properties and bottom depths from above-surface hyperspectral measurements.