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Phaeocystis globosa (P. globosa) is a unique causative species of harmful algal blooms, which can form gelatinous colonies. We, for the first time, used unmanned aerial vehicle (UAV) measurements to identify P. globosa blooms and to quantify the biomass. Based on in situ measured remote sensing reflectance (Rrs), it is found that, for P. globosa blooms, the maximum of the second-derivative ( dλ²Rrs) of Rrs(λ) in the 460-480-nm domain is beyond 466 nm. An analysis of the absorption properties from algal cultures suggested that this feature comes from the absorption of chlorophyll c₃ (Chl-/ c₃) around 466 nm, a prominent feature of P. globosa. This position of dλ²Rrs maximum was, thus, selected as the criterion for P. globosa identification. The spatial extent of P. globosa blooms in two bays off southern China was then mapped by applying the criterion to UAV-measured Rrs. Twelve out of 16 UAV and in situ match-up stations were consistently identified as dominated by P. globosa, indicating the accuracy of 75%. Furthermore, using localized empirical models, chlorophyll a (Chl-/ a) concentration and colony numbers of P. globosa were estimated from UAV-derived Rrs, where P. globosa colonies were found in a range of ~3-37 gel matrix/L, indicating the occurrence of weak to moderate P. globosa blooms during the surveys. The promising results suggest a high potential for detection and quantification of P. globosa blooms in near-shore bays or harbors using UAV-based hyperspectral remote sensing, where conventional ocean color satellite remote sensing runs into difficulties.

The database on ocean primary productivity comprises over two decades (1985–2008) of data that the authors have participated in collecting, using the assimilation of inorganic 14C through photosynthesis, in incubations carried out in situ. The dataset is perhaps unique in that it uses, overwhelmingly, consistent methodology while covering a wide geographic range. Ancillary data are included. Using the database, it is hoped that investigators can test for the relationships among the environmental drivers for ocean productivity, the meaning of the 14C method in terms of phytoplankton physiology and the dynamics in the water column, and as a resource for further development of productivity algorithms using satellite ocean color imagery.

A recently developed algorithm to estimate surface ocean chlorophyll a concentrations (Chl in milligrams per cubic meter), namely, the ocean color index (OCI) algorithm, has been adopted by the U.S. National Aeronautics and Space Administration to apply to all satellite ocean color sensors to produce global Chl maps. The algorithm is a hybrid between a band‐difference color index algorithm for low‐Chl waters and the traditional band‐ratio algorithms (OCx) for higher‐Chl waters. In this study, the OCI algorithm is revisited for its algorithm coefficients and for its algorithm transition between color index and OCx using a merged data set of high‐performance liquid chromatography and fluorometric Chl. Results suggest that the new OCI algorithm (OCI2) leads to lower Chl estimates than the original OCI (OCI1) for Chl less than 0.05 milligrams per cubic meter, but smoother algorithm transition for Chl between 0.25 and 0.40 milligrams per cubic meter. Evaluation using in situ data suggests that similar to OCI1, OCI2 has significantly improved image quality and cross‐sensor consistency between SeaWiFS (Sea-viewing Wide Field-of-view Sensor), MODISA (Moderate Resolution Imaging Spectroradiometer on Aqua), and VIIRS (Visible Infrared Imaging Radiometer Suite) over the OCx algorithms for oligotrophic oceans. Mean cross‐sensor difference in monthly Chl data products over global oligotrophic oceans reduced from approximately 10 percent for OCx to 1-2 percent for OCI2. More importantly, data statistics suggest that the current straylight masking scheme used to generate global Chl maps can be relaxed from 7 by 5 to 3 by 3 pixels without losing data quality in either Chl or spectral remote sensing reflectance (R (sub rs) by lambda (sensor wavelength), per steradian (sr (sup −1)) for not just oligotrophic oceans but also more productive waters. Such a relaxed masking scheme yields an average relative increase of 39 percent in data quantity for global oceans, thus making it possible to reduce data product uncertainties and fill data gaps.

An unprecedented devastating forest fire occurred in Australia from September 2019 to March 2020. Satellite observations revealed that this rare fire event in Australia destroyed a record amount of more than 202,387 km2 of forest, including 56,471 km2 in eastern Australia, which is mostly composed of evergreen forest. The released aerosols contained essential nutrients for the growth of marine phytoplankton and were transported by westerly winds over the Southern Ocean, with rainfall-induced deposition to the ocean beneath. Here, we show that a prominent oceanic bloom, indicated by the rapid growth of phytoplankton, took place in the Southern Ocean along the trajectory of fire-born aerosols in response to atmospheric deposition. Calculations of carbon released during the fire versus carbon absorbed by the oceanic phytoplankton bloom suggest that they were nearly equal. This finding illustrates the critical role of the oceans in mitigating natural and anthropogenic carbon dioxide releases to the atmosphere, which are a primary driver of climate change.

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.

Using simulations, error propagation theory, and measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS), we determined the minimal signal-to-noise ratio (SNR) required for ocean color measurements and product uncertainties at different spatial and temporal scales. First, based on typical top-of-atmosphere (TOA) radiance over the ocean, we evaluate the uncertainties in satellite-derived Rrs in the visible wavelengths (ΔRrs(vis)) due to sensor noise in both the near-infrared (NIR) and the visible bands. While the former induces noise in Rrs(vis) through atmospheric correction, the latter has a direct impact on Rrs(vis). Such estimated uncertainties are compared with inherent ΔRrs(vis) uncertainties from in situ measurements and from the operational atmosphere correction algorithm. The comparison leads to a conclusion that once SNR(NIR) is above 600:1, an SNR(vis) better than 400:1 will not make a significant reduction in product uncertainties at pixel level under typical conditions for a solar zenith angle of 45°. Then, such uncertainties are found to decrease significantly in data products of oceanic waters when the 1 km pixels from individual images are binned to lower spatial resolution (e.g., 4 km) or temporal resolution (e.g., monthly). Although these findings do not suggest that passive ocean color sensors should have SNR(vis) around 400:1, they do support the argument for more trade space in higher spatial and/or spectral resolutions once this minimal 400:1 SNR(vis) requirement is met.

The diffuse attenuation of downwelling irradiance, Kd (m−1), is an important property related to light penetration and availability in aquatic ecosystems. The standard Kd(490) product (the diffuse attenuation coefficient at 490 nm) of the global oceans from satellite remote sensing has been produced with an empirical algorithm, which limits its reliability and applicability in coastal regions. More importantly, as an apparent optical property (AOP), Kd is a function of the angular distribution of the light field (e.g., solar zenith angle). The empirically derived product thus contains ambiguities when compared with in situ measurements as there is no specification regarding the corresponding solar zenith angle associated with this Kd(490) product. To overcome these shortcomings, we refined the Kd product with a product termed as the normalized diffuse attenuation coefficient (nKd, m−1), which is equivalent to the Kd in the absence of the atmosphere and with the sun at zenith. Models were developed to get nKd from both in situ measurements and ocean color remote sensing. Evaluations using field measurements indicated that the semi-analytically derived nKd product will not only remove the ambiguities when comparing Kd values of different light fields, but will also improve the quality of such a product, therefore maximizing the value offered by satellite ocean color remote sensing. This article is protected by copyright. All rights reserved.

An approach to semianalytically derive waters' inherent optical properties (IOPs) from remote sensing reflectance (Rrs) and at the same time to take into account the residual errors in satellite Rrs is developed for open-ocean clear waters where aerosols are likely of marine origin. This approach has two components: (1) a scheme of combining a neural network and an algebraic solution for the derivation of IOPs, and (2) relationships between Rrs residual errors at 670 nm and other spectral bands. This approach is evaluated with both synthetic and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) data, and the results show that it can significantly reduce the effects of residual errors in Rrs on the retrieval of IOPs, and at the same time remove partially the Rrs residual errors for “low-quality” and “high-quality” data defined in this study. Furthermore, more consistent estimation of chlorophyll concentrations between the empirical blue-green ratio and band-difference algorithms can be derived from the corrected “low-quality” and “high-quality” Rrs. These results suggest that it is possible to improve both data quality and quantity of satellite-retrieved Rrs over clear open-ocean waters with a step considering the spectral relationships of the residual errors in Rrs after the default atmospheric correction procedure and without fixing Rrs at 670 nm to one value for clear waters in a small region such as 3 × 3 box.

Usable solar radiation (USR) represents spectrally integrated solar energy in the spectral range of 400–560 nm, a domain where photons penetrate the most in oceanic waters and thus contribute to photosynthesis and heating at deeper depths. Through purely numerical simulations, it was found that the diffuse attenuation coefficient of downwelling USR (Kd(USR), m−1) is nearly a constant vertically in the upper water column for clear waters and most turbid waters. Subsequently an empirical model was developed to estimate Kd(USR) based on the diffuse attenuation coefficient at 490 nm (Kd(490), m−1). We here evaluate this relationship using data collected from a wide range of oceanic and coastal environments and found that the relationship between Kd(490) and Kd(USR) developed via the numerical simulation is quite robust. We further refined this relationship to extend the applicability to “clearest” natural waters. This refined relationship was then used to produce sample distribution of Kd(USR) of global oceans. As expected, extremely low Kd(USR) (∼0.02 m−1) was observed in ocean gyres, while significantly higher Kd(USR) (∼5.2 m−1) was found in very turbid coastal regions. A useful application of Kd(USR) is to easily and accurately propagate surface USR to deeper depths, potentially to significantly improve the estimation of basin scale primary production and heat fluxes in the upper water column.

The spectral slope of the absorption coefficient of colored dissolved and detrital material (CDM), Scdm (units: nm−1), is an important optical parameter for characterizing the absorption spectral shape of CDM. Although highly variable in natural waters, in most remote sensing algorithms, this slope is either kept as a constant or empirically modeled with multiband ocean color in the visible domain. In this study, we explore the potential of semianalytically retrieving Scdm with added ocean color information in the ultraviolet (UV) range between 360 and 400 nm. Unique features of hyperspectral remote sensing reflectance in the UV-visible wavelengths (360–500 nm) have been observed in various waters across a range of coastal and open ocean environments. Our data and analyses indicate that ocean color in the UV domain is particularly sensitive to the variation of the CDM spectral slope. Here, we used a synthesized data set to show that adding UV wavelengths to the ocean color measurements will improve the retrieval of Scdm from remote sensing reflectance considerably, while the spectral band settings of past and current satellite ocean color sensors cannot fully account for the spectral variation of remote sensing reflectance. Results of this effort support the concept to include UV wavelengths in the next generation of satellite ocean color sensors.

Coastal water clarity varies at high temporal and spatial scales due to weather, climate, and human activity along coastlines. Systematic observations are crucial to assessing the impact of water clarity change on aquatic habitats. In this study, Secchi disk depths (ZSD) from Boston Harbor, Buzzards Bay, Cape Cod Bay, and Narragansett Bay water quality monitoring organizations were compiled to validate ZSD derived from Landsat 8 (L8) imagery, and to generate high spatial resolution ZSD maps. From 58 L8 images, acceptable agreement was found between in situ and L8 ZSD in Buzzards Bay (N = 42, RMSE = 0.96 m, MAPD = 28%), Cape Cod Bay (N = 11, RMSE = 0.62 m, MAPD = 10%), and Narragansett Bay (N = 8, RMSE = 0.59 m, MAPD = 26%). This work demonstrates the value of merging in situ ZSD with high spatial resolution remote sensing estimates for improved coastal water quality monitoring.

Ocean color measured from satellites provides daily global, synoptic views of spectral water-leaving 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.

Quantum yield of photosynthesis (phi) 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 phi 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 phi 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 (phi_m) and K_phi (PAR value at half phi_m) were calculated. Median phi_m values were ~0.040 and 0.063 mol C mol photons-1 at HOT and BATS, respectively, with higher values in winter. K_phi values were ~8.0 and 10.8 mol photons m-2 d-1 for HOT and BATS, respectively. Seasonal variability in K_phi showed its peak in summer. Dynamical parameterizations for both regions are indicated by their temporal behaviors, where phi_m is related to temperature at BATS while K_phi to PAR, in both stations. At HOT, phi_m 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-measurements 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-measurements 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 PP from satellite remote sensing by parameterized quantum yield along with phytoplankton absorption coefficient at the core.

This study used MODIS observed phytoplankton absorption coefficient at 443 nm (Aph) as a preferable index to characterize phytoplankton variability in optically complex waters. Aph derived from remote sensing reflectance (Rrs, both in situ and MODIS measured) with the Quasi-Analytical Algorithm (QAA) were evaluated by comparing them with match-up in situ measurements, collected in both oceanic and nearshore waters in the Taiwan Strait (TWS). For the data with matching spatial and temporal window, it was found that the average percentage error (ε) between MODIS derived Aph and field measured Aph was 33.8% (N=30, Aph ranges from 0.012 to 0.537 m−1), with a root mean square error in log space (RMSE_log) of 0.226. By comparison, ε was 28.0% (N=88, RMSE_log = 0.150) between Aph derived from ship-borne Rrs and Aph measured from water samples. However, values of ε as large as 135.6% (N=30, RMSE_log = 0.383) were found between MODIS derived chlorophyll-a (Chl, OC3M algorithm) and field measured Chl. Based on these evaluation results, we applied QAA to MODIS Rrs data in the period of 2003–2009 to derive climatological monthly mean Aph for the TWS. Three distinct features of phytoplankton dynamics were identified. First, Aph is low and the least variable in the Penghu Channel, where the South China Sea water enters the TWS. This region maintains slightly higher values in winter (~17% higher than that in the other seasons) due to surface nutrient entrainment under winter wind-driven vertical mixing. Second, Aph is high and varies the most in the mainland nearshore water, with values peaking in summer (June–August) when river plumes and coastal upwelling enhance surface nutrient loads. Interannual variation of bloom intensity in Hanjiang River estuary in June is highly correlated with alongshore wind stress anomalies, as observed by QuikSCAT. The year of minimum and maximum bloom intensity is in the midst of an El Niño and a La Niña event, respectively. Third, a high Aph patch appears between April and September in the middle of the southern TWS, corresponding to high thermal frontal probabilities, as observed by MODIS. Our results support the use of satellite derived Aph for time series analyses of phytoplankton dynamics in coastal ocean regions, whereas satellite Chl products derived empirically using spectral ratio of Rrs suffer from artifacts associated with non-biotic optically active materials.

We examined the temporal and spatial variabilities of phytoplankton absorption coefficients (αphλ)) and their relationships with physical processes in the northern South China Sea from two cruise surveys during spring (May 2001) and late autumn (November 2002). A large river plume induced by heavy precipitation in May stimulated a phytoplankton bloom on the inner shelf, causing significant changes in the surface water in αph values and B/R ratios (αph(440)/αph(675)). This was consistent with the observed one order of magnitude elevation of chlorophyll α and a shift from a pico/nano dominated phytoplankton community to one dominated by micro-algae. At the seasonal level, enhanced vertical mixing due to strengthened northeast monsoon in November has been observed to result in higher surface αph(675) (0.002–0.006 m−1 higher) and less pronounced subsurface maximum on the outer shelf/slope in November as compared that in May. Measurements of αph and B/R ratios from three transects in November revealed a highest surface αph(675) immediately outside the mouth of the Pearl River Estuary, whereas lower αph(675) and higher B/R ratios were featured in the outer shelf/slope waters, demonstrating the respective influence of the Pearl River plume and the oligotrophic nature of South China Sea water. The difference in spectral shapes of phytoplankton absorption (measured by B/R ratios and bathochromic shifts) on these three transects infers that picoprocaryotes are the major component of the phytoplankton community on the outer shelf/slope rather than on the inner shelf. In addition, a regional tuning of the phytoplankton absorption spectral model (Carder et al., 1999) demonstrated a greater spatial variation than seasonal variation in the lead parameter a0(λ). These results suggest that phytoplankton absorption properties in a coastal region such as the northern South China Sea are complex and region-based parameterization is mandatory in order for remote sensing algorithms.

Building a multi-decadal time series of large-scale estimates of net primary production (NPP) requires merging data from multiple ocean color satellites. The primary product of ocean color sensors is spectral remote sensing reflectance ( Rrs ). We found significant differences (13–18% median absolute percent error) between Rrs estimates at 443 nm of different satellite sensors. These differences in Rrs are transferred to inherent optical properties and further on to estimates of NPP. We estimated NPP for the California Current region from three ocean color sensors (SeaWiFS, MODIS-Aqua and MERIS) using a regionally optimized absorption based primary production model (Aph-PP) of Lee et al. (2011). Optimization of the Aph-PP model was required for each individual satellite sensor in order to make NPP estimates from different sensors compatible with each other. While the concept of Aph-PP has advantages over traditional chlorophyll-based NPP models, in practical application even the optimized Aph-PP model explained less than 60% of the total variance in NPP which is similar to other NPP algorithms. Uncertainties in satellite Rrs estimates as well as uncertainties in parameters representing phytoplankton depth distribution and physiology are likely to be limiting our current capability to accurately estimate NPP from space. Introducing a generic vertical profile for phytoplankton improved slightly the skill of the Aph-PP model.

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.

Satellite ocean color radiometry is a powerful method to study ocean biology but the relationships between satellite measurements and the in situ ocean properties are not well understood. Moreover, the measurements made with one satellite sensor may not be directly compatible with similar measurements from another sensor. We estimate inherent optical properties (IOPs) in the California Current by applying empirically optimized versions of the Quasi-Analytical Algorithm (QAA) of Lee et al. (2002) to satellite remote sensing reflectance (Rrs) from four ocean color sensors (OCTS, SeaWiFS, MODISA and MERIS). The set of estimated IOPs includes the total absorption coefficient at 490 nm (a490), phytoplankton absorption coefficient at 440 nm (aph440), absorption by dissolved and detrital organic matter at 440 nm (adg440) and particle backscattering coefficient at 490 nm (bbp490). The empirical inversion models are created by minimizing the deviations between satellite match-ups with in situ measurements and between the estimates of individual overlapping satellite sensors. The derived empirical algorithms were then applied to satellite Level-3 daily Rrs to create merged multi-sensor time series of the near-surface optical characteristics in the California Current region for a time period of over 16 years (November 1996–December 2012). Due to the limited number of in situ match-ups and their uneven distribution as well as the large errors in the satellite-derived Rrs, the uncertainty in the retrieved IOPs is still significant and difficult to quantify. The merged time series show the dominant annual cycle but also significant variability at interannual time scales. The ratio of adg440 to aph440 is around 1 in the transition zone, is >1 in the coastal zone and generally

Dinoflagellate and diatom blooms often occur in the East China Sea (ECS) during spring and summer. Some of the dinoflagellate blooms are toxic, resulting in widespread economic damage. In order to mitigate the negative impacts, remote-sensing methods that can effectively and accurately discriminate between bloom types are demanded for early warning and continuous monitoring of bloom events at large scales. An in situ bio-optical data set collected from diatom and dinoflagellate blooming waters indicates that the two types of blooms exhibited distinctive differences in the shapes and magnitudes of remote-sensing reflectance (Rrs). The ratio of in situ measured Rrs spectral slopes at two spectral ranges (443–488 and 531–555 nm, bands available with the moderate resolution imaging spectrometer (MODIS) sensor), abbreviated as BI (representing bloom index), was found effective in differentiating dinoflagellates from diatoms. Reflectance model simulations, which were carried out using in situ and algal culture data as input, provided consistent results. A classification approach for separating dinoflagellate from diatom blooms in the ECS was then developed: When fluorescence line height (FLH) is doubled over the background level and total absorption coefficient at 443 nm ≥ 0.5 m−1, if 0.0

University of Massachusetts Boston; NASA Ocean Biology and Biogeochemistry and Water and Energy Cycle Programs; JPSS VIIRS Ocean Color Cal/Val Project; National Natural Science Foundation of China [41071223, 40976068, 41121091]; Ministry of Science and Technology of China [2013BAB04B00]

Penetration of the photosynthetically available radiation (PAR, over 400–700 nm) in the upper ocean is important for many processes such as water radiant heating and primary productivity. Because of this importance, daily PAR at sea surface ( PAR ¯ ( 0 + ) ) is routinely generated from ocean-color images for global studies. To propagate this broadband solar radiation through the upper ocean, an attenuation coefficient of PAR ( K PAR ) is also generated from the same ocean-color measurements. However, due to the empirical nature of the K PAR algorithm, this K PAR product corresponds to an instantaneous PAR at a fixed sun angle, with no diurnal variability. It is hence necessary to have an attenuation coefficient matching the temporal characteristics of daily PAR. This paper represents an effort to meet this need. Using ECOLIGHT, the subsurface light field for a wide range of water bodies was simulated, from which the attenuation coefficient ( K PAR ¯ ) of daily PAR was calculated. We presented the diurnal and vertical variation of this attenuation coefficient, and found that it can be well predicted (within ∼ 7%) as a function of the total absorption coefficient and backscattering coefficient at 490 nm and the noontime solar zenith angle. This new model offers an efficient and reasonably accurate approach for quantifying daily upper water column PAR within the global ocean from satellite measurements of water color.

NASA; JPSS VIIRS; National Natural Science Foundation of China [41071223, 40976068, 41121091]; Ministry of Science and Technology of China [2013BAB04B00]; CNRS; INSU

In summer 2014, a toxic Karenia brevis bloom (red tide) occurred in the NE Gulf of Mexico, during which vertical migration of K. brevis has been observed from glider measurements. The current study shows that satellite observations from the Visible Infrared Imaging Radiometer Suite (VIIRS) can capture changes in surface reflectance and chlorophyll concentration occurring within 2h, which may be attributed this K. brevis vertical migration. The argument is supported by earlier glider measurements in the same bloom, by the dramatic changes in the VIIRS-derived surface chlorophyll, and by the consistency between the short-term reflectance changes and those reported earlier from field-measured K. brevis vertical migration. Estimates using the quasi-analytical algorithm also indicate significant increases in both total absorption coefficient and backscattering coefficient in two hours. The two observations in a day from a single polar-orbiting satellite sensor are thus shown to be able to infer phytoplankton vertical movement within a short timeframe, a phenomenon difficult to capture with other sensors as each sensor can provide at most one observation per day, and cross-sensor inconsistency may make interpretation of merged-sensor data difficult. These findings strongly support geostationary satellite missions to study short-term bloom dynamics.

Optical properties have fundamental importance to water quality, ecology, and remote sensing initiatives. Paired measurements of optically active constituents (OACs), and inherent optical properties (IOPs) and apparent optical properties (AOPs), were made in September 2010 across the optical gradients of Green Bay, extending from the Fox River to Sturgeon Bay (8 sites), and for three near-shore locations in the main basin of Lake Michigan. The array of laboratory and in situ measurements provided a robust characterization of the underwater and emergent light fields of these waters with respect to magnitudes and spectral features of the OACs, IOPs and AOPs. These measurements resolved the character and possible origins of the major gradients within the bay (5 to 10-fold differences) and the substantial differences between the bay and the main basin. The credibility of the characterizations was supported through closure analyses which demonstrated: (1) the approach to equivalence between various field and laboratory measurements, and (2) good matches of AOP observations by values predicted from measured IOPs using accepted radiative transfer expressions. The bay was demonstrated to be an optically complex case 2 system, with uncoupled variations along the spatial gradient(s) in OACs of phytoplankton biomass, colored dissolved organic material, and non-algal particulates. The documented spatial differences in optical properties rival those reported in much larger marine surveys. Radiative transfer expressions are used to predict changes in AOPs of the downwelling (underwater) attenuation coefficient and remote sensing signal in response to scenarios of changes in levels of OACs of potential ecological and management interest.

Spectral remote sensing reflectance (Rrs, sr−1) is the key for ocean color retrieval of water bio-optical properties. Since Rrs from in-situ and satellite systems are subject to errors or artifacts, assessment of the quality of Rrs data is critical. From a large collection of high quality in situ hyperspectral Rrs datasets, we developed a novel quality assurance (QA) system that can be used to objectively evaluate the quality of an individual Rrs spectrum. This QA scheme consists of a unique Rrs spectral reference and a score metric. The reference system includes Rrs spectra of 23 optical water types ranging from purple blue to yellow waters, with an upper and a lower bound defined for each water type. The scoring system is to compare any target Rrs spectrum with the reference and a score between 0 and 1 will be assigned to the target spectrum, with 1 for perfect Rrs spectrum and 0 for unusable Rrs spectrum. The effectiveness of this QA system is evaluated with both synthetic and in situ Rrs spectra and it is found to be robust. Further testing is performed with the NOMAD dataset as well as with satellite Rrs over coastal and oceanic waters, where questionable or likely erroneous Rrs spectra are shown to be well identifiable with this QA system. Our results suggest that applications of this QA system to in situ datasets can improve the development and validation of bio-optical algorithms and its application to ocean color satellite data can improve the short- and long-term products by objectively excluding questionable Rrs data. This article is protected by copyright. All rights reserved.

The signal-to-noise ratio (SNR, or sensitivity) of an ocean color instrument is a critical parameter to determine the accuracy and precision of the data products. Yet published literature showed various formats in SNR specifications under different conditions, making a direct cross-sensor comparison difficult. Here, we compared the SNRs of GOCI spectral bands with those of SeaWiFS and MODIS/Aqua under the same radiance inputs. We also compared their ability to resolve small changes in the retrieved chlorophyll-a data products (Chl). While GOCI visible bands showed similar at-sensor SNRs to SeaWiFS, the near-infrared (NIR) bands showed significantly higher SNRs. Because the NIR bands were used for atmospheric correction, the increases in SNRs led to reduced noise in the retrieved Chl, as shown in the GOCI and SeaWiFS Chl products for Chl < 0.1 mg m−3. The noise in the retrieved products also depends on the retrieval algorithms in addition to the sensor SNR. When a new band-subtraction algorithm (the Ocean Color Index or OCI algorithm) was applied to the same GOCI remotesensing reflectance data derived from the GDPS software package, significant noise reduction was found in the Chl product for low concentrations (< 0.25 mg m−3), leading to product precision (∼3% in Chl) comparable to those from MODIS/Aqua measurements. This is certainly a significant achievement, as GOCI spatial resolution is much higher than MODIS (500 m versus 1 km). In addition, artifacts across image mosaic edges over low-concentration waters have been removed nearly completely by the OCI algorithm. Data analyses also indicated that GOCI radiometric calibration requires further improvement.

Ocean-color imagers on conventional polar-orbiting satellites have a revisit time of ∼2 days for most regions, which is further reduced if the area is frequently cloudy. The Geostationary Ocean Color Imager (GOCI), the first ocean-color imager on a geostationary satellite, provides measurements 8 times a day, thus significantly improving the frequency of measurements for studies of ocean environments. Here, we use results derived from GOCI measurements over Taihu Lake to demonstrate that the extra sampling can be used to improve the accuracy of statistically averaged longer-term (daily) measurements. Additionally, using numerical simulations, we demonstrate that the coupling of diurnal variations of both biomass and photosynthetic available radiation can improve the accuracy of daily primary production estimates. These results echo that higher sampling frequency can improve our estimates of longer-term dynamics of biogeochemical processes and highlights the value of ocean color measurements from geostationary satellites.

High-Tech RD National Basic Research Program of China [2009CB421200, 2009CB421201]; NSF of China [40821063]; China Scholarship Council; NASA

We present a new approach that incorporates two models to estimate the underwater light field from remote sensing of ocean color. The first employs a series of analytical, semi-analytical, and empirical algorithms to retrieve the spectrum of inherent optical properties (IOPs), including the absorption and the backscatter coefficients, from the spectrum of remote sensing reflectance. The second model computes the profile of photosynthetically available radiation E 0,PAR (z) for a vertically homogeneous water column using the information of the retrieved IOPs and the ambient optical environment. This computation is based on an improved look-up table technology that possesses high accuracy, comparable with the full solution of the radiative transfer equation, and meets the computational requirement of remote sensing application. This new approach was validated by in situ measurements and an extensive model-to-model comparison with a wide range of IOPs. We successfully mapped the compensation depth by applying this new approach to process the SeaWiFS imagery. This research suggests that E 0,PAR (z) can be obtained routinely from ocean-color data and may have significant implications for the estimation of global heat and carbon budget.

Daily high-resolution Sea-viewing Wide Field-of-view Sensor (SeaWiFS) images of the central North Atlantic Ocean (1998-2003) show that temporal changes in the absorption coefficient of colored dissolved organic matter (CDOM) or "yellow substance" follow changes in phytoplankton pigment absorption coefficient in time. CDOM peaks (between January and March) and troughs (late summer and fall) followed pigment peaks and troughs by approximately two and four weeks, respectively. This phase shift is additional strong evidence that CDOM in the marine environment is derived from phytoplankton degradation. The common assumption of linear covariation between chlorophyll and CDOM is a simplification even in this ocean gyre. Due to the temporal changes in CDOM, chlorophyll concentration estimated based on traditional remote sensing band-ratio algorithms may be overestimated by about 10% during the spring bloom and underestimated by a similar 10% during the fall. These observations are only possible through use of synoptic, precise, accurate, and frequent measurements afforded by space-based sensors because in situ technologies cannot provide the required sensitivity or synoptic coverage to observe these natural phenomena.

“Case-1” has been a term frequently used to characterize water type since the seventies. However, the distribution of Case-1 waters in global scale has been vague, though open ocean waters are often referred to as Case-1 in the literature. In this study, based on recent bio-optical models for Case-1 waters, an inclusive and quantitative Case-1 criterion for remote sensing applications is developed. The criterion allows Case-1 waters to have about two-fold variations of non-pigment absorption and particle backscattering around their exact Case-1 values, allowing a large range of waters to be classified as Case-1. Even so, application of this criterion to ocean color data from the SeaWiFS satellite sensor suggests that Case-1 waters occupy only about 60% of the global ocean surface. Regionally, more Case-1 waters are found in the southern hemisphere than in the northern hemisphere, and most Indian Ocean waters are found to be Case-1. The Case-1 percentage and spatial distribution change with season, and with the boundaries chosen in the criterion. Nevertheless, this study for the first time provides a quantitative and geographical perspective of Case-1 waters in global scale, and further demonstrates that many open ocean waters are not necessarily Case-1.

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Aircraft images were collected near Lee Stocking Island (LSI), Bahamas, with wavelike features for bright sand bottoms during times when solar zenith angles were large. The image contrast between leading and trailing wave facets approached a 10‐15% difference because of algae accumulations in wave troughs or topographic variations of the bottom. Reflectance contrast for blue light was greater than for red and green wavelengths when algae or detritus was present in the troughs. However, the contrast at green and red wavelengths was greater than at blue wavelengths when caused by the interplay between bottom topography and oblique illumination. A three-dimensional backwards Monte Carlo (BMC) model was developed to evaluate the effect of oblique illumination on wavelike topographic features for various values of water clarity and bottom albedo. An inverse optical modeling approach, previously developed for flat, horizontally homogeneous bottoms, was applied to the BMC results. Bathymetric estimates for bright facets tilted 10 8 toward the sun were slightly smaller than actual depths, whereas shaded facet depth estimates were too high by about 5%. Larger errors were associated with albedo retrievals, where shaded facets produced albedo estimates up to 15% lower than actual values. Errors increased with tilt angles up to 208 but decreased with sea and sky turbidity. Averaging sunlit and shaded pixels before running the inverse model reduced the uncertainty of bathymetric and albedo estimates to about 2 and 5%, respectively, comparable to previous field evaluations of the inversion model.

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

We investigated 32 net primary productivity (NPP) models by assessing skills to reproduce integrated NPP in the Arctic Ocean. The models were provided with two sources each of surface chlorophyll‐a concentration (chlorophyll), photosynthetically available radiation (PAR), sea surface temperature (SST), and mixed‐layer depth (MLD). The models were most sensitive to uncertainties in surface chlorophyll, generally performing better with in situ chlorophyll than with satellite‐derived values. They were much less sensitive to uncertainties in PAR, SST, and MLD, possibly due to relatively narrow ranges of input data and/or relatively little difference between input data sources. Regardless of type or complexity, most of the models were not able to fully reproduce the variability of in situ NPP, whereas some of them exhibited almost no bias (i.e., reproduced the mean of in situ NPP). The models performed relatively well in low‐productivity seasons as well as in sea ice‐covered/deep‐water regions. Depth‐resolved models correlated more with in situ NPP than other model types, but had a greater tendency to overestimate mean NPP whereas absorption‐based models exhibited the lowest bias associated with weaker correlation. The models performed better when a subsurface chlorophyll‐a maximum (SCM) was absent. As a group, the models overestimated mean NPP, however this was partly offset by some models underestimating NPP when a SCM was present. Our study suggests that NPP models need to be carefully tuned for the Arctic Ocean because most of the models performing relatively well were those that used Arctic‐relevant parameters., Key Points The models reproduced primary productivity better using in situ chlorophyll‐a than satellite valuesThe models performed well in low‐productivity seasons and in sea ice‐covered/deep‐water regionsNet primary productivity models need to be carefully tuned for the Arctic Ocean

Using Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data as an example, we show in this study that the properties of the water column and bottom of a large, shallow area can be adequately retrieved using a model-driven optimization technique. The simultaneously derived properties include bottom depth, bottom albedo, and water absorption and backscattering coefficients, which in turn could be used to derive concentrations of chlorophyll, dissolved organic matter, and suspended sediments in the water column. The derived bottom depths were compared with a bathymetry chart and a boat survey and were found to agree very well. Also, the derived bottom albedo image shows clear spatial patterns, with end-members consistent with sand and seagrass. The image of absorption and backscattering coefficients indicates that the water is quite horizontally mixed. Without bottom corrections, chlorophyll a retrievals were ∼50 mg m−3, while the retrievals after bottom corrections were tenfold less, approximating real values. These results suggest that the model and approach used work very well for the retrieval of subsurface properties of shallow-water environments even for rather turbid environments like Tampa Bay, Florida.

This paper describes algorithms for retrieval of chlorophyll a concentration and phytoplankton and gelbstoff absorption coefficients for the Moderate-Resolution Imaging Spectrometer (MODIS) or sensors with similar spectral channels. The algorithms are based on a semianalytical, bio-optical model of remote sensing reflectance, Rrs(λ). The Rrs(λ) model has two free variables, the absorption coefficient due to phytoplankton at 675 nm, aϕ(675), and the absorption coefficient due to gelbstoff at 400 nm, ag(400). The Rrs model has several parameters that are fixed or can be specified based on the region and season of the MODIS scene. These control the spectral shapes of the optical constituents of the model. Rrs(λi) values from the MODIS data processing system are placed into the model, the model is inverted, and aϕ(675), ag(400), and chlorophyll a are computed. The algorithm also derives the total absorption coefficients a(λi) and the phytoplankton absorption coefficients aϕ(λi) at the visible MODIS wavelengths. MODIS algorithms are parameterized for three different bio-optical domains: (1) high photoprotective pigment to chlorophyll ratio and low self-shading, which for brevity, we designate as “unpackaged”; (2) low photoprotective pigment to chlorophyll ratio and high self-shading, which we designate as “packaged”; and (3) a transitional or global-average type. These domains can be identified from space by comparing sea-surface temperature to nitrogen-depletion temperatures for each domain. Algorithm errors of more than 45% are reduced to errors of less than 30% with this approach, with the greatest effect occurring at the eastern and polar boundaries of the basins.

Empirical algorithms for the total absorption coefficient and absorption coefficient by pigments for surface waters at 440 nm were developed by applying a quadratic formula that combines two spectral ratios of remote-sensing reflectance. For total absorption coefficients ranging from 0.02 to 2.0 m- a goodness of fit was achieved between the measured and modeled data with a root-mean-square difference between the measured and modeled values for log10 scale (RMSDog0) of 0.062 (15.3% for linear scale, number of samples N = 63), while RMSDlog0 is 0.111 (29.1% for linear scale, N = 126) for pigment absorption (ranging from 0.01 to 1.0 m-l). As alternatives to pigment concentration algorithms, the absorption algorithms developed can be applied to the coastal zone color scanner and sea-viewing wide-field-of-view sensor data to derive inherent optical properties of the ocean. For the same data sets, we also directly related the chlorophyll a concentrations to the spectral ratios and obtained an RMSDlog0 value of 0.218 (65.2% for linear scale, N = 120) for concentrations ranging from 0.06 to 50.0 mg m -3. These results indicate that it is more accurate to estimate the absorption coefficients than the pigment concentrations from remotely sensed data. This is likely due to the fact that for the broad range of waters studied the pigment-specific absorption coefficient at 440 nm ranged from 0.02 to 0.2 m 2 (mg chl) -. As an indirect test of the algorithms developed, the chlorophyll a concentration algorithm is applied to an independent global data set and an RMSDlog0 of 0.191 (55.2% for linear scale, N - 919) is obtained. There is no independent global absorption data set available as yet to test the absorption algorithms.