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Abstract
Introduction
Study Area Profile
Materials and Methods
Results and Discussion
Conclusions
References
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Earth Sciences
Oceanography

Remote Sensing of Ocean and Coastal Environments

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2021, Remote Sensing of Ocean and Coastal Environments

https://doi.org/10.1016/B978-0-12-819604-5.00003-2
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Abstract
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Remote sensing plays a critical role in the assessment of phytoplankton biomass in coastal and ocean environments, which is essential for understanding marine ecosystems. The study highlights the use of multispectral satellite images to monitor phytoplankton dynamics, emphasizing its effectiveness in observing seasonal variations and interactions with environmental factors such as salinity, temperature, and nutrient levels. The findings suggest that remote sensing offers a valuable method for estimating chlorophyll-a concentrations, providing insights into the productivity and diversity of aquatic systems over large spatial and temporal scales.

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High-resolution shipboard measurements of phytoplankton: a way forward for enhancing the utility of satellite SST and chlorophyll for mapping microscale features and frontal zones in coastal waters
Joaquim Goes

Proceedings of SPIE, 2016

Coastal eddies, frontal zones and microscale oceanographic features are now easily observable from satellite measurements of SST and Chl a. Enhancing the utility of these space-borne measurements for biological productivity, biogeochemical cycling and fisheries investigations will require novel bio-optical methods capable of providing information on the community structure, biomass and photo-physiology of phytoplankton associated on spatial scales that match these features. This study showcases high-resolution in-situ measurements of sea water hydrography (SeaBird CTD ®), CDOM (WetLabs ALF ®), phytoplankton functional types (PFTs, FlowCAM ®), biomass (bbe Moldaenke AlgaeOnlineAnalyzer ® and WetLabs ALF ®) and phytoplankton photosynthetic competency (mini-FIRe) across microscale features encountered during a recent (Nov. 2014) cruise in support of NOAA's VIIRS ocean color satellite calibration and validation activities. When mapped against binned daily, Level 2 satellite images of Chl a, K d 490 and SST over the cruise period, these high-resolution in-situ data showed great correspondence with the satellite data, but more importantly allowed for identification of PFTs and water types associated with microscale features. Large assemblages of phytoplankton communities comprising of diatoms and diatom-diazotroph associations (DDAs), were found in mesohaline frontal zones. Despite their high biomass, these populations were characterized by low photosynthetic competency, indicative of a bloom at the end of its active growth possibly due to nitrogen depletion in the water. Other prominent PFTs such as Trichodesmium spp., Synechococcus spp. and cryptophytes, were also associated with specific water masses offering the promise and potential that ocean remote sensing reflectance bands when examined in the context of water types also measurable from space, could greatly enhance the utility of satellite measurements for biological oceanographic, carbon cycling and fisheries studies.

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An optical model for the remote-sensing of absorption coefficients of phytoplankton in oceanic / coastal waters
Surya Prakash Tiwari

A new model for the remote sensing of absorption coefficients of phytoplankton a ph (λ) in oceanic and coastal waters is developed and tested with SeaWiFS and MODIS-Aqua data. The model is derived from a relationship of the remote sensing reflectance ratio R rs (670)/R rs (490) and a ph (λ) (from large in-situ data sets). When compared with over 470 independent in-situ data sets, the model provides accurate retrievals of the a ph (λ) across the visible spectrum, with mean relative error less than 8%, slope close to unity and R 2 greater than 0.8. Further comparison of the SeaWiFS-derived a ph (λ) with in-situ a ph (λ) values gives similar and consistent results. The model when used for analysis of MODIS-Aqua imagery, provides more realistic values of the phytoplankton absorption coefficients capturing spatial structures of the massive algal blooms in surface waters of the Arabian Sea. These results demonstrate that the new algorithm works well for both the coastal and open ocean waters observed and suggest a potential of using remote sensing to provide knowledge on the shape of phytoplankton absorption spectra that are a requirement in many inverse models to estimate phytoplankton pigment concentrations and for input into bio-optical models that predict carbon fixation rates for the global ocean.

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Remote estimation of phytoplankton size fractions using the spectral shape of light absorption
Joji Ishizaka

Optics express, 2015

Phytoplankton size structure plays an important role in ocean biogeochemical processes. The light absorption spectra of phytoplankton provide a great potential for retrieving phytoplankton size structure because of the strong dependence on the packaging effect caused by phytoplankton cell size and on different pigment compositions related to phytoplankton taxonomy. In this study, we investigated the variability in light absorption spectra of phytoplankton in relation to the size structure. Based on this, a new approach was proposed for estimating phytoplankton size fractions. Our approach use the spectral shape of the normalized phytoplankton absorption coefficient (a<sub>ph</sub>(λ)) through principal component analysis (PCA). Values of a<sub>ph</sub>(λ) were normalized to remove biomass effects, and PCA was conducted to separate the spectral variance of normalized a<sub>ph</sub>(λ) into uncorrelated principal components (PCs). Spectral variation...

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Global status of remote sensing of phytoplankton
Kedarnath Mahapatra

1999

The color of the ocean as measured by the space-borne sensors contains the quantitative information on phytoplankton pigments, especially chlorophyll. In this paper we have reviewed principle of ocean color remote sensing. The concept of spectral, spatial, temporal and radiometric resolutions are first described followed by a discussion on present status of ocean color sensors. A brief account on various applications of remotely sensed ocean color information is presented with the examples from several sensors and region. In the open ocean, the techniques for measuring chlorophyll is almost established, where as in coastal waters the retrieval of ocean color information becomes complicated because of the interference of suspended sediments. Ocean color images are found useful to trace the mesoscale dynamics. Remotely sensed phytoplankton are also found useful to understand Carbon at basin as well as global scale. The paper concludes with comment on future of ocean color remote sensi...

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Long-Time-Scale Investigation of Phytoplankton Biomass Through Reconstructed Chlorophyll-A Data Using DINEOF Method
arun inamdar

2019

The spatial and temporal changes of phytoplankton biomass in the Arabian Sea (AS), though a well-researched topic, its variability according to the phytoplankton size classes (PSCs) were less analysed and explored. Concisely, the chlorophyll-a (chl-a) concentration is not only considered as a proxy of phytoplankton biomass but also an essential factor for identifying phytoplankton community structure. Hence, the synoptic relationship between chl-a concentration and phytoplankton size classes (PSCs) (micro->20 μm, nano-2-20 μm and picoplankton-< 2 μm) provides the better understanding of phytoplankton communities at regional and global scales. In this study, Data INterpolating Empirical Orthogonal Function (DINEOF) is applied on the 16 years Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua Level-3 chl-a concentration of the AS. The spatial reconstruction is performed on eight-day composite time resolution to achieve considerably accurate gap-free chl-a data. Further ocean colour phytoplankton functional types (OCPFT) model is applied on gap-filled chl-a data of the time series 2003-2018 to study the spatial and temporal distribution of PSCs. The results show that microplankton is found highly distributed in northwest and northeast AS and southwest AS during summer monsoon (June-September) and winter monsoon (December-March), respectively, due to nutrient and light availability, constituting only 16.60% of the entire phytoplankton biomass in the mean-field of 2003-2018. Conversely, nanoplankton encounters ubiquitous nature throughout the AS, constituting 51.80% of chl-a. Whereas picoplankton appeared to be dominant in lower latitudes of AS (i.e., southeast AS) because of their survival capability (nutrient and lightindependent) in oligotrophic conditions, but constitute only 31.6% in the AS. Moreover, comparing sea surface temperature (SST) and photosynthetically active radiation (PAR) with the PSCs shows the relationship and distribution of PSCs towards favourable environmental conditions. Thus, PSCs estimation using reconstructed gap-free chlorophyll-a concentration provides a better understanding of PSCs shift and their dynamics on seasonal and temporal scales in the AS.

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Remote sensing of phytoplankton functional types
JESUS COTLAME MORALES

Remote Sensing of Environment, 2008

The 2013-2015 Hyperspectral Infrared Imager (HyspIRI) Preparatory Flight Campaign, using the Airborne Visible/ Infrared Imaging Spectrometer (AVIRIS) and MODIS/ASTER Airborne Simulator (MASTER), seeks to demonstrate appropriate sensor signal, spatial and spectral resolution, and orbital pass geometry for a global mission to reveal ecological and climatic gradients expressed in the selected California, USA study area. One of the awarded projects focused on the flight transects covering the coastal ocean to demonstrate that the AVIRIS data can be used to infer phytoplankton functional types at the land-sea interface. Specifically, this project directly assesses whether HyspIRI can provide adequate signal in the complex aquatic environment of the coastal zone to address questions of algal bloom dynamics, water quality, transient responses to human disturbance, river runoff, and red tides. Phytoplankton functional type (PFT), or biodiversity, can be determined from ocean color using the Phytoplankton Detection with Optics (PHYDOTax) algorithm and this information can be used to detect and monitor for harmful algal blooms. PHYDOTax is sensitive to spectral shape and accurate retrievals of ocean color across the visible spectral range is needed. The specific goal of this paper is to address the challenges of sensor capabilities and atmospheric correction in coastal environments by assessing two atmospheric correction methods using AVIRIS data for the retrieval of ocean color for use in derived products of chlorophyll-a and phytoplankton functional type. The atmospheric correction algorithms Atmospheric Removal (ATREM) and Tafkaa were applied to AVIRIS imagery of Monterey Bay, CA collected on 10 April 2013 and 31 October 2013. Data products from the imagery were compared with shipboard measurements including chlorophyll-a from whole-water samples and phytoplankton community structure estimated from diagnostic pigment markers using CHEMical TAXonomy (CHEMTAX). Using ATREM and Tafkaa and a selected set of input parameters for the scenes, we were unable to produce accurate retrievals of ocean color for the determination of chlorophyll-a and phytoplankton diversity. A modified ATREM correction produced science-quality data in which chlorophyll-a was accurately estimated using the Ocean Color 3 (OC3) chlorophyll-a algorithm, but biodiversity using PHYDOTax was not accurately estimated. Improvements in sensor calibration, sensitivity, and atmospheric correction of the HyspIRI imagery data set is needed in order to adequately estimate biogeochemically meaningful data products for the ocean such as chlorophyll-a, inherent optical properties, or PFTs. The HyspIRI Science Team is seeking improvements so the HyspIRI Airborne Campaign data set can be used for algorithm development to understand biodiversity and ecosystem function of coastal habitats that are facing increasing threats of human impact and climate change.

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The use of phytoplankton pigments for identifying and quantifying phytoplankton groups in coastal areas:testing the influence of light and nutrients on pigment/chlorophyll a ratios
Harry Havskum

Marine Ecology Progress Series, 2000

The influences of light and nutnents on ratios of pigments/chlorophyll a (chl a) were investigated for several species of different phytoplankton groups from estuaries and coastal areas lor the purpose of calculating the biornass of individual phytoplankton groups as chl a based on pig-menWch1 a ratios. Pigment ratios were constructed for varying Light intensities and qualities and for nutrient-starved algae cultured in the laboratory. The pigrnent ratios were tested on 4 data Sets obtained in estuanes and coastal areas using the CHEMTAX program, for calculating phytoplankton group abundance as chl a. Field samples were collected over a vanety of time and spatial scales as well as being subjects to variations in light and nutnent conditions. The pigmentkhl a ratios derived frorn the different treatments in culture experiments generally had a minor effect on the CHEMTAX biomass calculations, although the biomass of cyanobactena and prymnesiophytes was significantly influenced by the ratios chosen. In addition. interspecies variations in pigment/chl a ratios within the individual phytoplankton groups were even more pronounced than variations caused by the different growth conditions, indicating that the ratios chosen for CHEMTAX calculations should, if at ali possible. reflect the dominant phytoplankton species present in a given area. For 2 of the data sets, where larger algal cells dominated, the composition and the biomass of the individual phytoplankton groups, using our pigrnent ratios in the CHEMTAX prograrn, corresponded well to rnicroscopic determinations of the biomass of phytoplankton. One of the data sets, where small algal cells dominated, was counted under the microscope by 2 different laboratones. Their biomass estimates were not consistent, and both disagreed with the CHEMTAX results. This probably reflects the subjectivity of microscopic analysis, which is greatest when small phytoplankton cells dominate. In a fourth data set, owing to the high sensitivity and reproducibility of the pigment analysis, differences were detected between phytoplankton groups over a transect of 5 km. which might have been unresolved using standard microscopic techniques. KEY WORDS: Phytoplankton. Chlorophyll a. Pigments. HPLC. CHEMTAX 0 Inter-Research 2000 Resale of full article not permitted

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Satellite Estimation of Chlorophyll-a Using Moderate Resolution Imaging Spectroradiometer (MODIS) Sensor in Shallow Coastal Water Bodies: Validation and Improvement
Leonard Scinto

Water

The size and distribution of Phytoplankton populations are indicators of the ecological status of a water body. The chlorophyll-a (Chl-a) concentration is estimated as a proxy for the distribution of phytoplankton biomass. Remote sensing is the only practical method for the synoptic assessment of Chl-a at large spatial and temporal scales. Long-term records of ocean color data from the MODIS Aqua Sensor have proven inadequate to assess Chl-a due to the lack of a robust ocean color algorithm. Chl-a estimation in shallow and coastal water bodies has been a challenge and existing operational algorithms are only suitable for deeper water bodies. In this study, the Ocean Color 3M (OC3M) derived Chl-a concentrations were compared with observed data to assess the performance of the OC3M algorithm. Subsequently, a regression analysis between in situ Chl-a and remote sensing reflectance was performed to obtain a green-red band algorithm for coastal (case 2) water. The OC3M algorithm yielded ...

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Satellite-based water quality monitoring for improved spatial and temporal retrieval of chlorophyll-a in coastal waters
Petra Philipson

Remote Sensing of Environment, 2015

The coastal zones are the most inhabited areas of the world and are therefore strongly affected by humans, leading to undesirable environmental changes that may alter the ecosystems, such as eutrophication. In order to evaluate changes in the environment an effective water quality monitoring system for the coastal zones must be in place. The chlorophyll-a concentration is commonly used as a proxy for phytoplankton biomass and as indicator for eutrophication and it can be retrieved from ocean colour remote sensing data. Several operational monitoring systems based on remote sensing are in place to monitor the open sea and, to some extent, the coastal zones. However, evaluations of coastal monitoring systems based on satellite data are scarce. This paper compares the chlorophyll-a concentrations retrieved from an operational satellite system based on MERIS (Medium Resolution Imaging Spectrophotometer) data with ship-based monitoring for the productive seasons in 2008 and 2010, in a coastal area in the Baltic Sea. The comparisons showed that the satellite-based monitoring system is reliable and that the estimations of chlorophyll-a concentration are comparable to in situ measurements in terms of accuracy and quantitative retrieval. A very strong correlation was found between measurements from satellite-derived chlorophyll-a compared to in situ measurements taken close in time (0-3 days), with RMSE of 64% and a MNB of 17%. The comparison of the monthly means showed improved RMSE and a MNB of only 8%. Furthermore, this study shows that MERIS is better at capturing spatial dynamics and the extent of phytoplankton blooms than ship-based monitoring, since it has a synoptic view and higher temporal resolution. Satellite-based monitoring also increases the frequency of chlorophyll-a observations considerably, where the degree of improvement is dependent on the sampling frequency of the respective monitoring programme. Our results show that ocean colour remote sensing can, when combined with field sampling, provide an improved basis for more effective monitoring and management of the coastal zone. These results are important for eutrophication assessment and status classifications of water basins and can be applied to a larger extent within national and international agreements considering the coastal zones, e.g. the European Commission's Water Framework Directive.

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Specific absorption coefficient of phytoplankton off the Southwest coast of the Iberian Peninsula: A contribution to algorithm development for ocean colour remote sensing
S. Cristina

Continental Shelf Research, 2013

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Deriving optical metrics of coastal phytoplankton biomass from ocean colour
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An approach to develop accurate local models for the estimation of chlorophyll a concentration (Chl a) and spectral phytoplankton absorption (a ph (λ)) from hyperspectral in situ measurements of remote sensing reflectance (R rs (λ)) in an optically complex water body is presented. The models are based on empirical orthogonal function (EOF) analysis of integral-normalised R rs (λ) spectra, and spectral normalisation was found to be key to the models' success. Accurate model estimates of both Chl a and a ph (λ) were obtained, with R 2 values in log 10 space (N = 42) of 0.839 found for Chl a, and for a ph (λ), R 2 values ranging from 0.771 (547 nm) to 0.910 (655 nm). A statistical resampling exercise to create training and test data sets showed that stable models could be built with~15 training spectra and corresponding measurements of Chl a and a ph (λ), providing important guidance for the implementation of this approach at other locations. The applicability of the models to a reduced-wavelength resolution (8 wavebands) dataset was tested, and showed that reduction in wavelength resolution had little impact on the models' skill, with R 2 values obtained within~1% of the hyperspectral (101 wavelengths) R 2 values for both Chl a and a ph (λ). That the reduced-wavelength resolution models performed as well as the hyperspectral models points to their potential utility for satellite sensors.

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Retrieval of phytoplankton biomass from simultaneous inversion of reflectance, the diffuse attenuation coefficient, and Sun-induced fluorescence in coastal waters
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Journal of Geophysical Research, 2007

1] A model has been developed to retrieve phytoplankton absorption, a proxy for phytoplankton biomass, from observations of reflectance (R) and the diffuse attenuation coefficient (K d ) collected by moored radiometers in coastal waters, where high concentrations of chromophoric dissolved organic matter (CDOM) confound conventional ocean color algorithms. The inversion uses simultaneously two forward models: (1) a look-up table (LUT) that accounts for instrument geometry and the effect of the solar angle on both R and K d and (2) an analytical function describing the effects of Suninduced fluorescence (SIF) of chlorophyll on R. Estimates of phytoplankton biomass (mostly from SIF), the absorption by colored matter, and the particulate backscattering coefficients (mostly from the LUT) are obtained by optimizing the amplitude and shape of the absorption and backscattering coefficients in the forward model to best match the observations. An equation describing the quantum yield of fluorescence (photons fluoresced/photons absorbed) as a function of incident irradiance constrains the model and allows estimates of phytoplankton absorption. Innovations include: the utilization of both R and K d , providing good separation of the effects of backscattering from absorption; avoidance of reflectance bands between 400 and 600 nm, thereby avoiding interference from bottom reflection and CDOM fluorescence; utilization of the full emission band of SIF; and accounting for the irradiance-dependence of its quantum yield. The model effectively retrieved chlorophyll concentration from an independent data set -r = 0.76, n = 93, with a mean absolute percent error (MAPE) of 24%; this is better than a modern ocean color algorithm (OC4V4) on its validation data set, when restricted to the same range of chlorophyll (r = 0.67, n = 384, MAPE = 51%).

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An annual cycle of phytoplankton biomass in the Arabian Sea, 1994–1995, as determined by moored optical sensors
John F Marra

Deep Sea Research Part II: Topical Studies in Oceanography, 2001

A surface-to-bottom mooring in the central Arabian Sea (15.53N, 61.53E) deployed from October 1994 to October 1995, included #uorometers, PAR irradiance sensors, Lu sensors, and a spectral radiometer. An annual cycle of phytoplankton biomass was determined by transforming signals from the optical sensors into chlorophyll a (chl a). Half-yearly phytoplankton blooms with water-column strati"cation were observed near the end of each monsoon, as well as biomass increases in response to mesoscale #ow features. During the Northeast Monsoon, the integrate water-column chl a rose from 15 to 25 mg m\, while during the Southwest Monsoon, chl a increased from 15 to a maximum '40 mg m\. We present an empirical relationship between the ratio of downwelling E 443/E 550 (blue to green wavelength ratio) and integral euphotic zone chl a determined by moored #uorometers (r"0.73). There is a more signi"cant relationship between E 443/E 550 measured at one depth in the water column (65 m) and the average vertical attenuation coe$cient for PAR (K .0 ) between 0 and 65 m (r"0.845). Because biofouling was a signi"cant problem at times, data return from any one sensor was incomplete. However, optical sensor/data intercomparison helped "ll gaps while permitting investigation of the temporal variability in observed phytoplankton biomass.

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Phytoplankton (chl-a) Biomass Seasonal Variability in the Gulf of Mannar, South India: A Remote Sensing Perspective
Ramachandran Kizhur

Applications and Challenges of Geospatial Technology, 2018

The phytoplankton is being a primary producer that lies at the base of food web for aquatic flora and fauna of marine and coastal ecosystems. The study describes remote sensing applications for assessment of phytoplankton (chl-a) biomass variability and its major influencing factors (sea surface temperature, salinity, waves and currents) in the Gulf of Mannar (GoM), southeast coast of India. Multitemporal Landsat ETM+ images acquired on 2016 and 2017 are used for mapping assemblage of phytoplankton (chl-a) and its concentration at site-specific level. Band combination analysis along with mathematically derived coefficient of determination values has been used to extract phytoplankton (chl-a) concentration using spectral reflectance properties (0.4-10 μm). Multispectral images acquired on different times are used for the mapping of phytoplankton concentration with its seasonal variability. Highest assemblage of phytoplankton (chl-a) is measured at concentration of 48.8 μg/L, and it reflects 0.25% at wavelength of 0.55 μm (green) and 1.0 μm (SWIR), respectively. Coastal water comprises higher chl-a concentration which can observe majority wavelength in blue (0.45 μm) and red (0.65 μm) that distinguish phytoplankton from coastal water. The result reveals that chl-a concentration has significantly decreased to 36.20 μg/L, and this reflects 0.16% of wavelength at 0.55 μm (green). The chl-a concentration is further decreased to 33.33 μg/L, and it reflects 0.17% at the wavelength of 0.55 μm. Seasonal assessment shows higher chl-a concentration during pre-monsoon. Coastal water in shallow depth (<5.0 m) area has estimated higher chl-a concentration within a distance of 1.0 km from the shore during post-monsoon. However, the chl-a concentration has significantly decreased in monsoon due to highly fluctuating hydrodynamic conditions

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Related topics

  • Oceanography
  • Remote Sensing

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    Retrieval and Evaluation of Chlorophyll-A Spatiotemporal Variability Using GF-1 Imagery: Case Study of Qinzhou Bay, China
    Ke Chen Wen

    Sustainability

    Chlorophyll-a (Chl-a) concentration is a measure of phytoplankton biomass, and has been used to identify ‘red tide’ events. However, nearshore waters are optically complex, making the accurate determination of the chlorophyll-a concentration challenging. Therefore, in this study, a typical area affected by the Phaeocystis ‘red tide’ bloom, Qinzhou Bay, was selected as the study area. Based on the Gaofen-1 remote sensing satellite image and water quality monitoring data, the sensitive bands and band combinations of the nearshore Chl-a concentration of Qinzhou Bay were screened, and a Qinzhou Bay Chl-a retrieval model was constructed through stepwise regression analysis. The main conclusions of this work are as follows: (1) The Chl-a concentration retrieval regression model based on 1/B4 (near-infrared band (NIR)) has the best accuracy (R2 = 0.67, root-mean-square-error = 0.70 μg/L, and mean absolute percentage error = 0.23) for the remote sensing of Chl-a concentration in Qinzhou Bay...

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