Generalized ocean color inversion model for retrieving marine inherent optical properties

Anomalous trends in global ocean carbon concentrations following the 2022 eruptions of Hunga Tonga-Hunga Ha'apai

We report on observed trend anomalies in climate-relevant global ocean biogeochemical properties, as derived from satellite ocean color measurements, that show a substantial decline in phytoplankton carbon concentrations following eruptions of the submarine volcano Hunga Tonga-Hunga Ha'apai in January 2022. The anomalies are seen in remotely-sensed ocean color data sets from multiple satellite missions, but not in situ observations, thus suggesting that the observed anomalies are a result of ocean color retrieval errors rather than indicators of a major shift in phytoplankton carbon concentrations. The enhanced concentration of aerosols in the stratosphere following the eruptions results in a violation of some fundamental assumptions in the processing algorithms used to obtain marine biogeochemical properties from satellite radiometric observations, and it is demonstrated through radiative transfer simulations that this is the likely cause of the anomalous trends. We note that any future stratospheric aerosol disturbances, either natural or geoengineered, may lead to similar artifacts in satellite ocean color and other remote-sensing measurements of the marine environment, thus confounding our ability to track the impact of such events on ocean ecosystems.

Performance of two semi-analytical algorithms in deriving water inherent optical properties in the Southern Ocean

Remotely sensed inherent optical properties (IOPs) are key proxies for synoptic mapping of primary production and carbon export in the global ocean. However, the IOPs inversion algorithms are scarcely evaluated in the Southern Ocean (SO) because of limited field observations. In this study, the performance of two widely used semi-analytical algorithms (SAAs), i.e., the quasi-analytical algorithm (QAA) and the generalized IOP model (GIOP), were evaluated using a compiled in situ bio-optical dataset in SO, as well as measurements from the Visible Infrared Imaging Radiometer Suite (VIIRS). Evaluations with in situ data show that QAA and GIOP have comparable performance in retrieving the total absorption coefficient (a(λ)), absorption coefficients of phytoplankton (aph(λ)), and that of detritus and colored dissolved organic matter (adg(λ)). Overall, it was found that remotely sensed a(λ) and aph(λ) by both SAAs agreed well with field measurements, with the mean absolute percentage difference (MAPD) of derived a(λ) and aph(λ) in the blue-green bands being ∼20% and ∼40%, respectively. However, derived adg(λ) by both SAAs were higher than the measured values at the lower end (adg(443) < ∼0.01 m-1), but lower at the higher end (adg(443) > ∼0.02 m-1), with MAPD of ∼60%. Results of this effort suggest confident products of a(λ) and aph(λ) from VIIRS in SO, but more dedicated efforts on the measurements and evaluation of adg(λ) in SO would be desired.

Detection of harmful algal blooms from satellite-based inherent optical properties of the ocean in Paracas Bay - Peru

Harmful algal bloom (HAB) events in front of Pisco River, inside Paracas Bay and Lagunillas inlet on the southern coast of Peru was identified from a satellite index (IOPifa) generated with daily high-resolution satellite data of phytoplankton absorption (aphy,GIOP) and non-algal detrital material plus CDOM (adCDOM,GIOP) from the Generalized Inherent Optical Properties (GIOP) model of Modis-Aqua, Viirs-Snpp and Viirs-Jpss1 satellites were used. Phytoplankton density field data sampling from HAB's monitoring programs of IMARPE of 2018 and 2019 were used to validate and identify the extent and spatio-temporal variability of these events. The satellite index (IOPifa) identified for Modis-Aqua 9 active HABs, 8 events in final conditions and 6 events that do not represent HAB conditions, while for Viirs-Snpp found 14 active HABs, 7 events in decaying bloom conditions and 13 events that do not represent HABs; and for Viirs-Jpss1 the index identified 7 active events, 14 in final bloom conditions and 6 that do not represent HABs conditions. The one-factor anova model was applied (p-value = 0.32 > 0.05), indicating that there is no evidence of a difference in the population means of the indices for each sensor. Subsequently, the pairwise multiple comparisons analysis with a 95 % confidence level of Tukey's test confirmed that there are no significant differences in the satellite index value, the differences could be associated with the spectral characteristics of the cell density of the species community and the oceanographic and environmental conditions. The spatial overlap between the in situ harmful algal blooms areas and the calculated satellite index, shows the capacity of the IOP satellite data for the HABs detection. However, it was also evidenced that some HAB events with high phytoplankton cell density had low IOPifa values, while other events with lower cell density were easily identified by the satellite index. This would indicate the ability of the ocean inherent optical properties to differentiate the phytoplankton types that cause algal blooms.

UAV and satellite remote sensing for inland water quality assessments: a literature review

High spatial and temporal resolution data is crucial to comprehend the dynamics of water quality fully, support informed decision-making, and allow efficient management and protection of water resources. Traditional in situ water quality measurement techniques are both time-consuming and labor-intensive, resulting in databases with limited spatial and temporal frequency. To address these challenges, satellite-driven water quality assessment has emerged as an efficient and effective solution, offering comprehensive data on larger-scale water bodies. Numerous studies have utilized multispectral and hyperspectral remote sensing data from various sensors to assess water quality, yielding promising results. However, the recent popularity of unmanned aerial vehicle (UAV) remote sensing can be attributed to its high spatial and temporal resolution, flexibility, ability to capture data at different times of day, and relatively low cost compared to traditional platforms. This study presents a comprehensive review of the current state of the art in monitoring water quality in small inland water bodies using satellite and UAV remote sensing data. It encompasses an overview of atmospheric correction algorithms and the assessment of different water quality parameters. Furthermore, the review addresses the challenges associated with monitoring water quality in these bodies of water and emphasizes the potential of UAVs to overcome these challenges by providing accurate and reliable data.

Informing ocean color inversion products by seeding with ancillary observations

Ocean reflectance inversion algorithms provide many products used in ecological and biogeochemical models. While a number of different inversion approaches exist, they all use only spectral remote-sensing reflectances (Rrs(λ)) as input to derive inherent optical properties (IOPs) in optically deep oceanic waters. However, information content in Rrs(λ) is limited, so spectral inversion algorithms may benefit from additional inputs. Here, we test the simplest possible case of ingesting optical data ('seeding') within an inversion scheme (the Generalized Inherent Optical Property algorithm framework default configuration (GIOP-DC)) with both simulated and satellite datasets of an independently known or estimated IOP, the particulate backscattering coefficient at 532 nm (bbp(532)). We find that the seeded-inversion absorption products are substantially different and more accurate than those generated by the standard implementation. On global scales, seasonal patterns in seeded-inversion absorption products vary by more than 50% compared to absorption from the GIOP-DC. This study proposes one framework in which to consider the next generation of ocean color inversion schemes by highlighting the possibility of adding information collected with an independent sensor.

Deriving particulate backscattering coefficient at 400 nm from small-scale optically shallow waters using Landsat-8 data: a case study at Luhuitou Peninsula, Sanya

The particulate backscattering coefficient (bbp) plays an important role in the growth of coral reefs by influencing the light field conditions. Small-scale optically shallow waters are commonly found in coastal fringing reefs, making it challenging to monitor the spatial and temporal patterns accurately using Aqua satellites with a low spatial resolution. In this study, six existing optimization-based algorithms for deriving bbp at 400 nm (bbp(400)) were evaluated with three simulated Landsat-8 (spatial resolution = 30 m) data sets and in situ data from the Luhuitou Peninsula, Sanya. The comparison results indicated that the HOPE (hyperspectral optimization process exemplar) (Fix-H-error or Fix-H-error-free) algorithm which sets an input value of the water depth alone outperformed other algorithms. However, the estimated bbp(400) from all the algorithms tended to be either overestimated and underestimated due to the improper the spectral shape value of the backscattering coefficient. The HOPE (Fix-H-error) algorithm estimated-bbp(400) from in situ reflectance also had a good correlation with the in situ total suspended particle concentrations data derived-bbp(400), with a correlation coefficient of 0.83. Therefore, the HOPE (Fix-H-error) algorithm was selected to estimate the bbp(400) from satellite-based Landsat-8 data of the Luhuitou Peninsula, Sanya. Time-series (2014-2021) results from these Landsat-8 images reveal the seasonal variation of bbp(400). The bbp(400) was low from May to September every year. From October to December or January, bbp(400) had an increasing trend, and then it decreased until May. Spatial analysis indicated that bbp(400) decreased with increasing water depth. The spatial and temporal patterns of bbp(400) were consistent with in situ observations reported in the literature. This study preliminarily showed the efficiency of an optimization-based algorithm in deriving bbp(400) in small-scale optically shallow water region using Landsat-8 data.

Bayesian approach to a generalized inherent optical property model

Relationships between the absorption and backscattering coefficients of marine optical constituents and ocean color, or remote sensing reflectances R_rs(λ), can be used to predict the concentrations of these constituents in the upper water column. Standard inverse modeling techniques that minimize error between the modeled and observed R_rs(λ) break down when the number of products retrieved becomes similar to, or greater than, the number of different ocean color wavelengths measured. Furthermore, most conventional ocean reflectance inversion approaches, such as the default configuration of NASA's Generalized Inherent Optical Properties algorithm framework (GIOP-DC), require a priori definitions of absorption and backscattering spectral shapes. A Bayesian approach to GIOP is implemented here to address these limitations, where the retrieval algorithm minimizes both the error in retrieved ocean color and the deviation from prior knowledge, calculated using output from a mixture of empirically-derived and best-fit values. The Bayesian approach offers potential to produce an expanded range of parameters related to the spectral shape of absorption and backscattering spectra.

Estimation of chromophoric dissolved organic matter and non-algal particulate absorption coefficients of seawater in the ultraviolet by extrapolation from the visible spectral region

Extending the capabilities of optical remote sensing and inverse optical algorithms, which have been commonly focused on the visible (VIS) range of the electromagnetic spectrum, to derive the optical properties of seawater in the ultraviolet (UV) range is important to advancing the understanding of various optical, biological, and photochemical processes in the ocean. In particular, existing remote-sensing reflectance models that derive the total spectral absorption coefficient of seawater, a(λ), and absorption partitioning models that partition a(λ) into the component absorption coefficients of phytoplankton, a_ph(λ), non-algal (depigmented) particles, a_d(λ), and chromophoric dissolved organic matter (CDOM), a_g(λ), are restricted to the VIS range. We assembled a quality-controlled development dataset of hyperspectral measurements of a_g(λ) (N = 1294) and a_d(λ) (N = 409) spanning a wide range of values across various ocean basins, and evaluated several extrapolation methods to extend a_g(λ), a_d(λ), and a_dg(λ) ≡ a_g(λ) + a_d(λ) into the near-UV spectral region by examining different sections of the VIS as a basis for extrapolation, different extrapolation functions, and different spectral sampling intervals of input data in the VIS. Our analysis determined the optimal method to estimate a_g(λ) and a_dg(λ) at near-UV wavelengths (350 to 400 nm) which relies on an exponential extrapolation of data from the 400-450 nm range. The initial a_d(λ) is obtained as a difference between the extrapolated estimates of a_dg(λ) and a_g(λ). Additional correction functions based on the analysis of differences between the extrapolated and measured values in the near-UV were defined to obtain improved final estimates of a_g(λ) and a_d(λ) and then the final estimates of a_dg(λ) as a sum of final a_g(λ) and a_d(λ). The extrapolation model provides very good agreement between the extrapolated and measured data in the near-UV when the input data in the blue spectral region are available at 1 or 5 nm spectral sampling intervals. There is negligible bias between the modeled and measured values of all three absorption coefficients and the median absolute percent difference (MdAPD) is small, e.g., < 5.2% for a_g(λ) and < 10.5% for a_d(λ) at all near-UV wavelengths when evaluated with the development dataset. Assessment of the model on an independent dataset of concurrent a_g(λ) and a_d(λ) measurements (N = 149) yielded similar findings with only slight reduction of performance and MdAPD remaining below 6.7% for a_g(λ) and 11% for a_d(λ). These results are promising for integration of the extrapolation method with absorption partitioning models operating in the VIS.

Optimizing retrieval spaces of bio-optical models for remote sensing of ocean color

We investigated the optimal number of independent parameters required to accurately represent spectral remote sensing reflectances (R _rs) by performing principal component analysis on quality controlled in situ and synthetic R _rs data. We found that retrieval algorithms should be able to retrieve no more than four free parameters from R _rs spectra for most ocean waters. In addition, we evaluated the performance of five different bio-optical models with different numbers of free parameters for the direct inversion of in-water inherent optical properties (IOPs) from in situ and synthetic R _rs data. The multi-parameter models showed similar performances regardless of the number of parameters. Considering the computational cost associated with larger parameter spaces, we recommend bio-optical models with three free parameters for the use of IOP or joint retrieval algorithms.

Bio-optical trends of waters around Valdés Biosphere Reserve: An assessment of the temporal variability based on 20 years of ocean color satellite data

Satellite Image Time Series are becoming increasingly available and will continue to do so in the coming years thanks to the launch of space missions which aim to provide a high spatial resolution coverage of the Earth every few days. Bio-optical characteristics and their variation over time have been poorly studied in the Patagonian shelf. In this paper, we present the trends of time series analysis from satellite images that allows us to interpret the variations of bio-optical properties throughout time and their implications for planktonic organisms. The annual and seasonal trends of six variables were analyzed for two different gulfs, Nuevo and San José, in northern Patagonia from January 2003-December 2021. We present the dynamic temporal changes of chlorophyll a (Chla-sat), phytoplankton absorption (Ab_phy), detritus absorption as well as environmental features changes for the sea surface temperature (SST), depth of the euphotic zone (Z_eu) and photosynthetically active radiation (PAR). We found positive trends for SST, Ab_phy at 443 nm and PAR, but negative for Z_eu in Nuevo and San José gulfs. The positive trendlines for SST and negative for Z_eu suggest less availability of nutrients and light. These trends could change the bloom phenology and modify the phytoplankton community structure with implications for the entire food web and the ecosystem services in the VBR.

Transfer model to determine the above-water remote-sensing reflectance from the underwater remote-sensing ratio

Remote-sensing reflectance, R_rs(λ, θ, Δϕ, θ_s), contains the spectral color information of the water body below the sea surface and is a fundamental parameter to derive satellite ocean color products such as chlorophyll-a, diffuse light attenuation, or inherent optical properties. Water reflectance, i.e., spectral upwelling radiance, normalized by the downwelling irradiance, can be measured under- or above-water. Several models to extrapolate this ratio from underwater "remote-sensing ratio", r_rs(λ), to the above-water R_rs, have been proposed in previous studies, in which the spectral dependency of water refractive index and off-nadir viewing directions have not been considered in detail. Based on measured inherent optical properties of natural waters and radiative transfer simulations, this study proposes a new transfer model to spectrally determine R_rs from r_rs for different sun-viewing geometries and environmental conditions. It is shown that, compared to previous models, ignoring spectral dependency leads to a bias of ∼2.4% at shorter wavelengths (∼400 nm), which is avoidable. If nadir-viewing models are used, the typical 40°-off nadir viewing geometry will introduce a difference of ∼5% in R_rs estimation. When the solar zenith angle is higher than 60°, these differences of R_rs have implications for the downstream retrievals of ocean color products, e.g., > 8% difference for phytoplankton absorption at 440 nm and >4% difference for backward particle scattering at 440 nm by the quasi-analytical algorithm (QAA). These findings demonstrate that the proposed r_rs-to-R_rs model is applicable to a wide range of measurement conditions and provides more accurate estimates of R_rs than previous models.

Quantifying the Ocean's Biological Pump and Its Carbon Cycle Impacts on Global Scales

The biological pump transports organic matter, created by phytoplankton productivity in the well-lit surface ocean, to the ocean's dark interior, where it is consumed by animals and heterotrophic microbes and remineralized back to inorganic forms. This downward transport of organic matter sequesters carbon dioxide from exchange with the atmosphere on timescales of months to millennia, depending on where in the water column the respiration occurs. There are three primary export pathways that link the upper ocean to the interior: the gravitational, migrant, and mixing pumps. These pathways are regulated by vastly different mechanisms, making it challenging to quantify the impacts of the biological pump on the global carbon cycle. In this review, we assess progress toward creating a global accounting of carbon export and sequestration via the biological pump and suggest a path toward achieving this goal.

Modeling ocean surface chlorophyll-a concentration from ocean color remote sensing reflectance in global waters using machine learning

The spatial and temporal variations of Chlorophyll-a (Chl-a) in clear and coastal waters are critical for assessing the health of the marine environment. Machine learning models have been proven to model complex relationships and provide better accuracy estimates of the derived parameters compared to traditional empirical models. The present study proposes a novel approach to derive Chl-a by using multi-layer perceptron Neural Network (MLPNN) with Resilient backpropagation method based on the four ocean color bands existent in most of the ocean color sensors. The NNs are trained on NASA's bio-optical Marine Algorithm Dataset (NOMAD) and tested on three different datasets (i) SeaWiFS and, (ii) MODIS Aqua matchup dataset, and (iii) simulated dataset for the Red Sea. These three datasets cover significant variations range in Chl-a levels under both oligotrophic and eutrophic conditions. The influence of different variations in inputs used in NN training is assessed and hyperparameter tuning of the NN is performed to obtain best NN configuration to derive Chl-a. Accuracy assessment of the present study with other global algorithms are performed by comparing the modeled and observed values of the Chl-a. The performance matrices computed from the developed model were promising. Therefore, this study provides a potential approach for the retrieval of improved Chl-a estimates in the global clear and coastal waters as compared to the traditional blue-green band ratio algorithms. Furthermore, the developed algorithm and existing algorithms are applied to SeaWiFS, MODIS, VIIRS, and Hawkeye satellite ocean color data to demonstrate how it may be utilized to accurately depict the spatial distribution of ocean color features in global waters, phytoplankton blooms and some of the physical processes in the Arabian Sea and the Red Sea. The findings of this work have potential to advance the ocean color remote sensing and biogeochemical cycles and processes in coastal and open ocean waters.

MODIS-Landsat fusion-based single-band algorithms for TSS and turbidity estimation in an urban-waste-dominated river reach

Riverine ecosystem management along an urban stretch mostly depends on high-frequent (daily-scale) monitoring of water quality at finer spatial resolutions. However, with the decrease in the number of in-situ monitoring stations owing to their expensive maintenance cost, there is a need to develop the next-generation remote sensing (RS) tools as an alternate approach with better synoptic coverage of river water quality assessment. This study advocates three novel model variants to estimate the total suspended solids (TSS) concentration at daily-scale using the public-domain MODIS and Landsat satellite datasets. The MOD_T model variant uses the 1-day×250 m MODIS public domain datasets, and the FUS_T model is based on the 1-day×30 m MODIS-Landsat fusion datasets, whereas the CFUS_T model integrates the Frank Copula with the FUS_T model. These hierarchical model variants are assessed in the urban-waste-dominated lower Ganges, namely the Hooghly River and the Brahmani River, in eastern India using the measured in-situ TSS datasets at multiple monitoring stations from 2016 to 2019. The results reveal that the CFUS_T is the best TSS estimation model variant that performs with the average coefficient of determination of 0.88-0.93, mean absolute error of 0.17-0.19, and normal root mean square error of 0.05-0.09. Conclusively, the proposed CFUS_T and CFUS_TU stochastic models can be used as potential tools for TSS and turbidity assessment along the dynamic river systems, respectively.

Phytoplankton package effect in oceanic waters: Influence of chlorophyll-a and cell size

In this study, the interaction between the packaging effect (Q_a^⁎) and total chlorophyll-a concentration (C_t) or total size index (SI_t) was investigated to explore the potential bio-optical mechanism in phytoplankton cells in the global oceans. In addition, the long-term spatiotemporal characteristics of these interactions were necessary for grasping their variation. Numerous in situ surface measurements (phytoplankton pigment and absorption coefficients) from the global oceans were analyzed first, and then correlation and causality analyses were performed on the satellite-deduced Q_a^⁎, C_t, and SI_t in the global oceans during 2002-2020. The results show a negative correlation between Q_a^⁎ and C_t or SI_t in the low latitudes (30°S-30°N) and a positive correlation in the middle latitudes (30°S-55°S and 30°N-55°N). The causality analysis reveals a mutual and asymmetric cause-effect relationship between Q_a^⁎ and C_t or SI_t in the low latitudes. The stabilization effect of Q_a^⁎ contributes to a 10%-50% variation in C_t and SI_t, with 40%-60% uncertainty of Q_a^⁎ caused by C_t and SI_t in the low latitudes, which is inverse in the middle latitudes. The remaining contribution to each variable mainly originates from long-term trends and noise. Combining the analysis between Q_a^⁎ and the irradiance, the balancing processes in phytoplankton cells are different in the low (phytoplankton-driving mode) and middle latitudes (irradiance-driving mode), which is related to photoacclimation and photoinhibition. The analyses provide insights into the quantitative interpretation of the relationship between Q_a^⁎ and C_t or SI_t, which contribute knowledge that has not been previously reported.

Optimal estimation framework for ocean color atmospheric correction and pixel-level uncertainty quantification

Ocean color (OC) remote sensing requires compensation for atmospheric scattering and absorption (aerosol, Rayleigh, and trace gases), referred to as atmospheric correction (AC). AC allows inference of parameters such as spectrally resolved remote sensing reflectance (R_rs(λ);sr^-1) at the ocean surface from the top-of-atmosphere reflectance. Often the uncertainty of this process is not fully explored. Bayesian inference techniques provide a simultaneous AC and uncertainty assessment via a full posterior distribution of the relevant variables, given the prior distribution of those variables and the radiative transfer (RT) likelihood function. Given uncertainties in the algorithm inputs, the Bayesian framework enables better constraints on the AC process by using the complete spectral information compared to traditional approaches that use only a subset of bands for AC. This paper investigates a Bayesian inference research method (optimal estimation [OE]) for OC AC by simultaneously retrieving atmospheric and ocean properties using all visible and near-infrared spectral bands. The OE algorithm analytically approximates the posterior distribution of parameters based on normality assumptions and provides a potentially viable operational algorithm with a reduced computational expense. We developed a neural network RT forward model look-up table-based emulator to increase algorithm efficiency further and thus speed up the likelihood computations. We then applied the OE algorithm to synthetic data and observations from the moderate resolution imaging spectroradiometer (MODIS) on NASA's Aqua spacecraft. We compared the R_rs(λ) retrieval and its uncertainty estimates from the OE method with in-situ validation data from the SeaWiFS bio-optical archive and storage system (SeaBASS) and aerosol robotic network for ocean color (AERONET-OC) datasets. The OE algorithm improved R_rs(λ) estimates relative to the NASA standard operational algorithm by improving all statistical metrics at 443, 555, and 667 nm. Unphysical negative R_rs(λ), which often appears in complex water conditions, was reduced by a factor of 3. The OE-derived pixel-level R_rs(λ) uncertainty estimates were also assessed relative to in-situ data and were shown to have skill.

Direct measurement system of water-leaving albedo in the field by the skylight-blocked approach: Monte Carlo simulations

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 α_wSBA, 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 α_wSBA system (i.e., half cone angle, and the length of supporting arm). Based on sensitive analysis using MC simulations, an optimal configuration of α_wSBA is recommended. We further propose a mathematical expression to parameterize the shading error (ɛ), along with an error correction scheme (α_wOPT). It is found that, with the optimal configuration and α_wOPT, the uncertainties of obtained αw(λ) by α_wSBA are generally less than 7% for a wide range of waters with different IOPs and particulate scattering phase functions.

Rising dissolved organic carbon concentrations in coastal waters of northwestern Borneo related to tropical peatland conversion

Southeast Asia's peatlands are considered a globally important source of terrigenous dissolved organic carbon (DOC) to the ocean. Human disturbance has probably increased peatland DOC fluxes, but the lack of monitoring has precluded a robust demonstration of such a regional-scale impact. Here, we use a time series of satellite ocean color data from northwestern Borneo to show that DOC concentrations in coastal waters have increased between 2002 and 2021 by 0.31 μmol liter^-1 year^-1 (95% confidence interval, 0.18 to 0.44 μmol liter^-1 year^-1). We show that this was caused by a ≥30% increase in the concentration of terrigenous DOC and coincided with the conversion of 69% of regional peatland area to nonforest land cover, suggesting that peatland conversion has substantially increased DOC fluxes to the sea. This rise in DOC concentration has also increased the underwater light absorption by dissolved organic matter, which may affect marine productivity by altering underwater light availability.

An Operational Model for Remote Estimating Absorption of Optical Activity Constituents

The need for an accurate model that can derive the absorption coefficient of optical activity constituents from both marine water and coastal water remains necessary. This study aimed to develop an algorithm for determining the absorption coefficients for both phytoplankton and non-phytoplankton pigments [aph(λ) and adg(λ), respectively]. This algorithm included two portions: (1) the total absorption coefficients at the blue and green bands were computed using a neural network technology-based, quasi-analytical algorithm; and (2) the relationship between the adg(λ) coefficient and the coefficient of total absorption was analyzed. This algorithm was evaluated with both in-situ observations and remote-sensed satellite data. The results showed that the algorithm could produce acceptable results in the retrievals of adg(λ) and aph(λ) in both turbid and clear waters. The results also indicated that the proposed algorithm was effective for distinguishing between adg(λ) and aph(λ) from the total coefficients of absorption, even though more independent assessments using in-situ and remote-sensed data are required to further improve the approach.

Phytoplankton dynamics based on satellite inherent optical properties and oceanographic conditions in a patagonian gulf frontal system in relation to the adjacent continental shelf waters

The dynamics of phytoplankton across a seasonal frontal system formed in San José Gulf (SJG, Patagonia Argentina) and in neighbouring shelf waters was assessed based on bio-optical satellite data (2003-2018) and spring and summer in situ samplings. Bio-optical properties of the water masses on the eastern (ED) and western (WD) domains of the seasonal frontal system of SJG showed clear differences: the year-round-vertically-mixed waters from the WD, strongly connected with the adjacent shelf waters, evidenced a brief and strong single phytoplankton bloom, while those from the ED, showing lower exchange with shelf waters and a strong vertical stratification during the warm season, displayed an earlier and long-lasting spring phytoplankton bloom, followed by a late-summer and autumn bloom, both associated with the development and erosion of the seasonal thermocline. Waters from the entire system are optically influenced by the absorption of coloured dissolved organic matter and detritus (cdom + detritus), suggesting a strong sediment load contribution from the continent and the seabed. To remark, a strong correlation between satellite chlorophyll-a (Chla-sat) and absorption by phytoplankton (aphy₄₄₃) in the outer shelf waters differs from the weak correlation of those variables in the gulf's water masses, whose optical parameters are more complex. In situ Chla records may indicate wind-driven upwelling and downwelling areas in the northern and southern coasts of the ED. Dissolved nitrogen was identified as the limiting macronutrient for phytoplankton growth in the ED during summer. This work contributes relevant ecological information that may support management actions on the SJG shellfish artisanal fishery.

A Four-Step Method for Estimating Suspended Particle Size Based on In Situ Comprehensive Observations in the Pearl River Estuary in China

The suspended particle size has great impacts on marine biology environments and biogeochemical processes, such as the settling rates of particles and sunlight transmission in marine water. However, the spatial–temporal variations in particle sizes in coastal waters are rarely reported due to the paucity of appropriate observations and the limitations of particle size retrieval methods, especially in areas with complex optical properties. This study proposed a remote sensing-based method for estimating the median particle size Dv50 (calculated with a size range of 2.05–297 μm) that correlates Dv50 with the inherent optical properties (IOPs) retrieved from in situ remote sensing reflectance above the water’s surface (Rrs(λ)) in the Pearl River estuary (PRE) in China. Rrs(λ) was resampled to simulate the Multispectral Instrument (MSI) onboard Sentinel-2A/B, and the wavebands in 490, 560, and 705 nm were utilized for the retrieval of the IOPs. The results of this method had a statistical performance of 0.86, 18.52, 21.28%, and −1.85 for the R2, RMSE, MAPE, and bias values, respectively, in validation, which indicated that Dv50 could be estimated by Rrs(λ) with the proposed four-step method. Then, the proposed method was applied to Sentinel-2 MSI imagery, and a clear difference in Dv50 distribution which was retrieved from a different time could be seen. The proposed method holds great potential for monitoring the suspended particle size of coastal waters.

Seasonal bias in global ocean color observations

In this study, we identify a seasonal bias in the ocean color satellite-derived remote sensing reflectances (Rrs(λ);sr-1) at the ocean color validation site, Marine Optical BuoY. The seasonal bias in Rrs(λ) is present to varying degrees in all ocean color satellites examined, including the Visible Infrared Imaging Radiometer Suite, Sea-Viewing Wide Field-of-View Sensor, and Moderate Resolution Imaging Spectrometer. The relative bias in Rrs has spectral dependence. Products derived from Rrs(λ) are affected by the bias to varying degrees, with particulate backscattering varying up to 50% over a year, chlorophyll varying up to 25% over a year, and absorption from phytoplankton or dissolved material varying by up to 15%. The propagation of Rrs(λ) bias into derived products is broadly confirmed on regional and global scales using Argo floats and data from the cloud-aerosol lidar with orthogonal polarization instrument aboard the cloud-aerosol lidar and infrared pathfinder satellite. The artifactual seasonality in ocean color is prominent in areas of low biomass (i.e., subtropical gyres) and is not easily discerned in areas of high biomass. While we have eliminated several candidates that could cause the biases in Rrs(λ), there are still outstanding questions regarding potential contributions from atmospheric corrections. Specifically, we provide evidence that the aquatic bidirectional reflectance distribution function may in part cause the observed seasonal bias, but this does not preclude an additional effect of the aerosol estimation. Our investigation highlights the contributions that atmospheric correction schemes can make in introducing biases in Rrs(λ), and we recommend more simulations to discern these influence Rrs(λ) biases. Community efforts are needed to find the root cause of the seasonal bias because all past, present, and future data are, or will be, affected until a solution is implemented.

Particulate Backscattering in the Global Ocean: A Comparison of Independent Assessments

How well do we know the particulate backscattering coefficient (b_bp) in the global ocean? Satellite lidar b_bp has never been validated globally and few studies have compared lidar b_bp to b_bp derived from reflectances (via ocean color) or in situ observations. Here, we validate lidar b_bp with autonomous biogeochemical Argo floats using a decorrelation analysis to identify relevant spatiotemporal matchup scales inspired by geographical variability in the Rossby radius of deformation. We compare lidar, float, and ocean color b_bp at the same locations and times to assess performance. Lidar b_bp outperforms ocean color, with a median percent error of 18% compared to 24% in the best case and a relative bias of -11% compared to -21%, respectively. Phytoplankton carbon calculated from ocean color and lidar exhibits basin-scale differences that can reach ±50%.

A Semi-Analytical Optical Remote Sensing Model to Estimate Suspended Sediment and Dissolved Organic Carbon in Tropical Coastal Waters Influenced by Peatland-Draining River Discharges off Sarawak, Borneo

Coastal water quality degradation is a global challenge. Marine pollution due to suspended sediments and dissolved matter impacts water colour, biogeochemistry, benthic habitats and eventually human populations that depend on marine resources. In Sarawak (Malaysian Borneo), peatland-draining river discharges containing suspended sediments and dissolved organic carbon influence coastal water quality at multiple locations along the coast. Optical remote sensing is an effective tool to monitor coastal waters over large areas and across remote geographic locations. However, the lack of regional optical measurements and inversion models limits the use of remote sensing observations for water quality monitoring in Sarawak. To overcome this limitation, we have (1) compiled a regional spectral optical library for Sarawak coastal waters, (2) developed a new semi-analytical remote sensing model to estimate suspended sediment and dissolved organic carbon in coastal waters, and (3) demonstrated the application of our remote sensing inversion model on satellite data over Sarawak. Bio-optical data analysis revealed that there is a clear spatial variability in the inherent optical properties of particulate and dissolved matter in Sarawak. Our optical inversion model coupled with the Sarawak spectral optical library performed well in retrieving suspended sediment (bias = 3% and MAE = 5%) and dissolved organic carbon (bias = 3% and MAE = 8%) concentrations. Demonstration products using MODIS Aqua data clearly showed the influence of large rivers such as the Rajang and Lupar in discharging suspended sediments and dissolved organic carbon into coastal waters. The bio-optical parameterisation, optical model, and remote sensing inversion approach detailed here can now help improve monitoring and management of coastal water quality in Sarawak.

Evaluation of the CDOM Absorption Coefficient in the Arctic Seas Based on Sentinel-3 OLCI Data

Our work’s primary goal is to reveal the problematic issues related to estimates of the colored organic matter absorption coefficient in the northern seas from data of the Ocean and Land Color Instrument (OLCI) installed on the Sentinel-3 satellites, e.g., a comparison of the OLCI standard error assessment ADG443_NN_err relating to the measurement and the retrieval of the geophysical products and the uncertainties in the northern seas’ real situation. The natural conditions are incredibly unfavorable there, mainly due to frequent cloudiness and low sun heights. We conducted a comprehensive multi-sensor study of the uncertainties using various approaches. We directly compared the data from satellites (OLCI Sentinel-3 and four other ocean color sensors) and field measurements in five sea expeditions (2016–2019) using the different processing algorithms. Our analysis has shown that the final product’s real uncertainties are significantly (≥100%) higher than the calculated errors of the ADG443_NN_err (~10%). The main reason is the unsatisfactory atmospheric correction. We present the analysis of the various influential factors (satellite sensors, processing algorithms, and other parameters) and formulate future work goals.

In situ evaluation of spaceborne CALIOP lidar measurements of the upper-ocean particle backscattering coefficient

The spaceborne CALIOP lidar, initially designed for atmospheric measurements, was recently used to retrieve the particulate backscattering coefficient (b_bp) in ocean subsurface layers. However, extensive field evaluation of CALIOP estimates was never conducted due to the scarcity of in situ data. Here, year-round and basin-wide data from Biogeochemical Argo floats (BGC Argo) were used to evaluate CALIOP estimates in the North Atlantic. The high density of BGC Argo float profiles in this region allowed us to test different matchup strategies at different spatio-temporal scales. When averaged over 2° by 2° grid boxes and monthly time resolution, CALIOP data present reasonably good correlation with highly variable float b_bp values (correlation r = 0.44, root mean square relative error RMS% = 13.2%), suggesting that seasonal dynamics can be characterized at basin scale.

Bayesian retrieval of optically relevant properties from hyperspectral water-leaving reflectances

Current methods to retrieve optically relevant properties from ocean color observations do not explicitly make use of prior knowledge about property distributions. Here we implement a simplified Bayesian approach that takes into account prior probability distributions on two sets of five optically relevant parameters, and conduct a retrieval of these parameters using hyperspectral simulated water-leaving reflectances. We focus specifically on the ability of the model to distinguish between two optically similar phytoplankton taxa, diatoms and Noctiluca scintillans. The inversion retrieval gives most-likely concentrations and uncertainty estimates, and we find that the model is able to probabilistically predict the occurrence of Noctiluca scintillans blooms using these metrics. We discuss how this method can be expanded to include a priori covariances between different parameters, and show the effect of varying measurement uncertainty and spectral resolution on Noctiluca scintillans bloom predictions.

Optical proxy for particulate organic nitrogen from BGC-Argo floats

Using biogeochemical-Argo float measurements, we propose, for the first time, an optical proxy for particulate organic nitrogen concentration (PON) in the Western Tropical South Pacific, an area influenced by dinitrogen (N₂) fixation. Our results show a significant relationship between the backscattering coefficient at 700 nm (b_bp) and PON, especially when the latter is measured using the wet oxidation method (R²=0.87). b_bp may be used to estimate PON concentrations (PON^opt) between 0.02 and 0.95 µM, allowing for unprecedented monitoring using autonomous profiling floats. The b_bpvs PON relationship can be used to study phytoplanktonic biomass dynamics at relevant seasonal temporal scales, with clear evidence of PON^opt as a proxy of phytoplanktonic biomass, at least for this specific area. Temporal analyses of PON^opt show significant increases (from 0.16 to 0.80 µM) likely related to new production associated to N₂ fixation events measured during stratification periods in the Melanesian Archipelago.

Seasonal Variability of Diffuse Attenuation Coefficient in the Pearl River Estuary from Long-Term Remote Sensing Imagery

We evaluated six empirical and semianalytical models of the diffuse attenuation coefficient at 490 nm (Kd(490)) using an in situ dataset collected in the Pearl River estuary (PRE). A combined model with the most accurate performance (correlation coefficient, R2 = 0.92) was selected and applied for long-term estimation from 2003 to 2017. Physical and biological processes in the PRE over the 14-year period were investigated by applying satellite observations (MODIS/Aqua data) and season-reliant empirical orthogonal function analysis (S-EOF). In winter, the average Kd(490) was significantly higher than in the other three seasons. A slight increasing trend was observed in spring and summer, whereas a decreasing trend was observed in winter. In summer, a tongue with a relatively high Kd(490) was found in southeastern Lingdingyang Bay. In Eastern Guangdong province (GDP), the relatively higher Kd(490) value was found in autumn and winter. Based on the second mode of S-EOF, we found that the higher values in the eastern GDP extended westward and formed a distinguishable tongue in winter. The grey relational analysis revealed that chlorophyll-a concentration (Cchla) and total suspended sediment concentration (Ctsm) were two dominant contributors determining the magnitude of Kd(490) values. The Ctsm-dominated waters were generally located in coastal and estuarine turbid waters; the Cchla-dominated waters were observed in open clear ocean. The distribution of constituents-dominated area was different in the four seasons, which was affected by physical forces, including wind field, river runoff, and sea surface temperature.

Satellite-measured water properties in high altitude Lake Tahoe

It has been difficult in satellite remote sensing to derive accurate water optical, biological, and biogeochemical products over high-altitude inland waters due to issues in satellite data processing (i.e., atmospheric correction). In this study, we demonstrate that accurate normalized water-leaving radiance spectra nL_w(λ) can be derived for a high-altitude lake, Lake Tahoe, using improved Rayleigh radiance computations (Wang, M., Opt. Express, 24, 12414-12429, 2016) which accurately account for water surface altitude effects in the Multi-Sensor Level-1 to Level-2 (MSL12) ocean color data processing system. Satellite observations from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) between 2012 and 2018 are used to evaluate and validate satellite-derived nL_w(λ) spectra, and to quantitatively characterize water properties in the lake. Results show that VIIRS-derived nL_w(λ) spectra are quite comparable with those from the in situ measurements. Subsequent retrievals of water biological and biogeochemical products show that chlorophyll-a (Chl-a) concentration and Secchi depth (SD) are reasonably well-estimated, and captured normal seasonal variations in the lake, e.g., the annual highest Chl-a and SD normally occur in the winter while the lowest occur in the summer, which is consistent with in situ measurements. Interannual variability of these water quality parameters is also observed. In particular, Lake Tahoe experienced a significant environmental anomaly associated with an extreme weather condition event in 2017, showing considerably decreased nL_w(λ) at the spectral bands of 410, 443, and 486 nm, and significantly reduced SD values in the entire lake. The low SD measurements from VIIRS are consistent with in situ observations. Following the event in the 2017-2018 winter, Lake Tahoe recovered and returned to its typical conditions in spring 2018.

Variations of suspended particulate concentration and composition in Chinese lakes observed from Sentinel-3A OLCI images

The concentration and composition of suspended particulate matter provide important information for evaluating water quality and understanding the variability in the underwater light field in lakes. In this study, inherent optical property (IOP)-centered algorithms were developed to estimate the concentrations of chlorophyll-a (Chla, [mg/m³]) and suspended particulate matter (SPM, [g/m³]) and the Chla/SPM ratio (an indicator of the suspended particulate composition) of 118 lakes in the middle and lower reaches of the Yangtze and Huai Rivers (MLYHR) of China using Sentinel-3A/OLCI (Ocean and Land Colour Instrument) data collected from August 2016 to July 2018. The mean Chla concentration and Chla/SPM ratio were high in summer and low in winter, while the mean SPM peaked in winter and decreased in summer. The 94 lakes in the Yangtze River basin had a higher mean Chla concentration (30.94 ± 14.84) and Chla/SPM ratio (0.97 × 10^-3 ± 0.60 × 10^-3), but a lower mean SPM (44.87 ± 12.61) than the 24 lakes in the Huai River basin (Chla: 27.35 ± 12.18, Chla/SPM: 0.79 × 10^-3 ± 0.48 × 10^-3, SPM: 47.31 ± 13.40). Regarding the mean values of each lake, Chla and Chla/SPM ratio correlated well with temperature, whereas the wind speed and precipitation had little effect on the variations of suspended particulate matter. Moreover, shipping transportation and sand dredging activities affected the spatial distribution of Chla, SPM, and Chla/SPM in several large lakes (e.g., Lake Poyang and Lake Dongting). Chla/SPM related well with other proxies that express the suspended particulate composition, and had a significant correlation with the Chla-specific absorption coefficient of phytoplankton at 443 nm (a_ph^⁎(443)). The remotely sensed concentration and composition of suspended particulate matter can provide a comprehensive reference for water quality monitoring and expand our knowledge of the trophic status of the lakes.

A Virtual Geostationary Ocean Color Sensor to Analyze the Coastal Optical Variability

In the coastal environment the optical properties can vary on temporal scales that are shorter than the near-polar orbiting satellite temporal resolution (~1 image per day), which does not allow capturing most of the coastal optical variability. The objective of this work is to fill the gap between the near-polar orbiting and geostationary sensor temporal resolutions, as the latter sensors provide multiple images of the same basin during the same day. To do that, a Level 3 hyper-temporal analysis-ready Ocean Color (OC) dataset, named Virtual Geostationary Ocean Color Sensor (VGOCS), has been created. This dataset contains the observations acquired over the North Adriatic Sea by the currently functioning near-polar orbiting sensors, allowing approaching the geostationary sensor temporal resolution. The problem in using data from different sensors is that they are characterized by different uncertainty sources that can introduce artifacts between different satellite images. Hence, the sensors have different spatial and spectral resolutions, their calibration procedures can have different accuracies, and their Level 2 data can be retrieved using different processing chains. Such differences were reduced here by adjusting the satellite data with a multi-linear regression algorithm that exploits the Fiducial Reference Measurements data stream of the AERONET-OC water-leaving radiance acquired at the Acqua Alta Oceanographic Tower, located in the Gulf of Venice. This work aims to prove the suitability of VGOCS in analyzing the coastal optical variability, presenting the improvement brought by the adjustment on the quality of the satellite data, the VGOCS spatial and temporal coverage, and the inter-sensor differences. Hence, the adjustment will strongly increase the agreement between the satellite and in situ data and between data from different near-polar orbiting OC imagers; moreover, the adjustment will make available data traditionally masked in the standard processing chains, increasing the VGOCS spatial and temporal coverage, fundamental to analyze the coastal optical variability. Finally, the fulfillment by VGOCS of the three conditions for a hyper-temporal dataset will be demonstrated in this work.

Neural Network Reflectance Prediction Model for Both Open Ocean and Coastal Waters

Remote sensing of global ocean color is a valuable tool for understanding the ecology and biogeochemistry of the worlds oceans, and provides critical input to our knowledge of the global carbon cycle and the impacts of climate change. Ocean polarized reflectance contains information about the constituents of the upper ocean euphotic zone, such as colored dissolved organic matter (CDOM), sediments, phytoplankton, and pollutants. In order to retrieve the information on these constituents, remote sensing algorithms typically rely on radiative transfer models to interpret water color or remote-sensing reflectance; however, this can be resource-prohibitive for operational use due to the extensive CPU time involved in radiative transfer solutions. In this work, we report a fast model based on machine learning techniques, called Neural Network Reflectance Prediction Model (NNRPM), which can be used to predict ocean bidirectional polarized reflectance given inherent optical properties of ocean waters. This supervised model is trained using a large volume of data derived from radiative transfer simulations for coupled atmosphere and ocean systems using the successive order of scattering technique (SOS-CAOS). The performance of the model is validated against another large independent test dataset generated from SOS-CAOS. The model is able to predict both polarized and unpolarized reflectances with an absolute error (AE) less than 0.004 for 99% of test cases. We have also shown that the degree of linear polarization (DoLP) for unpolarized incident light can be predicted with an AE less than 0.002 for 99% of test cases. In general, the simulation time of SOS-CAOS depends on optical depth, and required accuracy. When comparing the average speeds of the NNRPM against the SOS-CAOS model for the same parameters, we see that the NNRPM is able to predict the Ocean BRDF 6000 times faster than SOS-CAOS. Both ultraviolet and visible wavelengths are included in the model to help differentiate between dissolved organic material and chlorophyll in the study of the open ocean and the coastal zone. The incorporation of this model into the retrieval algorithm will make the retrieval process more efficient, and thus applicable for operational use with global satellite observations.

Evaluating semi-analytical algorithms for estimating inherent optical properties in the South China Sea

Using large amounts of bio-optical data collected in the South China Sea (SCS) from 2003 to 2016, this study checks the consistency between well-known semi-analytical algorithms (SAAs)-the quasi-analytical algorithm (QAA) and the default generalized inherent optical property (GIOP-DC)-in retrieving the non-water absorption coefficient (a_nw(λ)), phytoplankton absorption coefficient (a_ph(λ)) and particulate backscattering coefficient (b_bp(λ)) from remote-sensing reflectance (R_rs(λ)) data at 412, 443, 490, 531, and 555 nm. The samples from the SCS are further separated into oligotrophic and mesotrophic water types for the comparison of the SAAs. Several findings are made: First, the values of an_w(λ) derived from the two SAAs deliver similar performance, with R² values ranging from 0.74 to 0.85 and 0.74 to 0.87, implying absolute percent error differences (APDs) from 37.93% to 74.88% and from 32.32% to 71.75% for the QAA and GIOP-DC, respectively. The QAA shows a value of R² between 0.64 and 0.91 and APDs between 43.57% to 83.53%, while the GIOP-DC yields R² between 0.76 to 0.89 and APDs between 44.65% to 79.46% when estimating a_ph(λ). The values of b_bp(λ) derived from the QAA are closer to the in-situ b_bp(λ) values, as indicated by the low values of the normalized centered root-mean-square deviation and normalized standard deviation, which are close to one. Second, a regionally tuned estimation of a_ph(λ) is proposed and recommended for the SCS. This consistency check of inherent optical properties products from SAAs can serve as reference for algorithm selection for further applications, including primary production, carbon, and biogeochemical models of the SCS, and can provide guidance for improving a_ph(λ) estimation.

Deriving consistent ocean biological and biogeochemical products from multiple satellite ocean color sensors

A methodology is developed for deriving consistent ocean biological and biogeochemical products from multiple satellite ocean color sensors that have slightly different sensor spectral characteristics. Specifically, the required coefficients for algorithm modifications are obtained using the hyperspectral in situ optical measurements from the Marine Optical Buoy (MOBY) in the water off Hawaii. It is demonstrated that using the proposed approach for modifying ocean biological and biogeochemical algorithms, satellite-derived ocean property data over the global open ocean are consistent from multiple satellite sensors, although their corresponding sensor-measured normalized water-leaving radiance spectra nLw(λ) are different. Therefore, the proposed approach allows satellite-derived ocean biological and biogeochemical products to be consistent and can therefore be routinely merged from various satellite ocean color sensors. The proposed approach can be applied to any satellite algorithms that use the input of sensor-measured nLw(λ) spectra.

Individual variation in migratory movements of chinstrap penguins leads to widespread occupancy of ice-free winter habitats over the continental shelf and deep ocean basins of the Southern Ocean

A goal of tracking migratory animals is to characterize the habitats they use and to interpret population processes with respect to conditions experienced en route to, and within, overwintering areas. For migratory seabirds with broad breeding ranges, inferring population-level effects of environmental conditions that are experienced during migratory periods would benefit by directly comparing how birds from different breeding aggregations disperse, characterizing the physical conditions of areas they use, and determining whether they occupy shared foraging areas. We therefore tracked 41 adult and juvenile chinstrap penguins (Pygoscelis antarctica) from three breeding locations in the northern Antarctic Peninsula region during the austral winter of 2017. The satellite tracking data revealed overlap of individuals over continental shelf areas during autumn months (Mar-May), shared outbound corridors that track the southern Antarctic circumpolar current front, followed by occupancy of progressively colder, deeper, and ice-free waters that spanned the entire western hemisphere south of the Polar Front. Despite broadly similar physical environments used by individuals from different colonies, the proportion of birds from each colony that remained within 500km of their colony was positively correlated with their local population trends. This suggests that local migration strategies near the Antarctic Peninsula may benefit breeding populations. However, the magnitude of inter-colony and intra-colony overlap was generally low given the broad scale of habitats occupied. High individual variation in winter movements suggests that habitat selection among chinstrap penguins is more opportunistic, without clear colony-specific preference for fine-scale foraging hotspots. Mixing of individuals from multiple colonies across broad regions of the Southern Ocean would expose chinstrap penguins from the Antarctic Peninsula to a shared environmental experience that helps explain the regional decline in their abundance.

On the Adequacy of Representing Water Reflectance by Semi-Analytical Models in Ocean Color Remote Sensing

Deterministic or statistical inversion schemes to retrieve ocean color from space often use a simplified water reflectance model that may introduce unrealistic constraints on the solution, a disadvantage compared with standard, two-step algorithms that make minimal assumptions about the water signal. In view of this, the semi-analytical models of Morel and Maritorena (2001), MM01, and Park and Ruddick (2005), PR05, used in the spectral matching POLYMER algorithm (Steinmetz et al., 2011), are examined in terms of their ability to restitute properly, i.e., with sufficient accuracy, water reflectance. The approach is to infer water reflectance at MODIS wavelengths, as in POLYMER, from theoretical simulations (using Hydrolight with fluorescence and Raman scattering) and, separately, from measurements (AERONET-OC network). A wide range of Case 1 and Case 2 waters, except extremely turbid waters, are included in the simulations and sampled in the measurements. The reflectance model parameters that give the best fit with the simulated data or the measurements are determined. The accuracy of the reconstructed water reflectance and its effect on the retrieval of inherent optical properties (IOPs) is quantified. The impact of cloud and aerosol transmittance, fixed to unity in the POLYMER scheme, on model performance is also evaluated. Agreement is generally good between model results and Hydrolight simulations or AERONET-OC values, even in optically complex waters, with discrepancies much smaller than typical atmospheric correction errors. Significant differences exist in some cases, but having a more intricate model (i.e., using more parameters) makes convergence more difficult. The trade-off is between efficiency/robustness and accuracy. Notable errors are obtained when using the model estimates to retrieve IOPs. Importantly, the model parameters that best fit the input data, in particular chlorophyll-a concentration, do not represent adequately actual values. The reconstructed water reflectance should be used in bio-optical algorithms. While neglecting cloud and aerosol transmittances degrades the accuracy of the reconstructed water reflectance and the retrieved IOPs, it negligibly affects water reflectance ratios and, therefore, any variable derived from such ratios.

Impacts of pure seawater absorption coefficient on remotely sensed inherent optical properties in oligotrophic waters

The spectral absorption coefficient of pure seawater (a_w(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 a_w(λ) may have significant impacts on retrieved IOPs in oligotrophic waters, especially for the phytoplankton absorption coefficient (a_ph(λ)). Two sets of a_w(λ) data, a_w_PF97 (Appl. Opt. 36, 8710, 1997) and a_w_Lee15 (Appl. Opt. 54, 546, 2015), were selected for optical inversion analysis. It is found that a_ph(λ) retrieved with a_w_Lee15 agree better with the in-situ measurements in oligotrophic waters. Further applications to satellite images show that the derived a_ph(λ) using a_w_Lee15 can be up to 238% higher than the retrievals using a_w_PF97 in the core zone of the subtropical ocean gyres. Given that a_w_PF97 is commonly accepted as the "standard" a_w(λ) by the ocean color community in the past decades, this study highlights the need and importance to update a_w(λ) with a_w_Lee15 for IOPs retrievals in oligotrophic waters.

Environmental Reservoirs of Vibrio cholerae: Challenges and Opportunities for Ocean-Color Remote Sensing

The World Health Organization has estimated the burden of the on-going pandemic of cholera at 1.3 to 4 million cases per year worldwide in 2016, and a doubling of case-fatality-rate to 1.8% in 2016 from 0.8% in 2015. The disease cholera is caused by the bacterium Vibrio cholerae that can be found in environmental reservoirs, living either in free planktonic form or in association with host organisms, non-living particulate matter or in the sediment, and participating in various biogeochemical cycles. An increasing number of epidemiological studies are using land- and water-based remote-sensing observations for monitoring, surveillance, or risk mapping of Vibrio pathogens and cholera outbreaks. Although the Vibrio pathogens cannot be sensed directly by satellite sensors, remotely-sensed data can be used to infer their presence. Here, we review the use of ocean-color remote-sensing data, in conjunction with information on the ecology of the pathogen, to map its distribution and forecast risk of disease occurrence. Finally, we assess how satellite-based information on cholera may help support the Sustainable Development Goals and targets on Health (Goal 3), Water Quality (Goal 6), Climate (Goal 13), and Life Below Water (Goal 14).

Evaluation of Satellite-Based Algorithms to Retrieve Chlorophyll-a Concentration in the Canadian Atlantic and Pacific Oceans

Remote-sensing reflectance data collected by ocean colour satellites are processed using bio-optical algorithms to retrieve biogeochemical properties of the ocean. One such important property is the concentration of chlorophyll-a, an indicator of phytoplankton biomass that serves a multitude of purposes in various ocean science studies. Here, the performance of two generic chlorophyll-a algorithms (i.e., a band ratio one, Ocean Colour X (OCx), and a semi-analytical one, Garver–Siegel Maritorena (GSM)) was assessed against two large in situ datasets of chlorophyll-a concentration collected between 1999 and 2016 in the Northeast Pacific (NEP) and Northwest Atlantic (NWA) for three ocean colour sensors: Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS). In addition, new regionally-tuned versions of these two algorithms are presented, which reduced the mean error (mg m−3) of chlorophyll-a concentration modelled by OCx in the NWA from −0.40, −0.58 and −0.45 to 0.037, −0.087 and −0.018 for MODIS, SeaWiFS, and VIIRS respectively, and −0.34 and −0.36 to −0.0055 and −0.17 for SeaWiFS and VIIRS in the NEP. An analysis of the uncertainties in chlorophyll-a concentration retrieval showed a strong seasonal pattern in the NWA, which could be attributed to changes in phytoplankton community composition, but no long-term trends were found for all sensors and regions. It was also found that removing the 443 nm waveband for the OCx algorithms significantly improved the results in the NWA. Overall, GSM performed better than the OCx algorithms in both regions for all three sensors but generated fewer chlorophyll-a retrievals than the OCx algorithms.

Algorithm to derive inherent optical properties from remote sensing reflectance in turbid and eutrophic lakes

Inherent optical properties play an important role in understanding the biogeochemical processes of lakes by providing proxies for a variety of biogeochemical quantities, including phytoplankton pigments. However, to date, it has been difficult to accurately derive the absorption coefficient of phytoplankton $[{a_{ph}}(\lambda )]$[a_ph(λ)] in turbid and eutrophic waters from remote sensing. A large dataset of remote sensing of reflectance $[{R_{rs}}(\lambda )]$[R_rs(λ)] and absorption coefficients was measured for samples collected from lakes in the middle and lower reaches of the Yangtze River and Huai River basin (MLYHR), China. In the process of scattering correction of spectrophotometric measurements, the particulate absorption coefficients $[{a_p}(\lambda )]$[a_p(λ)] were first assumed to have no absorption in the near-infrared (NIR) wavelength. This assumption was corrected by estimating the particulate absorption coefficients at 750 nm $[{a_p}({750})]$[a_p(750)] from the concentrations of chlorophyll-a (Chla) and suspended particulate matter, which was added to the ${a_p}(\lambda )$a_p(λ) as a baseline. The resulting mean spectral mass-specific absorption coefficient of the nonalgal particles (NAPs) was consistent with previous work. A novel iterative IOP inversion model was then designed to retrieve the total nonwater absorption coefficients $[{a_{nw}}(\lambda )]$[a_nw(λ)] and backscattering coefficients of particulates $[{b_{bp}}(\lambda )]$[b_bp(λ)], ${a_{ph}}(\lambda )$a_ph(λ), and ${a_{dg}}(\lambda )$a_dg(λ) [absorption coefficients of NAP and colored dissolved organic matter (CDOM)] from ${R_{rs}}(\lambda )$R_rs(λ) in turbid inland lakes. The proposed algorithm performed better than previously published models in deriving ${a_{nw}}(\lambda )$a_nw(λ) and ${b_{bp}}(\lambda )$b_bp(λ) in this region. The proposed algorithm performed well in estimating the ${a_{ph}}(\lambda )$a_ph(λ) for wavelengths $ > {500}\;{\rm nm}$>500nm for the calibration dataset [${\rm N} = {285}$N=285, unbiased absolute percentage difference $({\rm UAPD}) = {55.22}\% $(UAPD)=55.22%, root mean square error $({\rm RMSE}) = {0.44}\;{{\rm m}^{ - 1}}$(RMSE)=0.44m^-1] and for the validation dataset (${\rm N} = {57}$N=57, ${\rm UAPD} = {56.17}\% $UAPD=56.17%, ${\rm RMSE} = {0.71}\;{{\rm m}^{ - 1}}$RMSE=0.71m^-1). This algorithm was then applied to Sentinel-3A Ocean and Land Color Instrument (OLCI) satellite data, and was validated with field data. This study provides an example of how to use local data to devise an algorithm to obtain IOPs, and in particular, ${a_{ph}}(\lambda )$a_ph(λ), using satellite ${R_{rs}}(\lambda )$R_rs(λ) data in turbid inland waters.

Evaluating satellite estimates of particulate backscatter in the global open ocean using autonomous profiling floats

Satellite retrievals of particulate backscattering (b_bp) are widely used in studies of ocean ecology and biogeochemistry, but have been historically difficult to validate due to the paucity of available ship-based comparative field measurements. Here we present a comparison of satellite and in situ b_bp using observations from autonomous floats (n = 2,486 total matchups across three satellites), which provide b_bp at 700 nm. With these data, we quantify how well the three inversion products currently distributed by NASA ocean color retrieve b_bp. We find that the median ratio of satellite derived b_bp to float b_bp ranges from 0.77 to 1.60 and Spearman's rank correlations vary from r = 0.06 to r = 0.79, depending on which algorithm and sensor is used. Model skill degrades with increased spatial variability in remote sensing reflectance, which suggests that more rigorous matchup criteria and factors contributing to sensor noisiness may be useful to address in future work, and/or that we have built in biases in the current widely distributed inversion algorithms.

An Ocean-Colour Time Series for Use in Climate Studies: The Experience of the Ocean-Colour Climate Change Initiative (OC-CCI)

Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS); and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales; their role in marine biogeochemistry; the global carbon cycle; the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean; and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration; since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel.

Improved atmospheric correction algorithm for Landsat 8-OLI data in turbid waters: a case study for the Lake Taihu, China

Several atmospheric correction algorithms for turbid waters have been developed based on an assumption of zero reflection in short-wave infrared (SWIR) bands. However, for the Landsat8-Operational Land Imager (OLI), some water reflections are so strong in the 1609 nm band that they cannot be ignored. In this study, we developed a novel atmospheric correction algorithm based on a zero assumption for the short-wave infrared band (ACZI). The ACZI algorithm uses the black pixel index (BPI) and the floating algae index (FAI) to distinguish black pixels, which are used to estimate the aerosol scattering of non-black pixels based on the assumption of spatial homogeneity of aerosol types. In Lake Taihu, compared with the SeaDAS (SeaWiFS Data Analysis System) -SWIR algorithm, the ACZI algorithm achieved better precision for visible bands MAPE (the mean absolute percentage error), < 30%, RMSE (the root mean square error) < 0.0117 sr^-1) and provided more available water pixels. The accuracy of ACZI was close to that of the DSF (dark spectrum fitting) algorithm and was better than that of the EXP (exponential extrapolation) algorithm and L8SR (Landsat 8 OLI Surface Reflectance) product. The ACZI algorithm showed good applicability in turbid waters.

The Influence of Temperature and Community Structure on Light Absorption by Phytoplankton in the North Atlantic

We present a model that estimates the spectral phytoplankton absorption coefficient (aph(λ)) of four phytoplankton groups (picophytoplankton, nanophytoplankton, dinoflagellates, and diatoms) as a function of the total chlorophyll-a concentration (C) and sea surface temperature (SST). Concurrent data on aph(λ) (at 12 visible wavelengths), C and SST, from the surface layer (<20 m depth) of the North Atlantic Ocean, were partitioned into training and independent validation data, the validation data being matched with satellite ocean-colour observations. Model parameters (the chlorophyll-specific phytoplankton absorption coefficients of the four groups) were tuned using the training data and found to compare favourably (in magnitude and shape) with results of earlier studies. Using the independent validation data, the new model was found to retrieve total aph(λ) with a similar performance to two earlier models, using either in situ or satellite data as input. Although more complex, the new model has the advantage of being able to determine aph(λ) for four phytoplankton groups and of incorporating the influence of SST on the composition of the four groups. We integrate the new four-population absorption model into a simple model of ocean colour, to illustrate the influence of changes in SST on phytoplankton community structure, and consequently, the blue-to-green ratio of remote-sensing reflectance. We also present a method of propagating error through the model and illustrate the technique by mapping errors in group-specific aph(λ) using a satellite image. We envisage the model will be useful for ecosystem model validation and assimilation exercises and for investigating the influence of temperature change on ocean colour.

CHLOROPHYLL ALGORITHMS FOR OCEAN COLOR SENSORS - OC4, OC5 & OC6

A high degree of consistency and comparability among chlorophyll algorithms is necessary to meet the goals of merging data from concurrent overlapping ocean color missions for increased coverage of the global ocean and to extend existing time series to encompass data from recently launched missions and those planned for the near future, such as PACE, OLCI, HawkEye, EnMAP and SABIA-MAR. To accomplish these goals, we developed 65 empirical ocean color (OC) maximum band ratio (MBR) algorithms for 25 satellite instruments using the largest available and most globally representative database of coincident in situ chlorophyll a and remote sensing reflectances. Excellent internal consistency was achieved across these OC 'Version -7' algorithms, as demonstrated by a median regression slope and coefficient of determination (R²) of 0.985 and 0.859, respectively, between 903 pairwise comparisons of OC-modeled chlorophyll. SeaWiFS and MODIS-Aqua satellite-to-in situ match-up results indicated equivalent, and sometimes superior, performance to current heritage chlorophyll algorithms. During the past forty years of ocean color research the violet band (412 nm) has rarely been used in empirical algorithms to estimate chlorophyll concentrations in oceanic surface water. While the peak in chlorophyll-specific absorption coincides with the 443 nm band present on most ocean color sensors, the magnitude of chlorophyll-specific absorption at 412 nm can reach upwards of ~70% of that at 443 nm. Nearly one third of total chlorophyll-specific absorption between 400 and 700 nm occurs below 443 nm, suggesting that bands below 443 nm, such as the 412 nm band present on most ocean color sensors, may also be useful in detecting chlorophyll under certain conditions and assumptions. The 412 nm band is also the brightest band (that is, with the most dominant magnitude) in remotely sensed reflectances retrieved by heritage passive ocean color instruments when chlorophyll is less than ~0.1 mg m^-3, which encompasses ~24% of the global ocean. To attempt to exploit this additional spectral information, we developed two new families of OC algorithms, the OC5 and OC6 algorithms, which include the 412 nm band in the MBR. By using this brightest band in MBR empirical chlorophyll algorithms, the highest possible dynamic range of MBR may be achieved in these oligotrophic areas. The terms oligotrophic, mesotrophic, and eutrophic get frequent use in the scientific literature to designate trophic status; however, quantitative definitions in terms of chlorophyll levels are arbitrarily defined. We developed a new, reproducible, bio-optically based index for trophic status based on the frequency of the brightest, maximum band in the MBR for the OC6_SEAWIFS algorithm, along with remote sensing reflectances from the entire SeaWiFS mission. This index defines oligotrophic water as chlorophyll less than ~0.1 mg m^-3, eutrophic water as chlorophyll above 1.67 mg m^-3 and mesotrophic water as chlorophyll between 0.1 and 1.67 mg m^-3. Applying these criteria to the 40-year mean global ocean chlorophyll data set revealed that oligotrophic, mesotrophic, and eutrophic water occupy ~24%, 67%, and 9%, respectively, of the area of the global ocean on average.

Water Column Optical Properties of Pacific Coral Reefs Across Geomorphic Zones and in Comparison to Offshore Waters

Despite the traditional view of coral reefs occurring in oligotrophic tropical conditions, water optical properties over coral reefs differ substantially from nearby clear oceanic waters. Through an extensive set of optical measurements across the tropical Pacific, our results suggest that coral reefs themselves exert a high degree of influence over water column optics, primarily through release of colored dissolved organic matter (CDOM). The relative contributions of phytoplankton, non-algal particles, and CDOM were estimated from measurements of absorption and scattering across different geomorphic shallow-water reef zones (<10 m) in Hawaii, the Great Barrier Reef, Guam, and Palau (n = 172). Absorption was dominated at the majority of stations by CDOM, with mixtures of phytoplankton and CDOM more prevalent at the protected back reef and lagoon zones. Absorption could be dominated by sediments and phytoplankton at fringing reefs and terrestrially impacted sites where particulate backscattering was significantly higher than in the other zones. Scattering at three angles in the backward direction followed recent measurements of the particulate phase function. Optical properties derived from satellite imagery indicate that offshore waters are consistently lower in absorption and backscattering than reef waters. Therefore, the use of satellite-derived offshore parameters in modeling reef optics could lead to significant underestimation of absorption and scattering, and overestimation of benthic light availability. If local measurements are not available, average optical properties based on the general reef zone could provide a more accurate means of assessing light conditions on coral reefs than using offshore water as a proxy.

Approach for Propagating Radiometric Data Uncertainties Through NASA Ocean Color Algorithms

Spectroradiometric satellite observations of the ocean are commonly referred to as "ocean color" remote sensing. NASA has continuously collected, processed, and distributed ocean color datasets since the launch of the Sea-viewing Wide-field-of-view Sensor (SeaWiFS) in 1997. While numerous ocean color algorithms have been developed in the past two decades that derive geophysical data products from sensor-observed radiometry, few papers have clearly demonstrated how to estimate measurement uncertainty in derived data products. As the uptake of ocean color data products continues to grow with the launch of new and advanced sensors, it is critical that pixel-by-pixel data product uncertainties are estimated during routine data processing. Knowledge of uncertainties can be used when studying long-term climate records, or to assist in the development and performance appraisal of bio-optical algorithms. In this methods paper we provide a comprehensive overview of how to formulate first-order first-moment (FOFM) calculus for propagating radiometric uncertainties through a selection of bio-optical models. We demonstrate FOFM uncertainty formulations for the following NASA ocean color data products: chlorophyll-a pigment concentration (Chl), the diffuse attenuation coefficient at 490 nm (K _d,490), particulate organic carbon (POC), normalized fluorescent line height (nflh), and inherent optical properties (IOPs). Using a quality-controlled in situ hyperspectral remote sensing reflectance (R _rs,i ) dataset, we show how computationally inexpensive, yet algebraically complex, FOFM calculations may be evaluated for correctness using the more computationally expensive Monte Carlo approach. We compare bio-optical product uncertainties derived using our test R _rs dataset assuming spectrally-flat, uncorrelated relative uncertainties of 1, 5, and 10%. We also consider spectrally dependent, uncorrelated relative uncertainties in R _rs . The importance of considering spectral covariances in R _rs , where practicable, in the FOFM methodology is highlighted with an example SeaWiFS image. We also present a brief case study of two POC algorithms to illustrate how FOFM formulations may be used to construct measurement uncertainty budgets for ecologically-relevant data products. Such knowledge, even if rudimentary, may provide useful information to end-users when selecting data products or when developing their own algorithms.