Quasi-analytical Algorithm Research Articles

Accuracy Assessment of Bio-optical Models to Retrieve Backscattering Coefficients in Case 1 Waters of the Bay of Bengal

The backscattering coefficient (bb) has been obtained either in situ observations/measurements or semi-analytical/analytical and empirical algorithms that depend on the relationship between ‘bb’ and the remote sensing reflectance (Rrs). Several models were developed to estimate backscattering coefficient from the satellite imagery. The present study aims to assess the accuracy of bio-optical models and infer the best suitable model for Ocean Colour Monitoring (OCM-2) and for the upcoming OCM-3 sensors of Indian Space Research Organization. For this purpose, the bio-optical algorithms/models such as quasi-analytical algorithm (QAA), QAA version 5, Generalized Inherent Optical Properties (GIOP) and optimization of semi-analytical models in case 1 waters of the Bay of Bengal (BoB) were considered. For this analysis, three satellite sensors Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua, Visible Infrared Imaging Radiometer Suite (VIIRS), and OCM-2 datasets has been used to assess the model’s accuracy. The results showed that the optimization technique performs better (bias = 0.002, 0.179 and root-mean-square error (RMSE) = 8%, 20%) for MODIS-A and OCM-2, respectively, whereas GIOP performs better (bias = 0.08, RMSE = 14%) for VIIRS in case 1 waters of the BoB. From the statistical analysis of each model for all stations, it is recommended to use the optimization technique rather than GIOP technique for estimation of satellite based ‘bb’ in Indian waters.

An assessment of semi-analytical models based on the absorption coefficient in retrieving the chlorophyll-a concentration from a reservoir

Monitoring chlorophyll-a (Chl-a) concentrations in inland waters is crucial for water quality management, since Chl-a is a proxy for phytoplankton biomass and, thus, for ecological health of a water environment. Chl-a concentration can be retrieved through the inherent optical properties (IOPs) of a water system, which, in turn, can be remotely sensed obtained. Quasi-analytical algorithm (QAA), originally developed for ocean waters, can also retrieve IOPs for inland waters after re-parameterizations. This study is aimed at assessing the performance of sixteen schemes composed by QAA original and re-parameterized versions followed by models that use absorption coefficients as inputs for estimating Chl-a concentration in Ibitinga reservoir, located at Tietê River cascading system, São Paulo State, Brazil. It was verified that only QAAV5 based schemes were able to obtain reasonable estimates for image data and that by four models tested presented similar and acceptable results for QAAV5 outputs. The best model were applied to a Ocean and Land Colour Instrument (OLCI) image. Light absorption in the reservoir showed to be dominated by colored dissolved organic matter (CDOM), and wide spatial and temporal variability of optical and water quality properties was observed.

Validation of MODIS-Aqua bio-optical algorithms for phytoplankton absorption coefficient measurement in optically complex waters of El Rincón (Argentina)

El Rincón is a highly productive area in the inner-shelf of the Argentine Sea, southern Buenos Aires Province (39°S–62.5°W; 41.5°S–60°W), regarded as the habitat for commercially valuable fish species and important for local artisanal fisheries. The aims of this work were to characterize for the first time the in situ absorption coefficient of phytoplankton (aph(λ)) in relation to phytoplankton composition and chlorophyll-a (Chl-a) concentration, and to evaluate the satellite retrieval of aph(443) using two atmospheric correction algorithms and three bio-optical semi-analytical algorithms in order to define which is more suitable for the study area. A total of 28 oceanographic cruises were performed between November 2013 and March 2017 and the sampling included physical variables (temperature, salinity, suspended particulate matter, SPM), Chl-a, phytoplankton absorption and composition. The phytoplankton community was mainly dominated by chain-forming diatoms from the microplankton size class (40–80%), while a single coccolithophore, Gephyrocapsa oceanica (cell diameter <10 µm) was a key component (up to 40%) of the nanoplankon. In situ Chl-a (mean 1.8 mg m−3) and aph (443) (mean 0.052 m−1) generally covariated with no clear seasonality. Their correlation was rather low (R2 = 0.52) and with high scattering, in agreement with the high interannual variability of phytoplankton composition. Further, the relative low mean specific absorption coefficient of phytoplankton (aph*(443), 0.032 m2 (mg Chl-a)−1) and (aph*(676), 0.014 m2 (mg Chl-a)−1) denoted the presence of big cells and pigment packaging effect. Aside from the optical complexity of these waters, reasonable results were found using the Quasi Analytical Algorithm in retrieving aph coefficient (R2 = 0.55, Bias = 19%) and switching the atmospheric correction algorithm depending on the distance to the coast, the depth and turbidity. The results found in this paper present for the first time the phytoplankton composition and absorption of algal material characteristics and the derived implication on the performance of bio-optical algorithms in the optically complex waters of El Rincon.

Mapping the coloured dissolved organic matter absorption coefficient in a eutrophic reservoir using remotely sensed images

ABSTRACTDissolved organic matter (DOM) has 3 major sources: (1) autochthonous or surface water-derived of algal origin, (2) allochthonous or terrestrial material from soils, and (3) synthetic organic substances of man-made or industrial origin. Thus, its correlation with dissolved organic carbon (DOC) or chlorophyll a (Chl-a) depends on environmental dynamics and their related changes. Because coloured dissolved organic matter (CDOM) is the coloured fraction of DOM, historical measurements of DOM may be used as a suitable proxy for CDOM. In this research we (1) evaluated the performance of a set of bio-optical models, including empirical models and a quasi-analytical algorithm (QAA), by using simulated reflectance data based on the Thematic Mapper sensor parameters (TM); and (2) analyzed the possible sources of CDOM/DOM in a tropical eutrophic reservoir for a 15-year TM/Landsat-5 time series. Results showed that the literature-based models failed to retrieve the CDOM absorption coefficient () from simulated reflectance data. After correlation and collinearity analyses (2D colour correlation plot), an alternative wavelength (485 nm) and band ratio (B4/B1) gave the best relation to . The proposed model had satisfactory performance (R2 = 0.91, p < 0.0001, RMSE = 7%, Nash-Sutcliffe = 0.91). Two significantly different patterns were identified for the Funil hydroelectric reservoir (Brazil): one associated with land cover land use and rainfall/runoff occurrence and the other with elevated Chl-a associated with algal blooms.

Performance of existing QAAs in Secchi disk depth retrieval in phytoplankton and dissolved organic matter dominated inland waters

A semianalytical model developed to estimate the Secchi disk depth (ZSD) was used in eutrophic-to-hypereutrophic reservoirs (Ibitinga, Ibi, and Barra Bonita, BB) placed in the cascade system of the Tiete River, Brazil. The model was evaluated using the simulated remote sensing reflectance based on the Ocean and Land Color Instrument/Sentinel-3A and the Operational Land Imager/Landsat-8 from both reservoirs. Three quasianalytical algorithm (QAA) versions (QAAv5, QAAM14, and QAAW16) were evaluated to derive the absorption and backscattering coefficients, and then used for ZSD retrieval. For BB, where the chlorophyll-a concentration exceeded 200 mg m − 3, the model based on QAAv5 showed high uncertainties while the QAAW16, which was originally parameterized for BB showed better performance regarding the ZSD retrieval (mean absolute percentage errors—MAPE of 22%). However, QAAW16 did not perform satisfactorily for Ibi, which is dominated by colored dissolved organic matter (CDOM). For Ibi, QAAv5 provided the best result with MAPE of 34.60%, followed by QAAM14 with 34.65%. QAA-based ZSD models tend to perform poorly in waters with high concentration of chlorophyll-a possibly due to phytoplankton package effect, whereas the same models may require additional parameterization in waters dominated by CDOM. Landsat-8 data showed significant potential for ZSD retrieval in inland waters.

Assessment of quasi-analytical algorithm for estimating the inherent optical properties in a complex cascade system

Several quasi-analytical algorithm (QAA) versions were developed to make it suitable for different inland water systems. QAABBHR and QAAOMW were reparameterized based on two reservoirs from the Tiete River cascading system (Sao Paulo State, Brazil), which present widely differing compositions. Considering the purpose of monitoring the entire cascade through a unique QAA version, we aimed to assess the suitability of these two QAA versions and, in addition, another two QAA native forms (versions 5 and 6), for retrieving inherent optical properties (IOPs) in Ibitinga hydroelectric reservoir (IHR), situated in the same cascading system. In addition to that, we addressed bio-optical characterization of IHR, using spectral and water quality data collected in a field campaign conducted in July 2016. Wide spatial variability of optically significant constituent (OSC) in IHR and colored dissolved organic matter predominance in its absorption budget was observed. None of the tested QAA versions were completely suitable in retrieving absorption coefficients for IHR in all wavelengths. However, results for wavelengths commonly used as proxy for OSC concentration retrieval were satisfactory in some of the models. Therefore, the results obtained in this study shows that QAAs versions can be used for specific purposes (e.g., chlorophyll-a mapping), by employing the best model for IOPs retrieval at a specific wavelength. This highlights the challenge of copying with high optical variability in cascading systems. In this sense, further research is necessary, for either achieving a QAA reparameterized version appropriate for aquatic systems with widely differing optical properties or another analytical scheme.

Retrieval of absorption coefficients for a drinking water source using a green–red band quasianalytical algorithm

This study proposed a green–red band quasianalytical algorithm, QAA-GRI, and evaluates its performance using an in situ dataset from Lake Qiandaohu, a drinking water source, in China. First, by shifting the reference wavelength from 555 to 510 nm, a green–red index (GRI) can be calculated from remote sensing reflectance at 510, 560, and 620 nm, and the index was then used to retrieve the total absorption coefficients at the reference band, a ( 510 ) . Second, a semianalytical model based on a ( 510 ) and the GRI was deduced to establish the QAA-GRI, replacing the empirical model in quasianalytical algorithm version 5 (QAA-v5). The QAA-GRI was applied to retrieve absorption coefficients from an in situ dataset of Lake Qiandaohu, and the QAA-GRI’s performance was compared with that of QAA-v5 and another red–green QAA-based approach (QAA-RGR). The results demonstrate that, for this dataset, the QAA-GRI exhibited better performance (R2 = 0.81, mean absolute percentage error, MAPE = 15.7 % ), which indicated a clear improvement in the accuracy of absorption coefficient retrieval, compared with QAA-v5 (R2 = 0.56, MAPE = 21.2 % ) and QAA-RGR (R2 = 0.67, MAPE = 22.6 % ). In addition, to illustrate the QAA-GRI’s assumptions and its applicability, the QAA-GRI was also applied to an in situ dataset from Taihu Lake, a highly turbid and eutrophic water source. As predicted, when applied to Taihu Lake, the QAA-GRI did not perform as well as it did when applied to Lake Qiandaohu because of the former’s extremely high turbidity, which can cause greater uncertainty with regard to backscattering coefficients. This study suggests that the QAA-GRI is better suited to the retrieval of absorption coefficients from drinking water resources. Our algorithm will also have potential applications to satellite data from the Medium Resolution Imaging Spectrometer or Ocean and Land Color Instrument.

An estuarine-tuned quasi-analytical algorithm (QAA-V): assessment and application to satellite estimates of SPM in Galveston Bay following Hurricane Harvey

Abstract. The standard quasi-analytical algorithm (Lee et al., 2002) was tuned as QAA-V using a suite of synthetic data and in situ measurements to improve its performance in optically complex and shallow estuarine waters. Two modifications were applied to the standard QAA: (1) the semi-analytical relationship for obtaining remote sensing reflectance just below the water surface as a function of absorption and backscattering coefficients was updated using Hydrolight® simulations, and (2) an empirical model of the total non-water absorption coefficient was proposed using a ratio of green to red bands of an ocean color sensor, which is known to work well in various inland and estuarine environments. The QAA-V-derived total absorption and backscattering coefficients, which were evaluated in a variety of waters ranging from highly absorbing and turbid to relatively clear shelf waters, showed satisfactory performance on a Hydrolight-simulated synthetic dataset (R2 &gt; 0.87, MRE &lt; 17 %), an in situ estuarine and nearshore dataset (R2 &gt; 0.70, MRE &lt; 35 %), and the NOMAD (R2 &gt; 0.90, MRE &lt; 30 %). When compared to the standard QAA (QAA-v6), the QAA-V showed an obvious improvement with ∼ 30–40 % reduction in absolute mean relative error for the Hydrolight-simulated synthetic and in situ estuarine and nearshore datasets, respectively. The methodology of tuning QAA was applied to the VIIRS ocean color sensor and validation results suggest that the proposed methodology can also be applied to other ocean color and land-observing sensors. The QAA-V was also assessed on VIIRS imagery using a regional relationship between suspended particulate matter (SPM) and particulate backscattering coefficient at 532 nm (bbtnw532; R2 = 0.89, N = 33). As a case study, the QAA-V processing chain and VIIRS imagery were used to generate a sequence of SPM maps of Galveston Bay, Texas following the unprecedented flooding of Houston and the surrounding regions due to Hurricane Harvey in August 2017. The record discharge of floodwaters through two major rivers into the bay resulted in very high SPM concentrations over several days throughout the bay, with wind forcing additionally influencing its distribution into the coastal waters of the northern Gulf of Mexico. The promising results of this study suggest that the application of QAA-V to various ocean color and land-observing satellite imagery could be used to assess the bio-optical state and water quality dynamics in a variety of coastal systems around the world.

Spatio-temporal dynamics of inherent optical properties in oligotrophic northern Gulf of Mexico estuaries

Coastal and estuarine ecosystems provide numerous economic and environmental benefits to society. However, increasing anthropogenic activities and developmental pressure may stress these areas and hamper their ecosystem services. Satellite remote sensing could be used as a tool for monitoring water quality parameters, including inherent optical properties (IOP) in coastal regions. Spatio-temporal information on IOP variability will help in understanding the dynamics of the water quality of estuaries. The objective of this research was to develop a novel hybrid model by combining and parameterizing existing quasi analytical and semi-analytical algorithms to estimate IOPs in four oligotrophic northern Gulf of Mexico Florida estuaries. The hybrid model was applied to above surface remote sensing reflectance data representing the Medium Resolution Imaging Spectrometer (MERIS) and Sentinel-3's Ocean and Land Colour Instrument (OCLI) bands. The hybrid model produced a root means squared error (RMSE) of 0.32 m−1 (13.95% NRMSE) for total absorption (at), 0.21 m−1 (7.61% NRMSE) for detritus-gelbstoff absorption (adg), and 0.09 m−1 (22.77% NRMSE) for phytoplankton pigment absorption (aphi). Results showed that absorption by detritus and gelbstoff (adg) dominates the water in these estuaries. Monthly IOP variability in 2010 revealed that compared to other estuaries, magnitudes of IOPs was the highest in Pensacola Bay and therefore the highest attenuation. Findings also indicated that river discharge and precipitation predominantly govern the IOP variations in all four estuaries, showing an increase in IOP values following the high flow period. The hybrid model improved IOP retrieval in these low chlorophyll-a (Chl-a) estuaries where the existing spectral decomposition models did not perform satisfactorily.

Enhance field water-color measurements with a Secchi disk and its implication for fusion of active and passive ocean-color remote sensing.

Inversion of the total absorption (a) and backscattering coefficients of bulk water through a fusion of remote sensing reflectance (Rrs) and Secchi disk depth (ZSD) is developed. An application of such a system to a synthesized wide-range dataset shows a reduction of ∼3 folds in the uncertainties of inverted a(λ) (in a range of ∼0.01-6.8 m-1) from Rrs(λ) for the 350-560nm range. Such a fusion is further proposed to process concurrent active (ocean LiDAR) and passive (ocean-color) measurements, which can lead to nearly "exact" analytical inversion of an Rrs spectrum. With such a fusion, it is found that the uncertainty in the inverted total a in the 350-560nm range could be reduced to ∼2% for the synthesized data, which can thus significantly improve the derivation of a coefficients of other varying components. Although the inclusion of ZSD places an extra constraint in the inversion of Rrs, no apparent improvement over the quasi-analytical algorithm (QAA) was found when the fusion of ZSD and Rrs was applied to a field dataset, which calls for more accurate determination of the absorption coefficients from water samples.

Estimating the Optical Properties of Inorganic Matter-Dominated Oligo-to-Mesotrophic Inland Waters

Many studies over the years have focused on bio-optical modeling of inland waters to monitor water quality. However, those studies have been conducted mainly in eutrophic and hyper-eutrophic environments dominated by phytoplankton. With the launch of the Ocean and Land Colour Instrument (OLCI)/Sentinel-3A in 2016, a range of bands became available including the 709 nm band recommended for scaling up these bio-optical models for productive inland waters. It was found that one category of existing bio-optical models, the quasi-analytical algorithms (QAAs), when applied to colored dissolved organic matter (CDOM) and detritus-dominated waters, produce large errors. Even after shifting the reference wavelength to 709 nm, the recently re-parameterized QAA versions could not accurately retrieve the inherent optical properties (IOPs) in waterbodies dominated by inorganic matter. In this study, three existing versions of QAA were implemented and proved inefficient for the study site. Therefore, several changes were incorporated into the QAA, starting with the re-parameterization of the empirical steps related to the total absorption coefficient retrieval. The re-parameterized QAA, QAAOMW showed a significant improvement in the mean absolute percentage error (MAPE). MAPE decreased from 58.05% for existing open ocean QAA (QAALv5) to 16.35% for QAAOMW. Considerable improvement was also observed in the estimation of the absorption coefficient of CDOM and detritus from a MAPE of 91.05% for QAALv5 to 18.87% for QAAOMW. The retrieval of the absorption coefficient of phytoplankton ( a ϕ ) using the native form of QAA proved to be inaccurate for the oligo-to-mesotrophic waterbody due to the low a ϕ returning negative predictions. Therefore, a novel approach based on the normalized a ϕ was adopted to maintain the spectral shape and retrieve positive values, resulting in an improvement of 119% in QAAOMW. Further tuning and scale-up of QAAOMW to OLCI bands will aid in monitoring water resources and associated watershed processes.

Validation and Calibration of QAA Algorithm for CDOM Absorption Retrieval in the Changjiang (Yangtze) Estuarine and Coastal Waters

Distribution, migration and transformation of chromophoric dissolved organic matter (CDOM) in coastal waters are closely related to marine biogeochemical cycle. Ocean color remote sensing retrieval of CDOM absorption coefficient (ag(λ)) can be used as an indicator to trace the distribution and variation characteristics of the Changjiang diluted water, and further to help understand estuarine and coastal biogeochemical processes in large spatial and temporal scales. The quasi-analytical algorithm (QAA) has been widely applied to remote sensing inversions of optical and biogeochemical parameters in water bodies such as oceanic and coastal waters, however, whether the algorithm can be applicable to highly turbid waters (i.e., Changjiang estuarine and coastal waters) is still unknown. In this study, large amounts of in situ data accumulated in the Changjiang estuarine and coastal waters from 9 cruise campaigns during 2011 and 2015 are used to verify and calibrate the QAA. Furthermore, the QAA is remodified for CDOM retrieval by employing a CDOM algorithm (QAA_CDOM). Consequently, based on the QAA and the QAA_CDOM, we developed a new version of algorithm, named QAA_cj, which is more suitable for highly turbid waters, e.g., Changjiang estuarine and coastal waters, to decompose ag from adg (CDOM and non-pigmented particles absorption coefficient). By comparison of matchups between Geostationary Ocean Color Imager (GOCI) retrievals and in situ data, it reveals that the accuracy of retrievals from calibrated QAA is significantly improved. The root mean square error (RMSE), mean absolute relative error (MARE) and bias of total absorption coefficients (a(λ)) are lower than 1.17, 0.52 and 0.66 m−1, and ag(λ) at 443 nm are lower than 0.07, 0.42 and 0.018 m−1. These results indicate that the calibrated algorithm has a better applicability and prospect for highly turbid coastal waters with extremely complicated optical properties. Thus, reliable CDOM products from the improved QAA_cj can advance our understanding of the land-ocean interaction process by earth observations in monitoring spatial-temporal distribution of the river plume into sea.

Characterization of Particle Backscattering of Global Highly Turbid Waters From VIIRS Ocean Color Observations

AbstractNormalized water‐leaving radiance spectra nLw(λ) at the near‐infrared (NIR) from five years of observations (2012–2016) with the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar‐orbiting Partnership (SNPP) are used to derive the particle backscattering coefficients bbp(λ) for global highly turbid coastal and inland waters. Based on the fact that the absorption coefficient of sea water aw(λ) is generally much larger than those of the other constituents aiop(λ) at the NIR wavelengths in coastal and inland waters, an NIR‐based bbp(λ) algorithm for turbid coastal and inland waters has been developed and used in this study. This algorithm can be safely used for highly turbid waters with nLw(745) and nLw(862) &lt; ∼6 and ∼4 mW cm−2 μm−1 sr−1, respectively. Seasonal and interannual variations of bbp(λ) in China's east coastal region, the Amazon River Estuary, the La Plata River Estuary, the Meghna River Estuary, the Atchafalaya River Estuary, and Lake Taihu are characterized and quantified. The coefficient bbp(λ) can reach over ∼3–4 m−1 in the Amazon River Estuary and China's east coastal region. The Amazon River Estuary is identified as the most turbid region in the global ocean in terms of bbp(λ) magnitude. bbp(λ) spectra in these five highly turbid regions are also seasonal‐dependent and regional‐dependent. In the highly turbid waters of China's east coastal region and the Amazon River Estuary, bbp(λ) generally increases in wavelength from 410 to 862 nm, while it decreases in the La Plata River Estuary and Atchafalaya River Estuary. This is attributed to the different particle size distributions in these waters. The geophysical implication of the bbp(λ) spectral curvatures for different waters is discussed. To improve global bbp(λ) for both open oceans and coastal turbid waters, a new combined NIR‐based and Quasi‐Analytical Algorithm (QAA)‐based bbp(λ) algorithm is proposed and demonstrated.

Deriving colored dissolved organic matter absorption coefficient from ocean color with a neural quasi‐analytical algorithm

AbstractThe objective of this study is to develop an approach to estimate the gelbstoff absorption coefficient (ag) from remote sensing reflectance (Rrs). This approach includes two components: the inherent optical properties are semianalytically derived from the Rrs by a neural quasianalytical algorithm (NQAA), and then the derivations are semianalytically extended to ag estimations using a band difference approach. This method is then evaluated with the various type of ocean color data including synthetic, field measured, and satellite‐observed data. The results show that the method can produce an excellent quantitative agreement between the estimated and known ag in ocean waters with a wide range of optical properties, while significantly reducing the effects of residual error in SeaWiFS Rrs, primarily from the imperfect atmospheric correction algorithm on the retrieval of ag in the clear open oceans. Furthermore, with the application of this new algorithm, the SeaWiFS ag products exhibit more spatially and temporally uniform results than the band ratio approach‐based ag retrieval algorithm. These results indicate that the new algorithm is an encouraging approach to process ocean color images for ag retrieval, although a greater number of independent tests with in situ and satellite data are required to further validate and improve this approach.

Remote sensing of chlorophyll-a in coastal waters based on the light absorption coefficient of phytoplankton

Remote sensing of chlorophyll-a concentration, [Chl-a], has been difficult in coastal waters like the Chesapeake Bay owing largely to terrestrial substances (such as minerals and humus) that are optically significant but do not covary with phytoplankton. Here we revisit the semi-analytical pathway of deriving [Chl-a] based on the light absorption coefficient of phytoplankton by introducing the generalized stacked-constraints model (GSCM) to partition satellite-derived total light absorption coefficient of water (with pure-water contribution subtracted), anw(λ), into phytoplankton, aph(λ), and non-phytoplankton components, where anw(λ) is derived from satellite remote-sensing reflectance, Rrs(λ), using the Quasi-Analytical Algorithm. The GSCM-derived aph(λ) was compared with field matchups of [Chl-a]. We show that semi-analytical approaches can provide superior [Chl-a] product compared with reflectance-band-ratio algorithms when the accuracy of satellite-derived aph(λ) is sufficiently improved, in this case with the GSCM. However, the improvement is at the cost of significantly reduced data availability because the GSCM may provide no feasible solutions when input anw(λ) data are subject to large errors. This in turn highlights the needs for improved atmospheric correction and upstream models capable of preserving actual spectral shapes of Rrs(λ) and anw(λ), respectively.

Bathymetry of the Coral Reefs of Weizhou Island Based on Multispectral Satellite Images

Shallow water depth measurements using multispectral images are crucial for marine surveying and mapping. At present, relevant studies either depend on the use of other auxiliary data (such as field water depths or water column data) or contain too many unknown variables, thus making these studies suitable only for images that contain enough visible wavebands. To solve this problem, a Quasi-Analytical Algorithm (QAA) approach is proposed in this paper for estimating the water depths around Weizhou Island by developing a QAA to estimate the diffuse attenuation coefficients and simplifying the parameterization of the bathymetric model. The approach contains an initialization sub-approach and a novel global adjustment sub-approach. It is not only independent of other auxiliary data but also greatly reduces the number of unknowns. Experimental results finally demonstrated that the Root Mean Square Errors (RMSEs) were 1.01 m and 0.77 m for the ZY-3 image and the WorldView-3 (WV-3) image, respectively, so the approach is competitive to other QAA bathymetric methods. Besides, the global adjustment sub-approach was also seen to be superior to common smoothing filters: if the Signal-to-Noise Ratio (SNR) is as low as 42, i.e., ZY-3, it can smooth the water depths and improve the accuracies, otherwise can avoid the over-smoothing of water depths.

Retrieval of Secchi disk depth from a reservoir using a semi-analytical scheme

The mechanistic model reported in Lee et al. (2015) estimating the Secchi disk depth (ZSD) was applied to an oligo- to mesotrophic reservoir in Brazil. The model was originally validated with data covering lake, oceanic, and coastal waters; however, the model used the quasi-analytical algorithm (QAA) designed for optically deep waters as input and was applied to oceanic and coastal waters to derive absorption [a] and backscattering [bb] coefficients. The hypothesis is that the use of QAAv5 (http://www.ioccg.org/groups/Software_OCA/QAA_v5.pdf) to estimate both a and bb (step M1) to retrieve Kd (step M2) and ZSD (step M3) will lead to errors caused by M1 preventing an accurate estimate in oligo- to mesotrophic water. To test this hypothesis, data collected in three field trips were used to apply the mechanistic model based on the spectral bands from OLI/Landsat-8, (often applied to oceanic and coastal waters), and multispectral instrument (MSI)/Sentinel-2 bands (applied to QAA designed for very turbid inland water). The impact of step M1 over steps M2 and M3 was analyzed by the error analysis. The mean absolute percentage error (MAPE) for Kd using QAAv5 ranged between 10.35% and 19.76%, while the error using QAAM14 varied between 12.68% and 28.29%. Regarding the errors of step M3 and applying QAAv5, the total root-mean-square difference (RMSD) varied from 0.55 to 1.18m and MAPE ranged between 12.86% and 31.17%, while the RMSD ranged between 0.70 and 1.50m and MAPE varied from 14.33% to 39.13% when using QAAM14. However, the result from QAAv5 showed a better correlation with in situ data, although underestimating Kd and ZSD. Therefore, a modified version of QAAv5 (QAAR17) was evaluated. The results showed an improvement of Kd (MAPE ranging between 8.89% to 18.76%) and ZSD (RMSD ranging between 0.32 and 0.90m and MAPE ranging between 8.65 and 19.75%), bringing the values close to the 1:1 line. The largest error was observed for the data of the second field trip, where the bio-optical properties showed a horizontal gradient along the reservoir. In addition, the magnitude of the remote sensing reflectance (Rrs) also varied depending on the water quality. Thus, with respect to ZSD mapping, this research showed that environments with a high variability in Rrs can limit the accurate estimation of inherent optical properties (IOPs) based on QAAv5. Therefore, the limiting step of the model was attributed to M1, which means that the mechanistic model from Lee et al. (2015) can be considered an universal approach if M1 is modified based on the type of water.

VIIRS captures phytoplankton vertical migration in the NE Gulf of Mexico

In summer 2014, a toxic Karenia brevis bloom (red tide) occurred in the NE Gulf of Mexico, during which vertical migration of K. brevis has been observed from glider measurements. The current study shows that satellite observations from the Visible Infrared Imaging Radiometer Suite (VIIRS) can capture changes in surface reflectance and chlorophyll concentration occurring within 2h, which may be attributed this K. brevis vertical migration. The argument is supported by earlier glider measurements in the same bloom, by the dramatic changes in the VIIRS-derived surface chlorophyll, and by the consistency between the short-term reflectance changes and those reported earlier from field-measured K. brevis vertical migration. Estimates using the quasi-analytical algorithm also indicate significant increases in both total absorption coefficient and backscattering coefficient in two hours. The two observations in a day from a single polar-orbiting satellite sensor are thus shown to be able to infer phytoplankton vertical movement within a short timeframe, a phenomenon difficult to capture with other sensors as each sensor can provide at most one observation per day, and cross-sensor inconsistency may make interpretation of merged-sensor data difficult. These findings strongly support geostationary satellite missions to study short-term bloom dynamics.

A semianalytical MERIS green‐red band algorithm for identifying phytoplankton bloom types in the East China Sea

AbstractA new bio‐optical algorithm based on the green and red bands of the Medium Resolution Imaging Spectrometer (MERIS) is developed to differentiate the harmful algal blooms of Prorocentrum donghaiense Lu (P. donghaiense) from diatom blooms in the East China Sea (ECS). Specifically, a novel green‐red index (GRI), actually an indicator for a(510) of bloom waters, is retrieved from a semianalytical bio‐optical model based on the green and red bands of phytoplankton‐absorption and backscattering spectra. In addition, a MERIS‐based diatom index (DIMERIS) is derived by adjusting a Moderate Resolution Imaging Spectroradiometer (MODIS) diatom index algorithm to the MERIS bands. Finally, bloom types are effectively differentiated in the feature spaces of the green‐red index and DIMERIS. Compared with three previous MERIS‐based quasi‐analytical algorithm (QAA) algorithms and three existing classification methods, the proposed GRI and classification method have the best discrimination performance when using the MERIS data. Further validations of the algorithm by using several MERIS image series and near‐concurrent in situ observations indicate that our algorithm yields the best classification accuracy and thus can be used to reliably detect and classify P. donghaiense and diatom blooms in the ECS. This is the first time that the MERIS data have been used to identify bloom types in the ECS. Our algorithm can also be used for the successor of the MERIS, the Ocean and Land Color Instrument, which will aid the long‐term observation of species succession in the ECS.

A new algorithm of retrieving a petroleum substances absorption coefficient in sea water based on a remote sensing image

Establishing the remote sensing algorithm of retrieving the absorption coefficient of seawater petroleum substances is an efficient way to improve the accuracy of retrieving a seawater petroleum concentration using a remote sensing technology. A remote sensing reflectance is a basic physical parameter in water color remote sensing. Apply it to directly retrieve the absorption coefficient of seawater petroleum substances is of potential advantage. The absorption coefficient of waters containing petroleum [ACWCP, ao(λ)], consists of the absorption coefficient of pure water [ACPW, aw(λ)], plankton [ACP, aph(λ)], colored scraps [ACCS, ad, g(λ)], and petroleum substance [ACPS, aoil(λ)]. Among those, ACCS consists of the absorption coefficient of nonalgal particle [ACNP, ad (λ)] and colored dissolved organic matter [ACCDOM, ag(λ)]. For waters containing petroleum, the retrieved ACCS using the existing method is a combination absorption coefficient of ACNP, ACCDOM and ACPA [CAC, ad,g,oil (λ)]. Therefore, the principle question is how to extract ACPS from CAC. Through the analysis of the three proportion tests conducted between the year of 2013 and 2015 and the corresponding remote sensing data, an algorithm of retrieving the absorption coefficient of petroleum substances is proposed based on remote sensing reflectance. First of all, ACPS and CAC are retrieved from the reflectance using the quasi-analytical algorithm (QAA), with some parameter modified. Secondly, given the fact that the backscatter coefficient [BC, bbp(555)] of total particles at 555 nm can be obtained completely from the reflectance, the relation between BC and ACNP in petroleum contaminated water can be established. As a result, ACNP can be calculated. Then, combining the remote sensing retrieving algorithm of ag(440), the method of achieving the spectral slope of the absorption coefficient can be established, from which ACCDOM, can be calculated. Finally, ACPS can be computed as the residual. The accuracy of ACPS based on this algorithm is 86% compared with the in situ measurements.