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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.

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.

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]

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

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.

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.

A new empirical algorithm is proposed to estimate surface chlorophyll-a concentrations (Chl) in the global ocean for Chl less than or equal to 0.25 milligrams per cubic meters (approximately 77% of the global ocean area). The algorithm is based on a color index (CI), defined as the difference between remote sensing reflectance (R(sub rs), sr(sup -1) in the green and a reference formed linearly between R(sub rs) in the blue and red. For low Chl waters, in situ data showed a tighter (and therefore better) relationship between CI and Chl than between traditional band-ratios and Chl, which was further validated using global data collected concurrently by ship-borne and SeaWiFS satellite instruments. Model simulations showed that for low Chl waters, compared with the band-ratio algorithm, the CI-based algorithm (CIA) was more tolerant to changes in chlorophyll-specific backscattering coefficient, and performed similarly for different relative contributions of non-phytoplankton absorption. Simulations using existing atmospheric correction approaches further demonstrated that the CIA was much less sensitive than band-ratio algorithms to various errors induced by instrument noise and imperfect atmospheric correction (including sun glint and whitecap corrections). Image and time-series analyses of SeaWiFS and MODIS/Aqua data also showed improved performance in terms of reduced image noise, more coherent spatial and temporal patterns, and consistency between the two sensors. The reduction in noise and other errors is particularly useful to improve the detection of various ocean features such as eddies. Preliminary tests over MERIS and CZCS data indicate that the new approach should be generally applicable to all existing and future ocean color instruments.