Hyperspectral observations of ocean reflectance are essential for advancing ocean-color remote sensing, improving biogeochemical algorithms, and supporting climate-quality observations of marine ecosystems. Recent developments in BioGeoChemical-Argo (BGC-Argo) technology now enable autonomous acquisition of hyperspectral radiometric measurements across the global ocean. Here, we present the first assessment of a fleet of 21 hyperspectral BGC-Argo floats deployed between 2022 and 2025 in diverse open-ocean environments. Equipped with compact hyperspectral radiometers measuring downwelling irradiance and upwelling radiance, these floats provide sustained observations of hyperspectral remote-sensing reflectance (Rrs) from 380 to 710 nm. A dedicated processing framework was developed to derive surface Rrs, including automated quality control, radiance extrapolation to the surface, and uncertainty propagation. The resulting dataset comprises 519 hyperspectral Rrs spectra spanning a broad range of optical and biogeochemical conditions. The extrapolation methodology was evaluated through the dedicated DEMEL’ARGO experiment, designed to reproduce float measurement geometry while providing independent near-surface radiometric reference observations. Comparisons with Sentinel-3A, Sentinel-3B, and PACE satellite observations show generally good agreement in the blue-green spectral region, with median absolute relative differences typically between 7 and 15% from 380 to 560 nm, while larger discrepancies occur at red wavelengths because of weaker signals and increased uncertainties. In this study, we demonstrate the potential of the hyperspectral BGC-Argo floats to evolve into a homogeneous, globally distributed validation system for the open ocean. Beyond satellite validation, these observations provide a new global and autonomous source of hyperspectral bio-optical data with strong potential for developing and evaluating hyperspectral algorithms, characterizing phytoplankton community structure and physiology, improving radiative transfer studies, supporting coupled physical-biogeochemical models, and monitoring long-term ecosystem variability and climate-driven changes in the ocean.

Accurate characterization of photosynthetically available radiation (PAR) is essential for quantifying marine primary production and carbon cycling. However, current satellite sensors suffer from persistent data gaps due to orbital limitations, sun glint, and large solar zenith angles. Such gaps bias bloom phenology, obscure long-term trends, and degrade biogeochemical models requiring continuous radiative forcing. To address this, we developed a deep convolutional neural network (CNN) to generate gap-free daily PAR fields at 0.5 resolution using MODIS-Aqua and MERRA-2 datasets. Unlike traditional methods that rely solely on spatiotemporal patterns or reanalysis alone, the CNN utilizes cotemporal MODIS-Aqua satellitederived cloud fractional coverage (CC) and optical thickness, seamlessly substituted by MERRA-2 in gap regions, to account for cloud-modulated PAR variability. The model demonstrates high accuracy using MODIS-Aqua inputs [R-2 = 0.96, bias = -0.19 E/m(2) /day (-0.5%), root-mean-square error (RMSE) = 3.20 E/m(2) /day (8.3%)], though performance degrades with MERRA-2 cloud data [R-2 = 0.84, bias = 0.17 E/m(2) /day (-0.4%), RMSE = 6.47 E/m(2) /day (16.7%)]. Comparisons with independent earth polychromatic imaging camera (EPIC) estimates (R-2 = 0.82, RMSE = 6.43 E/m(2) /day, bias = -0.85 E/m(2) /day) confirm the physical realism of the reconstructed fields. This gapless product provides a robust forcing field for ocean-biogeochemistry models and data assimilation, enabling more accurate assessments of long-term climate trends. Future work will integrate multisensor fusion and higher-resolution cloud inputs

The influence of light availability and mixed layer depth (MLD) on phytoplankton bloom dynamics was examined across the Argentine Continental Shelf in the Southwest Atlantic Ocean (SWAO). Using satellite-derived chlorophyll-a concentration (Chl-a), photosynthetically available radiation (PAR), and euphotic depth (Zeu, defined as the depth at which the irradiance is 1 % of its PAR value at surface) data, together with reanalysis products for MLD and wind fields, we analyzed the spatial and temporal variability of key phenological parameters computed from the Chl-a time series, including bloom initiation, peak timing, and bloom intensity, over the 1998–2019 period. Distinct mean spatial distribution patterns in bloom dynamics were observed. In the Central Shelf (CS), blooms typically initiate (May–August) and peak (September–November) relatively early which correlated with shallow MLDs and increasing light, while coastal areas showed even earlier initiation (April) due to highly variable environmental conditions. In turn, the Patagonian Shelf (PS) experienced delayed initiation (September onwards) and peaks (December–January) probably due to deeper MLDs as result of the colder Subantarctic waters. Bloom intensity also exhibited spatial variability, with the highest values observed in the southern PS and regions influenced by frontal systems, where nutrient-rich upwelling and favorable light conditions enhanced phytoplankton growth. Statistical modeling revealed that light penetration (Zeu) and its interplay with vertical mixing (Zeu:MLD ratio) were the strongest predictors of bloom anomalies at most sites. However, the predictive power of these relationships varied in regions influenced by local processes, like tidal mixing or frontal zones. Predictive models need to be integrated with regional oceanographic features to improve assessments of bloom phenology and primary production in such highly variable shelf ecosystems.

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).

Accurate characterization of Photosynthetically Available Radiation (PAR) is essential for quantifying marine primary production and carbon cycling. However, current satellite sensors suffer from persistent data gaps due to orbital limitations, sun glint, and large solar zenith angles. Such gaps bias bloom phenology, obscure long-term trends, and degrade biogeochemical models requiring continuous radiative forcing. To address this, we developed a deep convolutional neural network (CNN) to generate gap-free daily PAR fields at 0.5° resolution using MODIS-Aqua and MERRA-2 datasets. Unlike traditional methods that rely solely on spatiotemporal patterns or reanalysis alone, the CNN utilizes co-temporal MODIS-Aqua cloud fractional coverage and optical thickness, seamlessly substituted by MERRA-2 in gap regions, to account for cloud-modulated PAR variability. The model demonstrates high accuracy using MODIS-Aqua inputs (R2 = 0.96, bias = -0.19 E/m2/day (-0.5%), RMSE = 3.20 E/m2/day (8.3%)), though performance degrades with MERRA-2 cloud data (R2 = 0.84, bias =0.17 E/m2/day (-0.4%), RMSE = 6.47 E/m2/day (16.7%)). Comparisons with independent EPIC estimates (R2 = 0.82, RMSE = 6.43 E/m2/day, bias = -0.85 E/m2/day) confirm the physical realism of the reconstructed fields. This gapless product provides a robust forcing field for ocean-biogeochemistry models and data assimilation, enabling more accurate assessments of long-term climate trends. Future work will integrate multi-sensor fusion and higher-resolution cloud inputs.

Biogeochemical (BGC) Argo floats currently measure downwelling planar irradiance (Ed) at three spectral wavelengths (380, 443, and 490 nm) and photosynthetically available radiation (PAR) in the 400-700 nm range. In next-generation floats, replacing the PAR sensor with a 555 nm band is under consideration to enhance spectral resolution while still enabling accurate PAR reconstruction from the existing measurements. This study proposes a General Additive Model (GAM) to estimate PAR at any given depth (z) from just below the surface (z = 0 m) to 200 m using Ed at 380, 443, 490, and 555 nm. The model coefficients are functions of z, and the possibility of introducing chlorophyll concentration ([Chl]) as an extra parameter is also evaluated. Theoretical simulations conducted under diverse environmental conditions show that PAR(z) can be estimated with high accuracy. When using depth as the sole explanatory variable, the estimate bias ranges from 0 to -0.59 µE/m2/s and root mean square deviations (RMSD) between 0.01 and 8.23 µE/m2/s. Percent bias is near-zero across all depths with slightly elevated values near the surface and around 200 m, i.e., -0.2% and 0.5%, respectively. Relative RMSD is about 1-2% at the surface depths and gradually increases to about 8% at 200 m. Including [Chl] as an extra explanatory variable did not significantly improve model performance, probably attributed to the uncertainties in [Chl] measurements. Validation against various in-situ Ed profiles confirms the model's robustness, i.e., with an overall model bias of -0.8% and an RMSD of 4.8% across 120,115 in-situ cases, accurately capturing near-surface variability and maintaining consistent performance, i.e., less than 10% relative error for PAR ranging from 103 to 10-2 µE/m2/s. Theoretical uncertainty of the PAR estimates was also quantified as a function of depth and estimated PAR, providing an uncertainty value for each estimate and showing good agreement with actual uncertainties. The proposed model benefits the BGC Argo program by expanding the Ed dataset within the photosynthetically active range and offering accurate PAR estimates across diverse environmental conditions.

Tropical islands 's oceanic biogeochemical environments may be influenced by continental forcing. The impact of the large Rewa River plume South of the main island of Viti Levu (Fiji) on the microbial processes i.e. carbon and nitrogen fluxes, fisheries richness or coral resources of the Fijian archipelago is unknown. SGLI imagery is helpful in describing ocean color South of Fiji. In order to examine the applicability of ocean colour algorithms for GCOM-C, in situ bio-optical and radiometric data were collected during the March-April 2022 SOKOWASA cruise. Typical mesotrophic waters with ranges of chlorophyll a concentration (Chla, 0.10-0.41 mg·m−3), MES (0.2 to 1.62 g.m-3), bbp550 (5.7 10-4-0.0037 m-1) and aCDOM440 (0.013- 0.047 m-1) are found on a South-North gradient between open ocean and the coast. The remote sensing reflectance obtained using HOCR were inverted to retrieve Inherent Optical Properties (IOPs) for the empirical and semianalytical (QAA, GIOP) algorithms and for OC3 for Chla. All IOPs could be derived from Chla (Case 1 waters). Results showed the capacity and usefulness of the derived products of SGLI (GCOM-C) to monitor the water quality of the Southern ocean South of Fiji. As concluded by bio-optical analysis, during the dry period experienced during SOKOWASA, mesotrophic waters extend far south of the Kadavu Island and small phytoplankton plumes issued from land are mostly formed by organic matter and may feed coral reefs.

Geostationary-orbit sensors provide high-frequency observations, offering advantages over sun-synchronous sensors for studying short-term ocean dynamics, particularly in regions such as the East Sea/Japan Sea and Yellow Sea, where daily variability is significant. Primary production (PP), crucial for analyzing the carbon cycle, is typically estimated on a global and daily scale from space, but understanding daily changes in PP at a regional scale is also important. Estimating instantaneous PP (iPP) requires knowledge of instantaneous photosynthetically available radiation (iPAR), which is the planar solar flux reaching the surface at wavelengths from 400 to 700 nm, a fundamental controlling variable. Here, using a plane-parallel theory-based PAR model, iPAR from GOCI-I was estimated for the ocean around the Korean Peninsula and evaluated against in-situ iPAR measurements from ECO-PAR sensors deployed at two ocean research stations, Socheong-cho and Ieodo, from 2015 to 2020. Data from 2015 to 2017 were used for the training set, and data from 2018 to 2020 for validation. In-situ measurements from 2015 to 2017 were checked against expected values from radiative transfer simulations, and a 2nd order polynomial regression was applied to correct the measurements. The GOCI-I iPAR estimates showed good agreement with the corrected data, with an RMSE of 10.36% and an MBE of 1.55% from 2015 to 2017. Evaluation against in-situ data from 2018 to 2020 also showed similar RMSE (10.04%) and MBE (0.74%). The accuracy of GOCI-I iPAR was further compared with that of iPAR data from the advanced Himawari imager (AHI) and the moderate resolution imaging spectrometer (MODIS). iPAR values from AHI and MODIS exhibited higher RMSE and MBE than GOCI-I iPAR and lower R2. These findings demonstrate that GOCI-I iPAR is a valuable dataset for assessing diurnal variability in oceanic PP around the Korean Peninsula, with implications for improved regional carbon cycle studies and ecosystem monitoring.

Current satellite radiation products from polar-orbiting sensors like MODIS, SGLI, VIIRS, and OCI provide only daily averaged quantities, missing critical diurnal variations. These fluctuations in light energy, ranging from none to ample throughout the day, significantly influence photosynthetic communities, impacting carbon export, nutrient cycling, and ecosystem functions. Incorporating hourly changes in phytoplankton light absorption and irradiance, as shown with GOCI data, has enhanced estimates of net primary productivity. However, the global oceans cannot be fully observed from a single geostationary platform, especially at high latitudes. EPIC, positioned at the first Lagrange point (L1) about 1.5 million km from Earth, offers a unique advantage by simultaneously capturing the entire sunlit ocean with high temporal resolution, enabling detailed observation of evolving systems and diurnal phenomena. Unlike polar orbiters, EPIC provides better coverage at low and middle latitudes, effectively mitigates Sun glint, and offers adequate views of polar regions. Utilizing EPIC data, we estimate hourly photosynthetically available radiation (PAR) over the global ocean. The algorithm is developed and validated through radiative transfer simulations under realistic conditions. Comparisons with geostationary AHI and GOCI data show strong performance. The diurnal PAR estimates, when combined with other ocean color products, are expected to advance studies of aquatic photosynthesis and biogeochemistry.

Atmospheric correction (AC) of ocean color imagery faces significant challenges in regions with absorbing aerosols, adjacency effects, and optically complex waters, common in coastal and inland areas. Traditional algorithms, which extrapolate the atmospheric signal from near and shortwave infrared to shorter wavelengths, often fail in these conditions. To overcome this, the top-of-atmosphere (TOA) signal undergoes principal component (PC) decomposition, retaining only PCs sensitive to water signals, which allows for precise non-linear mapping of TOA to water reflectance. This methodology is tailored for PACE OCI imagery with a 5 nm resolution across 340 to 895 nm. The algorithm excludes OCI observations affected by strong gaseous absorption, but maps TOA PCs to water PCs defined across all spectral bands, enabling water reflectance estimates over the entire 340-895 nm range. In situ measurements and Hydrolight simulations are used to generate TOA reflectance ensembles, with pixel-wise uncertainty estimation integrated. The method's performance under various angular and geophysical conditions is theoretically assessed and applied to OCI imagery of diverse oceanic regions. Results show that the PCA-based AC scheme effectively handles complex atmospheric settings, yielding realistic water reflectance. Comparisons with the operational OCI product (version 2) demonstrate improvements, particularly in mitigating negative or excessively low values at shorter wavelengths. Preliminary experimental validation using HyperNav measurements shows acceptable agreement between estimated and measured water reflectance values, but additional matchups are needed to confirm accuracy.

Ocean color (OC) remote sensing at a Pan-Arctic scale, with over 27 years of continuous daily data, provides critical insights into long-term trends and seasonal variability in phytoplankton abundance, indexed by Chlorophyll-a concentration (Chl-a). However, existing satellite algorithms for retrieving Chl-a in the Arctic Ocean (AO) exhibit significant limitations, including high uncertainties and inconsistent accuracy across different regions, which propagate errors in primary production estimates and biogeochemical models. In this study, we quantified the uncertainties of seven existing algorithms using harmonized, merged multi-sensor satellite remote sensing reflectance (Rrs) data from the ESA Climate Change Initiative (CCI) spanning 1998-2023. The existing algorithms exhibited varying performance, with Mean Absolute Differences (MAD) ranging from 0.8 to 4.2 mg m-3. To improve these results, we developed CIAO (Chlorophyll In the Arctic Ocean), a machine learning-based algorithm specifically designed for AO waters and trained with satellite Rrs data. The CIAO algorithm uses Rrs at four spectral bands (443, 490, 510 and 560 nm) and Day-Of-Year (DOY) to account for seasonal variations in bio-optical relationships. CIAO significantly outperformed seven existing algorithms, achieving a MAD of 0.5 mg m-3, thereby improving Chl-a retrievals by at least 30%, compared to the bestperforming existing algorithm. Furthermore, CIAO produced consistent spatial patterns without artifacts and provided more reliable Chl-a estimates in coastal waters, where other algorithms tend to overestimate. This enhanced the accuracy of seasonal variability tracking at a Pan-Arctic scale. By strengthening the precision of satellite-derived Chl-a estimates, CIAO contributes to more accurate ecological assessments and robust climate projections for the rapidly changing AO.

The annual periodicity present in spectral remote sensing reflectances (Rrs(λ)) derived from ocean color satellite data processed by NASA's Ocean Biology Processing Group has been shown to diverge from that observed in situ, with the source of the discrepancies remaining unresolved. Here we investigate such periodicity in reflectances derived from four satellite sensors and seven in situ locations. Periodicity in the time series of Rrs(λ) spectra are examined without prior assumption of the main constituent frequency through application of the floating-mean Lomb-Scargle periodogram. We show that a 1-term sinusoidal function specified at the annual frequency with a constant offset reproduces seasonal trends observed at various regional oceanic sites. Although unique regional features in mean, amplitude, and phase are generally well captured by Rrs(λ) derived from satellite measurements, discrepancies perpetuate the annual frequency into the satellite-to-in situ Rrs(λ) matchup difference. Specifying the origin of the discrepancy is challenging because the annual frequency is significantly contained in time series of many physical measurements, corrections, and ancillary data required for Rrs(λ) retrievals. Until differences in amplitude and phase between satellite and in situ Rrs(λ) time series are reduced, improved uncertainty estimates should be incorporated into downstream product analyses.

Long-term, global ocean-color observations are needed for biogeochemistry and climate applications and require integration across multiple satellite sensors. This study proposes a methodology for cross-calibrating polar-orbiting ocean-color sensors using a geostationary reference sensor. The geostationary sensor serves as an intermediary, offering numerous coincidences in time and geometry with polar-orbiting sensors, particularly over oceanic regions where radiance levels are typical for ocean-color remote sensing. The methodology is applied to cross-calibrate current ocean-color sensors, including the recently launched OCI, using AHI, a sensor expected to remain stable over short cross-calibration intervals. Accuracy is evaluated based on radiometric noise, acquisition time differences, solar and viewing geometry variations, and spectral band mismatch uncertainties. Cross-calibration coefficients derived from suitable imagery provide a foundation for consistent, normalized calibration of polar-orbiting sensors, enabling the generation of reliable long-term ocean-color products from multiple satellites.

Hyperspectral optical observations of the Earth’s surface oceans from space offer a means to improve our understanding of ocean biology and biogeochemistry. NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite mission, which includes a hyperspectral ocean color instrument (OCI), will provide radiometric observations of surface ocean with near continuous spectral resolution across the near UV to NIR range. Maintaining sufficient accuracy over the lifetime of satellite ocean color missions requires a robust program for system vicarious calibration (SVC) and product validation. The system vicarious calibration process combines satellite sensor data with in-situ radiometric/optical measurements to remove potential biases due to the combined errors from both satellite radiometric sensor calibration and atmospheric correction. As such, high accuracy, high-spectral resolution in-situ radiometric measurements are required to provide a principal source of truth for the satellite-derived products. To meet the requirements, a novel in-situ radiometric system, called HyperNav, has been developed, rigorously characterized and field tested. Key attributes of HyperNav are dual upwelling radiance heads coupled to individual spectrometers, spectral resolution of ∼2.2 nm (full width, half-maximum) across 320–900 nm, integrated shutter systems for dark measurements, and integrated tilt and pressure sensors. The HyperNav operational modes include traditional profiling and surface modes, as well as integration with an autonomous profiling float for unattended deployment, offering a new capability for a network of autonomous platforms to support the long-term needs for hyperspectral ocean color remote sensing observations. This paper describes the HyperNav design, in-situ operational modes, and field verification results.

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI

Ocean color satellites require a procedure known as System Vicarious Calibration (SVC) after launch as the pre-launch and on-orbit calibration accuracy is insufficient. The current approach for determination of post-launch SVC uses a single fixed measurement location and may be susceptible to unexpected biases in satellite processing algorithms. Here we describe a novel SVC program which is based on a high resolution and high accuracy radiometric system integrated with an autonomous profiling float (providing a buoyancy engine, physical observations, and communication). This float + radiometer (HyperNav) system can be shipped via air, land, ocean and is deployable from small boats. This SVC program relies on multiple deployment sites with associated facilities to collect a significant amount of SVC quality data in a relatively short time. It has centralized logistics and command-and-control centers ensuring easy access to information regarding the status of each asset and to ensure floats stay within a certain ocean area. The development of the program has been associated with the launch of NASA’s PACE satellite and has been executed by academic institutions in collaboration with an industrial partner. Other approaches for a future float-based operational SVC program are discussed.

Checking the radiometric calibration of satellite hyper-spectral sensors such as the PACE Ocean Color Instrument (OCI) while they operate in orbit and evaluating remote sensing reflectance, the basic variable from which a variety of optical and biogeochemical ocean properties can be derived, requires measuring upwelling radiance just above the surface (Lw) and downwelling planar irradiance reaching the surface (Es). For this, the current HyperNav systems measure Lw at about 2 nm spectral resolution in the ultraviolet to near infrared, but Es in only four 10 nm wide spectral bands centered on 412, 489, 555, and 705 nm. In this study, the Es data acquired in these spectral bands in clear sky conditions are used to reconstruct via a multi-linear regression model the hyper-spectral Es signal at 0.5 nm resolution from 315 to 900 nm, the OCI spectral range, allowing an estimate of Es at the HyperNav, OCI, and other sensors’ resolutions. After correction of gaseous absorption and normalization by the top-of-atmosphere incident solar flux, the atmospheric diffuse transmittance is expressed as a linear combination of Es measured in those 4 spectral bands. Based on simulations for Sun zenith angles from 0 to 75° and a wide range of (i.e., expected) atmospheric, surface, and water conditions, the Es spectrum is reconstructed with a bias of less than 0.4% in magnitude and an RMS error (RMSE) ranging from 0% to 2.5%, depending on wavelength. The largest errors occur in spectral regions with strong gaseous absorption. In the presence of typical noise on Es measurements and uncertainties on the ancillary variables, the bias and RMSE become −2.5% and 7.0%, respectively. Using a General Additive Model with coefficients depending on Sun zenith angle and aerosol optical thickness at 550 nm improves statistical performance in the absence of noise, especially in the ultraviolet, but provides similar performance on noisy data, indicating more sensitivity to noise. Adding spectral bands in the ultraviolet, e.g., centered on 325, 340, and 380 nm, yields marginally more accurate results in the ultraviolet, due to uncertainties in the gaseous transmittance. Comparisons between the measured and reconstructed Es spectra acquired by the MOBY spectroradiometer show agreement within predicted uncertainties, i.e., biases less than 2% in magnitude and RMS differences less than 5%. Reconstruction can also be achieved accurately with other sets of spectral bands and extended to cloudy conditions since cloud optical properties, like aerosol properties, tend to vary regularly with wavelength. These results indicate that it is sufficient, for many scientific applications involving hyper-spectral Es, to measure Es in a few coarse spectral bands in the ultraviolet to near infrared and reconstruct the hyperspectral signal using the proposed multivariate linear modeling.

A new methodology is presented to identify the spectral response of multi-band remote sensing optical camera. The proposed set-up is based in the system theory where the input-output relationship allows to characterize the internal parameters as it is the Absolute Spectral Response (ASR). The new approach allows one to minimize the instrumental set-up as well the time to develop the identification of the ASR.

We recently found a significant bias between spectral diffuse attenuation coefficient (Kd(λ)) retrievals by common ocean color algorithms and measurements from profiling floats [Remote. Sens.14, 4500 (2022)10.3390/rs14184500]. Here we show, using a multi-satellite match-up dataset, that the bias is markedly reduced by simple "tuning" of the algorithm's empirical coefficients. However, while the float dataset encompasses a larger proportion of the ocean's variability than previously used datasets, it does not cover the whole range of variability of observed remote sensing reflectance (Rrs). Thus, using algorithms tuned to this more comprehensive dataset may still result in a temporal and/or geographical bias in global application. To address this generalization issue, we evaluated a variety of analytical algorithms based on radiative transfer theory and settled on a specific one. This algorithm computes Kd(λ) from inherent optical properties (IOPs) obtained from an Rrs inversion and information about the angular distribution of the radiance transmitted through the air/ocean interface. The resulting Kd(λ) estimates at 412 and 490 nm were not appreciably biased against the float measurements. Evaluation using other in-situ datasets and radiative transfer simulations was also satisfactory. Statistical performance was good in both clear and turbid waters. Further work should be conducted to examine whether the tuned algorithms and/or the new analytical algorithm demonstrate adequate hyperspectral performance.