Ca-STANet: Spatio-Temporal Attention Network for Chlorophyll-a Prediction with Gap-Filled Remote Sensing Data

Long-term chlorophyll-a (Chl-a) prediction has the potential to provide an early warning of red tide, and support fishery management and marine ecosystem health. The existing learning-based Chl-a prediction methods mostly predict a single point or multiple points with monitoring data. However, the m...

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Veröffentlicht in:IEEE transactions on geoscience and remote sensing S. 1
Hauptverfasser: Ye, Min, Li, Bohan, Nie, Jie, Qian, Yuntao, Yang, Lie-Liang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 27.03.2023
Schlagworte:
Chlorophyll-a (Chl-a)
convolutional neural network
Correlation
Data models
deep learning (DL)
Feature extraction
Interpolation
Ocean temperature
Predictive models
Remote sensing
remote sensing data
spatio-temporal attention
spatio-temporal prediction
ISSN:0196-2892
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Zusammenfassung:Long-term chlorophyll-a (Chl-a) prediction has the potential to provide an early warning of red tide, and support fishery management and marine ecosystem health. The existing learning-based Chl-a prediction methods mostly predict a single point or multiple points with monitoring data. However, the monitoring data are subject to sparse sampling and difficult to be measured in a large-scale and synchronous way. Moreover, the advanced learning-based models for point Chl-a prediction, such as long short-term memory (LSTM) and convolutional neural network (CNN)-LSTM, are unable to fully mining the spatio-temporal correlation of Chl-a variations. Therefore, by using the satellite remote sensing data with extensive coverage, we design a framework, namely Ca-STANet, to simultaneously predict the Chl-a of all the locations in a large-scale area from the perspective of spatio-temporal field. Specifically in our method, the original data are firstly divided into multiple sub-regions to capture the spatial heterogeneity of large-scale area. Then, two modules are respectively operated to mine the spatial correlation and long-term dependency features. Finally, the outputs from the two modules are integrated by a fusion module to fully mine the spatio-temporal correlations, which are exploited to attain the final Chl-a prediction. In this paper, the proposed Ca-STANet is comprehensively evaluated and compared with the legacy methods based on the OC-CCI Chl-a 5.0 data of the Bohai Sea. The results demonstrate that the proposed Ca-STANet is highly effective for Chl-a prediction and achieves higher prediction accuracy than the baseline methods. Moreover, as the OC-CCI Chl-a 5.0 data have many missing areas, we introduce DINEOF method to fill the data gaps before using them for prediction.
ISSN:0196-2892
DOI:10.1109/TGRS.2023.3262749