Optically Active Constituents Research Articles

Bio-Optical Properties and Ocean Colour Satellite Retrieval along the Coastal Waters of the Western Iberian Coast (WIC)

Essential Climate Variables (ECVs) like ocean colour provide crucial information on the Optically Active Constituents (OACs) of seawater, such as phytoplankton, non-algal particles, and coloured dissolved organic matter (CDOM). The challenge in estimating these constituents through remote sensing is in accurately distinguishing and quantifying optical and biogeochemical properties, e.g., absorption coefficients and the concentration of chlorophyll a (Chla), especially in complex waters. This study evaluated the temporal and spatial variability of bio-optical properties in the coastal waters of the Western Iberian Coast (WIC), contributing to the assessment of satellite retrievals. In situ data from three oceanographic cruises conducted in 2019–2020 across different seasons were analyzed. Field-measured biogenic light absorption coefficients were compared to satellite estimates from Ocean-Colour Climate Change Initiative (OC-CCI) reflectance data using semi-analytical approaches (QAA, GSM, GIOP). Key findings indicate substantial variability in bio-optical properties across different seasons and regions. New bio-optical coefficients improved satellite data retrieval, reducing uncertainties and providing more reliable phytoplankton absorption estimates. These results highlight the need for region-specific algorithms to accurately capture the unique optical characteristics of coastal waters. Improved comprehension of bio-optical variability and retrieval techniques offers valuable insights for future research and coastal environment monitoring using satellite ocean colour data.

Superyachts could support satellite ocean colour validation

Visible spectral radiometry of seawater, often referred to as ocean colour, from space, provides a synoptic view of surface phytoplankton, and other optically-active constituents, at high temporal resolution, that is unsurpassed by any other technology. Yet, in-situ observations of ocean colour are critical to the success of the satellite, tracking the calibration of the radiometers and validating atmospheric correction algorithms. Owing to the high cost of commercial field radiometers, as well as the high costs associated with ocean-based field work, ocean colour scientists are plagued by a sparsity of high quality in-situ radiometric observations, particularly in remote regions. In this perspective article, we highlight potential to increase the number of in-situ observations of ocean colour by harnessing superyachts. Using openly-available data processing software, we show that automated ocean colour data collected using a superyacht can be used for the validation of an ocean colour satellite, with comparable results to traditional validation studies. Reaching out to wealthy citizen scientists may help fill gaps in our ability to monitor the colour of the ocean.

Analysis of bio-optical active constituents for lentic ecosystem through spectral-spatial and in-vitro observation.

The neural network algorithm approach was adopted in Kolavai Lake to retrieve the inherent optical properties (IOP) of active constituents. The retrieval of IOP by absorption and the scattering of optically active constituents (OAC) through employing Sentinel-2 MSI reflectance and field measured the salinity and temperature. The result illustrates the relationship between the IOP and measured OAC's concentrations and its sensitivity towards spectral wavelength. It shows that the phytoplankton absorption ap is highly related with chlorophyll-a concentration and has an R2 value of 0.808. Furthermore, at the total absorption of water has high correlation with chl-a which indicates the significant dominance in the lentic water. Also, the pigment constituents are showing an R2 value of 0.754. The total backscattering of water (btot) is strongly related to the total suspended matter with R value > 0.73. The spatial distribution of OAC in Kolavai Lake helps monitor the lake water quality. This approach is well-performed in estimating the inherent optical properties of optically active constituents that gives insight for assessing the relationship between IOP and water quality. The research has proved to be a good potential for monitoring lentic water quality through Sentinel-2 MSI.

An enhanced deep learning approach to assessing inland lake water quality and its response to climate and anthropogenic factors

Remote sensing has long been used for inland water quality monitoring. However, due to the complex correlation between water quality parameters (WQPs) and water optical properties, the interactions of WQPs, and the impacts of climate, using remote sensing reflectance (Rrs) to adequately estimate WQPs is still a grand challenge. Deep learning has the potential in capturing the correlation among Rrs, optically active constituents (OACs), and non-OACs, and is progressively used in remote sensing retrieval of inland water quality. In this study, the enhanced multimodal deep learning (EMDL) models were proposed for Chlorophyll-a, total phosphorous, total nitrogen, Secchi disk depth, dissolved organic carbon, and dissolved oxygen retrieval in Lake Simcoe (80 km north of Toronto, Canada). The EMDL models were developed and validated using the Rrs data derived from the harmonized Landsat and Sentinel-2 images, synchronized water quality measurements, water surface temperature, and climate data (N = 1173). The performance of the EMDL models was compared to that of several other machine learning, deep learning, and empirical models. Using the developed EMDL models, the spatial distributions and long-term variations of the WQPs in Lake Simcoe from 2013 to 2019 were reconstructed. The impacts of 12 potential natural and anthropogenic factors on the water quality of the entire Lake Simcoe and its two most concerned estuaries were also quantitatively discussed. The results showed that the EMDL models produced satisfactory performance in estimation of the six WQPs, with the Slope being close to 1 (0.84–0.95), normalized mean absolute error ≤20.17%, and Bias ≤14.68%. The EMDL models had the potential to reconstruct the spatial patterns and time-series dynamics of water quality and effectively detect the gradients of spatial patterns. This study provides a novel approach to supporting the environmental management and identification of the affecting factors for the Lake Simcoe watershed.

A hybrid remote sensing approach for estimating chemical oxygen demand concentration in optically complex waters: A case study in inland lake waters in eastern China

Chemical oxygen demand concentration (CCOD) is widely used to indicate the degree of organic pollution of lakes, reservoirs and rivers. Mastering the spatiotemporal distribution of CCOD is imperative for understanding the variation mechanism and controlling of organic pollution in water. In this study, a hybrid approach suitable for Sentinel 3A/Ocean and Land Colour Instrument (OLCI) data was developed to estimate CCOD in inland optically complex waters embedding the interaction between CCOD and the absorption coefficients of optically active constituents (OACs). Based on in-situ sampling in different waters, the independent validations of the proposed model performed satisfactorily in Lake Taihu (MAPE = 23.52 %, RMSE = 0.95 mg/L, and R2 = 0.81), Lake Qiandaohu (MAPE = 21.63 %, RMSE = 0.50 mg/L and R2 = 0.69), and Yangtze River (MAPE = 29.34 %, RMSE = 0.83 mg/L, and R2 = 0.64). In addition, the approach not only showed significant superiority compared with previous algorithms, but also was suitable for other common satellite sensors equipped same or similar bands. The hybrid approach was applied to OLCI images to retrieve CCOD of Lake Taihu from 2016 to 2020 and reveals substantial interannual and seasonal variations. The above results indicate that the proposed approach is effective and stable for studying spatiotemporal dynamic of CCOD in optically complex waters, and that satellite-derived products can provide reliable information for lake water quality management.

In-situ manipulations of aquatic optical depth and its effect on small unoccupied aerial systems–derived spectral reflectance

The collection of spectral data with sensors fixed to various platforms (e.g., satellites, occupied aerial vehicles, and small unoccupied aerial systems (sUAS)) has allowed for the estimation of optically active constituents (OACs) common in surface waters. However, in small, complex, and optically shallow waters where multiple OACs (e.g., chlorophyll-a and total suspended solids) impact the spectral signature, these technologies have experienced significant limitations. Altering the scale at which these examinations are performed on surface waters (e.g., ponds and lakes) to mesocosm systems (37 cm in height and 30 cm in diameter) provides information on the interactions between multiple OACs and insight on the impact aquatic optical depth has on remotely sensed spectra. This field study examines optically shallow and optically deep mesocosm systems simulated in five-gallon buckets to determine the role aquatic optical depth has on developing accurate surface-water quality models. Results demonstrated an accurate representation of OACs in optically deep mesocosms compared with optically shallow mesocosms when assessed with sUAS (i.e., relative percent differences in predicted iron concentrations of −86 and 16 for optically shallow and deep waters, respectively). The interferences observed under these conditions were comparable to literature values when studying optically complex water bodies with hyperspectral data. This study provides a basis for understanding the benefits and limitations of monitoring in-situ water quality via sUAS.

Performance of deep learning in mapping water quality of Lake Simcoe with long-term Landsat archive

Remote sensing provides full-coverage and dynamic water quality monitoring with high efficiency and low consumption. Deep learning (DL) has been progressively used in water quality retrieval because it efficiently captures the potential relationship between target variables and imagery. In this study, the multimodal deep learning (MDL) models were developed and rigorously validated using atmospherically corrected Landsat remote sensing reflectance data and synchronous water quality measurements for estimating long-term Chlorophyll-a (Chl-a), total phosphorus (TP), and total nitrogen (TN) in Lake Simcoe, Canada. Since TP and TN are non-optically active, their retrievals were based on the fact that they are closely related to the optically active constituents (OACs) such as Chl-a. We trained the MDL models with one in-situ measured data set (for Chl-a, N = 315, for TP and TN, N = 303), validated the models with two independent data sets (N = 147), and compared the model performances with several DL, machine learning, and empirical algorithms. The results indicated that the MDL models adequately estimated Chl-a (mean absolute error (MAE) = 32.57%, Bias = 10.61%), TP (MAE = 42.58%, Bias = −2.82%), and TN (MAE = 35.05%, Bias = 13.66%), and outperformed several other candidate algorithms, namely the progressively decreasing deep neural network (DNN), a DNN with trainable parameters similar to MDL but without splitting input features, the eXtreme Gradient Boosting, the support vector regression, the NASA Ocean Color two-band and three-band ratio algorithms, and another empirical algorithm of Landsat data in clear lakes. Using the MDL models, we reconstructed the historical spatiotemporal patterns of Chl-a, TP, and TN in Lake Simcoe since 1984, and investigated the effects of two water quality improvement programs. In addition, the physical mechanism and interpretability of the MDL models were explored by quantifying the contribution of each feature to the model outputs. The framework proposed in this study provides a practical method for long-term Chl-a, TP, and TN estimation at the regional scale.

A Low-Cost Digital Colorimetry Setup to Investigate the Relationship between Water Color and Its Chemical Composition.

Developments in digital image acquisition technologies and citizen science lead to more water color observations and broader public participation in environmental monitoring. However, the implications of the use of these simple water color indices for water quality assessment have not yet been fully evaluated. In this paper, we build a low-cost digital camera colorimetry setup to investigate quantitative relationships between water color indices and concentrations of optically active constituents (OACs). As proxies for colored dissolved organic matter (CDOM) and phytoplankton, humic acid and algae pigments were used to investigate the relationship between water chromaticity and concentration. We found that the concentration fits an ascending relationship with xy chromaticity values and a descending relationship with hue angle. Our investigations permitted us to increase the information content of simple water color observations, by relating them to chemical constituent concentrations in observed waters.

Variations in the light absorption coefficients of phytoplankton, non-algal particles and dissolved organic matter in reservoirs across China

Reservoirs were critical sources of drinking water for many large cities around the world, but progress in the development of large-scale monitoring protocols to obtain timely information about water quality had been hampered by the complex nature of inland waters and the various optical conditions exhibited by these aquatic ecosystems. In this study, we systematically investigated the absorption coefficient of different optically-active constituents (OACs) in 120 reservoirs of different trophic states across five eco-regions in China. The relationships were found between phytoplankton absorption coefficient at 675 nm (aph (675)) and Chlorophyll a (Chla) concentration in different regions (R2:0.60–0.82). The non-algal particle (NAP) absorption coefficient (aNAP) showed an increasing trend for reservoirs with trophic states. Significant correlation (p < 0.05) was observed between chromophoric dissolved organic matter (CDOM) absorption and water chemical parameters. The influencing factors for contributing the relative proportion of OACs absorption including the hydrological factors and water quality factors were analyzed. The non-water absorption budget from our data showed the variations of the dominant absorption types which underscored the need to develop and parameterize region-specific bio-optical models for large-scale assessment in water reservoirs.

Assessing the Impact of Tides and Atmospheric Fronts on Submesoscale Physical and Bio-Optical Distributions near a Coastal Convergence Zone

Optically-active constituents vary over short time and space scales in coastal waters, and they are impacted by a variety of complex, inter-related forcing processes. As part of the Integrated Coastal Bio-Optical Dynamics (ICoBOD) project, we conducted a field campaign in Mississippi Sound in the northern Gulf of Mexico during spring 2018 to examine the impact of the passage of atmospheric and tidal fronts on fine-scale physical and bio-optical property distributions in a shallow, dynamic, coastal environment. During a 25-day experiment, we deployed eight moorings over a roughly 7 × 7 km box encompassing a frontal zone, to collect a time series of physical and bio-optical measurements. We describe changes in diver visibility related to the passage of a short-duration, high-turbidity surface plume and nepheloid layer development/decay during a tidal cycle. Maximum nepheloid layer development was observed during low tide and lasted about 9–12 h. The strongest turbidity signal extended about 4–5 m above the bottom (approximately half of the water column), although anomalously elevated values were observed all the way to the surface. In addition, high-resolution (50 m) hydrodynamic model simulations provide insight into the frontal dynamics and aid interpretation of the observed patterns. Mooring observations confirmed model-predicted heat flux changes associated with the passage of an atmospheric cold front.

A semi-analytical approach for remote sensing of trophic state in inland waters: Bio-optical mechanism and application

The trophic state index (TSI) is a vital parameter for aquatic ecosystem assessment. Thus, information on the spatial and temporal distribution of TSI is critical for supporting scientifically sound water resource management decisions. We proposed a semi-analytical approach to remotely estimate TSI based on Landsat 8 OLI data for inland waters. The approach has two major steps: deriving the total absorption coefficient of optically active constituents (OACs) and building the relationship between the total absorption coefficient and TSI. First, version 6.0 of the Quasi-Analytical Algorithm (QAA_V6, developed by Zhongping Lee) was implemented with Landsat 8 OLI data to derive the total absorption coefficients of the OACs. Second, we modeled TSI using the total absorption coefficients of OACs at 440 nm based on a large in situ measurement dataset. The total absorption coefficient of OACs at 440 nm gave satisfactory validation results for modeling TSI with a mean absolute percent error of 6% and a root-mean-square error of 5.77. Then, we performed this approach in three inland waters with various eutrophic statuses to validate its results, and the approach demonstrated a robust and satisfactory performance. Finally, an application of the approach was demonstrated in Lake Qiandaohu. Our semi-analytical approach has a sound optical mechanism and extensive application for different trophic inland waters.

Hydrography-Driven Optical Domains in the Vaigat-Disko Bay and Godthabsfjord: Effects of Glacial Meltwater Discharge

We present here an assessment of in situ hyperspectral bio-optical variability in the Vaigat-Disko Bay and Godthabsfjord along the southwest coast of Greenland. The dataset consists of state-of-the-art profiler measurements of hyperspectral apparent and inherent optical properties of water complemented by traditional observations of Secchi disk and Forel Ule scale in the context of ocean color. Water samples were collected and analyzed for concentration of optically active constituents (OACs). Near-surface observations of hydrographic parameters revealed three different water masses in the Bay: meltwater plume, frontal zone, and Atlantic water mass. Underwater spectral light availability reveals three different spectral types. Low salinity, increased temperature, deep euphotic depths, and case-1 water type remote sensing reflectance spectra with tabletop peaks in the 400–500 nm wavelength range characterize the glacial meltwater plume in the Vaigat-Disko Bay. The conservative relationship between salinity and chromophoric dissolved organic matter (CDOM) commonly observed in estuarine and shelf seas is weaker in the Godthabsfjord and reverses in the Vaigat-Disko Bay. Efficiency of machine learning techniques such as cluster analysis is tested in delineating water masses in the bay w.r.t. hydrographic and bio-optical parameters. Tests of optical closure yield low root mean square error at longer wavelengths. The study provides strong evidence that despite similar geographic setting, fjord ecosystems exhibit contrasting bio-optical properties which necessitate fjord-specific investigations.

Seasonal changes of optical absorption properties of river and lake in East Liaohe River basin, Northeast China

The absorption characteristics of optically active constituents (OACs) in water column are important optical properties and basic parameters of establishing the inverse analysis model. Comparative analyses about seasonal variability of the optical absorption characteristics (phytoplankton, non-algal particles and chromophoric dissolved organic matter absorption characteristics) and water quality status of East Liaohe River basin were conducted based on the water samples in Erlong-hu Reservoir collected in June, September and October of 2011 and samples in East Liaohe River in October of 2012. The results demonstrated that the eutrophication status of Erlonghu Reservoir was lower in June, eutrophic in September and moderately eutrophic in October. Some of the sampling points of the East Liaohe River belonged to the middle trophic level and the other part belonged to the eutrophic level. The absorption coefficient of each component of water increased with increasing nutrient level. Besides, the absorption spectra of total suspended particulate of Erlonghu Reservoir in June and October were similar to that of non-algal particles, and chromophoric dissolved organic matter (CDOM) contributed most to the total absorption of water. The absorption spectra of total suspended particulate matter in September were similar to that of phytoplankton and phytoplankton was the dominant contributor to the total absorption. For samples of Erlonghu Reservoir in June and September, aph(440) and total phosphorus (TP) were correlated closely with each other. Significant correlation between aph(440) and dissolved organic carbon (DOC) of Erlonghu Reservoir in June was observed, while ad(440) was only correlated with Chla. There were positive correlations between aph(675) and Chla, Carlson index (TLI) in Erlonghu Reservoir (September) and East Liaohe River. Obvious differences of water optical properties were found between river and lake located in the East Liaohe River basin as follows: There were two types of phytoplankton spectra curves of East Liaohe River and the contribution rates of different components varied greatly from sites to sites; Phytoplankton dominated spectral absorption in waters with high nutrition level (Erlonghu Reservoir in September and the East Liaohe River) while CDOM and the particles were both the main components for Erlonghu Reservoir in June and October; CDOM was composed of large-size molecules in waters of Erlonghu Reservoir in June and in the East Liaohe River in October and mainly of small-size molecules in Erlonghu Reservoir in September and October.

Dynamics of limnological parameters in reservoirs: A case study in South Brazil using remote sensing and meteorological data

Reservoirs are important in Brazil for the production of hydroelectric power and human water consumption. The objective was to evaluate the variability of total suspended solids (TSS) and chlorophyll-a as well as the rainfall/temperature and land use impacts on these optically active constituents (OAC). The study area is the Passo Real reservoir in south Brazil. The methodology was divided in four steps. First, we used wavelet to detect anomalous periods of rainfall and temperature (2002–2014). Second, we carried out 12 field campaigns to obtain in situ measurements for limnological characterization (2009–2010). The third step was the analysis of Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra and Aqua satellites data corrected and non-corrected for bidirectional effects. Finally, we evaluated potential drivers of OAC changes over time using cross-correlation analysis. The results showed a decrease in the TSS and chlorophyll-a concentrations from the upper to the lower streams of the reservoir. The exponential regression between the MODIS red reflectance and TSS had an adjusted r2 of 0.63. It decreased to 0.53 for the relationship between the green reflectance and chlorophyll-a. MODIS data corrected for bidirectional effects provided better OAC estimates than non-corrected data. The validation of MODIS TSS and chlorophyll-a estimates using a separate set of measurements showed a RMSE of 2.98mg/l and 2.33μg/l, respectively. MODIS estimates indicated a gradual transition in OAC from the upper to the lower streams in agreement with the patterns observed using field limnological data. The analysis of land use (greenness) showed two well-defined crop cycles per year. The highest seasonal concentrations of TSS and chlorophyll-a were observed in December and the lowest concentrations in April. Despite the interrelationships between both factors, our cross-correlation analysis indicated that the great concentrations of TSS and chlorophyll-a were primarily controlled by rainfall and secondarily by land use.

Re-parameterization of a quasi-analytical algorithm for colored dissolved organic matter dominant inland waters

Quasi-Analytical Algorithms (QAAs) are based on radiative transfer equations and have been used to derive inherent optical properties (IOPs) from the above surface remote sensing reflectance (Rrs) in aquatic systems in which phytoplankton is the dominant optically active constituents (OACs). However, Colored Dissolved Organic Matter (CDOM) and Non Algal Particles (NAP) can also be dominant OACs in water bodies and till now a QAA has not been parametrized for these aquatic systems. In this study, we compared the performance of three widely used QAAs in two CDOM dominated aquatic systems which were unsuccessful in retrieving the spectral shape of IOPS and produced minimum errors of 350% for the total absorption coefficient (a), 39% for colored dissolved matter absorption coefficient (aCDM) and 7566.33% for phytoplankton absorption coefficient (aphy). We re-parameterized a QAA for CDOM dominated (hereafter QAACDOM) waters which was able to not only achieve the spectral shape of the OACs absorption coefficients but also brought the error magnitude to a reasonable level. The average errors found for the 400–750nm range were 30.71 and 14.51 for a, 14.89 and 8.95 for aCDM and 25.90 and 29.76 for aphy in Funil and Itumbiara Reservoirs, Brazil respectively. Although QAACDOM showed significant promise for retrieving IOPs in CDOM dominated waters, results indicated further tuning is needed in the estimation of a(λ) and aphy(λ). Successful retrieval of the absorption coefficients by QAACDOM would be very useful in monitoring the spatio-temporal variability of IOPS in CDOM dominated waters.

Spatial and seasonal variation in diffuse attenuation coefficients of downward irradiance at Ibitinga Reservoir, São Paulo, Brazil

Estimates of light availability in the water column require accurate quantification at different depths of the diffuse attenuation coefficient (K d). This study examines spatial and seasonal variability of K d derived variables [euphotic zone depth (Z eu), attenuation depth (Z att)], their relationship with optically active constituents (OACs), and their impact on the underwater light field at Ibitinga Reservoir (Sao Paulo, Brazil). Radiometric data were acquired using profiling spectroradiometers operating from 320 to 950 nm in four campaigns (February–July 2014) concurrently with OAC water samplings. Minimum and maximum values of K dPAR were, respectively, 0.99 and 3.45 m−1 for average ranges of OACs varying from 14.4 to 16.2 mg/l for dissolved total carbon, 11.5–100.5 μg/l for chlorophyll-a, 1.8–14.5 mg/l for total suspended solid, and from 0.73 to 1.71 m−1 for absorption coefficient of colored dissolved organic matter (CDOM) in 440 nm (a CDOM440). CDOM removed blue wavelengths in the first meter of the water column throughout seasons, while green wavelengths were predominant with increasing depth. Furthermore, this study demonstrated that K d variability is mainly influenced by the presence of phytoplankton and CDOM in the reservoir, and reveals that the seasonal variability of K d was much larger than spatial variability.

Application of a partial least-squares regression model to retrieve chlorophyll-a concentrations in coastal waters using hyper-spectral data

Coastal and inland waters represent a diverse set of resources that support natural habitats and provide valuable ecosystem services to the human population. Monitoring the quality of these waters is essential to maintaining the resources they provide, and long-term monitoring may offer a better understanding of the relationship between human development and the health of these resource producers. The implementation of conventional monitoring is typically time-intensive and limited in geographic scale. Alternatively, the use of airborne and spaceborne remote sensors provides a synoptic view of water quality with better spatial coverage to more accurately identify dynamic and unique parameters. Concentrations of optically active constituents (OACs) such as suspended sediments and the phytoplankton pigment chlorophylla (CHLa), act as proxies for water quality and can be detected by optical sensors. Traditional remote sensing techniques were developed using multispectral sensors, and employ band ratio algorithms that seek to predict the concentrations of OACs in relation to water quality. In complex coastal waters, overlapping spectral signatures of OACs often confound these algorithms and reduce their predictive capacity. The objective of this study was to develop a dataset to test the predictive capabilities of partial least-squares regression, a multivariate statistical method, for hyperspectral remote sensing and in situ CHLa concentrations. This paper presents the model performance for a dataset developed in Long Bay, a ~160 km arcuate bay that spans the border between North and South Carolina. The model uses multivariate-based statistical modeling to capitalize on the spectral advantage gained by hyperspectral sensors when observing such waters. Following this approach, a multivariate-based monitoring tool for the prediction of CHLa concentrations is presented with a partial least-squares regression (PLSR) method using hyperspectral and laboratory-analyzed field data. The PLSR model was used based on its ability to identify wavelengths that are more sensitive to CHLa relative to other OACs. The model was able to explain 80% of the observed CHLa variability in Long Bay with root mean square error (RMSE) = 2.03 µg/L. This enhanced mode of water quality monitoring has potential to deliver insights to point-sources and problem areas that may contribute to a decline in the water quality of valuable inland and coastal systems.

Light absorption by phytoplankton and minerogenic particles in Cayuga Lake, New York

Features of light absorption are critical determinants of the underwater and emergent light fields of lakes. Patterns in time and space of the components (ax) of the light absorption coefficient (a), and their dependence on various optically active constituents (OACs), in the form of material-specific ax, were resolved for Cayuga Lake, New York, USA. Component a was partitioned according to the additive components of ax that include phytoplankton (aϕ), nonalgal particles (aNAP), and colored dissolved organic material (aCDOM). Specific absorption coefficients were developed that couple the concentration of chlorophyll a (Chl-a, an OAC) to aϕ, and the concentration of inorganic suspended particulate material (ISPM, an OAC), or the projected area of minerogenic particles per unit volume of water (PAVm, an alternate OAC), to aNAP. The critical role of such specific coefficients in contemporary strategies to retrieve OACs through remote sensing for inland waters is described, and characteristics consistent with the influences of pigment packaging and accessory pigments on the aϕ–Chl-a relationship are demonstrated. Nonalgal partical aNAP is reported to be primarily regulated by the level of minerogenic particles. The specific absorption coefficients developed for Cayuga Lake to support retrieval of the OACs of Chl-a, PAVm, and ISPM are considered in the context of the limited populations of estimates available for inland waters.

Implications of scatter corrections for absorption measurements on optical closure of Amazon floodplain lakes using the Spectral Absorption and Attenuation Meter (AC-S-WETLabs)

Amazon floodplain lakes range widely in concentrations of optically active constituents (OAC) driven by seasonality in hydrological and biogeochemical processes, but in general they are characterized by high turbidity (NTU from 90 to 1645) compared to coastal waters. In this work, instruments for measuring inherent optical properties (IOPs) of water bodies were evaluated for the first time in floodplain lakes in the lower Amazon River during the falling limb of the hydrograph. Water column profiles of total attenuation and absorption were measured using the Spectral Absorption and Attenuation Meter (AC-S-WETLabs), and of backscattering using Hydroscat. These measurements, however, are subject to uncertainties and require corrections for turbid waters. In this paper, we assessed the implications of scattering correction methods for the absorption tube, proposed by the AC-S manufacturer, in the simulation of the Remote Sensing Reflectance (Rrs). The closure experiment comparing Hydrolight (Mobley & Sundman, 2001) simulated Rrs and in situ Rrs demonstrated that neither of the corrections was able to thoroughly account for the scattering errors which were propagated to the absorption measurements with AC-S and backscattering with Hydroscat. The three scattering correction methods (Flat, Proportional and “Kirk”) either under or overestimated the absorption coefficient that resulted in either under or overestimation of the simulated Rrs. Flat and Proportional Methods resulted in an underestimation of Rrs from 400 to 550nm and overestimation from 600 to 700nm, indicating that the assumption of zero (0) absorption in the near infrared does not apply to inland turbid water. The Rrs errors varied also according to water OAC composition. Overall, “Kirk” correction method provided the best results regarding the spectral shape of the Rrs, however, failed to account for magnitude. Based on the tuning tests, the errors in spectra magnitude seem to be sensitive to the constant fraction of scattering (CFS) used in the Kirk method. Tests carried out with CFS values varying from 0.18 to 0.38 indicated that magnitude error can be partially overcomed by tuning CFS according to water composition. Improvements in the scattering correction methods are required in order to obtain reliable IOPs in turbid inland Amazon lakes.

Remote quantification of phycocyanin in potable water sources through an adaptive model

Cyanobacterial blooms in water supply sources in both central Indiana USA (CIN) and South Australia (SA) are a cause of great concerns for toxin production and water quality deterioration. Remote sensing provides an effective approach for quick assessment of cyanobacteria through quantification of phycocyanin (PC) concentration. In total, 363 samples spanning a large variation of optically active constituents (OACs) in CIN and SA waters were collected during 24 field surveys. Concurrently, remote sensing reflectance spectra (Rrs) were measured. A partial least squares–artificial neural network (PLS–ANN) model, artificial neural network (ANN) and three-band model (TBM) were developed or tuned by relating the Rrs with PC concentration. Our results indicate that the PLS–ANN model outperformed the ANN and TBM with both the original spectra and simulated ESA/Sentinel-3/Ocean and Land Color Instrument (OLCI) and EO-1/Hyperion spectra. The PLS–ANN model resulted in a high coefficient of determination (R2) for CIN dataset (R2=0.92, R: 0.3–220.7μg/L) and SA (R2=0.98, R: 0.2–13.2μg/L). In comparison, the TBM model yielded an R2=0.77 and 0.94 for the CIN and SA datasets, respectively; while the ANN obtained an intermediate modeling accuracy (CIN: R2=0.86; SA: R2=0.95). Applying the simulated OLCI and Hyperion aggregated datasets, the PLS–ANN model still achieved good performance (OLCI: R2=0.84; Hyperion: R2=0.90); the TBM also presented acceptable performance for PC estimations (OLCI: R2=0.65, Hyperion: R2=0.70). Based on the results, the PLS–ANN is an effective modeling approach for the quantification of PC in productive water supplies based on its effectiveness in solving the non-linearity of PC with other OACs. Furthermore, our investigation indicates that the ratio of inorganic suspended matter (ISM) to PC concentration has close relationship to modeling relative errors (CIN: R2=0.81; SA: R2=0.92), indicating that ISM concentration exert significant impact on PC estimation accuracy.