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The development of regional satellite bio-optical models for natural waters with high temporal and spatial variability, such as inland seas, reservoirs, and coastal ocean waters, requires the implementation of an intermediate measuring link in the chain, "water sampling—bio-optical models", and this link must have certain intermediate characteristics. The most crucial of them are the high-precision measurements of the main water quality parameters, such as the concentration of chlorophyll a (Chl a), colored dissolved organic matter (CDOM), and total suspended sediments (TSS) in the upper water layer, together with a high operational rate and the ability to cover a large water area in a short time, which corresponds to a satellite overpass. A possible solution is to utilize laser-induced fluorescence (LIF) of water constituents measured by a marine LiDAR in situ with a high sampling rate from a high-speed vessel. This allows obtaining a large ground-truth dataset of the main water quality parameters simultaneously with the satellite overpass within the time interval determined by NASA protocols. This method was successfully applied to the oligotrophic Issyk-Kul Lake in Kyrgyzstan, where we obtained more than 4000 and 1000 matchups for the Chl a and TSS, respectively. New preliminary regional bio-optical models were developed on the basis of a one-day survey and tested for archive Sentinel-2A data for 2022. This approach can be applied for regular monitoring and further correction in accordance with seasonal variability. The obtained results, together with previously published similar studies for eutrophic coastal and productive inland waters, emphasize the applicability of the presented method for the development or adjustment of regional bio-optical models for water bodies of a wide trophic range. [ABSTRACT FROM AUTHOR]

The development of regional satellite bio-optical models for natural waters with high temporal and spatial variability, such as inland seas, reservoirs, and coastal ocean waters, requires the implementation of an intermediate measuring link in the chain, “water sampling—bio-optical models”, and this link must have certain intermediate characteristics. The most crucial of them are the high-precision measurements of the main water quality parameters, such as the concentration of chlorophyll a (Chl a), colored dissolved organic matter (CDOM), and total suspended sediments (TSS) in the upper water layer, together with a high operational rate and the ability to cover a large water area in a short time, which corresponds to a satellite overpass. A possible solution is to utilize laser-induced fluorescence (LIF) of water constituents measured by a marine LiDAR in situ with a high sampling rate from a high-speed vessel. This allows obtaining a large ground-truth dataset of the main water quality parameters simultaneously with the satellite overpass within the time interval determined by NASA protocols. This method was successfully applied to the oligotrophic Issyk-Kul Lake in Kyrgyzstan, where we obtained more than 4000 and 1000 matchups for the Chl a and TSS, respectively. New preliminary regional bio-optical models were developed on the basis of a one-day survey and tested for archive Sentinel-2A data for 2022. This approach can be applied for regular monitoring and further correction in accordance with seasonal variability. The obtained results, together with previously published similar studies for eutrophic coastal and productive inland waters, emphasize the applicability of the presented method for the development or adjustment of regional bio-optical models for water bodies of a wide trophic range.

Water is one of the most important and abundant resources on earth; therefore, determining easier and more accurate methods of measuring water quality has become challenging in recent years. Images of the Sentinel-2 multispectral (MSI) sensor and field measurements for three seasons and 39 spectral indices were evaluated to identify those that can be used as patterns for the generation of semi-empirical bio-optical models to predict the total dissolved solids (TDS) in surface water of freshwater wetlands. The Guartinaja and Momil wetlands, located in the Wetland Complex of Bajo Sinú, Northern Colombia, were used as references in the field. The individual bands of the Sentinel 2 MSI sensor and the spectral indices derived from these bands were tested (and will be referred to in this paper as spectral indices). They were classified as follows: 10 individual spectral bands of the MSI sensor of the Sentinel 2 satellite; 11 indices reported by the literature to determine water, vegetation, or soils; 11 indices reported to estimate salinity; and 7 to estimate total dissolved solids and proposed in this research. The indices were evaluated based on three exclusive conditions: 1) the correlation between the TDS values measured in the field and indices spectral values >0.7 for at least two sampling seasons; 2) for the three seasons, the regression models derived from the indices have a determination coefficient >0.7; and 3) the spatial correlation matrix between the images derived from the models is >0.8 for at least two out of the three analysis seasons. We achieved important results: 1) seven indices (B3, Brightness-3, SI-3, SI-5, Ferdous-2020, TDS-1, and TDS5) met the three conditions proposed above and therefore, they were preliminarily defined as patterns for estimating TDS in the selected wetlands; 2) Additionally, this research provided two new efficient indices for calculating TDS through bio-optical models: TDS-1 and TDS-5. The validation of the regression models derived from these indices indicated a high accuracy of the prediction surfaces, expressed in values <10% of the normalized root-mean-square deviation. The results of this study contribute to the determination of semi-empirical bio-optical models for predicting optically inactive water quality parameters, which are generally predicted with empirical models whose use is spatiotemporally limited. Additionally, the research contributions that are carried out to improve the methods of evaluating water quality from remote sensing products become investigations that positively impact the evaluation of water resources, especially in geographical locations in which there is no with sufficient financial resources for on-site sampling. [ABSTRACT FROM AUTHOR]

Ocean color remote sensing deals with the modulation of incident light by bio-optically active constituents present in the water column through the process of absorption and backscattering. The challenging task is to find out different approaches for exploiting water-leaving radiance measured from ocean color sensors to infer the absorption, scattering properties, and thereby concentrations of the bio-optical constituents present in different water types. In last few decades, these studies were carried out based on multispectral bands from different ocean color sensors in open ocean waters. Initially, the derived concentrations of chlorophyll-a (chl-a) were primarily based on the formulation of empirical approaches using large database of in situ measured values. Further advancement involved the use of bio-optical models such as quasi-analytical (Lee–Morel) and Garver–Seigel–Maritorena (GSM), which showed marginal improvements over the empirical approaches in deriving chl-a concentrations for optically complex Case-2 waters. A major drawback in these bio-optical models was the use of a constant value for the slope of colored dissolved and detrital organic matter absorption(acdm). In this study, a semianalytical bio-optical model (ABOM) is presented to derive concentration of chl-a in optically complex waters using hyperspectral data. Herein, we show that by modeling the variability in the acdm slope, the large errors associated with chlorophyll estimation in optically complex waters can be reduced significantly. The model was tested on a data set collected from an optically complex Chilika lagoon situated in the northeastern region of India. Results of the analysis indicate a significant change in the estimation of chl-a (mg/m3) with an mean absolute percentage difference (MAPD) of 58% as compared to GSM (175%) and Lee–Morel (189%) bio-optical model when compared with the in situ measurements. This study reveals that the bio-optical models developed for global oceans needs to be regionally parametrized for optical constituents in complex water bodies to estimate chlorophyll concentration with reduced uncertainty. [ABSTRACT FROM AUTHOR]

Chlorophyll-a (chl-a) is a central water quality parameter that has been estimated through remote sensing bio-optical models. This work evaluated the performance of three well established reflectance based bio-optical algorithms to retrieve chl-a from in situ hyperspectral remote sensing reflectance datasets collected during three field campaigns in the Funil reservoir (Rio de Janeiro, Brazil). A Monte Carlo simulation was applied for all the algorithms to achieve the best calibration. The Normalized Difference Chlorophyll Index (NDCI) got the lowest error (17.85%). The in situ hyperspectral dataset was used to simulate the Ocean Land Color Instrument (OLCI) spectral bands by applying its spectral response function. Therefore, we evaluated its applicability to monitor water quality in tropical turbid inland waters using algorithms developed for MEdium Resolution Imaging Spectrometer (MERIS) data. The application of OLCI simulated spectral bands to the algorithms generated results similar to the in situ hyperspectral: an error of 17.64% was found for NDCI. Thus, OLCI data will be suitable for inland water quality monitoring using MERIS reflectance based bio-optical algorithms.

Accurate calibration of oceanic LiDAR signals is essential for the accurate retrieval of ocean optical properties. Nowadays, there are many methods for aerosol LiDAR calibration, but fewer attempts have been made to implement specific calibration methods for oceanic LiDAR. Oceanic LiDAR often has higher vertical resolution, needs greater signal dynamic range, detects several orders of magnitude lower less depth of penetration, and suffers from the effects of the air-sea interface. Therefore the calibration methods for aerosol LiDAR may not be useful for oceanic LiDAR. In this paper, we present a new simple and feasible approach for oceanic LiDAR calibration via comparison of LiDAR backscatter against calculated scatter based on iteratively bio-optical models in clear, open ocean, Type 1 water. Compared with current aerosol LiDAR calibration methods, it particularly considers geometric losses and attenuation occurring in the atmosphere-sea interface. The mean relative error percentage (MREP) of LiDAR calibration constant at two different stations was all within 0.08%. The MREP between LiDAR-retrieved backscatter, chlorophyll after using LiDAR calibration constant with inversion results of measured data were within 0.18% and 1.39%, respectively. These findings indicate that the bio-optical methods for LiDAR calibration in clear ocean water are feasible and effective.

The kd(490) is a vertical light attenuation coefficient and an important parameter for water quality. The kd estimates are often based on empirical and semi-analytical algorithms, designed for oceanic and coastal waters. However, there is a lack of information about the performances of these models to inland waters dominated by chromophoric dissolved organic matter (CDOM). Therefore, to contribute to this investigation, nine empirical models based on the blue-to-green and blue-to-red ratios and chlorophyll-a (Chl-a) concentration were evaluated, as well as three semi-analytical models using bands from the Operational Land Imager (OLI)/Landsat-8. The errors (mean absolute percentage error, MAPE > 80%) presented by the empirical models confirmed that the blue-to-green ratio failed in retrieving kd(490) in an environment dominated by CDOM. Similar failures occurred with the models using the Chl-a concentration (MAPE ~60%) as input. A semi-analytical approach showed the lowest error (MAPE = 41.04%) in the estimate of the inherent optical properties for complex waters in order to reduce the errors above. After retrieval of kd(490) using the semi-analytical model, seasonal patterns were observed, and high values of kd(490) were detected in the dry season possibly due to the increase of the concentration of the optically-significant substances (OSS).

Quantifying Colored Detrital Matter (CDM) from satellite observations can improve our knowledge of carbon dynamics in coastal areas and the open oceans. Several bio-optical inversion models have been developed for this purpose. However, care must be taken when they are applied to waters where optical properties significantly differ from model assumptions, which is the case in the Mediterranean Sea. Algorithm testing and validation are thus required before routine use. Here, in situ radiometric measurements collected in the NW Mediterranean Sea (BOUSSOLE site) are used to evaluate three bio-optical inversion models that retrieve the CDM light absorption coefficients at 443 nm ( a cdm (443)). Although all methods reproduced the CDM seasonal cycles at the surface, comparisons of predicted and in situ a cdm (443) coefficients showed that the Quasi-Analytical Algorithm version 6 (QAAv6) and a locally-adapted version of the Garver-Siegel-Maritorena model (GSM-Med) were the two best algorithms. Applying these two models to SeaWiFS remote sensing reflectances, collected between 2003 and 2010, reproduced with good accuracy the a cdm (443) coefficients retrieved from field radiometric measurements at the BOUSSOLE site, with seasonal patterns consistent with previous observations. Finally, bio-optical relationships derived from satellite-retrieved a cdm (443) and chlorophyll values confirmed the higher-than-average CDM contribution for a given chlorophyll concentration in the Mediterranean Sea as compared to many oceanic regions. [ABSTRACT FROM AUTHOR]

Ocean color algorithms have been successfully developed to estimate chlorophyll a and total suspended solids concentrations in coastal and estuarine waters but few have been created to estimate light absorption due to colored dissolved inorganic matter (CDOM) and salinity from the spectral signatures of these waters. In this study, we used remotely sensed reflectances in the red and blue-green portions of the visible spectrum retrieved from Medium Resolution Imaging Spectrometer (MERIS) and the International Space Station (ISS) Hyperspectral Imager for the Coastal Ocean (HICO) images to create a model to estimate CDOM absorption. CDOM absorption results were then used to develop an algorithm to predict the surface salinities of coastal bays and estuaries in New England, Middle Atlantic, and Gulf of Mexico regions. Algorithm-derived CDOM absorptions and salinities were successfully validated using laboratory measured absorption values over magnitudes of ~0.1 to 7.0 m1 and field collected CTD data from oligohaline to polyhaline (S less than 5 to 18-30) environments in Narragansett Bay (Rhode Island); the Neuse River Estuary (North Carolina); Pensacola Bay (Florida); Choctawhatchee Bay (Florida); St. Andrews Bay (Florida); St. Joseph Bay (Florida); and inner continental shelf waters of the Gulf of Mexico. [ABSTRACT FROM AUTHOR]

Ocean color algorithms have been successfully developed to estimate chlorophyll a and total suspended solids concentrations in coastal and estuarine waters but few have been created to estimate light absorption due to colored dissolved inorganic matter (CDOM) and salinity from the spectral signatures of these waters. In this study, we used remotely sensed reflectances in the red and blue-green portions of the visible spectrum retrieved from Medium Resolution Imaging Spectrometer (MERIS) and the International Space Station (ISS) Hyperspectral Imager for the Coastal Ocean (HICO) images to create a model to estimate CDOM absorption. CDOM absorption results were then used to develop an algorithm to predict the surface salinities of coastal bays and estuaries in New England, Middle Atlantic, and Gulf of Mexico regions. Algorithm-derived CDOM absorptions and salinities were successfully validated using laboratory measured absorption values over magnitudes of ~0.1 to 7.0 m−1 and field collected CTD data from oligohaline to polyhaline (S less than 5 to 18–30) environments in Narragansett Bay (Rhode Island); the Neuse River Estuary (North Carolina); Pensacola Bay (Florida); Choctawhatchee Bay (Florida); St. Andrews Bay (Florida); St. Joseph Bay (Florida); and inner continental shelf waters of the Gulf of Mexico.

Chlorophyll-α (chl-α) is a central water quality parameter that has been estimated through remote sensing bio-optical models. This work evaluated the performance of three well established reflectance based bio-optical algorithms to retrieve chl-a from in situ hyperspectral remote sensing reflectance datasets collected during three field campaigns in the Funil reservoir (Rio de Janeiro, Brazil). A Monte Carlo simulation was applied for all the algorithms to achieve the best calibration. The Normalized Difference Chlorophyll Index (NDCI) got the lowest error (17.85%). The in situ hyperspectral dataset was used to simulate the Ocean Land Color Instrument (OLCI) spectral bands by applying its spectral response function. Therefore, we evaluated its applicability to monitor water quality in tropical turbid inland waters using algorithms developed for MEdium Resolution Imaging Spectrometer (MERIS) data. The application of OLCI simulated spectral bands to the algorithms generated results similar to the in situ hyperspectral: an error of 17.64% was found for NDCI. Thus, OLCI data will be suitable for inland water quality monitoring using MERIS reflectance based bio-optical algorithms. [ABSTRACT FROM AUTHOR]

Abstract: An image processing software has been developed which allows quantitative analysis of multi- and hyperspectral data from oceanic, coastal and inland waters. It has been implemented into the Water Colour Simulator WASI, which is a tool for the simulation and analysis of optical properties and light field parameters of deep and shallow waters. The new module WASI-2D can import atmospherically corrected images from airborne sensors and satellite instruments in various data formats and units like remote sensing reflectance or radiance. It can be easily adapted by the user to different sensors and to optical properties of the studied area. Data analysis is done by inverse modelling using established analytical models. The bio-optical model of the water column accounts for gelbstoff (coloured dissolved organic matter, CDOM), detritus, and mixtures of up to 6 phytoplankton classes and 2 spectrally different types of suspended matter. The reflectance of the sea floor is treated as sum of up to 6 substrate types. An analytic model of downwelling irradiance allows wavelength dependent modelling of sun glint and sky glint at the water surface. The provided database covers the spectral range from 350 to 1000nm in 1nm intervals. It can be exchanged easily to represent the optical properties of water constituents, bottom types and the atmosphere of the studied area. [Copyright &y& Elsevier]

Coastal ecosystem studies using remote visible/infrared spectroscopy typically invert an atmospheric model to estimate the water-leaving reflectance signal. This inversion is challenging due to the confounding effects of turbid backscatter, atmospheric aerosols, and sun glint. Simultaneous estimation of the surface and atmosphere can resolve the ambiguity enabling spectral reflectance maps with rigorous uncertainty quantification. We demonstrate a simultaneous retrieval method that adapts the Optimal Estimation (OE) formalism of Rodgers (2000) to the coastal domain. We compare two surface representations: a parametric bio-optical model based on Inherent Optical Properties (IOPs); and an expressive statistical model that estimates reflectance in every instrument channel. The latter is suited to both land and water reflectance, enabling a unified analysis of terrestrial and aquatic domains. We test these models with both vector and scalar Radiative Transfer Models (RTMs). We report field experiments by two airborne instruments: NASA's Portable Remote Imaging SpectroMeter (PRISM) in an overflight of Santa Monica, California; and NASA's Next Generation Airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG) in an overflight of the Wax Lake Delta and lower Atchafalaya River, Louisiana. In both cases, in situ validation measurements match remote water-leaving reflectance estimates to high accuracy. Posterior error predictions demonstrate a closed account of uncertainty in these coastal observations. • We adapt Optimal Estimation (OE) methods to VSWIR coastal imaging spectroscopy. • OE enables simultaneous surface and atmosphere retrieval with uncertainty estimates. • We compare surface parameterizations suited for land, deep water, and turbid water. • We perform field tests with multiple airborne imaging spectrometer field campaigns. • Remote reflectance accurately matches in situ validation measurements. [ABSTRACT FROM AUTHOR]

Accurate calibration of oceanic LiDAR signals is essential for the accurate retrieval of ocean optical properties. Nowadays, there are many methods for aerosol LiDAR calibration, but fewer attempts have been made to implement specific calibration methods for oceanic LiDAR. Oceanic LiDAR often has higher vertical resolution, needs greater signal dynamic range, detects several orders of magnitude lower less depth of penetration, and suffers from the effects of the air-sea interface. Therefore the calibration methods for aerosol LiDAR may not be useful for oceanic LiDAR. In this paper, we present a new simple and feasible approach for oceanic LiDAR calibration via comparison of LiDAR backscatter against calculated scatter based on iteratively bio-optical models in clear, open ocean, Type 1 water. Compared with current aerosol LiDAR calibration methods, it particularly considers geometric losses and attenuation occurring in the atmosphere-sea interface. The mean relative error percentage (MREP) of LiDAR calibration constant at two different stations was all within 0.08%. The MREP between LiDAR-retrieved backscatter, chlorophyll after using LiDAR calibration constant with inversion results of measured data were within 0.18% and 1.39%, respectively. These findings indicate that the bio-optical methods for LiDAR calibration in clear ocean water are feasible and effective. [ABSTRACT FROM AUTHOR]

The kd(490) is a vertical light attenuation coefficient and an important parameter for water quality. The kd estimates are often based on empirical and semi-analytical algorithms, designed for oceanic and coastal waters. However, there is a lack of information about the performances of these models to inland waters dominated by chromophoric dissolved organic matter (CDOM). Therefore, to contribute to this investigation, nine empirical models based on the blue-to-green and blue-to-red ratios and chlorophyll-a (Chl-a) concentration were evaluated, as well as three semi-analytical models using bands from the Operational Land Imager (OLI)/Landsat-8. The errors (mean absolute percentage error, MAPE > 80%) presented by the empirical models confirmed that the blue-to-green ratio failed in retrieving kd(490) in an environment dominated by CDOM. Similar failures occurred with the models using the Chl-a concentration (MAPE ~60%) as input. A semi-analytical approach showed the lowest error (MAPE = 41.04%) in the estimate of the inherent optical properties for complex waters in order to reduce the errors above. After retrieval of kd(490) using the semi-analytical model, seasonal patterns were observed, and high values of kd(490) were detected in the dry season possibly due to the increase of the concentration of the optically-significant substances (OSS). [ABSTRACT FROM AUTHOR]

Remote sensing data is a viable alternative for mapping pigment concentrations in water body, and consequently, the trophic. Chlorophyll- a (Chl-a ) is present in all phytoplankton species. This study therefore uses laboratory fluorometric and remote sensing extraction methods for assessing chlorophyll- a concentration in the Lagos Lagoon. The fluorometer was calibrated with a commercially available chlorophyll- a standard before used in the laboratory to estimates chlorophyll- a concentration. Landsat 7 (ETM+) and Landsat 8 (OLI) were acquired for the remote sensing method. The Landsat data were first geometrically rectified. Then brightness values were converted to reflectance through the radiometric correction process. For the regression models, logarithmically transformed chlorophyll- a was used as the dependent variable. Single bands, band ratios and logarithmically transformed band ratios were the independent variables. R2 values were computed and evaluated. •Chlorophyll-a contributes to productive water bodies•laboratory fluorometric and remote sensing extraction methods•Landsat data acquired for the remote sensing method.

Reservoirs are artificial environments built by humans, and the impacts of these environments are not completely known. Retention time and high nutrient availability in the water increases the eutrophic level. Eutrophication is directly correlated to primary productivity by phytoplankton. These organisms have an important role in the environment. However, high concentrations of determined species can lead to public health problems. Species of cyanobacteria produce toxins that in determined concentrations can cause serious diseases in the liver and nervous system, which could lead to death. Phytoplankton has photoactive pigments that can be used to identify these toxins. Thus, remote sensing data is a viable alternative for mapping these pigments, and consequently, the trophic. Chlorophyll-a (Chl-a) is present in all phytoplankton species. Therefore, the aim of this work was to evaluate the performance of images of the sensor Operational Land Imager (OLI) onboard the Landsat-8 satellite in determining Chl-a concentrations and estimating the trophic level in a tropical reservoir. Empirical models were fitted using data from two field surveys conducted in May and October 2014 (Austral Autumn and Austral Spring, respectively). Models were applied in a temporal series of OLI images from May 2013 to October 2014. The estimated Chl-a concentration was used to classify the trophic level from a trophic state index that adopted the concentration of this pigment-like parameter. The models of Chl-a concentration showed reasonable results, but their performance was likely impaired by the atmospheric correction. Consequently, the trophic level classification also did not obtain better results.