Diurnal changes of a harmful algal bloom in the East China Sea: Observations from GOCI

Lou, Xiulin; Hu, Chuanmin

doi:10.1016/j.rse.2013.09.031

Cited by 133 publications

(77 citation statements)

References 25 publications

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“…In addition to tidal flats, the GOCI atmospheric correction is expected to fail in extremely turbid waters near the estuary [2,18,19]. Moreover, the cloud-masking threshold currently implemented by GDPS is too tight to reveal valid data in more turbid areas [16].…”

Section: Diurnal Variation Of Tsm In the Yrementioning

confidence: 99%

Application of the Geostationary Ocean Color Imager to Mapping the Diurnal and Seasonal Variability of Surface Suspended Matter in a Macro-Tidal Estuary

et al. 2016

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Abstract:Total suspended particulate matter (TSM) in estuarine and coastal regions usually exhibits significant natural variations. The understanding of such variations is of great significance in coastal waters. The aim of this study is to investigate and assess the diurnal and seasonal variations of surface TSM distribution and its mechanisms in coastal waters based on Geostationary Ocean Color Imager (GOCI) data. As a case study, dynamic variations of TSM in the macro-tidal Yalu River estuary (YRE) of China were analysed. With regard to diurnal variability, there were usually two peaks of TSM in a tidal cycle corresponding to the maximum flood and ebb current. Tidal action appears to play a vital role in diurnal variations of TSM. Both the processes of tidal re-suspension and advection could be identified; however, the diurnal variation of TSM was mainly affected by a re-suspension process. In addition, spring-neap tides can affect the magnitude of TSM diurnal variations in the YRE. The GOCI-retrieved TSM results clearly showed the seasonal variability of surface TSM in this area, with the highest level occurring in winter and the lowest in summer. Moreover, although river discharge to the YRE was much greater in the wet season than the dry season, TSM concentrations were significantly higher in the dry season. Wind waves were considered to be the main factor affecting TSM seasonal variation in the YRE.

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Section: Diurnal Variation Of Tsm In the Yrementioning

confidence: 99%

Application of the Geostationary Ocean Color Imager to Mapping the Diurnal and Seasonal Variability of Surface Suspended Matter in a Macro-Tidal Estuary

et al. 2016

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“…CC BY 4.0 License. dominant on the ECS shelf sea in the spring (Furuya et al, 2003;Lou and Hu, 2014). Around the shelf break, upwelling frequently occurs at this region on the ECS to the Kuroshio water (Chen et al, 2009), transposing nutrients-rich waters from the subsurface layers to the euphotic zone.…”

Section: Seasonal Variations Of the Psc In The Ecsmentioning

confidence: 99%

Phytoplankton size class in the East China Sea derived from MODIS satellite data

Zhang¹,

Wang²,

Qiu³

et al. 2018

Preprint

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Abstract. The distribution of the phytoplankton size class (PSC) and the variations in the size classes are key to understanding 10 ocean biogeochemical processes and ecosystem. Remote sensing of the PSC in the East China Sea (ECS) remains a challenge, although many PSC algorithms have been developed. Here based on a local dataset from the ECS, a regional model was tuned to infer the PSC from the spectral features of normalized phytoplankton absorption (aph) using a principal component analysis approach. Before applying the refined model to the real MODIS (Moderate Resolution Imaging Spectroradiometer) data, reconstructing satellite Rrs at 412 and 443 nm becomes critical through modeling them from Rrs between 469 and 555 nm using 15 multiple regression analyses. Satellite-derived PSC values compare well with those derived from pigment composition, which demonstrates the potential of satellite ocean color data to estimate PSC distributions in the ECS from space. The refined model was applied to aph derived from Rrs observations collected by MODIS over the ECS from 2003 to 2016. Seasonal images show that the PSC distribution was heterogeneous in both temporal and spatial scales. Seasonal variations of the PSC in the ECS were probably affected by a combination of the water column stability, upwelling, sea surface temperature, and the Kuroshio 20Current. Additionally, human activity and riverine discharge may also influence the PSC distributions in the ECS, especially in coastal regions.Biogeosciences Discuss., https://doi

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“…Then, the distribution changed in the southern and central regions (i.e., the open area of Taihu Lake). However, the vertical migration of phytoplankton might also have affected the diurnal change of Cchl-a, particularly along the western lakeshore [45,46]. Based on the research of Reynolds [46], blue-green algae could increase at the water surface from early morning to 10:00 a.m. due to the increased buoyancy force of algae cells after photosynthesis.…”

Section: Assessment Of Atmospheric Correctionmentioning

confidence: 99%

A Weighted Algorithm Based on Normalized Mutual Information for Estimating the Chlorophyll-a Concentration in Inland Waters Using Geostationary Ocean Color Imager (GOCI) Data

2015

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Due to the spatiotemporal variations of complex optical characteristics, accurately estimating chlorophyll-a (Chl-a) concentrations in inland waters using remote sensing techniques remains challenging. In this study, a weighted algorithm was developed to estimate the Chl-a concentrations based on spectral classification and weighted matching using normalized mutual information (NMI). Based on the NMI algorithm, three water types (Class 1 to Class 3) were identified using the in situ normalized spectral reflectance data collected from Taihu Lake. Class-specific semi-analytic algorithms for the Chl-a concentrations were established based on the GOCI data. Next, weighted factors, which were used to determine the matching probabilities of different water types, were calculated OPEN ACCESS Remote Sens. 2015, 7 11732 between the GOCI data and each water type using the NMI algorithm. Finally, Chl-a concentrations were estimated using the weighted factors and the class-specific inversion algorithms for the GOCI data. Compared to the non-classification and hard-classification algorithms, the accuracies of the weighted algorithms were higher. The mean absolute error and root mean square error of the NMI weighted algorithm decreased to 22.63% and 9.41 mg/m 3 , respectively. The results also indicated that the proposed algorithm could reduce discontinuous or jumping effects associated with the hard-classification algorithm.

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Diurnal changes of a harmful algal bloom in the East China Sea: Observations from GOCI

Cited by 133 publications

References 25 publications

Application of the Geostationary Ocean Color Imager to Mapping the Diurnal and Seasonal Variability of Surface Suspended Matter in a Macro-Tidal Estuary

Application of the Geostationary Ocean Color Imager to Mapping the Diurnal and Seasonal Variability of Surface Suspended Matter in a Macro-Tidal Estuary

Phytoplankton size class in the East China Sea derived from MODIS satellite data

A Weighted Algorithm Based on Normalized Mutual Information for Estimating the Chlorophyll-a Concentration in Inland Waters Using Geostationary Ocean Color Imager (GOCI) Data

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