Improving Representation of Tropical Wetland Methane Emissions With CYGNSS Inundation Maps

Abstract

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 Cyclone Global Navigation Satellite System (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 ECMWF re‐analysis (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.

Using CYGNSS Data to Map Flood Inundation during the 2021 Extreme Precipitation in Henan Province, China

“…In fact, in addition to being used for flood monitoring, CYGNSS technology has also shown great potential in monitoring global climate change in recent years. For example, CYGNSS has been used for monitoring wetland area change [52], coastal flooding [53], and sea level rise [54]. These applications can help researchers better understand the framework of climate change, and can also be used as a reference when relevant departments formulate corresponding climate policies [55].…”

Underestimated Dry Season Methane Emissions from Wetlands in the Pantanal

“…When used in conjunction with top-down estimates of Investigating the role of wetland inundation on CH 4 emissions, Gerlein-Safdi et al compared modeled CH 4 emissions from WetCHARTs ensembles driven by three distinct inundation data sets. 15 Derived using differing observables, these products included the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) rainfall mode (https://www.ecmwf.int/), 16 the Surface Water Microwave Product Series (SWAMPS) inundated area fraction maps, 17,18 and inundation extents derived from the Cyclone Global Navigation Satellite System (CYGNSS) constellation. 15 The ERA5 reanalysis product is based on historical model dynamics and data assimilation, which directly link wetland inundation to precipitation rates.…”

“…15 Derived using differing observables, these products included the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) rainfall mode (https://www.ecmwf.int/), 16 the Surface Water Microwave Product Series (SWAMPS) inundated area fraction maps, 17,18 and inundation extents derived from the Cyclone Global Navigation Satellite System (CYGNSS) constellation. 15 The ERA5 reanalysis product is based on historical model dynamics and data assimilation, which directly link wetland inundation to precipitation rates. SWAMPS incorporates microwave data from several passive and active satellites; however, the spectral region from which it derives fractional surface water extent struggles to see through closed canopies.…”

“…The WetCHARTs ensemble here is further extended using the Surface Water Microwave Product Series (SWAMPS) fractional water maps 17,18 and the Cyclone Global Navigation Satellite System (CYGNSS)-based inundation map. 15 SWAMPS' watermask product is based on a combination of the European Remote-sensing Satellite (ERS) scatterometer, the U.S. NASA Quick Scatterometer (QuikSCAT), and the European Space Agency's Advanced Scatterometer (ASCAT) data: these instruments' wavelengths (about 6 cm for ERS and ASCAT, about 2.5 cm for QuikSCAT) allow them to penetrate clouds but not thick vegetation, limiting the ability of the SWAMPS product to provide an accurate water extent map in highly vegetated areas, such as tropical forests. CYGNSS inundation data are a relatively new inundation product that is based on the CYGNSS satellite constellation that operates in L-band and is therefore insensitive to the presence of clouds or vegetation canopies, 16 making CYGNSS a highly valuable data product in studying tropical regions such as the Pantanal.…”

“…CYGNSS inundation maps differ from the commonly used SWAMPS water maps in the seasonality of inundation and the resulting CH 4 fluxes they produce in WetCHARTS, as well as the interannual variability of predicted CH 4 fluxes. 15 This study uses WetCHARTs models driven by three inundation products (ERA5, SWAMPS, and CYGNSS), two static wetland extent maps (GLWD, and GLOBCOVER), three CH 4 :C temperature dependencies, and three global scale factors. One of the wetland extent maps, GLWD, synthesizes a range of sources published from 1992 to 2000 for lakes and wetlands on a global scale.…”