A global daily gap-filled chlorophyll-a dataset in open oceans during 2001-2021
Global daily gap-filled chlorophyll-a dataset for open oceans (2001-2021) reconstructed using a convolutional neural network integrating SST, salinity, and satellite observations.
doi.org
At a glance
- Region
- Global
- Coverage
- Global
- Access
- Free
- Category
- Research
- Last updated
- 2023
What's inside
- Remote Sensing
What you can do with it
Typical work that data of this type supports.
- 01
No-gauge areas
Monitor places with no ground stations.
Because it has: Remote Sensing
- 02
Long records
Use consistent records going back decades.
- 03
Hotspots and trends
Find where contamination is high and whether it is getting worse.
Where AI adds value
Ideas for machine learning and AI agents built on this kind of data.
- 01
Segmentation
Map water, crops and land use pixel by pixel with deep learning.
- 02
Data fusion
Blend satellite and ground data into one better estimate.
- 03
Risk maps
Predict contamination where no samples exist, from geology, land use and climate.
Machine access and documentation
Data paper
A global daily gap-filled chlorophyll-a dataset in open oceans during 2001-2021
Earth System Science Data 2023
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