Wavelet analysis on chlorophyll concentration change in the area around Bohai Bay area, Yangtze River Delta Region and South China Sea

Zhang, Y.; Hong, Jin; Chen, C.; Zhang, X.Y.; Wang, Y.

doi:10.1016/j.proenv.2012.01.130

Cited by 9 publications

(6 citation statements)

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“…This is because the phytoplankton growth is affected by the low nutrient concentration, because of the strong pycnocline formation, in addition to the effects of thermal stratification in the water column; all of this limits the vertical mixing so that the nutrients can rise to the surface (Doney, 2006). This study result is consistent with other studies regarding the seasonal changes (Al-Naimi et al, 2017;Zhang, Jiang, Chen, Zhang, & Wang, 2012;Zhao & Ghedira, 2014) The validation results of the product showed better accuracy to monitor low-concentration waters, while the accuracy in high-concentration waters decreases. Reviewing the extracted chlorophyll-a concentration using different equations and the OceanColor satellite sensors for several seas and different bays around the world, with different numbers of GTPs (30-114), we find that the accuracy varied in some literature from 0.18 to 0.64 mg.m -3 (Al-Yamani & Naqvi, 2019;Husar, Prospero, & Stowe, 1997;Johns, Yao, & Olson, 2003;Polikarpov, Saburova, & Al-Yamani, 2016).…”

Section: Resultssupporting

confidence: 91%

Trend and dynamic of chlorophyll-a concentration over the Arabian Gulf A long-term study using MODIS data (2004 – 2019)

Albanai¹

2021

JER is an international, peer-reviewed journal that publishes f

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Chlorophyll-a concentration in water is an indicator of phytoplankton density, which in turn is crucial, as it represents the base of the ecological life in seas and oceans. Any increase or decrease in the number of phytoplankton may contribute to disruption to the ecological cycle in the seawater. The density of phytoplankton is also an important indicator of water quality. Traditionally, water samples are collected in the field and analysed in the laboratory to find the density of phytoplankton in a specified amount of water. Recently, remote sensing has led to the development of advanced and remote methods to detect phytoplankton density, chiefly by extracting near-surface chlorophyll-a concentrations. In this study, MODIS (Aqua) Level 3 data (64 images) were used to extract average chlorophyll-a concentration at time points from 2004 to 2019 (over 16 years) for the Arabian Gulf, where the Level 2 (11 images) data were used to determine the accuracy of the estimated values via MODIS (Aqua) using field data taken from the waters of the State of Kuwait (25 points). The results showed good accuracy for MODIS (RMSE = ± 1.066), and it also shows that the temporal seasonal averages change in a annual-cycles, and that the trend decreased from 2004 to 2019, by about 0.7 mg/m−3

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Section: Resultssupporting

confidence: 91%

Trend and dynamic of chlorophyll-a concentration over the Arabian Gulf A long-term study using MODIS data (2004 – 2019)

Albanai¹

2021

JER is an international, peer-reviewed journal that publishes f

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“…In the long-term observation experiment, the trend of the Chl-a concentration obtained from retrieval of the spectra collected by the system is highly consistent with that measured by the Chl-a sensor during the selected time period. All the Chl-a concentration retrieved by the system are in the range of 0~0.15 mg/m³ , which agreed with the results from the literature in the South China Sea [45][46][47][48]. In addition, the Chl-a concentration obtained from the retrieval of the spectra collected by the system are basically consistent with the measured Chl-a concentration of the water samples.…”

Section: ) Chl-a Concentrations In the South China Seasupporting

confidence: 88%

Design of a Novel Fully Automatic Ocean Spectra Acquisition and Control System Based on the Real-Time Solar Angle Analyzing and Tracking

Yang

Gao

et al. 2021

IEEE Access

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The current manual spectra acquisition for monitoring water constituents has resulted in discontinuous data acquisition, insufficient amount of data, and small ocean coverage. This paper presents the design of a novel fully automatic ocean spectra acquisition and control system based on the real-time solar angle analyzing and tracking. To ensure that the requirements for spectra acquisition are met, the system is capable of accurately calculating the solar angle by collecting the information of latitude, longitude, date, time and direction, and automatically adjusting the position of instrument observation plane and the pointing angle of fiber optic probe in real-time. It achieves full automation of collecting the downward radiance of skylight, the upward radiance from reference panel and seawater separately through controlling the rotation of fiber optic probe. A 188-day observation experiment was carried out at the coastal ocean experimental station in Qingdao from September 11, 2018 to March 17, 2019. After that, the system was conducted onboard the Dongfanghong 3 scientific research vessel for a one-month demonstration and sea trial in June 2019. Comparative experiments including manual spectra collection, chlorophyll-a sensor measurement, and water samples collection were carried out. The experimental results show that the relative error of the spectra between the system and manual collection is less than 5%, and the relative error of the remote sensing reflectance calculated by the spectra is less than 4%. Considering the chlorophyll-a concentration obtained by the sensor and the water samples as the true value, the relative error of the chlorophyll-a concentration obtained by the system is 10% and 25% respectively. The results show its full and reliable capacity in collecting spectra of seawater automatically and continuously in real-time, with satisfactory accuracy and timeliness.

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“…Therefore, comprehensive understanding of the spatio-temporal variations of SST and Chl-a can be obtained by detecting of stationary and non-stationary variations. The wavelet Transform (WT) is a well-known methodology for stationary and non-stationary analysis of SST and Chl-a concentrations (Baliunas et al, 1997;Saco and Kumar, 2000;Belonenko, 2005;Zhang et al, 2012;Bashmachnikov et al, 2013;Liu et al, 2014;). Wavelet functions decompose a complex signal into component sub-signals.…”

Section: Introductionmentioning

confidence: 99%

“…Wavelet functions decompose a complex signal into component sub-signals. The recent studies demonstrated the capabilities of wavelet analysis for identifying the stability and abnormality of SST and Chl-a variations (Zhang et al, 2012;Bashmachnikov et al, 2013;Liu et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal variability of SST and Chlorophyll-a from MODIS data in the Persian Gulf

Moradi

Kabiri

2015

Marine Pollution Bulletin

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Wavelet analysis on chlorophyll concentration change in the area around Bohai Bay area, Yangtze River Delta Region and South China Sea

Cited by 9 publications

References 0 publications

Trend and dynamic of chlorophyll-a concentration over the Arabian Gulf A long-term study using MODIS data (2004 – 2019)

Trend and dynamic of chlorophyll-a concentration over the Arabian Gulf A long-term study using MODIS data (2004 – 2019)

Design of a Novel Fully Automatic Ocean Spectra Acquisition and Control System Based on the Real-Time Solar Angle Analyzing and Tracking

Spatio-temporal variability of SST and Chlorophyll-a from MODIS data in the Persian Gulf

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