Lachlan I.W. McKinna

Inter-annual variability of wet season freshwater plume extent into the Great Barrier Reef lagoon based on satellite coastal ocean colour observations.

Riverine freshwater plumes are the major transport mechanism for nutrients, sediments and pollutants into the Great Barrier Reef (GBR) lagoon and connect the land with the receiving coastal and marine waters. Knowledge of the variability of the freshwater extent into the GBR lagoon is relevant for marine park management to develop strategies for improving ecosystem health and risk assessments. In this study, freshwater extent has been estimated for the entire GBR lagoon area from daily satellite observations of the Moderate Resolution Imaging Spectroradiometer (MODIS) between 2002 and 2010. To enable a reliable mapping of freshwater plumes we applied a physics-based coastal ocean colour algorithm, that simultaneously retrieves chlorophyll-a, non-algal particulate matter and coloured dissolved organic matter (CDOM), from which we used CDOM as a surrogate for salinity (S) for mapping the freshwater extent.

On the surface circulation in the western Coral Sea and residence times in the Great Barrier Reef

Surface velocity observations from satellite tracked drifters made between 1987 and 2008 were used to resolve the surface circulation of the western Coral Sea, west of 158°E, and the Great Barrier Reef (GBR). The mean surface current map depicts well the major circulation patterns of the region, such as the position of the north Vanuatu and north Caledonia jets (NVJ and NCJ) and the western boundary currents. The North Queensland Current (NQC) and the East Australian Current (EAC) are well defined, flowing at speeds greater than 50 cm s−1 to the north, south of 15°S and 19°S, respectively. The NQC/EAC is mainly formed by the NVJ/NCJ flows, respectively. The presence of the Queensland Plateau greatly affects the westward flow, causing a zone of weak and highly variable currents that extends from 15°S to 18°S between the Queensland Plateau and the GBR shelf. Of the 235 drifters that crossed the western Coral Sea, 75 entered the GBR. Analysis of the drifter trajectories inside the GBR reveals the presence of a northwestward circulation at speeds of 22 cm s−1 north of 18°S and 0.5 cm s−1 south of 18°S. Drifter travel times used to evaluate the water residence times within the GBR indicate residence times of a few weeks for most of the lagoon.

A semianalytical ocean color inversion algorithm with explicit water column depth and substrate reflectance parameterization

A semianalytical ocean color inversion algorithm was developed for improving retrievals of inherent optical properties (IOPs) in optically shallow waters. In clear, geometrically shallow waters, light reflected off the seafloor can contribute to the water-leaving radiance signal. This can have a confounding effect on ocean color algorithms developed for optically deep waters, leading to an overestimation of IOPs. The algorithm described here, the Shallow Water Inversion Model (SWIM), uses pre-existing knowledge of bathymetry and benthic substrate brightness to account for optically shallow effects. SWIM was incorporated into the NASA Ocean Biology Processing Groups L2GEN code and tested in waters of the Great Barrier Reef, Australia, using the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua time series (2002–2013). SWIM-derived values of the total non-water absorption coefficient at 443 nm, at(443), the particulate backscattering coefficient at 443 nm, bbp(443), and the diffuse attenuation coefficient at 488 nm, Kd(488), were compared with values derived using the Generalized Inherent Optical Properties algorithm (GIOP) and the Quasi-Analytical Algorithm (QAA). The results indicated that in clear, optically shallow waters SWIM-derived values of at(443), bbp(443), and Kd(443) were realistically lower than values derived using GIOP and QAA, in agreement with radiative transfer modeling. This signified that the benthic reflectance correction was performing as expected. However, in more optically complex waters, SWIM had difficulty converging to a solution, a likely consequence of internal IOP parameterizations. Whilst a comprehensive study of the SWIM algorithms behavior was conducted, further work is needed to validate the algorithm using in situ data.

Bottom Reflectance in Ocean Color Satellite Remote Sensing for Coral Reef Environments

Most ocean color algorithms are designed for optically deep waters, where the seafloor has little or no effect on remote sensing reflectance. This can lead to inaccurate retrievals of inherent optical properties (IOPs) in optically shallow water environments. Here, we investigate in situ hyperspectral bottom reflectance signatures and their separability for coral reef waters, when observed at the spectral resolutions of MODIS and SeaWiFS sensors. We use radiative transfer modeling to calculate the effects of bottom reflectance on the remote sensing reflectance signal, and assess detectability and discrimination of common coral reef bottom classes by clustering modeled remote sensing reflectance signals. We assess 8280 scenarios, including four IOPs, 23 depths and 45 bottom classes at MODIS and SeaWiFS bands. Our results show: (i) no significant contamination (Rrscorr 17 m for MODIS and >19 m for SeaWiFS for the brightest spectral reflectance substrate (light sand) in clear reef waters; and (ii) bottom cover classes can be combined into two distinct groups, “light” and “dark”, based on the modeled surface reflectance signals. This study establishes that it is possible to efficiently improve parameterization of bottom reflectance and water-column IOP retrievals in shallow water ocean color models for coral reef environments.

An overview of approaches and challenges for retrieving marine inherent optical properties from ocean color remote sensing

Ocean color measured from satellites provides daily global, synoptic views of spectral waterleaving reflectances that can be used to generate estimates of marine inherent optical properties (IOPs). These reflectances, namely the ratio of spectral upwelled radiances to spectral downwelled irradiances, describe the light exiting a water mass that defines its color. IOPs are the spectral absorption and scattering characteristics of ocean water and its dissolved and particulate constituents. Because of their dependence on the concentration and composition of marine constituents, IOPs can be used to describe the contents of the upper ocean mixed layer. This information is critical to further our scientific understanding of biogeochemical oceanic processes, such as organic carbon production and export, phytoplankton dynamics, and responses to climatic disturbances. Given their importance, the international ocean color community has invested significant effort in improving the quality of satellite-derived IOP products, both regionally and globally. Recognizing the current influx of data products into the community and the need to improve current algorithms in anticipation of new satellite instruments (e.g., the global, hyperspectral spectroradiometer of the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission), we present a synopsis of the current state of the art in the retrieval of these core optical properties. Contemporary approaches for obtaining IOPs from satellite ocean color are reviewed and, for clarity, separated based their inversion methodology or the type of IOPs sought. Summaries of known uncertainties associated with each approach are provided, as well as common performance metrics used to evaluate them. We discuss current knowledge gaps and make recommendations for future investment for upcoming missions whose instrument characteristics diverge sufficiently from heritage and existing sensors to warrant reassessing current approaches.

Seasonal climate prediction for the Australian sugar industry using data mining techniques

The ability to predict rainfall with adequate certainty and lead time is beneficial to both industry and public. Periods of high or low seasonal rainfall can have many follow on effects to agriculture, industry, public health and, water supply and management. In order to implement decisions, planning and management strategies to contend with these issues, the ability to predict seasonal rainfall quantities is of great importance (Klopper et al., 2006). Climate conditions are known to influence the cultivation of Sugarcane influencing planting, harvesting and milling (Muchow and Wood, 1996; Everingham et al., 2002; Jones and Everingham, 2005). Unforeseen climate events such as excessive rainfall, can adversely effect the agricultural practices related to Sugarcane cultivation. The Australian Sugarcane harvest period commences in May/June and aims to finish by November/December before the start of the rainy season (Everingham et al., 2002). The risk of excessive rainfall disrupting harvest operations is greatest towards the end of the sugarcane harvest period (Muchow and Wood, 1996; Everingham et al., 2002). Therefore, improved seasonal rainfall prediction during the October-December period is beneficial.

From toes to top-of-atmosphere: Fowler’s Sneaker Depth index of water clarity for the Chesapeake Bay

Fowlers Sneaker Depth (FSD), analogous to the well known Secchi disk depth (Zsd), is a visually discerned citizen scientist metric used to assess water clarity in the Patuxent River estuary. In this study, a simple remote sensing algorithm was developed to derive FSD from space-borne spectroradiometric imagery. An empirical model was formed that estimates FSD from red-end remote sensing reflectances at 645 nm, Rrs(645). The model is based on a hyperbolic function relating water clarity to Rrs(645) that was established using radiative transfer modeling and fine tuned using in-water FSD measurements and coincident Rrs(645) data observed by NASAs Moderate Resolution Imaging Spectroradiometer aboard the Aqua spacecraft (MODISA). The resultant FSD algorithm was applied to Landsat-8 Operational Land Imager data to derive a short time-series for the Patuxent River estuary from January 2015 to June 2016. Satellite-derived FSD had an inverse, statistically significant relationship (p<0.005) with total suspended sediment concentration (TSS). Further, a distinct negative relationship between FSD and chlorophyll concentration was discerned during periods of high biomass (> 4 μg L-1). The complex nature of water quality in the mid-to-upper Chesapeake Bay was captured using a MODISA-based FSD time series (2002-2016). This study demonstrates how a citizen scientist-conceived observation can be coupled with remote sensing. With further refinement and validation, the FSD may be a useful tool for delivering scientifically relevant results and for informing and engaging local stakeholders and policy makers.