CYGNSS Daily Watermask: Inland Surface Water Classification

From September 2018 to present, this dataset provides daily binary inland surface water classification at a 0.01-degree (~1 km) resolution. It is derived from the CYGNSS satellite constellation using the UC Berkeley Random Walk Algorithm (Berkeley-RWAWC), generating maps with an approximate 6-day latency. The product is recommended for operational use, with a separate monthly version suggested for science applications.

Use Cases

• Flood mapping and monitoring based on daily binary water classification.
• Tracking seasonal changes in wetland and inland water body extent based on the 0.01-degree resolution data.
• Validating hydrological models using independent satellite-derived surface water observations.
• Operational situational awareness for disaster management based on the product's 6-day latency.

Strengths

• Provides daily global coverage with a mean satellite revisit time of 7 hours.
• Offers a consistent record from September 2018 to the present, enabling long-term trend analysis.
• Has a defined spatial resolution of 0.01 degrees (~1 km) and a clear binary classification scheme (land=0, water=1).

Limitations

• Key metadata such as row count, file size, and specific column names are not provided across any source.
• The license is listed as 'other-license-specified' without explicit details on reuse terms.
• The algorithm utilizes data from up to 30 days prior to generate a daily map, which may smooth out very rapid changes.

Provenance

Source

Developed by CYGNSS investigators at the University of California, Berkeley, Department of Civil and Environmental Engineering.

Collection Method

Derived from CYGNSS L1 SDR version 3.2 data using the Berkeley Random Walk Algorithm (RWAWC).

Time Range

September 2018 to present.

Freshness

Last updated on 2026-04-09 according to nasa_earthdata and datagov.

Geography

Global inland coverage, with data provided at a 0.01-degree grid.