Spatio-temporal patterns of Secchi depth in the waters around the Korean Peninsula using MODIS data

Kim, Sun-Hwa; Yang, Chan-Su; Ouchi, Kazuo

doi:10.1016/j.ecss.2015.07.003

Cited by 13 publications

(9 citation statements)

References 31 publications

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“…In situ sampling is essential as it provides, for example, accuracy and ground-truthing, and remotely sensed data increase the spatial and temporal coverage cost-efficiently. For example, Kim et al (2015) have stated that low frequency in situ measurements could not capture all the water transparency variability that were revealed by higher frequency satellite data in the coastal waters of the Korean Peninsula. Owing to technical limitations, much of the research on ocean colour remote sensing has been previously focused on open-ocean zones, but technical improvements and new data possibilities have enhanced the possibilities in coastal and inland waters as well (Mouw et al, 2015).…”

Section: Discussionmentioning

confidence: 97%

Spatio-temporal representativeness of euphotic depth in situ sampling in transitional coastal waters

Luhtala

Tolvanen

2016

Journal of Sea Research

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Section: Discussionmentioning

confidence: 97%

Spatio-temporal representativeness of euphotic depth in situ sampling in transitional coastal waters

Luhtala

Tolvanen

2016

Journal of Sea Research

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“…Previous studies have shown that SD is a function of hydrodynamic factors like velocity and water level [33,34]. Further, hydrodynamic variations may lead to variations in hydro-environmental indicators [35], and variations in hydro-environmental indicators might further cause variations in SD.…”

Section: Introductionmentioning

confidence: 98%

Water Transparency Prediction of Plain Urban River Network: A Case Study of Yangtze River Delta in China

Liao

Shu

et al. 2021

Sustainability

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Water transparency is commonly used to indicate the combined effect of hydrodynamics and the aquatic environment on water quality throughout a river network. However, how water transparency responds to these indicators still needs to be explored, especially their complicated nonlinear relationship; thus, this study represents an analysis of the Suzhou civil river network. Using an artificial neural network (ANN) hydrological model and a multiple linear model (MLR) with in-situ data between 2013–2019, we investigated the Suzhou River’s sensitivity to the six factors and water transparency, which including flow velocity and data from five categories of water-quality monitoring data: total suspended matter (TSS), water temperature (TE), dissolved oxygen (DO), chlorophyll (Chl) and chemical oxygen demand (COD). The results suggest that the ANN model can achieve better performance than the MLR model. Furthermore, results also show a well-established correlation between enhanced hydrodynamics and improved water transparency when the flow velocity ranged from 0.22 to 0.45 m/s. Overall, COD is a vital factor for the SD prediction because including the COD can see a notable improvement in the ANN model (with a correlation coefficient of 0.918). This study demonstrates that the ANN model with hydrodynamic and water quality parameters can achieve a better prediction of water transparency than other discussed models for a coastal plain urban river network.

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“…Furthermore, the monthly variation in Z sd in the Eastern China Seas is also significantly influenced by seasonal phytoplankton blooms and surface currents, showing seasonal cycles that differ from water column stratification. For example, low Z sd values in the central ECS in spring are closely related to spring phytoplankton blooms (Kim et al., 2015). Using MODIS‐Aqua data, Shang et al.…”

Section: Introductionmentioning

confidence: 99%

Multiple Timescale Variations in Water Transparency in the Eastern China Seas over the Period 1997–2019

et al. 2023

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The detection of water transparency (Zsd) changes over multiple timescales is an important task that has wide implications from water quality to climate change research. However, to date, the understanding of Zsd variations and their driving factors in the Eastern China Seas is fairly limited. Using the Data Interpolating Empirical Orthogonal Functions (DINEOF) method and a semi‐analytic algorithm, monthly cloud‐free Zsd products over the period 1997–2019 are retrieved from the European Space Agency Ocean Color Climate Change Initiative datasets. Significant Zsd variations on seasonal, interannual, and long‐term timescales, as well as their spatial discrepancies, are quantified in the Eastern China Seas. The first EOF mode of Zsd accounts for 57.46% of the total variance, which is dominated by a seasonal cycle in response to seasonal changes in oceanic and atmospheric factors (e.g., surface winds, sea surface temperature and ocean stratification). The second and third EOF modes are likely associated with seasonal phytoplankton blooms and ocean circulations, respectively. Factors that affect the interannual variation in Zsd, including sea surface temperature, wind speed, river discharge, and the Oceanic Niño Index, are quantitatively evaluated. The long‐term trends suggest different potential driving forces for the Zsd patterns at a regional scale, such as climate‐driven increased sea surface temperatures, weakened surface winds, reduced sediment discharge, and eutrophication of coastal waters. Overall, this study presents the first comprehensive investigation of Zsd variations in the Eastern China Seas at multiple timescales and an analysis of their underlying physical and environmental drivers.

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Spatio-temporal patterns of Secchi depth in the waters around the Korean Peninsula using MODIS data

Cited by 13 publications

References 31 publications

Spatio-temporal representativeness of euphotic depth in situ sampling in transitional coastal waters

Spatio-temporal representativeness of euphotic depth in situ sampling in transitional coastal waters

Water Transparency Prediction of Plain Urban River Network: A Case Study of Yangtze River Delta in China

Multiple Timescale Variations in Water Transparency in the Eastern China Seas over the Period 1997–2019

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