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.
Passive microwave remote sensing has made significant strides in the estimation of soil moisture; however, several challenges persist. A ground-based radiometry experiment conducted in an agricultural setting sought to enhance microwave emission models and retrieval algorithms for soil moisture and vegetation water content. The multi-frequency, dual-polarized brightness temperature (TB) data demonstrated a strong correlation with surface soil moisture. Importantly, surface roughness and vegetation water content were found to significantly influence the relationship between brightness temperature and soil moisture. Calibration of the τ-ω model led to improved performance. By utilizing the experimental data alongside the calibrated model, we evaluated the effectiveness of single-channel (SCA), dual-channel (DCA), and multi-channel (MCA) algorithms for estimating soil moisture and vegetation water content within single-objective estimation (SOE) and dual-objective estimation (DOE) frameworks. The findings revealed that SOE outperformed DOE, with MCA-SOE achieving the highest level of accuracy. Overall, this study lays the groundwork for the further development of passive microwave remote sensing methodologies aimed at estimating soil moisture and vegetation water content.
How to cite: Ma, C., Zhang, Y., and Li, X.: Ground-Based Microwave Radiometry Experiment for the Calibration of Emission Models and Retrieval of Soil Moisture, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7733, https://doi.org/10.5194/egusphere-egu25-7733, 2025.
Surface soil moisture is an important variable in the climate system, controlling the exchange of water, energy and carbon between the Earth's surface and the atmosphere. The quantification of surface soil moisture is also necessary for the simulation of climate change, the prediction of floods and droughts, and the optimal irrigation of agricultural land. Satellite altimeters are often used to monitor water levels (oceans and inland waters such as lakes, rivers and reservoirs) and the dynamics of ice sheets. However, due to the influence of surface roughness on the return waveforms captured by altimeters, they can also be used to estimate surface properties such as soil moisture. Against this background, the main objective of this study is to investigate the potential of conventional altimeters (Low Resolution Mode (LRM) satellite altimeters such as Jason series satellites, Envisat, Saral and...) and new generation altimeters (Synthetic Aperture Radar (SAR) satellite altimeters such as Cryosat-2, Sentinel-3 and Sentinel-6) in the estimation of surface soil moisture in the semi-arid region of Spain over the period 2016 to 2023. To achieve this goal, Level 2 (L2) data from the SRAL altimeter sensor of the Sentinel-3A satellite along the 644 pass and the Geophysical Data Record (GDR) from the Poseidon-3B altimeter sensor of the Jason-3 satellite along the 213 pass were used. In addition to the different acquisition geometry of these two altimetry satellites, the effectiveness of the re-tracking algorithms used in them for estimating soil surface moisture was also questioned in this study. The relationships between the observed backscatter coefficients derived from 4 re-tracking algorithms (re-tracker: Ocean re-tracker, OCOG re-tracker, Sea-ice re-tracker and Ice-sheet re-tracker) in the L2 data of the Sentinel-3A satellite and additionally 3 re-tracking algorithms (re-tracker: MLE-4 re-tracker, MLE-3 re-tracker and Ice re-tracker) in the GDR data of the Jason-3 satellite and the surface soil moisture obtained from ground stations (the ground station closest to the satellite pass was selected) were investigated. The results of the analysis show a strong linear relationship between the scattering coefficients derived from the satellite data and the corresponding soil moisture measurements obtained from ground stations along the coverage of the two satellites. The best results (highest correlation coefficient) for the Sentinel-3A and Jason-3 satellites were obtained with the Ocean Re-tracker (with a correlation coefficient of 0.75) and the Ice Re-tracker (with a correlation coefficient of 0.7), respectively. The MLE-3 re-tracker in the Jason-3 satellite has also obtained a result almost similar to the ICE re-tracker in one of the ground stations. While the results express the high performance of the Sentinel-3A and Jason-3 satellites in estimating surface soil moisture, they show the superiority of synthetic aperture radar altimeters over conventional altimeters in estimating surface soil moisture in the study area.
How to cite: Mardoukhi, M.: Investigate the potential of satellite altimeters in estimating surface soil moisture in semi-arid areas, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11200, https://doi.org/10.5194/egusphere-egu25-11200, 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 (SMCYGNSS) 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 SMCYGNSS (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.
Cosmic-ray neutron sensing (CRNS) is a non-invasive method to retrieve root zone soil moisture as daily time series for an area integrated that is up to 0.1 km². This area can be expanded by combining several CRNS-stations into one cluster, not only increasing its extent as an integral but also allowing for differences inbetween them to represent spatially varying conditions. Such a cluster has been established in Maquardt, Potsdam, Germany end of 2019, with a mixed land use of cropped fields, meadows and orchards. This CRNS cluster has been expanded by additional CRNS stations and side measurements during 2023 to improve its capability to serve as soil moisture reference network for satellite remote sensing such as the ESA Sentinel-1 Earth Observation mission. This was part of the EU-wide collaboration project SoMMet addressing soil moisture observations from a metrological perspective, with a focus on CRNS to bridge the scale between point measurements and remote sensing.
We will outline the design and capabilities of this specific CRNS cluster. It includes not only 16 CRNS stations, partly with very high device sensitivity, but also a soil moisture network of point sensors, as each CRNS stations includes a profile soil moisture probe down to 40 cm at least and additional shallow soil moisture measurements at 5 and 15 cm depths. Overall, this constitutes a triple network (CRNS, shallow soil moisture, root-zone soil moisture) covering about completely 0.5 km²; in its core area CRNS stations are placed densely, and with spacing increasing to the outside an outer area of about 2 km² is covered in a non-dense way. CRNS stations were individually calibrated by individual measurement campaigns to achieve a high-accuracy and representativeness for its location. We will present first results from the first-year of operation (2024) in its final, full cluster design and discuss its value in respect to future use as reference network or establishment as fiducial reference measurement.
Acknowledgment: The project Cosmic Sense has received funding from German Research Foundation (DFG, roject number 357874777) and 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: Oswald, S. E., Marret-Sicard, E., Große, P., Scheiffele, L., Dimitrova Petrova, K., Bíró, P., Schrön, M., Altdorff, D., Heistermann, M., and Francke, T.: A first CRNS cluster for soil moisture retrieval designed for comparison with remote sensing, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19901, https://doi.org/10.5194/egusphere-egu25-19901, 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.
Accurate soil moisture estimation is fundamental for optimizing irrigation strategies, enhancing crop yields, and managing water resources efficiently. This study harnesses time-series RGB-thermal imagery to assess soil moisture throughout various growth stages of corn, emphasizing depth-specific soil moisture estimation and time-series analysis of canopy information such as canopy structure and canopy spectral across growth stages. By integrating a comprehensive dataset that covers the full spectrum of the growing season from early to late stages. we evaluated soil moisture at multiple depths including 10, 20, 30, and 40 cm. Sophisticated regression models such as Gradient Boosting Machines (GBM), Least Absolute Shrinkage and Selection Operator (Lasso), and Support Vector Machines (SVM) were employed to analyze the effects of spectral indices, land surface temperature (LST), and structural canopy variables on soil moisture estimation accuracy. Our results reveal that thermal variables, particularly LST, exhibit significant correlations with soil moisture at shallower depths, especially in non-irrigated plots where moisture variability tends to be greater. The GBM model performed exceptionally well, achieving a coefficient of determination (R²) of 0.79 and a root mean square error (RMSE) of 1.86 % at a depth of 10 cm, showcasing its precision in moisture prediction. At a depth of 30 cm, the GBM model still demonstrated robust performance with an R² of 0.69 and an RMSE of 3.38 %, adapting effectively to different canopy densities and soil conditions. As canopy density increased, the effectiveness of LST in predicting soil moisture decreased, underscoring the dynamic interaction between plant growth stages and moisture estimation accuracy.
How to cite: shafian, S.: Depth-specific soil moisture estimation in vegetated corn fields using a canopy-informed model: A fusion of RGB-thermal drone data and machine learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20602, https://doi.org/10.5194/egusphere-egu25-20602, 2025.
The increase in extreme weather events due to climate change has led to irregular patterns in the hydrological cycle. In East Asia, a region characterized by a monsoon climate, natural disasters such as droughts and floods have become increasingly prevalent. This trend has underscored the necessity for effective soil moisture monitoring, as it is a crucial element in the hydrological cycle. To this end, various machine learning techniques based on satellite data combined with in-situ soil moisture observations are being actively researched for precise soil moisture estimation. However, the existing satellite images have limitations in temporal resolution compared to in-situ observations, and there is a need to improve the temporal resolution. In this study, soil moisture was estimated by linear regression using Cyclone Global Navigation Satellite System (CYGNSS) reflectivity and Soil Moisture Active Passive Level 2 (SMAP L2) soil moisture, vegetation opacity, surface roughness, and soil surface temperature in East Asia. The CYGNSS-based soil moisture was validated alongside SMAP Level 4 (L4) and Advanced SCATterometer (ASCAT) L2 data using Extended Triple Collocation (ETC) analysis, which demonstrated the high accuracy of CYGNSS. The results of this study provide high temporal resolution soil moisture data for East Asia, which can contribute to efficient hydrological factor monitoring and management.
Acknowledgment
This research was supported by the BK21 FOUR (Fostering Outstanding Universities for Research) funded by the Ministry of Education (MOE, Korea) and National Research Foundation of Korea (NRF). This work is financially supported by Korea Ministry of Land, Infrastructure and Transport (MOLIT) as「Innovative Talent Education Program for Smart City」. This work was supported by Korea Environment Industry & Technology Institute (KEITI) through Research and Development on the Technology for Securing the Water Resources Stability in Response to Future Change Project, funded by Korea Ministry of Environment (MOE)(RS-2024-00332300). This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2024-00416443). This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2022R1A2C2010266).
How to cite: Li, Y., Kim, D., and Choi, M.: Combining CYGNSS and SMAP for Soil Moisture Estimation in East Asia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21929, https://doi.org/10.5194/egusphere-egu25-21929, 2025.
Accurate and continuous root zone soil moisture (RZSM) data are crucial for important applications such as water resources management, flood and drought monitoring, irrigation planning and timing, agricultural productivity, climate change adaptation, and climate modeling. Direct measurement of RZSM using in-situ sensors can be time-consuming and costly, particularly over large areas. The Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellites provide surface soil moisture (SSM) data at L-Band and on a global scale. Due to the relationship between SSM and RZSM, satellite SSM data can be assimilated into land surface models to estimate RZSM data. The SMAP Level-4 Surface and Root Zone Soil Moisture (SMAP RZSM) product includes RZSM (0–100 cm), 3-hourly, 9 km on EASE-Grid 2; and the SMOS Level 4 RZSM CATDS (SMOS RZSM) product provides daily RZSM (0–100 cm), 25 km spatial sampling on EASE-Grid 2 on a global scale. This study aimed to evaluate the accuracy and efficiency of RZSM data derived from SMAP RZSM and SMOS RZSM products compared to in-situ measurements collected from IRIMO (Islamic Republic of Iran Meteorological Organization) sites from 2017 to 2020 in different regions of Iran. The evaluation results indicate that the SMOS RZSM data has higher accuracy compared to the SMAP RZSM data. The SMOS RZSM data has a root mean square error (RMSE) ranging from 0.03 to 0.09 m3m−3 and an unbiased root mean square error (ubRMSE) ranging from 0.03 to 0.05 m3m−3. The SMAP RZSM data has an RMSE range of 0.03 to 0.21 m3m−3 and an ubRMSE range of 0.02 to 0.08 m3m−3.
How to cite: Jamei, M., Asadi Oskouei, E., and Jamei, M.: Assessment of Root Zone Soil Moisture Products from the Passive Microwave sensors over Iran, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-549, https://doi.org/10.5194/egusphere-egu25-549, 2025.
In this study, a Convolutional long short-term memory network (ConvLSTM) is employed to forecast soil moisture for three Indian river basins with varying climatic conditions namely Godavari, Narmada and Cauveri. Utilizing a complete dataset from AgERA5 ranging over a decade, a number of meteorological forcings inducing soil moisture dynamics are incorporated to inform our forecast model. Starting with a global scale, the data undergo rigorous preprocessing, being refined to cater to the Indian basin scale, and subsequently tailored for our deep learning paradigm. By configuring the methodology around ConvLSTM network, the intrinsic patterns within the dataset were captured. This unification of Convolution neural network (CNN) and Long-short term memory network (LSTM) safeguarded complete data processing in both spatial and temporal perspectives, thereby bestowing an unparalleled basis for dismembering complex spatial-temporal sequences, making it ideal for tasks like soil moisture forecasting using extensive meteorological data. An all-inclusive evaluation of the proposed network is presented in form of a comparative analysis with four baseline models across all the river basins mentioned. Results in terms of evaluation metrices, underscore the ConvLSTM-based model’s ability in untying the nuanced spatial and temporal variability of soil moisture ahead of the baseline models. The robustness of the proposed network is further scrutinized by correlating ConvLSTM-derived soil moisture forecasts with those derived from another satellite-based product, namely the Soil Moisture and Ocean Salinity (SMOS), juxtaposed against the AgERA5 reanalysis data for 3-day and 5-day forecast horizons across the same river basins, showing good correlation. Such proficiency, overlay the means for possible progressions in agricultural approaches, improved drought prediction, and advanced management of water resources across various Indian river basins.
How to cite: Nath, K., Kasiviswanathan, K. S., and Nayak, P. C.: Forecasting soil moisture dynamics across diverse Indian river basins using a hybrid ConvLSTM model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-555, https://doi.org/10.5194/egusphere-egu25-555, 2025.
Recently, Korea and the rest of the world have been experiencing significant changes in rainfall patterns, leading to an increase in sudden runoff caused by abrupt rainfall events. This phenomenon, referred to as “Abnormal flood,” has resulted in accumulating damage. Runoff responses to rainfall events vary depending on surface cover conditions. In the mid- and upstream regions of rivers with relatively low impermeable layers, antecedent soil moisture saturation plays a significant role. In Korea, soil moisture observations are characterized by much lower observation density and shorter data records compared to variables such as rainfall, river water levels, and discharge, making statistical estimation challenging. Additionally, uncertain soil moisture data from unmeasured watersheds have been used in traditional watershed runoff estimation models to adjust assumed values of spatial hydrological characteristics within watersheds, thereby correcting and back-calculating runoff. While runoff calculated based on such assumed soil moisture saturation values demonstrated high reliability under traditional hydrological conditions, the reliability of runoff prediction results has relatively decreased in the face of vast data produced by advanced real-time monitoring technologies and hydrological changes due to climate change. To address these issues, since 2022, this study has developed a model to produce relatively reliable watershed antecedent soil moisture saturation observation data by synchronizing satellite and ground-based soil moisture data. In Korea, ground-based soil moisture sensors measure soil moisture at depths of 0–10 cm, while satellite observation data measure soil moisture at depths of 0–5 cm. Therefore, synchronization of these two data sources is essential. This study developed a synchronization model for satellite and ground-based soil moisture data using machine learning and analyzed the correlation between watershed soil moisture variations estimated by the model and the occurrence of sudden runoff within watersheds. Although soil moisture saturation of watershed soils significantly impacts runoff hydrologically, soil moisture has shown relatively low importance in runoff estimation models due to the lack of accurate data. The high-resolution watershed soil moisture data produced by this study are expected to enhance the accuracy of runoff analysis results, thereby improving the reliability of anomalous flood prediction and analysis models.
How to cite: Lee, J.-B., Jung, Y. B., Ha, M. S., and Yang, J.-S.: Watershed Runoff Correlation Analysis through the Development of a Synchronization Model for Satellite and Ground-Based Soil Moisture Data in Korea, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15566, https://doi.org/10.5194/egusphere-egu25-15566, 2025.
Accurate estimation of soil moisture is vital for sustainable water management in agriculture, particularly in olive orchards where precise irrigation strategies are crucial for maintaining productivity and crop quality. Climate change intensifies water scarcity, intensifying the need for advanced methodologies to optimize agricultural water use. Remote sensing technologies, such as Synthetic Aperture Radar (SAR), have emerged as promising tools for monitoring soil moisture over large areas. When combined with in situ measurements and data-driven models like Artificial Neural Networks (ANNs), these technologies offer scalable solutions for addressing the challenges of soil moisture estimation in heterogeneous agricultural landscapes.
This study integrates Sentinel-1 SAR data with ANN models to estimate soil moisture in olive orchards located in the Vilariça Valley, northeastern Portugal. Soil moisture measurements were recorded at a depth of 10 cm every 30 minutes from July 2020 to December 2021. Sentinel-1 SAR images were acquired in dual polarizations (VV and VH), and synthetic bands were generated through arithmetic operations combining polarization and calibration metrics (Beta, Sigma, Gamma, Gamma TF), yielding 24 features per image. Two datasets were constructed to evaluate the impact of orbit geometry: (1) D1, containing 161 images from ascending orbits, and (2) D2, comprising 246 images from ascending and descending orbits.
The ANN regression model, comprising six hidden layers and K-fold cross-validation (20 splits), demonstrated greater performance with the D1 dataset, achieving a Root Mean Square Error (RMSE) of 2.78, a coefficient of determination (R²) of 0.69, and a Mean Absolute Percentage Error (MAPE) of 8.26%. In contrast, the D2 dataset showed reduced accuracy (RMSE: 3.96, R²: 0.59, MAPE: 12.41%), likely due to variability introduced by combining ascending and descending orbits. These findings underscore the importance of dataset homogeneity in SAR-based soil moisture modeling.
This study highlights the potential of integrating Sentinel-1 SAR data with ANN models for soil moisture estimation in olive orchards, contributing to the development of sustainable agricultural practices. Future work should focus on addressing dataset imbalances by expanding the range of observed conditions, incorporating topographic features, and exploring advanced data augmentation techniques to enhance model robustness and scalability.
Acknowledgments
This work is financed by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia, within project LA/P/0063/2020. DOI 10.54499/LA/P/0063/2020 https://doi.org/10.54499/LA/P/0063/2020 and a doctoral scholarship in a non-academic environment at Fundação Côa Parque (PRT/BD/154871/2023).
How to cite: Carvalhais Teixeira, A. C., Marques, P., Bakon, M., Fernandes-Silva, A., Lopes, D., and Sousa, J.: Sentinel-1 SAR Data and Artificial Neural Networks for Soil Moisture Estimation in Olive Orchards, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19782, https://doi.org/10.5194/egusphere-egu25-19782, 2025.
Posters virtual: Fri, 2 May, 14:00–15:45 | vPoster spot A
EGU25-5173 | ECS | Posters virtual | VPS11
Validation of Satellite-Derived Soil Moisture Products Using Ground Observations in Southern EuropeFri, 02 May, 14:00–15:45 (CEST) | vPA.11
Soil Moisture (SM), acknowledged by the Global Climate Observing System (GCOS) and the European Space Agency’s Climate Change Initiative (ESA CCI) as an Essential Climate Variable (ECV), is a fundamental hydrological parameter that plays a pivotal role in bridging Earth's surface and atmospheric interactions. Understanding SM status and dynamics is critical for various meteorological, hydrological, and climatological applications. Furthermore, it provides insights into the water, energy, and carbon cycles while aiding in the forecasting of extreme climatic events, such as droughts and floods. In consequence, accurate global monitoring of SM with suitable temporal and spatial resolutions is imperative.
This study focuses on the validation of multiple satellite-derived near-surface SM products against field measurements to evaluate their accuracy and reliability. The research was conducted over the northeastern Spain and southern France, covering a 7-year span from January 2015 to December 2021. Ground truth data were obtained from the International Soil Moisture Network (ISMN) database, which included observations from 30 stations across four networks (COSMOS, FR-Aqui, IPE, and SMOSMANIA). The analysis assessed four microwave-based sensors, encompassing both active and passive systems: ASCAT (Advanced Scatterometer), SMOS (Soil Moisture and Ocean Salinity), SMAP (Soil Moisture Active Passive), and CCI.
Following data acquisition and processing for both satellite images and ground observations, a comprehensive validation was performed using statistical metrics, scatter plots, and linear regression analysis of the respective time series. Results highlighted that the SMAP mission delivered the most reliable outcomes, achieving a near-unity slope, an intercept close to zero, a correlation coefficient of R = 0.72, and a Root Mean Square Error of RMSE = 0.07 m³/m³. The CCI product followed, while ASCAT and SMOS showed larger uncertainties and weaker correlations, respectively. In addition, an analysis of the in situ depth effect using SMAP indicated that measurements at 0–6 cm (integrated) and 5 cm (point-specific) depths yielded optimal results. Nevertheless, despite remarkable advances in SM monitoring, this work underscores the need for further research to align satellite-derived data more closely with field-level precision.
Acknowledgements: This study was carried out in the framework of the PID2020-118797RBI00 (Tool4Extreme) project, funded by MCIN/AEI/10.13039/501100011033, and also the PROMETEO/2021/016 project, funded by Conselleria d’Educació, Universitats i Ocupació de la Generalitat Valenciana.
How to cite: Tomás-Portalés, G., Valor, E., Niclòs, R., and Puchades, J.: Validation of Satellite-Derived Soil Moisture Products Using Ground Observations in Southern Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5173, https://doi.org/10.5194/egusphere-egu25-5173, 2025.
EGU25-3139 | ECS | Posters virtual | VPS11
Influence of Spatial Heterogeneity in Error Characterization Using Triple CollocationFri, 02 May, 14:00–15:45 (CEST) | vPA.12
Accurate error characterization is essential for validating satellite-based geophysical products. Triple Collocation (TC) estimates random error variances of three mutually independent datasets but assumes a common spatial scale—a condition rarely met in practice. Spatial heterogeneity in the ground truth and mismatches in spatial resolution introduces "spatial representativeness errors", whose influence on error variance estimates remains unexamined. In this study, we have analyzed the sensitivity of the triple collocation estimates using the synthetically generated soil moisture dataset under varying sample sizes and spatial heterogeneity. Our results indicate that sample size (N) affects the TC estimates, with % bias decreasing from ±15% to ±2% for N ranging from 100 to 1000. The study finds that % bias also varies with the degree of spatial heterogeneity across the area under consideration. Additionally, the TC framework exhibits an equal likelihood of overestimation and underestimation. These findings underscore the critical importance of addressing spatial heterogeneity to enhance the reliability and robustness of error characterization in geophysical measurement systems. The study provides valuable insights for improving the applicability of TC in satellite product validation and underscores the need for more advanced approaches to handling spatially diverse datasets.
How to cite: Gupta, D. and Dhanya, C. T.: Influence of Spatial Heterogeneity in Error Characterization Using Triple Collocation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3139, https://doi.org/10.5194/egusphere-egu25-3139, 2025.
EGU25-15812 | Posters virtual | VPS11
Validation of SMAP and EOS-04 Soil Moisture Products Over Karnataka’s Heterogeneous Agricultural Landscapes Using Ground MeasurementsFri, 02 May, 14:00–15:45 (CEST) | vPA.13
Soil moisture is a crucial parameter that influences various environmental and socioeconomic processes, including flood and drought mitigation, sustainable agricultural productivity, and industrial applications. This study analyses soil moisture dynamics using data from 25 sensing stations distributed across various regions of Karnataka State. These sensing stations were installed under the REWARD (Rejuvenating Watersheds for Agricultural Resilience through Innovative Development Programme) project funded by World Bank. These stations encompass diverse topographic, soil, rainfall, and crop characteristics. High-frequency data collected from these stations at 15-minute intervals is aggregated into daily averages to analyse soil moisture responses to rainfall, recovery times, and depth-wise correlations between 5 cm and 50 cm. This study also validates soil moisture products from SMAP and EOS-04 satellites using ground-based measurements at these 25 locations. The validation was performed for both raw satellite data and data filtered using the Soil Wetness Index (SWI). The Soil Wetness Index (SWI) filter is applied as a background layer to effectively capture soil moisture dynamics across different spatial scales. The accuracy of soil moisture retrievals is evaluated for SMAP products at spatial resolutions of 9 km, 1 km, and 400 m, as well as for EOS-04 data at a 500 m resolution. When the SWI filter is applied, the remotely sensed retrievals show the strongest agreement with in-situ measurements across cultivated crop areas throughout the year. The findings from this study enhance the understanding of soil moisture dynamics and offer actionable recommendations for selecting the best satellite soil moisture products and optimizing soil moisture modelling. These insights are valuable for agricultural planning, water resource management, and disaster mitigation strategies in regions with diverse environmental conditions.
How to cite: Parekattuvalappil Shaju, A., Gupta, V., and Muddu, S.: Validation of SMAP and EOS-04 Soil Moisture Products Over Karnataka’s Heterogeneous Agricultural Landscapes Using Ground Measurements, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15812, https://doi.org/10.5194/egusphere-egu25-15812, 2025.
