Yuezhong Yang

Measuring natural phytoplankton fluorescence and biomass: a case study of algal bloom in the Pearl River estuary.

A moored optical buoy was deployed in the Pearl River estuarine waters for a 15-day period. A four-day algal bloom event occurred during this study period. Both chlorophyll a concentration and algal cell density (a proxy for biomass) changed dramatically before and after the event. The chlorophyll concentration at a 2.3m depth rose from 5.15 mg/m(-3) at 15:00 h on August 19 to 23.62 mg/m(-3) at 9:00 h on August 21, and then decreased to 3.24 mg/m(-3) at 15:00 h on August 24. The corresponding cell density ranged from 1.57 x 10(5) to 1.76 x 10(6)cells/L. We used normalized fluorescence line height (NFLH) and normalized fluorescence intensity (NFI) in order to determine fluorescence activity. Combined with the in situ sampling dataset, we were able to correlate natural fluorescence (NFLH and NFI) with chlorophyll a concentrations, and found correlation coefficients of 0.72 and 0.75, respectively. We also found correlations between natural fluorescence and cell density, with correlation coefficients of 0.71 and 0.65, respectively. These results indicate that applying continuous time series of natural fluorescence can reflect changes in biomass. This technique will prove extremely useful for in situ and real-time observations using an optical buoy. Although there are still problems to solve in the real-time observation of natural fluorescence in algal bloom events, we discuss the primary factors affecting fluorescence signals and suggest possible methods for mitigating these issues.

Variation of particulate organic carbon and its relationship with bio-optical properties during a phytoplankton bloom in the Pearl River estuary.

In this study, variations in the particulate organic carbon (POC) were monitored during a phytoplankton bloom event, and the corresponding changes in bio-optical properties were tracked at one station (114.29°E, 22.06°N) located in the Pearl River estuary. A greater than 10-fold increase in POC (112.29-1173.36 mg m⁻³) was observed during the bloom, with the chlorophyll a concentration (Chl-a) varying from 0.984 to 25.941 mg m⁻³. A power law function is used to describe the relationship between POC and Chl-a, and the POC:Chl-a ratio tends to change inversely with Chl-a. Phytoplankton carbon concentration is indirectly estimated using the conceptual model proposed by Sathyendranath et al. (2009), and this carbon is found to contribute 47.21% (±10.65%) to total POC. The estimated carbon-to-chlorophyll ratio of phytoplankton in diatom-dominated waters is found to be comparable with results reported in the literature. Empirical algorithms for determining the concentrations of Chl-a and POC were developed based on the relationships of these variables with the blue-to-green reflectance ratio. With these bio-optical models, the levels of particulate organic carbon and Chl-a could be predicted from the radiometric data measured by a marine optical buoy, which showed much more detailed information about the variability in biogeochemical parameters during this bloom event.

Spectral absorption coefficient of phytoplankton and its relation to chlorophyll a and remote sensing reflectance in coastal waters of southern China

The spectral absorption coefficient of phytoplankton in coastal waters of southern China is investigated. Large variations in the absorption coefficient of phytoplankton are found. The absorption coefficient of phytoplankton at 443 urn ranged from 0.006 m(-1) to 0.484 m(-1), with an average value of 0.067 m(-1). The chlorophyll-specific absorption coefficient of phytoplankton is also a bio-optical variable, with a spectrally averaged value of 0.025 m(2) (.) mg(-1). The variations of chlorophyll-specific absorption coefficient are mainly attributed to pigment composition of phytoplankton and package effect. The chlorophyll-specific absorption coefficient of phytoplankton decreases with the increasing of chlorophyll a concentration. This relationship can be described by a power law function, with the parameters and the coefficient of determination r(2) as functions of wavelength, but the parameters describing the relationships in present study differed from that in Case 1 waters, thus the regional adjustment of model parameters was of particular significance for improving the accuracy of bio-optical algorithms for estimation of Chl-a concentration and primary production from remotely sensed data. Regression analysis of reflectance (R-D) ratio and absorption coefficient of phytoplankton (a(ph)) indicates a close correlation between them, which means that it is possible to retrieve absorption coefficient of phytoplankton using ocean color remote sensing data in optically complex coastal waters.

Comparison of MERIS, MODIS, SeaWiFS-derived particulate organic carbon, and in situ measurements in the South China Sea

ABSTRACT Medium Resolution Imaging Spectrometer (MERIS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) particulate organic carbon (POC) concentration products for the South China Sea (SCS) were compared with in situ data collected from October 2007 to December 2013. Spectral remote-sensing reflectance (Rrs,λ) was also measured to help understand POC algorithm performance. A strict comparison of the satellite-derived POC and in situ measurements showed that MERIS, MODIS, and SeaWiFS underestimated in situ values by 29.1, 11.7, and 31.5%, respectively. Similar results were obtained with a relaxed matching criterion. Through analysis of the causes of product uncertainty, the results suggested that satellite retrieval of Rrs,λ and the global POC algorithm both have an impact on inversion accuracy. However, the formulation of the POC algorithm seems to be more critical. When a regional algorithm was developed to obtain satellite-derived POC, both the strict and relaxed comparison results showed significant improvement, but for coastal waters, both algorithms had larger errors. Other factors affecting the comparison are also discussed.

Estimating the Augmented Reflectance Ratio of the Ocean Surface When Whitecaps Appear

The presence of foam influences the accuracy of satellite-derived water-leaving radiance. A model has been developed to estimate the augmented reflectance ratio (A(λ,U)) due to differences in the fraction of whitecap coverage (w) on the ocean surface. A(λ,U) can be calculated from the product of w and ρ(λ,U), where ρ(λ,U) is the augmented ratio of the reflectance of background water (Rb(λ)) caused by the presence of whitecaps. Our results showed that the average A(400~700,U) in the visible region was approximately 1.3% at U = 9 m∙s−1, 2.2% at U = 10 m∙s−1, 4.4% at U = 12 m∙s−1, 7.4% at U = 14 m∙s−1, 19% at U = 19 m∙s−1 and 37.9% at U = 24 m∙s−1, making it is necessary to consider the augmented reflectance ratio for remote sensing applications. By estimating remote sensing augmented reflectance using A(λ,U), it was found that the result was in good agreement with previous studies conducted in other areas with U from 9 to 12 m∙s−1. Since Rb(λ) is temporally and spatially variable, our model considered the variation of Rb(λ), whereas existing models have assumed that Rb(λ) is constant. Therefore, the proposed model is more suitable for estimating the augmented reflectance ratio due to whitecaps.

A semianalytical algorithm for quantitatively estimating sediment and atmospheric deposition flux from MODIS‐derived sea ice albedo in the Bohai Sea, China

Quantitative estimates of particulate matter [ PM) concentration in sea ice using remote sensing data is helpful for studies of sediment transport and atmospheric dust deposition flux. In this study, the difference between the measured dirty and estimated clean albedo of sea ice was calculated and a relationship between the albedo difference and PM concentration was found using field and laboratory measurements. A semianalytical algorithm for estimating PM concentration in sea ice was established. The algorithm was then applied to MODIS data over the Bohai Sea, China. Comparisons between MODIS derived and in situ measured PM concentration showed good agreement, with a mean absolute percentage difference of 31.2%. From 2005 to 2010, the MODIS-derived annual average PM concentration was approximately 0.025 g/L at the beginning of January. After a month of atmospheric dust deposition, it increased to 0.038 g/L. Atmospheric dust deposition flux was estimated to be 2.50 t/km(2)/month, similar to 2.20 t/km(2)/month reported in a previous study. The result was compared with on-site measurements at a nearby ground station. The ground station was close to industrial and residential areas, where larger dust depositions occurred than in the sea, but although there were discrepancies between the absolute magnitudes of the two data sets, they demonstrated similar trends.