arXiv:2605.24003v2 Announce Type: replace-cross Abstract: Remote sensing techniques have been increasingly utilised in aquatic applications in recent years. A common challenge in using optical satellite data is the presence of missing observations due to cloud cover. These data gaps can lead to missed detection of critical events, such as algal blooms, in lakes of high interest to water authorities. As a result, enhancing the completeness of optical satellite datasets is crucial for improving the monitoring and prediction of algal blooms. In this study, we compared a traditional data imputatio
The increasing availability of satellite data combined with advancements in deep learning models makes this a timely development for addressing data gaps in remote sensing.
Improving the completeness of optical satellite data is crucial for accurate environmental monitoring, especially for critical natural resources like water, and informing water management policies.
The reliability and accuracy of remote sensing for environmental monitoring, particularly in cases of cloud cover, are significantly enhanced, enabling better detection and prediction of events like algal blooms.
- · Environmental monitoring agencies
- · Deep learning researchers
- · Water authorities
- · Satellite data providers
- · Traditional data imputation methods
- · Regions without access to advanced remote sensing capabilities
More accurate and timely detection of water quality issues like harmful algal blooms becomes possible.
Improved predictive models for water quality can lead to more proactive and effective water resource management and public health interventions.
Enhanced data could inform policy changes regarding agricultural runoff and industrial pollution, impacting local economies and regulatory frameworks.
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