Comparison of the Spatiotemporal Variation of Chl-a in the East China Sea and Bohai Sea based on long time series satellite data

Ma, Shuchang; Zhang, Xiaoyu; Ding, Changcheng; Han, Weili; Lu, Yujun

doi:10.1109/agro-geoinformatics50104.2021.9530337

Cited by 2 publications

(2 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the human activities and terrestrial pollutants from the surrounding areas, nutrients accumulate in coastal areas, together with shallow water and vertical mixing, creating favorable conditions for the proliferation of phytoplankton and making the offshore water of the Bohai Sea subject to frequent HABs [52]. Correspondingly, the spatial distribution of chl-a concentration in the Bohai Sea obviously tends to decrease from the nearshore towards the open-sea area, with a broad distribution of low-concentration areas [53,54]. Technologically, we can take advantage of this spatial distribution characteristic to define the stratification levels used in the CS-DINEOF algorithm.…”

Section: Study Areamentioning

confidence: 99%

An Improved Data Interpolating Empirical Orthogonal Function Method for Data Reconstruction: A Case Study of the Chlorophyll-a Concentration in the Bohai Sea, China

Hong,

Qin,

2024

Applied Sciences

View full text Add to dashboard Cite

Chlorophyll-a (chl-a) serves as a key indicator in water quality and harmful algal blooms (HABs) research. While satellite ocean color data have greatly advanced chl-a research and HABs monitoring, missing data caused by cloud cover and other factors limit the spatiotemporal continuity and the utility of remote sensing data products. The Data Interpolating Empirical Orthogonal Function (DINEOF) method, widely used to reconstruct missing values in remote sensing datasets, is open to improvement in terms of computational accuracy and efficiency. We propose an improved method called Concentration-Stratified DINEOF (CS-DINEOF), which uses a coordinate–value correlative data division strategy to stratify the study area into several subregions based on annual average chl-a concentration. The proposed method clusters data points with similar spatiotemporal patterns, allowing for more targeted and effective reconstruction in each sub-dataset. The feasibility and advantage of the proposed method are tested and evaluated in the experiments of chl-a data reconstruction in the water of the Bohai Sea. Compared with the ordinary DINEOF method, the CS-DINEOF method improves the reconstruction accuracy, with an average Root Mean Square Error (RMSE) reduction of 0.0281 mg/m3, and saves computational time by 228.9%. Furthermore, the gap-free images generated from CS-DINEOF are able to illustrate small variations and details of the chl-a distribution in local areas. We can conclude that the proposed CS-DINEOF method is superior in providing significant insights for water quality and HABs studies in the Bohai Sea region.

show abstract

Section: Study Areamentioning

confidence: 99%

An Improved Data Interpolating Empirical Orthogonal Function Method for Data Reconstruction: A Case Study of the Chlorophyll-a Concentration in the Bohai Sea, China

Hong,

Qin,

2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Remotely sensed environmental observations from the MODIS instrument, including SST and Chlo-a, have been validated profusely by the scientific community against a number of models and in situ measurements [51][52][53][54][55][56][57][58] and used in a diverse set of studies 13,14,19,[59][60][61][62][63][64][65][66][67] . For instance, validation of the SST observation uses accurate ship-based infrared radiometers and differing and moored buoys with thermometers a meter of depth 38,56,57 .…”

Section: Technical Validationmentioning

confidence: 99%

A database of global coastal conditions

et al. 2021

View full text Add to dashboard Cite

Remote sensing satellite imagery has the potential to monitor and understand dynamic environmental phenomena by retrieving information about Earth’s surface. Marine ecosystems, however, have been studied with less intensity than terrestrial ecosystems due, in part, to data limitations. Data on sea surface temperature (SST) and Chlorophyll-a (Chlo-a) can provide quantitative information of environmental conditions in coastal regions at a high spatial and temporal resolutions. Using the exclusive economic zone of coastal regions as the study area, we compiled monthly and annual statistics of SST and Chlo-a globally for 2003 to 2020. This ready-to-use dataset aims to reduce the computational time and costs for local-, regional-, continental-, and global-level studies of coastal areas. Data may be of interest to researchers in the areas of ecology, oceanography, biogeography, fisheries, and global change. Target applications of the database include environmental monitoring of biodiversity and marine microorganisms, and environmental anomalies.

show abstract

Comparison of the Spatiotemporal Variation of Chl-a in the East China Sea and Bohai Sea based on long time series satellite data

Cited by 2 publications

References 4 publications

An Improved Data Interpolating Empirical Orthogonal Function Method for Data Reconstruction: A Case Study of the Chlorophyll-a Concentration in the Bohai Sea, China

An Improved Data Interpolating Empirical Orthogonal Function Method for Data Reconstruction: A Case Study of the Chlorophyll-a Concentration in the Bohai Sea, China

A database of global coastal conditions

Contact Info

Product

Resources

About