Vittorio E. Brando

Satellite hyperspectral remote sensing for estimating estuarine and coastal water quality

The successful launch of Hyperion in November 2000 bridged the gap between the high-resolution (spatial and spectral) airborne remote sensing and the lower resolution satellite remote sensing. Although designed as a technical demonstration for land applications, Hyperion was tested for its capabilities over a range of water targets in Eastern Australia, including Moreton Bay in southern Queensland. Moreton Bay was the only Australian Earth Observing 1 (EO-1) Hyperion coastal site used for calibration/validation activities. This region was selected due to its spatial gradients in optical depth, water quality, bathymetry, and substrate composition. A combination of turbid and humic river inputs, as well as the open ocean flushing, determines the water quality of the bay. The field campaigns were coincident with Hyperion overpasses, retrieved inherent optical properties, apparent optical properties, substrate reflectance spectra, and water quality parameters. Environmental noise calculations demonstrate that Hyperion has sufficient sensitivity to detect optical water quality concentrations of colored dissolved organic matter, chlorophyll, and suspended matter in the complex waters of Moreton Bay. A methodology was developed integrating atmospheric and hydrooptical radiative transfer models (MODTRAN-4, Hydrolight) to estimate the underwater light field. A matrix inversion method was applied to retrieve concentrations of chlorophyll, colored dissolved organic matter, and suspended matter, which were comparable to those estimated in the field on the days of the overpass.

Generalized ocean color inversion model for retrieving marine inherent optical properties

Ocean color measured from satellites provides daily, global estimates of marine inherent optical properties (IOPs). Semi-analytical algorithms (SAAs) provide one mechanism for inverting the color of the water observed by the satellite into IOPs. While numerous SAAs exist, most are similarly constructed and few are appropriately parameterized for all water masses for all seasons. To initiate community-wide discussion of these limitations, NASA organized two workshops that deconstructed SAAs to identify similarities and uniqueness and to progress toward consensus on a unified SAA. This effort resulted in the development of the generalized IOP (GIOP) model software that allows for the construction of different SAAs at runtime by selection from an assortment of model parameterizations. As such, GIOP permits isolation and evaluation of specific modeling assumptions, construction of SAAs, development of regionally tuned SAAs, and execution of ensemble inversion modeling. Working groups associated with the workshops proposed a preliminary default configuration for GIOP (GIOP-DC), with alternative model parameterizations and features defined for subsequent evaluation. In this paper, we: (1) describe the theoretical basis of GIOP; (2) present GIOP-DC and verify its comparable performance to other popular SAAs using both in situ and synthetic data sets; and, (3) quantify the sensitivities of their output to their parameterization. We use the latter to develop a hierarchical sensitivity of SAAs to various model parameterizations, to identify components of SAAs that merit focus in future research, and to provide material for discussion on algorithm uncertainties and future emsemble applications.

Dispersal of suspended sediments and nutrients in the Great Barrier Reef lagoon during river-discharge events: conclusions from satellite remote sensing and concurrent flood-plume sampling.

Intense wet-season rainfall in January 2005 caused rivers in the Mackay–Whitsunday region of Queensland, Australia, to produce large discharges to the Great Barrier Reef (GBR) lagoon. The regional land use is dominated by sugarcane cultivation, beef grazing and urban uses. The high nutrient (nitrogen and phosphorus) fluxes from these land uses via river runoff produced a massive phytoplankton bloom in the GBR lagoon, which, after 9 days, had spread 150 km offshore. The plume and algal bloom surrounded inner-shelf reefs of the GBR such as Brampton Island Reef and its spread was tracked with a variety of satellite sensors including MODIS, SeaWiFS and Landsat over the 9-day period. The ability to be able to access imagery from a large number of satellite sensors allowed almost daily estimates of the extent of plume to be made, despite periods of cloud. Analysis of water samples from the plume revealed elevated (2–50 times higher) concentrations of Chlorophyll a (and hence phytoplankton biomass), up to 50 times higher than in non-flood conditions, nutrients (2–100 times higher) and herbicide residues (10–100 times higher) compared with GBR lagoon waters in non-discharge conditions. The concentration data from the samples and estimated exposure periods from the satellite images allowed estimates of the exposure of GBR marine ecosystems (coral reefs, the pelagic community, seagrass beds and mangrove forests) to the terrestrial contaminants to be made.

Guest Editorial: Coastal Aquatic Remote Sensing Applications for Environmental Monitoring and Management

Abstract not available.

Long term monitoring of photosystem II herbicides – Correlation with remotely sensed freshwater extent to monitor changes in the quality of water entering the Great Barrier Reef, Australia

Photosystem II (PSII) herbicides are used in large quantities on agricultural lands adjoining the Great Barrier Reef (GBR). Routine monitoring at 14 sites in inshore waters of the GBR using passive sampling techniques detected diuron (32-94% of sampling periods) at maximum concentrations of 1.7-430ng L(-1) in the relatively pristine Cape York Region to the Mackay Whitsunday Region, respectively. A PSII herbicide equivalent (PSII-HEq) index developed as an indicator for reporting was dominated by diuron (average contribution 89%) and typically increased during the wet season. The maximum PSII-HEq indicates the potential for photosynthetic inhibition of diatoms, seagrass and coral-symbionts. PSII herbicides were significantly positively correlated with remotely sensed coloured dissolved organic matter, a proxy for freshwater extent. Combining these methods provides for the first time the potential to cost-effectively monitor improvements in water quality entering the GBR with respect to exposure to PSII herbicides.

Adaptive semianalytical inversion of ocean color radiometry in optically complex waters

To address the challenges of the parameterization of ocean color inversion algorithms in optically complex waters, we present an adaptive implementation of the linear matrix inversion method (LMI) [J. Geophys. Res.101, 16631 (1996)], which iterates over a limited number of model parameter sets to account for naturally occurring spatial or temporal variability in inherent optical properties (IOPs) and concentration specific IOPs (SIOPs). LMI was applied to a simulated reflectance dataset for spectral bands representing measured water properties of a macrotidal embayment characterized by a large variability in the shape and amplitude factors controlling the IOP spectra. We compare the inversion results for the single-model parameter implementation to the adaptive parameterization of LMI for the retrieval of bulk IOPs, the IOPs apportioned to the optically active constituents, and the concentrations of the optically active constituents. We found that ocean color inversion with LMI is significantly sensitive to the a priori selection of the empirical parameters g0 and g1 of the equations relating the above-surface remote-sensing reflectance to the IOPs in the water column [J. Geophys. Res.93, 10909 (1988)]. When assuming the values proposed for open-ocean applications for g0 and g1 [J. Geophys. Res.93, 10909 (1988)], the accuracy of the retrieved IOPs, and concentrations was substantially lower than that retrieved with the parameterization developed for coastal waters [Appl. Opt.38, 3831 (1999)] because the optically complex waters analyzed in this study were dominated by particulate and dissolved matter. The adaptive parameterization of LMI yielded consistently more accurate inversion results than the single fixed SIOP model parameterizations of LMI. The adaptive implementation of LMI led to an improvement in the accuracy of apportioned IOPs and concentrations, particularly for the phytoplankton-related quantities. The adaptive parameterization encompassing wider IOP ranges were more accurate for the retrieval of bulk IOPs, apportioned IOPs, and concentration of optically active constituents.

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.

IMOS National Reference Stations: A Continental-Wide Physical, Chemical and Biological Coastal Observing System

Sustained observations allow for the tracking of change in oceanography and ecosystems, however, these are rare, particularly for the Southern Hemisphere. To address this in part, the Australian Integrated Marine Observing System (IMOS) implemented a network of nine National Reference Stations (NRS). The network builds on one long-term location, where monthly water sampling has been sustained since the 1940s and two others that commenced in the 1950s. In-situ continuously moored sensors and an enhanced monthly water sampling regime now collect more than 50 data streams. Building on sampling for temperature, salinity and nutrients, the network now observes dissolved oxygen, carbon, turbidity, currents, chlorophyll a and both phytoplankton and zooplankton. Additional parameters for studies of ocean acidification and bio-optics are collected at a sub-set of sites and all data is made freely and publically available. Our preliminary results demonstrate increased utility to observe extreme events, such as marine heat waves and coastal flooding; rare events, such as plankton blooms; and have, for the first time, allowed for consistent continental scale sampling and analysis of coastal zooplankton and phytoplankton communities. Independent water sampling allows for cross validation of the deployed sensors for quality control of data that now continuously tracks daily, seasonal and annual variation. The NRS will provide multi-decadal time series, against which more spatially replicated short-term studies can be referenced, models and remote sensing products validated, and improvements made to our understanding of how large-scale, long-term change and variability in the global ocean are affecting Australias coastal seas and ecosystems. The NRS network provides an example of how a continental scaled observing systems can be developed to collect observations that integrate across physics, chemistry and biology.

Validity of SeaDAS water constituents retrieval algorithms in Australian tropical coastal waters

[1] This paper reports an assessment of the performance of the seven global empirical and semi-analytical chlorophyll and bulk IOP retrieval algorithms implemented in SeaDAS, for the local conditions in tropical waters off the Australian northeast coast. An approach based on radiative transfer simulations was used providing systematic assessments. The study shows that when coloured dissolved organic matter (CDOM) and non-algal particle (NAP) concentrations are low, the seven algorithms tested, as expected, can retrieve chlorophyll with an error of, at best, 75% for 80-90% of the cases. The accuracy generally degrades rapidly with increasing CDOM and NAP concentrations. For the semi-analytical algorithms (bulk IOP retrieval enabled), the QAA algorithm achieved a better result at retrieving the total absorption, a, backscattering, bb and backscattering by particles, bbp. However, none of the algorithms can break down the total absorption into components with reasonable accuracy. The study indicates that without incorporating regional and seasonal knowledge of specific IOPs the accuracy in water constituent retrievals from remote sensing is poor, hindering the use of remote sensing in water quality management.

Remote Sensing of Seagrass Ecosystems: Use of Spaceborne and Airborne Sensors

The focus of this chapter lies in describing digi-tal multispectral and hyperspectral remote sensingdevelopments and applications in the mapping andmonitoring of seagrass ecosystems. Multispectralrefers to a sensor that registers light in a limitednumber of relatively broad spectral bands (band-widths of 20–60 nm); hyperspectral (also referred toas imaging spectrometry) is defined for sensors thatmeasure the entire spectrum under consideration incontiguous narrow spectral bands (bandwidths be-tween 2 and 20 nm).Currently, seagrass maps are still predominantlybeingproducedfromtheinterpretationofaerialpho-tographyalthoughitislikelythatairborneandspace-borne remote sensing methods will rapidly take overthisrolegiventheadvantagestheypresentintermsofaccuracy, repeatability, versatility, and informationcontent. Nevertheless, retrospective studies of sea-grass change using the more modern methodologieswillstillneedtomakeuseofresultsgeneratedbythemore traditional methods since aerial photographsare the dominant archival source of historical spatial