An overview of approaches and challenges for retrieving marine inherent optical properties from ocean color remote sensing

On the bubble-bubbleless ocean continuum and its meaning for the LiDAR equation: LiDAR measurement of underwater bubble properties during storm conditions

This paper presents the NRL shipboard LiDAR and the first LiDAR dataset of underwater bubbles. The meaning of these LiDAR observations, the algorithms used and their current limitations are discussed. The derivation of the LiDAR multiple scattering regime is derived from the LiDAR observations and theory. The detection of the underwater bubble presence and their depth is straightforward to estimate from the depolarized laser return. This dataset strongly suggest that the whitecaps term in the LiDAR equation formalism needs to be revisited. The retrieval of the fraction of air volume within a given volume of water (void fraction) is possible and the algorithm is stable with a simple ocean backscatter LiDAR system. The accuracy of the void fraction retrieval will increase significantly with future developments.

Model for partitioning the non-phytoplankton absorption coefficient of seawater in the ultraviolet and visible spectral range into the contributions of non-algal particulate and dissolved organic matter

Non-algal particles and chromophoric dissolved organic matter (CDOM) are two major classes of seawater constituents that contribute substantially to light absorption in the ocean within the ultraviolet (UV) and visible (VIS) spectral regions. The similarities in the spectral shape of these two constituent absorption coefficients, a d (λ) and a g (λ), respectively, have led to their common estimation as a single combined non-phytoplankton absorption coefficient, a d g (λ), in optical remote-sensing applications. Given the different biogeochemical and ecological roles of non-algal particles and CDOM in the ocean, it is important to determine and characterize the absorption coefficient of each of these constituents separately. We describe an ADG model that partitions a d g (λ) into a d (λ) and a g (λ). This model improves upon a recently published model [Appl. Opt.58, 3790 (2019)APOPAI0003-693510.1364/AO.58.003790] through implementation of a newly assembled dataset of hyperspectral measurements of a d (λ) and a g (λ) from diverse oceanic environments to create the spectral shape function libraries of these coefficients, a better characterization of variability in spectral shape of a d (λ) and a g (λ), and a spectral extension of model output to include the near-UV (350-400 nm) in addition to the VIS (400-700 nm) part of the spectrum. We developed and tested two variants of the ADG model: the ADG_UV-VIS model, which determines solutions over the spectral range from 350 to 700 nm, and the ADG_VIS model, which determines solutions in the VIS but can also be coupled with an independent extrapolation model to extend output to the near-UV. This specific model variant is referred to as A D G _ V I S-U V E x t . Evaluation of the model with development and independent datasets demonstrates good performance of both ADG_UV-VIS and A D G _ V I S-U V E x t . Comparative analysis of model-derived and measured values of a d (λ) and a g (λ) indicates negligible or small median bias, generally within ±5% over the majority of the 350-700 nm spectral range but extending to or above 10% near the ends of the spectrum, and the median percent difference generally below 20% with a maximum reaching about 30%. The presented ADG models are suitable for implementation as a component of algorithms in support of satellite ocean color missions, especially the NASA PACE mission.

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.

Neural network spectral relationship to improve an inherent optical properties data processing system for residual error correction

The residual error was a critical indicator to measure the data quality of ocean color products, which allows a user to decide the valuable envisioned application of these data. To effectively remove the residual errors from satellite remote sensing reflectance (Rrs) using the inherent optical data processing system (IDAS), we expressed the residual error spectrum as an exponential plus linear function, and then we developed neural network models to derive the corresponding spectral slope coefficients from satellite Rrs data. Coupled with the neural network models-based spectral relationship, the IDAS algorithm (IDASnn) was more effective than an invariant spectral relationship-based IDAS algorithm (IDAScw) in reducing the effects of residual errors in Rrs on IOPs retrieval for our synthetic, field, and Chinese Ocean Color and Temperature Scanner (COCTS) data. Particularly, due to the improved spectral relationship of the residual errors, the IDASnn algorithm provided more accurate and smoother spatiotemporal ocean color product than the IDAScw algorithm for the open ocean. Furthermore, we could monitor the data quality with the IDASnn algorithm, suggesting that the residual error was exceptionally large for COCTS images with low effective coverage. The product effective coverage should be rigorously controlled, or the residual error should be accurately corrected before temporal and spatial analysis of the COCTS data. Our results suggest that an accurate spectral relationship of residual errors is critical to determine how well the IDAS algorithm corrects for residual error.

Inversion diffuse attenuation coefficient of photosynthetically active radiation based on deep learning

Accurate estimation of the diffuse attenuation coefficient of photosynthetically active radiation, Kd(PAR), is critical for understanding and modeling key physical, chemical, and biological processes in waters. In this study, a deep learning model (DLKPAR) was developed for remotely estimating Kd(PAR). Compared to the traditional empirical algorithms and semi-analytical algorithm, DLKPAR demonstrated an improvement in the model's stability and accuracy. By using in situ NOMAD data to evaluate the model's performance, DLKPAR had lower root mean square difference (RMSD; 0.028 vs. 0.030-0.048 m-1) and mean absolute relative difference (MARD; 0.14 vs. 0.17-0.25) and higher R2 (0.94 vs. 0.82-0.94). The statistical results of the matchup NOMAD and Argo data to the MODIS also indicated DLKPAR improves the inversion accuracy of Kd(PAR) and could be applied to remotely estimate Kd(PAR) in the global oceans. Therefore, we anticipate that DLKPAR could yield reliable Kd(PAR) values from ocean color remote sensing, providing an accurate estimation of visible light attenuation in the upper ocean and facilitating biogeochemical cycle research.

Remote sensing for chromophoric dissolved organic matter (CDOM) monitoring research 2003-2022: A bibliometric analysis based on the web of science core database

Chromophoric dissolved organic matter (CDOM) occupies a critical part in biogeochemistry and energy flux of aquatic ecosystems. CDOM research spans in many fields, including chemistry, marine environment, biomass cycling, physics, hydrology, and climate change. In recent years, a series of remarkable research milestone have been achieved. On the basis of reviewing the research process of CDOM, combined with a bibliometric analysis, this study aims to provide a comprehensive review of the development and applications of remote sensing in monitoring CDOM from 2003 to 2022. The findings show that remote sensing data plays an important role in CDOM research as proven with the increasing number of publications since 2003, particularly in China and the United States. Primary research areas have gradually changed from studying absorption and fluorescence properties to optimization of remote sensing inversion models in recent years. Since the composition of oceanic and freshwater bodies differs significantly, it is important to choose the appropriate inversion method for different types of water body. At present, the monitoring of CDOM mainly relies on a single sensor, but the fusion of images from different sensors can be considered a major research direction due to the complex characteristics of CDOM. Therefore, in the future, the characteristics of CDOM will be studied in depth inn combination with multi-source data and other application models, where inversion algorithms will be optimized, inversion algorithms with low dependence on measured data will be developed, and a transportable inversion model will be built to break the regional limitations of the model and to promote the development of CDOM research in a deeper and more comprehensive direction.

Neural network approach for correction of multiple scattering errors in the LISST-VSF instrument

The LISST-VSF is a commercially developed instrument used to measure the volume scattering function (VSF) and attenuation coefficient in natural waters, which are important for remote sensing, environmental monitoring and underwater optical wireless communication. While the instrument has been shown to work well at relatively low particle concentration, previous studies have shown that the VSF obtained from the LISST-VSF instrument is heavily influenced by multiple scattering in turbid waters. High particle concentrations result in errors in the measured VSF, as well as the derived properties, such as the scattering coefficient and phase function, limiting the range at which the instrument can be used reliably. Here, we present a feedforward neural network approach for correcting this error, using only the measured VSF as input. The neural network is trained with a large dataset generated using Monte Carlo simulations of the LISST-VSF with scattering coefficients b=0.05-50m-1, and tested on VSFs from measurements with natural water samples. The results show that the neural network estimated VSF is very similar to the expected VSF without multiple scattering errors, both in angular shape and magnitude. One example showed that the error in the scattering coefficient was reduced from 103% to 5% for a benchtop measurement of natural water sample with expected b=10.6m-1. Hence, the neural network drastically reduces uncertainties in the VSF and derived properties resulting from measurements with the LISST-VSF in turbid waters.

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.

Chlorophyll-based model underpinned by measured inherent optical properties of Jerlov water types

An existing chlorophyll-based model has been updated and re-calibrated using measured data describing Jerlov water types, harvested from the World-wide Ocean Optics Database. This study has provided new chlorophyll concentration data, and used them in conjunction with recently published spectra of absorption and scattering coefficients to create an updated parameter set that describes eight of the 10 Jerlov water types. The updated model is consistent with other data, and it interprets the measured characteristics in terms of underlying properties. Techniques for inter-conversion between inherent and apparent optical properties have been further investigated, and the improved precision has uncovered new challenges that have been addressed using empirical techniques.

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.

Global climate-change trends detected in indicators of ocean ecology

Strong natural variability has been thought to mask possible climate-change-driven trends in phytoplankton populations from Earth-observing satellites. More than 30 years of continuous data were thought to be needed to detect a trend driven by climate change1. Here we show that climate-change trends emerge more rapidly in ocean colour (remote-sensing reflectance, Rrs), because Rrs is multivariate and some wavebands have low interannual variability. We analyse a 20-year Rrs time series from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Aqua satellite, and find significant trends in Rrs for 56% of the global surface ocean, mainly equatorward of 40°. The climate-change signal in Rrs emerges after 20 years in similar regions covering a similar fraction of the ocean in a state-of-the-art ecosystem model2, which suggests that our observed trends indicate shifts in ocean colour-and, by extension, in surface-ocean ecosystems-that are driven by climate change. On the whole, low-latitude oceans have become greener in the past 20 years.

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.

GLORIA - A globally representative hyperspectral in situ dataset for optical sensing of water quality

The development of algorithms for remote sensing of water quality (RSWQ) requires a large amount of in situ data to account for the bio-geo-optical diversity of inland and coastal waters. The GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments (GLORIA) includes 7,572 curated hyperspectral remote sensing reflectance measurements at 1 nm intervals within the 350 to 900 nm wavelength range. In addition, at least one co-located water quality measurement of chlorophyll a, total suspended solids, absorption by dissolved substances, and Secchi depth, is provided. The data were contributed by researchers affiliated with 59 institutions worldwide and come from 450 different water bodies, making GLORIA the de-facto state of knowledge of in situ coastal and inland aquatic optical diversity. Each measurement is documented with comprehensive methodological details, allowing users to evaluate fitness-for-purpose, and providing a reference for practitioners planning similar measurements. We provide open and free access to this dataset with the goal of enabling scientific and technological advancement towards operational regional and global RSWQ monitoring.

Assessment of the potential for quantifying multi-period suspended sediment concentration variations using satellites with different temporal resolution

Suspended sediment concentration (SSC) is a crucial indicator for coastal health and geomorphological evolution, featured by complex periodic processes on multiple timescales in response to different cyclic forcing factors. Although remote sensing has functioned as an important means for SSC estimation with sufficient spatio-temporal coverage, the low effective sampling rates and resulting unevenly spaced characteristics of the retrieved time series would hamper the extraction of the representative SSC portrayal (amplitude and phase) on multiple timescales, especially for low-resolution satellites. Here, we retrieved a 9-year hourly GOCI SSC time series (January 2012 to December 2020) at two coastal sites in China (Haimen and Haizhou Bay) as reference cases, and utilized them to obtain MODIS, Sentinel and Landsat sequences with average temporal resolutions of 0.5, 5.6 and 11.2 days as preliminary investigations into amplitude and phase extractions. Furthermore, we generated GOCI-based hypothetical satellite time series with temporal resolutions ranging from 1 to 16 days (1088 subsets) and their mutual combination (591,328 subsets) to explore general laws when extracting amplitudes and phases from satellites with different temporal resolutions by application of the Lomb-Scargle Periodogram and phase-folded diagram methods. The amplitude and phase deviations were found to increase with decreasing temporal resolution on seasonal and fortnightly timescales at Haimen and in Haizhou Bay, while by mutual combination of satellites the errors could be reduced as more data were utilized for the extraction. It is shown that larger amplitude and phase deviations occur on the seasonal timescale in comparison to the fortnightly timescale at Haimen, whereas the situation reverses in the case of Haizhou Bay. These results demonstrate that temporal resolution, data characteristics on the target timescale and absolute SSC amplitude codetermine the extraction accuracy. This further indicates that satellites with lower temporal resolutions can potentially be used on a global scale for extracting the feature changes of multi-period SSC variations, in particular as continuous improvements in data quantity and quality can be expected in the future.

Spatiotemporal dynamics of suspended particulate matter in the Bohai Sea, China over the past decade from the space perspective

Suspended particulate matter (SPM) concentration is an important biogeochemical parameter for water quality assessment and morphodynamic studies. In this study, the four recent SPM retrieval models developed for Bohai Sea were evaluated using in situ datasets, and the best performing model was selected to investigate the spatiotemporal dynamics of SPM in Bohai Sea from 2011 to 2021 based on 1164 satellite imageries. The results indicated that the satellite-derived SPM concentrations had a high accuracy (R² = 0.86, relative percentage difference = 33.71 %). The SPM concentrations in the Bohai Sea demonstrated a significant decadal decreasing trend (0.503 mg/L/yr), and the distribution area with low SPM (<30 mg/L) increased by 3.29 % annually. The southern Bohai Sea declined observably, involving the Bohai Bay (2.07 mg/L/yr), Laizhou Bay (1.916 mg/L/yr), and central Bohai Sea (-0.661 mg/L/yr). Monthly SPM was characterized by significant seasonality. The SPM circulation pattern in the Bohai Strait was generally northerly inflow and southerly outflow. Significant wave heights (Hs) dominated the SPM variations and explained 58.9 % of monthly SPM changes in the Bohai Sea. The strong waves reduction was the main reason for the decadal decline of SPM concentrations. Wind waves associated with monsoons controlled seasonal variations of SPM and promoted the output in winter through the southern Bohai Strait. Storms could cause a sharp increase in SPM concentrations, especially in Bohai Bay and Laizhou Bay which were highly sensitive to northerly winds and strong waves. After the storm ended, the effects of short-duration storm might fade away within a few hours, while that of long-duration storm could last for 2-3 days. High sediment transport from Yellow River (>500 × 10⁴ t/M) controlled 74.8 % of monthly SPM variations within 3-km area off the estuary, 45 % of that within 5-km area, and 28.4 % of that within 10-km area.

Measured IOPs of Jerlov water types

Inherent optical properties (IOPs) of typical ocean waters have been derived from a worldwide database of measured parameters. The optical quality of the world's oceans can be described in terms of their Jerlov water type, ranging from the clearest Jerlov I to the most turbid Jerlov 9C. These Jerlov classifications are defined in terms of an apparent optical property known as the downwelling diffuse attenuation coefficient (K_d). There is a need to relate these Jerlov water types to their IOPs, namely their absorption coefficient, a, and scattering coefficient, b. However, robust values of a and b for Jerlov water types have not previously existed. This study used the World-wide Ocean Optics Database to derive a series of experimentally measured a and b values for six Jerlov water types. Using data science techniques to group measurements in time and space, over 13.5 million data points were consolidated into 53 measured values for a and b. Established models were subsequently applied to generate a complete table of absorption and scattering coefficients from 300 to 800 nm for Jerlov IB to Jerlov 5C. The analysis includes the influence of changes in the solar zenith angle and the scattering phase function. These data are recommended for use in applications where IOPs are required to describe Jerlov water types.

Optical classification of an urbanized estuary using hyperspectral remote sensing reflectance

Optical water classification based on remote sensing reflectance (R_rs(λ)) data can provide insight into water components driving optical variability and inform the development and application of bio-optical algorithms in complex aquatic systems. In this study, we use an in situ dataset consisting of hyperspectral R_rs(λ) and other biogeochemical and optical parameters collected over nearly five years across a heavily urbanized estuary, the Long Island Sound (LIS), east of New York City, USA, to optically classify LIS waters based on R_rs(λ) spectral shape. We investigate the similarities and differences of discrete groupings (k-means clustering) and continuous spectral indexing using the Apparent Visible Wavelength (AVW) in relation to system biogeochemistry and water properties. Our R_rs(λ) dataset in LIS was best described by three spectral clusters, the first two accounting for the majority (89%) of R_rs(λ) observations and primarily driven by phytoplankton dynamics, with the third confined to measurements in river and river plume waters. We found AVW effective at tracking subtle changes in R_rs(λ) spectral shape and fine-scale water quality features along river-to-ocean gradients. The recently developed Quality Water Index Polynomial (QWIP) was applied to evaluate three different atmospheric correction approaches for satellite-derived R_rs(λ) from the Sentinel-3 Ocean and Land Colour Instrument (OLCI) sensor in LIS, finding Polymer to be the preferred approach. Our results suggest that integrative, continuous indices such as AVW can be effective indicators to assess nearshore biogeochemical variability and evaluate the quality of both in situ and satellite bio-optical datasets, as needed for improved ecosystem and water resource management in LIS and similar regions.

Data reconstruction of daily MODIS chlorophyll-a concentration and spatio-temporal variations in the Northwestern Pacific

Sea surface chlorophyll-a concentration (Chl-a) is a key proxy for phytoplankton biomass. Spatio-temporal continuous Chl-a data are important to understand the mechanisms of chlorophyll occurrence and development and track phytoplankton changes. However, the greatest challenge in utilizing daily Chl-a data is massive missing pixels due to orbital position and cloud coverage. This study proposes the application of a spatial filling method using the machine learning-based Extreme Gradient Boosting (BST) to reconstruct missing pixels of daily MODIS Chl-a data from 2007 to 2018. The approach is applied to different trophic biogeographical subregions of the Northwestern Pacific where it has complex phytoplankton dynamics and frequent data missing. Various environmental variables are taken into consideration, including meteorological forcing, geographic and topographic features, and oceanic physical components. The BST-reconstructed Chl-a (BST Chl-a) is validated using in-situ Chl-a measurements, VIIRS and Himawari-8 Chl-a products. The results show that the BST model is highly adaptive in reconstructing Chl-a data, and it performs well in pelagic, offshore and coastal with the best performance in pelagic. BST Chl-a improves coverage without significant quality degradation compared to the original MODIS Chl-a. BST Chl-a agrees better with in-situ data than that of MODIS, with CC of 0.742, RMSE of 0.247, MAE of 0.202 and Bias of 0.089. Cross-satellite validation using VIIRS and Himawari-8 Chl-a also shows promising results with the CC of 0.861 and 0.765, respectively, suggesting the high accuracy of BST Chl-a. The inter-annual trend of BST Chl-a decreases in coastal and increases in offshore and pelagic. BST Chl-a images present similar spatial patterns to MODIS Chl-a under different missing rates, with gradual decreases from coastal to pelagic. It indicates that phytoplankton bloom patterns can be identified by daily BST Chl-a images.

Variability of particulate organic carbon and assessment of satellite retrieval algorithms over the eastern Arabian Sea

Particulate organic carbon (POC) and its variability were studied to assess the accuracy of ocean colour retrieval algorithms over the eastern Arabian Sea (EAS) as it controls the carbon sequestration, oxygen minimum zone and biogeochemical (C, N and P) cycles. The seasonality in the physical and biological processes strongly influenced the distribution of POC along the EAS. Higher POC and chlorophyll a (chl a) during the spring inter monsoon (SIM) in the north EAS were due to detrainment bloom. The lower POC:chl a ratios during the winter monsoon (WM) (299.8 ± 190.8) than the SIM (482.1 ± 438.3) were due to the influence of freshly derived organic matter with high nutrient levels. The moderate coefficient of regression values of POC with chl a (R² = 0.49, N = 59) suggests the importance of dead organic materials in controlling the POC distribution in the EAS. Validation between satellite and in situ POC using the four ocean colour retrieval algorithms showed that the algorithm based on the ratio of remote sensing reflectance (Rrs) performed better (R² = 0.6, N = 17). It also showed a linear trend of POC with absorption coefficients suggesting it as an optical proxy for the POC retrieval.

Determination of optical markers of cyanobacterial physiology from fluorescence kinetics

Compared to other methods to monitor and detect cyanobacteria in phytoplankton populations, fluorometry gives rapid, robust and reproducible results and can be used in situ. Fluorometers capable of providing biomass estimates and physiological information are not commonly optimized to target cyanobacteria. This study provides a detailed overview of the fluorescence kinetics of algal and cyanobacterial cultures to determine optimal optical configurations to target fluorescence mechanisms that are either common to all phytoplankton or diagnostic to cyanobacteria. We confirm that fluorescence excitation channels targeting both phycocyanin and chlorophyll a associated to the Photosystem II are required to induce the fluorescence responses of cyanobacteria. In addition, emission channels centered at 660, 685 and 730 nm allow better differentiation of the fluorescence response between algal and cyanobacterial cultures. Blue-green actinic light does not yield a robust fluorescence response in the cyanobacterial cultures and broadband actinic light should be preferred to assess the relation between ambient light and photosynthesis. Significant variability was observed in the fluorescence response from cyanobacteria to the intensity and duration of actinic light exposure, which needs to be taken into consideration in field measurements.

Efficient Monte Carlo simulation reveals significant multiple scattering errors in underwater angular scattering measurements

Multiple scattering can severely affect the accuracy of optical instrumentation. Variance reduction methods have been implemented to improve a Monte Carlo model developed to simulate volume scattering functions measured by LISST-VSF instruments. The implemented methods can result in more than a tenfold increase in efficiency. The simulation is used to analyze multiple scattering errors for a range of Fournier-Forand (FF) phase functions. Our results demonstrate significant errors in the scattering coefficient, backscattering coefficient and phase function, where multiple scattering errors may only be considered negligible (<10%) for scattering coefficients <1 m^-1. The errors depend strongly on the scattering coefficient but also increase when phase functions become more forward-peaked.

Optimization and Evaluation of Widely-Used Total Suspended Matter Concentration Retrieval Methods for ZY1-02D’s AHSI Imagery

Total suspended matter concentration (CTSM) is an important parameter in aquatic ecosystem studies. Compared with multispectral satellite images, the Advanced Hyperspectral Imager (AHSI) carried by the ZY1-02D satellite can capture finer spectral features, and the potential for CTSM retrieval is enormous. In this study, we selected seven typical Chinese inland water bodies as the study areas, and recalibrated and validated 11 empirical models and two semi-analytical models for CTSM retrieval using the AHSI data. The results showed that the semi-analytical algorithm based on the 697 nm AHSI-band achieved the highest retrieval accuracy (R2 = 0.88, average unbiased relative error = 34.43%). This is because the remote sensing reflectance at 697 nm was strongly influenced by CTSM, and the AHSI image spectra were in good agreement with the in-situ spectra. Although further validation is still needed in highly turbid waters, this study shows that AHSI images from the ZY1-02D satellite are well suited for CTSM retrieval in inland waters.

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.

Satellites for long-term monitoring of inland U.S. lakes: The MERIS time series and application for chlorophyll-a

Lakes and other surface fresh waterbodies provide drinking water, recreational and economic opportunities, food, and other critical support for humans, aquatic life, and ecosystem health. Lakes are also productive ecosystems that provide habitats and influence global cycles. Chlorophyll concentration provides a common metric of water quality, and is frequently used as a proxy for lake trophic state. Here, we document the generation and distribution of the complete MEdium Resolution Imaging Spectrometer (MERIS; Appendix A provides a complete list of abbreviations) radiometric time series for over 2300 satellite resolvable inland bodies of water across the contiguous United States (CONUS) and more than 5,000 in Alaska. This contribution greatly increases the ease of use of satellite remote sensing data for inland water quality monitoring, as well as highlights new horizons in inland water remote sensing algorithm development. We evaluate the performance of satellite remote sensing Cyanobacteria Index (CI)-based chlorophyll algorithms, the retrievals for which provide surrogate estimates of phytoplankton concentrations in cyanobacteria dominated lakes. Our analysis quantifies the algorithms' abilities to assess lake trophic state across the CONUS. As a case study, we apply a bootstrapping approach to derive a new CI-to-chlorophyll relationship, Chl_BS, which performs relatively well with a multiplicative bias of 1.11 (11%) and mean absolute error of 1.60 (60%). While the primary contribution of this work is the distribution of the MERIS radiometric timeseries, we provide this case study as a roadmap for future stakeholders' algorithm development activities, as well as a tool to assess the strengths and weaknesses of applying a single algorithm across CONUS.

Interference of CDOM in remote sensing of suspended particulate matter (SPM) based on MODIS in the Persian Gulf and Oman Sea

The spatial and temporal variability of suspended particulate matter (SPM) in the Persian Gulf and Oman Sea coastal waters has remained challenging to understand among researchers. Here, for the first time in the region, we parametrized SPM concentration in the study area utilizing derived remote sensing reflectance (R_rs) values from Moderate-resolution Imaging Spectroradiometer (MODIS), using 555 and 667 nm wavelengths. Likewise, the findings showed that the developed optical model based on the optical ratio of R_rs (667)/R_rs (555) was sensitive to the concentration of Chromophoric dissolved organic matter (CDOM) in the seawater, within the visible wavelengths less than 600 nm. Comparing the new estimates of the SPM concentration with in situ measurements by Spearman's Rank correlation for validation revealed that the association between estimated and measured SPM concentration would be considered statistically significant (ρ up to 0.86, p < 0.05). This study increased the average accuracy of the estimates up to 73%.

Integrating Inland and Coastal Water Quality Data for Actionable Knowledge

Water quality measures for inland and coastal waters are available as discrete samples from professional and volunteer water quality monitoring programs and higher-frequency, near-continuous data from automated in situ sensors. Water quality parameters also are estimated from model outputs and remote sensing. The integration of these data, via data assimilation, can result in a more holistic characterization of these highly dynamic ecosystems, and consequently improve water resource management. It is becoming common to see combinations of these data applied to answer relevant scientific questions. Yet, methods for scaling water quality data across regions and beyond, to provide actionable knowledge for stakeholders, have emerged only recently, particularly with the availability of satellite data now providing global coverage at high spatial resolution. In this paper, data sources and existing data integration frameworks are reviewed to give an overview of the present status and identify the gaps in existing frameworks. We propose an integration framework to provide information to user communities through the the Group on Earth Observations (GEO) AquaWatch Initiative. This aims to develop and build the global capacity and utility of water quality data, products, and information to support equitable and inclusive access for water resource management, policy and decision making.

Monitoring Changes in the Transparency of the Largest Reservoir in Eastern China in the Past Decade, 2013–2020

Based on characteristics analysis about remote sensing reflectance, the Secchi Disk Depth (SDD) in the Qiandao Lake was predicted from the Landsat8/OLI data, and its changing rates on a pixel-by-pixel scale were obtained from satellite remote sensing for the first time. Using 114 matchups data pairs during 2013–2019, the SDD satellite algorithms suitable for the Qiandao Lake were obtained through both the linear regression and machine learning (Support Vector Machine) methods, with remote sensing reflectance (Rrs) at different OLI bands and the ratio of Rrs (Band3) to Rrs (Band2) as model input parameters. Compared with field observations, the mean absolute relative difference and root mean squared error of satellite-derived SDD were within 20% and 1.3 m, respectively. Satellite-derived results revealed that SDD in the Qiandao Lake was high in boreal spring and winter, and reached the lowest in boreal summer, with the annual mean value of about 5 m. Spatially, high SDD was mainly concentrated in the southeast lake area (up to 13 m) close to the dam. The edge and runoff area of the lake were less transparent, with an SDD of less than 4 m. In the past decade (2013–2020), 5.32% of Qiandao Lake witnessed significant (p < 0.05) transparency change: 4.42% raised with a rate of about 0.11 m/year and 0.9% varied with a rate of about −0.09 m/year. Besides, the findings presented here suggested that heavy rainfall would have a continuous impact on the Qiandao Lake SDD. Our research could promote the applications of land observation satellites (such as the Landsat series) in water environment monitoring in inland reservoirs.

Development and Validation of an Empirical Ocean Color Algorithm with Uncertainties: A Case Study with the Particulate Backscattering Coefficient

We explored how algorithm (model) and in situ measurement (observation) uncertainties can effectively be incorporated into empirical ocean color model development and assessment. In this study we focused on methods for deriving the particulate backscattering coefficient at 555 nm, b bp (555) (m-1). We developed a simple empirical algorithm for deriving b bp (555) as a function of a remote sensing reflectance line height (LH) metric. Model training was performed using a high-quality bio-optical dataset that contains coincident in situ measurements of the spectral remote sensing reflectances, R rs (λ) (sr-1), and the spectral particulate backscattering coefficients, b bp (λ). The LH metric used is defined as the magnitude of R rs (555) relative to a linear baseline drawn between R rs (490) and R rs (670). Using an independent validation dataset, we compared the skill of the LH-based model with two other models. We used contemporary validation metrics, including bias and mean absolute error (MAE), that were corrected for model and observation uncertainties. The results demonstrated that measurement uncertainties do indeed impact contemporary validation metrics such as mean bias and MAE. Zeta-scores and z-tests for overlapping confidence intervals were also explored as potential methods for assessing model skill.

A Self-Improving Framework for Joint Depth Estimation and Underwater Target Detection from Hyperspectral Imagery

Underwater target detection (UTD) is one of the most attractive research topics in hyperspectral imagery (HSI) processing. Most of the existing methods are presented to predict the signatures of desired targets in an underwater context but ignore the depth information which is position-sensitive and contributes significantly to distinguishing the background and target pixels. So as to take full advantage of the depth information, in this paper a self-improving framework is proposed to perform joint depth estimation and underwater target detection, which exploits the depth information and detection results to alternately boost the final detection performance. However, it is difficult to calculate depth information under the interference of a water environment. To address this dilemma, the proposed framework, named self-improving underwater target detection framework (SUTDF), employs the spectral and spatial contextual information to pick out target-associated pixels as the guidance dataset for depth estimation work. Considering the incompleteness of the guidance dataset, an expectation-maximum liked updating scheme has also been developed to iteratively excavate the statistical and structural information from input HSI for further improving the diversity of the guidance dataset. During each updating epoch, the calculated depth information is used to yield a more diversified dataset for the target detection network, leading to a more accurate detection result. Meanwhile, the detection result will in turn contribute in detecting more target-associated pixels as the supplement for the guidance dataset, eventually promoting the capacity of the depth estimation network. With this specific self-improving framework, we can provide a more precise detection result for a hyperspectral UTD task. Qualitative and quantitative illustrations verify the effectiveness and efficiency of SUTDF in comparison with state-of-the-art underwater target detection methods.

Deriving Water Quality Parameters Using Sentinel-2 Imagery: A Case Study in the Sado Estuary, Portugal

Monitoring water quality parameters and their ecological effects in transitional waters is usually performed through in situ sampling programs. These are expensive and time-consuming, and often do not represent the total area of interest. Remote sensing techniques offer enormous advantages by providing cost-effective systematic observations of a large water system. This study evaluates the potential of water quality monitoring using Sentinel-2 observations for the period 2018–2020 for the Sado estuary (Portugal), through an algorithm intercomparison exercise and time-series analysis of different water quality parameters (i.e., colored dissolved organic matter (CDOM), chlorophyll-a (Chl-a), suspended particulate matter (SPM), and turbidity). Results suggest that Sentinel-2 is useful for monitoring these parameters in a highly dynamic system, however, with challenges in retrieving accurate data for some of the variables, such as Chl-a. Spatio-temporal variability results were consistent with historical data, presenting the highest values of CDOM, Chl-a, SPM and turbidity during Spring and Summer. This work is the first study providing annual and seasonal coverage with high spatial resolution (10 m) for the Sado estuary, being a key contribution for the definition of effective monitoring programs. Moreover, the potential of remote sensing methodologies for continuous water quality monitoring in transitional systems under the scope of the European Water Framework Directive is briefly discussed.

A semi-analytical algorithm for deriving the particle size distribution slope of turbid inland water based on OLCI data: A case study in Lake Hongze

The particle size distribution (PSD) slope (ξ) can indicate the predominant particle size, material composition, and inherent optical properties (IOPs) of inland waters. However, few semi-analytical methods have been proposed for deriving ξ from the surface remote sensing reflectance due to the variable optical state of inland waters. A semi-analytical algorithm was developed for inland waters having a wide range of turbidity and ξ in this study. Application of the proposed model to Ocean and Land Color Instrument (OLCI) imagery of the water body resulted in several important observations: (1) the proposed algorithm (754 nm and 779 nm combination) was capable of retrieving ξ with R2 being 0.72 (p < 0.01, n = 60), and MAPE and RMSE being 4.37% and 0.22 (n = 30) respectively; (2) the ξ in HZL was lower in summer than other seasons during the period considered, this variation was driven by the phenological cycle of algae and the runoff caused by rainfall; (3) the band optimization proposed in this study is important for calculating the particle backscattering slope (η) and deriving ξ because it is feasible for both algae dominant and sediment governed turbid inland lakes. These observations help improve our understanding of the relationship between IOPs and ξ, which are affected by different bio-optic processes and algal phenology in the lake environment.

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.

An Artificial Neural Network to Infer the Mediterranean 3D Chlorophyll-a and Temperature Fields from Remote Sensing Observations

Remote sensing data provide a huge number of sea surface observations, but cannot give direct information on deeper ocean layers, which can only be provided by sparse in situ data. The combination of measurements collected by satellite and in situ sensors represents one of the most effective strategies to improve our knowledge of the interior structure of the ocean ecosystems. In this work, we describe a Multi-Layer-Perceptron (MLP) network designed to reconstruct the 3D fields of ocean temperature and chlorophyll-a concentration, two variables of primary importance for many upper-ocean bio-physical processes. Artificial neural networks can efficiently model eventual non-linear relationships among input variables, and the choice of the predictors is thus crucial to build an accurate model. Here, concurrent temperature and chlorophyll-a in situ profiles and several different combinations of satellite-derived surface predictors are used to identify the optimal model configuration, focusing on the Mediterranean Sea. The lowest errors are obtained when taking in input surface chlorophyll-a, temperature, and altimeter-derived absolute dynamic topography and surface geostrophic velocity components. Network training and test validations give comparable results, significantly improving with respect to Mediterranean climatological data (MEDATLAS). 3D fields are then also reconstructed from full basin 2D satellite monthly climatologies (1998–2015) and resulting 3D seasonal patterns are analyzed. The method accurately infers the vertical shape of temperature and chlorophyll-a profiles and their spatial and temporal variability. It thus represents an effective tool to overcome the in-situ data sparseness and the limits of satellite observations, also potentially suitable for the initialization and validation of bio-geophysical models.

Dynamic partitioning of tropical Indian Ocean surface waters using ocean colour data - management and modelling applications

Over the past few decades, partitioning of the surface ocean into ecologically-meaningful spatial domains has been approached using a range of data types, with the aim of improving our understanding of open ocean processes, supporting marine management decisions and constraining coupled ocean-biogeochemical models. The simplest partitioning method, which could provide low-latency information for managers at low cost, remains a purely optical classification based on ocean colour remote sensing. The question is whether such a simple approach has value. Here, the efficacy of optical classifications in constraining physical variables that modulate the epipelagic environment is tested for the tropical Indian Ocean, with a focus on the Chagos marine protected area (MPA). Using remote sensing data, it was found that optical classes corresponded to distinctive ranges of wind speed, wind stress curl, sea surface temperature, sea surface slope, sea surface height anomaly and geostrophic currents (Kruskal-Wallis and post-hoc Tukey honestly significantly different tests, α = 0.01). Between-class differences were significant for a set of sub-domains that resolved zonal and meridional gradients across the MPA and Seychelles-Chagos Thermocline Ridge, whereas between-domain differences were only significant for the north-south gradient (PERMANOVA, α = 0.01). A preliminary test of between-class differences in surface CO₂ concentrations from the Orbiting Carbon Observatory-2 demonstrated a small decrease in mean pCO₂ with increasing chlorophyll (chl), from 418 to 398 ppm. Simple optical class maps therefore provide an overview of growth conditions, the spatial distribution of resources - from which habitat fragmentation metrics can be calculated, and carbon sequestration potential. Within the 17 year study period, biotic variables were found to have decreased at up to 0.025%a^-1 for all optical classes, which is slower than reported elsewhere (Mann-Kendall-Sen regression, α = 0.01). Within the MPA, positive Indian Ocean Dipole conditions and negative Southern Oscillation Indices were weakly associated with decreasing chl, fluorescence line height (FLH), eddy kinetic energy, easterly wind stress and wind stress curl, and with increasing FLH/chl, sea surface temperature, SSH gradients and northerly wind stress, consistent with reduced surface mixing and increased stratification. The optical partitioning scheme described here can be applied in Google Earth Engine to support management decisions at daily or monthly scales, and potential applications are discussed.

Optical backscattering and linear polarization properties of the colony forming cyanobacterium Microcystis

Light scattering characteristics of the cyanobacterium Microcystis are investigated with numerical models for sphere aggregates. During summer bloom seasons, Microcystis is prevalent in many inland waters across the globe. Monitoring concentrations with remote sensing techniques requires knowledge of the inherent optical properties (IOPs), especially the backscattering properties of Microcystis cells and colonies in natural settings. In situ measurements in waters dominated by Microcystis blooms have previously detected extremely high backscattering ratios, i.e., b_b/b>0.043 at 443 nm [1], the highest to our knowledge in the natural environment. These highb_b/bvalues could hold promise as a diagnostic tool in identifying and monitoring Microcystis using optical approaches. However, it has been unclear how this type of optically 'soft' organic particle can generate such highb_b/bvalues. In this study, the Multiple Sphere T-matrix (MSTM) model is used to calculate the IOPs of model colonies, including b_b/b. Colony sizes in the model ranged from several cells to several hundred and both colony packing density and cell gas vacuole content were varied. Results are compared with model results for equivalent-volume spheres (EVS) and direct in situ measurements. Colony formation was required in the modeling to reproduce the high b_b/bconsistent with in situ measurements. The combination of moderate to very dense colony (packing density >30%) and high gas vacuole content in individual cells (volume percentage >20%) was the most favorable condition leading to rapid increases in b_b/bwith increasing number of cells N_cell of the colony. Significant linear correlations were observed betweenb_b/b and N_cell^1/3 for these colonies, whereb_b/b increased beyond 0.04 once cell number reached about 1000 cells in the case with the most densely packed cells and highest gas vacuole content. Within commonly observed colony sizes (N_cell <10⁶), colonies with high gas vacuole content exhibited b_b/bvalues up to 0.055, consistent with direct measurements from Lake Erie. Polarized scattering was also of interest as a diagnostic tool, particularly with future Earth-orbiting polarimeters being deployed for the NASA Plankton, Aerosols, Cloud, ocean Ecosystem (PACE) mission. The Degree of Linear Polarization (DoLP), expressed by the ratio of two Mueller matrix elements-P₁₂/P₁₁, decreased with increasing colony cell number for Microcystis. Another ratio of two Mueller matrix elementsP₂₂/P₁₁, an index for nonsphericity, also decreased with increasing colony size. In addition to higher relative backscattering, greater colony packing density and larger gas vacuole sizes both led to lower DoLP peak magnitude and lowerP₂₂/P₁₁. An optical opposition feature due to constructive phase interference that was observed previously for cosmic dusts is also present for these modeled colonies, manifested by a narrow intensity peak and negative polarization dip near exact backscattering direction, gradually forming as colony size increases.

In situ measurements of the volume scattering function with LISST-VSF and LISST-200X in extreme environments: evaluation of instrument calibration and validity

The LISST-VSF and LISST-200X are commercial instruments made available in recent years, enabling underwater measurements of the volume scattering function, which has not been routinely measured in situ due to lack of instrumentation and difficulty of measurement. Bench-top and in situ measurements have enabled absolute calibration of the instruments and evaluation of instrument validity ranges, even at environmental extremes such as the clear waters at the North Pole and turbid glacial meltwaters. Key considerations for instrument validity ranges are ring detector noise levels and multiple scattering. In addition, Schlieren effects can be significant in stratified waters.

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.

Polarized radiative transfer in dense dispersed media containing optically soft sticky particles

This paper focuses on polarized radiative transfer in dispersed layers composed of densely packed optically soft particles while considering the effects of dependent scattering and particle agglomeration. The radiative properties of the particles for different agglomeration degrees are calculated using the Lorenz-Mie theory combined with the Percus-Yevick sticky hard sphere model, and the vector radiative transfer equation is solved by using the spectral method. The normalized Stokes reflection matrix elements of the layers for different particle sizes, particle volume fractions and layer thicknesses are discussed. The results show that the effects of multiple scattering, dependent scattering and particle agglomeration have different degrees of influence on the polarized reflection characteristics of the layers. Due to the inhibition effect of far-field interference interaction on particle scattering, the dependent scattering weakens the depolarization caused by multiple scattering. However, as the particles form agglomerations, the scattering coefficients of the particles obviously increase with the agglomeration degree, which will lead to the significant enhancement of the multiple scattering and depolarization.

A Semianalytic Monte Carlo Simulator for Spaceborne Oceanic Lidar: Framework and Preliminary Results

Spaceborne lidar (light detection and ranging) is a very promising tool for the optical properties of global atmosphere and ocean detection. Although some studies have shown spaceborne lidar’s potential in ocean application, there is no spaceborne lidar specifically designed for ocean studies at present. In order to investigate the detection mechanism of the spaceborne lidar and analyze its detection performance, a spaceborne oceanic lidar simulator is established based on the semianalytic Monte Carlo (MC) method. The basic principle, the main framework, and the preliminary results of the simulator are presented. The whole process of the laser emitting, transmitting, and receiving is executed by the simulator with specific atmosphere–ocean optical properties and lidar system parameters. It is the first spaceborne oceanic lidar simulator for both atmosphere and ocean. The abilities of this simulator to characterize the effect of multiple scattering on the lidar signals of different aerosols, clouds, and seawaters with different scattering phase functions are presented. Some of the results of this simulator are verified by the lidar equation. It is confirmed that the simulator is beneficial to study the principle of spaceborne oceanic lidar and it can help develop a high-precision retrieval algorithm for the inherent optical properties (IOPs) of seawater.

Empirical Relationships between Remote-Sensing Reflectance and Selected Inherent Optical Properties in Nordic Sea Surface Waters for the MODIS and OLCI Ocean Colour Sensors

The Nordic Seas and the Fram Strait regions are a melting pot of a number of water masses characterized by distinct optical water properties. The warm Atlantic Waters transported from the south and the Arctic Waters from the north, combined with the melt waters contributing to the Polar Waters, mediate the dynamic changes of the year-to-year large-scale circulation patterns in the area, which often form complex frontal zones. In the last decade, moreover, a significant shift in phytoplankton phenology in the area has been observed, with a certain northward expansion of temperate phytoplankton communities into the Arctic Ocean which could lead to a deterioration in the performance of remote sensing algorithms. In this research, we exploited the capability of the satellite sensors to monitor those inter-annual changes at basin scales. We propose locally adjusted algorithms for retrieving chlorophyll a concentrations Chla, absorption by particles ap at 443 and 670 nm, and total absorption atot at 443 and 670 nm developed on the basis of intensive field work conducted in 2013–2015. Measured in situ hyper spectral remote sensing reflectance has been used to reconstruct the MODIS and OLCI spectral channels for which the proposed algorithms have been adapted. We obtained MNB ≤ 0.5% for ap(670) and ≤3% for atot(670) and Chla. RMS was ≤30% for most of the retrieved optical water properties except ap(443) and Chla. The mean monthly mosaics of ap(443) computed on the basis of the proposed algorithm were used for reconstructing the spatial and temporal changes of the phytoplankton biomass in 2013–2015. The results corresponded very well with in situ measurements.

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.

Contribution of Remote Sensing Technologies to a Holistic Coastal and Marine Environmental Management Framework: A Review

Coastal and marine management require the evaluation of multiple environmental threats and issues. However, there are gaps in the necessary data and poor access or dissemination of existing data in many countries around the world. This research identifies how remote sensing can contribute to filling these gaps so that environmental agencies, such as the United Nations Environmental Programme, European Environmental Agency, and International Union for Conservation of Nature, can better implement environmental directives in a cost-effective manner. Remote sensing (RS) techniques generally allow for uniform data collection, with common acquisition and reporting methods, across large areas. Furthermore, these datasets are sometimes open-source, mainly when governments finance satellite missions. Some of these data can be used in holistic, coastal and marine environmental management frameworks, such as the DAPSI(W)R(M) framework (Drivers–Activities–Pressures–State changes–Impacts (on Welfare)–Responses (as Measures), an updated version of Drivers–Pressures–State–Impact–Responses. The framework is a useful and holistic problem-structuring framework that can be used to assess the causes, consequences, and responses to change in the marine environment. Six broad classifications of remote data collection technologies are reviewed for their potential contribution to integrated marine management, including Satellite-based Remote Sensing, Aerial Remote Sensing, Unmanned Aerial Vehicles, Unmanned Surface Vehicles, Unmanned Underwater Vehicles, and Static Sensors. A significant outcome of this study is practical inputs into each component of the DAPSI(W)R(M) framework. The RS applications are not expected to be all-inclusive; rather, they provide insight into the current use of the framework as a foundation for developing further holistic resource technologies for management strategies in the future. A significant outcome of this research will deliver practical insights for integrated coastal and marine management and demonstrate the usefulness of RS to support the implementation of environmental goals, descriptors, targets, and policies, such as the Water Framework Directive, Marine Strategy Framework Directive, Ocean Health Index, and United Nations Sustainable Development Goals. Additionally, the opportunities and challenges of these technologies are discussed.

An Algorithm to Estimate Suspended Particulate Matter Concentrations and Associated Uncertainties from Remote Sensing Reflectance in Coastal Environments

Suspended Particulate Matter (SPM) is a major constituent in coastal waters, involved in processes such as light attenuation, pollutant propagation, and waterways blockage. The spatial distribution of SPM is an indicator of deposition and erosion patterns in estuaries and coastal zones and a necessary input to estimate the material fluxes from the land through rivers to the sea. In-situ methods to estimate SPM provide limited spatial data in comparison to the coverage that can be obtained remotely. Ocean color remote sensing complements field measurements by providing estimates of the spatial distributions of surface SPM concentration in natural waters, with high spatial and temporal resolution. Existing methods to obtain SPM from remote sensing vary between purely empirical ones to those that are based on radiative transfer theory together with empirical inputs regarding the optical properties of SPM. Most algorithms use a single satellite band that is switched to other bands for different ranges of turbidity. The necessity to switch bands is due to the saturation of reflectance as SPM concentration increases. Here we propose a multi-band approach for SPM retrievals that also provides an estimate of uncertainty, where the latter is based on both uncertainties in reflectance and in the assumed optical properties of SPM. The approach proposed is general and can be applied to any ocean color sensor or in-situ radiometer system with red and near-infra-red bands. We apply it to six globally distributed in-situ datasets of spectral water reflectance and SPM measurements over a wide range of SPM concentrations collected in estuaries and coastal environments (the focus regions of our study). Results show good performance for SPM retrieval at all ranges of concentration. As with all algorithms, better performance may be achieved by constraining empirical assumptions to specific environments. To demonstrate the flexibility of the algorithm we apply it to a remote sensing scene from an environment with highly variable sediment concentrations.

A global compilation of in situ aquatic high spectral resolution inherent and apparent optical property data for remote sensing applications

Light emerging from natural water bodies and measured by radiometers contains information about the local type and concentrations of phytoplankton, non-algal particles and colored dissolved organic matter in the underlying waters. An increase in spectral resolution in forthcoming satellite and airborne remote sensing missions is expected to lead to new or improved capabilities for characterizing aquatic ecosystems. Such upcoming missions include NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission; the NASA Surface Biology and Geology designated observable mission; and NASA Airborne Visible/Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG) airborne missions. In anticipation of these missions, we present an organized dataset of geographically diverse, quality-controlled, high spectral resolution inherent and apparent optical property (IOP-AOP) aquatic data. The data are intended to be of use to increase our understanding of aquatic optical properties, to develop aquatic remote sensing data product algorithms, and to perform calibration and validation activities for forthcoming aquatic-focused imaging spectrometry missions. The dataset is comprised of contributions from several investigators and investigating teams collected over a range of geographic areas and water types, including inland waters, estuaries, and oceans. Specific in situ measurements include remote-sensing reflectance, irradiance reflectance, and coefficients describing particulate absorption, particulate attenuation, non-algal particulate absorption, colored dissolved organic matter absorption, phytoplankton absorption, total absorption, total attenuation, particulate backscattering, and total backscattering. The dataset can be downloaded from https://doi.org/10.1594/PANGAEA.902230 (Casey et al., 2019).

Quantifying total suspended matter (TSM) in waters using Landsat images during 1984-2018 across the Songnen Plain, Northeast China

Understanding the spatiotemporal dynamics of total suspended matter (TSM) in waters is necessary to promote efficient water resource management. In our study, we have estimated the spatiotemporal pattern of TSM with the combination of time-series Landsat images and field survey. Among various remote sensing-derived parameters, the red/blue band turns to be robust and the most sensitive to the TSM from field measurements. In Songnen Plain, the mean annual TSM in 60.5% of the water bodies decreased from 1984 to 2018. The decreasing of TSM is likely due to the increasing of vegetation in the area. The TSM concentration in waters declined from April to July, and then increased from September onwards. We also found the TSM in water bodies in Songnen Plain has very high spatial variation. Our results indicated that the meteorological factors such as wind and precipitation may affect the variation of TSM. Our results demonstrate that long-term Landsat data are useful to examine TSM in inland waters. Our findings can support for water resource management under human activities and climate change.

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.

Information content of absorption spectra and implications for ocean color inversion

The increasing use of hyperspectral optical data in oceanography, both in situ and via remote sensing, holds the potential to significantly advance characterization of marine ecology and biogeochemistry because, in principle, hyperspectral data can provide much more detailed inferences of ecosystem properties via inversion. Effective inferences, however, require careful consideration of the close similarity of different signals of interest, and how these interplay with measurement error and uncertainty to reduce the degrees of freedom (DoF) of hyperspectral measurements. Here we discuss complementary approaches to quantify the DoF in hyperspectral measurements in the case of in situ particulate absorption measurements, though these approaches can also be used on other such data, e.g., ocean color remote sensing. Analyses suggest intermediate (${\sim}5 $∼5) DoF for our dataset of global hyperspectral particulate absorption spectra from the Tara Oceans expedition, meaning that these data can yield coarse community structure information. Empirically, chlorophyll is an effective first-order predictor of absorption spectra, meaning that error characteristics and the mathematics of inversion need to be carefully considered for hyperspectral data to provide information beyond that which chlorophyll provides. We also discuss other useful analytical tools that can be applied to this problem and place our results in the context of hyperspectral remote sensing.

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.