Benthic Reflectance Research Articles

An enhanced large-scale benthic reflectance retrieval model for the remote sensing of submerged ecosystems in optically shallow waters

Shallow water benthic habitats have been significantly degraded and seriously threatened by intensifying climate changes and anthropogenic stressors. Benthic reflectance (Rbλ) of optically shallow waters (OSWs) is a key parameter for remote sensing of benthic habitats’ composition and health status. The remote sensing reflectance (Rrsλ) just above the water surface contains the coupled spectral information of the water column and seabed in OSWs, which makes our effort challenging to separate the signals between seabed and water column properties. Due to too many unknowns of the water column and seabed optical properties, the current approaches are inaccurate or inadequate to retrieve benthic reflectance spectra over a large area from remotely sensed data. In this study, we proposed an enhanced large-scale benthic reflectance (LSBR) retrieval model from satellite data in OSWs with only input of Rrsλ. A new spectral library of benthic reflectance was built upon multi-source reflectance datasets obtained from several open-source field measurements. Then, the specific relationship between Rb443nm and Rb490nm was developed. By combining a semi-analytical benthic reflectance (SABR) model based on radiative transfer simulations and this relationship, water-column chlorophyll concentration (chl) was determined. Moreover, large-scale water depth was estimated by Rrsλ through satellite-derived bathymetry (SDB) technologies. Finally, benthic reflectance was retrieved with the inputs of the satellite-derived Rrsλ, chl, and water depth. In this way, the LSBR model fulfills the Rbλ retrieval with only input of satellite-derived Rrsλ. The new model was successfully applied to Sentinel-2/MSI data in the seagrass region of Xincun Bay and the coral region of Huaguang Reef and further verified by using field measurements and results of the along-track benthic reflectance (ATBR) retrieval model, which combined active lidar and passive high-resolution satellite images. Additionally, to gain insights into the long-term evolution of benthic communities, a typical application of the LSBR model for detecting benthic changes was conducted using eight years of Sentinel-2/MSI data in Heron Reef, depicting a similar consistency between the probable deterioration and restoration region and the sea surface temperature (SST) anomalies. The results demonstrated the robustness and accuracy of the LSBR model in retrieving benthic reflectance at large scales. The LSBR model should be helpful for further investigating benthic changes which can enhance the ability to monitor and assess the health status of submerged ecosystems in OSWs.

Semi-analytical inversion modelling of Chlorophyll a variability in the U.S. Virgin Islands

Coral reef health in the U.S. Virgin Islands (USVI) is in decline due to land-based sources of pollution associated with watershed development and global climate change. Water quality is a good indicator of stress in these nearshore environments as it plays a key role in determining the health and distribution of coral reef communities. Conventional water quality assessment methods based on in situ measurements are both time consuming and costly, and they lack the spatial coverage and temporal resolution that can be achieved using satellite remote sensing techniques. Water quality parameters (WQPs) such as Chlorophyll a (Chl-a), can be studied remotely using models that account for the inherent optical properties (IOPs) of the water. In this study, empirical based standard ocean color algorithm (OC4) and two semi-analytical algorithms, the Garver–Siegel–Maritorena (GSM) and the Generalized Inherent Optical Properties (GIOP) model, were evaluated in retrieving Chl-a in the nearshore waters of the USVI. GSM and GIOP were also evaluated for modeling inherent optical properties such as absorption coefficient of phytoplankton (aph (443)). Analysis of the results from each model using a field database from six cruises during May/June and December between 2016 and 2018, showed that the OC4 performed poorly with R2 of 0.14 and RMSE = 0.15. Effects of suspended particulates and benthic reflectance most likely contributed to the poor performance of the algorithm. GSM is a slightly better estimator for aph (443) and Chl-a (R2 = 0.55, RMSE = 0.04; R2 = 0.60, RMSE = 0.09) than GIOP (R2 = 0.52, RMSE = 0.05; R2 = 0.17, RMSE = 0.15). Performance of the semi-analytical models are limited in estimating particulate back scattering (bbp (443)) also due to the benthic albedo effects in the shallow waters. The calibrated GSM model was applied to Landsat 8 OLI satellite imagery spanning 2016–2018 to develop a time series of the spatial changes in Chl-a concentrations in the coastal waters of the USVI. The Landsat GSM Chl-a model produced promising results of R2 = 0.45, RMSE = 0.07, in an environment where signal-to-noise ratio is significantly low.

Bathymetry Retrieval Algorithm Based on Hyperspectral Features of Pure Water Absorption From 570 to 600 nm

Current efforts for improving the hyperspectral optimization processing exemplar (HOPE) model include further testing of remote-sensing reflectance (Rrs) features containing useful information for bathymetry retrieval via the minimization of the interference stemming from the variability in inherent optical properties and benthic reflectance. In this paper, we found a novel feature originating from the pure water absorption within the narrow spectral region of 570–600 nm. In most coastal regions of clear water, for example, in a coral reefs environment, pure water accounts for the majority of the total absorption in this spectral range. In addition to the depth variation, the spectral behavior of Rrs(570–600) is primarily dominated by a steep increase in pure water absorption with wavelength, whereas the influence of other optical properties such as phytoplankton/CDOM absorption, particle backscattering, and benthic reflectance can be simplified using the spectrally constant shape model. A HOPE pure water (HOPE-PW) algorithm using this feature was developed based on Rrs measurements with a spectral resolution of near 3.5 nm, wherein only four unknown parameters must be resolved. The validation from LiDAR data and comparison with HOPE-BRUCE using PRISM data at 15 sites located in five distinct regions of Palau, Guam, Great Barrier Reef, Hawaiian Islands and Florida Key, confirmed that the HOPW-PW yielded a considerable performance and provided adequate transferability to other sites with varying bottom and water environments. The sensitivity analysis based on Hydrolight-simulated datasets showed that HOPE-PW was less effected by bottom type variations but still had limitations in retrieving water optical properties.

Measurement of in-vivo spectral reflectance of bottom types: Implications for remote sensing of shallow waters

Field spectroscopy data serve as a standard for the inter-comparisons, calibration and validation of remote sensing data. It also greatly assists in the detection, characterization and mapping of the earth features from the satellite imagery. Strategies and methods for field-based spectro-radiometric measurements are well-established for land and deep ocean applications, however, there are certain challenges and limitations with respect to the shallow water coastal ecosystems. This paper focuses on the background theory, concepts, and measurement techniques of in-vivo sensing of benthic reflectance in shallow water environment. We investigated various measurement methods, influences of water column, air–water interface, sources of errors (introduced by experimental and environmental factors), and the correction methods that are involved in the determination of benthic substrate reflectance. Special emphasis is provided using in-vivo measurements made in the estuarine and shelf regions, illustrating the benthic substrate reflectance at various platforms, i.e., within the water column, above the water surface, at the top-of-atmosphere, in the air, and in a controlled (laboratory) environment. As part of the correction procedure, a new water column correction method is theoretically formulated and examined for varying water optical properties and water depth. A new step correction method is also developed (based on the additive approach) to correct the spectral reflectance measured by a full range spectroradiometer (350–2500 nm) that is in-built with two or more detectors. This study will have direct implications for improving the data quality of field-based spectro-radiometric measurements that are essential for shallow water remote sensing applications.

Satellite retrieval of benthic reflectance by combining lidar and passive high-resolution imagery: Case-I water

Under the background of global change, increasing attention has been paid to the changes of benthic habitats in shallow ocean ecosystems (e.g., seagrass beds and coral reefs). Optical satellite remote sensing via both active and passive methods plays an important role in monitoring the health of benthic habitats by retrieving benthic reflectance spectra, but it remains difficult to accurately retrieve benthic reflectance spectra from only active or passive remote sensing because of the coupling between water column scattering and benthic reflectance. Here, we developed a semi-analytical model to retrieve benthic reflectance spectra in Case-I waters by combining active lidar and passive high-resolution imagery. Based on two-stream radiative transfer theory, the analytical relationship among the remote sensing reflectance (Rrs) and water column reflectance (Rw), benthic reflectance (Rb), diffuse attenuation coefficient (Kd), and water depth was established. The lidar data at a certain wavelength were applied to derive the water depth and the chlorophyll concentration (chl) along the lidar track. Then, the values of Kd at different wavelengths were estimated from the derived chl. In addition, we established a look-up-table (LUT) for the relationship between Rw and chl and water depth using Hydrolight simulation, and the Rw values at different wavelengths were then estimated by the lidar-derived chl and water depth. Finally, Rb(λ) values at different wavelengths along the lidar track were retrieved from the Rrs(λ) values observed by passive high-resolution imagery and the values of Rw(λ), Kd(λ), and water depth derived by lidar observation. The accuracy of the model was verified by using the Hydrolight simulated datasets, and the high correlation coefficient (R) revealed promising model performance for different benthic habitats, e.g., R > 0.9 for typical clean shallow water (chl = 0.5 mg/m3, H < 4 m) for the wavelength range of 400–640 nm. The model was further applied to real satellite data from ICESat-2 lidar and passive high-resolution satellite imagery (multispectral imagery of Sentinel-2 and hyperspectral imagery of Zhuhai−1) at two different benthic habitat sites (seagrass beds in Xincun Bay and coral reefs in the Huaguang Reef) in the South China Sea, and the results revealed that the model could reproduce the benthic spectra in both magnitude and shape. Overall, the proposed model can reliably yield benthic reflectance spectra along the lidar track without any requirement on prior knowledge, which should be beneficial for further benthic habitat health monitoring.

Bathymetry and Benthic Habitat Mapping in Shallow Waters From Sentinel-2A Imagery: A Case Study in Xisha Islands, China

Mapping of benthic habitats and bathymetry is crucial for sustainable development and assessment of climate change and human activities. In this study, Hyperspectral Optimization Process Exemplar (HOPE) was modified, renamed as M-HOPE, to simultaneously obtain bathymetry and benthic habitat in shallow waters in Xisha Island, China. A local lookup table (LUT) for benthic reflectance spectra was established. Validation using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> measurements demonstrated good performance of M-HOPE with a R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.76 for bathymetry using the local LUT. Application of M-HOPE to Sentinel-2A imagery further proved good accuracy of M-HOPE derived bathymetry with a R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.86 against <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> observations and a R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.92 against ICESat-2 measurements. M-HOPE-derived benthic classification also agreed well with field observations with probability of detection (POD) > 0.6 and false alarm ratio (FAR) < 0.5. However, uncertainties exist, mainly caused by mixed benthic types, atmospheric correction, and similarity in spectral reflectance of different benthic types.

Coral Bleaching Detection in the Hawaiian Islands Using Spatio-Temporal Standardized Bottom Reflectance and Planet Dove Satellites

We present a new method for the detection of coral bleaching using satellite time-series data. While the detection of coral bleaching from satellite imagery is difficult due to the low signal-to-noise ratio of benthic reflectance, we overcame this difficulty using three approaches: 1) specialized pre-processing developed for Planet Dove satellites, 2) a time-series approach for determining baseline reflectance statistics, and 3) a regional filter based on a preexisting map of live coral. The time-series was divided into a baseline period (April-July 2019), when no coral bleaching was known to have taken place, and a bleaching period (August 2019-present), when the bleaching was known to have occurred based on field data. The identification of the bleaching period allowed the computation of a Standardized Bottom Reflectance (SBR) for each region. SBR transforms the weekly bottom reflectance into a value relative to the baseline reflectance distribution statistics, increasing the sensitivity to bleaching detection. We tested three scales of the temporal smoothing of the SBR (weekly, cumulative average, and three-week moving average). Our field verification of coral bleaching throughout the main Hawaiian Islands showed that the cumulative average and three-week moving average smoothing detected the highest proportion of coral bleaching locations, correctly identifying 11 and 10 out of 18 locations, respectively. However, the three-week moving average provided a better sensitivity in coral bleaching detection, with a performance increase of at least one standard deviation, which helps define the confidence level of a detected bleaching event.

Confidence Levels, Sensitivity, and the Role of Bathymetry in Coral Reef Remote Sensing

Remote sensing is playing an increasingly important role in the monitoring and management of coastal regions, coral reefs, inland lakes, waterways, and other shallow aquatic environments. Ongoing advances in algorithm development, sensor technology, computing capabilities, and data availability are continuing to improve our ability to accurately derive information on water properties, water depth, benthic habitat composition, and ecosystem health. However, given the physical complexity and inherent variability of the aquatic environment, most of the remote sensing models used to address these challenges require localized input parameters to be effective and are thereby limited in geographic scope. Additionally, since the parameters in these models are interconnected, particularly with respect to bathymetry, errors in deriving one parameter can significantly impact the accuracy of other derived parameters and products. This study utilizes hyperspectral data acquired in Hawaii in 2000–2001 and 2017–2018 using NASA’s Classic Airborne Visible/Infrared Imaging Spectrometer to evaluate performance and sensitivity of a well-established semi-analytical inversion model used in the assessment of coral reefs. Analysis is performed at several modeled spatial resolutions to emulate characteristics of different feasible moderate resolution hyperspectral satellites, and data processing is approached with the objective of developing a generalized, scalable, automated workflow. Accuracy of derived water depth is evaluated using bathymetric lidar data, which serves to both validate model performance and underscore the importance of image quality on achieving optimal model output. Data are then used to perform a sensitivity analysis and develop confidence levels for model validity and accuracy. Analysis indicates that derived benthic reflectance is most sensitive to errors in bathymetry at shallower depths, yet remains significant at all depths. The confidence levels provide a first-order method for internal quality assessment to determine the physical extent of where and to what degree model output is considered valid. Consistent results were found across different study sites and different spatial resolutions, confirming the suitability of the model for deriving water depth in complex coral reef environments, and expanding our ability to achieve automated widespread mapping and monitoring of global coral reefs.

Impact of water characteristics on the discrimination of benthic cover in and around coral reefs from imaging spectrometer data

Coral reefs are the foundation of productive ecosystems in the global, tropical oceans and are under threat from a variety of local to global scale stressors. Satellite imagery provides a tool to identify and understand the processes that control coral reef degradation, however due to the dynamic nature of seawater constituents, current spaceborne multispectral sensors cannot reliably discriminate between the many coral reef benthic classes necessary to detect change. Hyperspectral imagers may provide sufficient spectral resolution to estimate water column properties and differentiate benthic classes, however, the effects of depth, seawater constituents, and classification algorithm on the accuracy of benthic classifications have not been systematically assessed. Here, we simulate the ability of a spaceborne hyperspectral imager to accurately map fractional cover of coral reef benthic classes under a variety of conditions. Benthic reflectance is simulated by combining pure reflectance spectra of coral, algae, and sand and projecting these mixed spectra through a fully crossed set of water columns. We then use a semi-analytical optimization procedure to estimate the water column properties and multiple endmember spectral mixture analysis to estimate the fractional cover of the benthic classes using many independent endmember spectra. We compare our estimated benthic class fractions to the original, actual fractions used to produce the mixed coral reef spectra to quantify several measures of error. We found that multiple endmember spectral mixture analysis decreases fractional retrieval error, which is also reduced when the first derivative of the mixed and endmember spectra is used prior to unmixing. The estimation of fractional benthic class cover is most accurate for depths ≤3 m for most water conditions. Depths ≥5 m should be classified only if chlorophyll and sediment concentration are <0.1 mg m−3 and <0.1 g m−3, respectively. Our results indicate that the fractional cover of coral and algae should be at least 25% for accurate benthic class estimates (mean relative error < 50%), however there will be many ways to leverage the repeat measurements of a hyperspectral satellite sensor, such as a stable depth retrievals and benthic cover estimates, to produce more accurate and useful fractional cover data. We show how this simulation analysis can be used to generate maps of predicted benthic cover fractional retrieval uncertainty across a coral reef system using aerial hyperspectral imagery acquired over Hawaii, USA, although reef-specific, within pixel variations in depth and benthic class complexity should be considered.

Pushing the Limits of Seagrass Remote Sensing in the Turbid Waters of Elkhorn Slough, California

Remote sensing imagery has been successfully used to map seagrass in clear waters, but here we evaluate the advantages and limitations of different remote sensing techniques to detect eelgrass in the tidal embayment of Elkhorn Slough, CA. Pseudo true-color imagery from Google Earth and broadband satellite imagery from Sentinel-2 allowed for detection of the various beds, but retrievals particularly in the deeper Vierra bed proved unreliable over time due to variable image quality and environmental conditions. Calibrated water-leaving reflectance spectrum from airborne hyperspectral imagery at 1-m resolution from the Portable Remote Imaging SpectroMeter (PRISM) revealed the extent of both shallow and deep eelgrass beds using the HOPE semi-analytical inversion model. The model was able to reveal subtle differences in spectral shape, even when remote sensing reflectance over the Vierra bed was not visibly distinguishable. Empirical methods exploiting the red edge of reflectance to differentiate submerged vegetation only retrieved the extent of shallow alongshore beds. The HOPE model also accurately retrieved the water column absorption properties, chlorophyll-a, and bathymetry but underestimated the particulate backscattering and suspended matter when benthic reflectance was represented as a horizontal eelgrass leaf. More accurate water column backscattering could be achieved by the use of a darker bottom spectrum representing an eelgrass canopy. These results illustrate how high quality atmospherically-corrected hyperspectral imagery can be used to map eelgrass beds, even in regions prone to sediment resuspension, and to quantify bathymetry and water quality.

Influence of Three-Dimensional Coral Structures on Hyperspectral Benthic Reflectance and Water-Leaving Reflectance

Shading and inter-reflections created by the three-dimensional coral canopy structure play an important role on benthic reflectance and its propagation above the water. Here, a plane parallel model was coupled with a three-dimensional radiative transfer canopy model, incorporating measured coral shapes and hyperspectral benthic reflectances, to investigate this question under different illumination and water column conditions. Results indicated that a Lambertian treatment of the bottom reflectance can be a reasonable assumption if a variable shading factor is included. Without flexibility in the shading treatment, nadir view bottom reflectances can vary by as much as ±20% (or ±9% in above-water remote sensing reflectance) under solar zenith angles (SZAs) up to 50°. Spectrally-independent shading factors are developed for benthic coral reflectance measurements based on the rugosity of the coral. In remote sensing applications, where the rugosity is unknown, a shading factor could be incorporated as an endmember for retrieval in the inversion scheme. In dense coral canopies in clear shallow waters, the benthos cannot always be treated as Lambertian, and for large solar-view angles the bi-directional reflectance distribution functions (BRDF) hotspot propagated to above water reflectances can create up to a 50% or more difference in water-leaving reflectances, and discrepancies of 20% even for nadir-view geometries.

Multispectral Bathymetry via Linear Unmixing of the Benthic Reflectance

Bathymetry is important to shallow coral reef management. Remote sensing is one of the most important techniques for mapping bathymetry. For instance, multispectral remote sensing images have been widely used to map bathymetry of coral reefs. These physically based methods, which are able to retrieve bathymetry, water column inherent optical properties (IOPs), and benthic reflectance simultaneously, generally can achieve good bathymetry results. However, there are strong limitations when facing the scenarios of mixing benthic reflectance for bathymetry with multispectral images. In this study, aiming at handling the mixing phenomenon on the use of multispectral remote sensing images for mapping bathymetry, we propose a new unmixing-based method, namely unmixing-based multispectral optimization process exemplar method (UMOPE). The new method incorporates endmember variability with a linear combination of three fixed endmembers and the relaxation of the sum-to-one constrained. UMOPE was validated with two existing methods with in situ data and a WorldView-2 image in the South China Sea. Results from in situ data show that the proposed method performs the best, with the smallest absolute root mean square error ${\rm{(RMSE)}}$ (2.26 m) and the best agreement ( $R^{2}= {\text{0.91}}$ ) between the measured and estimated water depth. Moreover, results from WV-2 imagery demonstrate the superior performance for the UMOPE at depth range from about 9 to 26 m. Furthermore, since the errors from IOPs can propagate to bathymetry, we further take this issue into account by analyzing the rule of influence of bottom on IOPs. Finally, the result of benthic classification map from UMOPE is shown.

Machine learning-based retrieval of benthic reflectance and Posidonia oceanica seagrass extent using a semi-analytical inversion of Sentinel-2 satellite data

ABSTRACTIn the epoch of the human-induced climate change, seagrasses can mitigate the resulting negative impacts due to their carbon sequestration ability. The endemic and dominant in the Mediterranean Posidonia oceanica seagrass contains the largest stocks of organic carbon among all seagrass species, yet it undergoes a significant regression in its extent. Therefore, suitable quantitative assessment of its extent and optically shallow environment are required to allow good conservation and management practices. Here, we parameterise a semi-analytical inversion model which employs above-surface remote sensing reflectance of Sentinel-2A to derive water column and bottom properties in the Thermaikos Gulf, NW Aegean Sea, Greece (eastern Mediterranean). In the model, the diffuse attenuation coefficients are expressed as functions of absorption and backscattering coefficients. We apply a comprehensive pre-processing workflow which includes atmospheric correction using C2RCC (Case 2 Regional CoastColour) neural network, resampling of the lower spatial resolution Sentinel-2A bands to 10m/pixel, as well as empirical derivation of water bathymetry and machine learning-based classification of the resulting bottom properties using the Support Vector Machines. SVM-based classification of benthic reflectance reveals ~300 ha of P. oceanica seagrass between 2 and 16 m of depth, and yields very high producer and user accuracies of 95.3% and 99.5%, respectively. Sources of errors and uncertainties are discussed. All in all, recent advances in Earth Observation in terms of optical satellite technology, cloud computing and machine learning algorithms have created the perfect storm which could aid high spatio-temporal, large-scale seagrass habitat mapping and monitoring, allowing for its integration to the Analysis Ready Data era and ultimately enabling more efficient management and conservation in the epoch of climate change.

Airborne mapping of benthic reflectance spectra with Bayesian linear mixtures

Remote imaging spectroscopy from 400 to 800nm can use benthic reflectance signatures to map the composition and condition of shallow water ecosystems. We present a novel probabilistic approach to jointly estimate the seafloor reflectance and water properties while flexibly incorporating varied domain knowledge and in situ measurements. The inversion transforms remote radiance data with an atmospheric correction followed by a water column correction. Benthic reflectance and water optical properties are both represented by linear mixtures of endmember spectra. We combine remote measurements, prior knowledge and field data using a flexible Bayesian optimal estimation, solving for the Maximum A Posteriori (MAP) combination of water column properties, seafloor reflectance, and depth. We then demonstrate performance in controlled simulations and in overflights of a coral reef in Hawaii with coincident in situ measurements. The measurement approach helps lay a foundation for wide-area airborne mapping of the condition of threatened coastal ecosystems such as coral reefs.

Remote-sensing reflectance and true colour produced by a coupled hydrodynamic, optical, sediment, biogeochemical model of the Great Barrier Reef, Australia: Comparison with satellite data

Aquatic biogeochemical models are vital tools in understanding and predicting human impacts on water clarity. In this paper, we develop a spectrally-resolved optical model that produces remote-sensing reflectance as a function of depth-resolved biogeochemical model properties such as phytoplankton biomass, suspended sediment concentrations and benthic reflectance. We compare simulated remote-sensing reflectance from a 4 km resolution coupled hydrodynamic, optical, sediment and biogeochemical model configured for the Great Barrier Reef with observed remote-sensing reflectance from the MODIS sensor at the 8 ocean colour bands. The optical model is sufficiently accurate to capture the remote-sensing reflectance that would arise from a specific biogeochemical state. Thus the mismatch between simulated and observed remote-sensing reflectance provides an excellent metric for model assessment of the coupled biogeochemical model. Finally, we combine simulated remote-sensing reflectance in a red/green/blue colour model to produce simulated true colour images during the passage of Tropical Cyclone Yasi in February 2011.

Towards an Interoperable Field Spectroscopy Metadata Standard with Extended Support for Marine Specific Applications

This paper presents an approach to developing robust metadata standards for specific applications that serves to ensure a high level of reliability and interoperability for a spectroscopy dataset. The challenges of designing a metadata standard that meets the unique requirements of specific user communities are examined, including in situ measurement of reflectance underwater, using coral as a case in point. Metadata schema mappings from seven existing metadata standards demonstrate that they consistently fail to meet the needs of field spectroscopy scientists for general and specific applications (μ = 22%, σ = 32% conformance with the core metadata requirements and μ = 19%, σ = 18% for the special case of a benthic (e.g., coral) reflectance metadataset). Issues such as field measurement methods, instrument calibration, and data representativeness for marine field spectroscopy campaigns are investigated within the context of submerged benthic measurements. The implication of semantics and syntax for a robust and flexible metadata standard are also considered. A hybrid standard that serves as a “best of breed” incorporating useful modules and parameters within the standards is proposed. This paper is Part 3 in a series of papers in this journal, examining the issues central to a metadata standard for field spectroscopy datasets. The results presented in this paper are an important step towards field spectroscopy metadata standards that address the specific needs of field spectroscopy data stakeholders while facilitating dataset documentation, quality assurance, discoverability and data exchange within large-scale information sharing platforms.

Supervised Classification of Benthic Reflectance in Shallow Subtropical Waters Using a Generalized Pixel-Based Classifier across a Time Series

We tested a supervised classification approach with Landsat 5 Thematic Mapper (TM) data for time-series mapping of seagrass in a subtropical lagoon. Seagrass meadows are an integral link between marine and inland ecosystems and are at risk from upstream processes such as runoff and erosion. Despite the prevalence of image-specific approaches, the classification accuracies we achieved show that pixel-based spectral classes may be generalized and applied to a time series of images that were not included in the classifier training. We employed in-situ data on seagrass abundance from 2007 to 2011 to train and validate a classification model. We created depth-invariant bands from TM bands 1, 2, and 3 to correct for variations in water column depth prior to building the classification model. In-situ data showed mean total seagrass cover remained relatively stable over the study area and period, with seagrass cover generally denser in the west than the east. Our approach achieved mapping accuracies (67% and 76% for two validation years) comparable with those attained using spectral libraries, but was simpler to implement. We produced a series of annual maps illustrating inter-annual variability in seagrass occurrence. Accuracies may be improved in future work by better addressing the spatial mismatch between pixel size of remotely sensed data and footprint of field data and by employing atmospheric correction techniques that normalize reflectances across images.

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

AbstractA 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 Group's 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 algorithm's behavior was conducted, further work is needed to validate the algorithm using in situ data.

Benthic effects on the polarization of light in shallow waters

Measurements of the upwelling polarized radiance in relatively shallow waters of varying depths and benthic conditions are compared to simulations, revealing the depolarizing nature of the seafloor. The simulations, executed with the software package RayXP, are solutions to the vector radiative transfer equation, which depends on the incident light field and three types of parameters: inherent optical properties, the scattering matrix, and the benthic reflectance. These were measured directly or calculated from measurements with additional assumptions. Specifically, the Lambertian model used to simulate benthic reflectances is something of a simplification of reality, but the bottoms used in this study are found to be crucial for accurate simulations of polarization. Comparisons of simulations with and without bottom contributions show that only the former corroborate measurements of the Stokes components and the degree of linear polarization (DoLP) collected by the polarimeter developed at the City College of New York. Because this polarimeter is multiangular and hyperspectral, errors can be computed point-wise over a large range of scattering angles and wavelengths. Trends also become apparent. DoLP is highly sensitive to the benthic reflectance and to the incident wavelength, peaking in the red band, but the angle of linear polarization is almost spectrally constant and independent of the bottom. These results can thus facilitate the detection of benthic materials as well as future studies of camouflage by benthic biota; to hide underwater successfully, animals must reflect light just as depolarized as that reflected by benthic materials.

Capability of the Sentinel 2 mission for tropical coral reef mapping and coral bleaching detection

The Sentinel 2 mission offers continuity of service for the moderate resolution multispectral SPOT XS and Landsat Thematic Mapper series sensors, but also offers several design improvements that may lead to enhanced capability in coral reef mapping applications. In this study modeling and simulated image analyses were conducted to evaluate the relative capability of the Sentinel 2 instrument design compared to SPOT-4 and Landsat ETM+, for mapping bathymetry and benthic composition, and for coral bleaching detection. The analyses involved propagating noise and environmental uncertainties through a radiative transfer model inversion to quantify uncertainty in retrievable parameters from each sensor. The experiment structure included factors for sensor-environmental noise, band choice, and complexity of benthic reflectance model. Results indicate that while variables related to reef ‘health’ such as coral mortality and algal cover cannot be mapped accurately by this class of instrument; Sentinel 2 does have improved ability for discrimination of reef benthic composition over SPOT-4 and Landsat ETM+. The key enabling design factors are the narrowness of bands, increased spatial resolution and additional band at 443nm; instrument noise was a less significant factor. Rapid revisit times, global coverage, and freely available data suggest the potential for time series analyses. Sentinel 2 may also be capable of bleaching detection by change analysis given effective methods for precise cross-image image radiometric and spatial alignment.