Ca-STANet: Spatiotemporal Attention Network for Chlorophyll-a Prediction With Gap-Filled Remote Sensing Data-Reference-Cited by-同舟云学术

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Ca-STANet: Spatiotemporal Attention Network for Chlorophyll-a Prediction With Gap-Filled Remote Sensing Data

Published:2023 Issue: Volume:61 Page:1-14
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Ye Min¹,Li Bohan²^ORCID,Nie Jie¹^ORCID,Qian Yuntao³^ORCID,Yang Lie-Liang²^ORCID

Affiliation:

1. Department of Computer Science and Technology, Ocean University of China, Qingdao, China

2. School of Electronics and Computer Science, University of Southampton, Southampton, U.K

3. College of Computer Science, Zhejiang University, Hangzhou, China

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Chinese Scholarship Council (CSC) through the School of Electronics and Computer Science, University of Southampton, U.K

Engineering and Physical Sciences Research Council

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10006360/10083105.pdf?arnumber=10083105

Reference54 articles.

1. Forecasting of chlorophyll-a concentrations in South San Francisco Bay using five different models

2. Assessing the Skills of a Seasonal Forecast of Chlorophyll in the Global Pelagic Oceans

3. Development of early-warning protocol for predicting chlorophyll-a concentration using machine learning models in freshwater and estuarine reservoirs, Korea

4. An optimization of artificial neural network model for predicting chlorophyll dynamics

5. Modelling Chlorophyll-a Concentration using Deep Neural Networks considering Extreme Data Imbalance and Skewness

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1. Deep Neural Network-Based Earth System Forecasting Model Employing Non-Independent and Non-Identically Distributed Samples;IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium;2024-07-07

2. A spatiotemporal attention-augmented ConvLSTM model for ocean remote sensing reflectance prediction;International Journal of Applied Earth Observation and Geoinformation;2024-05

3. Space–Time Variations in the Long-Range Dependence of Sea Surface Chlorophyll in the East China Sea and the South China Sea;Fractal and Fractional;2024-02-07

4. Prediction on daily spatial distribution of chlorophyll-a in coastal seas using a synthetic method of remote sensing, machine learning and numerical modeling;Science of The Total Environment;2024-02

5. Self-Supervised Spatiotemporal Imputation Model for Highly Sparse Chl-a Data via Fusing Multisource Satellite Data;IEEE Transactions on Geoscience and Remote Sensing;2024

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