SMAP-HydroBlocks: A 30 m satellite-based soil moisture dataset for the conterminous US
Surface soil moisture at unprecedented 30 m resolution (2015-2019) for the conterminous U.S., merging high-resolution land surface modeling, ML, and SMAP satellite data.
doi.org
At a glance
- Region
- North America
- Coverage
- North America
- Access
- Free
- Category
- Research
- Last updated
- 2021
What's inside
- Soil Moisture
- Machine Learning
Terms explained
What you can do with it
Typical work that data of this type supports.
- 01
Irrigation scheduling
Decide when and how much to irrigate.
Because it has: Soil Moisture
- 02
Water accounting
Track how much water crops actually consume across a command area.
- 03
No-gauge areas
Monitor places with no ground stations.
Where AI adds value
Ideas for machine learning and AI agents built on this kind of data.
- 01
Field-scale ET
Estimate crop water use per field by combining satellite and weather with ML.
- 02
Demand forecasts
Predict irrigation demand weeks ahead for canal and pump planning.
- 03
Segmentation
Map water, crops and land use pixel by pixel with deep learning.
Machine access and documentation
Data paper
SMAP-HydroBlocks: A 30 m satellite-based soil moisture dataset for the conterminous US
Scientific Data 2021
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- Manitoba Hydrologic Forecast CentreNorth America | Free
- NRCS SCAN - Soil Climate Analysis NetworkNorth America | Free
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