Microwave radiometry and scatterometry, two complementary modes of sensing the composition and structure of the upper meters to hundreds of meters of the subsurface, are often difficult to reconcile, both on the Earth cryosphere and on icy moons of Saturn. To help interpret and model microwave scattering in porous, high-purity ices, we examine jointly 6.9-89 GHz AMSR2 radiometry in vertical (V) and horizontal (H) polarizations as well as 5.2 GHz ASCAT, 13.4 GHz QuikSCAT, and 13.5 GHz OSCAT scatterometry in the wind-glazed region of the East Antarctic ice sheet. The data are simulated using the Snow Microwave Radiative Transfer (SMRT) model, assuming a simplified snowpack characterized by constant temperature and a continuous increase in grain size (represented by optical radius) and density with depth. For the first time, we show that scatterometry and 6.9-37 GHz radiometry at V polarization can be successfully simulated with a unique simple snowpack model, indicating that incoherent volume scattering on subsurface heterogeneities dominates both the active and passive signals. To also simulate H-polarized radiometry, a thin surface ice layer as observed in the wind-glazed regions is one solution. Additional complexity, such as seasonal temperature variations, surface roughness, or non-continuous density variations, is necessary to explain the 89 GHz data and HH-polarized backscatter. Meanwhile, applying the same approach to simulate simultaneously passive and active Ku-band observations of icy moons improves on previous attempts but remains unable to reproduce the very high backscatter observed, highlighting the importance of coherent scattering and possibly unknown large icy structures (at least millimetric) in the subsurface. More work is still to be done to fully reproduce the microwave signatures of icy surfaces in the solar system.

We have presented in Microrad 2026 the results of an extensive observational campaign carried out with the Atacama Pathfinder Experiment (APEX) aimed at measuring with unprecedented spectral resolution (better that $\mathbf{1 ~ M H z}$) and calibration accuracy, the $159-752 \text{GHz}$ atmospheric spectrum under various clear sky conditions from the high and dry site of Chajnantor (Atacama Desert in Chile, 5000 meters above sea level). APEX was, at the time of these measurements, a collaboration of the Max-Planck-Institut für Radioastronomie (MPIfR), the European Southern Observatory (ESO), and Onsala Space Observatory (OSO). These measurements have implications on testing atmospheric radiative transfer models and, therefore, are important for both astrophysics and Earth's remote sensing.

Clouds are critical in the Arctic's water balance and energy budget. Especially, the cloud liquid water path (CLWP) modifies the cloud radiative properties and affects the surface energy balance. Spaceborne microwave radiometers provide a high sensitivity to CLWP at pan-Arctic scales, but extracting this information over sea ice requires separation of surface and cloud emission. Here, we assess CLWP detectability and retrieval accuracy over sea ice from a physical optimal estimation retrieval applied to airborne passive microwave observations during the HALO-(AC)3 campaign. Reference data on surface temperature, young ice fraction, hydrometeor occurrence, and cloud liquid layers are available from collocated airborne instruments. The retrieval estimates CLWP and five surface parameters by inverting a forward operator consisting of the Snow Microwave Radiative Transfer (SMRT) and Passive and Active Microwave radiative TRAnsfer (PAMTRA) models. We find a consistent representation of sea ice and snow emission from 22-183 GHz under clear-sky conditions in both observation and state space. The CLWP detectability, defined as the 95th percentile of retrieved CLWP under clear-sky conditions, is about 50 gm-2 in the Central Arctic and increases towards the marginal ice zone up to 350 gm-2. The CLWP retrieval accuracy increases with increasing CLWP, with a relative root mean squared error below 50 % for CLWP above 100 gm-2. Retrieval uncertainties occur due to ambiguities between cloud liquid water emission and scattering in the snowpack and emission by newly formed sea ice. We further analyze the impact of surface melt and a rain-on-snow event associated with the warm air intrusion on the surface parameters. Finally, we show CLWP distributions along the flight track for all airborne observations in comparison to ERA5 for different cloud regimes. The retrieval algorithm enhances the understanding of Arctic clouds and allows for an improved use of passive microwave satellite data in polar regions.

A Neural Network inversion method has been developed to estimate Above Ground Biomass (AGB) on a global scale using multiple microwave satellite observations from both passive and active instruments. The reference dataset is the AGB from the European Agency (ESA) Climate Climatology Initiative (CCI). The study evaluates the potential of each observation type individually and in combination, from current Advanced Microwave Scanning Radiometer 2 (AMSR2), Soil Moisture Active Passive (SMAP), and Advanced SCATterometer (ASCAT) satellite instruments. Additionally, auxiliary data from Normalized Difference Vegetation Index (NDVI from MODIS), land surface temperature (LST from ERA5), and soil moisture (SM from ERA5) are incorporated and evaluated into the retrieval process alongside microwave observations.Findings confirm that passive 1.4 GHz observations exhibit the highest sensitivity to AGB compared to other passive measurements up to 36 GHz. In contrast, active observations at 6 GHz demonstrate limited potential for AGB estimation when used in isolation, at least within the framework of this study. However, combining microwave observations between 1.4 and 36 GHz yields strong results compared to the CCI AGB dataset. The inclusion of NDVI, LST, and SM further enhances performance, achieving an R2 of 0.88 globally and an RMSE of 30 Mg/ha, as compared to the CCI AGB.The combination of passive microwave observations at 18 and 36 GHz, supplemented with auxiliary data, shows promise for assessing global AGB. With these passive microwave data available since the 1990s, long-term AGB dynamics could be estimated.

Icy surfaces across the solar system display unusual microwave radar and radiometry properties, including very high backscattering cross-sections and polarization ratios. At low temperature, snow and ice are very transparent to microwaves, leading to long path lengths and multiple scattering. Yet despite the large volume of available passive and active microwave satellite observations over the Earth cryosphere, physical interpretation of the co-variability of the multi-frequency observations is still challenging, especially when trying to reconcile radiometry and radar observations. To shed light on microwave scattering in icy regoliths, we focus on the Antarctic megadunes region, the coldest and driest area on Earth, which we propose as a new analog for icy satellites due to its very low precipitation (net zero snow accumulation) and temperature (averaging -50°C), combined with the highest microwave backscatter in Antarctica. We assemble a dataset consisting of 5.2 GHz ASCAT and 13.4 GHz QuikSCAT and OSCAT scatterometry, as well as AMSR2 radiometry at 6.9 to 89 GHz. Using the Snow Microwave Radiative Transfer (SMRT) model with a simplified snowpack with constant temperature and continuously increasing grain size and density with depth, we simulate simultaneously radar and radiometry. For the first time, we show that scatterometry and 6.9 to 37 GHz radiometry at V polarization can be successfully simulated with a unique simple snowpack model, indicating that incoherent volume scattering on subsurface heterogeneities dominates both the active and passive signal. The success of our approach encourages further work to analyze and simulate jointly active and passive microwave observations, both in the Earth cryosphere and on icy moons.

Wetlands and inundated areas cover only a few percent of the Earth's surface. However, they play an important role in freshwater regulation, biodiversity, and climate. In particular, a significant proportion of atmospheric methane is emitted from these areas [1]. There is therefore a need for data that can reliably capture surface water interannual variability over the past decades. The Global Inundation Extent from Multi-Satellites (GIEMS-2) [2] is based on microwave remote sensing data (SSM/I and SSMIS). It provides a 0.25° global monthly estimate of inundated and saturated areas and has been extended to 2020 to cover three decades (1992-2020). First, an evaluation of GIEMS-2 together with other products is presented. Key findings include consistent spatial patterns, seasonal cycles and time series anomalies observed by GIEMS-2 with the other observational datasets studied (MODIS-derived surface water, CYGNSS-derived surface water, river discharge). This highlights the interest of such a product for the calibration of hydrological models, as has been achieved for example by Xi et al. (2022) for TOPMODEL [3]. In a second part, the use of GIEMS-2 for the estimation of methane emissions from wetlands and inundated areas is discussed. GIEMS-2 has been processed with other data sources to derive a dynamic map of wetlands (including peatlands), open water (lakes, rivers, reservoirs) and rice paddies. This comprehensive product allows a consistent view of the area between the different classes, limiting problems of double counting and miss counting. This new database can then be used to constrain the extent of the water surface in models estimating methane flux rates, in order to study the influence of surface water changes on interannual variations in methane emissions. [1] Marielle Saunois et al. “The Global Methane Budget 2000–2017”. In: Earth System Science Data 12.3 (July 2020), pp. 1561–1623. doi: 10.5194/essd-12-1561-2020. url: https://doi.org/10.5194/essd-12-1561-2020.[2] C. Prigent, C. Jimenez, and P. Bousquet. “Satellite-Derived Global Surface Water Extent and Dynamics Over the Last 25 Years (GIEMS-2)”. In: Jour-nal of Geophysical Research: Atmospheres 125.3 (Feb. 2020). doi: 10.1029/2019jd030711. url: https://doi.org/10.1029/2019jd030711.[3] Yi Xi et al. “Gridded Maps of Wetlands Dynamics over Mid-Low Latitudes for 1980–2020 Based on TOPMODEL”. In: Scientific Data 9.1 (June 2022), p. 347. issn: 2052-4463. doi: 10.1038/s41597-022-01460-w

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 degrees x 0.25 degrees 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 Mkm(2) 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 10.5281/zenodo.13919644 .

Modeling sea ice microwave emissivities at large scales presents challenges, due to complex interactions between the microwave signal and the sea ice environment. For the preparation of the Copernicus Imaging Microwave Radiometer mission (CIMR) that focusses on the monitoring of polar regions, a pragmatic parameterization of the sea ice emissivity over the Arctic in winter is proposed, providing consistent emissivity parameterizations between 1.4 and 36 GHz, for both orthogonal polarizations. Satellite-derived microwave emissivities are calculated from the Advanced Microwave Scanning Radiometer 2, Soil Moisture Active Passive, and Soil Moisture Ocean Salinity observations, subtracting the atmospheric contributions and the surface temperature modulation using ERA5 meteorological reanalysis. The resulting Arctic sea ice emissivities are analyzed, alongside sea ice geophysical parameters from neXtSIM model outputs and ERA5, to identify the variables for the emissivity parameterization. Sea Ice Thickness emerges as a crucial factor, particularly at 18 and 36 GHz. A training database of coincident satellite-derived emissivities and geophysical parameters is set up, to develop a Neural Network parameterization of the emissivities based on the geophysical parameters. This pragmatic methodology establishes a direct link between calculated emissivities and physical sea ice properties, eliminating the need for a priori assumptions. Promising emissivity results are obtained, with Root Mean Square Error below 0.03 for most channels, and reaching 0.04 at 36 GHz. Part of the error is expected to come from uncertainties in the input geophysical parameters. The emissivity frequency dependence is checked, and the emissivity angular variation of the 1.4 GHz is calculated from SMOS-derived emissivities.

Global vegetation plays a major role in the Earth carbon budget, storing the largest carbon stock on land. Both direct human activities and natural evolution under a changing climate impact the state of global forests, leading to regional decreases or vegetation growth. Monitoring these variations over long time periods can help better constrain estimates of the land carbon sink, and understand the driving forces of the cyclical and long term variations. This enables a refined understanding of climate effects and policies impact on current and future global vegetation carbon uptake.Satellite records now span multiple decades, with microwave-based remote sensing providing complementary insights to optical observations. The lowest microwave frequencies are less affected by atmospheric perturbations and enable deeper penetration into the surface cover, with canopy penetration depth increasing with decreasing frequencies. However, achieving multi-decadal records requires the use of multiple instruments over time. These changes in instruments and observation types necessitate careful calibration and harmonization to produce consistent long-term time series of observations. The combination of different observation sources and different frequencies can be used as proxy to monitor geophysical variables variations such as the above ground biomass. In this work we used a statistical model to combine observations of the Special Sensor Microwave - Imager, Special Sensor Microwave Imager Sounder and the C-band ERS/Advanced Scatterometer and Ku-band QSCAT to estimate above ground biomass on a global scale. These models are applied to create a ~30 years time series of above ground biomass with R2>0.85 and RMSE

In recognition of the importance of inland waters, numerous datasets mapping their extents, types, or changes have been created using sources ranging from historical wetland maps to real-time satellite remote sensing. However, differences in definitions and methods have led to spatial and typological inconsistencies among individual data sources, confounding their complementary use and integration. The Global Lakes and Wetlands Database (GLWD), published in 2004, with its globally seamless depiction of 12 major vegetated and non-vegetated wetland classes at 1 km grid cell resolution, has emerged over the last few decades as a foundational reference map that has advanced research and conservation planning addressing freshwater biodiversity, ecosystem services, greenhouse gas emissions, land surface processes, hydrology, and human health. Here, we present a new iteration of this map, termed GLWD version 2, generated by harmonizing the latest ground- and satellite-based data products into one single database. Following the same design principle as its predecessor, GLWD v2 aims to avoid double counting of overlapping surface water features while differentiating between natural and non-natural lakes, rivers of multiple sizes, and several other wetland types. The classification of GLWD v2 incorporates information on seasonality (i.e., permanent vs. intermittent vs. ephemeral); inundation vs. saturation (i.e., flooding vs. waterlogged soils), vegetation cover (e.g., forested swamps vs. non-forested marshes), salinity (e.g., salt pans), natural vs. non-natural origins (e.g., rice paddies), and stratification of landscape position and water source (e.g., riverine, lacustrine, palustrine, coastal/marine). GLWD v2 represents 33 wetland classes and – including all intermittent classes – depicts a maximum of 18.2 ×106 km2 of wetlands (13.4 % of the global land area excluding Antarctica). The spatial extent of each class is provided as the fractional coverage within each grid cell at a resolution of 15 arcsec (approximately 500 m at the Equator), with cell fractions derived from input data at resolutions as small as 10 m. The upgraded GLWD v2 offers an improved representation of inland surface water extents and their classification for contemporary conditions (∼ 1984–2020). Despite being a static map, it includes classes that denote intrinsic temporal dynamics. GLWD v2 is designed to facilitate large-scale hydrological, ecological, biogeochemical, and conservation applications, aiming to support the study and protection of wetland ecosystems around the world. The GLWD v2 database is available at https://doi.org/10.6084/m9.figshare.28519994 (Lehner et al., 2025).

To assimilate passive microwave data in numerical weather prediction, a comprehensive understanding of the components of the radiative transfer equation is essential. Given the significant variability of emissivity in snow-covered regions - affected by frequency, polarisation, and the macro-and microstructural properties of snow - attention must be paid to the design of a forward model. However, existing physical models are unsuitable for global-scale studies due to their reliance on numerous inputs, such as snow grain size across different layers, which are typically unavailable at larger scales. In this study, we propose a method that utilises geophysical properties accessible at the continental scale to derive accurate emissivity values for frequencies ranging from 1 GHz to 90 GHz, in both vertical and horizontal polarisations, with a focus on the incident angles of conical scanners (approximately 50 degrees). Our approach employs neural networks to obtain a robust forward model using geophysical variables as input data. A training dataset was developed based on satellite-derived surface emissivity from the SMOS and AMSR2 instruments by subtracting atmospheric components and surface temperature modulation. The results, which accounts for the actual geophysical state of the surface and its temporal variability, outperform the emissivity climatologies. We achieved snow-covered surface emissivities at the continental scale with a correlation coefficient above 0.9 and a RMSE below 0.02 for frequencies up to 18.7 GHz, and around 0.03 for higher frequencies. Additionally, we demonstrate that, in a typical tundra snowpack where the macro-and microstructural properties of snow can be obtained, the emissivities retrieved by our neural network-based forward model are consistent with results from the physical model (SMRT). This proposed model will also support preparations for the CIMR mission.

L-band radiometer data collected by the Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions have shown potential for mapping the spatial distribution and temporal changes of the aboveground biomass (AGB) of forests. Most studies focussed on the relationships observed between AGB and estimates of the vegetation optical depth (VOD) derived from L-band radiometer data. We here present an approach for retrieving AGB from SMOS and SMAP brightness temperatures which builds upon existing AGB retrieval frameworks developed for active microwave data. A physically-based model was adapted to relate brightness temperatures to the percent canopy cover and height available from space-borne optical and LiDAR missions and, via modelled relationships between canopy cover, height, and AGB, to AGB. An initial set of 36 global AGB maps was produced from 10-days composites of a polarimetric index calculated from H and V polarization SMOS and SMAP brightness temperatures acquired in 2016. When compared to an ESA Climate Change Initiative Biomass AGB map, the AGB estimates produced from SMOS and SMAP presented a reasonable agreement with low systematic biases and explained, dependent on the type of forest, between 30 % and 80 % of the AGB variability in the reference map. A comparison with AGB reference information derived from plot-level inventory data for a limited number of sites across the major forest biomes indicated the merit of the suggested retrieval approach but also revealed a need for improving the retrieval algorithm locally.

Clouds and water vapor play a critical role in the water and energy balance of the Arctic. However, few field observations of these quantities over sea ice exist. Passive microwave observations provide high sensitivity to clouds and water vapor with high spatial and temporal coverage in polar regions. However, retrievals of atmospheric quantities from satellites and aircraft require a description of the variable sea ice emissivity, which depends on the properties of sea ice and snow. Recently, improved retrieval methods that derive sea ice and atmospheric properties simultaneously allowed for improved exploitation of the information from passive microwave observations. This work presents liquid water path (LWP), ice water path (IWP), and integrated water vapor (IWV) retrieved from the HALO Microwave Package (HAMP) operated onboard the HALO aircraft during the HALO-(AC)3 field campaign in spring 2022 in the Fram Strait. The nadir-viewing HAMP measures along two water vapor bands (22.24 and 183.31 GHz), two oxygen bands (50-60 and 118.75 GHz), and the atmospheric windows at 31 and 90 GHz over different surface types. The retrieval accounts for variable surface emission through a joint surface-atmosphere optimal estimation scheme with the Passive and Active Microwave Radiative Transfer (PAMTRA) model. The high spatial coverage of the HALO flights allows for assessing the spatial and temporal variability of the retrieved IWV, LWP, and IWP under various atmospheric and surface conditions. A particular focus lies on the warm air intrusion events and their related poleward changes in cloud properties and water vapor over sea ice that HALO captured. Furthermore, the hectometer-scale airborne observations allow statistical comparison with operational satellite products, reanalysis, and model simulations along the flight track. The HAMP observations will improve the characterization of clouds and water vapor in the Arctic and potentially improve the use of passive microwave satellite observations over sea ice.

Tropical wetlands account for similar to 20% of the global total methane (CH4) emissions, but uncertainties remain in emission estimation due to the inaccurate representation of wetland spatiotemporal variations. Based on the latest satellite observational inundation data, we constructed a model to map the long-term time series of wetland extents over the Sudd floodplain, which has recently been identified as an important source of wetland CH4 emissions. Our analysis reveals an annual, total wetland extent of 5.73 +/- 2.05 x 104 km2 for 2003-2022, with a notable accelerated expansion rate of 1.19 x 104 km2 yr-1 during 2019-2022 driven by anomalous upstream precipitation patterns. We found that current wetland products generally report smaller wetland areas, resulting in a systematic underestimation of wetland CH4 emissions from the Sudd wetland. Our study highlights the pivotal role of comprehensively characterizing the seasonal and interannual dynamics of wetland extent to accurately estimate CH4 emissions from tropical floodplains. Methane (CH4) plays an important role in global warming. About one-fifth of global CH4 emissions come from tropical wetlands, with the Sudd wetland in tropical Africa presenting as a hotspot for CH4 emissions. In this study, we generate a monthly wetland map series based on the latest satellite observation for the Sudd wetland. Our results show that wetland area dynamics present large seasonal and interannual variabilities. However, current widely used wetland products tend to indicate smaller sizes and variations of wetlands, which might lead to the underestimation of wetland CH4 emissions. We point out that refined maps of tropical wetlands can help reduce the uncertainties of CH4 emission estimations. Extended monthly inundation maps across the Sudd wetland for 2003-2022 Rapid growth of the Sudd wetland extent for 2019-2022 driven by upstream precipitation Underestimation of methane emissions from the Sudd wetland due to the inadequate characterization of wetland extent

Water resources play a crucial role in the global water cycle and are affected by human activities and climate change. However, the impacts of hydropower infrastructures on the surface water extent and volume cycle are not well known. We used a multi-satellite approach to quantify the surface water storage variations over the 2000-2020 period and relate these variations to climate-induced and anthropogenic factors over the whole basin. Our results highlight that dam operations have strongly modified the water regime of the Mekong River, exhibiting a 55 % decrease in the seasonal cycle amplitude of inundation extent (from 3178 km2 to 1414 km2) and a 70 % decrease in surface water volume (from 1109 km3 to 327 km3) over 2000-2020. In the floodplains of the Lower Mekong Basin, where rice is cultivated, there has been a decline in water residence time by 30 to 50 days. The recent commissioning of big dams (2010 and 2014) has allowed us to choose 2015 as a turning point year. Results show a trend inversion in rice production, from a rise of 40 % between 2000 and 2014 to a decline of 10 % between 2015 and 2020, and a strong reduction in aquaculture growth, from +730 % between 2000 and 2014, to +53 % between 2015 and 2020. All these results show the negative impact of dams on the Mekong basin, causing a 70 % decline in surface water volumes, with major repercussions for agriculture and fisheries over the period 2000-2020. Therefore, new future projects such as the Funan Techo canal in Cambodia, scheduled to start construction at the end of 2024, will particularly affect 1300 km2 of floodplains in the lower Mekong basin, with a reduction in the amount of water received, and other areas will be subjected to flooding. The human, material and economic damage could be catastrophic.

Upcoming submillimeter wave satellite missions require an improved understanding of sea ice emissivity to separate atmospheric and surface microwave signals under dry polar conditions. This work investigates hectometer-scale observations of airborne sea ice emissivity between 89 and 340 GHz, combined with high-resolution visual imagery from two Arctic airborne field campaigns that took place in summer 2017 and spring 2019 northwest of Svalbard, Norway. Using k-means clustering, we identify four distinct sea ice emissivity spectra that occur predominantly across multiyear ice, first-year ice, young ice, and nilas. Nilas features the highest emissivity, and multiyear ice features the lowest emissivity among the clusters. Each cluster exhibits similar nadir emissivity distributions from 183 to 340 GHz. To relate hectometer-scale airborne measurements to kilometer-scale satellite footprints, we quantify the reduction in the variability of airborne emissivity as footprint size increases. At 340 GHz, the emissivity interquartile range decreases by almost half when moving from the hectometer scale to a footprint of 16 km, typical of satellite instruments. Furthermore, we collocate the airborne observations with polar-orbiting satellite observations. After resampling, the absolute relative bias between airborne and satellite emissivities at similar channels lies below 3 %. Additionally, spectral variations in emissivity at nadir on the satellite scale are low, with slightly decreasing emissivity from 183 to 243 GHz, which occurs for all hectometer-scale clusters except those predominantly composed of multiyear ice. Our results will enable the development of microwave retrievals and assimilation over sea ice in current and future satellite missions, such as the Ice Cloud Imager (ICI) and EUMETSAT Polar System – Sterna (EPS–Sterna).

Inland waters, especially wetlands, play a crucial role in biodiversity, water resources and climate, and contribute significantly to global methane emissions. This study investigates the seasonal and inter-annual variability of the 0.25° × 0.25° surface water extent (SWE) from the Global Inundation Extent from Multi-Satellites (GIEMS-2) extended to a 30-year time series (1992–2020). Comparison with MODIS-derived SWE, CYGNSS-derived SWE and the Global Lakes and Wetlands Database (GLWD) shows consistent spatial patterns globally and over 10 different basins, although there are discrepancies in extent, partly due to different resolutions of the initial satellite observations. Strong cross-correlation (>0.8) in seasonal variability is observed when comparing GIEMS-2 with MODIS, CYGNSS and river discharge in most of the basins studied. Encouraging similarities were found in the inter-annual variability in most basins (cross-correlation >0.6) between GIEMS-2 and MODIS over 20 years, and between GIEMS-2 and river discharge over long time series, including over the Amazon and the Congo basins. These results highlight the reliability of GIEMS-2 in detecting changes in SWE in different environments, especially under dense vegetation, making it a valuable resource for calibrating hydrological models and studying global methane emissions.

Arctic sea ice volume (SIV) is a key climate indicator and memory source in sea ice predictions and projections, yet suffering from large observational and model uncertainty. Here, we test whether passive microwave (PMW) data constrain the long-term evolution of Arctic SIV, as recently hypothesized. We find many commonalities in Arctic SIV changes from a PMW sea ice thickness (SIT) 1992-2020 time series reconstructed with a neural network algorithm trained on lidar altimetry, and the reference PIOMAS reanalysis: relatively low differences in SIV mean (4615 km3, 37%), SIV trends (46 km3, 17%), and phased variability (r2=0.55). Key to reduced differences is the consistent evolution of many SIV contributors: seasonal and perennial ice coverage, their SIT contrast, whereas perennial SIT provides the largest remaining uncertainty source. We argue that PMW includes useful SIT information, reducing SIV uncertainty. We foresee progress from sea ice reanalyses combining dynamical models and data assimilation of PMW SIT estimates, in addition to the already assimilated PWM sea ice concentration.