Influence of atmospheric and sea-surface corrections on retrieval of bottom depth and reflectance using a semi-analytical model: a case study in Kaneohe Bay, Hawaii

Abstract

Hyperspectral instruments provide the spectral detail necessary for extracting multiple layers of information from inherently complex coastal environments. We evaluate the performance of a semi-analytical optimization model for deriving bathymetry, benthic reflectance, and water optical properties using hyperspectral AVIRIS imagery of Kaneohe Bay, Hawaii. We examine the relative impacts on model performance using two different atmospheric correction algorithms and two different methods for reducing the effects of sunglint. We also examine the impact of varying view and illumination geometry, changing the default bottom reflectance, and using a kernel processing scheme to normalize water properties over small areas. Results indicate robust model performance for most model formulations, with the most significant impact on model output being generated by differences in the atmospheric and deglint algorithms used for preprocessing.

Remote Sensing of Seagrass Leaf Area Index and Species: The Capability of a Model Inversion Method Assessed by Sensitivity Analysis and Hyperspectral Data of Florida Bay

“…This dynamic range would be covered by a signal to noise ratio (SNR) of 120 and 8-bit digitization, while PRISM is 14-bit with an SNR of 200 per band, and much greater when bands are combined as they are here (since fitting a spectrum is a effectively a kind of band-averaging) (Mouroulis et al, 2014). So for hyperspectral imagers such as PRISM the radiometric limiting factors, especially for physics based aquatic applications, lie not in the instrument specifications but in the data processing (see also Goodman et al, 2008;Hedley et al, 2012b). Since here discrepancies between the sensitivity analyses and practical performance appear to be due to radiometric differences between the model and data, this suggests future model based sensitivity analyses should include a term for "radiometric discrepancy."…”

“…In particular the input parameters and the model should encompass all the major sources of variation, otherwise spectra resulting from those variations may be non-physical from the point of view of the model, leading to errors in estimations and under-estimates of the uncertainty. For the same reason, atmospheric, and water interface corrections (sun-glint) must be performed with high accuracy (Goodman et al, 2008), any discrepancies in the radiometry of the imagery with respect to that of the model will lead to inaccurate results.…”

Sun Glint Correction of High and Low Spatial Resolution Images of Aquatic Scenes: a Review of Methods for Visible and Near-Infrared Wavelengths

“…In a case study by Goodman et al [1], uncorrected glint in high resolution imagery led to errors as large as 30% in the measurement of water depth. Hochberg et al [2] reported that in a set of 45 IKONOS images of coral reefs purchased by the National Aeronautics and Space Administration, 9 were badly contaminated by glint, and 13 more had significant areas of glint (Figure 1b).…”

Effects of Per-Pixel Variability on Uncertainties in Bathymetric Retrievals from High-Resolution Satellite Images

“…A partial improvement in retrieval error after glint correction of Image 2 resulted in an increase to the maximum depth to which accurate depth estimations were returned. Goodman [18] concluded that careful pre-processing of sub-optimal images has a significant influence on model output. Although the best available de-glinting algorithm for multispectral imagery was implemented, this procedure was only partially successful due to the severity of the glint affecting a large number of the image pixels.…”

“…As accurate as possible atmospheric correction during image pre-processing is important for reliable water column composition, benthic, and bathymetric information retrieval [18,26].…”