Evaluating the impact of sea surface temperature (SST) on spatial distribution of chlorophyll-a concentration in the East China Sea

Ji, Chenxu; Zhang, Yuanzhi; Cheng, Qiuming; Tsou, Jin Yeu; Jiang, Tingchen; Liang, Xue

doi:10.1016/j.jag.2018.01.020

Cited by 51 publications

(25 citation statements)

References 31 publications

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“…Within a certain temperature range, the positive correlation between Chl-a concentration and SST is significant, i.e., decreasing SST will inhibit the growth of phytoplankton, and Chl-a concentration will also decrease; on the contrary, as SST increases, phytoplankton grows vigorously, and the concentration of Chl-a also increases. However, excessively high temperatures will inhibit the rise of the Chl-a concentration, which is the reason why the concentration of Chl-a on 20 July is lower than that on December 17; this finding is consistent with previous research results [69][70][71][72][73]. The change of SSS is almost the opposite of that of the Chl-a concentration.…”

Section: Temporal Variationsupporting

confidence: 91%

An Improved Model for Chlorophyll-a Concentration Retrieval in Coastal Waters Based on UAV-Borne Hyperspectral Imagery: A Case Study in Qingdao, China

Gai

Zhou

et al. 2020

Water

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Chlorophyll-a (Chl-a) is an objective biological indicator, which reflects the nutritional status of coastal waters. However, the turbid coastal waters pose challenges to the application of existing Chl-a remote sensing models of case II waters. Based on the bio-optical models, we analyzed the suppression of coastal total suspended matter (TSM) on the Chl-a optical characteristics and developed an improved model using the imagery from a hyper-spectrometer mounted on an unmanned aerial vehicle (UAV). The new model was applied to estimate the spatiotemporal distribution of Chl-a concentration in coastal waters of Qingdao on 17 December 2018, 22 March 2019, and 20 July 2019. Compared with the previous models, the correlation coefficients (R2) of Chl-a concentrations retrieved by the new model and in situ measurements were greatly improved, proving that the new model shows a better performance in retrieving coastal Chl-a concentration. On this basis, the spatiotemporal variations of Chl-a in Qingdao coastal waters were analyzed, showing that the spatial variation is mainly related to the TSM concentration, wind waves, and aquaculture, and the temporal variation is mainly influenced by the sea surface temperature (SST), sea surface salinity (SSS), and human activities.

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Section: Temporal Variationsupporting

confidence: 91%

An Improved Model for Chlorophyll-a Concentration Retrieval in Coastal Waters Based on UAV-Borne Hyperspectral Imagery: A Case Study in Qingdao, China

Gai

Zhou

et al. 2020

Water

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“…The positive correlation between SST and Chl-a may be explained by the fact that increased SST enhances the water stratification, which in turn increases the light availability in turbid coastal waters, and thus promotes the growth of phytoplankton [19]. Similar results were obtained by Ji et al [35] for the coastal waters of East China Sea (north coast of Jiangsu Province, Yangtze River estuary region, and the Hangzhou Bay) using the DINEOF reconstructed monthly MODIS/Aqua Chl-a and SST datasets during 2003-2016. In the Bay of Bengal, where phytoplankton productivity is highly influenced by river discharge, Jutla et al [36] reported positive correlations between monthly SeaWiFS Chl-a and SST from 1997 to 2009.…”

Section: Eof Analysis: Monthly Variability Of Chl-asupporting

confidence: 59%

Analysis of the Monthly and Spring-Neap Tidal Variability of Satellite Chlorophyll-a and Total Suspended Matter in a Turbid Coastal Ocean Using the DINEOF Method

et al. 2021

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Missing spatial data is one of the major concerns associated with the application of satellite data. The Data INterpolating Empirical Orthogonal Functions (DINEOF) method has been proven to be an effective tool for filling spatial gaps in various satellite data products. The Ariake Sea, which is a turbid coastal sea, shows the large spatial and temporal variability of chlorophyll-a (Chl-a) and total suspended matter (TSM). However, ocean color satellite data for this region usually have large gaps, which affects the accurate analysis of Chl-a and TSM variability. In this study, we applied the DINEOF method to fill the missing pixels from the regionally tuned Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua (hereafter, MODIS) Chl-a and MODIS-derived TSM datasets for the period 2002–2017. The validation results showed that the DINEOF reconstructed data were accurate and reliable. Furthermore, the Empirical Orthogonal Functions (EOF) analysis based on the reconstructed data was used to quantitatively analyze the spatial and temporal variability of Chl-a and TSM at both monthly and individual events of spring-neap tidal scales. The first three EOF modes of Chl-a showed seasonal variability mainly caused by precipitation, the sea surface temperature (SST), and river discharge for the first EOF mode and the sea level amplitude for the second. The first three EOF modes of TSM exhibited both seasonal and spring-neap tidal variability. The first and second EOF modes of TSM displayed spring-neap tidal variability caused by the sea level amplitude. The second EOF mode of TSM also showed seasonal variability caused by the sea level amplitude. In this study, we first applied the DINEOF method to reconstruct the satellite data and to capture the major spatial and temporal variability of Chl-a and TSM for the Ariake Sea. Our results demonstrate that the DINEOF method can reconstruct patchy oceanic color datasets and improve spatio-temporal variability analysis.

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“…On the other hand, some others have reported that vegetation indices constructed in the near-infrared band were highly correlated with total chlorophyll content [64,65]. Although the vegetation indices constructed in the near-infrared band were highly accurate for the inversion of chlorophyll content [66,67] in the salt treatment in our study, this needs to be further refined in the future study. In the present study, vegetation indices D_RVI and D_NDVI were more suitable for than the red edge parameters of Suaeda salsa for estimating the total chlorophyll content under different salt conditions (Figure 7).…”

Section: Mechanisms and Potential For Monitoring Plant Stress Using Rcontrasting

confidence: 56%

Monitoring Suaeda salsa Spectral Response to Salt Conditions in Coastal Wetlands: A Case Study in Dafeng Elk National Nature Reserve, China

Xia¹,

Zhang²,

Tian³

et al. 2020

Remote Sensing

Self Cite

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This paper reports on monitored Suaeda salsa spectral response to salt conditions in coastal wetlands, using spectral measurements and remotely sensed algorithms. Suaeda salsa seedlings were collected from the Dafeng Elk National Nature Reserve (DENNR) in Jiangsu Province, China. We treated 21 Suaeda salsa seedlings planted in pots with 7 different salt concentrations (n = 3 for each concentration) to assess their response to varying salt conditions. Various plant growth indicators, including chlorophyll content, fresh weight, dry weight, and canopy hyperspectral reflectance, were measured. One-way analysis of variance (ANOVA) and post hoc multiple comparisons of least-significant difference (LSD) were used to explore the physiological indicators of sensitivity to salt treatment. Red edge parameters and spectral reflectance indices were used to analyze spectral response to salt conditions and to investigate the potential for remotely sensing physiological parameters which are sensitive to salt conditions. The results indicated that among these physiological indicators, the total chlorophyll content differed significantly with salt conditions, being highest at 50 mmol/L, whereas the differences observed for the morphological parameters were highest at 200 mmol/L. In addition, new vegetation indices were significantly more responsive to salt concentrations than were traditional red edge parameters. The two vegetation indices, D854/D792 and (D792 − D854)/(D792 + D854), were the most sensitive to the total chlorophyll content, and these also strongly correlated with salt concentrations. An analysis of the responses of plant growth indicators to salt treatment showed that soil having a salt concentration of 50~200 mmol/L is most suitable for the growth of Suaeda salsa. These results suggest the potential for using remote sensing to effectively interpret the causes of salt-induced spectral changes in Suaeda salsa. This methodology also provides a new reference for the inversion model of estimating the total chlorophyll content of Suaeda salsa under different salt concentrations in similar coastal wetlands, whether in China or elsewhere.

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Evaluating the impact of sea surface temperature (SST) on spatial distribution of chlorophyll-a concentration in the East China Sea

Cited by 51 publications

References 31 publications

An Improved Model for Chlorophyll-a Concentration Retrieval in Coastal Waters Based on UAV-Borne Hyperspectral Imagery: A Case Study in Qingdao, China

An Improved Model for Chlorophyll-a Concentration Retrieval in Coastal Waters Based on UAV-Borne Hyperspectral Imagery: A Case Study in Qingdao, China

Analysis of the Monthly and Spring-Neap Tidal Variability of Satellite Chlorophyll-a and Total Suspended Matter in a Turbid Coastal Ocean Using the DINEOF Method

Monitoring Suaeda salsa Spectral Response to Salt Conditions in Coastal Wetlands: A Case Study in Dafeng Elk National Nature Reserve, China

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