Parameterization of bottom spectral reflectance for shallow water ocean color inversion models

Abstract

Satellite-based ocean color remote sensing has been extensively applied in monitoring deep ocean environments. Yet, ocean color products cannot be used to quantify water column properties with any degree of accuracy in shallow water environments because of bottom reflectance contamination. Currently, there are no operational ocean color algorithms that can correct for bottom reflectance contamination in optically shallow waters, where light reflected from the seafloor contributes to the water-leaving radiance. To improve the accuracy of ocean color products, it is essential to understand the impact of bottom reflectance on the retrieval of inherent optical properties (IOPs) of the water column. However, there is a lack of knowledge of the appropriate selection and parameterization of bottom reflectance inputs in shallow water inversion algorithms. The aim of this research was to optimize bottom reflectance parameterization in shallow water inversion algorithms, and to assess the effects of the parameterization on the retrieval of IOPs. The research addressed the following three objectives, with investigations based in the Great Barrier Reef (GBR), Australia: (1) to assess the spectral separability and detectability of bottom reflectance in coral reef environments, (2) to test the sensitivity of bottom reflectance parameterization on the retrieval of IOPs using a Shallow Water Inversion Model (SWIM) and (3) to assess different approaches to create a spatially-resolved bottom reflectance map for shallow water areas, using different datasets. To address research Objective 1, the spectral separability and detectability of bottom types at Moderate Resolution Imaging Spectroradiometer (MODIS) and Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) bands were assessed. The results showed: (i) no significant contamination (Rrscorr l 0.0005) of bottom reflectance on the spectrally- averaged remote sensing reflectance signal at depths g19 m for the brightest spectral reflectance substrate (light sand) in clear reef waters; and (ii) bottom cover classes can be combined into two distinct groups, llightr and ldarkr, based on the modeled surface reflectance signals. This research established that it is possible to improve parameterization of bottom reflectance and water column IOP retrievals in shallow water ocean color models for coral reef environments.nTo address research Objective 2, the impact of bottom reflectance parameterization on IOP retrievals in SWIM was assessed. The results showed that there is no clear spatial pattern in mean IOP retrievals under different bottom reflectance scenarios. A GBR-wide assessment showed that retrieved IOP values vary considerably across the extent of the GBR and thus the differences in IOP retrievals due to bottom reflectance parameterization are also spatially variable. Water clarity was shown to further influence the differences in IOP retrievals between different bottom types. Analysis showed that most differences in SWIM IOP retrievals between sand and seagrass, as well as between sand and algae bottom reflectance scenarios are observed at depths above 20 m. The results also indicated that the magnitude of the bottom reflectance spectrum is not the only factor influencing the retrievals of IOPs, but also the spectral shape. To address research Objective 3, four different methods to create spatially explicit bottom reflectance maps using two different available datasets were evaluated. Application of all generated bottom reflectance maps to IOP retrievals produced comparable results. It was determined that any one of these methods may be applied to create a bottom reflectance map suitable for use in the SWIM algorithm bottom reflectance parameterization, to improve IOP retrievals in optically shallow waters. This thesis has successfully demonstrated that bottom reflectance parameterization can be optimized using only two bottom reflectance classes (llightr and ldarkr). The sensitivity of IOP retrievals to bottom reflectance in the SWIM algorithm is variable across the large extent of the GBR. Different methods to produce optimized bottom reflectance maps for ocean color shallow water inversion models have been presented to ensure wide applicability to shallow water environments. In a practical context, the findings of this thesis will help improve bottom reflectance parameterization, and thus IOP retrievals, in shallow water ocean color inversion algorithms, such as SWIM.