Wetlands are the single largest source of methane to the atmosphere and
their emissions are expected to respond to a changing climate.
Inaccuracy and uncertainty in inundation extent drives differences in
modeled wetland emissions and impacts representation of wetland
emissions on inter-annual and seasonal time frames. Existing wetland
maps are based on optical or NIR satellite data obscured by clouds and
vegetation, often leading to underestimates in wetlands extent,
especially in the Tropics. Here, we present new inundation maps based on
the CYGNSS satellite constellation, operating in L-band that is not
impacted by clouds or vegetation, providing reliable observations
through canopy and cloudy periods. We map the temporal and spatial
dynamics of the Pantanal and Sudd wetlands, two of the largest wetlands
in the world, using CYGNSS data and a computer vision algorithm. We link
these inundation maps to methane fluxes via WetCHARTs, a global wetland
methane emissions model ensemble. We contrast CYGNSS-modeled methane
emissions with WetCHARTs standard runs that use monthly rainfall data
from ERA5, as well as the commonly used SWAMPS wetland maps. We find
that the CYGNSS-based inundation maps modify the methane emissions in
multiple ways. The seasonality of inundation and methane emissions is
shifted by two months because of the lag in wetland recharge following
peak rainfall. Both inundation and methane emissions also respond
non-linearly to wet-season precipitation totals, leading to large
interannual variability in emissions. Finally, the annual magnitude of
emissions is found to be greater than previously estimated.