Daily map of where water vs land is (CYGNSS)

The CYGNSS Level 3 UC Berkeley Watermask Record Version 3.2 was developed by CYGNSS investigators in the Department of Civil and Environmental Engineering at the University of California, Berkeley. CYGNSS was launched on 15 December 2016, it is a NASA Earth System Science Pathfinder Mission that was launched with the purpose of collecting the first frequent space‐based measurements of surface wind speeds in the inner core of tropical cyclones. Originally made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35° from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours. This dataset is derived from version 3.2 of the CYGNSS L1 SDR dataset (<href>https://doi.org/10.5067/CYGNS-L1X32</href>). This is an update from the previous watermask monthly product (<href>https://doi.org/10.5067/CYGNS-L3W31</href>) which derived from the CYGNSS L1 SDR v3.1 (<href>https://doi.org/10.5067/CYGNS-L1X31</href>). The new product provides daily binary inland surface water classification data at a 0.01-degree (~1x1 kilometer) resolution with an approximate 6-day latency. The algorithm utilized data from up to 30 days prior to generate the daily map. This product, known as the UC Berkeley Random Walk Algorithm WaterMask from CYGNSS (Berkeley-RWAWC), generates water classification for a given location based on CYGNSS observations combined with a random walker algorithm. The watermask variable includes binary values indicating land (0), surface water (1), and no data/ocean (-99). The data product is archived in daily files in netCDF-4 format and covers the period from September 2018 to present. This product is recommended for operational use. For science applications, we recommend the use of the Berkeley-RWAWC monthly product instead: https://podaac.jpl.nasa.gov/dataset/CYGNSS_L3_UC_BERKELEY_WATERMASK_V3.1 Note that the daily product consist of maps constructed using the most recent 31 days of data to rapidly capture surface water dynamics without relying on historical data. While the oldest data within this 31 day-period is weighted less and replaced by newer observations as they become available, extreme flood events may still be detected with a delay due to the incorporation of prior days’ data into the algorithm. The incorporation of older data is necessary to maintain the spatial scale.