Abstract. A global in situ dataset for validation of satellite products from the ESA Ocean Colour Climate Change Initiative (OC-CCI) is presented. This version of the compilation, with data starting in 1996, extends to 2025, which is important for the validation of recent algorithms and satellite products. The dataset comprises in situ observations of the following variables: spectral remote-sensing reflectance (rrs), chlorophyll-a concentration ("chla"; HPLC and fluorometric), spectral inherent optical properties (IOPs: algal pigment absorption "aph", detritus plus gelbstoff absorption "adg", and particle backscattering "bbp"), spectral diffuse attenuation coefficient (kd) and total suspended matter (tsm). Data were obtained from multiple archives acquired via open internet services, or from individual projects, acquired directly from data providers. Compared to the previous version (Valente et al., 2022) this release incorporates updates from existing sources and introduces data from 13 additional sources. Notably, AERONET-OC coverage has been significantly expanded (39 sites compared to 11 in v2022) and MOBY Platinum data have been included from 2024. Methodologies were implemented for homogenisation, quality control and merging of all data. Minimal changes were made to the original data, other than conversion to a standard format, elimination of some points after quality control and averaging of observations that were close in time and space. The harmonisation procedures also include the application of a bidirectional reflectance distribution function (BRDF) correction to "rrs". The result is a merged table available in text format. Overall, the dataset increased by ~115 %, reaching 319,183 rows, with each row representing a unique station in space and time (cf 148,432 rows in Valente et al., 2022). Records of chlaovera increased by ~31 % (82,543 to 107,922); "rrs" observations nearly doubled +94 % (68,641 → 133,325); IOPs showed substantial expansion, with aph increasing by ~150 % (4,265 to 10,655) and adg by 78 % (1,654 to 2,948). The most pronounced change was for bbp, which increased by ~10,258 % (792 to 82,033), while moderate increases were observed for kd (+30 %; 2,454 to 3,197) and "tsm" (+51 %; 1,546 to 2,334). Metadata of each in situ measurement (original source, cruise or experiment, principal investigator) are included in the final table. By making the metadata available, provenance is better documented, and it is also possible to analyse each set of data separately. The compiled data are available at (Salem et al., 2026).

robust merging method for Sentinel-2 Multispectral Imager (MSI) and Landsat-8 Operational Land Imager (OLI) data products over inland and coastal waters greatly improves our capability for monitoring water quality indicators (WQIs) by significantly increasing the temporal observation frequency with high spatial resolution. Such merged surface reflectance products (e.g. HLS L30 and HLS S30) are publicly available through Harmonized Landsat and Sentinel2 (HLS) initiative for land applications. However, such a product for water reflectance, a principal quantity for deriving a series of WQIs, has not yet been available. Here, we propose a comprehensive spectral harmonization method for OLI and MSI imagery to improve the accuracy and consistency of water reflectance for retrieving chlorophyll-a (Chl-a) concentration and the light absorption coefficient of colored dissolved organic matter at 440 nm [a(cdom)(440)] with reasonable uncertainties. To do so, we first evaluate the performance of atmospheric correction (AC) schemes, including Acolite, Polymer, C2RCC, OC-SMART, and ICOR against the in situ rho(w). Our results indicate that compared to other ac schemes, the rho(w) products derived from OC-SMART processing are in good agreement with in situ data, with a median absolute percentage difference (MAPD) < 30% across all the visible bands for OLI and MSI. To acquire consistent rho(w) estimates from OLI and MSI, we apply a recently published machine learning (ML)-based bandpass adjustment (BA) model Asim et al., 2022, trained on near-simultaneous OLI and MSI rho(w) obtained through OC-SMART, to MSI images, with OLI as a reference. To better estimate Chl-a and a(cdom)(440), the performance of retrieval models, including the recently developed ocean color net (OCN) Asim et al., 2021, the Gaussian Process Regression and the band ratio based algorithms, are tuned and evaluated against in situ data. Chl-a and a(cdom)(440) estimates show that OCN is the top performer with a mean absolute error of 38% and 20% , respectively. The tuned OCN model is then applied to OLI and MSI images. Our results demonstrate that the proposed BA method properly aligns the OLI and MSI water reflectance spectra, resulting in improved Chl-a and a(cdom)(440) estimates in a variety of optically complex waters.

Absorption and scattering by optically active constituents (OACs) modify the sunlit aquatic light environment, facilitating the derivation of biogeochemical data products at scales spanning in situ to satellite observations. Excluding solar illumination, plus geometric and atmospheric effects, variability in an optical parameter arises from changing OAC concentrations, wherein observed patterns in the spectral evolution of data products are associated with the connectivity and spatiotemporal dynamics of OACs. In open-ocean waters far from terrestrial and riverine inputs, the content and mixture of OACs principally relates to the dynamics of phytoplankton and the microbial loop - a trophic pathway describing the cycling of microbial primary producers, remineralizers (e.g., bacteria and archaea), plus dissolved organic and inorganic materials (as applicable). Historical bio-optical models for the open ocean primarily invoke chlorophyll a concentration (Ca) - a commonly used proxy for phytoplankton biomass - as the ubiquitous independent variable governing optical data products such as the normalized water-leaving radiance, LW(lambda)N. Formulation of LW(lambda)N as a function of Ca invokes an idealized food chain, wherein phytoplankton are the dominant control of OACs, including the colored (or chromophoric, depending on the literature) portion of the dissolved organic matter (DOM) pool, hereafter CDOM. This prescription, in which Ca maximally explains oceanic light variability (hereafter primacy), is tested herein using eigenanalysis - e.g., an empirical orthogonal function analysis, principal component analysis, or other eigendecomposition depending on the literature. Analyses using three independent bio-optical datasets assess the shapes and associations of the principal and secondary eigenfunctions of aquatic LW(lambda)N observations. The analyses reveal LW(lambda)N variations to be more strongly associated with changes in CDOM rather than Ca - even for purely oceanic datasets - indicating that CDOM dynamics are more variable and exhibit greater independence from Ca than formerly ascribed. Blue and green band-ratio algorithms routinely used for remote sensing of Ca are found to be maximally sensitive to variability in CDOM rather than Ca based on validation tests of ocean chlorophyll (OC) algorithm performance (e.g., R2 of 0.85 versus 0.78), plus partial correlation coefficients relating eigenfunction scalar amplitude functions to field or derived observations. Eigenanalyses applied to spectral subsets of the data indicate expansive spectral range observing improves the independence in retrieving CDOM absorption and Ca. The spectral subset comparisons indicate expanded spectral observations supported by recent domestic and international satellite missions constitute a new and unique opportunity to optically characterize surface ocean phytoplankton stocks without relying on explicit or implied empiricisms requiring CDOM and other OACs to covary with Ca. The shapes and associations of the eigenfunctions suggest a greater diversity of trophic pathways determine OAC dynamics - e.g., in addition to phytoplankton contributing CDOM via cellular lysis, excretion, and grazing - and are consistent with advancing knowledge of the microbial loop in the decades after bio-optical formulations based on Ca were proposed.

Standard ocean colour algorithms exploiting only shorter visible wavelengths (less than 560 nm) perform poorly in the Arctic Ocean (AO) due to the interference from colored detrital material (CDM). The incorporation of longer wavelengths, which are less susceptible to interference from CDM, could prove beneficial in retrieving water properties, particularly in Arctic waters with high CDM content. Similarly, algorithms that exploit only the red region of the spectrum, such as fluorescence-based approaches, are also unsuitable for these waters. This is due to the difficulty in accurately describing the background elastic scattering signal. In this study, we propose an algorithm that accounts for elastic scattering and fluorescence of phytoplankton in the full visible spectral domain by coupling a tuned version of the Garver-Siegel-Maritorena (GSM) algorithm (GSMA) for the AO with an optimized fluorescence emission model. Our novel algorithm, FGSM, demonstrate comparable overall performance to an empirical algorithm derived for chlorophyll a concentration (Chl) estimates in the AO (AO.emp), with a mean absolute difference (MAD) of 1.83. In addition, FGSM outperforms both the GSMA and the fluorescence line height (FLH) algorithms, with an improvement in the MAD of Chl estimates up to 41 %. Assessments conducted using both in situ datasets and satellite data at the Lena River Delta, a region characterized by high productivity and the presence of coastal CDM, revealed that for eutrophic waters where Chl is generally high, FGSM significantly mitigate the underestimation of Chl by AO.emp and GSMA, and exhibit enhanced robustness to produce more retrievals than the other semi-analytical algorithms. FGSM also demonstrates superior performance compared to the other algorithms assessed in this study for waters with high suspended particulate matter (SPM). Further validations for Arctic waters, particularly turbid coastal waters, are still expected in the future.

Earth and planetary radiometry requires spectrally dependent observations spanning an expansive range in signal flux due to variability in celestial illumination, spectral albedo, and attenuation. Insufficient dynamic range inhibits contemporaneous measurements of dissimilar signal levels and restricts potential environments, time periods, target types, or spectral ranges that instruments observe. Next-generation (NO) advances in temporal, spectral, and spatial resolution also require further increases in detector sensitivity and dynamic range corresponding to increased sampling rate and decreased field of view (FOV), both of which capture greater intrapixel variability (i.e., variability within the spatial and temporal integration of a pixel observation). Optical detectors typically must support expansive linear radiometric responsivity, while simultaneously enduring the inherent stressors of field, airborne, or satellite deployment. Rationales for significantly improving radiometric observations of nominally dark targets are described herein, along with demonstrations of state-of-the-art (SOTA) capabilities and NO strategies for advancing SOTA. An evaluation of linear dynamic range and efficacy of optical data products is presented based on representative sampling scenarios. Low-illumination (twilight or total lunar eclipse) observations are demonstrated using a SOTA prototype. Finally, a ruggedized and miniaturized commercial-off-the-shelf (COTS) NO capability to obtain absolute radiometric observations spanning an expanded range in target brightness and illumination is presented. The presented NO technology combines a multipixel photon counter (MPPC) with a silicon photodetector (SiPD) to form a dyad optical sensing component supporting expansive dynamic range sensing, i.e., exceeding a nominal 10 decades in usable dynamic range documented for SOTA instruments.

The launch of the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) and the Surface Biology and Geology (SBG) satellite sensors will provide increased spectral resolution compared to existing platforms. These new sensors will require robust calibration and validation datasets, but existing field-based instrumentation is limited in its availability and potential for geographic coverage, particularly for coastal and inland waters, where optical complexity is substantially greater than in the open ocean. The minimum signal-to-noise ratio (SNR) is an important metric for assessing the reliability of derived biogeochemical products and their subsequent use as proxies, such as for biomass, in aquatic systems. The SNR can provide insight into whether legacy sensors can be used for algorithm development as well as calibration and validation activities for next-generation platforms. We extend our previous evaluation of SNR and associated uncertainties for representative coastal and inland targets to include the imaging sensors PRISM and AVIRIS-NG, the airborne-deployed C-AIR radiometers, and the shipboard HydroRad and HyperSAS radiometers, which were not included in the original analysis. Nearly all the assessed hyperspectral sensors fail to meet proposed criteria for SNR or uncertainty in remote sensing reflectance (Rrs) for some part of the spectrum, with the most common failures (>20% uncertainty) below 400 nm, but all the sensors were below the proposed 17.5% uncertainty for derived chlorophyll-a. Instrument suites for both in-water and airborne platforms that are capable of exceeding all the proposed thresholds for SNR and Rrs uncertainty are commercially available. Thus, there is a straightforward path to obtaining calibration and validation data for current and next-generation sensors, but the availability of suitable high spectral resolution sensors is limited.

The Arctic Ocean (AO) is the most river-influenced ocean. Located at the land-sea interface wherein phytoplankton blooms are common, Arctic coastal waterbodies are among the most affected regions by climate change. Given phytoplankton are critical for energy transfer supporting marine food webs, accurate estimation of chlorophyll a concentration (Chl), which is frequently used as a proxy of phytoplankton biomass, is critical for improving our knowledge of the Arctic marine ecosystem and its response to the ongoing climate change. Due to the unique and complex bio-optical properties of the AO, efforts are still needed to obtain more accurate Chl estimates, especially for coastal waters with high colored detrital material (CDM) content. In this study, we optimized the the Garver-Siegel-Maritorena (GSM) algorithm, using an Arctic bio-optical dataset comprised of seven wavelengths (the original GSM wavelengths plus 625 nm). Results suggested that our tuned algorithm, denoted GSMA, outperformed an alternative AO GSM algorithm denoted AO.GSM, but the accuracy of Chl estimates was only improved by 8%. In addition, GSMA showed appreciable robustness when assessed using a satellite image and two non-Arctic coastal datasets.

Planetary radiometric observations enable remote sensing of biogeochemical parameters to describe spatiotemporal variability in aquatic ecosystems. For approximately the last half century, the science of aquatic radiometry has established a knowledge base using primarily, but not exclusively, visible wavelengths. Scientific subdisciplines supporting aquatic radiometry have evolved hardware, software, and procedures to maximize competency for exploiting visible wavelength information. This perspective culminates with the science requirement that visible spectral resolution must be continually increased to extract more information. Other sources of information, meanwhile, remain underexploited, particularly information from nonvisible wavelengths. Herein, absolute radiometry is used to evaluate spectral limits for deriving and exploiting aquatic data products, specifically the normalized water-leaving radiance, Γ(λ), and its derivative products. Radiometric observations presented herein are quality assured for individual wavebands, and spectral verification is conducted by analyzing celestial radiometric results, comparing agreement of above- and in-water observations at applicable wavelengths, and evaluating consistency with bio-optical models and optical theory. The results presented include the first absolute radiometric field observations of Γ(λ) within the IR-B spectral domain (i.e. spanning 1400-3000 nm), which indicate that IR-B signals confer greater and more variable flux than formerly ascribed. Black-pixel processing, a routine correction in satellite and in situ aquatic radiometry wherein a spectrum is offset corrected relative to a nonvisible waveband (often IR-B or a shorter legacy waveband) set to a null value, is shown to degrade aquatic spectra and derived biogeochemical parameters.

The hardware and software capabilities of the compact-profiling hybrid instrumentation for radiometry and ecology (C-PHIRE) instruments on an unmanned surface vessel (USV) are evaluated. Both the radiometers and USV are commercial-off-the-shelf (COTS) products, with the latter being only minimally modified to deploy the C-PHIRE instruments. The hybridspectral C-PHIRE instruments consist of an array of 18 multispectral microradiometers with 10 nm wavebands spanning 320–875 nm plus a hyperspectral compact grating spectrometer (CGS) with 2048 pixels spanning 190–1000 nm. The C-PHIRE data were acquired and processed using two architecturally linked software packages, thereby allowing lessons learned in one to be applied to the other. Using standard data products and unbiased statistics, the C-PHIRE data were validated with those from the well-established compact-optical profiling system (C-OPS) and verified with the marine optical buoy (MOBY). Agreement between algorithm variables used to estimate colored dissolved organic matter (CDOM) absorption and chlorophyll a concentration were also validated. Developing and operating novel technologies, such as the C-PHIRE series of instruments, deployed on a USV increase the frequency and coverage of optical observations, which are required to fully support the present and next-generation validation exercises in radiometric remote sensing of aquatic ecosystems.

Coastal ecosystems are important in biogeochemical cycles, because they provide many pathways for chemical elements and compounds to flow between the physical environment and living organisms. The spatial and temporal scales of the biotic and abiotic interactions establish the inherent dynamics of the coastal zone and the requirement for high-resolution investigations. Using match-up data collected in Hokkaido coastal waters, the study herein evaluated the performance of deriving chlorophyll (Chl) a concentration and colored dissolved organic matter (CDOM) absorption ( $$a_\mathrm{CDOM}$$ ) as a function of wavelength ( $$\lambda$$ ) from satellite observations. The Japanese Aerospace Exploration Agency (JAXA) Second generation GLobal Imager (SGLI), which has 250 m spatial resolution, was compared with other satellite ocean color (OC) sensors. Our results show that the standard SGLI OC4 algorithm with the 530 nm band provided the best performance for Chl a retrievals, and the end-member analysis (EMA) technique improved the estimation of $$a_\mathrm{CDOM}\mathrm{(\lambda)}$$ . Overall differences between in situ radiometric data and satellite retrievals suggest that additional challenges remain, especially in the ultraviolet and blue spectral domains which are useful for studying CDOM and harmful algae blooms in coastal waters. To fulfill applications wherein high-quality remote sensing data are required, the improvement of the atmospheric correction is a likely research area where additional accomplishment will be beneficial for satellite observations of optically complex coastal waters.

The use of multispectral geostationary satellites to study aquatic ecosystems improves the temporal frequency of observations and mitigates cloud obstruction, but no operational capability presently exists for the coastal and inland waters of the United States. The Advanced Baseline Imager (ABI) on the current iteration of the Geostationary Operational Environmental Satellites, termed the R Series (GOES-R), however, provides sub-hourly imagery and the opportunity to overcome this deficit and to leverage a large repository of existing GOES-R aquatic observations. The fulfillment of this opportunity is assessed herein using a spectrally simplified, two-channel aquatic algorithm consistent with ABI wave bands to estimate the diffuse attenuation coefficient for photosynthetically available radiation, Kd(PAR). First, an in situ ABI dataset was synthesized using a globally representative dataset of above- and in-water radiometric data products. Values of Kd(PAR) were estimated by fitting the ratio of the shortest and longest visible wave bands from the in situ ABI dataset to coincident, in situKd(PAR) data products. The algorithm was evaluated based on an iterative cross-validation analysis in which 80% of the dataset was randomly partitioned for fitting and the remaining 20% was used for validation. The iteration producing the median coefficient of determination (R2) value (0.88) resulted in a root mean square difference of 0.319m-1, or 8.5% of the range in the validation dataset. Second, coincident mid-day images of central and southern California from ABI and from the Moderate Resolution Imaging Spectroradiometer (MODIS) were compared using Google Earth Engine (GEE). GEE default ABI reflectance values were adjusted based on a near infrared signal. Matchups between the ABI and MODIS imagery indicated similar spatial variability (R2=0.60) between ABI adjusted blue-to-red reflectance ratio values and MODIS default diffuse attenuation coefficient for spectral downward irradiance at 490 nm, Kd(490), values. This work demonstrates that if an operational capability to provide ABI aquatic data products was realized, the spectral configuration of ABI would potentially support a sub-hourly, visible aquatic data product that is applicable to water-mass tracing and physical oceanography research.

A compiled set of in situ data is important to evaluate the quality of ocean-colour satellite-data records. Here we describe the data compiled for the validation of the ocean-colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI). The data were acquired from several sources (MOBY, BOUSSOLE, AERONET-OC, SeaBASS, NOMAD, MERMAID, AMT, ICES, HOT, GeP&CO), span between 1997 and 2012, and have a global distribution. Observations of the following variables were compiled: spectral remote-sensing reflectances, concentrations of chlorophyll a, spectral inherent optical properties and spectral diffuse attenuation coefficients. The data were from multi-project archives acquired via the open internet services or from individual projects, acquired directly from data providers. Methodologies were implemented for homogenisation, quality control and merging of all data. No changes were made to the original data, other than averaging of observations that were close in time and space, elimination of some points after quality control and conversion to a standard format. The final result is a merged table designed for validation of satellite-derived ocean-colour products and available in text format. Metadata of each in situ measurement (original source, cruise or experiment, principal investigator) were preserved throughout the work and made available in the final table. Using all the data in a validation exercise increases the number of matchups and enhances the representativeness of different marine regimes. By making available the metadata, it is also possible to analyse each set of data separately. The compiled data are available at doi: 10.1594/PANGAEA.854832 (Valente et al., 2015).

The MALINA oceanographic campaign was conducted during summer 2009 to investigate the carbon stocks and the processes controlling the carbon fluxes in the Mackenzie River estuary and the Beaufort Sea. During the campaign, an extensive suite of physical, chemical and biological variables were measured across seven shelf–basin transects (south–north) to capture the meridional gradient between the estuary and the open ocean. Key variables such as temperature, absolute salinity, radiance, irradiance, nutrient concentrations, chlorophyll a concentration, bacteria, phytoplankton and zooplankton abundance and taxonomy, and carbon stocks and fluxes were routinely measured onboard the Canadian research icebreaker CCGS Amundsen and from a barge in shallow coastal areas or for sampling within broken ice fields. Here, we present the results of a joint effort to compile and standardize the collected data sets that will facilitate their reuse in further studies of the changing Arctic Ocean. The data set is available at https://doi.org/10.17882/75345 (Massicotte et al., 2020).

Intensive phytoplankton blooms, which mainly consist of diatoms, take place in the northern Bering and Chukchi Seas from spring to summer. Little is known, however, about the diatoms contributing to new production in these waters during summer when the water column is often stratified. In this study, using a C-13, N-15 dual isotope tracer technique plus scanning electron microscopy, we assessed the diatom genera or species contributing to new production in surface waters of the northern Bering and Chukchi Seas in July 2013. Relatively high concentrations of nitrate, nitrite, phosphate, and silicate were observed at Bering Strait and Chukchi Shelf stations, whereas at the other stations surface nitrate was generally depleted. Surface ammonium levels were relatively high (0.05-1.52 mu M), suggesting the high activity of heterotrophic organisms. In surface waters, hourly nitrate uptake rates ranged from 0.03 to 3.73 mg N m(-3)h(-1), while the ammonium uptake rates varied between 0.04 and 0.43 mg N m(-3)h(-1). As a result, the mean f-ratio was computed as 0.62 +/- 0.23 during observation. We found that the nitrate uptake rates were positively correlated with ambient nitrate levels, indicating that the activity was mainly regulated with the physical process of nitrate supply. The diatom Chaetoceros (mainly C. socialis/gelidus) numerically dominated the surface diatom assemblages. Contributions of C. socialis/gelidus (9 +/- 4 mu m in size) to the total armored phytoplankton abundance increased with an increase in the water column stratification, whereas Thalassiosila spp. (26 +/- 8 mu m) showed the opposite trend. These results were consistent with the survival strategy of small diatoms for nutrient uptake in oligotrophic waters through their higher cell surface area to volume ratios. On the other hand, the carbon biomass of Thalassiosira became relatively high in surface waters, and significantly correlated with f-ratio. We conclude that the centric diatom Thalassiosira plays a key role in determining new production in surface waters of the study area during summer.

The colored (or chromophoric, depending on the literature) dissolved organic matter (CDOM) spectral absorption coefficient, aCDOM(λ), is a variable of global interest that has broad application in the study of biogeochemical processes. Within the funding for scientific research, there is an overarching trend towards increasing the scale of observations both temporally and spatially, while simultaneously reducing the cost per sample, driving a systemic shift towards autonomous sensors and observations. Legacy aCDOM(λ) measurement techniques can be cost-prohibitive and do not lend themselves toward autonomous systems. Spectrally rich datasets carefully collected with advanced optical systems in diverse locations that span a global range of water bodies, in conjunction with appropriate quality assurance and processing, allow for the analysis of methods and algorithms to estimate aCDOM(440) from spectrally constrained one- and two-band subsets of the data. The resulting algorithms were evaluated with respect to established fit-for-purpose criteria as well as quality assured archival data. Existing and proposed optical sensors capable of exploiting the algorithms and intended for autonomous platforms are identified and discussed. One-band in-water algorithms and two-band above-water algorithms showed the most promise for practical use (accuracy of 3.0% and 6.5%, respectively), with the latter demonstrated for an airborne dataset.

The optically active component of dissolved organic material in aquatic ecosystems, or colored dissolved organic matter (CDOM), is represented by the coefficient of absorption due to the dissolved aquatic constituents at 440 nm, a(CDOM)(440). Remote sensing of a(CDOM)(440) enables characterization of ecosystem processes and aids in retrieval of chlorophyll a, a proxy for phytoplankton biomass. Spectrally adjacent band-ratio domains, e.g., blue to green, have previously been applied for remote sensing of a(CDOM)(440) in coastal and oceanic waters with similar results compared to more complex semi-analytical algorithms. Estimation of a(CDOM)(440) from ratios of the most spectrally separated ocean color wavebands (end members), e.g., ultraviolet (UV) to near-infrared (NIR), termed end-member analysis (EMA), has previously been shown to increase the accuracy of global a(CDOM)(440) retrievals from in-water observations of diffuse attenuation and to enable a unified algorithmic perspective without requiring regional adjustment of internal bio-optical parameters. EMA of above-water observations is evaluated herein, with a focus on coastal and inland waters in which increasing optical complexity and likelihood of bottom reflectance challenge the oceanic algorithms developed for deep and optically simple (case-1) waters. Analysis herein of three independent, in situ, bio-optical datasets indicates significant correlation between a(CDOM)(440) and end-member band ratios (next-generation 320 and 780 nm or legacy 412 and 670 nm ratios) with a coefficient of determination, R-2, of 0.87 (log-scale) or higher based on a dataset spanning the dynamic range of global, conservative water bodies. For applicable wavelengths, EMA algorithms are shown to agree with case-1 relationships and to produce consistent log-scale uncertainties across more than three orders of magnitude in a(CDOM)(440) values (0.001-2.305 m(-1)). EMA using UV and NIR wavelengths (320 and 780 nm) is applied to low-altitude airborne observations and satisfies 25% uncertainty based on unbiased percent differences (UPDs) within each of three dissimilar match-up sites ranging in a(CDOM)(440) from 0.02-0.57 m(-1). Results demonstrate that EMA is a useful and robust approach for the remote sensing of a(CDOM)(440) in coastal and inland waters, which are generally shallower, contain more optically complex environments, and span a greater range in a(CDOM)(440) than oceanic waters.

The case-1 (optically simple) oceanic environment is described by a smoothly transitioning gradient in optical properties arising from the concentration of the primary algal photopigment, chlorophyll a, plus its covarying and optically relevant constituents. As such, the spectral transition that defines case-1 water types is captured by a single primary spectral mode—the transition in peak wavelength between oligotrophic blue and mesotrophic green waters—which has substantiated blue-green algorithm designs and sensor capabilities for oceanographic observations that primarily focus on retrieving the visible (VIS) spectral domain. For case-2 water types, e.g., optically complex inland and coastal waters interacting with the continent and shelf, a similar unifying mode that captures common spectral changes is not defined. This study evaluates the potential to formulate optical complexity as a progression of discrete or continuous parameters in order to advance radiometry for a global range of water types. Within a gradient of increasing optical complexity, this study uses a comprehensive set of case-2 scenarios to compare spectral changes as a function of increasing water mass complexity, based on simple diagnostic tools and definitions. The spectral comparisons rely on instrument and data processing improvements—developed from an end-member analysis (EMA) perspective—that optimize a spectral domain spanning wavelengths shorter and longer than the primarily case-1 VIS domain, i.e., the ultraviolet (UV) and near-infrared (NIR) domains, and that enable sampling of shallow or non-navigable water bodies. The spectral comparisons in the case-2 scenarios indicate five primary modes capture the dominant spectral changes associated with increasing optical complexity. The primary modes and diagnostic tools presented herein enable the organization of global (i.e., oceanic, coastal, and inland) water bodies along a continuous complexity gradient defined by optical complexity instead of as a binary and discontinuous partition (i.e., case-1 and case-2). The expression and dynamic range of the modes establish the spectral regions most sensitive to water mass transitions in optically complex environments. These spectral expressions provide a basis for evaluating the spectral sensing requirements for next-generation remote sensing missions, which seek to characterize optically complex coastal (i.e., the continental shelf and its environs) and inland water bodies. In particular, three of five spectral modes indicate the spectral response to increasing optical complexity is greatest at the spectral end members. The results presented indicate that both discretized values and a continuum are useful for formulating a hierarchy in optical complexity and suggest next-generation activities should optimize the retrieval of the UV and NIR domains for improving the development of global algorithms.

We present the performance of JAXA’s SGLI standard algorithms for estimating chlorophyll a (chl a) concentration and the light absorption coefficients of colored dissolved organic matter (CDOM) using recently compiled high-quality data obtained from oceanic to coastal waters. Prior to the evaluation of the algorithms, we first compare two forward models (Gordon et al. in J Geophys Res 93(D9):10909-10924, 1988: G88 and Park and Ruddick in Appl Opt 44(7):1236–1249, 2005: PR05) using a wide range of inherent optical properties (IOPs) to (1) examine if the water reflectance is appropriately reproduced and (2) correct measured reflectance in terms of its bidirectionality. Based on a good reproducibility of water reflectance using the PR05, the optimized IOPs are further used for explaining errors in estimates of chl a concentration and CDOM absorption when using the SGLI, the GSM (Maritorena et al. in Appl Opt 41:2705–2714, 2002), and the QAA (Lee et al. in Appl Opt 41:5755, 2002) inversion algorithms. Results show that the mean error for estimating chl a concentration using the SGLI algorithm is 110% for our dataset. Although this error is lower than that of the GSM and the QAA algorithms, a significant underestimate at chl a higher than 2.0 mg m−3 is observed, which is further shown by independent match-up analyses. Another SGLI CDOM product includes the mean error of 50% and shows deviation at high CDOM range (> 1.0 m−1). A similar trend is observed for the GSM but not for the QAA when a global relationship of CDOM to colored detrital matter is applied. The sources of errors are discussed for potentially improving the retrievals.