The increased climatic variability in the present scenario intensifies the coupled interaction of land-atmosphere component extremes. Spatiotemporal dependence of soil moisture (SM) and rainfall forms a crucial aspect of this interaction, which contributes to extreme events such as floods. Rainfall creates distinct signatures of SM throughout various soil profile, leading to antecedent SM that influence surface runoff. This underscores the need for a deeper understanding of SM– precipitation preconditioning, particularly in regions of high flood risk. In this study, we investigate the SM-precipitation coupling over India using an event-based, non-parametric Event Coincidence Analysis (ECA) approach. The analysis is carried out for the year 2017, using the surface and root-zone SM (RZSM) data from the Global Land Evaporation Amsterdam Model and corresponding rainfall data from the India Meteorological Department (IMD). Extreme events of SM and rainfall across specific locations inside major river basins within different Indian regions (as per the IMD- based precipitation categories) are marked using the 95 ^th percentile threshold. The strength of coincidence between the two event-series was subsequently inferred using the two statistical ECA parameters: precursor (r _p) and trigger (r _t) coincidence rate. Results reveals a strong directional relationship of SM event that triggers rainfall extremes over the southern peninsular and central India, as indicated by their high trigger rate (r _t =0.842 to 0.895) and moderate precursor rate (r _p = 0.526 to 0.579). in contrast, northern India (both eastern and western region), exhibits a low EC (0.263–0.368), indicating an inconsistent time lag between the two extreme event series. Additionally, the RZSM demonstrates a comparatively moderate triggering and preconditioning effect on precipitation extremes in most of the regions, except for the Northwest, which reveals a lower coincidence value. Notably, this observation was revealed in one of the key locations within the Yamuna River basin which showed an identical r _p and r _t values of 0.053. Altogether, the present study enhances our understanding of SM-precipitation dynamics, offering critical insights for flood risk assessment. The findings of this study significantly contribute to disaster management over one of the globally recognized flood risk regions.

How to cite: k s, S., dash, S. K., krishnan, S., and thampi, S. G.: Inferring the spatiotemporal interdependency of soil moisture–rainfall Coincidences over the Indian region, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-936, https://doi.org/10.5194/egusphere-egu25-936, 2025.

Forests are one of the most essential components of the Earth system. They account for a large part of the total global photosynthetic activity, store a significant amount of the total carbon, and provide a habitat for countless species. At the same time, they offer critical resources to anthropogenic activities, such as timber, food, and firewood. Soil moisture (SM) plays a pivotal role in the processes governing all these functions. Low-frequency remote sensing is the only way to acquire a large spatial distribution of the forest SM because of its ability to carry the signal from the forest floor through the forest canopy to the satellite. Studies have shown that NASA's SMAP (Soil Moisture Active Passive) mission, measuring brightness temperature at 1.4 GHz (L-band), is sensitive to SM changes in forests despite the interference by the forest canopy. The challenge is to accurately account for the attenuation, scattering, and emission by the canopy. The SMAP Validation Experiment 2019-2022 (SMAPVEX19-22) in the temperate forests of the northeast US collected a vast amount of in situ and other experimental data to improve SMAP's SM and L-band vegetation optical depth (L-VOD) retrievals in forested areas. The results from the experiment have shown that the transmissivity is substantially higher in the spring no-leaf conditions than later in the season, suggesting that the seasonal water content changes and phenology significantly affect L-band TB. While the effect is seasonal, substantial changes in the L-VOD response occurred within days as the water content and phenological changes occurred harmoniously across the large SMAP footprint (tens of km). Moreover, the frozen season effect on the tree permittivity affected the SMAP L-VOD at daily timescales as the trees within the SMAP footprint underwent changes between frozen and thawed states. The results underline the need for the SM and L-VOD retrieval algorithms to account for the short-timescale changes.

How to cite: Colliander, A., Cosh, M., Kraatz, S., Bourgeau-Chavez, L., Chaubell, J., Xu, X., Kelly, V., Siqueira, P., McDonald, K., Steiner, N., Kurum, M., Roy, A., Berg, A., Vittucci, C., Tsang, L., Entekhabi, D., and Yueh, S.: Impact of L-band Vegetation Optical Depth Temporal Variation on Soil Moisture Retrieval, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7207, https://doi.org/10.5194/egusphere-egu25-7207, 2025.

Despite global coverage, remote sensing of soil moisture (SM) is challenged by coarse spatial sensor resolution and shallow sensing depth which result in systematic differences when compared to reference SM measured in-situ. Although improvements have been documented with assimilation of SMAP radiometer data with land surface models, a regionalized solution is needed that leverages crucial physical signatures (SM recessions) to provide further improved estimations, addressing systematic deviations that persist. A key drawback of existing algorithms is the lack of consideration of the uncertainty associated with different physical factors that modulate the SM time series. Specifically, SM drawdown is not influenced by precipitation, which reduces uncertainty considerably. In the present study, a novel approach is demonstrated that splits the SMAP Level 4 SM series, mechanically segregating the recession limbs that last at least 2 days and uses them to modify the complete time series. A bivariate recursive filtering approach is introduced that models the association of initial soil wetness and drying rate during the recession periods, minimizing the disparity to represent the same observed in-situ. Consequently, the modified drying attributes (initial wetness and recession rates) are utilized to reconstruct the complete time series. The approach is validated by comparing ensued estimates with the in-situ measurements from dense and sparse networks from April 2015 to March 2020. The validation metrics show improvements in the reconstructed SM series, with significant enhancements observed for the recession parts of the series. The combined procedure has performed well, demonstrating the importance of associativity of physical processes into SMAP assimilation observations for regional studies. 

How to cite: Sharma, A., Sinha, J., and Marshall, L.: A Physics Based Satellite Soil Moisture Reconstruction Algorithm, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7602, https://doi.org/10.5194/egusphere-egu25-7602, 2025.

 Soil Moisture (SM) plays a crucial role in the hydrological characteristics of the Earth’s land surface, energy exchange, and climate change. The spaceborne GNSS-R (Global Navigation Satellite System Reflectometry) technique has developed as an effective method to retrieve land surface soil moisture. This study proposes a new semi-empirical method to estimate the land surface soil moisture from the spaceborne GNSS-R data. First, the Fresnel reflectivity is linearly modeled with the GNSS-R-derived reflectivity and coherency ratio and the environment variables (i.e. vegetation water content and surface roughness). Then the Fresnel reflectivity is used to estimate the soil moisture by the dielectric constant model. We apply our method to the Cyclone GNSS (CYGNSS) reflectometry data globally collected from 2021 to 2023. The CYGNSS-derived reflectivity and coherency ratio and the Soil Moisture Active Passive (SMAP) data in 2021 are used to construct the linear model. Then the global daily soil moisture data with a spatial resolution of 36 km from 2022 to 2023 are retrieved with the model. Our soil moisture retrievals and the official CYGNSS soil moisture product (SM_CYGNSS) are compared to the SMAP SM. The results show that our soil moisture retrievals perform well (ubRMSE=0.043 cm³/cm³; R=0.62) and are superior to that of the SM_CYGNSS (ubRMSE=0.059 cm³/cm³; R=0.34), with ubRMSE decreasing by 27.1%. The proposed method improves the soil moisture estimation and will benefit the physical interpretation of hydrological issues with GNSS-R.

How to cite: Hu, Y. and Gong, J.: A new method for retrieving daily land surface soil moisture using CYGNSS reflectometry data and coherency ratio, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11598, https://doi.org/10.5194/egusphere-egu25-11598, 2025.

One of the key research questions in terrestrial carbon cycling is concerned about how to advance our understanding of the processes underlying terrestrial CO2 fluxes and subsequently reduce related uncertainties in an integrated approach exploiting both observations (satellite and in situ) and modelling. Here, we demonstrate the synergistic exploitation of remotely sensed soil moisture observations together with additional observations from passive microwave and optical sensors for an improved understanding of the terrestrial carbon and water cycles. As such, the Terrestrial Carbon Community Assimilation System (TCCAS), an activity funded by the European Space Agency within its Carbon Science Cluster, has been developed. TCCAS has at its core the community terrestrial ecosystem model D&B that is based on the well-established DALEC and BETHY models, and thus building on the strengths of each component model. In particular, it combines the dynamic simulation of the carbon pools and canopy phenology of DALEC with the dynamic simulation of water pools, and the canopy model of photosynthesis and energy balance of BETHY.  A suite of observation operators allows the simulation of surface layer soil moisture as well as solar-induced fluorescence, fraction of absorbed photosynthetically active radiation, and vegetation optical depth from passive microwave sensors. TCCAS employs a variational assimilation system (making use of efficient tangent and adjoint code) that adjusts a combination of initial pool sizes and process parameters to match the observational data streams. The system is applied to two ICOS sites and regions around these sites: Sodankylä, Finland, representing a boreal forest biome, and Majadas de Tietar, Spain, representing a temperate savanna biome.  The model performance is assessed against independent observations at site scale as well as at approximately 500 km x 500 km regions around each site. We find that the assimilation of soil moisture in combination with the three other data streams has a profound impact on simulated ecosystem function and carbon fluxes at both sites/regions.

How to cite: Scholze, M. and the TCCAS team: Assimilation of soil moisture observations to constrain carbon fluxes in the  Terrestrial Carbon Community Assimilation System  (TCCAS), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11822, https://doi.org/10.5194/egusphere-egu25-11822, 2025.

Soil moisture is a critical variable in precision agriculture, hydrological modeling, and environmental monitoring, influencing crop productivity, irrigation planning, hydrological processes and water resource management. Advances in Earth Observation (EO) technologies enable high-resolution soil moisture estimation by integrating synthetic aperture radar (SAR), multispectral imagery, and ground-based measurements. This study describes a comprehensive methodology which is currently under development for near surface soil moisture estimation tailored to the diverse agricultural landscapes of Greece.

The primary objective is to develop and implement a national-scale soil moisture estimation methodology utilizing data from Sentinel-1 and Sentinel-2 satellites, supplemented by an in-situ soil moisture sensors network. The study region encompasses agricultural areas with heterogeneous soil types, land cover, and topographic variations, addressing the complexity of soil moisture dynamics in Mediterranean climates.

Ground truth data for model calibration and validation is provided by a network of IoT-based soil moisture sensors strategically placed to capture diverse soil textures and land cover classes. The network builds on existing stations and introduces additional sensors to enhance spatial coverage and data representativeness for top-soil moisture dynamics. The monitoring network was designed using geospatial analysis techniques considering all the biophysical features influencing soil moisture dynamics.

The methodology includes preprocessing dual-polarization backscatter data (VV and VH) from Sentinel-1 SAR imagery. Vegetation effects on the backscatter signal are corrected using the Water Cloud Model (WCM), parameterized with NDVI from Sentinel-2 and empirical coefficients derived from field measurements. Corrected soil backscatter is combined with ancillary data and fed into machine learning models, including Random Forest and Artificial Neural Networks, trained on in-situ soil moisture observations. Model performance is evaluated using metrics such as RMSE and R² to ensure predictive accuracy. The resulting high-resolution soil moisture maps reflect dynamic spatial and temporal variations with enhanced precision.

Preliminary results highlight the feasibility of integrating satellite and in-situ data for national-scale soil moisture mapping. WCM-based corrections significantly enhance SAR-derived backscatter accuracy, while machine learning models demonstrate strong predictive performance. The scalable methodology offers valuable insights for optimizing agricultural practices and water resource management.

How to cite: Kalivas, D., Dosiadis, E., and Soulis, K.: Estimating top-soil moisture at high spatiotemporal resolution in a highly complex landscape, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11864, https://doi.org/10.5194/egusphere-egu25-11864, 2025.

Soil moisture (SM) is a critical variable within the hydrological cycle, closely linked to weather patterns and climate change. However, the limited availability of high-resolution SM datasets, constrained by the resolution of satellite sensors, poses significant challenges for field-scale research, which is essential for agricultural management and precision irrigation planning. This study addresses this limitation by estimating daily SM across surface (0-5 cm), active rootzone (0-30 cm), and unsaturated zone (0-120 cm) depth at a spatial resolution of 10 m. A random forest (RF) regression model was developed using in-situ SM measurements as the training dataset. The predictor variables included meteorological parameters, topographic features, soil texture properties, Sentinel-1 synthetic aperture radar (SAR) signals, groundwater depth, and vegetation indices derived from Sentinel-2 imagery.

Model predictions were validated in the Twente and Raam regions of the Netherlands over a one-year period (2023-05-18 to 2024-05-18), using independent observations from six in-situ SM stations that were excluded from both the training and testing phases. The results indicated strong model performance, with unbiased root mean square error (ubRMSE) values ranging from 0.03 to 0.08 cm³/cm³ and Pearson correlation coefficients (R) from 0.71 to 0.90. Comparisons with the European Space Agency Climate Change Initiative (ESA CCI) SM product further corroborated the model’s accuracy. While the model effectively captured daily SM dynamics, particularly during winter months, some discrepancies, such as over- or under-estimations, were noted during the summer. These high-resolution SM estimates provide valuable insights for precision agriculture and hydrological research, enhancing decision-making processes in these fields.

How to cite: Duan, T., Zeng, Y., and Su, Z.: Estimating multi-depth daily soil moisture at 10 m resolution using SMAP SSM and Sentinel-1/2 data based on random forest regression algorithm, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12272, https://doi.org/10.5194/egusphere-egu25-12272, 2025.

Remote sensing and model-based soil moisture datasets now provide global, frequent, and overall consistent surface soil moisture estimates. However, the complexity of hydrological processes involved in soil moisture evolution, especially over challenging environments may exceed the capabilities of these datasets. 

Each type of soil moisture product has inherent limitations depending on their technique (e.g., coverage, interferences, signal-to-noise ratio, residual trends). However, there are soil moisture processes' characteristics, such as heterogeneity and transient states, that can affect each product differently depending on their capabilities across a range of spatial and temporal scales. Often, the performance of soil moisture products concerning these process-related factors is overlooked. Given the wide range of features (e.g., resolution, frequency, coverage) of current global soil moisture products, intercomparing them in complex soil moisture regimes can inform about their suitability for monitoring soil moisture in challenging environments of compromised resilience at regional and global scale. 

This study evaluates several cutting-edge surface soil moisture products for effective monitoring, including (1) active remote sensing (from ASCAT and Sentinel-1 radar data), (2) passive remote sensing (ESA Climate Change Initiative passive dataset (CCIp) and NASA Soil Moisture Active Passive (SMAP) mission), and (3) model-based products (GLOFASv4 using the LISFLOOD model). The intercomparison is applied across regions with distinct challenging soil moisture regimes, such as Africa's monsoonal belts, Europe's convective storm corridors, and the Mediterranean basin. These areas, often less understood and instrumented, are characterized by regime transitions differing in spatial and temporal scale, and range and pace of soil moisture alteration, making them useful for testing soil moisture products beyond the ordinary range used to test their performance. The study period is 2016-2022, with a 5 x 5 km resolution and two temporal resolutions 10-day period and daily scale (considering the revisit times often span several days). Validation uses surface soil moisture data from the International Soil Moisture Network (ISMN) across Europe and Africa. 

Results indicate that hydrological monitoring focused on the long-term evolution of soil moisture (e.g., water resources assessment, drought, rainfed agriculture monitoring) is consistent across scales and environments for most products. However, monitoring soil moisture in areas with high spatial and temporal heterogeneity is more uncertain. Sentinel-1 data, with its high spatial resolution, excels in identifying patterns even at local scale but has limitations in temporal coverage, better addressed by products of short revisit times like ASCAT or model-based datasets less sensitive to time. CCIp, despite resolution constraints, effectively reproduces heterogeneity of the spatial patterns in the semi-arid areas of quick regime transitions, where active remote sensing and model-based estimates struggle. The more event-driven the process, the more uncertain the estimate of soil moisture evolution becomes, thus highlighting the need for higher temporal frequency over spatial resolution for near-real-time monitoring of impactful short-term events (e.g., floods, flash droughts). The study emphasizes the worth of evaluating products from the perspective of the target processes and encourages further research on their suitability to monitor soil moisture in unconventional conditions of regional and global relevance. 

How to cite: Gaona, J., Brocca, L., and Filippucci, P.: Suitability of remotely sensed and model-based soil moisture datasets for effective monitoring over regions of distinct challenging soil moisture regimes. , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13383, https://doi.org/10.5194/egusphere-egu25-13383, 2025.

Information about wetness conditions of the soil is beneficial to hazard prediction and monitoring, water resources management, and weather and climate predictions. For most of these applications, it is important not only that the estimates are accurate, but also at high spatial resolution. Soil moisture can be measured in-situ, remotely or estimated by models. While in-situ measurements are considered the most accurate, networks are very expensive to maintain and provide very sparse coverage, allowing to monitor specific locations but not obtain spatially distributed information. Conversely, remote sensing products can provide estimates over large areas (globally), but lack the spatial resolution required for these applications.

Here, we focus on Switzerland, with the goal of exploring different alternatives for obtaining soil moisture information. We compare existing products (satellite products, in situ observations, hydrological models) with two methods developed here: a downscaling approach, downscaling satellite observations (SMAP), and one based on upscaling of in situ observations. The former overcomes classical limitations of downscaling (i.e., being spatially limited to existing soil moisture observations stations and the scale mismatch) by training a Machine Learning (ML) downscaling model on physic-based simulations (ParFlow-CLM). The latter, similarly, takes advantage of physics-based simulations (Tethys-Chloris) to train a ML model able to predict soil moisture at any given location provided local meteorology (precipitation and temperature) as well as observed soil moisture at existing stations.

We compare all these products among each other and with in situ observations, both temporally, comparing timeseries at stations’ locations, and spatially, comparing daily values at the different station locations. This comparison allows to assess the quality of the different products and even to identify issues with stations’ observations. We find the upscaling approach compares best to observations, but it also uses them as inputs. Interestingly, when looking at the spatial standard deviation (std) of the different products at stations, the lack of variability of the satellite product (too small std) is improved in the downscaled version. This demonstrates that while the scale mismatch does not allow direct comparison with stations (250x250m² resolution of the downscaled product, a few centimeters for the in-situ measurements), the downscaling is very beneficial, adding higher resolution spatial variability.

How to cite: Leonarduzzi, E., Bircher-Adrot, S., Humphrey, V., Maxwell, R. M., and Stähli, M.: Towards a high spatial resolution soil moisture product for Switzerland: observations, modeling, downscaling, and upscaling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16425, https://doi.org/10.5194/egusphere-egu25-16425, 2025.

The need for more accurate, higher-resolution and longer-lasting weather forecasts 
has continued to increase in recent years. In order to meet this need, the input data 
used must, among other things, be provided in high resolution. This is a problem for
soil moisture. 
Large-scale observations of soil moisture are usually carried out using space-born 
microwave instruments. A high spatial resolution requires a large antenna. The maximum 
spatial resolution is therefore limited by the design of the satellite. 

The proposed algorithm for a higher resolution soil moisture product combines a low 
resolution microwave based soil moisture product with higher resolution reflectivities 
in the red and near infrared. This allows to use any microwave product in combination 
with any imager featuring AVHRR heritage channels.
The soil moisture for vegetated pixel is derived by the NDVI itself and for bare land
from water absorption. To prevent soil moisture values from drifting and to make
sure the overall good quality on the rough scale is preserved, a scaling towards
microwave product is performed.

In this presentation we will show intercomparisons of derived soil moisture with in
situ surface observations. Different sources of errors will be discussed and possibilities
to reduce their influence.

How to cite: Scheirer, R., Dybbroe, A., and Raspaud, M.: Towards High Resolution Soil Moisture Observation from Space, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16848, https://doi.org/10.5194/egusphere-egu25-16848, 2025.

Soil moisture (SM) is the amount of water contained in soil and is a key variable at earth surface which controls many processes like erosion, evapotranspiration and infiltration. In deeper layers and root zone area, it controls vegetation health and surface coverage conditions. Traditional methods for SM monitoring, such as field-based measurements, are accurate but provide only point-based results. Given the heterogeneous nature of SM across time and space, remote sensing and machine learning (ML) techniques have emerged as valuable tools. These approaches can efficiently handle large datasets, and provide measurements at regular intervals, offering an innovative alternative for SM estimation in large scale regions.

In this study we evaluated the potential of multi-spectral optical remote sensing for SM estimation by examining the dependency between Sentinel-2 images and SM measurements from two datasets: the REMEDHUS network (Spain) and the SMOSMANIA network (France). The REMEDHUS network is located in an agricultural region while the SMOSMANIA network spans a 400 km transect from the Mediterranean Sea to the Atlantic Ocean. Both networks provide hourly SM measurements at the depth of -5cm, with additional measurements at depths of -10cm, -20cm, -30cm for the SMOSMANIA network. To achieve this, Harmonized Sentinel-2 (MSI) data, from Google Earth Engine, were used to predict SM. Surface reflectance from 12 spectral bands, along with Normalized Difference Vegetation Index (NDVI), Normalized Difference water Index (NDWI) and Enhanced Vegetation Index (EVI) were used as features in regression models and recorded SM (close to the time of satellite overpass) was taken as the target variable. To ensure consistency in the analysis, the Sentinel-2 image collection was filtered by location (the coordinate of each sensor), study period (2017-2022) and cloud cover (maximum acceptable cloud cover = 10%).  Later all spectral bands were resampled to a uniform spatial resolution of 10 meters.

Three ML algorithms were applied to model the relationship between predictor variables and SM: Random Forest Regression (RF), Support Vector Regression (SVR), and Gradient Boosting Regression (GBR). Model performance was assessed using Root Mean Square Error (RMSE). For the REMEDHUS network, RF achieved the best performance with an RMSE of 0.08319 m³/m³. In the SMOSMANIA network, all three algorithms performed best at a depth of -20 cm, with SVR achieving the lowest RMSE (0.0591 m³/m³). Additionally, the weighted vertical average from the SVR model yielded the lowest overall RMSE of 0.0551 m³/m³.

Comparisons between actual and predicted SM values for each testing sensor confirm the role of land-use type on model’s performance. Another consideration is the model's ability to predict SM within specific moisture content ranges. Although data from the SMOSMANIA and REMEDHUS networks exhibit completely different measured SM values and originate from different land use types, both models demonstrate optimal performance within the range of 0.1 to 0.4 m³/m³, with RMSE = 0.034 for REMEDHUS network and RMSE= 0.04 for SMOSMANIA network.

How to cite: Gachpaz, S., Boni, G., Moser, G., and Federici, B.: Machine Learning-Based Soil Moisture Estimation Using Sentinel-2 MSI Data: Case Studies from the REMEDHUS (Spain) and SMOSMANIA (France) Networks, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18781, https://doi.org/10.5194/egusphere-egu25-18781, 2025.

Soil moisture is crucial for ecosystem functioning, as it influences biological and biogeochemical processes. It regulates water, energy, and carbon cycles, playing a key role in ecosystem organization, biodiversity, and vegetation resilience. However, soil moisture dynamics are increasingly impacted by climate change. Shifts in precipitation patterns, rising temperatures, and intensifying droughts are amplifying spatio-temporal variability. These challenges highlight the need for reliable representations of soil moisture, to enable adaptive forestry practices, and strategies to mitigate ecosystem vulnerabilities. In general, topographic models are considered as reliable sources for representing soil moisture.

Extensive research has validated topographic modelling of soil moisture, but most studies have focused on northern regions, leaving a scarcity of empirical data for Central Europe. This study investigated five sites in temperate forests of Germany, dominated by cambisols, with over 2,000 measurement locations. The objectives were to (1) analyse the spatio-temporal variability of soil moisture, (2) examine correlations with topographic indices under varying seasonal conditions, and (3) validate soil moisture estimates provided by the ERA5-Land dataset.

The results indicated that temporal variability in soil moisture was approximately 3.6 times greater than spatial variability. Flow-accumulation-based indices were poor predictors of spatial moisture patterns. The variability explained (R²) by indices such as the depth-to-water index ranged between 1% and 4% only and did not align with expected seasonal trends. Mesorelief, represented by the topographic position index, showed weak but consistent correlations at selected sites. Temporal variations in soil moisture were effectively captured by ERA5-Land reanalysis data, with site-specific adaptations yielding R² values of up to 98%.

These findings reveal the limitations and potential applications of soil moisture modelling. Moreover, they can contribute to improving soil–plant–atmosphere models and inform sustainable forest management strategies in the context of a changing climate.

How to cite: Schönauer, M., Winkler, M., and Drollinger, S.: Spatial variations in soil moisture in temperate forest independent of topographic moisture indices, yet ERA5-Land retrievals accurately reflect their temporal variations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19477, https://doi.org/10.5194/egusphere-egu25-19477, 2025.

Surface soil moisture (SSM) products derived from microwave remote sensing technology are currently operational at coarse resolutions (10-40 km) and global scale. Despite the utility of existing Earth Observation (EO) SSM products, there is significant scientific interest in enhancing the ability to resolve fine-scale surface heterogeneity. High spatial resolution soil moisture patterns (e.g., 0.1-1 km) can improve our quantitative understanding of the soil-vegetation-atmosphere system and enhance applications such as mapping the impact of irrigation on local water budgets, assessing the effects of local soil moisture variability on atmospheric instability, and improving numerical weather prediction (NWP) and hydrological modeling at regional scales. Additionally, these high-resolution data are crucial for hydrometeorological research focusing on extreme weather events in the context of climate change.

The European Copernicus program, with its sustained observation strategy using Synthetic Aperture Radar (SAR) sensors, including the European Radar Observatory Sentinel-1 (S-1), the S-1 Next Generation satellites, and the forthcoming EU L-band Radar Observation System for Europe (ROSE-L), motivates and stimulates the development of operational land surface monitoring at high spatial resolution.

From the EO SSM validation perspective, significant efforts have been made to define protocols, identify reference measurements (RMs), and address the spatial mismatch between EO SSM products and RMs, which are typically point-scale measurements from hydrologic networks. However, this process is still ongoing, particularly for high-resolution SSM products, and requires a collaborative effort among different scientific communities to achieve metrologically traceable EO SSM.

This paper presents the European project “Metrology for Multi-Scale Monitoring of Soil Moisture” (SoMMet), which aims to establish a metrological basis and harmonization in soil moisture measurements across scales, from point scale to remote sensing, through cosmic ray neutron sensors (CRNS). These sensors are characterized by different measurement supports in the horizontal, vertical, and temporal dimensions. A key aspect of the project is to conduct field campaigns at three high-level field sites across Europe: Marquardt in Northern Germany, Bondeno in Northern Italy, and the Apulian Tavoliere in Southern Italy. The comparison of soil moisture data from point scale, CRNS, and S-1 SSM at these experimental sites is discussed, and recommendations on EO SSM validation practices are provided.

Acknowledgment: The project 21GRD08 SoMMet has received funding from the European Partnership on Metrology, co-financed from the European Union’s Horizon Europe Research and Innovation Programme and by the Participating States.

How to cite: Balenzano, A., Mattia, F., Satalino, G., Palmisano, D., Lovergine, F. P., Oswald, S. E., Schrön, M., Baroni, G., Emamalizadeh, S., Kjeldsen, H., Klahn, E. A., Rinaldi, M., Ciavarella, F., and Zboril, M.: Metrology for Multi-Scale Soil Moisture Monitoring (SoMMet) and High-Resolution Earth Observation Validation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13440, https://doi.org/10.5194/egusphere-egu25-13440, 2025.

Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) extracts information about the surrounding environment of a ground-based GNSS antenna by analyzing the differences between direct signals and reflected multipath signals. In recent years, the use of GNSS-IR has become increasingly prevalent, driven by the growing demand for environmental monitoring. One of the most critical parameters for monitoring the environment is soil moisture due to its role in the water-heat transfer and energy exchange between the soil and the atmosphere, influencing the hydrological cycle. Soil moisture has been estimated via GNSS-IR using the GNSS multipath signal phase, which is determined by using observed Signal-to-Noise Ratio (SNR) values, because of a strong link between the multipath signal phase and the soil moisture. However, although there are thousands of continuously operating GNSS stations worldwide, most are used for navigation, and their potential to be used in GNSS-IR is yet to be fully explored both as standalone stations and as part of a broader global network.

The International GNSS Service (IGS) network is one of the most expansive global GNSS networks.  While the IGS stations vary in installation and surroundings, they collectively provide global coverage and continuously accessible and reliable data. We evaluated each station against criteria such as minimum antenna height and relevant surrounding topography. We found that several IGS stations can be utilized to estimate soil moisture, as approximately 33% are suitable for GNSS-IR. Each station's coverage can reach hundreds of square meters depending on the GNSS antenna height. Next, we use discrete soil moisture estimates based on optical remote sensing multispectral data, such as Sentinel-2 data, to fit a model that continuously estimates soil moisture using the suitable IGS stations' GNSS multipath signal SNR data. These discrete estimates can estimate volumetric soil moisture with a precision of around 0.02 [m^3/m^3] for a pixel's area of around [10 x 10] [m]. When combined with GNSS-IR data, they enable continuous soil moisture estimation with a comparable precision for the same area.

This approach enables establishing a global, continuous soil moisture monitoring system that leverages the continuous observations of GNSS-IR and the extensive coverage of the IGS network. Unlike optical remote sensing satellites, which are constrained by a 3–5-day revisit time, this system provides consistent, weather-independent measurements. By combining the continuous monitoring capabilities of GNSS-IR with the discrete, in-situ-independent soil moisture estimates from optical remote sensing, the system holds promise for global and continuous soil moisture monitoring with decent precision.

In this study, we present the initial results of a global soil moisture monitoring system utilizing data from several IGS stations located across various regions worldwide. Over a limited timeframe, we provide daily and sub-daily soil moisture estimates, demonstrating the system's potential for continuous and reliable environmental monitoring.

How to cite: Awad, R., Kizel, F., and Even-Tzur, G.: Proposal for A Global Soil Moisture Monitoring System Using GNSS-IR and Optical Remote Sensing, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20282, https://doi.org/10.5194/egusphere-egu25-20282, 2025.

Soil Moisture is a vital state variable influencing land surface dynamics across hydrological, meteorological, and climatological contexts. The Soil Moisture Operational Product System (SMOPS), developed by the National Environmental Satellite, Data, and Information Service (NESDIS) of National Oceanic and Atmospheric Administration (NOAA), has been operationally providing satellite soil moisture observational data products for scientific studies and numerical weather and water predictions. However, the lack of a high-quality long-term SMOPS product has led to pronounced fluctuations in data quality across distinct versions and notable uncertainties for climatological studies and prolonged data assimilation operations. To address these issues, NESDIS has reprocessed SMOPS with all available satellite soil moisture observations to generate a Climate Data Record (SMOPScdr). SMOPScdr incorporates advancements of using machine learning approaches, satellite radiances calibration, inter-satellite bias correction, and observation-driven quality control. The reprocessed product offers improved accuracy, expanded spatial coverage, and an extended observation period from 2002 to present. The advancement makes this product valuable for both meteorological and climatological studies. SMOPScdr has been compared to in situ observations and Soil Moisture Active and Passive data, demonstrating consistent performance and superior spatiotemporal coverage. We showcase a range of successful scientific and operational applications of this new product in climate change research, flood and drought monitoring. The initial release of the SMOPScdr data is now available to the public and will undergo further refinement based on feedback from the scientific, operational and industrial communities. This study outlines the development and evaluation of the SMOPScdr product, highlights its potential applications, and invites users to shape future directions for its improvement.   

How to cite: Yin, J., Zhan, X., and Liu, J.: Reprocessed NOAA SMOPS Blended Soil Moisture Product as a Climate Data Record , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20312, https://doi.org/10.5194/egusphere-egu25-20312, 2025.

Soil moisture is fundamentally important for drought monitoring, hydrologic forecasting, weather and climate predictions, agriculture and forest management, and many other applications. Advancements in satellite observations and land data assimilation systems (LDAS) have created new opportunities for field-scale soil moisture monitoring locally and across the globe. Using satellite and LDAS data to estimate soil moisture across multiple scales would benefit many applications and scientific research, especially for locations where ground soil moisture observations are not available. In this study, we introduce a deep learning emulator, namely Scalable Deep Learning for Soil Moisture Monitoring (SDLS), for root-zone soil moisture monitoring from the field to the regional scales. The SDLS method uses LDAS forcings and simulations from sampled locations to a bidirectional long short-term memory (B-LSTM) deep learning model, and is further applied to 30-m satellite-based evapotranspiration (ET), land cover, and topographic data, and digital soil property data. Evaluation of SDLS emulations demonstrates robust performance, with a mean squared error (MSE) below 0.0004, a Pearson correlation coefficient exceeding 0.8, and a Kling-Gupta Efficiency (KGE) score above 0.75 against LDAS soil moisture. SDLS method can generate daily soil moisture at 30-m resolution and can capture field-scale variability and drought, well matching with in situ observations. With additional deep learning postprocessing, the performance of the SDLS soil moisture against in situ observations can be further improved. The strength of the SDLS method lies in its ability to leverage process-based physical knowledge in land surface models to estimate soil moisture using satellite observations in a scalable way, which can be readily applied to new locations without the need for ground observations.  

How to cite: Kumar, S. and Tian, D.: A deep learning emulator for scalable soil moisture monitoring based on satellite and land data assimilation system, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21925, https://doi.org/10.5194/egusphere-egu25-21925, 2025.