Reconstruction and analysis of long-term satellite-derived sea surface temperature for the South China Sea

Huynh, Thi Hong Ngu; Azcarate, Aïda Alvera; Barth, Alexander; Beckers, Jean-Marie

doi:10.1007/s10872-016-0365-1

Cited by 17 publications

(17 citation statements)

References 51 publications

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“…However, as illustrated here, long gaps between scenes, such as periods of missing data during winter months, can lead to calculation of irrelevant EOFs and unrealistically high/low chla ( Figure A1), similar to results by [47]. The missing data of this study (Table 1) is comparable relative to other DINEOF studies (e.g., 75.2% missing daily data in [12], 63.3-75.5% in [14]; or, 39.4% missing for week composite time series in [16]) or even lower (e.g., 88.0% missing data in [22], or 86.0% missing in [56]). Improved results may be achieved in the future by using a longer time series to better constrain the EOFs near long gaps, or by using a longer temporal covariance matrix filter [47].…”

supporting

confidence: 86%

“…Recent DINEOF applications include spatial reconstructions of satellite-derived time series of sea surface temperature (SST) [2,[11][12][13], sea surface salinity (SSS) [14], chla [15][16][17], turbidity [18], and total suspended matter (TSM) [19], or in multivariate form to exploit natural correlations between variables such as for SST + chla [20,21]. Existing implementations of DINEOF utilize input data at different time scales, for instance, varied study periods and time resolutions (e.g., from less than one year [12] to more than a decade using daily [15] or week composite imagery [16]), for different oceanographic regions, such as open ocean [22] and coastal [23] waters. Some studies have considered the impact of input dataset time resolution on the results [12,17] as it impacts the ability of DINEOF to capture regional oceanographic features.…”

Section: Introductionmentioning

confidence: 99%

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Applications of DINEOF to Satellite-Derived Chlorophyll-a from a Productive Coastal Region

Hilborn

Costa

2018

Remote Sensing

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A major limitation for remote sensing analyses of oceanographic variables is loss of spatial data. The Data INterpolating Empirical Orthogonal Functions (DINEOF) method has demonstrated effectiveness for filling spatial gaps in remote sensing datasets, making them more easily implemented in further applications. However, the spatial and temporal coverage of the input image dataset can heavily impact the outcomes of using this method and, thus, further metrics derived from these datasets, such as phytoplankton bloom phenology. In this study, we used a three-year time series of MODIS-Aqua chlorophyll-a to evaluate the DINEOF reconstruction output accuracy corresponding to variation in the form of the input data used (i.e., daily or week composite scenes) and time series length (annual or three consecutive years) for a dynamic region, the Salish Sea, Canada. The accuracy of the output data was assessed considering the original chla pixels. Daily input time series produced higher accuracy reconstructing chla (95.08–97.08% explained variance, RMSExval 1.49–1.65 mg m−3) than did all week composite counterparts (68.99–76.88% explained variance, RMSExval 1.87–2.07 mg m−3), with longer time series producing better relationships to original chla pixel concentrations. Daily images were assessed relative to extracted in situ chla measurements, with original satellite chla achieving a better relationship to in situ matchups than DINEOF gap-filled chla, and with annual DINEOF-processed data performing better than the multiyear. These results contribute to the ongoing body of work encouraging production of ocean color datasets with consistent processing for global purposes such as climate change studies.

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supporting

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Applications of DINEOF to Satellite-Derived Chlorophyll-a from a Productive Coastal Region

Hilborn

Costa

2018

Remote Sensing

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“…As noted by Huynh et al (2016), this semi-annual variability is mostly driven by oceanic 290 thermal advection along the northeast-southwest diagonal of the basin from two opposite 291 directions. These authors partially associate the spatial and temporal variabilities of the 292 second SST mode with the influence of an atmospheric anticyclone.…”

Section: °65'e)mentioning

confidence: 93%

Seasonal and interannual (ENSO) climate variabilities and trends in the South China Sea over the last three decades

Piton

Delcroix

2018

Preprint

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1We present a short overview of the long-term mean and variability of five Essential Climate 2Variables observed in the South China Sea over the last 3 decades, including sea surface 3 temperature (SST), sea level anomaly (SLA), precipitation (P), surface wind and water 4 discharge (WD) from the Mekong and Red Rivers. At the seasonal time scale, SST and SLAs 5 increase in the summer (up to 4.2°C and 14 cm, respectively), and P increases in the north. 6The summer zonal and meridional winds reverse and intensify (mostly over the ocean), and 20 21 22 23 24 25 26 27 28

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“…Because the EOF and wavelet analyses generally require a complete time series of input maps without data voids, the decompositions from Data Interpolating Empirical Orthogonal Functions (DINEOF) were used to obtain complete Chl- a and SST datasets [33–34]. DINEOF has been used widely to construct the ocean parameters widely [25, 35–37]. EOF analysis was applied to the monthly Chl- a dataset.…”

Section: Datasets and Methodologymentioning

confidence: 99%

Variability in phytoplankton biomass and effects of sea surface temperature based on satellite data from the Yellow Sea, China

et al. 2019

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A time series of satellite data on Chlorophyll- a concentration (Chl- a ) that used ocean color was studied to determine mechanisms of phytoplankton variation in recent decade in the Yellow Sea, China during 2003–2015. The variability patterns on seasonal and inter-annual oscillation periods were confirmed using the Empirical Orthogonal Function (EOF), and Morlet wavelet transform analyses, respectively. The first EOF mode for Chl- a was dominated by obvious spring and fall blooms in a spatial pattern that was related to the strong mixing of the water masses from the Yellow Sea Cold Warm Mass (YSCWM) and the Yellow Sea Warm Current (YSWC) in winter. The second EOF mode for Chl- a showed an opposite spatial pattern between the northern and southern regions. The temporal coefficient showed differences in the timing of blooms. On an inter-annual scale, Chl- a indicated variation at periods of 2–4 years during 2003–2015. Chl- a showed a significantly negative correlation with the sea surface temperature (r = -0.21, p<0.01), with time lags of 4 months (Chl- a ahead). Chl- a was coupled with El Niño Southern Oscillation (ENSO) events, with a positive correlation (r = 0.46, p<0.01) at a lag of 3–5 months (ENSO ahead). The present study demonstrated that the variation in phytoplankton biomass was controlled primarily by water mass seasonally, and it was influenced by ENSO events on an inter-annual scale.

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Reconstruction and analysis of long-term satellite-derived sea surface temperature for the South China Sea

Cited by 17 publications

References 51 publications

Applications of DINEOF to Satellite-Derived Chlorophyll-a from a Productive Coastal Region

Applications of DINEOF to Satellite-Derived Chlorophyll-a from a Productive Coastal Region

Seasonal and interannual (ENSO) climate variabilities and trends in the South China Sea over the last three decades

Variability in phytoplankton biomass and effects of sea surface temperature based on satellite data from the Yellow Sea, China

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