Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS); and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in ...the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales; their role in marine biogeochemistry; the global carbon cycle; the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean; and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration; since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel.
Human activity and natural climate trends constitute a major threat to coral reefs worldwide. Models predict a significant reduction in reef spatial extension together with a decline in biodiversity ...in the relatively near future. In this context, monitoring programs to detect changes in reef ecosystems are essential. In recent years, coral reef mapping using remote sensing data has benefited from instruments with better resolution and computational advances in storage and processing capabilities. However, the water column represents an additional complexity when extracting information from submerged substrates by remote sensing that demands a correction of its effect. In this article, the basic concepts of bottom substrate remote sensing and water column interference are presented. A compendium of methodologies developed to reduce water column effects in coral ecosystems studied by remote sensing that include their salient features, advantages and drawbacks is provided. Finally, algorithms to retrieve the bottom reflectance are applied to simulated data and actual remote sensing imagery and their performance is compared. The available methods are not able to completely eliminate the water column effect, but they can minimize its influence. Choosing the best method depends on the marine environment, available input data and desired outcome or scientific application.
The inverse ocean color problem, i.e., the retrieval of marine reflectance from top-of-atmosphere (TOA) reflectance, is examined in a Bayesian context. The solution is expressed as a probability ...distribution that measures the likelihood of encountering specific values of the marine reflectance given the observed TOA reflectance. This conditional distribution, the posterior distribution, allows the construction of reliable multi-dimensional confidence domains of the retrieved marine reflectance. The expectation and covariance of the posterior distribution are computed, which gives for each pixel an estimate of the marine reflectance and a measure of its uncertainty. Situations for which forward model and observation are incompatible are also identified. Prior distributions of the forward model parameters that are suitable for use at the global scale, as well as a noise model, are determined. Partition-based models are defined and implemented for SeaWiFS, to approximate numerically the expectation and covariance. The ill-posed nature of the inverse problem is illustrated, indicating that a large set of ocean and atmospheric states, or pre-images, may correspond to very close values of the satellite signal. Theoretical performance is good globally, i.e., on average over all the geometric and geophysical situations considered, with negligible biases and standard deviation decreasing from 0.004 at 412nm to 0.001 at 670nm. Errors are smaller for geometries that avoid Sun glint and minimize air mass and aerosol influence, and for small aerosol optical thickness and maritime aerosols. The estimated uncertainty is consistent with the inversion error. The theoretical concepts and inverse models are applied to actual SeaWiFS imagery, and comparisons are made with estimates from the SeaDAS standard atmospheric correction algorithm and in situ measurements. The Bayesian and SeaDAS marine reflectance fields exhibit resemblance in patterns of variability, but the Bayesian imagery is less noisy and characterized by different spatial de-correlation scales. Experimental errors obtained from match-up data are similar to the theoretical errors determined from simulated data. Regionalization of the inverse models is a natural development to improve retrieval accuracy, for example by including explicit knowledge of the space and time variability of atmospheric variables.
•Inverse ocean color problem is examined in a Bayesian context.•Solution is expressed as a probability distribution.•Expectation and covariance are computed.•Methodology is evaluated theoretically and experimentally.•Inverse models are applied to SeaWiFS imagery.
Abstract
Here we present retrievals of aerosol optical depth
τ
from an Aerosol Robotic Network (AERONET) station in the southeastern corner of California, an area where dust storms are frequent. By ...combining AERONET data with collocated ceilometer measurements, camera imagery, and satellite data, we show that during significant dust outbreaks the AERONET cloud-screening algorithm oftentimes classifies dusty measurements as cloud contaminated, thus removing them from the aerosol record. During dust storms we estimate that approximately 85% of all dusty retrievals of
τ
and more than 95% of retrievals when
τ
> 0.1 are rejected, resulting in a factor-of-2 reduction in dust-storm averaged
τ
. We document the specific components in the screening algorithm responsible for the misclassification. We find that a major reason for the loss of these dusty measurements is the high temporal variability in
τ
during the passage of dust storms over the site, which itself is related to the proximity of the site to the locations of emission. We describe a method to recover these dusty measurements that is based on collocated ceilometer measurements. These results suggest that AERONET sites that are located close to dust source regions may require ancillary measurements to aid in the identification of dust.
Significance Statement
In this study we demonstrate that, during dust storms, measurements made with a sun photometer at an AERONET site in the western Sonoran Desert are frequently classified as cloud contaminated by the network’s processing algorithm. We identify the various algorithmic tests that result in the misclassification and discuss the physical reasons why dust typically fails those tests. We then present a method to restore these data that utilizes measurements from a collocated ceilometer. This work highlights the challenges, and one solution, to operating an AERONET site in a region that is close to the sources of airborne dust.
The biodiversity and high productivity of coastal terrestrial and aquatic habitats are the foundation for important benefits to human societies around the world. These globally distributed habitats ...need frequent and broad systematic assessments, but field surveys only cover a small fraction of these areas. Satellite-based sensors can repeatedly record the visible and near-infrared reflectance spectra that contain the absorption, scattering, and fluorescence signatures of functional phytoplankton groups, colored dissolved matter, and particulate matter near the surface ocean, and of biologically structured habitats (floating and emergent vegetation, benthic habitats like coral, seagrass, and algae). These measures can be incorporated into Essential Biodiversity Variables (EBVs), including the distribution, abundance, and traits of groups of species populations, and used to evaluate habitat fragmentation. However, current and planned satellites are not designed to observe the EBVs that change rapidly with extreme tides, salinity, temperatures, storms, pollution, or physical habitat destruction over scales relevant to human activity Making these observations requires a new generation of satellite sensors able to sample with these combined characteristics: ( 1) spatial resolution on the order of 30 to 100-m pixels or smaller; (2) spectral resolution on the order of 5 nm in the visible and 10 nm in the short-wave infrared spectrum (or a t least two or more bands a t 1,030, 1,240, 1,630, 2,125, and/or 2,260 nm) for atmospheric correction and aquatic and vegetation assessments; (3) radiometric quality with signal to noise ratios (SNR) above 800 (relative to signal levels typical of the open ocean), 14-bit digitization, absolute radiometric calibration <2%, relative calibration of 0.2%, polarization sensitivity <1%, high radiometric stability and linearity, and operations designed to minimize sunglint; and (4) temporal resolution of hours to days. We refer to these combined specifications as H4 imaging. Enabling H4 imaging is vital for the conservation and management of global biodiversity and ecosystem services, including food provisioning and water security. An agile satellite in a 3-d repeat low-Earth orbit could sample 30-km swath images of several hundred coastal habitats daily Nine H4 satellites would provide weekly coverage of global coastal zones. Such satellite constellations are now feasible and are used in various applications.
The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission will carry into space the Ocean Color Instrument (OCI), a spectrometer measuring at 5 nm spectral resolution in the ultraviolet (UV) to ...near infrared (NIR) with additional spectral bands in the shortwave infrared (SWIR), and two multi-angle polarimeters that will overlap the OCI spectral range and spatial coverage, i. e., the Spectrometer for Planetary Exploration (SPEXone) and the Hyper-Angular Rainbow Polarimeter (HARP2). These instruments, especially when used in synergy, have great potential for improving estimates of water reflectance in the post Earth Observing System (EOS) era. Extending the top-of-atmosphere (TOA) observations to the UV, where aerosol absorption is effective, adding spectral bands in the SWIR, where even the most turbid waters are black and sensitivity to the aerosol coarse mode is higher than at shorter wavelengths, and measuring in the oxygen A-band to estimate aerosol altitude will enable greater accuracy in atmospheric correction for ocean color science. The multi-angular and polarized measurements, sensitive to aerosol properties (e.g., size distribution, index of refraction), can further help to identify or constrain the aerosol model, or to retrieve directly water reflectance. Algorithms that exploit the new capabilities are presented, and their ability to improve accuracy is discussed. They embrace a modern, adapted heritage two-step algorithm and alternative schemes (deterministic, statistical) that aim at inverting the TOA signal in a single step. These schemes, by the nature of their construction, their robustness, their generalization properties, and their ability to associate uncertainties, are expected to become the new standard in the future. A strategy for atmospheric correction is presented that ensures continuity and consistency with past and present ocean-color missions while enabling full exploitation of the new dimensions and possibilities. Despite the major improvements anticipated with the PACE instruments, gaps/issues remain to be filled/tackled. They include dealing properly with whitecaps, taking into account Earth-curvature effects, correcting for adjacency effects, accounting for the coupling between scattering and absorption, modeling accurately water reflectance, and acquiring a sufficiently representative dataset of water reflectance in the UV to SWIR. Dedicated efforts, experimental and theoretical, are in order to gather the necessary information and rectify inadequacies. Ideas and solutions are put forward to address the unresolved issues. Thanks to its design and characteristics, the PACE mission will mark the beginning of a new era of unprecedented accuracy in ocean-color radiometry from space.
This paper reviews the state of the art of protocols for measurement of water-leaving radiance in the context of fiducial reference measurements (FRM) of water reflectance for satellite validation. ...Measurement of water reflectance requires the measurement of water-leaving radiance and downwelling irradiance just above water. For the former there are four generic families of method, based on: (1) underwater radiometry at fixed depths; or (2) underwater radiometry with vertical profiling; or (3) above-water radiometry with skyglint correction; or (4) on-water radiometry with skylight blocked. Each method is described generically in the FRM context with reference to the measurement equation, documented implementations and the intra-method diversity of deployment platform and practice. Ideal measurement conditions are stated, practical recommendations are provided on best practice and guidelines for estimating the measurement uncertainty are provided for each protocol-related component of the measurement uncertainty budget. The state of the art for measurement of water-leaving radiance is summarized, future perspectives are outlined, and the question of which method is best adapted to various circumstances (water type, wavelength) is discussed. This review is based on practice and papers of the aquatic optics community for the validation of water reflectance estimated from satellite data but can be relevant also for other applications such as the development or validation of algorithms for remote-sensing estimation of water constituents including chlorophyll a concentration, inherent optical properties and related products.
Estimating water reflectance accurately from satellite optical data requires implementing an accurate atmospheric correction (AC) scheme, a particularly challenging task over optically complex water ...bodies, where the signal that comes from the water prevents using the near-infrared (NIR) bands to separate the perturbing atmospheric signal. In the present work, we propose a new AC scheme specially designed for the Río de la Plata—a funnel-shaped estuary in the Argentine–Uruguayan border—highly scattering turbid waters. This new AC scheme uses far shortwave infrared (SWIR) bands but unlike previous algorithms relates the atmospheric signal in the SWIR to the signal in the near-infrared (NIR) and visible (VIS) bands based on the decomposition into principal components of the atmospheric signal. We describe the theoretical basis of the algorithm, analyze the spectral features of the simulated principal components, theoretically address the impact of noise on the results, and perform match-ups exercises using in situ measurements and Moderate Resolution Imaging Spectrometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) imagery over the region. Plausible water reflectance retrievals were obtained in the NIR and VIS bands from both simulations and match-ups using field data—with better performance (i.e., lowest errors and offsets, and slopes closest to 1) compared to existing AC schemes implemented in the NASA Data Analysis Software (SeaDAS). Moreover, retrievals over images in the VIS and NIR bands showed low noise, and the correlation was low between aerosol and water reflectance spatial fields.
An underwater volcanic eruption off the Vava’u island group in Tonga on 7 August 2019 resulted in the creation of floating pumice on the ocean’s surface extending over an area of 150 km2. The ...pumice’s far-reaching effects from its origin in the Tonga region to Fiji and the methods of automatic detection using satellite imagery are described, making it possible to track the westward drift of the pumice raft over 43 days. Level 2 Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), Sentinel-3 Ocean and Land Color Instrument (OLCI), and Sentinel-3 Sea and Land Surface Temperature Radiometer (SLSTR) imagery of sea surface temperature, chlorophyll-a concentration, quasi-surface (i.e., Rayleigh-corrected) reflectance, and remote sensing reflectance were used to distinguish consolidated and fragmented rafts as well as discolored and mesotrophic waters. The rafts were detected by a 1 to 3.5 °C enhancement in the MODIS-derived “sea surface temperature” due to the emissivity difference of the raft material. Large plumes of discolored waters, characterized by higher satellite reflectance/backscattering of particles in the blue than surrounding waters (and corresponding to either submersed pumice or associated white minerals), were associated with the rafts. The discolored waters had relatively lower chlorophyll-a concentration, but this was artificial, resulting from the higher blue/red reflectance ratio caused by the reflective pumice particles. Mesotrophic waters were scarce in the region of the pumice rafts, presumably due to the absence of phytoplanktonic response to a silicium-rich pumice environment in these tropical oligotrophic environments. As beach accumulations around Pacific islands surrounded by coral shoals are a recurrent phenomenon that finds its origin far east in the ocean along the Tongan trench, monitoring the events from space, as demonstrated for the 7 August 2019 eruption, might help mitigate their potential economic impacts.
A generic methodology is presented to cross-calibrate satellite ocean-color sensors in polar orbit
via
an intermediary geostationary sensor of reference. In this study, AHI onboard Hiwamari-8 is used ...as the intermediary sensor to cross-calibrate SGLI onboard GCOM-C and MODIS onboard Aqua and Terra (MODIS-A and MODIS-T) after system vicarious calibration (SVC). Numerous coincidences were obtained near the Equator using 3 days of imagery, i.e., 11 May 2018, 22 January 2019, and 25 January 2020. Spectral matching to AHI spectral bands was first performed for a wide range of angular geometry, aerosol conditions, and Case 1 waters using a single band or multiple bands of SGLI, MODIS-A and MODIS-T, yielding root mean square differences of 0.1–0.7% in the blue and green and 0.7%–3.7% in the red depending on the band combination. Limited by the inherent AHI instrument noise and the system vicarious calibration of individual polar-orbiting sensors, cross-calibration was only performed for equivalent AHI bands centered on at 471, 510, and 639 nm. Results show that MODIS-A and MODIS-T are accurately cross-calibrated, with cross-calibration ratios differing by 0.1%–0.8% in magnitude. These differences are within or slightly outside the estimated uncertainties of ±0.6% to ±1.0%. In contrast, SGLI shows larger cross-calibration differences, i.e., 1.4%, 3.4%, and 1.1% with MODIS-A and 1.5%, 4.6%, and 1.5% with MODIS-T, respectively. These differences are above uncertainties of ±0.8–1.0% at 471 and 510 nm and within uncertainties of ±2.3% and ±1.9% at 639 nm. Such differences may introduce significant discrepancies between ocean-color products generated from SGLI and MODIS data, although some compensation may occur because different atmospheric correction schemes are used to process SGLI and MODIS imagery, and SVC is based on the selected scheme. Geostationary sensors with ocean color capability have potential to improve the spectral matching and reduce uncertainties, as long as they provide imagery at sufficient cadence over equatorial regions. The methodology is applicable to polar-orbiting optical sensors in general and can be implemented operationally to ensure consistency of products generated by individual sensors in establishing long-term data records for climate studies.
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