UC Berkeley RWAWC: Monthly Inland Water Classification from CYGNSS

August 2018 to present monthly binary inland surface water classification data at a 0.01-degree (~1 km) resolution. This dataset, known as the Berkeley-RWAWC, is derived from NASA's CYGNSS satellite constellation observations and uses a random walker algorithm to classify land (0), surface water (1), and no data/ocean (-99). The data is provided in netCDF-4 format with a one-month latency.

Use Cases

• Tracking seasonal and interannual changes in inland water extent based on monthly binary classification.
• Validating other surface water products or hydrological models using the 1 km resolution geospatial data.
• Analyzing flood or drought impacts by observing changes in the watermask variable over time.
• Studying the utility of GNSS reflectometry (from the CYGNSS mission) for terrestrial hydrology applications.

Strengths

• Provides consistent global coverage with a mean satellite revisit time of seven hours.
• Offers a long temporal record, with data coverage from August 2018 to the present.
• Derived from a well-documented source (CYGNSS L1 SDR v3.1) and a specific algorithm (Berkeley-RWAWC).

Limitations

• Key metadata such as row count, file size, and specific column names are unavailable across all platforms.
• The license is ambiguously listed as 'other-license-specified' without explicit details.
• The binary classification may not distinguish between different types of surface water bodies.

Provenance

Source

Developed by CYGNSS investigators in the Department of Civil and Environmental Engineering at the University of California, Berkeley, for NASA's CYGNSS Earth System Science Pathfinder Mission.

Collection Method

Derived from version 3.1 of the CYGNSS L1 SDR dataset using a random walker algorithm.

Time Range

August 2018 to present

Freshness

2026-04-09 21:17:09.743207

Geography

Global coverage with an orbital inclination of approximately 35° from the equator.