Performance metrics for the assessment of satellite data products: an ocean color case study

Expanding the Application of Sentinel-2 Chlorophyll Monitoring across United States Lakes

Eutrophication of inland lakes poses various societal and ecological threats, making water quality monitoring crucial. Satellites provide a comprehensive and cost-effective supplement to traditional in situ sampling. The Sentinel-2 MultiSpectral Instrument (S2 MSI) offers unique spectral bands positioned to quantify chlorophyll a, a water-quality and trophic-state indicator, along with fine spatial resolution, enabling the monitoring of small waterbodies. In this study, two algorithms-the Maximum Chlorophyll Index (MCI) and the Normalized Difference Chlorophyll Index (NDCI)-were applied to S2 MSI data. They were calibrated and validated using in situ chlorophyll a measurements for 103 lakes across the contiguous U.S. Both algorithms were tested using top-of-atmosphere reflectances (ρ t), Rayleigh-corrected reflectances (ρ s), and remote sensing reflectances (R rs ). MCI slightly outperformed NDCI across all reflectance products. MCI using ρ t showed the best overall performance, with a mean absolute error factor of 2.08 and a mean bias factor of 1.15. Conversion of derived chlorophyll a to trophic state improved the potential for management applications, with 82% accuracy using a binary classification. We report algorithm-to-chlorophyll-a conversions that show potential for application across the U.S., demonstrating that S2 can serve as a monitoring tool for inland lakes across broad spatial scales.

Satellite imagery as a management tool for monitoring water clarity across freshwater ponds on Cape Cod, Massachusetts

Water clarity serves as both an indicator and a regulator of biological function in aquatic systems. Large-scale, consistent water clarity monitoring is needed for informed decision-making. Inland freshwater ponds and lakes across Cape Cod, a 100-km peninsula in Massachusetts, are of particular interest for water clarity monitoring. Secchi disk depth (SDD), a common measure of water clarity, has been measured intermittently for over 200 Cape Cod ponds since 2001. Field-measured SDD data were used to estimate SDD from satellite data, leveraging the NASA/USGS Landsat Program and Copernicus Sentinel-2 mission, spanning 1984 to 2022. Random forest machine learning models were generated to estimate SDD from satellite reflectance data and maximum pond depth. Spearman rank correlations (rs) were "strong" for Landsat 5 and 7 (rs = 0.78 and 0.79), and "very strong" for Landsat 8, 9, and Sentinel-2 (rs = 0.83, 0.86, and 0.80). Mean absolute error also indicated strong predictive capacity, ranging from 0.65 to 1.05 m, while average bias ranged from -0.20 to 0.06 m. Long- and recent short-term changes in satellite-estimated SDD were assessed for 193 ponds, selected based on surface area and the availability of maximum pond depth data. Long-term changes between 1984 and 2022 established a retrospective baseline using the Mann-Kendall test for trend and Theil-Sen slope. Generally, long-term water clarity improved across the Cape; 149 ponds indicated increasing water clarity, and 8 indicated deteriorating water clarity. Recent short-term changes between 2021 and 2022 identified ponds that may benefit from targeted management efforts using the Mann-Whitney U test. Between 2021 and 2022, 96 ponds indicated deteriorations in water clarity, and no ponds improved in water clarity. While the 193 ponds analyzed here constitute only one quarter of Cape Cod ponds, they represent 85% of its freshwater surface area, providing the most spatially and temporally comprehensive assessment of Cape Cod ponds to date. Efforts are focused on Cape Cod, but can be applied to other areas given the availability of local field data. This study defines a framework for monitoring and assessing change in satellite-estimated SDD, which is important for both local and regional management and resource prioritization.

A tuned ocean color algorithm for the Arctic Ocean: a solution for waters with high CDM content

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

Assessing the suitability of lakes and reservoirs for recreation using Landsat 8

Water clarity has long been used as a visual indicator of the condition of water quality. The clarity of waters is generally valued for esthetic and recreational purposes. Water clarity is often assessed using a Secchi disk attached to a measured line and lowered to a depth where it can be no longer seen. We have applied an approach which uses atmospherically corrected Landsat 8 data to estimate the water clarity in freshwater bodies by using the quasi-analytical algorithm (QAA) and Contrast Theory to predict Secchi depths for more than 270 lakes and reservoirs across the continental US. We found that incorporating Landsat 8 spectral data into methodologies created to retrieve the inherent optical properties (IOP) of coastal waters was effective at predicting in situ measures of the clarity of inland water bodies. The predicted Secchi depths were used to evaluate the recreational suitability for swimming and recreation using an assessment framework developed from public perception of water clarity. Results showed approximately 54% of the water bodies in our dataset were classified as "marginally suitable to suitable" with approximately 31% classed as "eminently suitable" and approximately 15% classed as "totally unsuitable-unsuitable". The implications are that satellites engineered for terrestrial applications can be successfully used with traditional ocean color algorithms and methods to measure the water quality of freshwater environments. Furthermore, operational land-based satellite sensors have the temporal repeat cycles, spectral resolution, wavebands, and signal-to-noise ratios to be repurposed to monitor water quality for public use and trophic status of complex inland waters.

Demonstration of a Modular Prototype End-to-End Simulator for Aquatic Remote Sensing Applications

This study introduces a prototype end-to-end Simulator software tool for simulating two-dimensional satellite multispectral imagery for a variety of satellite instrument models in aquatic environments. Using case studies, the impact of variable sensor configurations on the performance of value-added products for challenging applications, such as coral reefs and cyanobacterial algal blooms, is assessed. This demonstrates how decisions regarding satellite sensor design, driven by cost constraints, directly influence the quality of value-added remote sensing products. Furthermore, the Simulator is used to identify situations where retrieval algorithms require further parameterization before application to unsimulated satellite data, where error sources cannot always be identified or isolated. The application of the Simulator can verify whether a given instrument design meets the performance requirements of end-users before build and launch, critically allowing for the justification of the cost and specifications for planned and future sensors. It is hoped that the Simulator will enable engineers and scientists to understand important design trade-offs in phase 0/A studies easily, quickly, reliably, and accurately in future Earth observation satellites and systems.

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.

Algal biomass mapping of eutrophic lakes using a machine learning approach with MODIS images

Algal blooms are a widespread issue in eutrophic lakes. Compared with the satellite-derived surface algal bloom area and chlorophyll-a (Chla) concentration, algae biomass is a more stable way to reflect water quality. Although satellite data have been adopted to observe the water column integrated algal biomass, the previous methods mostly are empirical algorithms, which are not stable enough for widespread use. This paper proposed a machine learning algorithm based on Moderate Resolution Imaging Spectrometer (MODIS) data to estimate the algal biomass, which was successfully applied to a eutrophic lake in China, Lake Taihu. This algorithm was developed by linking Rayleigh-corrected reflectance to in situ algae biomass data in Lake Taihu (n = 140), and the different mainstream machine learning (ML) methods were compared and validated. The partial least squares regression (PLSR) (R² = 0.67, mean absolute percentage error (MAPE) = 38.88 %) and support vector machines (SVM) (R² = 0.46, MAPE = 52.02 %) performed poor satisfactory. In contrast, random forest (RF) and extremely gradient boosting tree (XGBoost) algorithms had higher accuracy (RF: R² = 0.85, MAPE = 22.68 %; XGBoost: R² = 0.83, MAPE = 24.06 %), demonstrating greater application potential in algal biomass estimation. Field biomass data were further used to estimate the RF algorithm, which showed acceptable precision (R² = 0.86, MAPE < 7 mg Chla). Subsequently, sensitivity analysis showed that the RF algorithm was not sensitive to high suspension and thickness of aerosols (rate of change <2 %), and inter-day and consecutive days verification showed stability (rate of change <5 %). The algorithm was also extended to Lake Chaohu (R² = 0.93, MAPE = 18.42 %), demonstrating its potential in other eutrophic lakes. This study for algae biomass estimation provides technical means with higher accuracy and greater universality for the management of eutrophic lakes.

Satellite and in situ cyanobacteria monitoring: Understanding the impact of monitoring frequency on management decisions

Cyanobacterial harmful algal blooms (cyanoHABs) in reservoirs can be transported to downstream waters via scheduled discharges. Transport dynamics are difficult to capture in traditional cyanoHAB monitoring, which can be spatially disparate and temporally discontinuous. The introduction of satellite remote sensing for cyanoHAB monitoring provides opportunities to detect where cyanoHABs occur in relation to reservoir release locations, like canal inlets. The study objectives were to assess (1) differences in reservoir cyanoHAB frequencies as determined by in situ and remotely sensed data and (2) the feasibility of using satellite imagery to identify conditions associated with release-driven cyanoHAB export. As a representative case, Lake Okeechobee and the St. Lucie Estuary (Florida, USA), which receives controlled releases from Lake Okeechobee, were examined. Both systems are impacted by cyanoHABs, and the St. Lucie Estuary experienced states of emergency for extreme cyanoHABs in 2016 and 2018. Using the European Space Agency's Sentinel-3 OLCI imagery processed with the Cyanobacteria Index (CI cyano ), cyanoHAB frequencies across Lake Okeechobee from May 2016-April 2021 were compared to frequencies from in situ data. Strong agreement was observed in frequency rankings between the in situ and remotely sensed data in capturing intra-annual variability in bloom frequencies across Lake Okeechobee (Kendall's tau = 0.85, p-value = 0.0002), whereas no alignment was observed when evaluating inter-annual variation (Kendall's tau = 0, p-value = 1). Further, remotely sensed observations revealed that cyanoHABs were highly frequent near the inlet to the canal connecting Lake Okeechobee to the St. Lucie Estuary in state-of-emergency years, a pattern not evident from in situ data alone. This study demonstrates how remote sensing can complement traditional cyanoHAB monitoring to inform reservoir release decision making.

Application of a PLS-Augmented ANN Model for Retrieving Chlorophyll-a from Hyperspectral Data in Case 2 Waters of the Western Basin of Lake Erie

We present results that demonstrate the utility of machine learning techniques that are based on partial least squares (PLS) and artificial neural networks (ANNs) for estimating low-moderate chlorophyll-a (chl-a) concentrations in the western basin of Lake Erie (WBLE). Previous ocean color studies have resulted in a large number of algorithms that are based on spectral indices to estimate water quality parameters (WQPs) such as chl-a concentration from remote sensing reflectance. However, these spectral index algorithms are based on reflectance features at specific wavelengths and do not take advantage of the wealth of spectral information that is contained in hyperspectral data, and are often not easily adaptable to waters with conditions that are different from those in the datasets that were used to originally calibrate the indices. Recently, there have been efforts to use machine learning techniques that are based on ANNs and PLS regression to exploit the spectral richness contained in hyperspectral data and retrieve WQPs. In this study, we have combined an ANN model with output from PLS regression to retrieve chl-a concentration from hyperspectral data in the WBLE. We compared the results from the PLS-ANN method to those that were obtained from a band-ratio algorithm that is based on reflectances in the blue and green spectral regions, a band ratio algorithm that is based on reflectances in the red and near-infrared (NIR) spectral regions, and a PLS-only approach. For a dataset that was collected in 2012, with chl-a concentrations ranging from 0.48 to 21.2 µg/L, the PLS-ANN method yielded a root mean square error (RMSE) of 1.22 µg/L, whereas the blue-green ratio algorithm yielded an RMSE of 1.75 µg/L, the NIR-red ratio algorithm yielded an RMSE of 1.95 µg/L, and the PLS-only approach yielded an RMSE of 1.95 µg/L. The PLS-ANN method takes advantage of the PLS regression to identify specific wavelengths that contain most information about the variation in chl-a concentration, minimize spectral collinearity and redundancy in the data, and simplify the neural network’s input structure. The better performance of the PLS-ANN method can also be attributed to the neural network’s ability to account for nonlinearity in the relationship between chl-a concentration and spectral reflectance. The results indicate that the PLS-ANN method can be reliably used to estimate and monitor low-moderate chl-a concentrations in optically complex waters.

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.

Improvement and Assessment of Ocean Color Algorithms in the Northwest Pacific Fishing Ground Using Himawari-8, MODIS-Aqua, and VIIRS-SNPP

Chlorophyll-a (Chl-a) is an important marine indicator, and the improvement in Chl-a concentration retrieval for ocean color remote sensing is always a major challenge. This study focuses on the northwest Pacific fishing ground (NPFG) to evaluate and improve the Chl-a products of three mainstream remote sensing satellites, Himawari-8, MODIS-Aqua, and VIIRS-SNPP. We analyzed in situ data and found that an in situ Chl-a concentration of 0.3 mg m−3 could be used as a threshold to distinguish the systematic deviation of remote sensing Chl-a data in the NPFG. Based on this threshold, we optimized the Chl-a algorithms of the three satellites by data grouping, and integrated multisource satellite Chl-a data by weighted averaging to acquire high-coverage merged data. The merged data were thoroughly verified by Argo Chl-a data. The Chl-a front of merged Chl-a data could be represented accurately and completely and had a good correlation with the distribution of the NPFG. The most important marine factors for Chl-a are nutrients and temperature, which are affected by mesoscale eddies and variations in the Kuroshio extension. The variation trend of merged Chl-a data is consistent with mesoscale eddies and Kuroshio extension and has more sensitive responses to the marine climatic conditions of ENSO.

An Artificial Neural Network Algorithm to Retrieve Chlorophyll a for Northwest European Shelf Seas from Top of Atmosphere Ocean Colour Reflectance

Chlorophyll-a (Chl) retrieval from ocean colour remote sensing is problematic for relatively turbid coastal waters due to the impact of non-algal materials on atmospheric correction and standard Chl algorithm performance. Artificial neural networks (NNs) provide an alternative approach for retrieval of Chl from space and results for northwest European shelf seas over the 2002–2020 period are shown. The NNs operate on 15 MODIS-Aqua visible and infrared bands and are tested using bottom of atmosphere (BOA), top of atmosphere (TOA) and Rayleigh corrected TOA reflectances (RC). In each case, a NN architecture consisting of 3 layers of 15 neurons improved performance and data availability compared to current state-of-the-art algorithms used in the region. The NN operating on TOA reflectance outperformed BOA and RC versions. By operating on TOA reflectance data, the NN approach overcomes the common but difficult problem of atmospheric correction in coastal waters. Moreover, the NN provides data for regions which other algorithms often mask out for turbid water or low zenith angle flags. A distinguishing feature of the NN approach is generation of associated product uncertainties based on multiple resampling of the training data set to produce a distribution of values for each pixel, and an example is shown for a coastal time series in the North Sea. The final output of the NN approach consists of a best-estimate image based on medians for each pixel, and a second image representing uncertainty based on standard deviation for each pixel, providing pixel-specific estimates of uncertainty in the final product.

Simulated response of St. Joseph Bay, Florida, seagrass meadows and their belowground carbon to anthropogenic and climate impacts

Seagrass meadows are degraded globally and continue to decline in areal extent due to human pressures and climate change. This study used the bio-optical model GrassLight to explore the impact of climate change and anthropogenic stressors on seagrass extent, leaf area index (LAI) and belowground organic carbon (BGC) in St. Joseph Bay, Florida, using water quality data and remotely-sensed sea surface temperature (SST) from 2002 to 2020. Model predictions were compared with satellite-derived measurements of seagrass extent and shoot density from the Landsat images for the same period. The GrassLight-derived area of potential seagrass habitat ranged from 36.2 km2 to 39.2 km2, averaging 38.0 ± 0.8 km2 compared to an observed seagrass extent of 23.0 ± 3.0 km2 derived from Landsat (range = 17.9-27.4 km2). GrassLight predicted a mean seagrass LAI of 2.7 m2 leaf m-2 seabed, compared to a mean LAI of 1.9 m2 m-2 estimated from Landsat, indicating that seagrass density in St. Joseph Bay may have been below its light-limited ecological potential. Climate and anthropogenic change simulations using GrassLight predicted the impact of changes in temperature, pH, chlorophyll a, chromophoric dissolved organic matter and turbidity on seagrass meadows. Simulations predicted a 2-8% decline in seagrass extent with rising temperatures that was offset by a 3-11% expansion in seagrass extent in response to ocean acidification when compared to present conditions. Simulations of water quality impacts showed that a doubling of turbidity would reduce seagrass extent by 18% and total leaf area by 21%. Combining climate and water quality scenarios showed that ocean acidification may increase seagrass productivity to offset the negative effects of both thermal stress and declining water quality on the seagrasses growing in St. Joseph Bay. This research highlights the importance of considering multiple limiting factors in understanding the effects of environmental change on seagrass ecosystems.

Spatio-Temporal Variability of Suspended Particulate Matter in a High-Arctic Estuary (Adventfjorden, Svalbard) Using Sentinel-2 Time-Series

Arctic coasts, which feature land-ocean transport of freshwater, sediments, and other terrestrial material, are impacted by climate change, including increased temperatures, melting glaciers, changes in precipitation and runoff. These trends are assumed to affect productivity in fjordic estuaries. However, the spatial extent and temporal variation of the freshwater-driven darkening of fjords remain unresolved. The present study illustrates the spatio-temporal variability of suspended particulate matter (SPM) in the Adventfjorden estuary, Svalbard, using in-situ field campaigns and ocean colour remote sensing (OCRS) via high-resolution Sentinel-2 imagery. To compute SPM concentration (CSPMsat), a semi-analytical algorithm was regionally calibrated using local in-situ data, which improved the accuracy of satellite-derived SPM concentration by ~20% (MRD). Analysis of SPM concentration for two consecutive years (2019, 2020) revealed strong seasonality of SPM in Adventfjorden. Highest estimated SPM concentrations and river plume extent (% of fjord with CSPMsat > 30 mg L−1) occurred during June, July, and August. Concurrently, we observed a strong relationship between river plume extent and average air temperature over the 24 h prior to the observation (R2 = 0.69). Considering predicted changes to environmental conditions in the Arctic region, this study highlights the importance of the rapidly changing environmental parameters and the significance of remote sensing in analysing fluxes in light attenuating particles, especially in the coastal Arctic Ocean.

Ocean mover's distance: using optimal transport for analysing oceanographic data

Remote sensing observations from satellites and global biogeochemical models have combined to revolutionize the study of ocean biogeochemical cycling, but comparing the two data streams to each other and across time remains challenging due to the strong spatial-temporal structuring of the ocean. Here, we show that the Wasserstein distance provides a powerful metric for harnessing these structured datasets for better marine ecosystem and climate predictions. The Wasserstein distance complements commonly used point-wise difference methods such as the root-mean-squared error, by quantifying differences in terms of spatial displacement in addition to magnitude. As a test case, we consider chlorophyll (a key indicator of phytoplankton biomass) in the northeast Pacific Ocean, obtained from model simulations, in situ measurements, and satellite observations. We focus on two main applications: (i) comparing model predictions with satellite observations, and (ii) temporal evolution of chlorophyll both seasonally and over longer time frames. The Wasserstein distance successfully isolates temporal and depth variability and quantifies shifts in biogeochemical province boundaries. It also exposes relevant temporal trends in satellite chlorophyll consistent with climate change predictions. Our study shows that optimal transport vectors underlying the Wasserstein distance provide a novel visualization tool for testing models and better understanding temporal dynamics in the ocean.

Monitoring Optical Variability in Complex Inland Waters Using Satellite Remote Sensing Data

Optical classification for water bodies was carried out based on satellite remote sensing data, which avoided the limitation of having a limited amount of in situ measured spectral data. Unsupervised cluster analysis was performed on 53,815 reflectance spectra extracted at 500-m intervals based on the same season or quasi-same season Landsat 8 SR data using the algorithm of fuzzy c-means. Lakes and reservoirs in the study area were comprehensively identified as three optical types representing different limnological features. The shape and amplitude characteristics of the reflectance spectra for the three optical water types indicated that one corresponds to the clearest water, one corresponds to turbid water, and the other is moderate clear water. The novelty detection technique was further used to label the match-ups of the in situ data set collected during 2006 to 2019 in 12 field surveys based on mathematical rules of the three optical water types. The results confirmed that each optical water type was associated with different bio-optical properties, and the total suspended matter of the clearest, moderate clear and turbid water types were 14.99 mg/L, 41.06 mg/L and 83.81 mg/L, respectively. Overall, the clearest, moderate clear and turbid waters in the study area accounted for 49.3%, 36.7% and 14.0%, respectively. The spatial distribution of optical water types in the study area was seamlessly mapped. Results showed that the bio-optical conditions of the water distributed across the southeast region were roughly homogeneous, but in most of other regions and within some water bodies, they showed a patchy distribution and heterogeneity. This study is useful for monitoring water quality and provides a useful foundation to develop or tuning algorithms to retrieve water quality parameters.

Why Is It Important to Consider Dust Aerosol in the Sevastopol and Black Sea Region during Remote Sensing Tasks? A Case Study

Atmospheric correction of satellite optical data is based on an assessment of the optical characteristics of the atmosphere, such as the aerosol optical thickness of the atmosphere and the spectral slope, the so-called Angstrom parameter. Inaccurate determination of these parameters is one of the causes of error in the retrieval of remote-sensed reflectance spectra. In this work, a large array of field and satellite data measured in Sevastopol and the northeastern part of the Black Sea were used, including ship-based measurements of atmospheric characteristics and sea reflectance, MODIS Aqua/Terra, and VIIRS NOAA/NPP Level 2 remote-sensed reflectance and atmospheric data. In total, three episodes of Saharan dust transfer over the Black Sea region were considered, mainly in the autumn-winter period. The purpose of this study was to show the numerical differences between the atmospheric parameters measured at the surface level and by satellites, and show their relationship with the differences between in situ and satellite remote-sensed reflectance. Based on the information identified, we propose an algorithm for additional correction of satellite level 2 data that uses a two-parametric model of the Black Sea remote-sensed reflectance as a first approximation. Moreover, additional correction significantly reduces the discrepancy between in situ and retrieved remote-sensed reflectance, especially in short-wave spectral bands.

OC4-SO: A New Chlorophyll-a Algorithm for the Western Antarctic Peninsula Using Multi-Sensor Satellite Data

Chlorophyll-a (Chl-a) underestimation by global satellite algorithms in the Southern Ocean has long been reported, reducing their accuracy, and limiting the potential for evaluating phytoplankton biomass. As a result, several regional Chl-a algorithms have been proposed. The present work aims at assessing the performance of both global and regional satellite algorithms that are currently available for the Western Antarctic Peninsula (WAP) and investigate which factors are contributing to the underestimation of Chl-a. Our study indicates that a global algorithm, on average, underestimates in-situ Chl-a by ~59%, although underestimation was only observed for waters with Chl-a > 0.5 mg m−3. In high Chl-a waters (>1 mg m−3), Chl-a underestimation rose to nearly 80%. Contrary to previous studies, no clear link was found between Chl-a underestimation and the pigment packaging effect, nor with the phytoplankton community composition and sea ice contamination. Based on multi-sensor satellite data and the most comprehensive in-situ dataset ever collected from the WAP, a new, more accurate satellite Chl-a algorithm is proposed: the OC4-SO. The OC4-SO has great potential to become an important tool not only for the ocean colour community, but also for an effective monitoring of the phytoplankton communities in a climatically sensitive region where in-situ data are scarce.

Merging of the Case 2 Regional Coast Colour and Maximum-Peak Height chlorophyll-a algorithms: validation and demonstration of satellite-derived retrievals across US lakes

Water quality monitoring is relevant for protecting the designated, or beneficial uses, of water such as drinking, aquatic life, recreation, irrigation, and food supply that support the economy, human well-being, and aquatic ecosystem health. Managing finite water resources to support these designated uses requires information on water quality so that managers can make sustainable decisions. Chlorophyll-a (chl-a, µg L-1) concentration can serve as a proxy for phytoplankton biomass and may be used as an indicator of increased anthropogenic nutrient stress. Satellite remote sensing may present a complement to in situ measures for assessments of water quality through the retrieval of chl-a with in-water algorithms. Validation of chl-a algorithms across US lakes improves algorithm maturity relevant for monitoring applications. This study compares performance of the Case 2 Regional Coast Colour (C2RCC) chl-a retrieval algorithm, a revised version of the Maximum-Peak Height (MPH(P)) algorithm, and three scenarios merging these two approaches. Satellite data were retrieved from the MEdium Resolution Imaging Spectrometer (MERIS) and the Ocean and Land Colour Instrument (OLCI), while field observations were obtained from 181 lakes matched with U.S. Water Quality Portal chl-a data. The best performance based on mean absolute multiplicative error (MAEmult) was demonstrated by the merged algorithm referred to as C15-M10 (MAEmult = 1.8, biasmult = 0.97, n = 836). In the C15-M10 algorithm, the MPH(P) chl-a value was retained if it was > 10 µg L-1; if the MPH(P) value was ≤ 10 µg L-1, the C2RCC value was selected, as long as that value was < 15 µg L-1. Time-series and lake-wide gradients compared against independent assessments from Lake Champlain and long-term ecological research stations in Wisconsin were used as complementary examples supporting water quality reporting requirements. Trophic state assessments for Wisconsin lakes provided examples in support of inland water quality monitoring applications. This study presents and assesses merged adaptations of chl-a algorithms previously reported independently. Additionally, it contributes to the transition of chl-a algorithm maturity by quantifying error statistics for a number of locations and times.

Variability of Chlorophyll-a and Secchi Disk Depth (1997–2019) in the Bohai Sea Based on Monthly Cloud-Free Satellite Data Reconstructions

Ocean colour data are crucial for monitoring and assessing marine ecosystems. In this study, the Data Interpolating Empirical Orthogonal Functions (DINEOF) approach was applied to the Ocean Colour Climate Change Initiative (OC-CCI), chlorophyll-a (Chl-a) and Secchi disk depth (Zsd) to completely reconstruct the missing pixels in the Bohai Sea during 1997–2019. The results of cross-validation demonstrate that the DINEOF reconstructed data have a good agreement with the satellite-measured data. Based on monthly cloud-free satellite data reconstructions, the Zsd series showed high negative correlation with log10 (Chl-a). The Zsd as a function of log10 (Chl-a) can be well fitted by the cubic polynomial in the offshore waters. The Chl-a in the entire Bohai Sea showed a significant decreasing trend (−0.013 mg/m3/year), while the Zsd exhibited a significant increasing trend (0.0065 m/year), and both had regional-seasonal variations. In addition, the ensemble empirical mode decomposition (EEMD) results reveal highly nonlinear trends of Chl-a and Zsd. The linear and nonlinear trends of Chl-a and Zsd suggest the deterioration of water quality in the Bohai Sea was not continued over the past two decades. This study presents the first simultaneous investigation of Chl-a and Zsd using the 23 years of cloud-free reconstructions in the Bohai Sea.

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.

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.

One- and Two-Band Sensors and Algorithms to Derive aCDOM(440) from Global Above- and In-Water Optical Observations

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

Analyzing Satellite Ocean Color Match-Up Protocols Using the Satellite Validation Navy Tool (SAVANT) at MOBY and Two AERONET-OC Sites

The satellite validation navy tool (SAVANT) was developed by the Naval Research Laboratory to help facilitate the assessment of the stability and accuracy of ocean color satellites, using numerous ground truth (in situ) platforms around the globe and support methods for match-up protocols. The effects of varying spatial constraints with permissive and strict protocols on match-up uncertainty are evaluated, in an attempt to establish an optimal satellite ocean color calibration and validation (cal/val) match-up protocol. This allows users to evaluate the accuracy of ocean color sensors compared to specific ground truth sites that provide continuous data. Various match-up constraints may be adjusted, allowing for varied evaluations of their effects on match-up data. The results include the following: (a) the difference between aerosol robotic network ocean color (AERONET-OC) and marine optical Buoy (MOBY) evaluations; (b) the differences across the visible spectrum for various water types; (c) spatial differences and the size of satellite area chosen for comparison; and (d) temporal differences in optically complex water. The match-up uncertainty analysis was performed using Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) SNPP data at the AERONET-OC sites and the MOBY site. It was found that the more permissive constraint sets allow for a higher number of match-ups and a more comprehensive representation of the conditions, while the restrictive constraints provide better statistical match-ups between in situ and satellite sensors.

Inter-Comparison of Methods for Chlorophyll-a Retrieval: Sentinel-2 Time-Series Analysis in Italian Lakes

Different methods are available for retrieving chlorophyll-a (Chl-a) in inland waters from optical imagery, but there is still a need for an inter-comparison among the products. Such analysis can provide insights into the method selection, integration of products, and algorithm development. This work aims at inter-comparison and consistency analyses among the Chl-a products derived from publicly available methods consisting of Case-2 Regional/Coast Colour (C2RCC), Water Color Simulator (WASI), and OC3 (3-band Ocean Color algorithm). C2RCC and WASI are physics-based processors enabling the retrieval of not only Chl-a but also total suspended matter (TSM) and colored dissolved organic matter (CDOM), whereas OC3 is a broadly used semi-empirical approach for Chl-a estimation. To pursue the inter-comparison analysis, we demonstrate the application of Sentinel-2 imagery in the context of multitemporal retrieval of constituents in some Italian lakes. The analysis is performed for different bio-optical conditions including subalpine lakes in Northern Italy (Garda, Idro, and Ledro) and a turbid lake in Central Italy (Lake Trasimeno). The Chl-a retrievals are assessed versus in situ matchups that indicate the better performance of WASI. Moreover, relative consistency analyses are performed among the products (Chl-a, TSM, and CDOM) derived from different methods. In the subalpine lakes, the results indicate a high consistency between C2RCC and WASI when aCDOM(440) < 0.5 m−1, whereas the retrieval of constituents, particularly Chl-a, is problematic based on C2RCC for high-CDOM cases. In the turbid Lake Trasimeno, the extreme neural network of C2RCC provided more consistent products with WASI than the normal network. OC3 overestimates the Chl-a concentration. The flexibility of WASI in the parametrization of inversion allows for the adaptation of the method for different optical conditions. The implementation of WASI requires more experience, and processing is time demanding for large lakes. This study elaborates on the pros and cons of each method, providing guidelines and criteria on their use.

Assessment of Polymer Atmospheric Correction Algorithm for Hyperspectral Remote Sensing Imagery over Coastal Waters

Spaceborne imaging spectroscopy, also called hyperspectral remote sensing, has shown huge potential to improve current water colour retrievals and, thereby, the monitoring of inland and coastal water ecosystems. However, the quality of water colour retrievals strongly depends on successful removal of the atmospheric/surface contributions to the radiance measured by satellite sensors. Atmospheric correction (AC) algorithms are specially designed to handle these effects, but are challenged by the hundreds of narrow spectral bands obtained by hyperspectral sensors. In this paper, we investigate the performance of Polymer AC for hyperspectral remote sensing over coastal waters. Polymer is, in nature, a hyperspectral algorithm that has been mostly applied to multispectral satellite data to date. Polymer was applied to data from the Hyperspectral Imager for the Coastal Ocean (HICO), validated against in situ multispectral (AERONET-OC) and hyperspectral radiometric measurements, and its performance was compared against that of the hyperspectral version of NASA’s standard AC algorithm, L2gen. The match-up analysis demonstrated very good performance of Polymer in the green spectral region. The mean absolute percentage difference across all the visible bands varied between 16% (green spectral region) and 66% (red spectral region). Compared with L2gen, Polymer remote sensing reflectances presented lower uncertainties, greater data coverage, and higher spectral similarity to in situ measurements. These results demonstrate the potential of Polymer to perform AC on hyperspectral satellite data over coastal waters, thus supporting its application in current and future hyperspectral satellite missions.

Intercalibration of MERIS, MODIS, and OLCI Satellite Imagers for Construction of Past, Present, and Future Cyanobacterial Biomass Time Series

Satellite imagery has been used to monitor and assess Harmful Algal Blooms (HABs), specifically, cyanobacterial blooms in Lake Erie (the USA and Canada) for over twelve years. In recent years, imagery has been applied to the other Great Lakes as well as other U.S. lakes. The key algorithm used in this monitoring system is the cyanobacterial index (CI), a measure of the chlorophyll found in cyanobacterial blooms. The CI is a “spectral shape” (or curvature) algorithm, which is a form of the second derivative around the 681 nm (MERIS/OLCI) or 678 nm (MODIS) band, which is robust and implicitly includes an atmospheric correction, allowing reliable use for many more scenes than analytical algorithms. Monitoring of cyanobacterial blooms with the CI began with the European Space Agency’s (ESA) Medium Resolution Imaging Spectrometer (MERIS) sensor (2002–2012). With the loss of data from MERIS in the spring of 2012, the monitoring system shifted to using NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS). MODIS has bands that allow computation of a CI product, which was intercalibrated with MERIS at the time to establish a conversion of MODIS CI to MERIS CI. In 2016, ESA launched the Ocean and Land Color Imager (OLCI), the replacement for MERIS, on the Sentinel-3 spacecraft. MODIS can serve two purposes. It can provide a critical data set for the blooms of 2012–2015, and it offers a bridge from MERIS to OLCI. We propose a basin-wide integrated technique for intercalibrating the CI algorithm from MODIS to both MERIS and OLCI. This method allowed us to intercalibrate OLCI CI to MERIS CI, which would then allow the production of a 20-year and ongoing record of cyanobacterial bloom activity. This approach also allows updates as sensor calibrations change or new sensors are launched, and it could be readily applied to spectral shape algorithms.

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 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.

Water Quality Retrieval from PRISMA Hyperspectral Images: First Experience in a Turbid Lake and Comparison with Sentinel-2

A new era of spaceborne hyperspectral imaging has just begun with the recent availability of data from PRISMA (PRecursore IperSpettrale della Missione Applicativa) launched by the Italian space agency (ASI). There has been pre-launch optimism that the wealth of spectral information offered by PRISMA can contribute to a variety of aquatic science and management applications. Here, we examine the potential of PRISMA level 2D images in retrieving standard water quality parameters, including total suspended matter (TSM), chlorophyll-a (Chl-a), and colored dissolved organic matter (CDOM) in a turbid lake (Lake Trasimeno, Italy). We perform consistency analyses among the aquatic products (remote sensing reflectance (Rrs) and constituents) derived from PRISMA and those from Sentinel-2. The consistency analyses are expanded to synthesized Sentinel-2 data as well. By spectral downsampling of the PRISMA images, we better isolate the impact of spectral resolution in retrieving the constituents. The retrieval of constituents from both PRISMA and Sentinel-2 images is built upon inverting the radiative transfer model implemented in the Water Color Simulator (WASI) processor. The inversion involves a parameter (gdd) to compensate for atmospheric and sun-glint artifacts. A strong agreement is indicated for the cross-sensor comparison of Rrs products at different wavelengths (average R ≈ 0.87). However, the Rrs of PRISMA at shorter wavelengths (<500 nm) is slightly overestimated with respect to Sentinel-2. This is in line with the estimates of gdd through the inversion that suggests an underestimated atmospheric path radiance of PRISMA level 2D products compared to the atmospherically corrected Sentinel-2 data. The results indicate the high potential of PRISMA level 2D imagery in mapping water quality parameters in Lake Trasimeno. The PRISMA-based retrievals agree well with those of Sentinel-2, particularly for TSM.

Exploring the potential value of satellite remote sensing to monitor chlorophyll-a for US lakes and reservoirs

Assessment of chlorophyll-a, an algal pigment, typically measured by field and laboratory in situ analyses, is used to estimate algal abundance and trophic status in lakes and reservoirs. In situ-based monitoring programs can be expensive, may not be spatially, and temporally comprehensive and results may not be available in the timeframe needed to make some management decisions, but can be more accurate, precise, and specific than remotely sensed measures. Satellite remotely sensed chlorophyll-a offers the potential for more geographically and temporally dense data collection to support estimates when used to augment or substitute for in situ measures. In this study, we compare available chlorophyll-a data from in situ and satellite imagery measures at the national scale and perform a cost analysis of these different monitoring approaches. The annual potential avoided costs associated with increasing the availability of remotely sensed chlorophyll-a values were estimated to range between $5.7 and $316 million depending upon the satellite program used and the timeframe considered. We also compared sociodemographic characteristics of the regions (both public and private lands) covered by both remote sensing and in situ data to check for any systematic differences across areas that have monitoring data. This analysis underscores the importance of continued support for both field-based in situ monitoring and satellite sensor programs that provide complementary information to water quality managers, given increased challenges associated with eutrophication, nuisance, and harmful algal bloom events.

A New Remote Sensing Method to Estimate River to Ocean DOC Flux in Peatland Dominated Sarawak Coastal Regions, Borneo

We present a new remote sensing based method to estimate dissolved organic carbon (DOC) flux discharged from rivers into coastal waters off the Sarawak region in Borneo. This method comprises three steps. In the first step, we developed an algorithm for estimating DOC concentrations using the ratio of Landsat-8 Red to Green bands B4/B3 (DOC (μM C) = 89.86 ·e0.27·(B4/B3)), which showed good correlation (R = 0.88) and low mean relative error (+5.71%) between measured and predicted DOC. In the second step, we used TRMM Multisatellite Precipitation Analysis (TMPA) precipitation data to estimate river discharge for the river basins. In the final step, DOC flux for each river catchment was then estimated by combining Landsat-8 derived DOC concentrations and TMPA derived river discharge. The analysis of remote sensing derived DOC flux (April 2013 to December 2018) shows that Sarawak coastal waters off the Rajang river basin, received the highest DOC flux (72% of total) with an average of 168 Gg C per year in our study area, has seasonal variability. The whole of Sarawak represents about 0.1% of the global annual riverine and estuarine DOC flux. The results presented in this study demonstrate the ability to estimate DOC flux using satellite remotely sensed observations.

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.

Correcting non-photochemical quenching of Saildrone chlorophyll-a fluorescence for evaluation of satellite ocean color retrievals

In vivo chlorophyll fluorescence (ChlF) can serve as a reasonable estimator of in situ phytoplankton biomass with the benefits of efficiently and affordably extending the global chlorophyll (Chl) data set in time and space to remote oceanic regions where routine sampling by other vessels is uncommon. However, in vivo ChlF measurements require correction for known, spurious biases relative to other measures of Chl concentration, including satellite ocean color retrievals. Spurious biases affecting in vivo ChlF measurements include biofouling, colored dissolved organic matter (CDOM) fluorescence, calibration offsets, and non-photochemical quenching (NPQ). A more evenly distributed global sampling of in vivo ChlF would provide additional confidence in estimates of uncertainty for satellite ocean color retrievals. A Saildrone semi-autonomous, ocean-going, solar- and wind-powered surface drone recently measured a variety of ocean and atmospheric parameters, including ChlF, during a 60-day deployment in mid-2018 in the California Current region. Correcting the Saildrone ChlF data for known biases, including deriving an NPQ-correction, greatly improved the agreement between the drone measurements and satellite ocean color retrievals from MODIS-Aqua and VIIRS-SNPP, highlighting that once these considerations are made, Saildrone semi-autonomous surface vehicles are a valuable, emerging data source for ocean and ecosystem monitoring.

Monitoring Water Transparency in Shallow and Eutrophic Lake Waters Based on GOCI Observations

Water transparency represented by the Secchi disk depth (Zsd) plays an important role in understanding water ecology environment variations, especially for optically complex and shallow lake waters. In this study, using in situ measured remote sensing reflectance (Rrs), diffuse attenuation coefficient (Kd), and Zsd data collected in Lake Taihu (China), a regional algorithm for estimating Kd from Rrs was designed, and the semi-analytical model proposed by Lee et al. (2015) (hereafter called Lee_2015 model) was refined using a linear scaling correction for remote sensing of Zsd. The results showed that a good agreement between the derived Kd and in situ measured data (mean absolute percentage error (MAPE) = 26% for Kd(490); MAPE < 5% for Kd at 443, 555, and 660 nm). The in situ Rrs-derived Zsd results using the refined Lee_2015 model compared well with the in situ measured Zsd (R2 = 0.72 and MAPE = 36%), which was an obvious improvement over the Lee_2015 model in our study region. Subsequently, the refined Lee_2015 model was applied to the geostationary ocean color imager (GOCI) observations between 2012 and 2018 to yield the spatial and temporal variations of water transparency in the Lake Taihu waters. The long-term mean distribution of Zsd revealed that water transparency values in the northeastern Lake Taihu were generally higher than those in the southwest part. Monthly climatological Zsd patterns suggested that the Zsd distributions had large temporal variability, and distinct monthly patterns of Zsd existed in different subregions of Lake Taihu. The significant interannual variations of Zsd in Lake Taihu are probably affected by a combination of the water column stability mainly caused by wind, water temperature, human activity, and riverine discharge. The present study can provide a new approach for quantifying water visibility and serve for water-color remote sensing of optically complex and highly turbid waters.

Deriving Particulate Organic Carbon in Coastal Waters from Remote Sensing: Inter-Comparison Exercise and Development of a Maximum Band-Ratio Approach

Recently, different algorithms have been developed to assess near-surface particulate organic matter (POC) concentration over coastal waters. In this study, we gathered an extensive in situ dataset representing various contrasted bio-optical coastal environments at low, medium, and high latitudes, with various bulk particulate matter chemical compositions (mineral-dominated, 50% of the data set, mixed, 40%, or organic-dominated, 10%). The dataset includes 606 coincident measurements of POC concentration and remote-sensing reflectance, Rrs, with POC concentrations covering three orders of magnitude. Twelve existing algorithms have then been tested on this data set, and a new one was proposed. The results show that the performance of historical algorithms depends on the type of water, with an overall low performance observed for mineral-dominated waters. Furthermore, none of the tested algorithms provided satisfactory results over the whole POC range. A novel approach was thus developed based on a maximum band ratio of Rrs (red/blue, red/yellow or red/green ratio). Based on the standard statistical metric for the evaluation of inverse models, the new algorithm presents the best performance. The root-mean square deviation for log-transformed data (RMSDlog) is 0.25. The mean absolute percentage difference (MAPD) is 37.48%. The mean bias (MB) and median ratio (MR) values are 0.54 μg L−1 and 1.02, respectively. This algorithm replicates quite well the distribution of in situ data. The new algorithm was also tested on a matchup dataset gathering 154 coincident MERIS (MEdium Resolution Imaging Spectrometer) Rrs and in situ POC concentration sampled along the French coast. The matchup analysis showed that the performance of the new algorithm is satisfactory (RMSDlog = 0.24, MAPD = 34.16%, MR = 0.92). A regional illustration of the model performance for the Louisiana continental shelf shows that monthly mean POC concentrations derived from MERIS with the new algorithm are consistent with those derived from the 2016 algorithm of Le et al. which was specifically developed for this region.

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.

Optical classification of inland waters based on an improved Fuzzy C-Means method

Water optical clustering based on water color information is important for many ecological and environmental application studies, both regionally and globally. The fuzzy clustering method avoids the sharp boundaries in type-memberships produced by hard clustering methods, and thus presents its advantages. However, to make good use of the fuzzy clustering methods on water color spectra data sets, the determination of the fuzzifier parameter (m) of FCM (fuzzy c-means) is the key factor. Usually, the m is set to 2 by default. Unfortunately, this method assigned some membership degrees to non-belonging water type, failing to obtain the unitarity of cluster structure in some cases, especially in inland eutrophic water. To overcome this shortcoming, we proposed an improved FCM method (namely FCM-m) for water color spectra classification by optimizing the fuzzifier parameter. We collected an inland data set containing 1280 in situ spectral data and co-measured water quality parameters with a wide range of biogeochemical variability in China. Using FCM-m, seven spectrally distinct water optical clusters on Sentinel-3 OLCI (Ocean and Land Colour Imager) bands were obtained with the optimized fuzzifier (m=1.36), and the well-performed clustering result is assessed by the validated index (Fuzzy Silhouette Index=0.513). Also, the FCM-m-based soft classification framework was successfully applied to the atmospherically corrected OLCI images, which was evaluated by previous case studies. Besides, by testing FCM-m on three coastal and oceanic data sets, we verified that the optimized m should be adjusted based on the data set itself, and in general, the value gradually approaches 1 with the increase of the band number (or dimension). Finally, the effect of the improved method was tested by Chlorophyll-a concentration estimation. The results show that the algorithm------- blending by FCM-m performs better than that by original FCM, which is mainly because the FCM-m reduces the estimation error from non-belonging clusters by a stricter membership value assignation. To sum up, we believe that FCM-m is an adaptive algorithm, whose R codes are available at https://github.com/bishun945, and needs to be tested by more public data sets.

Impacts of Physical and Biological Processes on Spatial and Temporal Variability of Particulate Organic Carbon in the North Pacific Ocean during 2003-2017

The North Pacific Ocean is a significant carbon sink region, but little is known about the dynamics of particulate organic carbon (POC) and the influences of physical and biological processes in this region at the basin scale. Here, we analysed high-resolution surface POC data derived from MODIS-Aqua during 2003-2017, together with satellite-derived sea surface chlorophyll and temperature (SST). There are large spatial and temporal variations in surface POC in the North Pacific. Surface POC is much lower in the subtropical region (<50 mg m-3) than in the subarctic region (>100 mg m-3), primarily resulting from the south-to-north variability in biological production. Our analyses show significant seasonal and interannual variability in surface POC. In particular, there is one peak in winter-spring in the western subtropical region and two peaks in late spring and fall in the western subarctic region. Surface POC is positively correlated with chlorophyll (r = ~1) and negatively correlated with SST (r = ~-0.45, P < 0.001) south of 45°N, indicating the strong influence of physically driven biological activity on the temporal variability of POC in the subtropical region. There is a significantly positive but relatively lower correlation coefficient (0.6-0.8) between POC and chlorophyll and an overall non-significantly positive correlation between POC and SST north of 45°N, reflecting the reduction in the POC standing stock due to the fast sinking of large particles. The climate modes of the Pacific Decadal Oscillation, El Niño-Southern Oscillation and North Pacific Gyre Oscillation have large impacts on POC in various seasons in the subtropical region and weak influences in the subarctic region. Surface POC was anomalously high after 2013 (increased by ~15%) across the basin, which might be the result of complex interactions of physical and biological processes associated with an anomalous warming event (the Blob).

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.

Comparing level-2 and level-3 satellite ocean color retrieval validation methodologies

Many ocean color data applications leverage global spatially composited level-3 (L3) satellite data because of their regular Earth-grid frame of reference. However, ocean color satellite retrieval performance is routinely evaluated on level-2 (L2) data at the native satellite swath resolution and geometries. This study assesses how accurately binned and gridded L3 data represent L2 satellite data products via satellite-to-in situ match-up activities. L2 and L3 satellite data retrievals of the photosynthetic pigment chlorophyll-a are compared with a common in situ dataset, revealing similar L2 and L3 satellite-to-in situ performance for both MODIS-Aqua and VIIRS-SNPP. This agreement implies that L2 validation results are generally applicable to L3 data. However, uncertainties are introduced during the generation of L3 data from L2 data. L3 data comparisons introduce a wider temporal window between the time of in situ measurement and the time of the satellite observation, which can unintentionally reflect on the quality of the satellite retrieval or algorithm performance. The choice of L3 map projection may introduce additional uncertainty by spatially distorting the true location of the satellite retrievals. Each manipulation of satellite data beyond the instrument's native spatiotemporal reference (L2) reduces the applicability of L2 validation results to higher data processing levels.

Model for deriving benthic irradiance in the Great Barrier Reef from MODIS satellite imagery

We demonstrate a simple, spectrally resolved ocean color remote sensing model to estimate benthic photosynthetically active radiation (bPAR) for the waters of the Great Barrier Reef (GBR), Australia. For coastal marine environments and coral reefs, the underwater light field is critical to ecosystem health, but data on bPAR rarely exist at ecologically relevant spatio-temporal scales. The bPAR model presented here is based on Lambert-Beer's Law and uses: (i) sea surface values of the downwelling solar irradiance, Es(λ); (ii) high-resolution seafloor bathymetry data; and (iii) spectral estimates of the diffuse attenuation coefficient, Kd(λ), calculated from GBR-specific spectral inherent optical properties (IOPs). We first derive estimates of instantaneous bPAR. Assuming clear skies, these instantaneous values were then used to obtain daily integrated benthic PAR values. Matchup comparisons between concurrent satellite-derived bPAR and in situ values recorded at four optically varying test sites indicated strong agreement, small bias, and low mean absolute error. Overall, the matchup results suggest that our benthic irradiance model was robust to spatial variation in optical properties, typical of complex shallow coastal waters such as the GBR. We demonstrated the bPAR model for a small test region in the central GBR, with the results revealing strong patterns of temporal variability. The model will provide baseline datasets to assess changes in bPAR and its external drivers and may form the basis for a future GBR water-quality index. This model may also be applicable to other coastal waters for which spectral IOP and high-resolution bathymetry data exist.

A Secchi Depth Algorithm Considering the Residual Error in Satellite Remote Sensing Reflectance Data

A scheme to semi-analytically derive waters’ Secchi depth (Zsd) from remote sensing reflectance (Rrs) considering the effects of the residual errors in satellite Rrs was developed for the China Eastern Coastal Zone (CECZ). This approach was evaluated and compared against three existing algorithms using field measurements. As it was challenging to provide the accurately inherent optical properties data for running the three existing algorithms in the extremely turbid waters, the new developed algorithm worked more effective than the latter. Moreover, with both synthetic and match-up data, the results indicated that the proposed algorithm was able to minimize some residual errors in Rrs, and thus could generate inter-mission consistent Zsd results from two ocean color missions. Finally, after application of new model to satellite images, we presented the spatial and temporal variations of Secchi depth and trophic state in the CECZ during 2002–2014. The study led to several findings: Firstly, the Zsd-based trophic state index (TSI) in the East China Sea first increased since 2002, and then gradually dropped during 2008–2014. Secondly, more and more waters within 30–35 m and 20–25 m isobaths were deteriorating from oligotrophic to mesotrophic type and from mesotrophic to eutrophic water, respectively, during 2002–2014. Lastly, the TSI increased on average 0.091 and 0.286 m per year respectively in Bohai Sea and Yellow Sea since 2002, and it might only take 14 and 67 years for Bohai Sea and Yellow Sea to deteriorate from mesotrophic to eutrophic water, following their current yearly deterioration rate and trophic trend. These results highlighted the importance to make some strict regulations for protecting the aquatic environment in the CECZ.

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.

Evaluation of Chlorophyll-a and POC MODIS Aqua Products in the Southern Ocean

The Southern Ocean (SO) is highly sensitive to climate change. Therefore, an accurate estimate of phytoplankton biomass is key to being able to predict the climate trajectory of the 21st century. In this study, MODerate resolution Imaging Spectroradiometer (MODIS), on board EOS Aqua spacecraft, Level 2 (nominal 1 km × 1 km resolution) chlorophyll-a (C S a t ) and Particulate Organic Carbon (POC s a t ) products are evaluated by comparison with an in situ dataset from 11 research cruises (2008–2017) to the SO, across multiple seasons, which includes measurements of POC and chlorophyll-a (C i n s i t u ) from both High Performance Liquid Chromatography (C H P L C ) and fluorometry (C F l u o ). Contrary to a number of previous studies, results highlighted good performance of the algorithm in the SO when comparing estimations with HPLC measurements. Using a time window of ±12 h and a mean satellite chlorophyll from a 5 × 5 pixel box centered on the in situ location, the median C S a t :C i n s i t u ratios were 0.89 (N = 46) and 0.49 (N = 73) for C H P L C and C F l u o respectively. Differences between C H P L C and C F l u o were associated with the presence of diatoms containing chlorophyll-c pigments, which induced an overestimation of chlorophyll-a when measured fluorometrically due to a potential overlap of the chlorophyll-a and chlorophyll-c emission spectra. An underestimation of ∼0.13 mg m − 3 was observed for the global POC algorithm. This error was likely due to an overestimate of in situ POC i n s i t u measurements from the impact of dissolved organic carbon not accounted for in the blank correction. These results highlight the important implications of different in situ methodologies when validating ocean colour products.

First Results of Phytoplankton Spatial Dynamics in Two NW-Mediterranean Bays from Chlorophyll-a Estimates Using Sentinel 2: Potential Implications for Aquaculture

Shellfish aquaculture has a major socioeconomic impact on coastal areas, thus it is necessary to develop support tools for its management. In this sense, phytoplankton monitoring is crucial, as it is the main source of food for shellfish farming. The aim of this study was to assess the applicability of Sentinel 2 multispectral imagery (MSI) to monitor the phytoplankton biomass at Ebro Delta bays and to assess its potential as a tool for shellfish management. In situ chlorophyll-a data from Ebro Delta bays (NE Spain) were coupled with several band combination and band ratio spectral indices derived from Sentinel 2A levels 1C and 2A for time-series mapping. The best results (AIC = 72.17, APD < 10%, and MAE < 0.7 mg/m3) were obtained with a simple blue-to-green ratio applied over Rayleigh corrected images. Sentinel 2–derived maps provided coverage of the farm sites at both bays allowing relating the spatiotemporal distribution of phytoplankton with the environmental forcing under different states of the bays. The applied methodology will be further improved but the results show the potential of using Sentinel 2 MSI imagery as a tool for assessing phytoplankton spatiotemporal dynamics and to encourage better future practices in the management of the aquaculture in Ebro Delta bays.

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.

Optimal Cyanobacterial Pigment Retrieval from Ocean Colour Sensors in a Highly Turbid, Optically Complex Lake

To date, several algorithms for the retrieval of cyanobacterial phycocyanin (PC) from ocean colour sensors have been presented for inland waters, all of which claim to be robust models. To address this, we conducted a comprehensive comparison to identify the optimal algorithm for retrieval of PC concentrations in the highly optically complex waters of Lake Balaton (Hungary). MEdium Resolution Imaging Spectrometer (MERIS) top-of-atmosphere radiances were first atmospherically corrected using the Self-Contained Atmospheric Parameters Estimation for MERIS data v.B2 (SCAPE-M_B2). Overall, the Simis05 semi-analytical algorithm outperformed more complex inversion algorithms, providing accurate estimates of PC up to ±7 days from the time of satellite overpass during summer cyanobacteria blooms (RMSElog < 0.33). Same-day retrieval of PC also showed good agreement with cyanobacteria biomass (R2 > 0.66, p < 0.001). In-depth analysis of the Simis05 algorithm using in situ measurements of inherent optical properties (IOPs) revealed that the Simis05 model overestimated the phytoplankton absorption coefficient [aph(λ)] by a factor of ~2. However, these errors were compensated for by underestimation of the mass-specific chlorophyll absorption coefficient [a*chla(λ)]. This study reinforces the need for further validation of algorithms over a range of optical water types in the context of the recently launched Ocean Land Colour Instrument (OLCI) onboard Sentinel-3.

Evaluation of Atmospheric Correction Algorithms over Spanish Inland Waters for Sentinel-2 Multi Spectral Imagery Data

The atmospheric contribution constitutes about 90 percent of the signal measured by satellite sensors over oceanic and inland waters. Over open ocean waters, the atmospheric contribution is relatively easy to correct as it can be assumed that water-leaving radiance in the near-infrared (NIR) is equal to zero and it can be performed by applying a relatively simple dark-pixel-correction-based type of algorithm. Over inland and coastal waters, this assumption cannot be made since the water-leaving radiance in the NIR is greater than zero due to the presence of water components like sediments and dissolved organic particles. The aim of this study is to determine the most appropriate atmospheric correction processor to be applied on Sentinel-2 MultiSpectral Imagery over several types of inland waters. Retrievals obtained from different atmospheric correction processors (i.e., Atmospheric correction for OLI ‘lite’ (ACOLITE), Case 2 Regional Coast Colour (here called C2RCC), Case 2 Regional Coast Colour for Complex waters (here called C2RCCCX), Image correction for atmospheric effects (iCOR), Polynomial-based algorithm applied to MERIS (Polymer) and Sen2Cor or Sentinel 2 Correction) are compared against in situ reflectance measured in lakes and reservoirs in the Valencia region (Spain). Polymer and C2RCC are the processors that give back the best statistics, with coefficients of determination higher than 0.83 and mean average errors less than 0.01. An evaluation of the performance based on water types and single bands–classification based on ranges of in situ chlorophyll-a concentration and Secchi disk depth values- showed that performance of these set of processors is better for relatively complex waters. ACOLITE, iCOR and Sen2Cor had a better performance when applied to meso- and hyper-eutrophic waters, compare with oligotrophic. However, other considerations should also be taken into account, like the elevation of the lakes above sea level, their distance from the sea and their morphology.

HABscope: A tool for use by citizen scientists to facilitate early warning of respiratory irritation caused by toxic blooms of Karenia brevis

Blooms of the toxic microalga Karenia brevis occur seasonally in Florida, Texas and other portions of the Gulf of Mexico. Brevetoxins produced during Karenia blooms can cause neurotoxic shellfish poisoning in humans, massive fish kills, and the death of marine mammals and birds. Brevetoxin-containing aerosols are an additional problem, having a severe impact on beachgoers, triggering coughing, eye and throat irritation in healthy individuals, and more serious respiratory distress in those with asthma or other breathing disorders. The blooms and associated aerosol impacts are patchy in nature, often affecting one beach but having no impact on an adjacent beach. To provide timely information to visitors about which beaches are low-risk, we developed HABscope; a low cost (~$400) microscope system that can be used in the field by citizen scientists with cell phones to enumerate K. brevis cell concentrations in the water along each beach. The HABscope system operates by capturing short videos of collected water samples and uploading them to a central server for rapid enumeration of K. brevis cells using calibrated recognition software. The HABscope has a detection threshold of about 100,000 cells, which is the point when respiratory risk becomes evident. Higher concentrations are reliably estimated up to 10 million cells L-1. When deployed by volunteer citizen scientists, the HABscope consistently distinguished low, medium, and high concentrations of cells in the water. The volunteers were able to collect data on most days during a severe bloom. This indicates that the HABscope can provide an effective capability to significantly increase the sampling coverage during Karenia brevis blooms.