Deriving optical metrics of coastal phytoplankton biomass from ocean colour

Craig, Susanne E.; Jones, Chris T.; Li, William K. W.; Lazin, Gordana; Horne, Edward P. W.; Caverhill, Carla; Cullen, John J.

doi:10.1016/j.rse.2011.12.007

Cited by 77 publications

(83 citation statements)

References 64 publications

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“…Satellite remote sensing, on the other hand, provides frequent and synoptic measurements, but the accuracy of the satellite-based data products for the optically complex coastal and inland waters are often questionable. Recent algorithm development efforts have led to significant progress in improving the Chla data product accuracy using either spectral bands in the green and red [16][17][18][19][20][21][22][23][24][25], neural-networks [26],or empirical orthogonal function (EOF) approaches [26,27]. However, most of these studies are based on in situ measured reflectance data.…”

Section: Introductionmentioning

confidence: 99%

An EOF-Based Algorithm to Estimate Chlorophyll a Concentrations in Taihu Lake from MODIS Land-Band Measurements: Implications for Near Real-Time Applications and Forecasting Models

Duan

et al. 2014

Remote Sensing

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For near real-time water applications, the Moderate Resolution Imaging Spectroradiometers (MODIS) on Terra and Aqua are currently the only satellite instruments that can provide well-calibrated top-of-atmosphere (TOA) radiance data over the global aquatic environments. However, TOA radiance data in the MODIS ocean bands over turbid atmosphere in east China often saturate, leaving only four land bands to use. In this study, an approach based on Empirical Orthogonal Function (EOF) analysis has been developed and validated to estimate chlorophyll a concentrations (Chla, μg/L) in surface waters of Taihu Lake, the third largest freshwater lake in China. The EOF approach analyzed the spectral variance of normalized Rayleigh-corrected reflectance (Rrc) data at 469, 555, 645, and 859 nm, and subsequently related that variance to Chla using 28 concurrent MODIS and field measurements. This empirical algorithm was then validated using another 30 independent concurrent MODIS and field measurements. Image analysis and radiative transfer simulations indicated that the algorithm appeared to be tolerant to aerosol perturbations, with unbiased RMS uncertainties of <80% for Chla ranging between 3 and 100 μg/L. Application OPEN ACCESSRemote Sens. 2014, 6 10695 of the algorithm to a total of 853 MODIS images between 2000 and 2013 under cloud-free conditions revealed spatial distribution patterns and seasonal changes that are consistent to previous findings based on floating algae mats. The current study can provide additional quantitative estimates of Chla that can be assimilated in an existing forecast model, which showed improved performance over the use of a previous Chla algorithm. However, the empirical nature, relatively large uncertainties, and limited number of spectral bands all point to the need of further improvement in data availability and accuracy with future satellite sensors.

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Section: Introductionmentioning

confidence: 99%

An EOF-Based Algorithm to Estimate Chlorophyll a Concentrations in Taihu Lake from MODIS Land-Band Measurements: Implications for Near Real-Time Applications and Forecasting Models

Duan

et al. 2014

Remote Sensing

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“…However, ocean color sensors restrict us to using only specific numbers of wavelengths for PCA because of the limitation of available observation bands. Although additional wavelengths are better for a PCA approach, previous studies have reported that reduced wavelength information has little influence on model performance [23,25]. Initially, we attempted to conduct PCA for a std ph (λ) resampled at 10 MODIS bands between 400 and 700 nm; however, the estimation accuracy of the QAA-v6 became quite poor at longer wavelengths such as 645, 667, and 678 nm with huge RRMSE values of 67.2%, 77.9%, and 58.1%, respectively (Table 3).…”

Section: Empirical Development Of the Csd Modelmentioning

confidence: 99%

“…Some recent studies have demonstrated the utility of the principal component analysis (PCA) approach in deriving information on phytoplankton community structure [23][24][25]. For example, Wang et al [25] developed a method to estimate phytoplankton size structure using the spectral features of normalized a ph (λ) captured by PCA.…”

Section: Introductionmentioning

confidence: 99%

Differences in Rate and Direction of Shifts between Phytoplankton Size Structure and Sea Surface Temperature

et al. 2017

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Species distributions are changing with various rates and directions in response to recent global warming. The velocity of sea surface temperature (SST) has been used to predict species migration and persistence as an expectation of how species track their thermal niches; however, several studies have found that evidence for species shifts has deviated from the velocity of SST. This study investigated whether estimation of the velocity of shifts in phytoplankton size structure using remote sensing data could contribute to better prediction of species shifts. A chlorophyll-a (Chla) size distribution (CSD) model was developed by quantifying the relationships between the size structure of the phytoplankton community and the spectral features of the phytoplankton absorption coefficient (a ph (λ)), based on the principal component analysis approach. Model validation demonstrated that the exponent of CSD (hereafter, CSD slope), which can describe the synoptic size structure of a phytoplankton community, was derived successfully with a relative root mean square error of 18.5%. The median velocity of CSD slope across the ocean was 485.2 km·decade −1 , broadly similar to Chla (531.5 km·decade −1 ). These values were twice the velocity of SST, and the directions of shifts in CSD slope and Chla were quite different from that of SST. Because Chla is generally covariant with the size structure of a phytoplankton community, we believe that spatiotemporal changes in Chla can explain the variations of phytoplankton size structure. Obvious differences in both rate and direction of shifts were found between the phytoplankton size structure and SST, implying that shifts of phytoplankton size structure could be a powerful tool for assessing the distributional shifts of marine species. Our results will contribute to generate global and regional maps of expected species shifts in response to environmental forcing.

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“…The development of the model was based on an empirical orthogonal function (EOF) analysis of the fluorescence spectra. Several previous studies have successfully modeled water constituents, chla or phytoplankton community/size composition using EOF analysis of spectral optical signatures, such as remote-sensing reflectance and absorption (Craig et al 2012;Bracher et al 2015;Wang et al 2015). Similarly, we extracted the dominant modes of excitation spectra from the EOF analysis using the MATLAB statistical toolbox (MathWorks Inc.).…”

Section: Measurement Of High-resolution Pigment Distribution Using Mumentioning

confidence: 99%

Changes in phytoplankton community structure during wind-induced fall bloom on the central Chukchi shelf

et al. 2018

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The recent increasing of atmospheric turbulence has had considerable impact on the oceanic environment and ecosystems of the Arctic. To understand its effect on phytoplankton community structure, a Eulerian fixed-point observation (FPO) was conducted on the Chukchi shelf in fall 2013. Temporal and vertical distributions of the phytoplankton community were inferred from algal pigment signatures. A strong wind event (SWE) occurred during the observation term, and significant convection supplied nutrients from the bottom layer to the surface. Before the SWE, pigment composition in the warmer, less saline, and nutrient-poor surface waters was diverse with low concentration of chlorophyll-a (chla). Vertical mixing induced by the SWE weakened the stratification and brought sufficient nutrients to enhance diatom-derived pigment concentrations (e.g., fucoxanthin and chlc3), suggesting increases in diatoms. We also developed a model to predict the distribution of major phytoplankton pigment/chla ratios using a profiling multi-wavelength fluorometer (Multi-Exciter) with higher spatio-temporal resolution. The Multi-Exciter also captured changes in pigment composition with environmental changes at the FPO site and at four observation sites 16 km from the location of the FPO. Furthermore, we investigated the change in grazing rates of the major Arctic copepod Calanus glacialis copepodid stage five to assess the interaction between primary and secondary producers during the fall bloom. Increased diatom biomass caused a significant increase in the grazing rate on microphytoplankton (> 20 µm) and a decrease on nanophytoplankton (2-20 µm), indicative of a strong cascade effect because of the reduction of microzooplankton due to the grazing from C. glacialis. We conclude that SWEs during fall might affect food webs via the alternation of seasonal succession of phytoplankton community structure.

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Deriving optical metrics of coastal phytoplankton biomass from ocean colour

Cited by 77 publications

References 64 publications

An EOF-Based Algorithm to Estimate Chlorophyll a Concentrations in Taihu Lake from MODIS Land-Band Measurements: Implications for Near Real-Time Applications and Forecasting Models

An EOF-Based Algorithm to Estimate Chlorophyll a Concentrations in Taihu Lake from MODIS Land-Band Measurements: Implications for Near Real-Time Applications and Forecasting Models

Differences in Rate and Direction of Shifts between Phytoplankton Size Structure and Sea Surface Temperature

Changes in phytoplankton community structure during wind-induced fall bloom on the central Chukchi shelf

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