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Abstract Upwelling in the Equatorial Pacific nurtures an expansive, westward-stretching chlorophyll-rich tongue (CRT), supporting 18% of the annual global new production. Surrounding the CRT are the oligotrophic subtropical gyres to the north and south, which are suggested to be expanding under global warming. Yet, how this productive CRT has changed, expanding or contracting, remains unknown. By applying the empirical mode decomposition (EMD) method to 20-year monthly measurements of chlorophyll-a concentration from MODIS-Aqua satellite (2002-2022), we demonstrate that the CRT exhibited a significant westward extension, at an average expanding rate of 1.87 ( ± 0.82) × 105 km2/yr. The westward extension of the CRT is attributed to strengthened equatorial upwelling and a strengthened South Equatorial Current from 2002 to 2022, driven by intensified easterly trade winds as the Pacific Decadal Oscillation predominantly remains in its negative phase during this period. Interestingly, EMD analysis on central locations of the Pacific gyres suggested simultaneous extension of the gyres and the CRT during 2002-2022, with the gyres extending poleward. Our findings imply a broader cover of productive water along the equator, while its impact on tropical climate, ecosystems, and carbon cycle deserves further investigation.

The Landsat-8 Collection 2 provides Level-2 surface temperature product (L8-L2ST) at a spatial resolution of 30 m, catering to various applications. However, discrepancies in the spatial resolution of certain parameters involved in L8-L2ST production often result in noticeable “checkerboard” patterns in images over oceanic waters. To enhance the accuracy of sea surface temperature (SST) products derived from the Landsat-8 measurements, this study introduces a neural network (NN) based algorithm for the estimation of SST. By sidestepping the conventional radiative-transfer-based method, which relies on numerous auxiliary data products, the SST generated by the NN-based algorithm could avoid the “checkerboard” issues encountered in the L8-L2ST products. Compared to the reference MODIS SST products, the root mean square error (RMSE) of NN-based SST is 0.7 °C, whereas the RMSE of L8-L2ST is 1.42 °C. In comparison to buoy data, the RMSE of this method is 1.18 °C, while the RMSE of L8-L2ST is 2 °C. This work thus presents a valuable framework for acquiring more consistent and better-quality SST products from Landsat-8 measurements.

Consistent bio-optical properties across multiple ocean color satellites are the key prerequisite to merging products from these satellites, thereby enhancing spatial coverage and extending temporal spans. However, due to factors such as sensor specifics and separate data processing algorithms, bio-optical properties (e.g., remote sensing reflectance, Rrs ) from different ocean color missions exhibit varying discrepancies in oceanic, coastal, and inland waters. Here, we introduce a cross-satellite atmospheric correction (CSAC) scheme, which could greatly improve the consistency of Rrs products between MODIS-Aqua and other satellite ocean color missions. Specifically, using an inclusive high-quality Rrs dataset of oceanic waters obtained from MODIS-Aqua as the reference, and as an example, top-of-atmosphere reflectance from SeaWiFS (Sea-Viewing Wide Field-of-View Sensor) is directly processed to MODIS-Aqua-equivalent Rrs ([Formula: see text]) via CSAC. As a demonstration, for independent space-time matched measurements between MODIS-Aqua and SeaWiFS, the mean absolute percent difference (MAPD) between [Formula: see text] and MODIS-Aqua Rrs ranges from 5.9% to 22.2% across wavelengths from 412 to 667 nm. In contrast, the MAPD values between the NASA standard SeaWiFS and MODIS-Aqua Rrs products range from 10.1% to 55.1% for the same spectral bands. These results highlight the potential of CSAC in obtaining consistent Rrs products and, subsequently, Rrs -derived bio-optical properties, from various ocean color satellites, facilitating extensive and long-term ocean color observations of the global ocean.

Absorption coefficient and chlorophyll concentration (Chl) are important optical and biological properties of the aquatic environment, which can be estimated from the spectrum of water color, commonly measured by the remote sensing reflectance (Rrs). In this study, we extended the band-difference scheme for Chl of oceanic waters developed a decade ago to the estimation of absorption coefficient at 440 nm (a(440)). As demonstrated earlier for the estimation of Chl, a(440) product from the band difference of Rrs showed much smoother spatial pattern than that from a semianalytical algorithm. More importantly, it is found that the upper limit of using band difference of Rrs can be extended from −0.0005 sr−1 (the upper limit set a decade ago for the estimation of Chl) to ~0.0005 sr−1 (corresponding to a(440) ~0.08 m−1), which covers ~91% of the global ocean. We further converted a(440) to Chl based on the “Case-1” water assumption and found that the standard Chl product of oligotrophic waters (Chl ~ 0.1 mg/m3) distributed by NASA is generally ~20% higher than Chl converted from a(440), possibly a result of different datasets used to determine the algorithm coefficients. These results not only extended the application of the band-difference scheme for more oceanic waters but also highlighted the need of more accurate field measurements of Chl and Rrs in oligotrophic oceans in order to minimize the discrepancies observed in satellite Chl products derived using the same algorithm concept but different empirical approaches.

Measuring the characteristics of seawater constituent is in great demand for studies of marine ecosystems and biogeochemistry. However, existing techniques based on remote sensing or in situ samplings present various tradeoffs with regard to the diversity, synchronism, temporal-spatial resolution, and depth-resolved capacity of their data products. Here, we demonstrate a novel oceanic triple-field-of-view (FOV) high-spectral-resolution lidar (HSRL) with an iterative retrieval approach. This technique provides, for the first time, comprehensive, continuous, and vertical measurements of seawater absorption coefficient, scattering coefficient, and slope of particle size distribution, which are validated by simulations and field experiments. Furthermore, it depicts valuable application potentials in the accuracy improvement of seawater classification and the continuous estimation of depth-resolved particulate organic carbon export. The triple-FOV HSRL with high performance could greatly increase the knowledge of seawater constituents and promote the understanding of marine ecosystems and biogeochemistry.

Based on the optical properties of water constituents, the vertical variation of photosynthetically available radiation (PAR) can be well modeled with hyperspectral resolution; the intensive computing load, however, demands simplified modeling that can be easily embedded in marine physical and biogeochemical models. While the vertical PAR profile in homogeneous waters can now be accurately modeled with simple parameterization, it is still a big challenge to model the PAR profile in stratified waters with limited variables. In this study, based on empirical equations and simulations, we propose a dual-band model to characterize the vertical distribution of PAR using the chlorophyll concentration (Chl). With an inclusive dataset including cruise data collected in the Southeast Pacific and BGC-Argo data in the global ocean, the model was thoroughly evaluated for its general applicability in three aspects: 1) estimating the entire PAR profile from sea-surface PAR and the Chl profile, 2) estimating the euphotic layer depth from the Chl profile, and 3) estimating PAR just below the sea surface from in situ radiometry measurements. It is demonstrated that the proposed dual-band model is capable of generating similar estimates as that from a hyperspectral model, thus offering an effective module that can be incorporated in large-scale ecosystem and/or circulation models for efficient calculations.

Bottom depth (H) of optically shallow waters can be retrieved from multiband imagery, where remote sensing reflectance (Rrs) are commonly used as the input. Because of the difficulties of removing the atmospheric effects in coastal areas, quite often, there are no valid Rrs from satellites for the retrieval of H. More importantly, the empirical algorithms for H are hardly portable to new measurements. In this study, using data from Landsat-8 and ICESat-2 as examples, we present an approach to retrieve H directly from the top-of-atmosphere (TOA) data. It not only bypasses the requirement to correct the effects of aerosols but also shows promising portability to areas not included in algorithm development. Specifically, we use Rayleigh-corrected TOA reflectance (ρrc) in the 443–2300 nm range as input, along with a multilayer perceptron (MLPHρrc), for the retrieval of H. More than 78,000 matchup points of ρrc and H (0–25 m) were used to train MLPHρrc, which resulted in a Mean Absolute Percentage Difference (MARD) of 8.8% and a coefficient of determination (R2) of 0.96. This MLPHρrc was further applied to Landsat-8 data of six regions not included in the training phase, generating MARD and R2 values of 8.3% and 0.98, respectively. In contrast, a conventional two-band ratio algorithm with Rrs as the input generated MARD and R2 values of 31.6% and 0.68 and significantly fewer H retrievals due to failures in atmospheric correction. These results indicate a breakthrough of algorithm portability of MLPHρrc in sensing H of optically shallow waters.

Morel’s “Optical modeling of the upper ocean in relation to its biogenous matter content (Case I waters)” (J. Geophys. Res. - Oceans, Vol. 93, pp. 107,49-10,768, 1988) laid the groundwork to model the optical properties of natural waters based on the concentration of chlorophyll ([Chl], in mg/m3). As stated in the abstract, it aims “tentatively to interpret the optical behavior of oceanic case-I waters,” where “Chlorophyll-like pigment concentration is used as the index to quantify the algal materials,” because [Chl] is routinely measured in marine/oceanic surveys. Specifically, Morel developed “statistical relationships between this index and the depth of euphotic layer, the spectral values of the attenuation coefficient for downwelling irradiance, or the scattering coefficient,” and further, “a pigment-dependent optical model is developed.” Thus, such a system allows many aspects of oceanographic applications when [Chl] (“this index”) is provided. In part, this system established [Chl] at the core of traditional ocean color remote sensing. To implement this system, however, it is necessary to have a complete understanding of the definition and evolution of this Case-1/Case-2 system, especially the qualitative definition of Case-1/Case-2 vs. the practical separation of Case-1/Case-2 as well as the quantitative modeling of the optical properties of Case-1 waters.

One of the primary goals of launching an ocean color satellite is to obtain over the global ocean synoptic measurements of primary production (PP), a measure of phytoplankton photosynthesis. To reach this ultimate goal, in addition to precise measurements of radiance at the satellite altitude and robust data processing systems, a key requirement is to link primary production with satellite-derived products, where a model must be developed and applied. Although many models have been developed in the past decades, the vertically generalized production model (VGPM) developed by Behrenfeld and Falkowski, due to its simplicity and ease of use with satellite products, has been a de facto “standard” for the estimation of PP from ocean color measurements over the past 20+ years. Thus, it has significantly influenced the ocean color remote sensing and the biological oceanographic communities. In this article, we discuss the limitations of VGPM (and PP models based on chlorophyll concentration) in estimating primary production.

Sentinel-2 mission has been shown to have promising applications in coral reef remote sensing because of its superior properties. It has a 5-day revisit time, spatial resolution of 10 m, free data, etc. In this study, Sentinel-2 imagery was investigated for bleaching detection through simulations and a case study over the Lizard Island, Australia. The spectral and image simulations based on the semianalytical (SA) model and the sensor spectral response function, respectively, confirmed that coral bleaching cannot be detected only using one image, and the change analysis was proposed for detection because there will be a featured change signal for bleached corals. Band 2 of Sentinel-2 is superior to its other bands for the overall consideration of signal attenuation and spatial resolution. However, the detection capability of Sentinel-2 is still limited by the water depth. With rapid signal attenuation due to the water absorption effect, the applicable water depth for bleaching detection was recommended to be less than 10 m. The change analysis was conducted using two methods: one radiometric normalization with pseudo invariant features (PIFs) and the other with multi-temporal depth invariant indices (DII). The former performed better than the latter in terms of classification. The bleached corals maps obtained using the PIFs and DII approaches had an overall accuracy of 88.9 and 57.1%, respectively. Compared with the change analysis based on two dated images, the use of a third image that recorded the spectral signals of recovered corals or corals overgrown by algae after bleaching significantly improved the detection accuracy. All the preliminary results of this article will aid in the future studies on coral bleaching detection based on remote sensing.

There has been a long history of interest on how (if) the absorption coefficient of “pure” fresh water (afwλ) and “pure” seawater (aswλ) changes with temperature (T), yet the impact of T reported in the literature differs significantly in the blue domain. Unlike the previous studies based on laboratory measurements, we took an approach based on ~18 years (2002–2020) of MODIS ocean color and temperature measurements in the oligotrophic oceans, along with field measured chlorophyll concentration and phytoplankton absorption coefficient, to examine the relationship between T and the total absorption coefficient (aλ) at 412 and 443 nm. We found that the values of a412 and a443 in the summer are nearly flat (slightly decreasing) for the observed T range of ~19–27 °C. Since there are no detectable changes of chlorophyll during this period, the results suggest that T has a negligible impact on asw412 and asw443 in this T range. As a complement, the impact of salinity on afwλ was also evaluated using three independent determinations of aswλ and afwλ, where good agreements were found from these observations.

With the advancement of Lidar technology, bottom depth (H) of optically shallow waters (OSW) can be measured accurately with an airborne or space-borne Lidar system (HLidar hereafter), but this data product consists of a line format, rather than the desired charts or maps, particularly when the Lidar system is on a satellite. Meanwhile, radiometric measurements from multiband imagers can also be used to infer H (Himager hereafter) of OSW with variable accuracy, though a map of bottom depth can be obtained. It is logical and advantageous to use the two data sources from collocated measurements to generate a more accurate bathymetry map of OSW, where usually image-specific empirical algorithms are developed and applied. Here, after an overview of both the empirical and semianalytical algorithms for the estimation of H from multiband imagers, we emphasize that the uncertainty of Himager varies spatially, although it is straightforward to draw regressions between HLidar and radiometric data for the generation of Himager. Further, we present a prototype system to map the confidence of Himager pixel-wise, which has been lacking until today in the practices of passive remote sensing of bathymetry. We advocate the generation of a confidence measure in parallel with Himager, which is important and urgent for broad user communities.

The research frontiers of radiative transfer (RT) in coupled atmosphere-ocean systems are explored to enable new science and specifically to support the upcoming Plankton, Aerosol, Cloud ocean Ecosystem (PACE) satellite mission. Given (i) the multitude of atmospheric and oceanic constituents at any given moment that each exhibits a large variety of physical and chemical properties and (ii) the diversity of light-matter interactions (scattering, absorption, and emission), tackling all outstanding RT aspects related to interpreting and/or simulating light reflected by atmosphere-ocean systems becomes impossible. Instead, we focus on both theoretical and experimental studies of RT topics important to the science threshold and goal questions of the PACE mission and the measurement capabilities of its instruments. We differentiate between (a) forward (FWD) RT studies that focus mainly on sensitivity to influencing variables and/or simulating data sets, and (b) inverse (INV) RT studies that also involve the retrieval of atmosphere and ocean parameters. Our topics cover (1) the ocean (i.e., water body): absorption and elastic/inelastic scattering by pure water (FWD RT) and models for scattering and absorption by particulates (FWD RT and INV RT); (2) the air-water interface: variations in ocean surface refractive index (INV RT) and in whitecap reflectance (INV RT); (3) the atmosphere: polarimetric and/or hyperspectral remote sensing of aerosols (INV RT) and of gases (FWD RT); and (4) atmosphere-ocean systems: benchmark comparisons, impact of the Earth’s sphericity and adjacency effects on space-borne observations, and scattering in the ultraviolet regime (FWD RT). We provide for each topic a summary of past relevant (heritage) work, followed by a discussion (for unresolved questions) and RT updates.

Quantum yield of photosynthesis (ϕ) expresses the efficiency of phytoplankton carbon fixation given certain amount of absorbed light. This photophysiological parameter is key to obtaining reliable estimates of primary production (PPsat) in the ocean based on remote sensing information. Several works have shown that ϕ changes temporally, vertically, and horizontally in the ocean. One of the primary factors ruling its variability is light intensity and thereby, it can be modeled as a function of Photosynthetically Available Radiation (PAR). We estimated ϕ utilizing long time-series collected in the North Subtropical Oligotrophic Gyres, at HOT and BATS stations (Pacific and Atlantic oceans, respectively). Subsequently the maximum quantum yield (ϕm) and Kϕ (PAR value at half ϕm) were calculated. Median ϕm values were ~0.040 and 0.063 mol C mol photons−1 at HOT and BATS, respectively, with higher values in winter. Kϕ values were ~8.0 and 10.8 mol photons m−2 d−1 for HOT and BATS, respectively. Seasonal variability in Kϕ showed its peak in summer. Dynamical parameterizations for both regions are indicated by their temporal behaviors, where ϕm is related to temperature at BATS while Kϕ to PAR, in both stations. At HOT, ϕm was weakly related to temperature and its median annual value was used for the whole data series. Differences in the study areas, even though both belong to Subtropical Gyres, reinforced the demand for regional parameterizations in PPsat models. Such parameterizations were finally included in a PPsat model based on phytoplankton absorption (PPsat−aphy−based), where results showed that the PPsat−aphy−based model coupled with dynamical parameterization improved PPsat estimates. Compared with PPsat estimates from the widely used VGPM, a model based on chlorophyll concentration (PPsat−chl−based), PPsat−aphy−based reduced model-measurement differences from ~62.8 to ~8.3% at HOT, along with well-matched seasonal cycle of PP (R2 = 0.76). There is not significant reduction in model-measurement differences between PPsat−chl−based and PPsat−aphy−based PP at BATS though (37.8 vs. 36.4%), but much better agreement in seasonal cycles with PPsat−aphy−based (R2 increased from 0.34 to 0.71). Our results point to improved estimation of PPsat by parameterized quantum yield along with phytoplankton absorption coefficient at the core.

Commonly we see the diffuse attenuation coefficient of downwelling irradiance (Kd) expressed as a sum of the contributions of various constituents. We show here that, both theoretically and numerically, because Kd is an apparent optical property (AOP), this approach is not consistent with radiative transfer. We further advocate the application of models of Kd developed in past decades that are not only consistent with radiative transfer but also provide more accurate estimates, in particular for coastal turbid waters.

The Secchi disk depth (ZSD, m) has been used globally for many decades to represent water clarity and an index of water quality and eutrophication. In recent studies, a new theory and model were developed for ZSD, which enabled its semi-analytical remote sensing from the measurement of water color. Although excellent performance was reported for measurements in both oceanic and coastal waters, its reliability for highly turbid inland waters is still unknown. In this study, we extend this model and its evaluation to such environments. In particular, because the accuracy of the inherent optical properties (IOPs) derived from remote sensing reflectance (Rrs, sr−1) plays a key role in determining the reliability of estimated ZSD, we first evaluated a few quasi-analytical algorithms (QAA) specifically tuned for turbid inland waters and determined the one (QAATI) that performed the best in such environments. For the absorption coefficient at 443 nm (a(443), m−1) ranging from ~0.2 to 12.5 m−1, it is found that the QAATI-derived absorption coefficients agree well with field measurements (r2 > 0.85, and mean absolute percentage difference (MAPD) smaller than ~39%). Furthermore, with QAATI-derived IOPs, the MAPD was less than 25% between the estimated and field-measured ZSD (r2 > 0.67, ZSD in a range of 0.1−1.7 m). Furthermore, using matchup data between Rrs from the Medium Resolution Imaging Spectrometer (MERIS) and in-situ ZSD, a similar performance in the estimation of ZSD from remote sensing was obtained (r2 = 0.73, MAPD = 37%, ZSD in a range of 0.1−0.9 m). Based on such performances, we are confident to apply the ZSD remote sensing scheme to MERIS measurements to characterize the spatial and temporal variations of ZSD in Lake Taihu during the period of 2003−2011.

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.

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.

This paper reviews the state of the art of protocols for the measurement of downwelling irradiance in the context of Fiducial Reference Measurements (FRM) of water reflectance for satellite validation. The measurement of water reflectance requires the measurement of water-leaving radiance and downwelling irradiance just above water. For the latter, there are four generic families of method, using: (1) an above-water upward-pointing irradiance sensor; (2) an above-water downward-pointing radiance sensor and a reflective plaque; (3) a Sun-pointing radiance sensor (sunphotometer); or (4) an underwater upward-pointing irradiance sensor deployed at different depths. Each method—except for the fourth, which is considered obsolete for the measurement of above-water downwelling irradiance—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 the measurement of downwelling irradiance is summarized, future perspectives are outlined, and key debates such as the use of reflectance plaques with calibrated or uncalibrated radiometers are presented. This review is based on the practice and studies of the aquatic optics community and the validation of water reflectance, but is also relevant to land radiation monitoring and the validation of satellite-derived land surface reflectance.

Detecting changes to the transparency of the water column is critical for understanding the responses of marine organisms, such as corals, to light availability. Long-term patterns in water transparency determine geographical and depth distributions, while acute reductions cause short-term stress, potentially mortality and may increase the organisms’ vulnerability to other environmental stressors. Here, we investigated the optimal, operational algorithm for light attenuation through the water column across the scale of the Great Barrier Reef (GBR), Australia. We implemented and tested a quasi-analytical algorithm to determine the photic depth in GBR waters and matched regional Secchi depth (ZSD) data to MODIS-Aqua (2002–2010) and SeaWiFS (1997–2010) satellite data. The results of the in situ ZSD/satellite data matchup showed a simple bias offset between the in situ and satellite retrievals. Using a Type II linear regression of log-transformed satellite and in situ data, we estimated ZSD and implemented the validated ZSD algorithm to generate a decadal satellite time series (2002–2012) for the GBR. Water clarity varied significantly in space and time. Seasonal effects were distinct, with lower values during the austral summer, most likely due to river runoff and increased vertical mixing, and a decline in water clarity between 2008–2012, reflecting a prevailing La Niña weather pattern. The decline in water clarity was most pronounced in the inshore area, where a significant decrease in mean inner shelf ZSD of 2.1 m (from 8.3 m to 6.2 m) occurred over the decade. Empirical Orthogonal Function Analysis determined the dominance of Mode 1 (51.3%), with the greatest variation in water clarity along the mid-shelf, reflecting the strong influence of oceanic intrusions on the spatio-temporal patterns of water clarity. The newly developed photic depth product has many potential applications for the GBR from water quality monitoring to analyses of ecosystem responses to changes in water clarity.

The remote sensing of chlorophyll a concentration from ocean color satellites has been an essential variable quantifying phytoplankton in the past decades, yet estimation of accessory pigments from ocean color remote sensing data has remained largely elusive. In this study, we validated the concentrations of multiple pigments (Cpigs) retrieved from in situ and MEdium Resolution Imaging Spectrometer (MERIS) measured remote sensing reflectance (Rrs(λ)) in the global oceans. A multi-pigment inversion model (MuPI) was used to semi-analytically retrieve Cpigs from Rrs(λ). With a set of globally optimized parameters, the accuracy of the retrievals obtained with MuPI is quite promising. Compared with High-Performance Liquid Chromatography (HPLC) measurements near Bermuda, the concentrations of chlorophyll a, b, c ([Chl-a], [Chl-b], [Chl-c]), photoprotective carotenoids ([PPC]), and photosynthetic carotenoids ([PSC]) can be retrieved from MERIS data with a mean unbiased absolute percentage difference of 38%, 78%, 65%, 36%, and 47%, respectively. The advantage of the MuPI approach is the simultaneous retrievals of [Chl-a] and the accessory pigments [Chl-b], [Chl-c], [PPC], [PSC] from MERIS Rrs(λ) based on a closure between the input and output Rrs(λ) spectra. These results can greatly expand scientific studies of ocean biology and biogeochemistry of the global oceans that are not possible when the only available information is [Chl-a].

About 30 years ago, NASA launched the first ocean-color observing satellite:the Coastal Zone Color Scanner. CZCS had 5 bands in the visible-infrared domain with anobjective to detect changes of phytoplankton (measured by concentration of chlorophyll) inthe oceans. Twenty years later, for the same objective but with advanced technology, theSea-viewing Wide Field-of-view Sensor (SeaWiFS, 7 bands), the Moderate-ResolutionImaging Spectrometer (MODIS, 8 bands), and the Medium Resolution ImagingSpectrometer (MERIS, 12 bands) were launched. The selection of the number of bands andtheir positions was based on experimental and theoretical results achieved before thedesign of these satellite sensors. Recently, Lee and Carder (2002) demonstrated that foradequate derivation of major properties (phytoplankton biomass, colored dissolved organicmatter, suspended sediments, and bottom properties) in both oceanic and coastalenvironments from observation of water color, it is better for a sensor to have ~15 bands inthe 400 – 800 nm range. In that study, however, it did not provide detailed analysesregarding the spectral locations of the 15 bands. Here, from nearly 400 hyperspectral (~ 3-nm resolution) measurements of remote-sensing reflectance (a measure of water color)taken in both coastal and oceanic waters covering both optically deep and optically shallowwaters, first- and second-order derivatives were calculated after interpolating themeasurements to 1-nm resolution. From these derivatives, the frequency of zero values foreach wavelength was accounted for, and the distribution spectrum of such frequencies wasobtained. Furthermore, the wavelengths that have the highest appearance of zeros wereidentified. Because these spectral locations indicate extrema (a local maximum orminimum) of the reflectance spectrum or inflections of the spectral curvature, placing the bands of a sensor at these wavelengths maximizes the potential of capturing (and then restoring) the spectral curve, and thus maximizes the potential of accurately deriving properties of the water column and/or bottom of various aquatic environments with a multi-band sensor.

After publication of the research paper [1], the authors wish to make the following correction[...]

Hyperspectral remote sensing inversion models utilize spectral information over optically shallow waters to retrieve optical properties of the water column, bottom depth and reflectance, with the latter used in benthic classification. Accuracy of these retrievals is dependent on the spectral endmember(s) used to model the bottom reflectance during the inversion. Without prior knowledge of these endmember(s) current approaches must iterate through a list of endmember—a computationally demanding task. To address this, a novel lookup table classification approach termed HOPE-LUT was developed for selecting the likely benthic endmembers of any hyperspectral image pixel. HOPE-LUT classifies a pixel as sand, mixture or non-sand, then the latter two are resolved into the three most likely classes. Optimization subsequently selects the class (out of the three) that generated the best fit to the remote sensing reflectance. For a coral reef case, modeling results indicate very high benthic classification accuracy (>90%) for depths less than 4 m of common coral reef benthos. These accuracies decrease substantially with increasing depth due to the loss of bottom information, especially the spectral signatures. We applied this technique to hyperspectral airborne imagery of Heron Reef, Great Barrier Reef and generated benthic habitat maps with higher classification accuracy compared to standard inversion models.

Phytoplankton pigments absorb sunlight for photosynthesis, protect the chloroplast from damage caused by excess light energy, and influence the color of the water. Some pigments act as bio-markers and are important for separation of phytoplankton functional types. Among many efforts that have been made to obtain information on phytoplankton pigments from bio-optical properties, Gaussian curves decomposed from phytoplankton absorption spectrum have been used to represent the light absorption of different pigments. We incorporated the Gaussian scheme into a semi-analytical model and obtained the Gaussian curves from remote sensing reflectance. In this study, a series of sensitivity tests were conducted to explore the potential of obtaining the Gaussian curves from multi-spectral satellite remote sensing. Results showed that the Gaussian curves can be retrieved with 35% or less mean unbiased absolute percentage differences from MEdium Resolution Imaging Spectrometer (MERIS) and Moderate Resolution Imaging Spectroradiometer (MODIS)-like sensors. Further, using Lake Erie as an example, the spatial distribution of chlorophyll a and phycocyanin concentrations were obtained from the Gaussian curves and used as metrics for the spatial extent of an intense cyanobacterial bloom occurred in Lake Erie in 2014. The seasonal variations of Gaussian absorption properties in 2011 were further obtained from MERIS imagery. This study shows that it is feasible to obtain Gaussian curves from multi-spectral satellite remote sensing data, and the obtained chlorophyll a and phycocyanin concentrations from these Gaussian peak heights demonstrated potential application to monitor harmful algal blooms (HABs) and identification of phytoplankton groups from satellite ocean color remote sensing semi-analytically.

The penetration of visible solar radiation (VSR) in the upper ocean contributes to heating in the upper water column, and this process is modulated by constituents in water such as phytoplankton. Various schemes have been developed to propagate surface VSR to deeper depths, which are incorporated in ocean circulation models to study basin-scale impacts on ocean and atmosphere by phytoplankton. However, none of these studies evaluated sensitivity of the schemes of VSR transmission to upper-water dynamics, especially when the required input is derived from ocean color remote sensing. We used an idealized one-dimensional (1-D) Regional Ocean Modeling System (ROMS) to simulate profiles of upper water column temperature at six locations with different ocean color characteristics (containing different water constituents). We incorporated and tested four different schemes of VSR transmission: the default water-type scheme within the ROMS, two schemes based on concentrations of chlorophyll (Chl), and one scheme based on water's inherent optical properties (IOPs). The results showed that, although using the same ocean color information and the same circulation model, the IOPs- and Chl-based schemes resulted in very different vertical temperature and upper-water mixing compared to those using the default scheme, with differences in sea surface temperature up to 1.5-2°C. These results highlight the importance of developing and incorporating more appropriate VSR transmission schemes into large-scale ocean circulation simulations, where the inputs for the transmission are inherently estimates from ocean color--the actual measurement from satellite remote sensing. [ABSTRACT FROM AUTHOR]