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The role of remote sensing observations in quantifying the biomass of forests is frequently debated because of both their strengths and limitations. Satellite remote sensing is nowadays standard in research activities thanks to missions designed to last over decades. Nonetheless, satellites cannot measure the organic mass stored in trees. As such, indirect approaches are developed that combine multiple observations and mathematical models together with ground-based observations to provide a set of estimates presented in the form of a map.While small-scale studies profit from a strategy that collects observations best suited to estimate biomass, continental and global mapping efforts need to restrict to datasets that have been collected following observation plans and are free of charge. In turn, this increases the demand on the performance of the models selected to link the predictor metrics derived from remote sensing and the response variable biomass. A map of biomass is eventually the result of an interplay between sensitivity of the remote sensing data to response forest variables, the spatial resolution of the sensors, the number of remote sensing observations and the capability of the models to reproduce the relationship between predictors and response variables. A consequence of such interplay is the level of accuracy affecting the biomass estimate, which ultimately is a key parameter to inform user communities on the reliability and efficiency of biomass maps. A comparison of biomass estimates obtained with different predictors and models for the same region provides additional measures to increase our understanding of the uncertainty affecting current biomass maps derived from satellite data.In this presentation, we explore such uncertainties by comparing four maps of forest aboveground biomass (AGB) based on satellite images acquired in 2020 and covering Europe. The maps were based on different predictors (Sentinel-1 and ALOS-2 PALSAR-2, ASCAT, SMOS as well as spaceborne LiDAR metrics) but share the same modelling framework for biomass retrieval. Depending on the spatial resolution of the satellite data, spatial scales ranging between 100 m and 25 km were covered.Validation of each of the datasets indicates that the overall spatial distribution of AGB is well captured even in regions with dense mature forests. However, the maps show substantial discrepancies at the level of individual pixels, regardless of the set of predictors. In addition, the precision of individual AGB estimates is rather low, between 30 and 50% of the estimated value. AGB biases were identified in specific regions and were mostly explained as imperfect modelling of the relationship between predictors and forest variables. The maps’ precision increases with spatial averaging; nonetheless, the spatial correlation of errors implies that the resulting estimates can still be affected by non-negligible uncertainty. These results in turn explain why AGB values from the different maps are highly correlated although the magnitudes can be substantially different. In conclusion, the reliability of biomass maps from satellite data is questionable at the scale of the spatial resolution; their use is instead advised at the landscape scale and for understanding broad spatial patterns.

The forest age can be defined as the time since the last stand-replacement event. Within the local context, it determines the forest successional stage, a fundamental variable for diagnosing the net carbon fluxes in terrestrial ecosystems. To accurately quantify the carbon sink-source strength in these ecosystems and inform adaptation-mitigation strategies, it is essential to be able to infer forest age at high resolution.This study presents an updated version of the MPI-BGC forest age product, featuring global distributions of forest age for 2010, 2017, 2018, and 2020 at 100m spatial resolution. We employed two machine learning approaches, XGBoost and a multi-layer perceptron model, to create data-driven estimates of forest age based on over 40,000 forest inventory plots, biomass, remote-sensing, and climate data. One key innovation of our approach is the incorporation of Landsat-based disturbance history metrics as input variables. Our updated estimates show better precision in identifying old-growth forests and reduce overestimation biases in young forests and underestimation biases in old forests, but not completely. Additionally, we found substantial regional variations related to changes in covariate strength and improvement in the model. Also, we discussed the uncertainty layers, created using model ensembles, that materialize the quantification of methodological uncertainty in the forest age estimates.An analysis of the global distribution of forest age reveals significant variations across the years studied. We also quantified the changes in forest age in regions with high deforestation or forest degradation rates, where younger stands are becoming more prevalent. We discuss the challenges and limitations of using regression-based mapping approaches, including the choice of machine learning algorithm, spatial cross-validation techniques, and the caveats of extrapolation, given data limitations. Our research highlights the complementary biomass-based approaches for determining forest age and underscores the importance of detailed global estimates at high spatial resolutions. Overall, this study advances our understanding of forest age, a key variable for understanding the carbon cycle in terrestrial ecosystems.

Under polar and subpolar climatic conditions, volcano edifice growth and stability are affected by extreme erosion rates, mass wasting, glacier loading (and unloading), and permafrost soil conditions. Relatively small changes in temperature can lead to very different snow and ice conditions in relation to all of the above. Therefore active, shallow magmatic plumbing systems and magmatic pathways might react sensitively to even minor changes of their surrounding environmental conditions. Almost constant degassing from the summit crater of Mount Curry (Zavodovski Island) and the presence of an active lava lake within the summit crater of Mount Michael (Saunders Island) suggest the existence of shallow magmatic plumbing systems at both volcanoes. They therefore represent exceptional study sites for investigating volcano processes under subpolar climatic conditions. Because of their remoteness, none of these islands are equipped with permanently installed ground-based instruments. We observe and quantify surface displacements related to volcanic activity, fumarolic activity, tectonic activity in the Scotia arc, as well as glacier flow from high-resolution combined TerraSAR-X and PAZ interferometry and amplitude offsets. Multi-temporal topographic data are available through the TanDEM-X SAR satellite mission and photogrammetric surveys conducted in April-Mai 2019 at Saunders Island and in January-February 2023 on Zavodovski Island. Here we introduce the first results of combining and exploring UAV photogrammetry with SAR satellite data. We present a geomorphological and structural analysis of Zavodovski Island and the outer subaerial and shallower submarine flanks of Saunders Island. We also estimate the glacier volume and volume change over time on Saunders as well as surface dynamics at Zavodovski. With this study we highlight the unprecedented detail and the valuable information that can be retrieved from tasked and targeted TerraSAR-X, TanDEM-X, and PAZ satellite acquisitions coupled

The interferometric coherence of repeat-pass C-band synthetic aperture radar observations has shown potential for mapping forest above-ground biomass (AGB) when images were acquired with short temporal repeat intervals and under stable environmental conditions. Since 2014, Sentinel-1 collects repeated interferometric image pairs at global scale with repeat intervals of 6 or 12 days. For a semiarid forest site in California, we evaluated one year of Sentinel-1 6- and 12-day (60 and 59 images, respectively) repeat-pass coherence images with the objective of retrieving AGB. The retrieval was performed by inverting the semiempirical Interferometric Water Cloud Model. The retrieval based on individual coherence images was most accurate (~50% relative root-mean-square difference with respect to a Lidar-derived map) when images were acquired in extended periods of dry conditions. The retrieval accuracy improved by approximately 15% units when combining multiple coherence images in the retrieval. Finally, the accuracy of the retrieval with multitemporal 6- or 12-day repeat-pass coherence images differed by only 2% units. The results of this study advocate consideration of Sentinel-1 repeat-pass coherence in AGB mapping efforts in semiarid forest areas.

Vegetation turnover time (τ) is a central ecosystem property to quantify the global vegetation carbon dynamics. However, our understanding of vegetation dynamics is hampered by the lack of long-term observations of the changes in vegetation biomass. Here we challenge the steady state assumption of τ by using annual changes in vegetation biomass that derived from remote-sensing observations. We evaluate the changes in magnitude, spatial patterns, and uncertainties in vegetation carbon turnover times from 1992 to 2016. We found that the forest ecosystem is close to a steady state at global scale, contrasting with the larger differences between τ under steady state and τ under non-steady state at the grid cell level. The observation that terrestrial ecosystems are not in a steady state locally is deemed crucial when studying vegetation dynamics and the potential response of biomass to disturbance and climatic changes.

The 2015/16 El Niño brought severe drought and record-breaking temperatures in the tropics. Here, using satellite-based L-band microwave vegetation optical depth, we mapped changes of above-ground biomass (AGB) during the drought and in subsequent years up to 2019. Over more than 60% of drought-affected intact forests, AGB reduced during the drought, except in the wettest part of the central Amazon, where it declined 1 y later. By the end of 2019, only 40% of AGB reduced intact forests had fully recovered to the predrought level. Using random-forest models, we found that the magnitude of AGB losses during the drought was mainly associated with regionally distinct patterns of soil water deficits and soil clay content. For the AGB recovery, we found strong influences of AGB losses during the drought and of γ . γ is a parameter related to canopy structure and is defined as the ratio of two relative height (RH) metrics of Geoscience Laser Altimeter System (GLAS) waveform data—RH25 (25% energy return height) and RH100 (100% energy return height; i.e., top canopy height). A high γ may reflect forests with a tall understory, thick and closed canopy, and/or without degradation. Such forests with a high γ ( γ ≥ 0.3) appear to have a stronger capacity to recover than low- γ ones. Our results highlight the importance of forest structure when predicting the consequences of future drought stress in the tropics.

Quantifying the responses of forest dynamics to fluctuations in the atmosphere, hydrosphere and land surface conditions at global scales have been rather challenging. Despite the understanding that favourable environmental conditions promote forest growth, the effects have been challenging to observe across different ecosystems and climate gradients. Based on a global annual time series of aboveground biomass (AGB) from 1992 to 2018, we present forest carbon changes and provide insights on the controls of atmospheric (e.g., climate), hydrosphere (e.g., soil water availability) and land surface (e.g., changes in forest cover) conditions on forest carbon changes from local to global scales. Our findings indicate a gradient of forest gains and losses across AGB classes, with regions with carbon stocks of 50-100 MgC ha-1 depicting both the highest forest gains and losses. Furthermore, we observe that changes in forest carbon stocks were systematically positively correlated with changes in forest cover, while it was not necessarily the case with other environmental variables, such as air temperature and water availability at the uni-variate level. We also used a gradient boosted decision tree model and a variable importance metric (i.e., SHAP values) to demonstrate that atmospheric conditions largely dictate forest carbon changes followed by land surface and hydrosphere conditions. Interestingly, the observed functional relationships indicate a strong sensitivity of forest carbon changes to recent-past carbon stocks and both recent-past and concurrent atmospheric water demand. Regionally, we find evidence that carbon gains from long-term forest growth covary with long-term carbon sink inferred from atmospheric inversions at the ecosystem level. Our study quantifies the contributions from the atmosphere, hydrosphere, and land surface conditions to forest carbon changes and provides new insights into the underlying mechanisms of forest growth on the global carbon cycle.

African forests suffer from severe fragmentation that further causes forest degradation near forest edges. The impact of fires used for slash-and-burn on forest edge effects remains unclear. Here, using high-resolution satellite-based forest-cover and biomass datasets, we find that edge effects extend a median distance and an interquartile range of $$0.11_{ - 0.04}^{ + 0.06}\,{\mathrm{km}}$$ and $$0.15_{ - 0.05}^{ + 0.09}\,{\mathrm{km}}$$ into moist and dry forests, and biomass within the forest edge zones has a carbon deficit of 4.1 PgC. Fires occurred in 52% of the forest edges and increased the carbon deficit by $$5.5_{ - 2.9}^{ + 4.3}\,{\mathrm{MgC}}\,{\mathrm{ha}^{{-1}}}$$ , compared with non-fire edges, through both the direct impact of fires intruding into forests and the indirect impact of changes in the local atmospheric circulations increasing canopy dryness. If small-scale slash-and-burn practices continue, increased fragmentation during 2010–2100 will result in a carbon loss from edge effects of 0.54–4.6 PgC. Fragmentation-caused forest degradation could be avoided by implementing dedicated forest protection policies supported by satellite monitoring of forest edges. Fire exacerbates forest degradation in the forest edge zones in Africa, increasing the carbon deficit caused by forest fragmentation, according to analyses of high-resolution satellite data on forest cover and biomass.

Soil moisture observations are of broad scientific interest and practical value for a wide range of applications. The scientific community has made significant progress in estimating soil moisture from satellite-based Earth observation data, particularly in operationalizing coarse-resolution (25-50 km) soil moisture products. This review summarizes existing applications of satellite-derived soil moisture products and identifies gaps between the characteristics of currently available soil moisture products and the application requirements from various disciplines. We discuss the efforts devoted to the generation of high-resolution soil moisture products from satellite Synthetic Aperture Radar (SAR) data such as Sentinel-1 C-band backscatter observations and/or through downscaling of existing coarse-resolution microwave soil moisture products. Open issues and future opportunities of satellite-derived soil moisture are discussed, providing guidance for further development of operational soil moisture products and bridging the gap between the soil moisture user and supplier communities.

The responses of forest carbon dynamics to fluctuations in environmental conditions at a global scale remain elusive. Despite the understanding that favourable environmental conditions promote forest growth, these responses have been challenging to observe across different ecosystems and climate gradients. Based on a global annual time series of aboveground biomass (AGB) estimated from radar satellites between 1992 and 2018, we present forest carbon changes and provide insights on their sensitivities to environmental conditions across scales. Our findings indicate differences in forest carbon changes across AGB classes, with regions with carbon stocks of 50-125 MgC ha-1 depict the highest forest carbon gains and losses, while regions with 125-150 MgC ha-1 have the lowest forest carbon gains and losses in absolute terms. Net forest carbon change estimates show that the arc-of-deforestation and the Congo Basin were the main hotspots of forest carbon loss, while a substantial part of European forest gained carbon during the last three decades. Furthermore, we observe that changes in forest carbon stocks were systematically positively correlated with changes in forest cover fraction. At the same time, it was not necessarily the case with other environmental variables, such as air temperature and water availability at the bivariate level. We also used a model attribution method to demonstrate that atmospheric conditions were the dominant control of forest carbon changes (56% of the total study area) followed by water-related (29% of the total study area) and vegetation (15% of the total study area) conditions. Regionally, we find evidence that carbon gains from long-term forest growth covary with long-term carbon sinks inferred from atmospheric inversions. Our results describe the contributions from the atmosphere, water-related and vegetation conditions to forest carbon changes and provide new insights into the underlying mechanisms of the coupling between forest growth and the global carbon cycle.

This study presents an assessment of a pre-operational soil moisture product at 1 km resolution derived from satellite data acquired by the European Radar Observatory Sentinel-1 (S-1), representing the first space component of the Copernicus program. The product consists of an estimate of surface soil volumetric water content (SSM) [m/m] and its uncertainty [m/m], both at 1 km. The retrieval algorithm relies on a time series based Short Term Change Detection (STCD) approach, taking advantage of the frequent revisit of the S-1 constellation that performs C-band Synthetic Aperture Radar (SAR) imaging. The performance of the S-1 SSM product is estimated through a direct comparison between 1068 S-1 SSM images against in situ SSM measurements acquired by 167 ground stations located in Europe, America and Australia, over 4 years between January 2015 and December 2020, depending on the site. The paper develops a method to estimate the spatial representativeness error (SRE) that arises from the mismatch between the S-1 SSM retrieved at 1 km resolution and the in situ point-scale SSM observations. The impact of SRE on standard validation metrics, i.e., root mean square error (RMSE), Pearson correlation (R) and linear regression, is quantified and experimentally assessed using S-1 and ground SSM data collected over a dense hydrologic network (4 - 5 stations/km) located in the Apulian Tavoliere (Southern Italy). Results show that for the dense hydrological network the RMSE and correlation are ~0.06 m/m and 0.71, respectively, whereas for the sparse hydrological networks, i.e., 1 station/km, the SRE increases the RMSE by ~0.02 m/m (70% Confidence Level). Globally, the S-1 SSM product is characterized by an intrinsic (i.e., with SRE removed) RMSE of ~0.07 m/m over the SSM range [0.03, 0.60] m/m and R of 0.54. A breakdown of the RMSE per dry, medium and wet SSM ranges is also derived and its implications for setting realistic requirements for SAR-based SSM retrieval are discussed together with recommendations for the density of in situ SSM observations.

C-band scatterometer data are among the longest time record of spaceborne observations, dating back to 1991. Their use in the context of terrestrial carbon cycle studies has not been investigated yet. In spite of a weak sensitivity of the C-band backscatter to forest above-ground biomass (AGB), repeated observations have been shown to support the retrieval of biomass. In this paper, we investigate the use of C-band backscatter data at 25 km spatial resolution from the ERS WindScat and MetOp ASCAT sensors arranged in daily mosaics to estimate forest AGB. A retrieval approach based on self-calibration and regression between SAR backscatter and canopy density was developed, producing daily estimates of AGB that were found to well reproduce the global spatial patterns of AGB. A weighted average of the daily AGB estimates was then applied to generate annual maps of AGB. The evaluation of the AGB dataset with recent global datasets of AGB for 2010 confirms the overall reliability of our estimates.

Forests cover about 30% of the terrestrial surface of our planet and store a large part of the terrestrial carbon (C), indicating their fundamental role in terrestrial C dynamics. In recent years, significant advances have been made in understanding terrestrial C cycling across scales, albeit uncertainties remain about fundamental processes, such as photosynthesis, allocation, and mortality, which exert dominant controls on vegetation C dynamics. Allocation plays a critical role in forest ecosystem C cycling by partitioning the products of net photosynthesis into leaves, wood, and below-ground components but is still poorly represented mostly given limitations in process understanding as well as in both suitable and commensurate observations.Here, we explore different approaches in constraining C allocation alongside processes driving assimilation and out fluxes in a terrestrial ecosystem model based on novel forest biomass datasets. More specifically, we use a series of temporally changing above-ground biomass (AGB) data from local (i.e. in-situ forest inventory data) to global (i.e. long-term C-band satellite retrievals from 1992 to 2018) scales, in a multi-constraint approach. We explore the information contained in a novel AGB time series to diagnose the potential of using changes in vegetation C stocks, jointly with C and water fluxes, to constrain and parameterize different C allocation modeling approaches. Both at FLUXNET site level and global scale, we will: i) present these novel AGB datasets, their strengths and limitations, ii) demonstrate the relevance of constraining C allocation with such temporally changing AGB estimates, and iii) provide a comparison of different C allocation approaches (i.e. fixed versus dynamic allocation, and an hybrid modeling approach) and their implications in representing ecosystem dynamics.

The large uncertainty characterizing the terrestrial carbon (C) cycle is a consequence of the sparse and irregular observations on the ground. In terms of observations, spaceborne remote sensing has been achieving global, repeated coverages of the Earth since the late 1970s, with a continuous increase in terms of density of observations in time and spatial resolution, thus potentially qualifying as data source to fill such gap in knowledge. Above-ground biomass is a baseline for quantifying the terrestrial C pool; however, remote sensing observations do not measure the organic mass of vegetation. Above-ground biomass (AGB) of forests can only be inferred by inverting numerical models relating and combining multiple remote sensing observations. One of the longest time record of observations from space is represented by the backscattered intensity from the European Remote Sensing Wind Scatterometer (ERS WindScat) and the MetOp Advanced Scatterometer (ASCAT), both operating at C-band (wavelength of 6 cm). An almost unbroken time series of backscatter observations at 0.25° spatial resolution exists since 1991 and data continuity is guaranteed in the next decades. In spite of the weak sensitivity of C-band backscatter to AGB, wall-to-wall estimates of AGB have been derived from high-resolution SAR observations by exploiting multiple observations acquired in a relatively short time period (Santoro et al., Rem. Sens. Env., 2011; Santoro et al., Rem. Sens. Env., 2015). We have now applied this approach to generate a global time series of AGB estimates for each year between 1992 and 2018 from the C-band scatterometer data at 0.25° spatial resolution. The spatial patterns of AGB match known patterns from in situ records and other remote sensing datasets. The uncertainty of our AGB estimates is between 30% and 40% of the estimated value at the pixel level, providing strong confidence in multi-decadal AGB trends. We identify a constant increase of biomass across most boreal and temperate forests of the northern hemisphere. In contrast, we detect severe loss of biomass throughout the wet tropics during the 1990s and the beginning of the 2000 decade in consequence of massive deforestation. This loss in biomass is followed by a steady increase during the 2000s and the beginning of the most recent decade, coming more recently into saturation. Overall, we find that the global AGB density at 0.25° steadily increased by 9% from 71.8 Mg ha-1 Pg in the 1990s to 78.1 Mg ha-1 in the 2010s. Combining our AGB density estimates with the annual maps of the Climate Change Initiative (CCI) Land Cover dataset, we show that total AGB in forests decreased slightly from 566 Pg in the 1990s to 560 Pg in the 2000s, then increased to 593 Pg in the 2010s, resulting in an almost 5% net increase during the last three decades.

The global biomass is determined by the vegetation turnover times (τ) and carbon fixation through photosynthesis. Vegetation turnover time is a central parameter that not only partially determines the terrestrial carbon sink but also the response of terrestrial vegetation to the future changes in climate. However, the change of magnitude, spatial patterns and uncertainties in τ as well as the sensitivity of these processes to climate change is not well understood due to lack of observations on global scale. In this study, we explore a new dataset of annual above-ground biomass (AGB) change from 1993 to 2018 from spaceborne scatterometer observations. Using the long-term, spatial-explicit global dynamic dataset, we investigated how τ change over almost three decades including the uncertainties. Previous estimations of τ under steady-state assumption can now be challenged acknowledging that terrestrial ecosystems are, for the most of cases, not in balance. In this study, we explore this new dataset to derive global maps of τ in non-steady-state for different periods of time. We used a non-steady-state carbon model in which the change of AGB is a function of Gross Primary Production (GPP) and τ (ΔAGB = α*GPP-AGB/ τ). The parameter α represents the percentage of incorporation of carbon from GPP to biomass. By exploring the AGB change in 5 to 10 years of time step, we were able to infer τ and α from the observations of AGB and GPP change by solving the linear equation. We show how τ changes after potential disturbances in the early 2000s in comparison to the previous decade. We also show the spatial distributions of α from the change of AGB. By accessing the change in biomass, τ and α as well as their associated uncertainties, we provide a comprehensive diagnostic on the vegetation dynamics and the potential response of biomass to disturbance and to climate change.

Soil moisture content is an essential climate variable that plays an important role in the exchange of water, energy and biochemical fluxes between the land surface and atmosphere at local, regional and global scales. Near surface soil moisture (SSM) content is operationally delivered at low resolution (e.g. >=25 km) by earth observation missions, such as ESA/SMOS, NASA/SMAP and EUMETSAT/ASCAT. However numerous land applications would benefit from the availability of SSM at higher resolution. The launch of the C-band Sentinel-1 (S-1) European Radar Observatory, with its frequent revisit, large geographical coverage and data continuity, has opened new perspectives to SAR derived SSM products and stimulated a large research effort to develop SSM products at high resolution (e.g. 1 km). The objective of Exploit-S-1 has been to demonstrate and quantify the capabilities of the S-1 mission to support systematic SSM product generation at high resolution and at regional/continental scale. A suite of four SSM retrieval methods, based on previous research into C-band soil moisture retrieval, has been selected, tailored to S-1 data and implemented. The selection has been driven by the most promising trade-off among robustness, retrieval accuracy and potential matching with the requirements of different applications, such as numerical weather prediction, watershed management, runoff prediction, estimation of evapotranspiration, flood forecasting, in terms of accuracy, resolution and product frequency. A key component of Exploit-S-1 has been the validation activity. To this regard, Exploit-S-1 has also benefitted from the advice and support of an External Advisory Team (EAT) consisting of International leading experts in microwave SSM retrieval and application that has provided their expertise in all the phases of the project and, particularly, in the validation phase. In addition, a pivotal element of Exploit-S-1 has been the assessment of the optimal pre-processing of S-1 time series for SSM retrieval, which can be considered a standard also for other S-1 retrieval studies. The project has been organized in three main tasks: Scientific Review and Requirements Consolidation, Implementation and Validation, Ideas for further scientific research. Based on the specific characteristics of each retrieval approach, a sensitivity analysis, the assessment of algorithm performance on experimental data, the time series Short term Change Detection (STCD) approach has been selected as the most promising approach for further development as a pre-operational S-1 SSM product and its production has been extended over the entire Mediterranean basin. The developed product is derived from time series of VV&VH S-1 observations and delivered at a spatial resolution of ~1 km. A companion SSM standard deviation product at 1 km is delivered as an additional information layer.

The objective of the ESA SEOM Exploit-S-1 project has been to demonstrate and quantify the capabilities of the Sentinel-1 (S-1) mission to support the systematic surface soil moisture (SSM) generation at high resolution and regional/continental scale. Moreover, the consistency and complementarity between S-1 SSM and ESA/SMOS, NASA/SMAP and EUMETSAT/ASCAT SSM products have been assessed. The scientific relevance of satellite high-resolution SSM measurements for many applications (e.g., agriculture, hydrology, weather forecast, etc.) and the associated requirements - in terms of accuracy and temporal/spatial resolution - have been initially surveyed through a gap analysis based on interviews. In parallel, a review of the main Synthetic Aperture Radar (SAR) SSM retrieval methods proposed in the literature, with a specific emphasis on those exploiting single/dual polarized SAR data, has supported the selection of the SAR SSM retrieval methods. The most promising trade-off among robustness, retrieval accuracy and potential matching with the requirements of different applications have been identified into a suite of four SSM retrieval methods that have been tailored to S-1 data and implemented. A key component of Exploit-S-1 has been the extensive validation activity, likely unprecedented for a SAR SSM product at C-band. To this regard, Exploit-S-1 has benefitted from the advice and support of an External Advisory Team (EAT) consisting of International leading experts in microwave SSM retrieval and application that has provided their expertise in all the phases of the project and, particularly, in the validation phase. Exploit-S-1 has adopted the procedures and the metrics recommended by the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration and Validation (WGCV) (https://lpvs.gsfc.nasa.gov/). In particular, the implemented validation activity has included i) a comparison of S-1 SSM estimates against in situ observations collected over seven cal/val sites located in USA, Canada, Australia and Europe (referred to as "validation at local scale") and ii) a cross-comparison among S-1, SMAP, SMOS and ASCAT SSM fields observed over a large area of the Mediterranean basin (referred to as "validation at regional scale"). In this study, an emphasis has been put on addressing the spatial representativeness error for S-1 SSM retrieval at 1 km. Based on the specific characteristics of each retrieval approach, a sensitivity analysis and the assessment of the performance of the algorithms on the experimental data basis, one method has been selected as the most promising approach for a pre-operational S-1 SSM product and its production has been extended over the entire Mediterranean basin.

Much attention is paid to the estimation of forest biomass-related variables (stem volume and above-ground biomass) with synthetic aperture radar (SAR) backscatter images because of the increasing number of sensors in space providing global and repeated coverage and the sensitivity of the backscattered intensity to forest properties. One of the most popular models used to estimate a biomass-related variable from SAR backscatter observations is the Water Cloud Model (WCM) because of its simplicity allowing for a straightforward retrieval. Nonetheless, a common feature of these estimates is the tendency to over- or underestimate specific ranges due to simplifying assumptions in the model. In this study, the WCM has been revisited by exploring pathways for a physically-based, Light Detection and Ranging (LiDAR)-aided, model parameterization at larger scale with the overall aim to reduce systematic retrieval errors associated with empirical assumptions in the model. The study was undertaken in Sweden where repeated observations of backscatter by the Advanced Land Observing Satellite (ALOS) Phased Array-type L-band Synthetic Aperture Radar (PALSAR) were available. The integration was prototyped in Sweden thanks to detailed allometries relating forest variables in the WCM. These were derived from spatially dense estimates of canopy density and vegetation height from observations by the Ice, Cloud and land Elevation Satellite (ICESat) Geoscience Laser Altimeter System (GLAS) and measurements of height and stem volume from the Swedish National Forest Inventory (NFI). The SAR backscatter predicted by the revisited WCM was in strong agreement with the observations. When evaluated against stem volumes estimated from the NFI data, the SAR-based stem volumes presented strong dispersion at the pixel level. Average stem volume at the level of five or more pixels, i.e., for an area larger than 0.3 ha, were instead unbiased and similar to the average values obtained from the NFI data (relative root mean square error: 21.4%, estimation bias: 0.9 m3/ha and coefficient of determination: 0.67). This study demonstrates that the integration of allometries in the WCM effectively reduces estimation errors. The method here prototyped in Sweden qualifies to provide large-scale estimates of biomass-related variables using multiple observations of L-band backscatter with potential application worldwide.

The amount and spatial distribution of forest aboveground biomass (AGB) were estimated using a range of regionally developed methods using Earth Observation data for Poland, Sweden and regions in Indonesia (Kalimantan), Mexico (Central Mexico and Yucatan peninsula), and South Africa (Eastern provinces) for the year 2010. These regions are representative of numerous forest biomes and biomass levels globally, from South African woodlands and savannas to the humid tropical forest of Kalimantan. AGB retrieval in each region relied on different sources of reference data, including forest inventory plot data and airborne LiDAR observations, and used a range of retrieval algorithms. This is the widest inter-comparison of regional-to-national AGB maps to date in terms of area, forest types, input datasets, and retrieval methods. The accuracy assessment of all regional maps using independent field data or LiDAR AGB maps resulted in an overall root mean square error (RMSE) ranging from 10 t ha−1 to 55 t ha−1 (37% to 67% relative RMSE), and an overall bias ranging from −1 t ha−1 to +5 t ha−1 at pixel level. The regional maps showed better agreement with field data than previously developed and widely used pan-tropical or northern hemisphere datasets. The comparison of accuracy assessments showed commonalities in error structures despite the variety of methods, input data, and forest biomes. All regional retrievals resulted in overestimation (up to 63 t ha−1) in the lower AGB classes, and underestimation (up to 85 t ha−1) in the higher AGB classes. Parametric model-based algorithms present advantages due to their low demand on in situ data compared to non-parametric algorithms, but there is a need for datasets and retrieval methods that can overcome the biases at both ends of the AGB range. The outcomes of this study should be considered when developing algorithms to estimate forest biomass at continental to global scale level.