Spectral correlation in MODIS water-leaving reflectance retrieval uncertainty.

Spectral remote sensing reflectance, Rrs(λ) (sr-1), is the fundamental quantity used to derive a host of bio-optical and biogeochemical properties of the water column from satellite ocean color measurements. Estimation of uncertainty in those derived geophysical products is therefore dependent on knowledge of the uncertainty in satellite-retrieved Rrs. Furthermore, since the associated algorithms require Rrs at multiple spectral bands, the spectral (i.e., band-to-band) error covariance in Rrs is needed to accurately estimate the uncertainty in those derived properties. This study establishes a derivative-based approach for propagating instrument random noise, instrument systematic uncertainty, and forward model uncertainty into Rrs, as retrieved using NASA's multiple-scattering epsilon (MSEPS) atmospheric correction algorithm, to generate pixel-level error covariance in Rrs. The approach is applied to measurements from Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite and verified using Monte Carlo (MC) analysis. We also make use of this full spectral error covariance in Rrs to calculate uncertainty in phytoplankton pigment chlorophyll-a concentration (chla, mg/m3) and diffuse attenuation coefficient of downwelling irradiance at 490 nm (Kd(490), m-1). Accounting for the error covariance in Rrs generally reduces the estimated relative uncertainty in chla by ∼1-2% (absolute value) in waters with chla < 0.25 mg/m3 where the color index (CI) algorithm is used. The reduction is ∼5-10% in waters with chla > 0.35 mg/m3 where the blue-green ratio (OCX) algorithm is used. Such reduction can be higher than 30% in some regions. For Kd(490), the reduction by error covariance is generally ∼2%, but can be higher than 20% in some regions. The error covariance in Rrs is further verified through forward-calculating chla from MODIS-retrieved and in situ Rrs and comparing estimated uncertainty with observed differences. An 8-day global composite of propagated uncertainty shows that the goal of 35% uncertainty in chla can be achieved over deep ocean waters (chla ≤ 0.1 mg/m3). While the derivative-based approach generates reasonable error covariance in Rrs, some assumptions should be updated as our knowledge improves. These include the inter-band error correlation in top-of-atmosphere reflectance, and uncertainties in the calibration of MODIS 869 nm band, in ancillary data, and in the in situ data used for system vicarious calibration.

Optimal estimation framework for ocean color atmospheric correction and pixel-level uncertainty quantification.

Ocean color (OC) remote sensing requires compensation for atmospheric scattering and absorption (aerosol, Rayleigh, and trace gases), referred to as atmospheric correction (AC). AC allows inference of parameters such as spectrally resolved remote sensing reflectance (Rrs(λ);sr-1) at the ocean surface from the top-of-atmosphere reflectance. Often the uncertainty of this process is not fully explored. Bayesian inference techniques provide a simultaneous AC and uncertainty assessment via a full posterior distribution of the relevant variables, given the prior distribution of those variables and the radiative transfer (RT) likelihood function. Given uncertainties in the algorithm inputs, the Bayesian framework enables better constraints on the AC process by using the complete spectral information compared to traditional approaches that use only a subset of bands for AC. This paper investigates a Bayesian inference research method (optimal estimation [OE]) for OC AC by simultaneously retrieving atmospheric and ocean properties using all visible and near-infrared spectral bands. The OE algorithm analytically approximates the posterior distribution of parameters based on normality assumptions and provides a potentially viable operational algorithm with a reduced computational expense. We developed a neural network RT forward model look-up table-based emulator to increase algorithm efficiency further and thus speed up the likelihood computations. We then applied the OE algorithm to synthetic data and observations from the moderate resolution imaging spectroradiometer (MODIS) on NASA's Aqua spacecraft. We compared the Rrs(λ) retrieval and its uncertainty estimates from the OE method with in-situ validation data from the SeaWiFS bio-optical archive and storage system (SeaBASS) and aerosol robotic network for ocean color (AERONET-OC) datasets. The OE algorithm improved Rrs(λ) estimates relative to the NASA standard operational algorithm by improving all statistical metrics at 443, 555, and 667 nm. Unphysical negative Rrs(λ), which often appears in complex water conditions, was reduced by a factor of 3. The OE-derived pixel-level Rrs(λ) uncertainty estimates were also assessed relative to in-situ data and were shown to have skill.

Model for deriving benthic irradiance in the Great Barrier Reef from MODIS satellite imagery: erratum.

Corrections for equations in our recently published paper [Opt. Express27, A1350 (2019)] are presented.

Model for deriving benthic irradiance in the Great Barrier Reef from MODIS satellite imagery.

We demonstrate a simple, spectrally resolved ocean color remote sensing model to estimate benthic photosynthetically active radiation (bPAR) for the waters of the Great Barrier Reef (GBR), Australia. For coastal marine environments and coral reefs, the underwater light field is critical to ecosystem health, but data on bPAR rarely exist at ecologically relevant spatio-temporal scales. The bPAR model presented here is based on Lambert-Beer's Law and uses: (i) sea surface values of the downwelling solar irradiance, Es(λ); (ii) high-resolution seafloor bathymetry data; and (iii) spectral estimates of the diffuse attenuation coefficient, Kd(λ), calculated from GBR-specific spectral inherent optical properties (IOPs). We first derive estimates of instantaneous bPAR. Assuming clear skies, these instantaneous values were then used to obtain daily integrated benthic PAR values. Matchup comparisons between concurrent satellite-derived bPAR and in situ values recorded at four optically varying test sites indicated strong agreement, small bias, and low mean absolute error. Overall, the matchup results suggest that our benthic irradiance model was robust to spatial variation in optical properties, typical of complex shallow coastal waters such as the GBR. We demonstrated the bPAR model for a small test region in the central GBR, with the results revealing strong patterns of temporal variability. The model will provide baseline datasets to assess changes in bPAR and its external drivers and may form the basis for a future GBR water-quality index. This model may also be applicable to other coastal waters for which spectral IOP and high-resolution bathymetry data exist.

Approach for identifying optically shallow pixels when processing ocean-color imagery.

Ocean-color remote sensing is routinely used to derive marine geophysical parameters from sensor-observed spectral water-leaving radiances. However, in clear geometrically shallow regions, traditional ocean-color algorithms can be confounded by light reflected from the seafloor. Such regions are typically referred to as "optically shallow". When performing spatiotemporal analyses of ocean color datasets, optically shallow features such as submerged sand banks and coral reefs can lead to unexpected regional biases. Most contemporary approaches mask or flag suspected optically shallow pixels based on ancillary bathymetric data. However, the extent to which seafloor reflectance contaminates the water-leaving radiance is dependent on bathymetry, water clarity and seafloor albedo. In this paper, an approach for flagging optically shallow pixels has been developed that considers all three of these variables. In the method, the optical depth of the water column at 547 nm, ζ(547), is predicted from bathymetric data and estimated water-column optical properties. If ζ(547) is less then the pre-defined threshold, a pixel is flagged as potentially optically shallow. Radiative transfer modeling was used to identify a conservative threshold value of ζ(547) = 20 for a bright sand seafloor. In addition, pixels in waters shallower than 5 m are also flagged. We also examined how varying bathymetric datasets may affect the optically shallow flag using MODIS data. It is anticipated that the optically shallow flag will benefit end-users when quality controlling derived ocean color products. Further, the flag may prove useful as a mechanism for switching between optically deep and shallow algorithms during ocean color processing.

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.

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

Fowler's 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 NASA's 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.

Implementation of an analytical Raman scattering correction for satellite ocean-color processing.

Raman scattering of photons by seawater molecules is an inelastic scattering process. This effect can contribute significantly to the water-leaving radiance signal observed by space-borne ocean-color spectroradiometers. If not accounted for during ocean-color processing, Raman scattering can cause biases in derived inherent optical properties (IOPs). Here we describe a Raman scattering correction (RSC) algorithm that has been integrated within NASA's standard ocean-color processing software. We tested the RSC with NASA's Generalized Inherent Optical Properties algorithm (GIOP). A comparison between derived IOPs and in situ data revealed that the magnitude of the derived backscattering coefficient and the phytoplankton absorption coefficient were reduced when the RSC was applied, whilst the absorption coefficient of colored dissolved and detrital matter remained unchanged. Importantly, our results show that the RSC did not degrade the retrieval skill of the GIOP. In addition, a time-series study of oligotrophic waters near Bermuda showed that the RSC did not introduce unwanted temporal trends or artifacts into derived IOPs.

Development and Validation of an Empirical Ocean Color Algorithm with Uncertainties: A Case Study with the Particulate Backscattering Coefficient.

We explored how algorithm (model) and in situ measurement (observation) uncertainties can effectively be incorporated into empirical ocean color model development and assessment. In this study we focused on methods for deriving the particulate backscattering coefficient at 555 nm, b bp (555) (m-1). We developed a simple empirical algorithm for deriving b bp (555) as a function of a remote sensing reflectance line height (LH) metric. Model training was performed using a high-quality bio-optical dataset that contains coincident in situ measurements of the spectral remote sensing reflectances, R rs (λ) (sr-1), and the spectral particulate backscattering coefficients, b bp (λ). The LH metric used is defined as the magnitude of R rs (555) relative to a linear baseline drawn between R rs (490) and R rs (670). Using an independent validation dataset, we compared the skill of the LH-based model with two other models. We used contemporary validation metrics, including bias and mean absolute error (MAE), that were corrected for model and observation uncertainties. The results demonstrated that measurement uncertainties do indeed impact contemporary validation metrics such as mean bias and MAE. Zeta-scores and z-tests for overlapping confidence intervals were also explored as potential methods for assessing model skill.

A benthic light index of water quality in the Great Barrier Reef, Australia.

Good water quality is essential to the health of marine ecosystems, yet current metrics used to track water quality in the Great Barrier Reef are not strongly tied to ecological outcomes. There is a need for a better water quality index (WQI). Benthic irradiance, the amount of light reaching the seafloor, is critical for coral and seagrass health and is strongly affected by water quality. It therefore represents a strong candidate for use as a water quality indicator. Here, we introduce a new index based on remote sensing benthic light (bPAR) from ocean color. Resulting bPAR index timeseries, based on the extent to which the observed bPAR fell short of the locally- and seasonally-specific optimum, showed strong spatial and temporal variability, which was consistent with the dynamics that govern changes in water clarity in the Great Barrier Reef. Our new index is ecologically relevant, responsive to changes in light availability and provides a robust metric that may complement current Great Barrier Reef water quality metrics.

Sensitivity of inherent optical properties from ocean reflectance inversion models to satellite instrument wavelength suites.

The Earth science community seeks to develop climate data records (CDRs) from satellite measurements of ocean color, a continuous data record which now exceeds twenty years. Space agencies will launch additional instruments in the coming decade that will continue this data record, including the NASA PACE spectrometer. Inherent optical properties (IOPs) quantitatively describe the absorbing and scattering constituents of seawater and can be estimated from satellite-observed spectroradiometric data using semi-analytical algorithms (SAAs). SAAs exploit the contrasting optical signatures of constituent matter at spectral bands observed by satellite sensors. SAA performance, therefore, depends on the spectral resolution of the satellite spectroradiometer. A CDR spanning SeaWiFS, MODIS, OLCI, and PACE, for example, would include IOPs derived using varied wavelength suites if all available wavelengths were considered. Here, we explored differences in derived IOPs that stem simply from the use of (eight) different wavelength suites of input radiometric measurements. Using synthesized data and SeaWiFS Level-3 mission-long composites, we demonstrated equivalent SAA performance for all wavelength suites, but that IOP retrievals vary by several percent across wavelength suites and as a function of water type. The differences equate to roughly ≤ 6, 12, and 7% for adg (443), aph (443), and bbp (443), respectively, for waters with Ca ≤ 1 mg m-3. These values shrink for sensors with similar wavelength suites (e.g., SeaWiFS, MODIS, and MERIS) and rise to substantially larger values for higher Ca waters. Our results also indicate that including 400 nm (in the case of OLCI) influences the derived IOPs, using longer wavelengths (>600 nm) influences the derived IOPs when there is a red signal, and, including additional spectral information shows potential for improved IOP estimation, but not without revisiting SAA parameterizations and execution. While modest in scope, we believe this study contributes to the knowledge base for CDR development. The implication of ignoring such an analysis as CDRs continue to be developed is a prolonged inability to distinguish between algorithmic and environmental contributions to trends and anomalies in the IOP time-series.

Approach for Propagating Radiometric Data Uncertainties Through NASA Ocean Color Algorithms.

Spectroradiometric satellite observations of the ocean are commonly referred to as "ocean color" remote sensing. NASA has continuously collected, processed, and distributed ocean color datasets since the launch of the Sea-viewing Wide-field-of-view Sensor (SeaWiFS) in 1997. While numerous ocean color algorithms have been developed in the past two decades that derive geophysical data products from sensor-observed radiometry, few papers have clearly demonstrated how to estimate measurement uncertainty in derived data products. As the uptake of ocean color data products continues to grow with the launch of new and advanced sensors, it is critical that pixel-by-pixel data product uncertainties are estimated during routine data processing. Knowledge of uncertainties can be used when studying long-term climate records, or to assist in the development and performance appraisal of bio-optical algorithms. In this methods paper we provide a comprehensive overview of how to formulate first-order first-moment (FOFM) calculus for propagating radiometric uncertainties through a selection of bio-optical models. We demonstrate FOFM uncertainty formulations for the following NASA ocean color data products: chlorophyll-a pigment concentration (Chl), the diffuse attenuation coefficient at 490 nm (K d,490), particulate organic carbon (POC), normalized fluorescent line height (nflh), and inherent optical properties (IOPs). Using a quality-controlled in situ hyperspectral remote sensing reflectance (R rs,i ) dataset, we show how computationally inexpensive, yet algebraically complex, FOFM calculations may be evaluated for correctness using the more computationally expensive Monte Carlo approach. We compare bio-optical product uncertainties derived using our test R rs dataset assuming spectrally-flat, uncorrelated relative uncertainties of 1, 5, and 10%. We also consider spectrally dependent, uncorrelated relative uncertainties in R rs . The importance of considering spectral covariances in R rs , where practicable, in the FOFM methodology is highlighted with an example SeaWiFS image. We also present a brief case study of two POC algorithms to illustrate how FOFM formulations may be used to construct measurement uncertainty budgets for ecologically-relevant data products. Such knowledge, even if rudimentary, may provide useful information to end-users when selecting data products or when developing their own algorithms.

Digitise this! A quick and easy remote sensing method to monitor the daily extent of dredge plumes.

Technological advancements in remote sensing and GIS have improved natural resource managers' abilities to monitor large-scale disturbances. In a time where many processes are heading towards automation, this study has regressed to simple techniques to bridge a gap found in the advancement of technology. The near-daily monitoring of dredge plume extent is common practice using Moderate Resolution Imaging Spectroradiometer (MODIS) imagery and associated algorithms to predict the total suspended solids (TSS) concentration in the surface waters originating from floods and dredge plumes. Unfortunately, these methods cannot determine the difference between dredge plume and benthic features in shallow, clear water. This case study at Barrow Island, Western Australia, uses hand digitising to demonstrate the ability of human interpretation to determine this difference with a level of confidence and compares the method to contemporary TSS methods. Hand digitising was quick, cheap and required very little training of staff to complete. Results of ANOSIM R statistics show remote sensing derived TSS provided similar spatial results if they were thresholded to at least 3 mg L(-1). However, remote sensing derived TSS consistently provided false-positive readings of shallow benthic features as Plume with a threshold up to TSS of 6 mg L(-1), and began providing false-negatives (excluding actual plume) at a threshold as low as 4 mg L(-1). Semi-automated processes that estimate plume concentration and distinguish between plumes and shallow benthic features without the arbitrary nature of human interpretation would be preferred as a plume monitoring method. However, at this stage, the hand digitising method is very useful and is more accurate at determining plume boundaries over shallow benthic features and is accessible to all levels of management with basic training.