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The database includes the dataset used for validation and the corresponding model outputs presented in the manuscript entitled "On the spatial and temporal variations of primary production in the South China Sea" submitted to JGR-Ocean.

For the first time the vertical variation in light quality in the global ocean is quantified with a single parameter--the hue angle (αE, in degree) in chromaticity of downwelling irradiance. For oceanic waters, αE is ~140° at surface, but it becomes ~230° at the bottom of the euphotic zone; αE changes rapidly near the surface, and we term this layer of rapid change in light quality as chromocline, analogous to the thermocline or pycnocline in oceanography. The 3-D variations in light quality are further highlighted with data from satellite ocean color measurements, where global distributions of αE for depths of 99%, 37%, and 1% of surface photosynthetically available radiation (PAR) are presented. As an example to demonstrate the importance to consider the change in light quality, the relationship between the light quality and the ratio of phytoplankton absorbed light to PAR is presented where this ratio may vary by a factor of 3 or more under different chlorophyll-a concentrations; otherwise, the ratio would be constant vertically. We advocate quantitative measurement and report the light quality in the upper ocean with such a single and objective parameter to accompany the routine measurement and report the light intensity, which will greatly improve our understanding of light-related processes and further bridge ocean optics and oceanography.

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.

Atmospheric correction (AC) is a key step in ocean color remote sensing for obtaining oceanic and coastal water ecosystem products. However, traditional NIR-based AC scheme ( AC NIR ) often fails in turbid coastal waters dominated by high suspended particulate matter (SPM). SWIR-based AC is valid for turbid waters, but it cannot be applied to some satellite sensors (e.g., Sea-viewing Wide Field-of-view Sensor (SeaWiFS), MEdium Resolution Imaging Spectrometer (MERIS), and future small satellite sensors) that have no SWIR bands. To obtain reliable data of turbid coastal waters by these sensors without SWIR bands, an improved AC NIR ( AC NIR-PRO ) scheme is proposed herein based on the frame of traditional AC NIR ( AC NIR-STA ). The synthesized and in situ datasets are analyzed to extend bio-optical models of absorption and backscattering coefficients in AC NIR-STA to highly turbid waters. AC NIR-PRO utilizes concentrations of both chlorophyll-a ([Chla]) and SPM ([SPM]) to estimate water-leaving contributions at the NIR bands. Similar to NIR-SWIR AC, AC NIR-PRO can obtain the information of global coastal waters with remote sensing reflectance at 862 nm ( R r s ( 862 ) ) up to 0 . 025 ∼ 0 . 03 sr − 1 . R r s of AC NIR-PRO is also validated by AERONET-OC datasets (mean absolute percent difference (MAPD) of R r s ( 551 ) is approximately 16%) for various coastal waters including high colored dissolved organic matter. Further, the magnitude and spatial patterns of [SPM] and [Chla] derived from the R r s products after performing AC NIR-PRO are significantly better than those from AC NIR-STA , especially for turbid coastal waters.

Water-leaving albedo (αw(λ)) is an important component of the ocean surface albedo. Direct measurement of αw(λ) in the field is not yet available due to difficulties in removing the contribution of surface-reflected solar radiation. Following the concept of the skylight-blocked approach (SBA), a novel system, termed αwSBA, is proposed in this study to directly measure Ew(λ), where a wide-angle black cone is used to block the surface-reflected radiance. The shading errors associated with the cone and the measuring system are examined via Monte-Carlo (MC) simulations for a wide range of water inherent optical properties (IOPs), solar zenith angle, and different configurations of the αwSBA system (i.e., half cone angle, and the length of supporting arm). Based on sensitive analysis using MC simulations, an optimal configuration of αwSBA is recommended. We further propose a mathematical expression to parameterize the shading error (ɛ), along with an error correction scheme (αwOPT). It is found that, with the optimal configuration and αwOPT, the uncertainties of obtained αw(λ) by αwSBA are generally less than 7% for a wide range of waters with different IOPs and particulate scattering phase functions.

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.

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

Himawari-8 (H8) is a geostationary meteorological satellite launched by JAXA (Japan Aerospace Exploration Agency) and is now operated by JMA (Japan Meteorological Agency). It takes measurements at a temporal resolution of 10 min for full disk view. Although designed as a meteorological satellite, the Advanced Himawari Imager (AHI) onboard H8 has three visible (460, 510 and 640 nm), one near infrared (860 nm) and two shortwave infrared bands (1610 and 2257 nm) to observe the Earth system. In this study, the Floating Algae Index (FAI) developed for ocean color satellites (Hu, 2009) is adapted to process AHI data for the first time and applied for waters of Lake Taihu, China. For a total of 18 near-cloud-free images, a correlation coefficient (r) of 0.92 was obtained between the algae area derived from AHI FAI and that from the Moderate Resolution Imaging Spectroradiometer (MODIS) FAI, and the mean percentage difference is ~5% in algae coverage. More concurrent images (n = 80) were collected for a comparison between AHI and Geostationary Ocean Color Imager (GOCI), as GOCI is also a geostationary satellite, resulting in a correlation coefficient of 0.91 and percentage deviation of ~8% in observed algae coverage. These results indicate that H8/AHI can obtain reliable observations of floating algae at ultrahigh temporal resolutions (10 min). Especially, such ultrahigh-frequency measurements show that part of Lake Taihu (e.g., Meiliang Bay) experienced more frequent events of floating algae (mostly >60%) than that observed by GOCI (generally

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.

Small water bodies are an important part of the Earth’s freshwater system, protecting biodiversity and providing ecosystem services. Because of various surrounding features, it is unknown to what extent we can obtain accurate remote-sensing reflectance ( R rs ) of such an environment by the conventional above-water approach (AWA). In this study, we used both AWA and the skylight-blocked approach (SBA) side-by-side to measure R rs in a typical small water body. It was found that the variation of R rs in the UV-blue domain from AWA is around 50% and is inconsistent with the variation of the total absorption coefficient ( a t ) obtained from water samples; on the contrary, the variation of R rs obtained from SBA is highly consistent, with a coefficient of variation under ∼ 5 % . These results highlight the large uncertainties in the measured R rs from AWA due to the complexity of such an environment and further echo the robustness of SBA to measure R rs in the field, even in such challenge environments.

Water-leaving albedo (αw(λ)) is an important component of the ocean surface albedo and is conventionally estimated based on remotely sensed chlorophyll-a concentration (Chl) (termed Chl-αw). We show that estimated αw(λ) by Chl-αw could be significantly biased in global oceans, because there is no guarantee of closure between the modeled remote sensing reflectance (Rrs(λ)) from Chl-inferred inherent optical properties (IOPs) and the input Rrs(λ) that is used to derive Chl. We thus propose a simple and improved scheme, termed Chl-αw_new, and show that the step to infer IOPs from Chl is not necessary, where αw(λ) can be accurately estimated from satellite-measured Rrs(λ) and a Chl-based look-up-table (LUT) for the bidirectional relationships of angular Rrs(λ). Evaluations with both HydroLight simulations and satellite measurements show that Chl-αw_new is equivalent to the recently developed αw scheme based on IOPs (IOPs-αw, [Remote Sens. Environ. 269, 112807]), where both schemes could significantly improve the estimation of αw(λ) compared to Chl-αw. Comparisons among Chl-αw, Chl-αw_new, and IOPs-αw highlight that optical closure of Rrs(λ) is essential for accurate remote sensing of αw(λ), while the model for Rrs(λ) bidirectionality has rather minor impacts. The impact of improved αw(λ) estimations on the solar flux exchanges at the air-sea interface is preliminarily evaluated in this effort, where the use of Chl-αw_new could increase the estimation of reflected solar radiation by over 68.7% in turbid waters compared to that using Chl-αw, highlighting the necessity of incorporating accurate αw schemes into the coupled ocean-atmosphere models, especially for regional models in coastal oceans.

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.

Accurate determination of the water-leaving radiance ( L w ) is key to correctly interpret in-water optical properties and to validate the atmospheric correction schemes in ocean color studies. Among the various approaches adopted to measure L w in the field, the skylight-blocked approach (SBA) is the only scheme that can potentially measure L w directly. However, the apparatus associated with an SBA system will introduce self-shading effects to the measured L w , which is required to be corrected for an accurate L w determination. In this study, we experimentally evaluate several factors that could contribute to the self-shading effects of the SBA-measured L w , including solar zenith angle ( ∼ 18 ∘ − 64 ∘ ), water’s optical properties, and cone size (radius of 22 mm and 45 mm). For waters with the total absorption coefficient at 440 nm as high as ∼ 6.0 m − 1 , the normalized root-mean-square difference between the SBA-measured L w after shade correction and the “true” L w is generally between ∼ 5 % and ∼ 10 % for wavelengths in the range of 400–750 nm. These results suggest that SBA can obtain highly accurate and precise L w in nearly all natural aquatic environments.

Low-power, lightweight, off-the-shelf imaging spectrometers, deployed on above-water fixed platforms or on low-altitude aerial drones, have significant potential for enabling fine-scale assessment of radiometrically derived water quality properties (WQPs) in oceans, lakes, and reservoirs. In such applications, it is essential that the measured water-leaving spectral radiances be corrected for surface-reflected light, i.e., glint. However, noise and spectral characteristics of these imagers, and environmental sources of fine-scale radiometric variability such as capillary waves, complicate the glint correction problem. Despite having a low signal-to-noise ratio, a representative lightweight imaging spectrometer provided accurate radiometric estimates of chlorophyll concentration—an informative WQP—from glint-corrected hyperspectral radiances in a fixed-platform application in a coastal ocean region. Optimal glint correction was provided by a spectral optimization algorithm, which outperformed both a hardware solution utilizing a polarizer and a subtractive algorithm incorporating the reflectance measured in the near infrared. In the same coastal region, this spectral optimization approach also provided the best glint correction for radiometric estimates of backscatter at 650 nm, a WQP indicative of suspended particle load.

The Chinese Ocean Colour and Temperature Scanner (COCTS), Coastal Zone Imager (CZI) and the novel Ultra-Violet Imager (UVI) which on-board the Chinese Ocean Satellite HY-1C was launched in September 2018. The atmospheric correction of ocean color sensors was a critical step for accurate retrieval of the remote sensing reflectance, and the look-up-tables (LUTs), for the Rayleigh scattering, the aerosol scattering, and the diffuse transmittance, which were built bases on a Successive Order Scattering Vector Radiative Transfer Solver, played an important role in the processing step. Preliminary evaluation has been performed using the SeaWiFS LUTs and the MODIS data, it showed that COCTS can provide accurate ocean color products.

The Operational Land Imager (OLI) onboard Landsat-8 satellite can provide remote sensing reflectance (Rrs) of aquatic environments with high spatial resolution (30 m), allowing for benthic habitat mapping and monitoring of bathymetry and water column optical properties. To facilitate these applications, accurate sensor-derived Rrs is required. In this study, we assess atmospheric correction schemes, including NASA's NIR-SWIR approach, Acolite's NIR and SWIR approaches and the cloud-shadow approach. We provide the first comprehensive evaluation for Landsat-8 Rrs retrievals in optically shallow coral reefs, along with an investigation of Landsat-8 Rrs products in a temperate turbid embayment. The obtained Landsat-8 Rrs data products are evaluated with concurrent in situ hyperspectral Rrs measurements. Our analyses show that the NASA and the cloud-shadow approaches generated reliable Rrs products across shallow coral reefs and optically deep waters. This evaluation suggests that high quality Rrs products are achievable from the Landsat-8 satellite in optically shallow environments, which supports further application of Landsat-8 type measurements for coral reef studies.

In this study, six algorithms (both empirical and semi-analytical) developed for the estimation of Kd in the ultraviolet (UV) domain (specifically 360, 380, and 400 nm) were evaluated from a dataset of 316 stations covering oligotrophic ocean and coastal waters. In particular, the semi-analytical algorithm (Lee et al. 2013) used remote sensing reflectance in these near-blue UV bands estimated from a recently developed deep learning system as the input. For Kd(380) in a range of 0.018 - 2.34 m−1, it is found that the semi-analytical algorithm has the best performance, where the mean absolute relative difference (MARD) is 0.19, and the coefficient of determination (R2) is 0.94. For the empirical algorithms, the MARD values are 0.23–0.90, with R2 as 0.70–0.92, for this evaluation dataset. For a VIIRS and in situ matchup dataset (N = 62), the MARD of Kd(380) is 0.21 (R2 as 0.94) by the semi-analytical algorithm. These results indicate that a combination of deep learning system and semi-analytical algorithms can provide reliable Kd(UV) for past and present satellite ocean color missions that have no spectral bands in the UV, where global Kd(UV) products are required for comprehensive studies of UV radiation on marine primary productivity and biogeochemical processes in the ocean.

Water-leaving albedo (αw), defined as the ratio of water-leaving irradiance to downwelling irradiance just above the surface, is a major component of ocean surface albedo (α) but has long been ignored or underrepresented. A semi-analytical scheme based on inherent optical properties (IOPs), termed IOPs-αw, is proposed in this study to estimate spectral αw(λ) from ocean color measurements. Evaluations with numerical simulations of radiative transfer show that IOPs-αw outperforms the conventional scheme based on chlorophyll-a (Chl) concentration. The median absolute percentage difference (MAPD) of derived αw(λ) from IOPs-αw is generally less than 3% in the blue-green spectral domain, in comparison to MAPD of over 40% for estimated αw(λ) from the Chl-based scheme. IOPs-αw is later implemented to monthly composite data of the Visible Infrared Imaging Radiometer Suite (VIIRS), where reasonable spatial distributions and seasonal patterns of αw(λ) are obtained. In particular, broadband αw in the visible domain, termed αw_VIS, obtained via IOPs-αw is over 50% higher than the previous estimation by the Chl-based scheme in most oceanic waters. Furthermore, this study concludes that αw_VIS could contribute up to 20% to α in oceanic waters under low solar-zenith angles. Thus, we suggest that neither the spatial variability of αw_VIS nor the contribution of αw_VIS to α shall be neglected, and it is necessary to incorporate IOPs-αw into current parameterizations of α in coupled ocean-atmosphere and climate models.

Deriving inherent optical properties (IOPs) and other water quality parameters from satellite remote sensing data covering optically shallow environments has historically been problematic due to difficulties in separating the benthic signal from that of the water column. While recent advances have improved such retrievals, most methods have high uncertainties for very shallow (

We propose an innovative multi-pixel atmospheric correction approach (MPACA) to process high-spatial-resolution satellite measurements over coastal waters based on a revised POLYMER model. MPACA assumes the aerosol type to be uniform within a relatively small region, while the aerosol load and water properties are allowed to vary. Landsat-8 OLI images over six coastal locations with various turbidities were utilized to evaluate the performance of MPACA. The retrieved remote sensing reflectance (Rrs(λ)) by MPACA is validated with in situ matchups obtained from two sources: ship-based field campaigns and the AERONET-OC networks. It is found that, at each of OLI's four visible bands, MPACA provided accurate Rrs(λ) products over such coastal environments, with the Root Mean Square Difference (RMSD) and Mean Absolute Percentage Difference (MAPD) less than 0.0006 sr−1 and 16.2%, respectively. In contrast, the Rrs(λ) values retrieved with NASA's SeaDAS (v7.5), where each pixel was treated independently, showed RMSD and MAPD as ~0.0018 sr−1 and ~38.8%, respectively. Acolite-DSF, which assumed some spatial dependency, obtained MAPD almost two times that of SeaDAS for each visible band. Further, it appears that Acolite-EXP did not perform well for this evaluation dataset, where RMSD is ~0.0062 sr−1 and MAPD is ~228.2%. These results suggest that MPACA is a promising scheme for atmospheric correction in coastal waters, especially for measurements from multi-band satellites that have a high spatial resolution along with at least two bands in the NIR or SWIR domain.

Remote imaging spectroscopy from 400 to 800 nm can use benthic reflectance signatures to map the composition and condition of shallow water ecosystems. We present a novel probabilistic approach to jointly estimate the seafloor reflectance and water properties while flexibly incorporating varied domain knowledge and in situ measurements. The inversion transforms remote radiance data with an atmospheric correction followed by a water column correction. Benthic reflectance and water optical properties are both represented by linear mixtures of endmember spectra. We combine remote measurements, prior knowledge and field data using a flexible Bayesian optimal estimation, solving for the Maximum A Posteriori (MAP) combination of water column properties, seafloor reflectance, and depth. We then demonstrate performance in controlled simulations and in overflights of a coral reef in Hawaii with coincident in situ measurements. The measurement approach helps lay a foundation for wide-area airborne mapping of the condition of threatened coastal ecosystems such as coral reefs.

Rapid assessment of algal blooms in bays and estuaries has been difficult due to lack of timely shipboard measurements and lack of spatial resolution from current ocean color satellites. Airborne measurements may fill the gap, yet they are often hindered by the high cost and difficulty in deployment. Here we demonstrate the capacity of a low-cost, low-altitude unmanned aerial vehicle (UAV) in assessing an intense phytoplankton ( Phaeocystis globosa ) bloom (chlorophyll concentrations ranged from 7.3 to 45.6 mg/m 3 ) in Weitou Bay in the western Taiwan Strait. The UAV was equipped with a hyperspectral sensor to measure the water color with a footprint of 5 m at every 30 m distance along the flight track. A novel approach was developed to obtain remote sensing reflectance ( R rs ) from the UAV at-sensor radiometric measurements. Compared with concurrent and co-located field measured R rs (14 stations in total), the UAV-derived R rs showed reasonable agreement with root mean square difference ranging 0.0028–0.0043 sr − 1 (relative difference ~ 20–32%) of such turbid waters for the six MODIS bands (412–667 nm). The magnitude of the bloom was further evaluated from the UAV-derived R rs . For the bloom waters, the estimated surface chlorophyll a concentration ( Chl ) ranged 6–98 mg/m 3 , which is 3–50 times of the Chl under normal conditions. This effort demonstrates for the first time a successful retrieval of both water color ( i.e. , R rs ) and Chl in a nearshore environment from UAV hyperspectral measurements, which advocates the use of UAVs for rapid assessment of water quality, especially for nearshore or difficult-to-reach waters, due to its flexibility, low cost, high spatial resolution, and sound accuracy.

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 spectral absorption coefficient of pure seawater (aw(λ)) in published studies differ significantly in the blue domain, yet the impacts of such discrepancies on the inherent optical properties (IOPs) derived from ocean color have been scarcely documented. In this study, we confirm that changes in aw(λ) may have significant impacts on retrieved IOPs in oligotrophic waters, especially for the phytoplankton absorption coefficient (aph(λ)). Two sets of aw(λ) data, aw_PF97 (Appl. Opt. 36, 8710, 1997) and aw_Lee15 (Appl. Opt. 54, 546, 2015), were selected for optical inversion analysis. It is found that aph(λ) retrieved with aw_Lee15 agree better with the in-situ measurements in oligotrophic waters. Further applications to satellite images show that the derived aph(λ) using aw_Lee15 can be up to 238% higher than the retrievals using aw_PF97 in the core zone of the subtropical ocean gyres. Given that aw_PF97 is commonly accepted as the “standard” aw(λ) by the ocean color community in the past decades, this study highlights the need and importance to update aw(λ) with aw_Lee15 for IOPs retrievals in oligotrophic waters.

This report summarizes the research frontiers on radiative transfer (RT) in coupled atmosphere-atmosphere systems to enable new science and specifically to support simulations and interpretations of remote sensing observations from the upcoming Plankton, Aerosol, Cloud ocean Ecosystem (PACE) mission. Given that (i) there is a multitude of atmospheric and oceanic constituents at any given moment that each exhibit a large variety of physical and chemical properties and that (ii) light-matter interaction can occur in various ways (scattering, absorption, and emission), it becomes clear that it is impossible to tackle all outstanding RT aspects related to interpreting and/or simulating light reflected by atmosphere-ocean systems. Instead, we performed both theoretical and experimental studies on a discrete set of RT topics that are of importance to (a) the science threshold and goal questions of the PACE mission and (b) the measurement capabilities of the instruments currently envisioned for this mission. These topics cover (1) the ocean (i.e., water body): exact (elastic and inelastic scattering) and approximate (bio-optical) forward RT models used for scattering and absorption by pure water and particulates, (2) the air-water interface: extracting and/or accounting for variations in surface refractive index and whitecaps and underwater bubbles in actual imagery, (3) the atmosphere: aerosol and gas RT models for polarimetric and/or hyperspectral remote sensing, and (4) atmosphere-ocean systems: RT benchmark results for horizontally homogeneous plane-parallel systems, and impact of the Earth’s sphericity and adjacency effects on RT analyses of space-borne observations. We provide for each topic a summary of past relevant (heritage) work, followed by a discussion (for unresolved questions) and RT updates.

Visibility, a property measured based on human vision, is a common and widely used measurement to represent the quality of air and water. To visibility, theories have been developed in the past century for measurements in both air and water. This includes the century-old Koschmieder model for visibility in air and the Duntley-Preisendorfer model for visibility (Secchi disk depth) in water, as well as recent model by Lee et al. Here we present a general relationship for the Law of Contrast Reduction based on radiative transfer, which is the key to deduce visibility in both media. And from this general relationship, it suggests the Koschmieder model is applicable only to situations when a common-size object can be viewed 10's of kilometers away, while the Duntley-Preisendorfer model is not applicable for a common size Secchi disk when viewed in water. We further highlight the difference in "visibility" between "simple detection" and "clear recognition", a key difference of "visibility" in air and water.

In ocean-color remote sensing, subsurface remote-sensing reflectance (r r s ) of optically deep waters can be linked to its absorption (a) and backscattering coefficients (b b ) by various models. The use of such models allows for quick calculations r r s from such coefficients, eliminating the need to solve the radiative transfer equation. In particular, r r s can be expressed as a function of bb/(a+bb). HydroLight and Monte Carlo simulations showed that commonly used models underestimate r r s in waters with high suspended sediment loads. Monte Carlo simulations confirmed that this issue is due to a sharp increase in multiple scattering events at high turbidity levels. A quartic polynomial model is derived relating r r s and inherent optical properties (IOPs) for waters of any turbidity, to avoid significant errors in waters of high turbidity.

To achieve a smooth transition between algorithms for “clear” water and “turbid” water, we propose a single formula to calculate the input parameter (ip) used for empirical retrieval of absorption coefficients (a) or chlorophyll concentration ([Chl]) from remote-sensing reflectance (R r s ). This formula for ip takes the ratio of the maximum R r s in the blue-green bands to the sum of R r s (green) and the scaled R r s in the red and infrared bands (termed as ipMax-Sum). We found that, compared to the widely used OC4-type formula for ip, ipMax-Sum can improve the coefficient of determination from ∼0.88 to 0.99 for absorption coefficient at 440 nm [a(440)] in ∼0.01–20.0 m–1 ([Chl] ∼0.01–500 mg m–3). Especially, the sensitivity of ipMax-Sum to the change in a(440) is about five times greater than that of OC4-type for a(440)>∼1.0 m−1 ([Chl]>∼10 mg m−3). These results indicate an advantage of ipMax-Sum for generating robust and seamless a(440) or [Chl] from clear to highly turbid waters. The inclusion of such a scheme in a quasi-analytical algorithm is also presented.

Secchi disk depth (ZSD) and Forel-Ule index (FUI) are the two oldest and easiest measurements of water optical properties based on visual determination. With an overarching objective to obtain water inherent optical properties (IOPs) using these historical measurements, this study presents a model for associating remote-sensing reflectance (Rrs) with FUI and ZSD. Based upon this, a scheme (FZ2ab) for converting FUI and ZSD to absorption (a) and backscattering coefficients (bb) is developed and evaluated. For a data set from HydroLight simulations, the difference is