Berkeley-RWAWC: a new CYGNSS-based watermask unveils unique observations
of seasonal dynamics in the Tropics
Abstract
The UC Berkeley Random Walk Algorithm WaterMask from CYGNSS
(Berkeley-RWAWC) is a new data product designed to address the
challenges of monitoring inundation in regions hindered by dense
vegetation and cloud cover as is the case in most of the Tropics. The
Cyclone Global Navigation Satellite System (CYGNSS) constellation
provides data with a higher temporal repeat frequency compared to
single-satellite systems, offering the potential for generating moderate
spatial resolution inundation maps with improved temporal resolution
while having the capability to penetrate clouds and vegetation. This
paper details the development of a computer vision algorithm for
inundation mapping over the entire CYGNSS domain (37.4°N to 37.4°S).
The unique reliance on CYGNSS data sets our method apart in the field,
highlighting CYGNSS’s indication of water existence. Berkeley-RWAWC
provides monthly, near-real-time inundation maps starting in August 2018
and across the CYGNSS latitude range, with a spatial resolution of
0.01° × 0.01°. Here we present our workflow and parameterization
strategy, alongside a comparative analysis with established surface
water datasets (SWAMPS, WAD2M) in four regions: the Amazon Basin, the
Pantanal, the Sudd, and the Indo-Gangetic Plain. The comparisons reveal
Berkeley-RWAWC’s enhanced capability to detect seasonal variations,
demonstrating its usefulness in studying tropical wetland hydrology. We
also discuss potential sources of uncertainty and reasons for variations
in inundation retrievals. Berkeley-RWAWC represents a valuable addition
to environmental science, offering new insights into tropical wetland
dynamics.