Monitoring vegetation water status is key to understanding forest canopy hydraulics, stomatal regulation, and ultimately the biosphere's drought response under a changing climate. Yet direct, in situ measurements of hydraulic state are labor-intensive and rarely sustained long enough to produce the multi-year time series needed for model development and drought-impact forecasting. Continuous proxies such as sap flow or stem water potential provide vital information about fluxes, but their representativeness for entire trees and stand-scale canopy water status remains very limited.Here, we highlight the potential of Global Navigation Satellite Systems Transmissometry (GNSS-T) to bridge this observation gap. GNSS-T retrieves vegetation optical depth (VOD), the effective canopy opacity at L-band (1-2 GHz), by measuring one-way attenuation of GNSS microwave signals along their path from the transmitting satellite to a receiver located below the canopy. GNSS-T VOD integrates information on canopy biomass and water content of the canopy (plant and interception storage) and has demonstrated sensitivity to stand-scale vegetation water dynamics. However, its sensitivity to changes in vegetation water dynamics is expected to vary with stand biomass and canopy cover, species hydraulic strategies, and climatic conditions. These dependencies remain poorly quantified. To date, progress has been limited due to the novelty of this emerging technique as existing GNSS-T records are rather short in time and largely confined to individual sites.In this contribution, we present the first data from VODnet, a community-driven network that builds, maintains, and advances GNSS-T for ecological research. The emerging dataset spans 10 forest stations across diverse biomes, including temperate, Mediterranean, savanna, and tropical ecosystems in South America, and Southern and Central Europe, enabling cross-site analyses of GNSS-T VOD sensitivity under contrasting climate conditions and vegetation properties.The goal of this study is to understand the sensitivity of GNSS-T VOD to changes in vegetation water status across climate gradients, plant traits, and forest structural conditions. We do this by calculating partial correlations of VOD with hydrological drivers such as soil moisture deficit, sap flow and water potential anomalies while accounting for structural properties such as LAI, total biomass and canopy cover, and measure the degree to which site factors drive this correlation. Beyond in situ applications, VODnet provides a unique opportunity to study uncertainty in widely used spaceborne VOD data sets (e.g., SMAP, AMSR-2) through validation across forest ecosystems. Based on our results, we can now provide a first assessment of whether GNSS-T can serve as a validation reference for satellite-derived VOD.

Soil moisture is an essential climate variable exhibiting strong spatio-temporal dynamics, especially in the topsoil. Therefore, it is assessed multiple times by sensors within in situ networks, satellites, and by modeling of the Earth system. The resulting soil moisture fields from all methods are individual and non-congruent due to the imperfection of the methods and retrievals. But their spatial patterns have valuable similarities that call for investigation to foster intercomparison or even fusion of soil moisture products. In this research study, the similarity of spatial soil moisture patterns between passive microwave remote sensing products and Earth system modeling is investigated. We configure and apply spatial similarity metrics to enable a spatial comparison of the operational SMAP Dual Channel Algorithm (DCA) radiometer soil moisture product with the soil moisture output from IFS model runs of the ECMWF. The pattern assessment spans over the whole of Europe and aims to find the drivers behind the spatial soil moisture distributions at scales ranging from single grid cells (minimum) to continental (maximum) spatial scales, and between growing periods of wet (2021) and dry (2022) years. The two specifically configured metrics, total disagreement and mean category distance, showcase the opportunities and challenges when assessing spatial similarity in soil moisture fields across different scales. In addition, the potential drivers of the spatial moisture patterns were screened. Here, soil texture is the most influential single driver of spatial patterns in the IFS soil moisture runs, when analyzed in absolute terms [m3 m−3]. In relative terms of soil moisture [-] (soil wetness index), precipitation and soil temperature explain most of the variability of the IFS soil moisture for Europe. The SMAP retrievals are predominantly driven by the brightness temperatures, mostly influenced by surface temperature, vegetation water content, and soil roughness. These differences in drivers, as well as in methodology, culminate in an inherent discrepancy between the two soil moisture products. However, the assessment of their spatial patterns reveals the underlying similarity from the local to the continental scale.

Water status, water dynamics, and ecohydrological resilience of a protected German beech forest during the 2018-2020 multi-year drought are assessed using over six years of multi-frequency remote sensing data, integrating active, passive, and optical sensors with varying canopy penetration depths, highlighting the importance of monitoring forests under extreme conditions. In this study, we investigate Sentinel-1 C-band backscatter (S-1 gamma 0) and the relative water content estimated from vegetation optical depth (RWCVOD) of AMSR2 (X- and C-bands) and SMAP (L-band) within the soil-plant-atmosphere system (SPAS). In addition, time series are intercompared and examined through correlation and sensitivity analyses applying tailored environmental and newly developed hydrological selection strategies. Our results show that S-1 gamma VH is most influenced by leaf area index and thus leaf biomass when sensed during dense vegetation (leaf-on), no-frost, and very wet conditions (r = -0.94). In contrast, during sparse vegetation (leaf-off), no-frost, stable dry-down, and extremely dry conditions, S-1 gamma VH is very sensitive to topsoil moisture (r = 0.91). Due to increased microwave attenuation, resulting in reduced S-1 gamma VH backscatter, an anti-cyclical behavior (negative correlations) is observed between almost all SPAS-based variables/proxies and S-1 gamma VH during leaf-on conditions. Conversely, this reverses to a cyclical behavior (positive correlations) during leaf-off conditions. Our results reveal that X- and C-band RWCVOD effectively detect drought onset by capturing fast water content changes in leaves and twigs of the top canopy due to shallow sensing depth, while L-band RWCVOD captures legacy effects after repetitive droughts through slower water content changes in branches and trunks of lower tree compartments.

This study evaluates temporal variability and algorithm differences in soil moisture estimates over Europe using the European Center for Medium-range Weather Forecasts (ECMWF) operational analysis and the passive Soil Moisture Active Passive (SMAP) soil moisture product. While models and satellite retrievals have improved in capturing the timing of soil moisture dynamics, absolute accuracy and temporal variability magnitudes still diverge. This study compares the representation of short-term and seasonal variability of soil moisture in absolute and normalized terms over two different hydrometeorological growing periods (2021 and 2022). Both datasets exhibit intermediate to high temporal correlations with in situ measurements at selected stations (median Pearson correlation coefficients of all stations range between 0.65 and 0.79), confirming previous studies. However, they overestimate the magnitude of absolute soil moisture variability at most stations (median interquartile range of all stations at 0.085 (0.10) m3m−3 for ECMWF and 0.072 (0.079) m3m−3 for SMAP opposed to 0.063 (0.072) m3m−3 for in situ in 2021 (2022)) due to an overestimation of short-term fluctuations, especially at dry stations in southern France and Eastern Europe. The soil wetness index is underestimated, particularly within SMAP estimates. The performance of both is sensitive to hydrometeorological conditions, with the 2022 European drought causing strong seasonal and weak short-term fluctuations. This is easier to capture than conditions with pronounced short-term and weaker seasonal fluctuations, as in 2021. Overall, SMAP and ECMWF time series show considerable coincident timing, whereas the magnitude of temporal variability and accuracy depend on site-specific characteristics and the pre-processing of the data.

Decreasing water availability due to climate change reduces the vegetation water pool. This affects the capacity of vegetation to mediate land-atmosphere feedbacks through photosynthesis and transpiration and impacts vegetation health worldwide [1]. Thus, it is paramount to model vegetation water storage (VWS; the mass of water per ground area) in order to monitor vegetation function. Passive microwave sensors on satellites like the Soil Moisture Active-Passive (SMAP) quantify the attenuation that vegetation exerts over land microwave emissions expressed as the vegetation optical depth (VOD). The VOD is linearly related to VWS via the b factor (VOD = b·VWS) and is a good proxy of VWS. Still, satellite-based VWS estimates have been scarcely validated and, importantly, values of b are purely empirical and are time-invariant, omitting relevant phenological changes in VWS [2]. The lack of accurate estimation of b values limits our capacity to better understand the VOD-VWS relationship, to accurately model VWS, or to further explore the transit time of water in vegetation [3]. Here, we bridge this gap by using newly generated, quasi-global benchmark maps of VWS for leaves (VWSleaf), wood (VWSwood) and their summation (VWStotal). These maps are based on ground information of plant traits (specific leaf area and wood density) from the TRY database [4] and their relationship with leaf and wood water storage [5]. Here, we first find that the linear relationship between SMAP L-band VOD and VWS holds when VWStotal is used. We extend this analysis to AMSR2 X- and Ku-VOD data and find linear relationships with VWSleaf (we test against leaves due to the shallow sensing depth of X- and Ku-VOD). Second, we assess the SMAP VWS datasets against VWStotal and find that spatial differences in VWS are biome-dependent. Third, we divide global maps of annual averages of VOD by VWStotal (for L-VOD) and by VWSleaf (for X- and Ku-VOD) to derive global, multi-frequency maps of b and to study its spatiotemporal variation. Results provide new insights on the accuracy of VOD-derived VWS estimates and open a new path towards estimating VWS for different canopy layers, which has wide implications for the remote sensing and the plant ecology research communities.[1] Grossiord, C., et al. (2020). Plant responses to rising vapor pressure deficit. New Phytologist, 226, 1550–1566.[2] Togliatti, K., et al. (2019). Satellite L-band vegetation optical depth is directly proportional to crop water in the US Corn Belt. Remote Sensing of Environment, 233, 111378.[3] Felton, A. J., et al. (2025). Global estimates of the storage and transit time of water through vegetation. Nature Water, 3(1), 59-69.[4] Kattge, J., et al. (2020). TRY plant trait database–enhanced coverage and open access. Global Change Biology, 26, 119-188.[5] Stewart, L., et al. (submitted). Wood You Be-Leaf It? The First Trait-Based Map of Global Vegetation Water Storage. To be presented at EGU 2026.

Automated Individual Tree Stem Detection (ITSD) is an important technique for forest management and conservation. In this study, a new method is tested, using unoccupied aerial vehicle (UAV)-derived point clouds from RGB imagery depicting deciduous dense forests in Germany. For the ITSD, the Vertical Complexity Index (VCI) was applied on five different datasets, including four Structure-from-Motion point clouds of a site in the Hainich National Park and one UAV LiDAR point cloud located at the Research Centre Jülich in Germany for comparison. First, four parameters were calibrated on one dataset and then used on the remaining point clouds to test the robustness of the respective method. The results showed that, for the SfM datasets, up to 82% of overstory trees could be detected with a Precision of up to 0.82 and F1-Score of up to 0.78. The LiDAR point cloud achieved an F1-Score of 0.69 using the same parameter set, but performance could be improved up to an F1-Score of 0.86 when adjusting one parameter. False negatives of the SfM datasets can be traced back to leaning trees and a low diameter at breast height (DBH). The application of this new method proved to be robust across different datasets and study sites, showing promising results for RGB-derived point clouds, and considering the low computational power, holding great potential for future analysis. Especially in forest management, the application of this method on low-cost RGB point clouds would be beneficial compared to the more expensive LiDAR imaging.

Microwave satellite vegetation parameters are widely used to monitor ecosystem spatiotemporal dynamics. Among these, vegetation optical depth (VOD) stands out as a critical microwave vegetation indicator, widely used for applications such as monitoring crop yield, estimating carbon stocks, and assessing risks threatening forests and their resilience to them. However, current VOD products are available at a coarse resolution, often around tens of kilometers, limiting their usefulness to only large-scale applications or those that do not need high spatial precision. Nevertheless, although extensive research has been conducted to improve the spatial resolution of several geophysical indicators, such as soil moisture, advances have been scarce for VOD. Here, we review the advances conducted to estimate VOD at medium-high spatial resolutions, overviewing the state of the art of different methods and their potentials and limitations. Basedon the available literature, we propose a taxonomy that classifies existing VOD downscaling approaches into proxy-based methods, which exploit the relationship between VOD and auxiliary variables, and data-fusion strategies that combine complementary microwave observations across sensors and frequencies. Also, we synthesize the suitability of different proxy variables according to the VOD frequency band, showing that while optical vegetation indices perform well for high-frequency VOD, the downscaling of low-frequency VOD benefits from the integration of complementary radar-derived proxies. Additionally, we examine the effectiveness and affordability of current validation methods and review the potential of emerging ones, such as GNSS technology and land surface models, to guarantee the reliable quality of future VOD downscaled products. Finally, we highlight the capabilities of future missions, particularly the upcoming CIMR multiresolution capability across frequency bands, which could considerably aid in obtaining VOD at better spatial scales.

Monitoring the water status of forests is paramount for assessing vegetation health, particularly in the context of increasing duration and intensity of droughts. In this study, a methodology was developed for estimating forest water potential at the canopy scale from ground-based L-band radiometry. The study uses radiometer data from a tower-based experiment of the SMAPVEX 19-21 campaign from April to October 2019 at Harvard Forest, MA, USA. The gravimetric and the relative water content of the forest stand was retrieved from radiometer-based vegetation optical depth. A model-based methodology was adapted and assessed to transform the relative water content estimates into values of forest water potential. A comparison and validation of the retrieved forest water potential was conducted with in situ measurements of leaf and xylem water potential to understand the limitations and potentials of the proposed approach for diurnal, weekly and monthly time scales. The radiometer-based water potential estimates of the forest stand were found to be consistent in time with rPearson correlations up to 0.6 and similar in value, down to RMSE = 0.14 [MPa], compared to their in situ measurements from individual trees in the radiometer footprint, showing encouraging retrieval capabilities. However, a major challenge was the bias between the radiometer-based estimates and the in situ measurements over longer times (weeks & months). Here, an approach using either air temperature or soil moisture to update the minimum water potential of the forest stand (FWPmin) was developed to adjust the mismatch. These results showcase the potential of microwave radiometry for continuous monitoring of plant water status at different spatial and temporal scales, which has long been awaited by forest ecologists and tree physiologists.

Storage of interception water in the canopy (S c ) heavily affects measurements of vegetation optical depth (VOD) from rain, dew and fog, impeding the direct retrieval of tree physiological parameters such as biomass and plant water content. This study presents a time series decomposition of VOD from Global Navigation Satellite System-Transmissometry (GNSS-T) into biomass, plant moisture content (M g ) and S c . The experiment was conducted at eddy covariance (EC) towers in two temperate forest types in Germany, over the entire vegetation period of 2023 and under fairly wet conditions. S c -values were 1.5 times (needleleaf) to two times (broadleaf) higher than the average diurnal M g cycle, allowing partitioning of interception water storage from plant water. Furthermore, we found indications that S c maxima did not linearly increase with precipitation, suggesting sensitivity of VOD to saturation effects when canopy interception storage reaches a maximum during strong precipitation events. Results indicate the sensitivity of VOD from GNSS-T to canopy wetness. This allows partitioning of canopy water storage from other VOD components and improves the usefulness of VOD as a remote sensing metric for forest canopy water relations. Moreover, it opens pathways to quantify S c and evaporation fluxes independently from EC measurements and field experiments.

Monitoring vegetation moisture conditions is paramount to better understand and assess drought impacts on vegetation, enhance crop yield predictions, and improve ecosystem models. Passive microwave remote sensing allows retrievals of the vegetation optical depth (VOD; [unitless]), which is directly proportional to the vegetation water content (VWC; in units of water mass per unit area [kg/m(2)]). However, VWC is largely dependent on the dry biomass and structure imprints on the VOD signal. Previously, statistical models have been used to isolate the water component from the biomass and structure components. Physically-based approaches have not yet been proposed for this goal. In this study, we present a multi-sensor semi-physical approach to retrieve the vegetation moisture from the VOD and express it as Live Fuel Moisture Content (LFMC [%]; the percentage of water mass per dry biomass unit). The study is performed in the western United States for the period April 2015 - December 2018. There, in situ LFMC samples are available for assessment. We rely on a VOD model based on vegetation height data from GEDI/Sentinel-2 and radar backscatter from Sentinel-1, which account for the biomass and structure components. Vegetation moisture is retrieved at L-, X- and Ku-bands by minimizing the difference between the modeled VOD and the VOD estimates from SMAP (L-band) and AMSR-2 (X- and Ku-band) satellites. Results show that the LFMC retrievals are independent of canopy height, land cover, and radar backscatter, demonstrating the capability of the proposed algorithm to separate water dynamics from the biomass/structure component in VOD. LFMC estimates at X- and Ku-bands reproduce well the expected spatio-temporal dynamics of in situ LFMC. Results show good agreement with in situ at a regional scale, with Pearson's correlations (r) between in situ LFMC samples and LFMC estimates of 0.64 (Ku-band), 0.60 (X-band) and 0.47 (L-band). Similar results are obtained independently for shrub and forest sites at X- and Ku-bands. In most comparisons between in situ and estimated LFMC, biases are below 10% of the dynamic range of LFMC. Performance at L-band is limited by the fact that this frequency senses the full vertical extent of the canopy, while in situ samples are taken only from top of canopy leaves to which X- and Ku-bands are much more sensitive. More insight will be needed for grasslands (r = 0.44 at X-band) using time-dynamic canopy height data. Furthermore, a pixel-scale assessment is conducted, showing a good agreement in most sites (r > 0.6). The proposed method can be tailored to exploit the synergies of past (e.g., AMSR-E), current (e.g., AMSR-2) and future satellite sensors such as CIMR and ROSE-L for global vegetation moisture mapping at different canopy layers.

Understanding the key variables that characterise fire propagation is important for a better estimation of fire events and their impacts. This study uses machine learning combined with satellite remote sensing and atmospheric modelled data to enhance estimations of burned areas. It focuses on the intense early summer weather patterns in South Asia during April and May 2022 and explores the relationship between environmental factors and fire spread. The study employs various algorithms, including random forest, extra trees, extreme gradient boosting (XGBoost), gradient boosting regressor, support vector regressor and neural networks. XGBoost proves to be the most accurate approach. An isolation forest algorithm is used to adjust for outliers in burned area estimations. The comprehensive analysis conducted includes the identification of key variables and sensitivity tests incorporating changes of up to 25 % in natural environmental conditions to assess the model’s consistency. The results indicate that integrating vegetation, atmospheric, and human-related variables with the XGBoost algorithm, and incorporating outlier adjustments leads to the most effective performance (R2 ≥ 0.7), with jet stream variables enhancing the accuracy by approximately 11.5 %. The study highlights the notable impact on fire propagation of increases in the value of 300-hPa meridional circulation index flow (MCI300) and a high 500-hPa geopotential height anomaly (ΔZ500), indicating the development of strong atmospheric blocking (upper tropospheric ridge). As compared to other factors, e.g. land surface temperature, vapour pressure deficit, soil moisture and vegetation optical depth, the impact of changes in jet stream metrics (MCI300 and ΔZ500) was more pronounced, indicating greater sensitivity. These insights emphasise the complexity of fire spread, and the importance of using atmospheric factors to estimate burned areas, particularly during severe heatwaves.

This study addresses discerning causal relationships in complex systems, a key aspect of interpretable machine learning. It focuses on the unusual and intense early summer weather in South Asia during April and May 2022 that led to an increased number of forest fires. This work employs a Bayesian network (BN), constructed using the NOTEARS algorithm, to analyse the contribution of various land and atmospheric variables on the extent of burned areas. In a scenario analysis using peak values of 300-hPa meridional circulation index and 500-hPa Geopotential Height Anomalies, indicative of a strong atmospheric block, the likelihood of large burned areas (>3.06 log ha or >1150 ha) increases from 36.6% to 41.6%. This is due to a rise of conditional probabilities in the Vapor Pressure Deficit (VPD) (> 5.21 kPa) by 24.8%, and the Land Surface Temperature (LST) (>45.7 degrees C) by 15.6%. In addition, sensitivity and spatial analyses indicate that extreme dry conditions, characterized by high LST and VPD due to the trapping effects of the omega block jet stream pattern, were the primary factors influencing the extent of burned areas during the 2022 South Asia heatwave.

Publication: Chaparro et al. (2024) This dataset contains estimates of Live Fuel Moisture Content (LFMC) in the Western United States. LFMC is the percentage of vegetation water mass over the dry biomass of the plants. Here, LFMC is retrieved by isolating the water component of the passive microwaves vegetation optical depth (VOD) signal at three frequencies: L-band (1.4 GHz), X-band (10.65 GHz) and Ku-band (18.7 GHz). Each frequency represents a different canopy sensing depth. To isolate LFMC from VOD, auxiliary information to account for the biomass and structure of the vegetation has been used: radar backscatter data from Sentinel-1 and canopy height data from GEDI/Sentinel-2. The dataset spans between April 2015 and December 2018 for L- and X-bands, and between April 2015 and July 2018 for Ku-band retrievals. Details: Period: April 2015 - December 2018 (daily resolution) Gridding: 0.25º Grid type: lat/lon Size: 73 (lat) x 104 (lon) x 1371 (time)

Climate change is amplifying the duration, frequency, and intensity of droughts, harming global ecosystems. During droughts, plants can close their stomata to save water, at the expense of a reduced carboxylation rate. When in a carboxylation-limited regime, plants benefit from an increase in water availability, as it increases their photosynthetic rate. The sun-induced chlorophyll fluorescence (SIF) signal, measurable from satellites, is mechanistically linked to this rate. Like canopy photosynthesis, SIF carries an imprint from the available irradiation (PAR) as well as the canopy structure and the efficiency of the photosynthesis at the photosystem level. Normalizing the global TROPOMI SIF observations with TROPOMI reflectance and MODIS Normalised Difference Vegetation Index (NDVI) data, we extracted the fluorescence quantum yield (ϕF), which lab-scale experiments have found to be linked to the photosynthetic electron transport. Plant physiologists have long proved the photosynthetic electron transport to be sensitive to plant water status. Here, the plant water status is controlled by the soil moisture (SM) and the vapour pressure deficit (VPD). Combining data from the TROPOMI, AIRS and SMAP satellite sensors, this study describes how SM and VPD control the ϕF at the global scale. We identify a VPD range (VPD<1.5 kPa) in which the ϕF is mainly controlled by VPD, and another (VPD>1.5 kPa) in which the ϕF is co-regulated by SM and VPD. The precise values of this range, as well as the magnitude of ϕF values, are modulated by the plant isohydricity. To gain a deeper understanding of the link between ϕF and photosynthetic efficiency at large scale, we used the link between ϕF and data on the canopy conductance (Gs), which were calculated using remote sensing data-driven models. A comparison found that the ϕF-Gs relationship at large scale is in line with the ϕF-Gs relationship described in plant-level studies.

Tracking seasonal dynamics of evapotranspiration (ET) across global biomes and along seasonal time periods using remote sensing is vital for monitoring ecosystem health and indicating early signals of drought. In this study, we assess the potential of adding weather and illumination-independent signals from active and passive microwave remote sensing (SAR backscatter & vegetation optical depth, VOD) to the established set of ET products, like from optical/thermal remote sensing (MODIS, SEVIRI) and reanalysis (ERA-5 land, GLDAS) data.Our study covers a four-year period (2017-2020), including dry (2018 & 2019) and wet (2017) years. The study was conducted over eight ICOS sites across Europe. These sites are predominantly forested with a low biomass dynamic over the observation period.We find that the ET products from in situ Eddy Covariance (EC), MODIS, and GLDAS deviate relatively minor along the seasons (< 1 [mm/day]), but differ between years. Here, the years (2017-2020) indicate a slightly different ET rate between in situ measurements (EC) and derived products (MODIS & GLDAS), which is currently being investigated. The microwave-based indicators (backscatter & VOD) are proxies by their nature and serve as first-order indicators of relative dynamics allowing the identification of seasonal patterns of ET as well as their spatio-temporal anomalies along both dry and wet years.

Jet streams’ persistent tropospheric ridging plays a crucial role in temperature extremes, heatwaves, and consequent wildfires in subtropical and polar regions. To address this, the research presented in this study incorporates jet stream variables into atmospheric data to enhance the forecast of burned areas using machine learning (ML). Focusing on the anomalous early and intense summer weather in South Asia during April and May 2022, this research employed ML algorithms such as Random Forest, Support Vector Regression (SVR), Gradient Boosting Regressor (GBR), Extreme Gradient Boosting (XGBoost), and Neural Network (NN). Notably, the XGBoost model outperformed others, and its accuracy improved by approximately 11.5% (with R 2 scores rising from 0.61 to 0.68) when jet stream features were included, which emphasized their importance. These findings highlight the importance of both natural and anthropogenic factors, including upper tropospheric patterns, in predicting burned areas.

In the work a random forest model has been implemented as an interpretable machine learning tool in the effort to estimate the burned areas caused by fire outbreaks in India, Pakistan, and Myanmar in April and May 2022. The proposed model combines environmental and atmospheric (including upper tropospheric) factors suggested to drive patterns of burned areas, and determines the weight of each factor on the propagation of fires. Results demonstrate that the model mimics the actual burned area by considering a combination of vegetation, atmosphere, and human-related variables and improves accuracy by approximately 7% after adding jet stream features. This approach could lead to implement a semi-operational forecast system that may be tested in multiple demonstration sites.

A Random Forest (RF) regression-tree method to derive high-resolution (60 m) surface soil moisture maps is proposed in this study. The developed methodology integrates multi-source synergies by incorporating information from the visible, near-infrared until short-wave infrared spectrum (Sentinel-2), reanalysis data (ERA5-Land) and terrain information (SRTM), using exclusively open access data. The analysis focuses on the central part of the Iberian Peninsula and covers a four-year period (2018-2021). The resulting high-resolution soil moisture maps exhibit greater spatial heterogeneity compared to the ESA Climate Change Initiative (CCI) soil moisture, which was used as a reference in the training of the RF model. These maps have been evaluated using in situ soil moisture measurements from the REMEDHUS network, and show good agreement in terms of Pearson's correlation (0.83), and uRMSE (0.028 m 3 •m -3 ), demonstrating the method’s significant potential for deriving high-resolution soil moisture information.