# The GIEMS-MethaneCentric database: a dynamic and comprehensive global product of methane-emitting aquatic areas

Juliette Bernard, Catherine Prigent, Carlos Jimenez, Etienne Fluet-Chouinard, Bernhard Lehner, Elodie Salmon, Philippe Ciais, Zhen Zhang, Shushi Peng, Marielle Saunois

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**To cite this version:**

Juliette Bernard, Catherine Prigent, Carlos Jimenez, Etienne Fluet-Chouinard, Bernhard Lehner, et al.. The GIEMS-MethaneCentric database: a dynamic and comprehensive global product of methane-emitting aquatic areas. Earth System Science Data, 2025, 17 (6), pp.2985-3008. 10.5194/essd-17-2985-2025. hal-05198935

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# The GIEMS-MethaneCentric database: a dynamic and comprehensive global product of methane-emitting aquatic areas

Juliette Bernard¹,², Catherine Prigent¹,³, Carlos Jimenez³,¹, Etienne Fluet-Chouinard⁴,
Bernhard Lehner⁵, Elodie Salmon², Philippe Ciais², Zhen Zhang⁶, Shushi Peng⁷, and Marielle Saunois²

¹LERMA, Paris Observatory, CNRS, PSL, Paris, France

²Laboratoire des Sciences du Climat et de l'Environnement, LSCE-IPSL (CEA-CNRS-UVSQ), Université Paris-Saclay, 91191 Gif-sur-Yvette, France

³Estellus, Paris, France

⁴Earth System Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA

⁵Department of Geography, McGill University, Montreal, QC H3A 0B9, Canada

⁶Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China

⁷College of Urban and Environmental Sciences, Peking University, Beijing 100871, China

**Correspondence:** Juliette Bernard (juliette.bernard@obspm.fr) and Catherine Prigent (catherine.prigent@obspm.fr)

Received: 11 October 2024 – Discussion started: 17 October 2024

Revised: 6 March 2025 – Accepted: 20 March 2025 – Published: 28 June 2025

**Abstract.** The Global Inundation Extent from Multi-Satellites (GIEMS) database first published in 2001 was a key advance toward the accurate representation of wetlands globally by providing dynamic time series of global surface water based on passive microwave observations. This study supplements the second version of GIEMS (GIEMS-2) with other datasets to produce GIEMS-MethaneCentric (GIEMS-MC), a dynamically mapped dataset of methane-emitting waterlogged and inundated ecosystems. We separated open water from wetlands in GIEMS-MC by using the Global Lakes and Wetlands Database version 2 (GLWDv2) while adding unsaturated peatland areas undetected by GIEMS-2. Rice paddies are identified using the Monthly Irrigated and Rainfed Crop Areas (MIRCA2000) product. A specific coastal zone filtering is applied to avoid ocean artifacts while preserving coastal wetlands. GIEMS-MC covers the period 1992–2020 on a monthly scale at 0.25° × 0.25° spatial resolution. The GIEMS-MC product includes two layers of monthly wetland time series – one for flooded and saturated wetlands and another for all wetlands and peatlands – together with seven layers of compatible static maps of open water bodies (lakes, rivers, reservoirs) and seasonal rice paddy maps used in its production. The dominant vegetation and wetland types per pixel are also provided in GIEMS-MC variables. GIEMS-MC is compared to Wetland Area and Dynamics for Methane Modelling (WAD2M), a dataset providing dynamic wetland information. In terms of wetland extent, all wetlands and peatlands in GIEMS-MC and WAD2M show similar results, with a mean annual maximum of 7.7 Mkm² for GIEMS-MC and 6.8 Mkm² for WAD2M, with similar spatial patterns in most regions. The GIEMS-MC seamless time series represents a significant advance in wetland representation for methane modelling, although limitations remain in the accurate identification of rice, coastal, and peatland areas. This resource provides harmonized dynamic maps of aquatic methane-emitting surfaces and is available at https://doi.org/10.5281/zenodo.13919644 (Bernard et al., 2024a).
# 1 Introduction

Following a stable period from 1999 to 2006, atmospheric methane levels have started to rise again, reaching a record growth rate of +18 ppb yr⁻¹ in 2021 (Lan et al., 2024). This increase is a cause for concern, particularly given that anthropogenic emissions of this potent greenhouse gas account for approximately one-third of the human-induced radiative forcing (Szopa et al., 2021). As a chemically active greenhouse gas with multiple, time-varying sources and sinks (Saunois et al., 2025), closing the methane budget is challenging. The causes of the observed increase in atmospheric methane remain uncertain. Potential factors include increased human or natural emissions, reduced sinks, or a combination of these factors. However, isotopic evidence suggests that biogenic sources (livestock, wetlands, waste, etc.) may play a significant role in the observed increase (Nisbet et al., 2016, 2019).

Among the sources, natural emissions from wetlands and freshwater ecosystems account for 145 to 369 Tg CH₄ yr⁻¹, i.e. 25% to 51% of global methane emissions (Saunois et al., 2025). Wetland emissions show significant inter-annual variability (Bousquet et al., 2006; Bridgham et al., 2013) and are sensitive to climate (Bridgham et al., 2013; Zhang et al., 2023). Thus, better understanding natural methane emissions variability in the past will inform future predictions of wetland emissions and their feedback on climate. Large uncertainties remain for both wetlands and freshwater ecosystems methane emissions (Saunois et al., 2020; Canadell et al., 2021). This is due to the difficulty in modelling methane fluxes, which depend on many biotic and abiotic factors (Bridgham et al., 2013; Ge et al., 2024), to the small number of flux observations (Canadell et al., 2021), and to uncertainties in wetland and freshwater area (Bridgham et al., 2013; Melton et al., 2013; Saunois et al., 2020; Canadell et al., 2021), including issues of double counting, where the same area may be counted twice under different categories, inflating estimated emissions (Canadell et al., 2021; Thornton et al., 2016). Yet the area covered by seasonal wetlands remains the single largest source of uncertainty in wetland CH₄ emissions (Melton et al., 2013; Peltola et al., 2019; Poulter et al., 2017; Zhang et al., 2017).

The first global wetland map was produced by Matthews and Fung (1987), providing composite static information on wetland types. Since then, new static wetland products have been established, either from composite information (Lehner and Döll, 2004; Tootchi et al., 2019; Tuanmu and Jetz, 2014) or from remote sensing approaches (Loveland et al., 2000; Friedl et al., 2002; Carroll et al., 2009; Feng et al., 2016; Bartholomé Belward, 2005). Further datasets have been developed based on hydrological model outputs (Ringeval et al., 2012; Wania et al., 2013; Xi et al., 2022), presenting their advantages and disadvantages compared to satellite-derived products. Those models can be used both to reconstruct the historical distribution of wetlands and to

predict their future evolution. Modelling can be an effective method for producing a global map of wetlands, particularly where physics-based models can reflect the mechanisms by which wetlands are formed. The two main limitations of these model outputs are (1) that hydrological models are simplified representations of the real-world complexity of wetlands (e.g. models often focus on a single water surface generation process (Obled and Zin, 2004)) and (2) that human interference is not well accounted for in the models (Hu et al., 2017). Moreover, observations are required to constrain and/or validate these model predictions.

However, there are only a few available observational dynamic time series of surface water maps at a global scale. Notably, these include (1) the Global Inundation Extent from Multi-Satellites (GIEMS and GIEMS-2) (Prigent et al., 2001, 2007; Papa et al., 2010; Prigent et al., 2020) and its downscaled versions (Fluet-Chouinard et al., 2015; Aires et al., 2017) and (2) the Surface Water Microwave Product Series (SWAMPS) (Schroeder et al., 2015; Jensen and McDonald, 2019).

GIEMS-2 and SWAMPS both provide monthly fractions of surface water at 0.25° × 0.25° for 1992–2020, mainly based on passive microwave observations from Special Sensor Microwave Imager (SSM/I) and the Special Sensor Microwave Imager Sounder (SSMIS). Although SWAMPS and GIEMS-2 both aim to represent both inundated surfaces and are produced using similar input data, they present significant differences both in terms of spatial distribution and inter-annual variations (Pham-Duc et al., 2017; Bernard et al., 2024b).

GIEMS-2 and SWAMPS products do not differentiate surface water categories, e.g. wetland, lake, reservoir, pond, or rice paddy, and are therefore not directly usable for wetland studies modelling seasonally inundated wetlands separately from open water bodies. Recent efforts have been made by Zhang et al. (2021b) to produce the Wetland Area and Dynamics for Methane Modeling (WAD2M) product based on SWAMPS, which represents a pioneering attempt to dynamically map wetlands, including peatlands. Using additional high-resolution static estimates of wetlands and open permanent water, as well as seasonal information on rice paddies, Zhang et al. (2021b) were able to apply these correction layers to SWAMPS to distinguish wetlands from other surface water. WAD2M version 2.0 (Zhang et al., 2021a) provides monthly estimates on a global scale for 2000–2020 at 0.25° × 0.25°. However, WAD2M has encountered difficulties in capturing reliable inter-annual trends (Zhang et al., 2021b; Bernard et al., 2024b). In fact, issues in SWAMPS are propagated into WAD2M, such as ocean and desert artifacts leading to overestimation and abrupt changes in time series partly due to changes in satellites (Pham-Duc et al., 2017; Bernard et al., 2024b).

In an attempt to compare the corrected wetland extent of WAD2M and GIEMS, McNicol et al. (2023) applied the same correction layers to GIEMS-2, but this exercise did not
eliminate the large differences between these two datasets. In particular, the WAD2M procedure rescales the SWAMPS surface water extent fractions, which are always positive, with other high-resolution static wetland datasets, which potentially produces some unreliable seasonality where the wetland fractions in SWAMPS are below the instrumental noise level. On the contrary, GIEMS-2 shows some zero fractions over numerous pixels where no water is detected. This makes it impossible to use the same procedure as in Zhang et al. (2021b) to produce WAD2M from SWAMPS. The correction procedure needs to be modified to adapt to GIEMS-2. Furthermore, since the release of WAD2M, the most recent maps of aquatic ecosystems have been aggregated into the Global Lakes and Wetlands Database version 2 (GLWDv2), which now offers the most comprehensive and up-to-date representation of global wetland classes (Lehner et al., 2025a).

This study presents a new comprehensive database of methane-emitting surfaces, named the Global Inundation Extent from Multi-Satellites-MethaneCentric (GIEMS-MC). GIEMS-MC aims at providing spatially and dynamically consistent maps of the different methane-emitting ecosystems, with the purpose of providing data for modelling methane emissions at the global scale (0.25° × 0.25°) over 1992–2020. In particular, two time series of wetland maps are developed at a monthly timescale: inundated and saturated wetlands (ISW) and all wetlands including non-inundated peatlands (inundated and saturated wetlands + peatlands, ISW+P). GIEMS-MC also provides compatible information from the ancillary data used, including static surface extents of open permanent waters (lakes, rivers, reservoirs, estuaries, deltas) and seasonal surface extents of rice paddies, along with dominant vegetation on wetland classes. GIEMS-MC takes advantage of the GIEMS-2 product that offers ~30-year seamless time series of surface water with realistic seasonality and inter-annuality (Prigent et al., 2020; Bernard et al., 2024b) and largely benefits from the recently developed comprehensive static map of GLWDv2 (Lehner et al., 2025a). This article outlines the methodology behind the production of GIEMS-MC and provides an analysis and comparison with existing datasets: WAD2M (Zhang et al., 2021b), GLWDv2 (Lehner et al., 2025a), and the original GIEMS-2 (Prigent et al., 2020). Two inundation products based on Cyclone Global Navigation Satellite System (CYGNSS) data are also used for comparison over the Sudd (Zeiger et al., 2023; Gerlein-Safdi et al., 2021). The sensitivity of the wetland estimates to the different process steps is also discussed.

## 2 Datasets

This section presents the three types of data used in the production of GIEMS-MC: (1) the surface data of the different aquatic ecosystems, (2) the data used for the masks and ad-

ditional ecosystem layers, and (3) the WAD2M comparison dataset.

### 2.1 Input datasets to GIEMS-MC (GIEMS-2, GLWDv2, MIRCA2000)

The GIEMS-2 dataset, spanning 1992 to 2015 and extended to 2020 in this study, provides monthly global maps of surface water extent with a spatial resolution of 0.25° × 0.25° (Prigent et al., 2020). GIEMS-2 includes all continental water surfaces, such as wetlands, rice paddies, rivers, reservoirs, and lakes, but excludes large lakes (> 15 000 km²). The original GIEMS-1 methodology (Prigent et al., 2001, 2007; Papa et al., 2006, 2008, 2010) and the GIEMS-2 algorithm (Prigent et al., 2020; Bernard et al., 2024b) have been extensively evaluated against other observational products, demonstrating robust capture of seasonal and inter-annual variability, despite uncertainties in wetland extent between currently available products. GIEMS-2 relies primarily on inter-calibrated passive microwave observations from the SSM/I and SSMIS satellites (Fennig et al., 2020) at frequencies from 19 to 85 GHz. The microwave signal is influenced by atmospheric conditions (e.g. water vapour, clouds) and surface variables (e.g. surface temperature, vegetation, snow) by other ancillary information to limit these artifacts, as described in Prigent et al. (2020). Microwave observations are complemented by other satellite information to limit these artifacts, as described in Prigent et al. (2020). Specifically, active microwave satellite data and the normalized difference vegetation index (NDVI) derived from visible and near-infrared measurements are used to characterize vegetation and mitigate its influence on the passive microwave signal. Since snow contamination prevents the calculation of surface water, snow-covered pixels (as defined by meteorological re-analysis) are assigned a water fraction of 0 for the previous studies and in the distributed GIEMS-2 product. Passive microwaves are sensitive to the presence of water, including estuarine and offshore marine waters. To avoid misinterpretation of the data, coastal pixels were filtered out in the distributed GIEMS-2 product, leading to a possible underestimation of inundated surface extent in the coastal areas. Here we use an unfiltered version of GIEMS-2 in which coastal regions are not excluded, in order to improve the coastal cleaning process during the production of GIEMS-MC based on GLWDv2, as described in Sect. 3.

The Global Lakes and Wetlands Database version 2 (GLWDv2) (Lehner et al., 2025a, b) provides comprehensive global maps of aquatic ecosystems synthesized from a variety of ground- and satellite-based data products. GLWDv2 combines various data products to generate consolidated and harmonized static maps representative of the period 1990–2020. The GLWDv2 product contains 33 wetland and water body classes, which are listed in Table S1 in the Supplement. GLWDv2 represents the maximum extent of each of its 33 classes (in pixel fraction) at a resolution of 15 arcsec
(approximately 500 m at the Equator). For this study, the 33 GLWDv2 class maps were aggregated at 0.25° × 0.25°.

Rice cultivation varies seasonally according to cropping calendars, and their inundated cover can be confused with that of wetlands. The majority of global rice paddy maps are static representations, typically for a specific time period. A notable data source that gives insights into the seasonality of rice paddy at global scale is the MIRCA2000 dataset (Portmann et al., 2010b, a). MIRCA2000 provides data on irrigated and rainfed cultivated areas at a resolution of 5 arcmin for each month of a reference year (representative of circa 2000). The dataset integrates several data sources, including agricultural statistics such as cropping calendar and remote sensing data. This study uses both irrigated and rainfed rice data extracted from the MIRCA2000 dataset.

## 2.2 Ancillary and correction datasets (ERA5, ESA CCI)

The European Centre for Medium-range Weather Forecasts reanalysis (ECMWF-ERA5) (Hersbach et al., 2020; Copernicus Climate Change Service, 2019) is a state-of-the-art reanalysis for climate applications. It provides global climate and weather data spanning from 1940 to the present. ERA-5 uses assimilation techniques by integrating a wide diversity of observational data to deliver hourly estimates of multiple atmospheric, land, and oceanic variables at a resolution of 31 km. ERA5 can be downloaded at a resolution of 0.25° × 0.25° from https://cds.climate.copernicus.eu (last access: 12 January 2023). In the GIEMS-2 and GIEMS-MC process, the area covered with snow in a pixel is derived from the ERA5 variables snow density and snow depth.

The European Space Agency (ESA) Climate Change Initiative (CCI) Land Cover dataset (Defourny et al., 2017; Harper et al., 2023) provides a classification of land cover features at a spatial resolution of 300 m for each year from 1992 to 2022. The dataset is derived from various satellite Earth observation data. According to the standards of the United Nations Land Cover Classification System (Di Gregorio and Jansen, 2005), it contains 22 land cover classes (Table S2), including 18 vegetation categories and urban, bare, water bodies, and snow/ice categories. The ESA CCI Land Cover dataset can be accessed via the ESA CCI Land Cover project website: https://maps.elie.ucl.ac.be/CCI/viewer/download.php (last access: 12 May 2023). Here, we aggregated a version to 0.25° × 0.25°, where the dominant class within each pixel is determined based on the highest fractional coverage.

## 2.3 Comparison dataset (WAD2M)

The Wetland Area and Dynamics for Methane Modeling (WAD2M) version 2.0 dataset (Zhang et al., 2021b, a) is a comprehensive global product designed to support methane modelling. It provides the fraction of wetland area, including peatlands, at a resolution of 0.25° × 0.25° and at a monthly

time step for 2000–2020. The WAD2M dataset uses dynamic data from the Surface Water Microwave Product Series (SWAMPS) dataset (Jensen and Mcdonald, 2019), which provides a monthly inundation fraction at 0.25° × 0.25°. Similar to GIEMS-2, SWAMPS is derived mainly from passive microwave observations from SSM/I and SSMIS, but the methodology and ancillary data used differ between the two products (Schroeder et al., 2015; Prigent et al., 2020), resulting in important differences in some regions (Pham-Duc et al., 2017; Bernard et al., 2024b). The creation of WAD2M involved combining SWAMPS surface inundation time series with static datasets to distinguish between different wetland types. The static datasets used in WAD2M production are four peatland maps (NCSCD from Hugelius et al., 2013, CAWASAR from Widhalm et al., 2015, GLWDv1 from Lehner and Döll, 2004, and CIFOR from Gumbricht et al., 2017), one inland open water map (GSW from Pekel et al., 2016), one coastal mask (MOD44W from Carroll et al., 2009), and a seasonal irrigated rice map (MIRCA2000 from Portmann et al., 2010b). These static layers allow the wetland fractions to be rescaled to include non-inundated wetlands (peatlands) and exclude non-wetland inundated areas (irrigated rice paddies and open waters).

# 3 Methods

## 3.1 Overview of the methodology

GIEMS-2 uses satellite passive microwave data, which are particularly responsive to the presence of water, to determine the fraction of inundated and saturated soil per pixel. However, modifications to the GIEMS-2 dataset are required in order to remove inundated or saturated areas that are not wetlands (e.g. rice paddies, lakes, rivers, reservoirs) and to add wetlands where the water table may be undetectable below ground level (e.g. some peatlands). As a consequence of the aforementioned remote sensing approach, the present study will first distinguish the inundated wetlands identified by GIEMS-2 and then add the unsaturated wetlands. In addition to GIEMS-2, the GLWDv2 dataset will be used.

The original GIEMS-2 product (Prigent et al., 2020) has been extended to 2020 (Bernard et al., 2024b), and a special version without coastal filtering is used here. In total, seven steps, described in the following subsections, are required to derive wetland maps from these data. The operations are made in terms of pixel fraction *f* on a regular grid of 0.25° × 0.25°. Multiplication by pixel area is then needed to derive wetland extent. A summary of the procedure is shown in Fig. 1, and the seven steps are described in detail in the following subsections.

### 3.1.1 Applying ocean mask

For consistency with GLWDv2, we here used the regional shapefiles of the HydroATLAS database (version 1.0; Linke
<figure><img src="image_4.png" /><figcaption>**Figure 1.** Schematic of the GIEMS-MC dataset production process. All operations are performed in terms of pixel fractions at a resolution of 0.25° × 0.25°. Ocean pixels are set to -999, snow-covered pixels are set to -998, and urban pixels are set to -997 in a revised version of GIEMS-2. Open permanent water and then rice paddies areas are subtracted from the surface water areas. A specific coastal cleaning is applied to remove ocean contamination, resulting in a dynamic map of inundated and saturated wetland (GIEMS-MC<sub>ISW</sub>). Peatland areas undetected by GIEMS-2 are added to derive a dynamic map of all wetlands including peatlands, called inundated and saturated wetland + peatland (GIEMS-MC<sub>ISW+P</sub>). Finally, initial ancillary data information is added to the product so that users can easily access the different fraction maps of all surface water categories, including inundated and saturated wetland, inundated and saturated wetland + peatland, open permanent waters, rice paddies, and the dominant wetland and vegetation classes. $f_i$ refers to the fraction of a pixel i before the corresponding step. “pos” (f) refers to the positive part of f, i.e. pos(f) = max(f, 0). GLWDv2 open permanent water (GLWDv2<sub>OPW</sub>) is the sum of all GLWDv2 classes 1 to 5 and 30. GLWDv2 inundated and saturated wetland (GLWDv2<sub>ISW</sub>) is the sum of all GLWDv2 wetlands excluding peatlands corresponding to classes 8 to 21, 28, 29, 31, and 32. GLWDv2 peatland (GLWDv2<sub>peat</sub>) is the sum of all GLWDv2 peatlands corresponding to classes 22 to 27.</figcaption></figure>
et al., 2019), which provides near-identical coastlines as GLWDv2. This allowed us to calculate the ocean fraction for each 0.25° × 0.25° pixel. The ocean water fraction is set to −999 if the ocean fraction of a pixel is greater than 99 %, to avoid confusion between ocean pixels and pixels where no surface water was detected (zero fraction pixels).

### 3.1.2 Applying snow mask

GIEMS-2 surface water detection relies primarily on passive microwave observations, which are affected by the presence of snow (Foster et al., 1984). Thus, the surface water fraction cannot be reliably quantified in the presence of snow. Consequently, the surface water detection algorithm in the GIEMS-2 production is not run when snow is present in a pixel. To exclude these snow-covered pixels, ECMWF snow information from ERA5 is used in the GIEMS-2 processing, and pixels with a snow fraction above 2 % are set to a surface water fraction of 0 (Prigent et al., 2020). In GIEMS-MC, the pixel value is given its dedicated snow flag value of −998 when the snow fraction of a pixel is greater than 2 %. It should be noted that this mask remains for all subsequent steps and is therefore also applied to the peatlands (step 7).

### 3.1.3 Applying urban mask

It has been observed that unexpectedly large water surfaces are detected by GIEMS-2 in areas of high urban density. This could be due to the different surface materials used in buildings, some of which strongly reflect microwaves. For example, highly reflective areas over Paris are misinterpreted as water due to predominance of zinc roofs. To apply an urban mask, the urban class product of the ESA CCI land cover map aggregated at 0.25° × 0.25° is used. The grid cells with urban percentage above 40 % are systematically masked to −997 to avoid any confusion between urban and water surfaces. Note that applying this urban mask results in neglecting change in terms of surface water global area (< 1 % change on mean extent), but this avoids local artifacts over the high-urban density areas.

### 3.1.4 Subtracting open permanent waters

Inland open permanent waters are considered separately from wetlands in methane budgets (Saunois et al., 2020; Canadell et al., 2021), as different methane production and transport processes are involved. To derive wetland maps, these open permanent surface water areas must be subtracted from the GIEMS-2 estimates. Here, we define open permanent water as non-vegetated, permanently inundated areas that are not wetland. Some dynamic datasets could have been used, but consistency was preferred, so GLWDv2 harmonized maps were used in GIEMS-MC production. Then, open permanent water areas of GLWDv2 corresponding to layers 1 to 5 and 30 (freshwater lake, salt lake, reservoir, large river,

large estuarine river, and large river delta) are subtracted from the GIEMS-2 fractions. These GLWDv2 areas are derived from HydroLAKES (Messager et al., 2016; lakes), the Global Dam Watch (GDWv1) database (Lehner et al., 2024; reservoirs), and the Global River Width from the Landsat (GRWL) dataset (Allen and Pavelsky, 2018; large rivers) and augmented with the Global Surface Water (GSW) database (Pekel et al., 2016). Note that the floodplain ecosystems are included in other GLWDv2 classes (10 to 15) and are therefore retained in GIEMS-2 when constructing GIEMS-MC.

### 3.1.5 Subtracting rice paddies

Rice paddies are intermittently saturated or inundated depending on irrigation practices, and their methane emissions are considered to be an anthropogenic source that should be separated from those of natural wetlands. GLWDv2 contains a static rice paddy map, but the seasonal variation of rice paddies is important in terms of extent and needs to be taken into account to avoid over-subtraction of rice paddies in the GIEMS-MC process. However, there is to our knowledge no dynamic (intra-annual resolution) product available that represents rice paddies at global scale over our observation period. As the MIRCA2000 product provides maps with a typical seasonality (circa 2000) of global rice paddies, it appears to be the most appropriate product available. Consequently, the MIRCA2000 12-month seasonality of irrigated and rainfed rice paddy areas is subtracted from the area estimates. This rice paddy processing and its uncertainties are discussed further in Sect. 5.2.2.

### 3.1.6 Correcting ocean contamination

The GIEMS-2 version used here has not been filtered in coastal areas, as it is usually done in the distributed GIEMS-2 version. The SSM/I and SSMIS passive microwave observations used in GIEMS-2 production are very sensitive to the presence of water, including the ocean. The GIEMS-2 fraction and seasonality estimates are less reliable for pixels with larger ocean fractions. Thus, pixels containing more than 10 % ocean in GLWDv2 (GLWDv2ocean > 10 %) are set to 0. Tests were made to tune this 10 % threshold, to avoid masking all pixels containing a small fraction of ocean area while ensuring reasonable seasonality. However, pixels containing up to 10 % ocean area will undergo an additional coastal cleaning procedure that follows. Ocean contamination can arise not only from the presence of ocean within the pixel but also from the ocean in neighbouring pixels. A GIEMS-2 pixel (~ 800 km² at the Equator, ~ 400 km² at 60°N or S) is smaller than the −3 dB footprint of the original microwave satellite observations (69 km × 43 km at 19 GHz and 37 km × 28 km at 37 GHz). Moreover, microwave energy is also measured in the side lobes of the satellite instrument footprint. Coastal areas should then undergo a cleaning process to reduce these artifacts. Pixels whose centres
are between 0 and 50 km from a coastline or large lakes (> 15 000 km²) are considered coastal areas.

In an attempt to correct for ocean contamination, the fol-
lowing procedure is applied to ensure that the wetland ar-
eas in GIELS-MC in coastal regions are equal to or lower
than the GLWDv2 inventory. This is done by calculating
GLWDv2 inundated and saturated wetland (GLWDv2ISW),
i.e. the sum of all GLWDv2 wetlands excluding peatlands
(which are not necessarily saturated surface water) corre-
sponding to classes 8 to 21, 28, 29, 31, and 32. The frac-
tion of the modified version of GIELS-2 up to this step, in-
corporating the five aforementioned corrections and cleaning
processes (ocean, snow, urban masks and open water, and
rice paddies removal), is called f. Its mean annual maxi-
mum fMAmax is calculated by taking a monthly average over
all years and taking the maximum of this monthly seasonal-
ity for each pixel i. In the coastal region, for each time step
and for each pixel i, with the resulting areas called the inun-
dated and saturated wetland map in GIELS-MC (GIEMS-
MCISW):

- If GLWDv2ocean, i > 10%, then GIELS-MCISW, i = 0.

- If GLWDv2ocean, i ≤ 10% and fMAmax, i < GLWDv2ISW, i, then GIELS-MCISW, i = fᵢ.

- If GLWDv2ocean, i ≤ 10% and fMAmax, i > GLWDv2ISW, i, then GIELS-MCISW, i = fᵢ · 
  GLWDv2ISW, i
  fMAmax, i

3.1.7 Adding peatlands

Finally, in order to have a complete map of wetlands, the
peatlands not detected by GIELS-2 (monthly unsaturated or
unflooded peatlands) have to be taken into account in the
wetland fraction. This is done using the following procedure.
The sum of GLWDv2 peatlands, i.e. GLWDv2 classes 22 to
27, is denoted here as GLWDv2peat. GLWDv2 peatland in-
formation is a composite product relying on the most up-to-
date peatland maps: PeatMap (Xu et al., 2018, global), Soil-
Grids250m (Hengl et al., 2017, global), NCSCD (Hugelius
et al., 2013, north of 23° N), and CIFOR (Gumbricht et al.,
2017, only south of 23.5° N). More details can be found in
Lehner et al. (2025a). GLWDv2peat represents 4.26 Mkm²,
which is consistent with primary PeatMap estimates of
4.23 Mkm². The peatlands detected by GIELS-2 are derived
by the difference, if positive, between GIELS-MCISW and
GLWDv2ISW. The undetected peatlands are then derived for
each month as the difference between GLWDv2peat and the
peatlands detected by GIELS-2. These undetected peatlands
are added to GIELS-MCISW, resulting in GIELS-MC inun-
dated and saturated wetland + peatland (GIEMS-MCISW+P).

That is, for each pixel i,

$$
\begin{equation}
\begin{aligned}
\text{undetected peatlands}_i = \operatorname{pos}[&\text{GLWDv2}_{\text{peat}_i} \\
& - \operatorname{pos}(\text{GIEMS-MC}_{\text{ISW}_i} - \text{GLWDv2}_{\text{ISW}_i})] \\
\text{GIEMS-MC}_{\text{ISW}+\text{P}} = \text{GIEMS-MC}_{\text{ISW}} \\
& + \text{undetected peatlands.}
\end{aligned}
\tag{1}
\end{equation}
$$

The uncertainty in terms of areas of this step is discussed in
Sect. 5.2.3.

3.2 Comparison

GIEMS-MCISW is compared with the original GIELS-
2 product and GLWDISW. GIELS-MCISW+P is compared
to GLWDv2ISW+P (GLWDv2ISW+GLWDv2peat) and to
WAD2M, as all also include peatlands (Table 3, Fig. 2, and
Fig. S1). As GLWDv2 is a static map representing long-term
maximum extent, the GIELS-MC long-term maximum (LT-
max) will be used for comparison with GLWDv2 instead of
MAmax (Table 3). To derive this LTmax, the maximum of
each pixel over the whole time period is selected. This can
lead to the selection of extreme values with moderate relia-
bility, and LTmax should then be interpreted with caution.

3.3 Description of GIELS-MC dataset

Following the seven steps outlined, a NetCDF product at
0.25° × 0.25° resolution containing the derived variables and
ancillary variables is created. The variables included in this
product are detailed in Table 1. Its components include both
dynamic monthly maps of GIELS-MCISW and GIELS-
MCISW+P. Open permanent water classes (GLWDv2), i.e.
freshwater lake, saline lake, reservoir, river, large estuarine
river, and large river delta, are also added as static variables in
GIEMS-MC. The 12-month seasonality of irrigated and rain-
fed rice paddy (MIRCA2000) is included. Three static maps
provide information on the main ecosystems per pixel: the
dominant aquatic class (GLWDv2), the dominant wetland or
peatland class (GLWDv2), and the dominant land cover class
(ESA CCI Land Cover map). This vegetation information is
added in GIELS-MC as it is expected to help improve the es-
timation of wetland methane emissions (Pangala et al., 2017;
Vroom et al., 2022; Feron et al., 2024; Girkin et al., 2025; Ge
et al., 2024).

4 GIELS-MC results

4.1 Global inland water areas

To quantify the variations in terms of extent, the mean an-
nual maximum (MAmax), mean annual mean (MAmean),
and mean annual minimum (MAmin) are calculated by av-
eraging the 29 years of data (21 years for WAD2M) to a typi-
cal 12-month seasonality for each pixel. Then, the maximum,
the mean, and the minimum are respectively selected for each
<table><caption>**Table 1.** Summary of GIEMS-MC variables with corresponding data sources and temporal resolution. For details about data sources, see Sect. 2 or Prigent et al. (2020) for GIEMS-2, Lehner et al. (2025a) for GLWDv2, Portmann et al. (2010b) for MIRCA2000, and Defourny et al. (2017) for ESA CCI Land Cover.</caption><thead><tr><th>GIEMS-MC variable</th><th>Long name</th><th>Type</th><th>Primary or main data source</th><th>Time resolution</th></tr></thead><tbody><tr><td>inund_sat_wetland.frac</td><td>inundated and saturated wetland</td><td>fraction</td><td>GIEMS-2</td><td>monthly</td></tr><tr><td>inund_sat_peat_wetland.frac</td><td>inundated and saturated wetland + peatland</td><td>fraction</td><td>GIEMS-2 + GLWDv2</td><td>monthly</td></tr><tr><td>fresh_lake.frac</td><td>freshwater lake</td><td>fraction</td><td>GLWDv2</td><td>static</td></tr><tr><td>saline_lake.frac</td><td>saline lake</td><td>fraction</td><td>GLWDv2</td><td>static</td></tr><tr><td>reservoir.frac</td><td>reservoir</td><td>fraction</td><td>GLWDv2</td><td>static</td></tr><tr><td>river.frac</td><td>large river</td><td>fraction</td><td>GLWDv2</td><td>static</td></tr><tr><td>estu_river.frac</td><td>large estuarine river</td><td>fraction</td><td>GLWDv2</td><td>static</td></tr><tr><td>delta.frac</td><td>large river delta</td><td>fraction</td><td>GLWDv2</td><td>static</td></tr><tr><td>rice_irri.frac</td><td>irrigated rice paddy</td><td>fraction</td><td>MIRCA2000</td><td>12-month seasonality</td></tr><tr><td>rice_rainfed.frac</td><td>rainfed rice paddy</td><td>fraction</td><td>MIRCA2000</td><td>12-month seasonality</td></tr><tr><td>dom_aqua_class</td><td>dominant aquatic class</td><td>33 classes (Table S1)</td><td>GLWDv2</td><td>static</td></tr><tr><td>dom_wet_peat_class</td><td>dominant wetland or peatland class</td><td>25 classes (Table S1)</td><td>GLWDv2</td><td>static</td></tr><tr><td>dom_land_cover_class</td><td>dominant land cover class</td><td>37 classes (Table S2)</td><td>ESA CCI Land Cover</td><td>static</td></tr></tbody></table>

pixel. Spatial correlation is defined as the pixel-by-pixel correlation between two maps, considering a specific unmasked region.

Globally, GIEMS-MC<sub>ISW</sub> represents 3.80 Mkm<sup>2</sup>, with a MAmean of 1.23 Mkm<sup>2</sup> (Table 2). The addition of peatlands greatly increases these global areas, with GIEMS-MC<sub>ISW+P</sub> reaching 7.75 Mkm<sup>2</sup> (+3.95 Mkm<sup>2</sup>) in terms of MAmax and 3.54 Mkm<sup>2</sup> (+2.27 Mkm<sup>2</sup>) in terms of MAmean. This increase is mainly due to Europe–Siberia and North America, where large peatlands contribute significantly to the total wetland area (74 % and 58 % respectively).

GIEMS-MC<sub>ISW</sub> consistently shows a much lower extent than the original GIEMS-2 (MAmax reduced from 6.80 to 3.80 Mkm<sup>2</sup>) that comprises all inundated and saturated areas, including non-wetland categories. The lower areas in GIEMS-MC<sub>ISW</sub> are mainly due to the removal of open permanent waters in Europe, Siberia, and North America and to rice paddy subtraction in Asia. The LTmax of GIEMS-MC<sub>ISW</sub> reaches 8.77 Mkm<sup>2</sup>, close to the GLWDv2<sub>ISW</sub> estimates of 8.22 Mkm<sup>2</sup>.

Globally, the MAmean estimates of GIEMS-MC<sub>ISW+P</sub> and WAD2M are in agreement (MAmean of 3.50 and 4.21 Mkm<sup>2</sup>, respectively), and the two datasets show a spatial correlation coefficient of 0.61 for MAmax over submerged land. However, regional differences exist in Africa (MAmean of 295 and 719 × 10<sup>3</sup> km<sup>2</sup>, respectively) and Oceania (MAmean of 429 and 572 × 10<sup>3</sup> km<sup>2</sup>, respectively). In those regions, WAD2M detects comparatively more water, likely due to desert contamination in the SWAMPS product used in the WAD2M production (see Figs. 2 and S1). In Asia, Europe, Siberia, and North America, GIEMS-MC<sub>ISW+P</sub> shows similar MAmean areas but has a larger MAmax–MAmin amplitude, possibly due to (1) higher peatland estimates in GLWDv2 than in the ancillary data used in WAD2M production, which could explain the higher MAmax, and (2) more stringent snow and coastal filtering in GIEMS-MC, which could explain the lower MAmin. As GLWDv2 peatlands are used to derive GIEMS-MC<sub>ISW+P</sub> from GIEMS-MC<sub>ISW</sub>, similar total extents are consistently found between the GIEMS-MC<sub>ISW+P</sub> LTmax of 12.25 Mkm<sup>2</sup> and the GLWDv2<sub>ISW+P</sub> LTmax of 12.49 Mkm<sup>2</sup>.

Figure 3 provides the latitudinal distribution of (a) GIEMS-MC variables and (b) GIEMS-MC<sub>ISW+P</sub> against WAD2M. GIEMS-MC<sub>ISW</sub> shows a relatively uniform distribution across all latitudinal zones, with a peak just south of the Equator due to the Amazon basin. The inclusion of peatlands in GIEMS-MC<sub>ISW+P</sub> increases largely the wetland area in the boreal (> 55° N; e.g. the Hudson Bay and the Siberian Lowland) and tropical (10° S–5° N; e.g. the Congo Basin) bands, leading to a similar distribution in WAD2M (Figs. 2 and 3c). Differences between GIEMS-MC<sub>ISW+P</sub> and WAD2M are observed around 15° N and 10–30° S, due to discrepancies between the SWAMPS and GIEMS-2 methodologies (Pham-Duc et al., 2017; Bernard
<table><caption>**Table 2.** Global and continental surfaces of GIEMS-MC variables in $10^3$ km$^2$. For dynamic classes, MAmax and MAmin are shown. Total MAmax is the sum of GIEMS-MC inundated and saturated wetland + peatland MAmax, open permanent water (freshwater lake, saline lake, reservoir, river, large estuarine river, large river delta) from GLWDv2, and rice paddy from MIRCA2000 MAmax (irrigated and rainfed). Regions correspond to the shapefiles of HydroATLAS database (version 1.0; Linke et al., 2019).</caption><thead><tr><th></th><th></th><th>Global</th><th>Africa</th><th>Asia</th><th>Europe + Siberia</th><th>Oceania</th><th>North America</th><th>South America</th></tr></thead><tbody><tr><td rowspan="3">GIEMS-MC<sub>ISW</sub><br>(inundated and saturated wetland)</td><td>MAmax</td><td>3796</td><td>515</td><td>972</td><td>616</td><td>207</td><td>827</td><td>207</td></tr><tr><td>MAmean</td><td>1227</td><td>163</td><td>252</td><td>154</td><td>103</td><td>258</td><td>294</td></tr><tr><td>MAmin</td><td>251</td><td>46</td><td>29</td><td>11</td><td>46</td><td>30</td><td>85</td></tr><tr><td rowspan="3">GIEMS-MC<sub>ISW+P</sub><br>(inundated and saturated wetland + peatland)</td><td>MAmax</td><td>7745</td><td>636</td><td>1153</td><td>2369</td><td>522</td><td>1985</td><td>1001</td></tr><tr><td>MAmean</td><td>3500</td><td>295</td><td>382</td><td>888</td><td>429</td><td>790</td><td>654</td></tr><tr><td>MAmin</td><td>1308</td><td>182</td><td>96</td><td>79</td><td>377</td><td>76</td><td>449</td></tr><tr><td>Freshwater lake</td><td>static</td><td>2045</td><td>197</td><td>76</td><td>429</td><td>22</td><td>1214</td><td>81</td></tr><tr><td>Saline lake</td><td>static</td><td>359</td><td>33</td><td>97</td><td>88</td><td>40</td><td>21</td><td>20</td></tr><tr><td>Reservoir</td><td>static</td><td>316</td><td>40</td><td>62</td><td>71</td><td>6</td><td>87</td><td>47</td></tr><tr><td>River</td><td>static</td><td>384</td><td>40</td><td>72</td><td>109</td><td>13</td><td>53</td><td>93</td></tr><tr><td>Large estuarine river</td><td>static</td><td>79</td><td>5</td><td>12</td><td>16</td><td>9</td><td>10</td><td>13</td></tr><tr><td>Large river delta</td><td>static</td><td>280</td><td>18</td><td>139</td><td>15</td><td>0</td><td>35</td><td>57</td></tr><tr><td rowspan="3">Irrigated rice paddy</td><td>MAmax</td><td>639</td><td>14</td><td>509</td><td>16</td><td>46</td><td>14</td><td>22</td></tr><tr><td>MAmean</td><td>431</td><td>8</td><td>355</td><td>6</td><td>30</td><td>8</td><td>10</td></tr><tr><td>MAmin</td><td>64</td><td>0</td><td>62</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td rowspan="3">Rainfed rice paddy</td><td>MAmax</td><td>614</td><td>42</td><td>452</td><td>0</td><td>71</td><td>3</td><td>25</td></tr><tr><td>MAmean</td><td>251</td><td>21</td><td>178</td><td>0</td><td>29</td><td>1</td><td>12</td></tr><tr><td>MAmin</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Total</td><td>MAmax</td><td>12271</td><td>1013</td><td>2495</td><td>3102</td><td>731</td><td>3400</td><td>1326</td></tr><tr><td>Total distribution</td><td>MAmax</td><td>100%</td><td>8.3%</td><td>20.3%</td><td>25.3%</td><td>6.0%</td><td>27.7%</td><td>10.8%</td></tr></tbody></table>

et al., 2024b), mostly related to desert contamination in SWAMPS (the Sahel and Australia in Fig. 2).

## 4.2 Regional spatial patterns over main basins

GIEMS-MC<sub>ISW</sub> and GIEMS-MC<sub>ISW+P</sub> data are analysed in the following sections over large wetland complexes representing different environments: the Siberian Lowland, the Sudd, the Amazon, and South-East Asia.

As expected, peatland addition noticeably amplifies the extent between GIEMS-MC<sub>ISW</sub> (MAmax of 0.19 Mkm$^2$) and GIEMS-MC<sub>ISW+P</sub> (MAmax of 0.70 Mkm$^2$) over the Ob basin (Western Siberian Lowland; Fig. 4). WAD2M and GIEMS-MC<sub>ISW+P</sub> consistently present similar patterns for MAmax (spatial correlation coefficient of 0.80). However, discrepancies occur in the southern part of the Ob basin that can be attributed to different snow filtering between SWAMPS (used for WAD2M) and GIEMS-2 (used for GIEMS-MC). The Boreal–Arctic Wetland and Lake Dataset (BAWLD) is also shown for comparison (Fig. 4), as it covers the upper part of the basin (Olefeldt et al., 2021b, a). The BAWLD wetland fraction, including peatland classes,

exhibits patterns similar to GIEMS-MC<sub>ISW+P</sub> MAmax, with a spatial correlation coefficient of 0.74. BAWLD covers a slightly larger area (+6%) compared to GIEMS-MC<sub>ISW+P</sub> MAmax when comparing the common area covered by the two datasets.

In the Sudd basin shown in Fig. 5, GIEMS-MC<sub>ISW+P</sub> extent corresponds essentially to GIEMS-MC<sub>ISW</sub>, indicating minimal presence of peatlands. For comparison, two surface water extent products, both derived from Cyclone Global Navigation Satellite System L-band remote sensing observations, are also shown (Zeiger et al., 2023; Zeiger, 2023; Gerlein-Safdi and Ruf, 2021; Gerlein-Safdi et al., 2021). Gerlein-Safdi and Ruf (2021) estimates are available for the southern part of the basin (MAmax of 0.27 Mkm$^2$ for 2018–2019) and are much higher than the GIEMS-MC (MAmax of 0.04 Mkm$^2$ for 2018–2019) and WAD2M (MAmax of 0.1 Mkm$^2$ for 2018–2019) estimates. The Zeiger (2023) product provides an MAmax of 0.06 over August 2018 to July 2019, which is within the GIEMS-MC and WAD2M estimates. While good agreement is observed in the southern part of the basin between the spatial pattern of GIEMS-MC<sub>ISW+P</sub>, WAD2M, and the product of Zeiger (2023), sig-
<table><caption>**Table 3.** Comparison of GIEMS-MC<sub>ISW</sub> and GIEMS-MC<sub>ISW+P</sub> surface extents with WAD2M (Zhang et al., 2021b) and GLWDv2 (Lehner et al., 2025a) datasets, in 10<sup>3</sup> km<sup>2</sup>. For dynamic classes, MAmax and MAmin are shown. GLWDv2<sub>ISW</sub> refers to the sum of GLWDv2 classes 8 to 21, 28, 29, 31, and 32, while GLWDv2<sub>ISW+P</sub> refers to the sum of GLWDv2 classes 8 to 29, 31, and 32. Regions correspond to the shapefiles of the HydroATLAS database (version 1.0; Linke et al., 2019).</caption><thead><tr><th></th><th></th><th>Global</th><th>Africa</th><th>Asia</th><th>Europe + Siberia</th><th>Oceania</th><th>North America</th><th>South America</th></tr></thead><tbody><tr><td rowspan="4">GIEMS-MC<sub>ISW</sub><br>(inundated and<br>saturated wetland)</td><td>LTmax</td><td>8766</td><td>1409</td><td>2097</td><td>1548</td><td>648</td><td>1594</td><td>1465</td></tr><tr><td>MAmax</td><td>3796</td><td>515</td><td>972</td><td>616</td><td>207</td><td>827</td><td>207</td></tr><tr><td>MAmean</td><td>1227</td><td>163</td><td>252</td><td>154</td><td>103</td><td>258</td><td>294</td></tr><tr><td>MAmin</td><td>251</td><td>46</td><td>29</td><td>11</td><td>46</td><td>30</td><td>85</td></tr><tr><td rowspan="3">Original GIEMS-2</td><td>MAmax</td><td>6796</td><td>631</td><td>1793</td><td>1071</td><td>339</td><td>1804</td><td>945</td></tr><tr><td>MAmean</td><td>2730</td><td>236</td><td>659</td><td>339</td><td>216</td><td>647</td><td>506</td></tr><tr><td>MAmin</td><td>795</td><td>88</td><td>149</td><td>42</td><td>133</td><td>93</td><td>218</td></tr><tr><td>GLWDv2<sub>ISW</sub> (inundated<br>and saturated wetland)</td><td>static</td><td>8223</td><td>1010</td><td>1958</td><td>1416</td><td>541</td><td>1755</td><td>1269</td></tr><tr><td rowspan="4">GIEMS-MC<sub>ISW+P</sub><br>(inundated and saturated<br>wetland + peatland)</td><td>LTmax</td><td>12 251</td><td>1512</td><td>2242</td><td>3115</td><td>915</td><td>2628</td><td>1760</td></tr><tr><td>MAmax</td><td>7745</td><td>636</td><td>1153</td><td>2369</td><td>522</td><td>1985</td><td>1001</td></tr><tr><td>MAmean</td><td>3500</td><td>295</td><td>382</td><td>888</td><td>429</td><td>790</td><td>654</td></tr><tr><td>MAmin</td><td>1308</td><td>182</td><td>96</td><td>79</td><td>377</td><td>76</td><td>449</td></tr><tr><td rowspan="3">WAD2M</td><td>MAmax</td><td>6756</td><td>1077</td><td>743</td><td>1675</td><td>678</td><td>1308</td><td>985</td></tr><tr><td>MAmean</td><td>4208</td><td>719</td><td>370</td><td>776</td><td>572</td><td>760</td><td>778</td></tr><tr><td>MAmin</td><td>2437</td><td>479</td><td>184</td><td>176</td><td>482</td><td>293</td><td>639</td></tr><tr><td>GLWDv2<sub>ISW+P</sub><br>(inundated and saturated<br>wetland + peatland)</td><td>static</td><td>12 486</td><td>1147</td><td>2156</td><td>3280</td><td>878</td><td>3023</td><td>1646</td></tr></tbody></table>

nificant disparities emerge between WAD2M and the two products in the north-eastern desert region of the Sudd basin, probably due to contamination in the original SWAMPS dataset.

Over the Amazon (Fig. 6), GIEMS-MC<sub>ISW</sub> fractions are high (> 0.5) along the main river channel, while including peatlands adds smaller surfaces along smaller secondary channels, resulting in finer spatial patterns and higher MAmax (0.31 to 0.56 Mkm<sup>2</sup>). The resulting GIEMS-MC<sub>ISW+P</sub> MAmax map closely resembles that of WAD2M (spatial correlation coefficient of 0.77), with a slightly higher MAmax for WAD2M (0.47 Mkm<sup>2</sup>). GIEMS-MC<sub>ISW</sub> and GIEMS-MC<sub>ISW+P</sub> extents can be potentially underestimated in this basin, as Fleischmann et al. (2022) found that GIEMS-2 estimates of inundation were likely slightly underestimated compared to higher-resolution remote sensing estimates based on synthetic aperture radar (SAR), with Chapman et al. (2015) finding +4 % and Rosenqvist et al. (2020) +36 % compared to GIEMS-2 long-term maximum inundation.

GIEMS-MC<sub>ISW+P</sub> and WAD2M agree well in South-East Asia (Fig. 7, with a MAmax spatial correlation coefficient of 0.67), with GIEMS-MC<sub>ISW+P</sub> showing greater peatland extent than WAD2M due to higher peatland areas estimated by GLWDv2 used in GIEMS-MC production than the earlier estimates used in WAD2M.

These findings underline the legacy of the two original microwave-based datasets (GIEMS-2 and SWAMPS) used respectively in GIEMS-MC and WAD2M production, despite the corrections. Indeed, methodological disparities between GIEMS-2 and SWAMPS production may lead to distinct spatial inundation detection patterns, particularly in regions where contamination from ocean, desert, and snow needs careful consideration.

## 4.3 Temporal seasonal and inter-annual variations

The temporal dynamics of GIEMS-2 were extensively examined in Prigent et al. (2020) and evaluated in Bernard et al. (2024b), where it was compared with other hydrological observations, including MODIS-derived surface water extent (Frappart et al., 2018; Normandin et al., 2018, 2024), CYGNSS-derived (Zeiger et al., 2023) surface water extent, and river discharge. The evaluation showed that GIEMS-2 reliably captures temporal variations, including seasonality and inter-annual variabilities, even in regions with dense vegetation cover (Figs. 8 and 9).

### 4.3.1 Seasonal variations

The 2000–2020 mean seasonality of GIEMS-MC<sub>ISW</sub>, GIEMS-MC<sub>ISW+P</sub>, and WAD2M over the Ob, the Sudd,
<figure><img src="image_5.png" /><figcaption>**Figure 2.** Global distribution of the MAmax of GIEMS-MC<sub>ISW</sub>, GIEMS-MC<sub>ISW+P</sub>, and WAD2M (Zhang et al., 2021b), as well as the difference in MAmax from GIEMS-MC<sub>ISW+P</sub> and WAD2M. Refer to Fig. S1 for maps with MAmin.</figcaption></figure>

the Amazon, and the Congo basins and South-East Asia are presented in Fig. 8 (left). The seasonal variations of the GIEMS-MC variables are driven by the dynamics of saturated and inundated wetlands (GIEMS-MC<sub>ISW</sub>), with peatlands contributing to an offset effect between GIEMS-MC<sub>ISW</sub> and GIEMS-MC<sub>ISW+P</sub>. In the Ob, the Amazon, and the Congo basins and South-East Asia, GIEMS-MC<sub>ISW+P</sub> exhibits comparable magnitudes to WAD2M. In the Sudd region, WAD2M shows distinct seasonality to GIEMS-MC and the two CYGNSS-derived products, probably due to the SWAMPS artifacts in desert regions mentioned above.

Across latitudinal bands, the global seasonality of GIEMS-MC<sub>ISW</sub> and GIEMS-MC<sub>ISW+P</sub> is mainly driven by the boreal and temperate northern regions, due to snow cover changes (Fig. 9, left). However, notable differences in terms of seasonal cycle between GIEMS-MC<sub>ISW+P</sub> and WAD2M exist over the middle to high latitudes. Indeed, larger peat-land surfaces are included in GIEMS-MC than in WAD2M (higher amplitude), and the more widespread snow masking in GIEMS-MC in the temperate zone leads to a stronger sea-sonal cycle compared to WAD2M. The seasonal cycles over the tropics and the Southern Hemisphere are more similar be-tween GIEMS-MC<sub>ISW+P</sub> and WAD2M, but surface extents are larger in WAD2M, predominantly due to desert and ocean contamination in SWAMPS, as discussed in Sect. 4.1.

### 4.3.2 Inter-annual variations and trends

Figure 8 (right) shows the deseasonalized monthly anomalies of GIEMS-MC<sub>ISW</sub>, GIEMS-MC<sub>ISW+P</sub>, and WAD2M over different regions, while Fig. 9 (right) corresponds to lati-tudinal bands. The reference seasonality period subtracted is the 2000–2020 seasonal average. As expected, GIEMS-MC<sub>ISW</sub> and GIEMS-MC<sub>ISW+P</sub> have the same anomalies for latitudes below 30°N because the temporal dynamics come from the inundated and saturated wetlands, not from the static peatland map. For northern temperate and boreal ar-eas, the snow cover also imposes a seasonality on peatlands, which explains larger anomalies in GIEMS-MC<sub>ISW+P</sub> than in GIEMS-MC<sub>ISW</sub>. For GIEMS-MC<sub>ISW+P</sub> over the boreal region (55–90°N), a positive trend is detected over May and June (+10 × 10<sup>3</sup> km<sup>2</sup> yr<sup>-1</sup>) and September and October (+24 × 10<sup>3</sup> km<sup>2</sup> yr<sup>-1</sup>) months that can possibly be attributed to earlier snow melt and delayed snow cover arrival.

No long-term trends were found at regional scales in GIEMS-MC<sub>ISW</sub>, except for South-East Asia, where a small positive trend was found (+1.7 × 10<sup>3</sup> km<sup>2</sup> yr<sup>-1</sup>, i.e. ~ +50 × 10<sup>3</sup> km<sup>2</sup> for 30 years). This is likely mostly due to the in-creasing trend in rice paddies, which is not taken into account because only the MIRCA2000 climatology is used, and new rice paddies over the years are then aliased to wetlands over time (see Sect. 5.2.2).
<figure><img src="image_6.png" /></figure>

**Figure 3.** Latitudinal distributions of (a) the GIEMS-MC wetland variables (inundated and saturated wetlands, inundated and saturated wetlands + peatlands), (b) GIEMS-MC wetland ancillary variables (rice paddy, and the sum of open permanent water), and (c) GIEMS-MC<sub>ISW+P</sub> and WAD2M products. For dynamic variables, solid lines represent the MAmean, while coloured fillings represent the MAmax-MAmin interval. The extents are given per 1° latitudinal bin. The metrics (MAmin, MAmean, and MAmax) are calculated over the entire product availability period, i.e. from 1992 to 2020 for the GIEMS-MC variables and from 2000 to 2020 for WAD2M.

An abrupt change in WAD2M inter-annual variability am-
plitude occurs over the Amazon and Congo basins in 2009,
attributed to a change in one of the satellite data used in
SWAMPS, together with a decreasing trend also found in the
SWAMPS data (Fig. 8). Due to these problems in SWAMPS,
the inter-annual variability of WAD2M should be consid-
ered with caution and makes time-series comparison with
GIEMS-MC<sub>ISW+P</sub> difficult over these regions.

5 Discussion

The production of GIEMS-MC involves seven steps, each of
which not only contributes to the transition from inundation
time series to wetland map time series, but also to the un-
certainties of the final product. It has been estimated that the
GIEMS product possibly underestimates surface water areas
by less than 10% (Prigent et al., 2007). This value can be
used as an order of magnitude of the uncertainty in GIEMS-
2, although methodological improvements have been made
between GIEMS and GIEMS-2 (Prigent et al., 2020). This is
also likely a realistic approximation for the GIEMS-MC<sub>ISW</sub>
error, as it uses mainly GIEMS-2 information. A quantifica-
tion of the uncertainties of the GIEMS-MC variables would
require a deeper knowledge of the measurement and detec-
tion uncertainties of all the products used, some of which
are not calculated in the original source, which is beyond the
scope of this study. However, it is possible to quantify the

influence of each step in the GIEMS-MC procedure and to
study the sensitivity of the results to the processing choices.

5.1 Quantification of the influence of each process step
on the GIEMS-MC global extents

Table 4 shows the influence of the successive steps in terms
of MAmax, MAmean, and MAmin. The removal of open
permanent water (step 4) and the subtraction of rice pad-
dies (step 5) have a significant impact on the global extent,
both resulting in a subtraction of -2.34 Mkm² on MAmax
and -1.16 Mkm² on MAmean. Coastal cleaning also has a
large influence on the reduction of the area: -2.60 Mkm²
in MAmax and -1.60 Mkm² in MAmean and especially on
MAmin, which decreases from 1.17 to 0.26 Mkm². In fact,
the coastal region has an artificially high minimum value due
to ocean contamination before this cleaning step. Finally, the
addition of peatlands turns out to be an extremely signifi-
cant step in terms of surface area for GIEMS-MC<sub>ISW+P</sub>, with
large increases observed in both MAmax (+3.94 Mkm²) and
MAmean (+2.27 Mkm²) extents.

5.2 Sensitivity to the GIEMS-MC procedure

The three critical stages in the production of GIEMS-MC are
further discussed in this section, along with the use of a snow
mask.

5.2.1 Coastal processing

In the methods in Sect. 3.1.6, we chose to apply a clean-
ing procedure to coastal pixels located within 50 km of
the coast. Figure 10a shows the global mean seasonality
of GIEMS-MC<sub>ISW</sub> and GIEMS-MC<sub>ISW+P</sub> when consider-
ing coastal bands ranging between 0 and 100 km from the
coast to be processed using GLWDv2 information, following
the methodology in Sect. 3. After removing the pixels with
more than 10 % ocean, the cleaning of the coastal band up
to 30 km from the coast reduces GIEMS-MC<sub>ISW</sub> MAmax by
0.7 Mkm². Cleaning also the 30–50 km band reduces it fur-
ther by 0.5 Mkm², while the 50–70 km (−0.12 Mkm²) and
70–100 km (−0.08 Mkm²) bands have smaller effects. The
cleaning over 50 km is consistent with our technical under-
standing of the contamination.

5.2.2 Rice subtraction

An issue concerning rice in GIEMS-MC production stems
from the classification used in the MIRCA2000 dataset,
which separates rice paddies into irrigated and rainfed types.
Irrigated paddies are typically fully inundated at least part
of the year. Rainfed paddies have variable levels of submer-
gence, with approximately 80 % inundated and 20 % remain-
ing upland paddies (Maclean et al., 2013). Under these condi-
tions, the upland rice (9 % of total rice paddies area; Maclean
et al., 2013) should not be subtracted from GIEMS-2 in the
<figure><img src="image_7.png" /><figcaption>**Figure 4.** MAmax and MAmin maps of GIEMS-MC<sub>ISW</sub> (1992 to 2020), GIEMS-MC<sub>ISW+P</sub> (1992 to 2020), and WAD2M (2000 to 2020) over the Ob, as well as GLWDv2 peatland (static) and BAWLD wetland (static) maps. Low MAmin values are due to the snow mask.</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>**Figure 5.** MAmax and MAmin maps over the Sudd region of GIEMS-MC<sub>ISW</sub>, GIEMS-MC<sub>ISW+P</sub> (1992 to 2020), and WAD2M (2000 to 2020) and CYGNSS-based estimates from Gerlein-Saftti et al. (2021) (June 2017 to April 2020) and Zeiger et al. (2023) (August 2018 to July 2020). The available periods differ between the products.</figcaption></figure>

GIEMS-MC processing steps if we could distinguish upland rice from the rest of the rainfed paddies. To explore this, we attempted a classification based on topographic information to separate inundated from non-inundated within the rainfed rice category. The resulting maps, shown in Table S3, lead to surface extent inconsistent with FAO statistics per country (FAO, 2002). In the absence of any reliable distinction possibility, the total rice paddies were subtracted, acknowledging that the subtracted area might be overestimated by about 9%. Note that this light overestimation of subtracted rice paddies might be counterbalanced by the fact that MIRCA2000 areas estimates are underestimated when compared to FAOSTAT (Fig. 11).

In WAD2M production, the MIRCA2000 is also used to differentiate rice paddies from wetlands, but only irrigated paddy class is subtracted (Zhang et al., 2021b). To evaluate the impact of rice handling in GIEMS-MC, Fig. 10b shows global seasonality of GIEMS-MC<sub>ISW</sub> and GIEMS-MC<sub>ISW+P</sub> if only irrigated rice paddies are subtracted or if both irrigated and rainfed are subtracted. The difference occurs mainly between June and October, in the Northern Hemisphere sum-

mer, corresponding to a difference of 0.25 Mkm² in terms of MAmax (6% of GIEMS-MC<sub>ISW</sub> and 3% GIEMS-MC<sub>ISW+P</sub> MAmax). While this has a moderate influence on global extent, this difference can be important in rice-cultivating countries, e.g. a difference of 30% in GIEMS-MC<sub>ISW</sub> MAmax over India depending on the type of subtraction used. For GIEMS-MC<sub>ISW+P</sub>, as the total surfaces are higher, the influence of rice paddy subtraction is proportionally less important.

Finally, subtracting the MIRCA2000 climatology in the GIEMS-MC processing and not taking into account the inter-annual variation of rice paddies and their inter-annual changes in inundation over the period 1992–2020 can lead to misclassification of rice paddies as wetlands. First, rainfed rice paddies in particular show significant inter-annual changes in inundation. Furthermore, the surface covered by rice paddies also changes over the years. The MIRCA2000 product is compared in Fig. 11 with the estimates from the Food and Agriculture Organization (FAO) of the United Nations FAOSTAT (https://www.fao.org/faostat/en/#data/QCL, last access: 30 June 2023). FAOSTAT is widely used for
<figure><img src="image_9.png" /><figcaption>**Figure 6.** MAmax and MAmin maps of GIELMS-MC<sub>ISW</sub> (1992 to 2020), GIELMS-MC<sub>ISW+P</sub> (1992 to 2020), and WAD2M (2000 to 2020) over the Amazon basin.</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>**Figure 7.** MAmax and MAmin maps of GIELMS-MC<sub>ISW</sub> (1992 to 2020), GIELMS-MC<sub>ISW+P</sub> (1992 to 2020), and WAD2M (2000 to 2020) over South-East Asia.</figcaption></figure>

global estimates of methane emissions from rice paddies, notably in the Emissions Database for Global Atmospheric Research (EDGAR; Janssens-Maenhout et al., 2019). The crop-land area of rice paddies is increasing in South-East Asia, with FAOSTAT estimating +60 × 10³ km² between 1992 and 2020 in this region, which corresponds to the increasing trend of ~ +50 × 10³ km² in GIELMS-MC<sub>ISW</sub> over this period (Sect. 4.3.2).

MIRCA2000 (MAmax of 1.25 Mkm²) presents smaller rice paddy extent than FAOSTAT (1.47 Mkm² in 1992 to 1.64 Mkm² in 2020). In GLWDv2, the map from Salmon et al. (2015) is used as primary information but undergoes numerous corrections related to artifacts in the product, including double-checking information using the RiceAtlas (Laborte et al., 2017). This led to a static map of rice paddies of 1.2 Mkm², close to MIRCA2000 MAmax estimates. Then, various inventories of anthropogenic methane emissions that account for rice methane emissions do not use the same maps for rice paddies, which can lead to mismatches

across the estimates (surfaces double counting or miscounting). Efforts to use similar compatible rice maps between the two research communities would greatly improve the consistency of wetland time series and then the methane emission estimates. A dynamic map that accurately reflects the temporal variation of inundated rice paddies would better meet the needs of remote sensing wetland mapping. This approach would address the limitations of existing classifications, such as MIRCA's irrigated/rainfed or FAO's yearly irrigated/rainfed/upland categories, which do not adequately address the specific needs of the community.

### 5.2.3 Peatland integration

Peatlands contribute to more than half of areas in GIELMS-MC<sub>ISW+P</sub> (Table 4) and depend highly on the GLWDv2 peatland map used here.

Most of the peatlands are not saturated or inundated areas, although some can have their water table above the peat
<figure><img src="image_11.png" /><figcaption>**Figure 8.** Left: monthly mean seasonal cycle of GIEMS-MC<sub>ISW</sub>, GIEMS-MC<sub>ISW+P</sub>, and WAD2M over different regions. Right: deseasonalized monthly anomalies of the same three variables. To derive the deseasonalized monthly anomalies, the average monthly seasonal cycle was subtracted from the long-term monthly time series. For the Sudd basin seasonality comparison, estimations from Gerlein-Safdi et al. (2021) (June 2017 to April 2020) and Zeiger et al. (2023) (August 2018 to July 2020) are added.</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>**Figure 9.** Left: monthly mean seasonal cycle of GIEMS-MC<sub>ISW</sub>, GIEMS-MC<sub>ISW+P</sub>, and WAD2M over different latitudinal bands. Right: deseasonalized monthly anomalies of the same three variables. To derive the deseasonalized monthly anomalies, the average monthly seasonal cycle was subtracted from the long-term monthly time series.</figcaption></figure>

surface intermittently (Lourenco et al., 2023). A large part of peatlands are then not detected by GIEMS-2. Three ways of integrating GLWDv2 peatlands were tested to assess how sensitive the peatland integration method is. For each pixel *i*, we did the following:

1. Set GLWDv2<sub>peat</sub> fraction as the minimum of GIEMS-MC<sub>ISW+P</sub>, minimizing GIEMS-MC<sub>ISW+P</sub> areas: if GIEMS-MC<sub>ISW</sub> < GLWDv2<sub>peat</sub>, then GIEMS-MC<sub>ISW+P</sub> = GLWDv2<sub>peat</sub>.
<table><caption>**Table 4.** Global MAmax, MAmean, and MAmin (in Mkm²) after each step of GIEMS-MC production. It should be noted that snow and oceans were already set to zero fraction in GIEMS-2 and that the snow mask is applied to all wetlands, including peatlands, and is then responsible for the low MAmin values.</caption><thead><tr><th>Step</th><th>Step description</th><th>MAmax</th><th>MAmean</th><th>MAmin</th></tr></thead><tbody><tr><td>0</td><td>GIEMS-2 revised version</td><td>9.02</td><td>4.15</td><td>1.63</td></tr><tr><td>1 to 3</td><td>After masking oceans, snow, and urban areas</td><td>8.83</td><td>4.03</td><td>1.58</td></tr><tr><td>4</td><td>After subtracting open permanent water</td><td>6.89</td><td>3.06</td><td>1.23</td></tr><tr><td>5</td><td>After subtracting rice paddies</td><td>6.30</td><td>2.77</td><td>1.12</td></tr><tr><td>6</td><td>After cleaning coasts (GIEMS-MC<sub>ISW</sub>)</td><td>3.80</td><td>1.23</td><td>0.25</td></tr><tr><td>7</td><td>After adding peatlands (GIEMS-MC<sub>ISW+P</sub>)</td><td>7.75</td><td>3.50</td><td>1.31</td></tr></tbody></table>

<figure><img src="image_13.png" /><figcaption>**Figure 10.** Sensitivity of GIEMS-MC averaged seasonality to the different steps of the production procedure: (a) the coastal threshold for coastal cleaning, (b) the rice procedure, and (c) the way peatlands are added. Solid lines represent the extent of GIEMS-MC<sub>ISW</sub>, while dashed lines represent GIEMS-MC<sub>ISW+P</sub>. Colours show the different GIEMS-MC treatments.</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>**Figure 11.** Rice paddy surface extent estimations from MIRCA2000 (Portmann et al., 2010b), FAOSTAT (https://www.fao.org/faostat/en/#data/QCL, last access: 30 June 2023), and GLWDv2 (Lehner et al., 2025a). MIRCA2000 MAmax is higher than the maximum of the MIRCA2000 seasonality plot because not all pixels have their maximum in the same month.</figcaption></figure>

2. Attempt to add only the peatlands not detected by GIEMS-2 as described in the methods in Sect. 3.1.7:
GIEMS-MC<sub>ISW+P<sub>i</sub></sub> = GIEMS-MC<sub>ISW<sub>i</sub></sub> + pos[ GLWDv2<sub>peat<sub>i</sub></sub> - pos(GIEMS-MC<sub>ISW<sub>i</sub></sub> - GLWDv2<sub>ISW+P<sub>i</sub></sub>)].

3. Add all GLWDv2 peatland, maximizing GIEMS-MC<sub>ISW+P</sub> areas:

$$GIEMS-MC_{ISW+P_i} = GIEMS-MC_{ISW_i} + GLWDv2_{peat_i}.$$

The effects of these three peatland integration approaches on GIEMS-MC<sub>ISW+P</sub> extent are represented in Fig. 10c. A difference of 0.85 Mkm² (11%) is found for GIEMS-MC<sub>ISW+P</sub> MAmax between the two extreme approaches 1 and 3. Approach 1 likely underestimates peatland integration, as some pixels can contain both inundated or saturated wetlands and peatlands. Approach 3 likely overestimates peatland integration, as some peatlands (inundated and saturated) should be detected in GIEMS-2. Method 2 appears as a sensible consensus, but this approach also likely overestimates peatland surfaces, as GLWDv2 wetland categories, used to discriminate detected and non-detected peatlands by GIEMS-2 (see Sect. 3.1.7), are a long-term maximum.

Non-inundated peatlands account for a significant proportion of the GIEMS-MC<sub>ISW+P</sub> in terms of maximum area (50.2% of the MAmax; see Table 2). The peatland map used in this study to derive GIEMS-MC<sub>ISW+P</sub> comes from GLWDv2 and is a composite product based on four different estimates (see Sect. 3.1.7 or Lehner et al., 2025a). It should be emphasized that although this product is based on the best current knowledge, there is still limited consensus in the literature on the extent of peatlands. Mapping of peatlands remains challenging, as it is primarily a soil characteristic re-
lated to organic carbon content that cannot be directly de-
tected by satellite data. Consequently, mapping efforts rely
on approaches based on observations (in situ data and/or re-
mote sensing proxies for, for example, vegetation or hydro-
logical data) to map their extent using models or machine
learning. This task is particularly difficult in tropical regions,
where in situ data are sparse and where dense cloud cover
and vegetation cover limit remote sensing observations; this
results in fewer regional maps over the tropics, with frequent
revisions towards higher estimates of tropical peatland areas
(Dargie et al., 2017; Hastie et al., 2024).

5.2.4 Snow-covered pixel masking

Due to the influence of snow on passive microwave ob-
servations, snow-covered pixels are masked in the estima-
tion of GIEMS-2 and GIEMS-MC inundation fractions (see
Sect. 3.1.2). This masking prevents models from accounting
for methane emissions from snow-covered areas. However,
cold-season methane fluxes in arctic peatlands and tundra
have been shown to contribute between 25 % and 50 % of
the annual local fluxes (Bao et al., 2021; Ito et al., 2023;
Mastepanov et al., 2008; Zona et al., 2016; Rößger et al.,
2022). Therefore, the sensitivity of microwave remote sens-
ing to snow is a limitation in boreal regions. Nevertheless, the
boreal zones are estimated to contribute only about 5 % of
annual global wetland and inland freshwater emissions using
a top-down approach and about 10 % using a bottom-up ap-
proach (Saunois et al., 2025), with only up to half of these bo-
real emissions potentially occurring during the cold season.
Consequently, the exclusion of snow-covered areas is likely
to add only a few percent of uncertainty to global methane
emissions from wetlands and inland waters.

For consistency with the snow mask used in GIEMS-2 pro-
duction, we have used the same mask here in the GIEMS-
MC generation. This mask is derived from the ERA5 prod-
uct, which might overestimate the extent of snow cover but
still captures interannual changes well (Kouki et al., 2023).
The snow mask in GIEMS-MC is only a filter for pixels po-
tentially contaminated by the presence of snow. The potential
overestimation of snow cover extent should have limited im-
plications for methane emissions, as methane emissions in
these regions during the snow season should be a small frac-
tion of global emissions, as discussed above.

6 Perspectives

Several key areas for future improvement of the GIEMS-
MC production process were identified. First, taking into
account the inter-annual variations of rice paddies would
help improve the accuracy and in particular the long-term
trend of GIEMS-MC. Ideally, these estimates should be con-
sistent with those used in anthropogenic greenhouse gas
emission inventories such as FAOSTAT. Secondly, a better
distinction between inundated and dry peatlands would al-

low a more accurate integration of peatlands into GIEMS-
MC<sub>ISW+P</sub>. New satellite data, such as the 2022-launched
Surface Water and Ocean Topography (SWOT) with its Ka-
band radar interferometer, hold promise for the monitoring
of continental surface water area and height at high spa-
tial resolution and temporal sampling (Neeck et al., 2012;
Pedinotti et al., 2014; Biancamaria et al., 2016; Prigent et al.,
2016; Peral and Esteban-Fernandez, 2018). In particular, ei-
ther high-resolution SWOT data or water table depth from
a hydrological model combined with the 500 m GLWDv2
data could help to better distinguish the inundated from the
non-inundated peatlands to improve the integration of non-
inundated peatlands in GIEMS-MC<sub>ISW+P</sub>. In addition, the
upcoming NASA-ISRO Synthetic Aperture Radar (NISAR),
scheduled for launch in 2025, will provide high-resolution
(below 7 m) observations in L-bands and S-bands (Chuang
et al., 2016; Adeli et al., 2021). These frequencies are partic-
ularly advantageous for mapping of subcanopy inundation in
forested wetlands, as they penetrate vegetation more effec-
tively than the Ka-band used for SWOT.

GIEMS-MC dynamics are derived from GIEMS-2, which
provides valuable insights into global water surface dynam-
ics with seamless time series of surface water extent. The
continuity of GIEMS-2 production holds the potential to ex-
tend the temporal coverage of the GIEMS-MC maps. How-
ever, no new SSMIS instrument is planned to be launched
when the current instruments (F15 to F18) are decommis-
sioned. Adaptations to the GIEMS-2 process, such as the
incorporation of Advanced Microwave Scanning Radiome-
ter (AMSR) data, will be required to extend the observa-
tion period despite critical changes in satellite overpassing
local time and spatial resolution. Other passive microwave
future missions are expected to cover all or part of the SS-
MIS microwave frequency range, such as the MicroWave Im-
ager (MetOp-SG; D'Addio et al., 2014) or the Copernicus
Imaging Microwave Radiometer (CIMR; Vanin et al., 2020),
offering alternative data sources for GIEMS-MC production
but also requiring adjustment to the methodology. In addi-
tion, plans to increase the temporal resolution of GIEMS-2
to a 10 d data record could provide a more detailed under-
standing of wetland dynamics over time.

7 Data availability

The GIEMS-MC dataset in NetCDF for-
mat and its documentation are available at
https://doi.org/10.5281/zenodo.13919644 (Bernard et al.,
2024a).

8 Conclusion

Despite significant advances in methane measurement and
modelling, accurate mapping of wetland extent remains
a key challenge. In this context, we introduce GIEMS-
MethaneCentric (GIEMS-MC). It is a new product that im-
proves the temporal variability of the wetland extent by accu-
rately capturing seasonal and interannual dynamics without
discontinuities while also enhancing spatial patterns.

GIEMS-2 product was combined with other information
to produce GIEMS-MC, a dataset containing spatially and
dynamically consistent maps of different methane-emitting
aquatic ecosystems. In particular, the GLWDv2 dataset en-
ables the separation of open water surfaces (lakes, rivers,
reservoirs) in GIEMS-2, as well as the addition of peat-
lands not detected by microwave satellite observations used
in GIEMS-2 production. Rice paddies are identified using
the MIRCA2000 product. Updated coastal zone filtering im-
proves the previous complete masking in the distributed ver-
sion of GIEMS-2.

GIEMS-MC provides two harmonized times series maps at 0.25° × 0.25° and monthly time steps of wetland surfaces from 1992 to 2020: one representing inundated and saturated wetlands and the other covering all wetlands, including peatlands. In addition, GIEMS-MC provides consistent maps of rice paddies and categories of open permanent water. Information on the dominant vegetation and wetland type for each pixel is also provided, as these factors help improve the understanding and accurate modelling of methane emissions from wetlands.

This comprehensive database will hopefully set a new
standard for harmonizing and consistently mapping methane
emission from the different aquatic ecosystems.

**Supplement.** The supplement related to this article is available online at https://doi.org/10.5194/essd-17-2985-2025-supplement.

**Author contributions.** JB, CP, CJ, MS, and EF-C conceived the main ideas of this study. CJ and CP developed and produced the GIEMS-2 data. BL provided the GLWDv2 product. JB, CJ, and CP built the database and performed the numerical analyses. JB drafted the manuscript with input from CP, MS, and EF-C All authors provided critical feedback and expertise on the manuscript.

**Competing interests.** The contact author has declared that none of the authors has any competing interests.

**Disclaimer.** Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors.

**Acknowledgements.** The authors would like to thank Pierre Zeiger and Frédéric Frappart for the discussions on their CYGNSS-

derived wetland extent dataset. The authors thank John Melack and
the two anonymous referees for their comments and suggestions.

**Financial support.** Juliette Bernard is funded by a PhD grant from the Institut National des Sciences de l'Univers (INSU) of the Centre National de la Recherche Scientifique (CNRS). Partial funding has been provided by the ESA CCI RECAPP2 project (grant no. 4000123002/18/INB), and a preliminary version of this database has been developed under that contract. The Agence Nationale de la Recherche also provided support through the project Advanced Methane Budget through Multi-constraints and Multi-data streams Modelling (grant no. AMB-M3-ANR-21-CE01-0030). Etienne Fluet-Chouinard was supported by COMPASS-FME, a multi-institutional project supported by the U.S. Department of Energy, Office of Science, Biological and Environmental Research as part of the Environmental System Science Program.

**Review statement.** This paper was edited by Hanqin Tian and reviewed by John Melack and two anonymous referees.

**References**

Adeli, S., Salehi, B., Mahdianpari, M., Quackenbush, L. J., and Chapman, B.: Moving Toward L-Band NASA-ISRO SAR Mission (NISAR) Dense Time Series: Multipolarization Object-Based Classification of Wetlands Using Two Machine Learning Algorithms, Earth Space Sci., 8, e2021EA001742, https://doi.org/10.1029/2021EA001742, 2021.

Aires, F., Miolane, L., Prigent, C., Pham, B., Fluet-Chouinard, E., Lehner, B., and Papa, F.: A Global Dynamic Long-Term Inundation Extent Dataset at High Spatial Resolution Derived through Downscaling of Satellite Observations, J. Hydrometeorol., 18, 1305–1325, https://doi.org/10.1175/JHM-D-16-0155.1, 2017.

Allen, G. H. and Pavelsky, T. M.: Global Extent of Rivers and Streams, Science, 361, 585–588, https://doi.org/10.1126/science.aat0636, 2018.

Bao, T., Xu, X., Jia, G., Billesbach, D. P., and Sullivan, R. C.: Much Stronger Tundra Methane Emissions during Autumn Freeze than Spring Thaw, Global Change Biol., 27, 376–387, https://doi.org/10.1111/gcb.15421, 2021.

Bartholomé, E. and Belward, A. S.: GLC2000: A New Approach to Global Land Cover Mapping from Earth Observation Data, Int. J. Remote Sens., 26, 1959–1977, https://doi.org/10.1080/01431160412331291297, 2005.

Bernard, J., Prigent, C., Jimenez, C., Fluet-Chouinard, E., Lehner, B., Salmon, E., Ciais, P., Zhen, Z., Peng, S., and Saunois, M.: GIEMS-MethaneCentric, Zenodo [data set], https://doi.org/10.5281/zenodo.13919644, 2024a.

Bernard, J., Prigent, C., Jimenez, C., Frappart, F., Normandin, C., Zeiger, P., Xi, Y., and Peng, S.: Assessing the Time Variability of GIEMS-2 Satellite-Derived Surface Water Extent over 30 Years, Front. Remote Sens., 5, 1399234, https://doi.org/10.3389/frsen.2024.1399234, 2024b.

Biancamaria, S., Lettenmaier, D. P., and Pavelsky, T. M.: The SWOT Mission and Its Capabilities for Land Hydrology,
Surv. Geophys., 37, 307–337, https://doi.org/10.1007/s10712-015-9346-y, 2016.

Bousquet, P., Ciais, P., Miller, J. B., Dlugokencky, E. J., Hauglustaine, D. A., Prigent, C., Van Der Werf, G. R., Peylin, P., Brunke, E.-G., Carouge, C., Langenfelds, R. L., Lathière, J., Papa, F., Ramonet, M., Schmidt, M., Steele, L. P., Tyler, S. C., and White, J.: Contribution of Anthropogenic and Natural Sources to Atmospheric Methane Variability, Nature, 443, 439–443, https://doi.org/10.1038/nature05132, 2006.

Bridgham, S. D., Cadillo-Quiroz, H., Keller, J. K., and Zhuang, Q.: Methane Emissions from Wetlands: Biogeochemical, Microbial, and Modeling Perspectives from Local to Global Scales, Global Change Biol., 19, 1325–1346, https://doi.org/10.1111/gcb.12131, 2013.

Copernicus Climate Change Service: ERA5 hourly data on single levels from 1950 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS), https://doi.org/10.24381/CDS.68D2BB30, 2019.

Canadell, J., Monteiro, P., Costa, M., Cotrim da Cunha, L., Cox, P., Eliseev, A., Henson, S., Ishii, M., Jaccard, S., Koven, C., Lohila, A., Patra, P., Piao, S., Rogelj, J., Syampungani, S., Zahle, S., and Zickfeld, K.: Global Carbon and other Biogeochemical Cycles and Feedbacks, in: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., book Sect. 5, Cambridge University Press, Cambridge, UK and New York, NY, USA, https://doi.org/10.1017/9781009157896.007, 2021.

Carroll, M., Townshend, J., DiMiceli, C., Noojipady, P., and Sohlberg, R.: A New Global Raster Water Mask at 250 m Resolution, Int. J. Digital Earth, 2, 291–308, https://doi.org/10.1080/17538940902951401, 2009.

Chapman, B., McDonald, K., Shimada, M., Rosenqvist, A., Schroeder, R., and Hess, L.: Mapping Regional Inundation with Spaceborne L-Band SAR, Remote Sens., 7, 5440–5470, https://doi.org/10.3390/rs70505440, 2015.

Chuang, C.-L., Shaffer, S., Niamsuwan, N., Li, S., Liao, E., Lim, C., Duong, V., Volain, B., Vines, K., Yang, M.-W., and Wheeler, K.: NISAR L-band Digital Electronics Subsystem: A Multichannel System with Distributed Processors for Digital Beam Forming and Mode Dependent Filtering, in: 2016 IEEE Radar Conference (RadarConf), 1–5 pp., IEEE, Philadelphia, PA, USA, ISBN 978-1-5090-0863-6, https://doi.org/10.1109/RADAR.2016.7485225, 2016.

D'Addio, S., Kangas, V., Klein, U., Loiselet, M., and Mason, G.: The Microwave Radiometers On-Board MetOp Second Generation Satellites, in: 2014 IEEE Metrology for Aerospace (MetroAeroSpace), 599–604 pp., IEEE, Benevento, Italy, ISBN 978-1-4799-2069-3, https://doi.org/10.1109/MetroAeroSpace.2014.6865995, 2014.

Dargie, G. C., Lewis, S. L., Lawson, I. T., Mitchard, E. T. A., Page, S. E., Bocko, Y. E., and Ifo, S. A.: Age, Extent and Carbon Storage of the Central Congo Basin Peatland Complex, Nature, 542, 86–90, https://doi.org/10.1038/nature21048, 2017.

Defourny, P., Lamarche, C., Bontemps, S., De Maet, T., Van Boogaert, E., Moreau, I., Brockmann, C., Boettcher, M., Kirches, G., Wevers, J., Santoro, M., Ramoino, F., and Arino, O.: Land Cover Climate Change Initiative – Product User Guide v2, Issue 2.0, http://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf (last access: 7 June 2025), 2017.

Di Gregorio, A. and Jansen, L. J.: Land Cover Classification System (LCCS): Classification Concepts and User Manual, https://www.fao.org/3/x0596e/x0596e00.htm (last access: 16 September 2024), 2005.

FAO: FAO Rice Information, Volume 3, FAO, https://www.fao.org/4/y4347e/y4347e00.htm (last access: 7 June 2025), 2002.

Feng, M., Sexton, J. O., Channan, S., and Townshend, J. R.: A Global, High-Resolution (30-m) Inland Water Body Dataset for 2000: First Results of a Topographic-Spectral Classification Algorithm, Int. J. Digital Earth, 9, 113–133, https://doi.org/10.1080/17538947.2015.1026420, 2016.

Fennig, K., Schröder, M., Andersson, A., and Hollmann, R.: A Fundamental Climate Data Record of SMMR, SSM/I, and SS-MIS brightness temperatures, Earth Syst. Sci. Data, 12, 647–681, https://doi.org/10.5194/essd-12-647-2020, 2020.

Feron, S., Malhotra, A., Bansal, S., Fluet-Chouinard, E., McNicol, G., Knox, S. H., Delwiche, K. B., Cordero, R. R., Ouyang, Z., Zhang, Z., Poulter, B., and Jackson, R. B.: Recent Increases in Annual, Seasonal, and Extreme Methane Fluxes Driven by Changes in Climate and Vegetation in Boreal and Temperate Wetland Ecosystems, Global Change Biol., 30, e17131, https://doi.org/10.1111/gcb.17131, 2024.

Fleischmann, A. S., Papa, F., Fassoni-Andrade, A., Melack, J. M., Wongchuig, S., Paiva, R. C. D., Hamilton, S. K., Fluet-Chouinard, E., Barbedo, R., Aires, F., Al Bitar, A., Bonnet, M.-P., Coe, M., Ferreira-Ferreira, J., Hess, L., Jensen, K., McDonald, K., Ovando, A., Park, E., Parrens, M., Pinel, S., Prigent, C., Resende, A. F., Revel, M., Rosenqvist, A., Rosenqvist, J., Rudorff, C., Silva, T. S. F., Yamazaki, D., and Collischonn, W.: How Much Inundation Occurs in the Amazon River Basin?, Remote Sens. Environ., 278, 113099, https://doi.org/10.1016/j.rse.2022.113099, 2022.

Fluet-Chouinard, E., Lehner, B., Rebelo, L.-M., Papa, F., and Hamilton, S. K.: Development of a Global Inundation Map at High Spatial Resolution from Topographic Downscaling of Coarse-Scale Remote Sensing Data, Remote Sens. Environ., 158, 348–361, https://doi.org/10.1016/j.rse.2014.10.015, 2015.

Foster, J. L., Hall, D. K., Chang, A. T. C., and Rango, A.: An Overview of Passive Microwave Snow Research and Results, Rev. Geophys., 22, 195–208, https://doi.org/10.1029/RG022i002p00195, 1984.

Frappart, F., Biancamaria, S., Normandin, C., Blarel, F., Bourrel, L., Aumont, M., Azemar, P., Vu, P.-L., Le Toan, T., Lubac, B., and Darrozes, J.: Influence of Recent Climatic Events on the Surface Water Storage of the Tonle Sap Lake, Sci. Total Environ., 636, 1520–1533, https://doi.org/10.1016/j.scitotenv.2018.04.326, 2018.

Friedl, M., McIver, D., Hodges, J., Zhang, X., Muchoney, D., Strahler, A., Woodcock, C., Gopal, S., Schneider, A., Cooper, A., Baccini, A., Gao, F., and Schaaf, C.: Global Land Cover Mapping from MODIS: Algorithms and Early Results, Remote
Sens. Environ., 83, 287–302, https://doi.org/10.1016/S0034-4257(02)00078-0, 2002.

Ge, M., Korrensalo, A., Laiho, R., Kohl, L., Lohila, A., Pihlatie, M., Li, X., Laine, A. M., Anttila, J., Putkinen, A., Wang, W., and Koskinen, M.: Plant-Mediated CH₄ Exchange in Wetlands: A Review of Mechanisms and Measurement Methods with Implications for Modelling, Sci. Total Environ., 914, 169662, https://doi.org/10.1016/j.scitotenv.2023.169662, 2024.

Gerlein-Safdi, C. and Ruf, C.: CYGNSS-based inundation maps of the Pantanal and the Sudd wetlands from June 2017 to December 2019, Zenodo, https://doi.org/10.5281/ZENODO.5621107, 2021.

Gerlein-Safdi, C., Bloom, A. A., Plant, G., Kort, E. A., and Ruf, C. S.: Improving Representation of Tropical Wetland Methane Emissions With CYGNSS Inundation Maps, Global Biogeochem. Cy., 35, 12, https://doi.org/10.1029/2020GB006890, 2021.

Girkin, N. T., Siegenthaler, A., Lopez, O., Stott, A., Ostle, N., Gauci, V., and Sjögersten, S.: Plant Root Carbon Inputs Drive Methane Production in Tropical Peatlands, Sci. Rep., 15, 3244, https://doi.org/10.1038/s41598-025-87467-w, 2025.

Gumbricht, T., Roman-Cuesta, R. M., Verchot, L., Herold, M., Wittmann, F., Householder, E., Herold, N., and Murdiyarso, D.: An Expert System Model for Mapping Tropical Wetlands and Peatlands Reveals South America as the Largest Contributor, Global Change Biol., 23, 3581–3599, https://doi.org/10.1111/gcb.13689, 2017.

Harper, K. L., Lamarche, C., Hartley, A., Peylin, P., Ottlé, C., Bastrikov, V., San Martín, R., Bohnenstengel, S. I., Kirches, G., Boettcher, M., Shevchuk, R., Brockmann, C., and Defourny, P.: A 29-year time series of annual 300 m resolution plant-functional-type maps for climate models, Earth Syst. Sci. Data, 15, 1465–1499, https://doi.org/10.5194/essd-15-1465-2023, 2023.

Hastie, A., Householder, J. E., Honorio Coronado, E. N., Hidalgo Pizango, C. G., Herrera, R., Lähteenjoja, O., De Jong, J., Winton, R. S., Aymard Corredor, G. A., Reyna, J., Montoya, E., Paukku, S., Mitchard, E. T. A., Åkesson, C. M., Baker, T. R., Cole, L. E. S., Córdova Oroche, C. J., Dávila, N., Águila, J. D., Draper, F. C., Fluet-Chouinard, E., Grández, J., Janovec, J. P., Reyna, D., W Tobler, M., Del Castillo Torres, D., Roucoux, K. H., Wheeler, C. E., Fernandez Piedade, M. T., Schöngart, J., Wittmann, F., Van Der Zon, M., and Lawson, I. T.: A New Data-Driven Map Predicts Substantial Undocumented Peatland Areas in Amazonia, Environ. Res. Lett., 19, 094019, https://doi.org/10.1088/1748-9326/ad677b, 2024.

Hengl, T., Mendes de Jesus, J., Heuvelink, G. B. M., Ruiperez Gonzalez, M., Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M. N., Geng, X., Bauer-Marschallinger, B., Guevara, M. A., Vargas, R., MacMillan, R. A., Batjes, N. H., Leenaars, J. G. B., Ribeiro, G., Wheeler, I., Mantel, S., and Kempen, B.: SoilGrids250m: Global Gridded Soil Information Based on Machine Learning, PLOS ONE, 12, e0169748, https://doi.org/10.1371/journal.pone.0169748, 2017.

Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S.,

Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., De Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 Global Reanalysis, Q. J. Roy. Meteorol. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.

Hu, S., Niu, Z., and Chen, Y.: Global Wetland Datasets: A Review, Wetlands, 37, 807–817, https://doi.org/10.1007/s13157-017-0927-z, 2017.

Hugelius, G., Tarnocai, C., Broll, G., Canadell, J. G., Kuhry, P., and Swanson, D. K.: The Northern Circumpolar Soil Carbon Database: spatially distributed datasets of soil coverage and soil carbon storage in the northern permafrost regions, Earth Syst. Sci. Data, 5, 3–13, https://doi.org/10.5194/essd-5-3-2013, 2013.

Ito, A., Li, T., Qin, Z., Melton, J. R., Tian, H., Kleinen, T., Zhang, W., Zhang, Z., Joos, F., Ciais, P., Hopcroft, P. O., Beerling, D. J., Liu, X., Zhuang, Q., Zhu, Q., Peng, C., Chang, K.-Y., Fluet-Chouinard, E., McNicol, G., Patra, P., Poulter, B., Sitch, S., Riley, W., and Zhu, Q.: Cold-Season Methane Fluxes Simulated by GCP-CH₄ Models, Geophys. Res. Lett., 50, e2023GL103037, https://doi.org/10.1029/2023GL103037, 2023.

Janssens-Maenhout, G., Crippa, M., Guizzardi, D., Muntean, M., Schaaf, E., Dentener, F., Bergamaschi, P., Pagliari, V., Olivier, J. G. J., Peters, J. A. H. W., van Aardenne, J. A., Monni, S., Doering, U., Petrescu, A. M. R., Solazzo, E., and Oreggioni, G. D.: EDGAR v4.3.2 Global Atlas of the three major greenhouse gas emissions for the period 1970–2012, Earth Syst. Sci. Data, 11, 959–1002, https://doi.org/10.5194/essd-11-959-2019, 2019.

Jensen, K. and Mcdonald, K.: Surface Water Microwave Product Series Version 3: A Near-Real Time and 25-Year Historical Global Indundated Area Fraction Time Series From Active and Passive Microwave Remote Sensing, IEEE Geosci. Remote Sens. Lett., 16, 1402–1406, https://doi.org/10.1109/LGRS.2019.2898779, 2019.

Kouki, K., Luojus, K., and Riihelä, A.: Evaluation of snow cover properties in ERA5 and ERA5-Land with several satellite-based datasets in the Northern Hemisphere in spring 1982–2018, The Cryosphere, 17, 5007–5026, https://doi.org/10.5194/tc-17-5007-2023, 2023.

Laborte, A. G., Gutierrez, M. A., Balanza, J. G., Saito, K., Zwart, S. J., Boschetti, M., Murty, M., Villano, L., Aunario, J. K., Reinke, R., Koo, J., Hijmans, R. J., and Nelson, A.: RiceAtlas, a Spatial Database of Global Rice Calendars and Production, Sci. Data, 4, 170074, https://doi.org/10.1038/sdata.2017.74, 2017.

Lan, X., K.W., T., and E.J., D.: Trends in globally-averaged CH₄, N₂O, and SF₆ determined from NOAA Global Monitoring Laboratory measurements, NOAA GML, https://doi.org/10.15138/P8XG-AA10, 2024.

Lehner, B. and Döll, P.: Development and Validation of a Global Database of Lakes, Reservoirs and Wetlands, J. Hydrol., 296, 1–22, https://doi.org/10.1016/j.jhydrol.2004.03.028, 2004.

Lehner, B., Beames, P., Mulligan, M., Zarfl, C., De Felice, L., Van Soesbergen, A., Thieme, M., Garcia De Leaniz, C., Anand, M., Belletti, B., Brauman, K. A., Januchowski-Hartley, S. R., Lyon, K., Mandle, L., Mazany-Wright, N., Messager, M. L., Pavelsky, T., Pekel, J.-F., Wang, J., Wen, Q., Wishart, M., Xing, T., Yang, X., and Higgins, J.: The Global Dam Watch Database of River Barrier and Reservoir Information for Large-Scale Applications, Sci. Data, 11, 1069, https://doi.org/10.1038/s41597-024-03752-9, 2024.
Lehner, B., Anand, M., Fluet-Chouinard, E., Tan, F., Aires, F., Allen, G. H., Bousquet, P., Canadell, J. G., Davidson, N., Ding, M., Finlayson, C. M., Gumbricht, T., Hilarides, L., Hugelius, G., Jackson, R. B., Korver, M. C., Liu, L., McIntyre, P. B., Nagy, S., Olefeldt, D., Pavelsky, T. M., Pekel, J.-F., Poulter, B., Prigent, C., Wang, J., Worthington, T. A., Yamazaki, D., Zhang, X., and Thieme, M.: Mapping the world's inland surface waters: an upgrade to the Global Lakes and Wetlands Database (GLWD v2), Earth Syst. Sci. Data, 17, 2277–2329, https://doi.org/10.5194/essd-17-2277-2025, 2025a.

Lehner, B., Anand, M., Fluet-Chouinard, E., Tan, F., Aires, F., Allen, G. H., Bousquet, P., Canadell, J. G., Davidson, N., Ding, M., Finlayson, C. M., Gumbricht, T., Hilarides, L., Hugelius, G., Jackson, R. B., Korver, M. C., Liu, L., McIntyre, P. B., Nagy, S., Olefeldt, D., Pavelsky, T. M., Pekel, J.-F., Poulter, B., Prigent, C., Wang, J., Worthington, T. A., Yamazaki, D., Zhang, X., and Thieme, M.: Global Lakes and Wetlands Database (GLWD) version 2.0, figshare, https://doi.org/10.6084/M9.FIGSHARE.28519994.V1, 2025b.

Linke, S., Lehner, B., Ouellet Dallaire, C., Ariwi, J., Grill, G., Anand, M., Beames, P., Burchard-Levine, V., Maxwell, S., Moidu, H., Tan, F., and Thieme, M.: Global Hydro-Environmental Sub-Basin and River Reach Characteristics at High Spatial Resolution, Sci. Data, 6, 283, https://doi.org/10.1038/s41597-019-0300-6, 2019.

Lourenco, M., Fitchett, J. M., and Woodborne, S.: Peat Definitions: A Critical Review, Prog. Phys. Geogr.-Earth Environ., 47, 506–520, https://doi.org/10.1177/03091333221118353, 2023.

Loveland, T. R., Reed, B. C., Brown, J. F., Ohlen, D. O., Zhu, Z., Yang, L., and Merchant, J. W.: Development of a Global Land Cover Characteristics Database and IGBP DISCover from 1 Km AVHRR Data, Int. J. Remote Sens., 21, 1303–1330, https://doi.org/10.1080/014311600210191, 2000.

Maclean, J., Hardy, B., and Hettel, G.: GRiSP (Global Rice Science Partnership). 2013. Rice almanac, 4th Edn., Los Baños (Philippines): International Rice Research Institute, 283 p., http://books.irri.org/9789712203008_content.pdf (last access: 6 May 2024), 2013.

Mastepanov, M., Sigsgaard, C., Dlugokencky, E. J., Houweling, S., Ström, L., Tamstorf, M. P., and Christensen, T. R.: Large Tundra Methane Burst during Onset of Freezing, Nature, 456, 628–630, https://doi.org/10.1038/nature07464, 2008.

Matthews, E. and Fung, I.: Methane Emission from Natural Wetlands: Global Distribution, Area, and Environmental Characteristics of Sources, Global Biogeochem. Cy., 1, 61–86, https://doi.org/10.1029/GB001i001p00061, 1987.

McNicol, G., Fluet-Chouinard, E., Ouyang, Z., Knox, S., Zhang, Z., Aalto, T., Bansal, S., Chang, K.-Y., Chen, M., Delwiche, K., Feron, S., Goeckede, M., Liu, J., Malhotra, A., Melton, J. R., Riley, W., Vargas, R., Yuan, K., Ying, Q., Zhu, Q., Alekseychik, P., Aurela, M., Billesbach, D. P., Campbell, D. I., Chen, J., Chu, H., Desai, A. R., Euskirchen, E., Goodrich, J., Griffis, T., Helbig, M., Hirano, T., Iwata, H., Jurasinski, G., King, J., Koepsch, F., Kolka, R., Krauss, K., Lohila, A., Mammarella, I., Nilson, M., Noormets, A., Oechel, W., Peichl, M., Sachs, T., Sakabe, A., Schulze, C., Sonnentag, O., Sullivan, R. C., Tuittila, E.-S., Ueyama, M., Vesala, T., Ward, E., Wille, C., Wong, G. X., Zona, D., Windham-Myers, L., Poulter, B., and Jackson, R. B.: Upscaling Wetland Methane Emissions From the FLUXNET-CH4

Eddy Covariance Network (UpCH4 v1.0): Model Development, Network Assessment, and Budget Comparison, AGU Adv., 4, e2023AV000956, https://doi.org/10.1029/2023AV000956, 2023.

Melton, J. R., Wania, R., Hodson, E. L., Poulter, B., Ringeval, B., Spahni, R., Bohn, T., Avis, C. A., Beerling, D. J., Chen, G., Eliseev, A. V., Denisov, S. N., Hopcroft, P. O., Lettenmaier, D. P., Riley, W. J., Singarayer, J. S., Subin, Z. M., Tian, H., Zürcher, S., Brovkin, V., van Bodegom, P. M., Kleinen, T., Yu, Z. C., and Kaplan, J. O.: Present state of global wetland extent and wetland methane modelling: conclusions from a model intercomparison project (WETCHIMP), Biogeosciences, 10, 753–788, https://doi.org/10.5194 bg-10-753-2013, 2013.

Messager, M. L., Lehner, B., Grill, G., Nedeva, I., and Schmitt, O.: Estimating the Volume and Age of Water Stored in Global Lakes Using a Geo-Statistical Approach, Nat. Commun., 7, 13603, https://doi.org/10.1038/ncomms13603, 2016.

Neeck, S. P., Lindstrom, E. J., Vaze, P. V., and Fu, L.-L.: Surface Water and Ocean Topography (SWOT) Mission, in: SPIE Remote Sensing, edited by: Meynart, R., Neeck, S. P., and Shimoda, H., p. 85330G, Edinburgh, United Kingdom, https://doi.org/10.1117/12.981151, 2012.

Nisbet, E. G., Dlugokencky, E. J., Manning, M. R., Lowry, D., Fisher, R. E., France, J. L., Michel, S. E., Miller, J. B., White, J. W. C., Vaughn, B., Bousquet, P., Pyle, J. A., Warwick, N. J., Cain, M., Brownlow, R., Zazzeri, G., Lanoisellé, M., Manning, A. C., Gloor, E., Worthy, D. E. J., Brunke, E.-G., Labuschagne, C., Wolff, E. W., and Ganesan, A. L.: Rising Atmospheric Methane: 2007–2014 Growth and Isotopic Shift, Global Biogeochem. Cy., 30, 1356–1370, https://doi.org/10.1002/2016GB005406, 2016.

Nisbet, E. G., Manning, M. R., Dlugokencky, E. J., Fisher, R. E., Lowry, D., Michel, S. E., Myhre, C. L., Platt, S. M., Allen, G., Bousquet, P., Brownlow, R., Cain, M., France, J. L., Hermansen, O., Hossaini, R., Jones, A. E., Levin, I., Manning, A. C., Myhre, G., Pyle, J. A., Vaughn, B. H., Warwick, N. J., and White, J. W. C.: Very Strong Atmospheric Methane Growth in the 4 Years 2014–2017: Implications for the Paris Agreement, Global Biogeochem. Cy., 33, 318–342, https://doi.org/10.1029/2018GB006009, 2019.

Normandin, C., Frappart, F., Lubac, B., Bélanger, S., Marieu, V., Blarel, F., Robinet, A., and Guiastrennec-Faugas, L.: Quantification of surface water volume changes in the Mackenzie Delta using satellite multi-mission data, Hydrol. Earth Syst. Sci., 22, 1543–1561, https://doi.org/10.5194/hess-22-1543-2018, 2018.

Normandin, C., Frappart, F., Bourrel, L., Diepkilé, A. T., Mougin, E., Zwarts, L., Blarel, F., Egon, F., and Wigneron, J.-P.: Quantification of Surface Water Extent and Volume in the Inner Niger Delta (IND) over 2000–2022 Using Multispectral Imagery and Radar Altimetry, Geocart. Int., 39, 2311203, https://doi.org/10.1080/10106049.2024.2311203, 2024.

Obled, C. and Zin, I.: TOPMODEL : principes de fonctionnement et application, La Houille Blanche, 90, 65–77, https://doi.org/10.1051/lhb:200401009, 2004.

Olefeldt, D., Hovemyr, M., Kuhn, M., Bastviken, D., Bohn, T., Connolly, J., Crill, P., Euskirchen, E., Finkelstein, S., Genet, H., Grosse, G., Harris, L., Heffernan, L., Helbig, M., Hugelius, G., Hutchins, R., Juutinen, S., Lara, M., Malhotra, A., Manies, K., McGuire, D., Natali, S., O'Donnell, J., Parmentier, F.-J., Räsänen, A., Schädel, C., Sonnentag, O., Strack, M., Tank, S., Treat, C., Varner, R., Virtanen, T., Warren, R., and Watts, J.:
The fractional land cover estimates from the Boreal-Arctic Wetland and Lake Dataset (BAWLD), 2021, NSF Arctic Data Center, https://doi.org/10.18739/A2C824F9X, 2021a.

Olefeldt, D., Hovemyr, M., Kuhn, M. A., Bastviken, D., Bohn, T. J., Connolly, J., Crill, P., Euskirchen, E. S., Finkelstein, S. A., Genet, H., Grosse, G., Harris, L. I., Heffernan, L., Helbig, M., Hugelius, G., Hutchins, R., Juutinen, S., Lara, M. J., Malhotra, A., Manies, K., McGuire, A. D., Natali, S. M., O'Donnell, J. A., Parmentier, F.-J. W., Räsänen, A., Schädel, C., Sonnentag, O., Strack, M., Tank, S. E., Treat, C., Varner, R. K., Virtanen, T., Warren, R. K., and Watts, J. D.: The Boreal–Arctic Wetland and Lake Dataset (BAWLD), Earth Syst. Sci. Data, 13, 5127–5149, https://doi.org/10.5194/essd-13-5127-2021, 2021b.

Pangala, S. R., Enrich-Prast, A., Basso, L. S., Peixoto, R. B., Bastviken, D., Hornibrook, E. R. C., Gatti, L. V., Marotta, H., Calazans, L. S. B., Sakuragui, C. M., Bastos, W. R., Malm, O., Gloor, E., Miller, J. B., and Gauci, V.: Large Emissions from Floodplain Trees Close the Amazon Methane Budget, Nature, 552, 230–234, https://doi.org/10.1038/nature24639, 2017.

Papa, F., Prigent, C., Durand, F., and Rossow, W. B.: Wetland Dynamics Using a Suite of Satellite Observations: A Case Study of Application and Evaluation for the Indian Subcontinent, Geophys. Res. Lett., 33, 2006GL025767, https://doi.org/10.1029/2006GL025767, 2006.

Papa, F., Güntner, A., Frappart, F., Prigent, C., and Rossow, W. B.: Variations of Surface Water Extent and Water Storage in Large River Basins: A Comparison of Different Global Data Sources, Geophys. Res. Lett., 35, 2008GL033857, https://doi.org/10.1029/2008GL033857, 2008.

Papa, F., Prigent, C., Aires, F., Jimenez, C., Rossow, W. B., and Matthews, E.: Interannual Variability of Surface Water Extent at the Global Scale, 1993–2004, J. Geophys. Res.-Atmos., 115, 2009JD012674, https://doi.org/10.1029/2009JD012674, 2010.

Pedinotti, V., Boone, A., Ricci, S., Biancamaria, S., and Mognard, N.: Assimilation of satellite data to optimize large-scale hydrological model parameters: a case study for the SWOT mission, Hydrol. Earth Syst. Sci., 18, 4485–4507, https://doi.org/10.5194/hess-18-4485-2014, 2014.

Pekel, J.-F., Cottam, A., Gorelick, N., and Belward, A. S.: High-Resolution Mapping of Global Surface Water and Its Long-Term Changes, Nature, 540, 418–422, https://doi.org/10.1038/nature20584, 2016.

Peltola, O., Vesala, T., Gao, Y., Räty, O., Alekseychik, P., Aurela, M., Chojnicki, B., Desai, A. R., Dolman, A. J., Euskirchen, E. S., Friborg, T., Göckede, M., Helbig, M., Humphreys, E., Jackson, R. B., Jocher, G., Joos, F., Klatt, J., Knox, S. H., Kowalska, N., Kutzbach, L., Lienert, S., Lohila, A., Mammarella, I., Nadeau, D. F., Nilsson, M. B., Oechel, W. C., Peichl, M., Pypker, T., Quinton, W., Rinne, J., Sachs, T., Samson, M., Schmid, H. P., Sonnentag, O., Wille, C., Zona, D., and Aalto, T.: Monthly gridded data product of northern wetland methane emissions based on upscaling eddy covariance observations, Earth Syst. Sci. Data, 11, 1263–1289, https://doi.org/10.5194/essd-11-1263-2019, 2019.

Peral, E. and Esteban-Fernandez, D.: Swot Mission Performance and Error Budget, in: IGARSS 2018–2018 IEEE International Geoscience and Remote Sensing Symposium, 8625–8628 pp., IEEE, Valencia, ISBN 978-1-5386-7150-4, https://doi.org/10.1109/IGARSS.2018.8517385, 2018.

Pham-Duc, B., Prigent, C., Aires, F., and Papa, F.: Comparisons of Global Terrestrial Surface Water Datasets over 15 Years, J. Hydrometeorol., 18, 993–1007, https://doi.org/10.1175/JHM-D-16-0206.1, 2017.

Portmann, F. T., Siebert, S., and Döll, P.: MIRCA2000, Zenodo, https://doi.org/10.5281/ZENODO.7422506, 2010a.

Portmann, F. T., Siebert, S., and Döll, P.: MIRCA2000-Global Monthly Irrigated and Rainfed Crop Areas around the Year 2000: A New High-Resolution Data Set for Agricultural and Hydrological Modeling: MONTHLY IRRIGATED AND RAINFED CROP AREAS, Global Biogeochem. Cy., 24, 1, https://doi.org/10.1029/2008GB003435, 2010b.

Poulter, B., Bousquet, P., Canadell, J. G., Ciais, P., Peregon, A., Saunois, M., Arora, V. K., Beerling, D. J., Brovkin, V., Jones, C. D., Joos, F., Gedney, N., Ito, A., Kleinen, T., Koven, C. D., McDonald, K., Melton, J. R., Peng, C., Peng, S., Prigent, C., Schroeder, R., Riley, W. J., Saito, M., Spahni, R., Tian, H., Taylor, L., Viovy, N., Wilton, D., Wiltshire, A., Xu, X., Zhang, B., Zhang, Z., and Zhu, Q.: Global Wetland Contribution to 2000–2012 Atmospheric Methane Growth Rate Dynamics, Environ. Res. Lett., 12, 094013, https://doi.org/10.1088/1748-9326/aa8391, 2017.

Prigent, C., Matthews, E., Aires, F., and Rossow, W. B.: Remote Sensing of Global Wetland Dynamics with Multiple Satellite Data Sets, Geophys. Res. Lett., 28, 4631–4634, https://doi.org/10.1029/2001GL013263, 2001.

Prigent, C., Papa, F., Aires, F., Rossow, W. B., and Matthews, E.: Global Inundation Dynamics Inferred from Multiple Satellite Observations, 1993–2000, J. Geophys. Res., 112, D12107, https://doi.org/10.1029/2006JD007847, 2007.

Prigent, C., Lettenmaier, D. P., Aires, F., and Papa, F.: Toward a High-Resolution Monitoring of Continental Surface Water Extent and Dynamics, at Global Scale: From GIEMS (Global Inundation Extent from Multi-Satellites) to SWOT (Surface Water Ocean Topography), Surv. Geophys., 37, 339–355, https://doi.org/10.1007/s10712-015-9339-x, 2016.

Prigent, C., Jimenez, C., and Bousquet, P.: Satellite-Derived Global Surface Water Extent and Dynamics Over the Last 25 Years (GIEMS-2), J. Geophys. Res.-Atmos., 125, e2019JD030711, https://doi.org/10.1029/2019JD030711, 2020.

Ringeval, B., Decharme, B., Piao, S. L., Ciais, P., Papa, F., de Noblet-Ducoudré, N., Prigent, C., Friedlingstein, P., Gouttevin, I., Koven, C., and Ducharne, A.: Modelling sub-grid wetland in the ORCHIDEE global land surface model: evaluation against river discharges and remotely sensed data, Geosci. Model Dev., 5, 941–962, https://doi.org/10.5194/gmd-5-941-2012, 2012.

Rosenqvist, J., Rosenqvist, A., Jensen, K., and McDonald, K.: Mapping of Maximum and Minimum Inundation Extents in the Amazon Basin 2014–2017 with ALOS-2 PALSAR-2 ScanSAR Time-Series Data, Remote Sens., 12, 1326, https://doi.org/10.3390/rs12081326, 2020.

Rößger, N., Sachs, T., Wille, C., Boike, J., and Kutzbach, L.: Seasonal Increase of Methane Emissions Linked to Warming in Siberian Tundra, Nat. Clim. Change, 12, 1031–1036, https://doi.org/10.1038/s41558-022-01512-4, 2022.

Salmon, J., Friedl, M. A., Frolking, S., Wisser, D., and Douglas, E. M.: Global Rain-Fed, Irrigated, and Paddy Croplands: A New High Resolution Map Derived from Remote Sensing, Crop In-
ventories and Climate Data, Int. J. Appl. Earth Observ. Geoinfo., 38, 321–334, https://doi.org/10.1016/j.jag.2015.01.014, 2015.

Saunois, M., Stavert, A. R., Poulter, B., Bousquet, P., Canadell, J. G., Jackson, R. B., Raymond, P. A., Dlugokencky, E. J., Houweling, S., Patra, P. K., Ciais, P., Arora, V. K., Bastviken, D., Bergamaschi, P., Blake, D. R., Brailsford, G., Bruhwiler, L., Carlson, K. M., Carrol, M., Castaldi, S., Chandra, N., Crevoisier, C., Crill, P. M., Covey, K., Curry, C. L., Etiope, G., Frankenberg, C., Gedney, N., Hegglin, M. I., Höglund-Isaksson, L., Hugelius, G., Ishizawa, M., Ito, A., Janssens-Maenhout, G., Jensen, K. M., Joos, F., Kleinen, T., Krummel, P. B., Langenfelds, R. L., Laruelle, G. G., Liu, L., Machida, T., Maksyutov, S., McDonald, K. C., McNorton, J., Miller, P. A., Melton, J. R., Morino, I., Müller, J., Murguia-Flores, F., Naik, V., Niwa, Y., Noce, S., O'Doherty, S., Parker, R. J., Peng, C., Peng, S., Peters, G. P., Prigent, C., Prinn, R., Ramonet, M., Regnier, P., Riley, W. J., Rosentreter, J. A., Segers, A., Simpson, I. J., Shi, H., Smith, S. J., Steele, L. P., Thornton, B. F., Tian, H., Tohjima, Y., Tubiello, F. N., Tsuruta, A., Viovy, N., Voulgarakis, A., Weber, T. S., van Weele, M., van der Werf, G. R., Weiss, R. F., Worthy, D., Wunch, D., Yin, Y., Yoshida, Y., Zhang, W., Zhang, Z., Zhao, Y., Zheng, B., Zhu, Q., Zhu, Q., and Zhuang, Q.: The Global Methane Budget 2000–2017, Earth Syst. Sci. Data, 12, 1561–1623, https://doi.org/10.5194/essd-12-1561-2020, 2020.

Saunois, M., Martinez, A., Poulter, B., Zhang, Z., Raymond, P. A., Regnier, P., Canadell, J. G., Jackson, R. B., Patra, P. K., Bousquet, P., Ciais, P., Dlugokencky, E. J., Lan, X., Allen, G. H., Bastviken, D., Beerling, D. J., Belikov, D. A., Blake, D. R., Castaldi, S., Crippa, M., Deemer, B. R., Dennison, F., Etiope, G., Gedney, N., Höglund-Isaksson, L., Holgerson, M. A., Hopcroft, P. O., Hugelius, G., Ito, A., Jain, A. K., Janardanan, R., Johnson, M. S., Kleinen, T., Krummel, P. B., Lauerwald, R., Li, T., Liu, X., McDonald, K. C., Melton, J. R., Mühle, J., Müller, J., Murguia-Flores, F., Niwa, Y., Noce, S., Pan, S., Parker, R. J., Peng, C., Ramonet, M., Riley, W. J., Rocher-Ros, G., Rosentreter, J. A., Sasakawa, M., Segers, A., Smith, S. J., Stanley, E. H., Thanwerdas, J., Tian, H., Tsuruta, A., Tubiello, F. N., Weber, T. S., van der Werf, G. R., Worthy, D. E. J., Xi, Y., Yoshida, Y., Zhang, W., Zheng, B., Zhu, Q., Zhu, Q., and Zhuang, Q.: Global Methane Budget 2000–2020, Earth Syst. Sci. Data, 17, 1873–1958, https://doi.org/10.5194/essd-17-1873-2025, 2025.

Schroeder, R., McDonald, K., Chapman, B., Jensen, K., Podest, E., Tessler, Z., Bohn, T., and Zimmermann, R.: Development and Evaluation of a Multi-Year Fractional Surface Water Data Set Derived from Active/Passive Microwave Remote Sensing Data, Remote Sens., 7, 16688–16732, https://doi.org/10.3390/rs71215843, 2015.

Szopa, S., Naik, V., Adhikary, B., Artaxo, P., Berntsen, T., Collins, W., Fuzzi, S., Gallardo, L., Kiendler-Scharr, A., Klimont, Z., Liao, H., Unger, N., and Zanis, P.: Short-Lived Climate Forcers, in: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Conors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., book Sect. 6, Cambridge University Press, Cambridge, UK and New York, NY, USA, https://doi.org/10.1017/9781009157896.008, 2021.

Thornton, B. F., Wik, M., and Crill, P. M.: Double-counting Challenges the Accuracy of High-latitude Methane Inventories, Geophys. Res. Lett., 43, 12569–12577, https://doi.org/10.1002/2016GL071772, 2016.

Tootchi, A., Jost, A., and Ducharne, A.: Multi-source global wetland maps combining surface water imagery and groundwater constraints, Earth Syst. Sci. Data, 11, 189–220, https://doi.org/10.5194/essd-11-189-2019, 2019.

Tuanmu, M.-N. and Jetz, W.: A Global 1-km Consensus Land-cover Product for Biodiversity and Ecosystem Modelling, Global Ecol. Biogeogr., 23, 1031–1045, https://doi.org/10.1111/geb.12182, 2014.

Vanin, F., Laberinti, P., Donlon, C., Fiorelli, B., Barat, I., Sole, M. P., Palladino, M., Eggers, P., Rudolph, T., and Galeazzi, C.: Copernicus Imaging Microwave Radiometer (CIMR): System Aspects and Technological Challenges, in: IGARSS 2020–2020 IEEE International Geoscience and Remote Sensing Symposium, 6535–6538 pp., IEEE, Waikoloa, HI, USA, ISBN 978-1-72816-374-1, https://doi.org/10.1109/IGARSS39084.2020.9324259, 2020.

Vroom, R., Van Den Berg, M., Pangala, S., Van Der Scheer, O., and Sorrell, B.: Physiological Processes Affecting Methane Transport by Wetland Vegetation – A Review, Aqua. Bot., 182, 103547, https://doi.org/10.1016/j.aquabot.2022.103547, 2022.

Wania, R., Melton, J. R., Hodson, E. L., Poulter, B., Ringeval, B., Spahni, R., Bohn, T., Avis, C. A., Chen, G., Eliseev, A. V., Hopcroft, P. O., Riley, W. J., Subin, Z. M., Tian, H., van Bodegom, P. M., Kleinen, T., Yu, Z. C., Singarayer, J. S., Zürcher, S., Lettenmaier, D. P., Beerling, D. J., Denisov, S. N., Prigent, C., Papa, F., and Kaplan, J. O.: Present state of global wetland extent and wetland methane modelling: methodology of a model inter-comparison project (WETCHIMP), Geosci. Model Dev., 6, 617–641, https://doi.org/10.5194/gmd-6-617-2013, 2013.

Widhalm, B., Bartsch, A., and Heim, B.: A Novel Approach for the Characterization of Tundra Wetland Regions with C-band SAR Satellite Data, Int. J. Remote Sens., 36, 5537–5556, https://doi.org/10.1080/01431161.2015.1101505, 2015.

Xi, Y., Peng, S., Ducharne, A., Ciais, P., Gumbricht, T., Jimenez, C., Poulter, B., Prigent, C., Qiu, C., Saunois, M., and Zhang, Z.: Gridded Maps of Wetlands Dynamics over Mid-Low Latitudes for 1980–2020 Based on TOPMODEL, Sci. Data, 9, 347, https://doi.org/10.1038/s41597-022-01460-w, 2022.

Xu, J., Morris, P. J., Liu, J., and Holden, J.: PEATMAP: Refining Estimates of Global Peatland Distribution Based on a Meta-Analysis, CATENA, 160, 134–140, https://doi.org/10.1016/j.catena.2017.09.010, 2018.

Zeiger, P.: Clustering and inundation maps based on CYGNSS reflectivity, Odatis, https://doi.org/10.6096/3003, 2023.

Zeiger, P., Frappart, F., Darrozes, J., Prigent, C., Jiménez, C., and Bourrel, L.: Weekly Mapping of Surface Water Extent in the Intertropical Wetlands Using Spaceborne GNSS Reflectometry, J. Hydrol., 626, 130305, https://doi.org/10.1016/j.jhydrol.2023.130305, 2023.

Zhang, B., Tian, H., Lu, C., Chen, G., Pan, S., Anderson, C., and Poulter, B.: Methane Emissions from Global Wetlands: An Assessment of the Uncertainty Associated with Various Wetland Extent Data Sets, Atmos. Environ., 165, 310–321, https://doi.org/10.1016/j.atmosenv.2017.07.001, 2017.
Zhang, Z., Fluet-Chouinard, E., Jensen, K., McDonald, K., Hugelius, G., Gumbricht, T., Carroll, M., Prigent, C., Bartsch, A., and Poulter, B.: Development of a global dataset of Wetland Area and Dynamics for Methane Modeling (WAD2M), Zenodo, https://doi.org/10.5281/zenodo.5553187, 2021a.

Zhang, Z., Fluet-Chouinard, E., Jensen, K., McDonald, K., Hugelius, G., Gumbricht, T., Carroll, M., Prigent, C., Bartsch, A., and Poulter, B.: Development of the global dataset of Wetland Area and Dynamics for Methane Modeling (WAD2M), Earth Syst. Sci. Data, 13, 2001–2023, https://doi.org/10.5194/essd-13-2001-2021, 2021b.

Zhang, Z., Poulter, B., Feldman, A. F., Ying, Q., Ciais, P., Peng, S., and Li, X.: Recent Intensification of Wetland Methane Feedback, Nat. Clim. Change, 13, 430–433, https://doi.org/10.1038/s41558-023-01629-0, 2023.

Zona, D., Gioli, B., Commane, R., Lindaas, J., Wofsy, S. C., Miller, C. E., Dinardo, S. J., Dengel, S., Sweeney, C., Karion, A., Chang, R. Y.-W., Henderson, J. M., Murphy, P. C., Goodrich, J. P., Moreaux, V., Liljedahl, A., Watts, J. D., Kimball, J. S., Lipson, D. A., and Oechel, W. C.: Cold Season Emissions Dominate the Arctic Tundra Methane Budget, P. Natl. Acad. Sci. USA, 113, 40–45, https://doi.org/10.1073/pnas.1516017113, 2016.