This session invites contributions that push the boundaries of how we observe, measure, and monitor soil and/or soil-water dynamics. We are looking to showcase and discuss research that spans a wide range of scales, from local plots to global systems, and employs a variety of sensing techniques, from proximal sensors (i.e., in-field and machinery sensors) to remote sensing (i.e., UAVs, airborne systems, and satellites).
We also welcome studies that explore the fusion of diverse geospatial datasets (e.g., soil sensors, LiDAR, photogrammetry, and satellite imagery) to gain a more holistic, multi-scale understanding of these processes. This also includes research that uses sensed data as input for modelling and/or aim at predict future scenarios under changing conditions.
This session is open to, but not limited to, the following topics and application fields (e.g., agriculture, forestry, urban development, and mountain environments):
Advanced proximal sensing and ground-based monitoring
• Remote sensing, UAV, airborne, and satellite remote sensing of soil and water investigations
• Fusion and integration of multi-source geospatial data
• AI and machine learning for soil and water analysis
• Novel monitoring workflows, protocols, and open-source tools
• Linking sensing data with process-based or distributed models
• Critical evaluations of opportunities and limitations of emerging technologies
• Translating sensing and modeling into decision-support frameworks
Interdisciplinary contributions are more then welcome, as well as the participation of early career scientists (ECS).
Orals: Mon, 4 May, 08:30–10:15 | Room 0.15
Check dams, widely implemented across the Chinese Loess Plateau as a key soil and water conservation measure, have played a critical role in mitigating soil erosion. Over recent decades, these structures, combined with vegetation restoration and terracing, have contributed significantly to sediment reduction, leading to a marked decline in sediment discharge in the Yellow River. However, due to the absence of a comprehensive and spatially explicit check dam database, the distribution and precise sediment retention benefits of these structures remain poorly quantified. Moreover, as important terrestrial depositional environments, check dams not only intercept substantial amounts of sediment but also bury large quantities of organic carbon, thereby influencing terrestrial carbon cycling—a process that remains poorly understood in terms of its magnitude and stabilization mechanisms. To address these gaps, this study developed the first vector-based dataset of check dams across the Loess Plateau using an object-based classification approach, supported by very-high-resolution satellite imagery (0.3–1.0 m). The dataset provides detailed spatial coordinates, dam land areas, sediment storage volume, and sediment retention capacity for each check dam. Validation based on 1,947 field-surveyed check dams demonstrated high accuracy, with an overall accuracy of 94.4%, producer’s accuracy of 88.9%, and user’s accuracy of 99.5%. Building on this dataset, we integrated satellite and UAV remote sensing with extensive field sampling to derive empirical relationships between dam land area and sediment volume. Our estimates indicate that check dams have retained approximately 10.2 billion tons of sediment, equivalent to 46% of the Yellow River’s sediment flux to the sea between 1970 and 2020. This highlights check dams as a major driver behind the dramatic reduction in river sediment load. Furthermore, we estimated the organic carbon stock trapped within check dam deposits to be about 21.6 ± 9.9 Tg. Notably, the carbon burial rate (468 g C m⁻² yr⁻¹) and burial efficiency (~80%) in these environments significantly exceed those observed in other typical depositional systems, underscoring the role of check-dams as effective sinks for eroded organic carbon and their importance in the terrestrial carbon budget. Our study provides a robust dataset and scientific foundation for quantifying the impacts of soil and water conservation measures on sediment and carbon dynamics, supporting informed decision-making in land management under changing environmental conditions.
How to cite: Zeng, Y., Fang, N., and Ni, L.: Assessing Sediment Retention and Carbon Sequestration Benefits of Check Dams on the Chinese Loess Plateau: Integrating Multi-Source Remote Sensing and Field Data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1965, https://doi.org/10.5194/egusphere-egu26-1965, 2026.
Aluminum oxide extraction from bauxite generates over 3,000 tons of filtered bauxite residue daily at Rio Tinto’s Vaudreuil plant. Managing these mine tailings includes addressing the risks of fugitive dust scattering from their surfaces while they are momentarily stored in the open at the disposal site in Jonquière, Quebec. To mitigate these risks, continuous monitoring of the drying process is imperative. A real-time quantification of their surface moisture content (SMC) is necessary to predict and prevent fugitive dust scattering, thereby reducing managing and mitigating costs. Albedo measurements in the near infrared (λ = 1200–1550 nm) will be shown to be a precise, sensitive and selective optical method for characterizing the mine tailings’ SMC and monitoring their drying rate. A portable device has been designed for continuous in situ SMC measurement under adverse environmental and operational conditions. This device also facilitates laboratory investigations into the dependencies of the mine tailings’ drying rates on ambient air temperature and relative humidity, revealing how atmospheric boundary conditions influence water transport mechanisms within their interconnected porous network, namely capillary pumping and gaseous diffusion. Frozen tailings show reduced drying rates due to suppressed capillary transport, while increasing temperatures and decreasing relative humidity of the ambient air accelerate drying rates. A comprehensive and quantitative knowledge of the impact of these key parameters, along with that of meteorological conditions such as wind speed and solar irradiance, and their recent history, should improve our description of water transport mechanisms and kinetics within mine tailing. The quantification of their drying rates should also improve our prediction of the evolution of their SMC and promote the development and implementation of models and tools necessary for the forecasting and prevention of fugitive dust scattering events thereby contributing to the reduction of the environmental impact of mine tailings disposal site.
How to cite: Dessap Pefete, G.-A. and Ayotte, P.: Spectroscopic investigations of mining residues drying kinetics to predict and prevent fugitive dust emission, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14594, 2026.
Climate change and intensified human activities have profoundly altered hydrological processes in watersheds worldwide. Soil erosion and sediment yield are key indicators of watershed hydrological responses, with far-reaching consequences for water resources, landscape evolution, ecosystem functioning, and socio-economic sustainability. While most existing studies focus on rainfall-driven water erosion, hydrological processes in climatically transitional regions remain less well understood. In such regions, the alternating and combined effects of multiple erosive agents can increase ecological vulnerability and amplify erosion and sediment yield risks. Here, we present a long-term investigation of hydrological and sediment dynamics in representative wind-water erosion crisscross watersheds in northern China. These watersheds are located within an arid and semi-arid climatic transition zone and feature a complex geomorphic setting comprising aeolian sand landscapes and loess hilly terrain. This combination gives rise to distinct hydrological behaviors. Multi-temporal analyses show that precipitation and runoff are similar between the two geomorphic units. In contrast, the loess hilly region exhibits significantly higher annual sediment yields and a much stronger seasonal concentration, with sediment transport being highly episodic and dominated by a few flood events during the rainy season. Attribution analyses indicate that watershed topography, soil texture, and landscape complexity jointly control spatial variability in sediment yield. In particular, the complex composition of underlying surfaces promotes the formation of hyperconcentrated floods, during which peak suspended sediment concentrations frequently exceed 300 kg m-3. River discharge-sediment hysteresis analyses further demonstrate that ecological restoration has reduced flood process complexity and sediment source variability. However, hyperconcentrated flows remain the dominant driver of sediment production, sustaining a high erosion risk. Futhermore, we observe an increasing alternation of drought and flood extremes in this region, pointing to growing hydrological instability. Under future scenarios of intensified climate extremes, such variability is likely to further amplify erosion and sediment yield risks. These findings highlight the importance of integrated monitoring of soil and water processes across contrasting geomorphic units to improve erosion risk assessment and watershed management under global change.
How to cite: Jing, T. and Fang, N.: Hydrological Processes and Sediment Dynamics in Climatically Transitional Watersheds under Climate Change and Human Activities, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2385, https://doi.org/10.5194/egusphere-egu26-2385, 2026.
Productive agricultural areas of Italy’s northeast are under ever-increasing strain from extreme events, such as drought. Largely irrigated agriculture, the northeast is a producer of key staple crops like corn and wheat that underpin food security both locally and globally. Irrigation water management in the area typically falls under the control of regional water authorities (Consorzi di Bonifica). Staff within water authorities include both farmers and technical experts; thus, knowledge of water management may vary. Regional drought risk and the limits of technical capacity among water authority employees highlight the need for a framework to bridge gaps in technical knowledge. Identification of priority areas and hot spots during the key irrigation months, May to September, is critical to sustaining agricultural production and improving resilience to extremes such as drought. A threshold-based framework using standard drought-related variables, Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), and Surface Soil Moisture (SSM) from largely code-free sources at a 1 km spatial resolution and at a monthly timestep can help to identify regional priority areas and persistence-based hot spots. When comparing monthly priority from May to September across three distinct rainfall years, a wet (2019), normal (2020) and dry year (2022), results showed greater variability across months in 2019 and 2020, regardless of one year being wetter than the other. In the dry year, there was a notable increase in priority areas moving into the middle summer months, with a peak in July of the highest priority level. Results align with existing 2022 drought research, which shows that extremes were highest in the middle summer months. Persistence-based hot spots indicated that, for each month across the three years of interest, higher-priority hot spots were more prevalent in June and July, with the highest-priority hot spots primarily located in the central and eastern parts of the study area, respectively. Past drought events, such as the extreme event in 2022, have led to water shortages and water-use restrictions. The increasing frequency of such events may require decision-makers within water authorities to prioritize irrigation water use in the face of shortages or restrictions, and a reproducible framework can aid in such decision-making.
How to cite: Lippa, M. N. and Tarolli, P.: Mapping Irrigation Priority Areas and Hot Spots in Northeast Italy: A Simplified Framework for Water Authorities, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10400, 2026.
With the progressive advancement of the global “Blue Transition” strategy, extensive areas of natural wetlands have been converted into high-intensity fishponds to secure the global supply of aquatic protein. Aquaculture currently accounts for nearly 50% of the world’s edible fish production, with Asia contributing approximately 88% of the total output. Despite its critical role in food security, this large-scale transformation of land cover has generated substantial environmental risks, including wetland degradation and loss, declining water quality, and disruptions to regional hydrological cycles. Owing to the fragmented spatial configuration of aquaculture ponds and their pronounced spectral heterogeneity, existing global land-cover products remain inadequate for accurately detecting these subtle yet widespread human modifications, frequently misclassifying aquaculture ponds as natural water bodies or agricultural land. The absence of consistent, long-term benchmark datasets has therefore severely constrained rigorous assessments of the global environmental footprint of aquaculture expansion.
To address this critical data gap, this study leverages the Google Earth Engine (GEE) cloud-computing platform to integrate more than 200,000 multi-source satellite images from the Landsat and Sentinel missions spanning the period 2000–2025. Based on this extensive archive, a global dynamic monitoring framework for fishponds was developed at a spatial resolution of 30 m. A standardized validation dataset comprising 15,000 reference points was established across 12 representative geographic regions worldwide through systematic, expert-level visual interpretation. The proposed methodological framework combines a newly developed Aquaculture Pond Index (API) with a Support Vector Machine (SVM) classifier, explicitly targeting the persistent spectral confusion between aquaculture ponds and paddy fields in complex inland environments.
Using this framework, preliminary quantitative analyses yielded the following key findings. (1) Classification accuracy: The proposed approach achieved a global Overall Accuracy (OA) of 81.6% with a Kappa coefficient of 0.79. In complex inland landscapes, classification performance improved by approximately 12% relative to existing mainstream global land-cover products, demonstrating the robustness of the API in discriminating aquaculture ponds from spectrally similar land features. (2) Areal dynamics: Between 2000 and 2025, global fishponds exhibited a persistent expansion trend, with total area increasing by approximately 35%. Approximately 65% of this expansion was attributed to the conversion of natural wetlands and low-lying agricultural land. (3) Spatial patterns: A pronounced pattern of land-oriented clustering was identified, with inland aquaculture expansion rates surpassing those of traditional coastal regions. Emerging economies in Southeast Asia and East Africa have increasingly become new growth centers for global aquaculture development.
This study fills a critical gap in long-term, spatially explicit monitoring of inland aquaculture at the global scale. The findings provide a robust scientific basis for evaluating the long-term impacts of human-driven aquaculture expansion on surface water resources and offer essential spatial benchmark information to support the reconciliation of global food security objectives with wetland conservation priorities under the United Nations Sustainable Development Goals (SDGs).
How to cite: Liu, X., Yang, X., Niu, L., Zhong, K., and Tong, Y.: Spatio-temporal Dynamics and Driving Mechanisms of Global Human-Transformed fishponds the “Blue Transformation” Paradigm, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4331, 2026.
Soil organic carbon (SOC) has a significant impact on soil health and is vital for achieving net zero emissions in crop production systems. Therefore, developing cost-effective measuring and monitoring methods for SOC is an urgent priority. Visible near-infrared (Vis-NIR) spectroscopy has the potential to aid in SOC monitoring as an efficient and low-cost method. Using the Canadian Prairie Soil Spectral Library (CPSSL, n = 8392), which encompasses the provinces of Manitoba, Saskatchewan and Alberta, we aimed to examine the potential to predict SOC content for subsequent samplings at the local and provincial scales in the Canadian prairies. The local scale involved a long-term field experiment where SOC was measured in 1987 and 2003, with the last sampling in 2015 (n = 74). At the provincial scale, SOC was measured on producer fields in Saskatchewan in 1996, 2005 and 2011, with the last sampling in 2018 (n = 360). Partial least squares regression (PLSR), Cubist and Global-Local were compared by using site-specific (local scale, n = 150), study-specific (provincial scale, n = 1985) and the remaining CPSSL samples (global, n = 2628 and 2633 for the local and provincial scale, respectively) as the calibration sets. The site-specific with PLSR gave the best prediction of SOC for the 2015 sampling at the local scale (root mean square error (RMSE) = 0.14% SOC, ratio of performance to interquartile range (RPIQ) = 7.5) followed by the site-specific with Cubist (RMSE = 0.15%, RPIQ = 6.8), Global-Local (RMSE = 0.20%, RPIQ = 5.1) and the global calibration with Cubist (RMSE = 0.29%, RPIQ = 3.6). The study-specific with Cubist best predicted the 2018 sampling at the provincial scale (RMSE = 0.36%, RPIQ = 2.2) followed by the study-specific with PLSR (RMSE = 0.47%, RPIQ = 1.7), Global-Local (RMSE = 0.46%, RPIQ = 1.8) and the global calibration with Cubist (RMSE = 0.52%, RPIQ = 1.6). While the site- or study-specific calibrations, as expected, provided the most accurate predictions, data mining and machine learning models with the CPSSL showed great promise, especially at the local scale. Our results showed that SOC monitoring at the local scale with Vis-NIR using site-specific samples can be reliable, thereby reducing costs. At the larger provincial scale, models based on soil texture and/or soil classification, and inclusion of covariates may be necessary to improve prediction precision depending on the level of accuracy required. Overall, this study suggests that using Vis-NIR in combination with soil spectral libraries with temporal data and machine learning models can improve efficiency in SOC monitoring.
How to cite: St. Luce, M. and the CPSSL Team: Soil organic carbon monitoring at the local and provincial scales using visible near-infrared spectroscopy, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5834, 2026.
Straw incorporation has been increasingly recommended to control rill initiation and development. However, the unpredictable impact of continuous straw incorporation on residual straw and soil physicochemical properties leads to uncertain variations in rill erosion resistance. In our experiment, maize straw was annually incorporated into runoff plots for two consecutive years. The plots were configured with three fixed factors: straw length (0–2 cm and 2–5 cm), straw amount (4000 kg ha-1 and 8000 kg ha-1), and straw incorporation depth (15 cm and 20 cm). Two rill erosion resistance parameters, soil critical shear stress (τc) and rill erodibility (kd), were measured using a submerged jet apparatus after maize harvesting. The results revealed a 10.98% decrease in τc and a 93.03% increase in kd after two rounds of straw incorporation compared to after the first incorporation, indicating that soil resistance to rill erosion decreased during continuous straw incorporation. Structural equation modeling suggested that the incorporated depth was the dominant contributor to variations in rill erosion resistance following the first straw incorporation, primarily by influencing total porosity and saturated water content. As soil agglomeration progressed driven by straw decomposition, the straw amount had an increasingly indirect effect on rill erosion resistance, with primary factors shifting to capillary porosity, straw residues, water-stable aggregates, humic substances, and the humus fraction. Following continuous straw incorporation, both τc and kd increased with the straw amount but decreased with the incorporated depth, indicating that excessive or shallow incorporation of straw can effectively prevent rill scouring under low shear stress but is less effective under high-stress conditions. Our study contributes to a deeper understanding of the dynamics of rill erosion resistance during continuous straw incorporation and provides a valuable reference for optimizing straw-returning strategies on sloping farmland.
How to cite: Zhang, Y. and Fang, N.: Dramatical variation in rill erosion resistance during two-year continuous straw incorporation on sloping farmland, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2261, https://doi.org/10.5194/egusphere-egu26-2261, 2026.
Soil water content dynamics describe the response of soil functions to atmospheric forcing and provide insight into soil hydraulic properties and soil health. Abrupt changes in climatic conditions may lead to persistent shifts in this response, reflecting structural alteration rather than short-term variability. Detecting and reproducing such changes remains challenging, as most modelling approaches assume stationary soil properties and are not designed for long-term monitoring.
In this study, to detect persistent changes, we analyse multi-year lysimeter observations from the SOILCan network that is part of the TERrestrial ENvironmental Observatories (TERENO). For that purpose, we use a data-driven approach that combines a neural network with seasonal–trend decomposition. The model is trained on a lysimeter exhibiting stable soil water dynamics and subsequently applied to lysimeters of different soil origins exposed to contrasting climatic conditions. Differences between observed and modelled soil water content are tracked over time to test whether the soil moisture-climate relationship remains stable under changing conditions.
Persistent changes in soil water response are identified when model residuals exhibit a sustained bias over time, indicating a shift in the underlying soil–climate interaction. Based on this behaviour, soil dynamics are classified as stable, resilient, or changed. Application to the extreme drought of summer 2018 in Germany shows that while soil water dynamics are often preserved under typical conditions, extreme drought and exposure to new climatic regimes can induce lasting changes, even when soil texture remains unchanged. The proposed approach thus provides an early-warning capability for detecting emerging changes in soil hydraulic functioning from long-term monitoring data.
How to cite: Aqel, N., Groh, J., Weihermüller, L., Gründling, R., Carminati, A., and Lehmann, P.: Detecting changes in soil water content response under climate extremes using long-term lysimeter data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9668, 2026.
Soil aggregates influence soil physical functioning, water retention, infiltration, and soil resistance to erosion and is thus important for a wide range of soil ecosystem services. However, the characteristics of soil particle aggregates and their stability in forest soils are not sufficiently understood. In this study, we investigate how soil physical and chemical properties influence mechanical aggregate resistance by combining laser diffraction grain size analyses with an extensive set of physical and chemical parameters.
Particle size analyses (PSA) were conducted using a Malvern (Mastersizer 2000 and Mastersizer 3000E) laser diffractometer on 1862 soil samples from Austrian forest sites in Styria, Upper and Lower Austria, and Burgenland taken in four depth levels (0-10, 10-20, 20-50 and 50-80 cm). Each sample was analysed twice: once without ultrasonic pre-treatment, preserving naturally occurring aggregates, and once after 4 min ultrasonic dispersion (Bandelin Sonorex RK 255, 35 kHz) to achieve complete disaggregation. Differences between the two treatments provide a proxy for aggregate stability. In addition, the same samples were characterised by a comprehensive set of physical and chemical analyses, including density of mineral soil, pH, content of carbonate, organic carbon, and total nitrogen, C/N ratio, plant-available nutrients, exchangeable cations, base saturation, and selected trace elements. These parameters capture potential key physical and chemical controls on aggregation, such as carbonate cementation.
In a next step, we will analyse differences between dispersed and non-dispersed PSAs statistically and relate them to chemical and physical soil properties in order to identify controls on aggregate resilience. We assume that in particular carbonate cementation and cation exchange capacity affect the mechanical stability of soil aggregates and their response to ultrasonic dispersion. By linking grain size distribution changes to soil chemistry, this study aims to improve the understanding of physical and chemical controls on soil aggregation and to highlight the importance of considering pre-treatment effects when interpreting laser diffraction data.
This work was carried out within the FORSITE I, FORSITE II and FORSITE II+ projects, funded by the Federal Ministry of Agriculture and Forestry, Climate and Environmental Protection, Regions and Water Management, Republic of Austria.
How to cite: Horn, L., Klebinder, K., Kohl, B., Kitzler, B., and Michel, K.: Assessing mechanical aggregate resilience of forest soils using a laser diffractometer with and without pre-treatment , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20243, 2026.
Soil moisture is a key regulator of forest health, drought risk, and hydrological extremes, yet its variability in space and time remains poorly understood, particularly in forested and topographically complex terrain. This knowledge gap hinders our ability to anticipate and respond effectively to forest disruptions under climate change. We combine long-term in-situ soil moisture data from a Swiss-wide network of topsoil sensors with high-resolution remote sensing data to identify the principal drivers of fine-scale (5m) seasonal soil moisture patterns in Swiss forests, with a particular focus on quantifying the effects of vegetation structure using radiative transfer modelling. We show that vegetation characteristics are important drivers of local soil moisture dynamics, markedly outweighing the effects of topography. Topsoils under denser canopies are significantly wetter, more likely to be at field capacity and less likely to dry out completely compared to topsoils under sparse canopies. The seasonal consistency of this effect hints at the critical importance of processes outside the growing season, and at the capacity of dense forests to enhance infiltration, as well as minimize evaporation losses. We further show how tree species and their ecological preferences imprint distinct signatures on soil-moisture dynamics and discuss future soil moisture modelling avenues. Our study underscores the importance and feasibility of accounting for local-scale drivers governing forest hydrology. The predictive approach allows for generating high-resolution Swiss-wide (5m) soil moisture maps for past, present and future climatic conditions, which offer practical value for forest management, provide reference data for validating satellite-based soil moisture products in heterogeneous forested terrain, and help us better understand past disturbances and future risks to forest functioning and ecosystem service provisioning.
How to cite: Dietzel, A., Zellweger, F., and Meusburger, K.: High-resolution modelling of seasonal soil moisture patterns in Swiss forests: disentangling the role of vegetation structure using radiative transfer modelling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18240, 2026.
Posters on site: Mon, 4 May, 16:15–18:00 | Hall X3
The particle size sorting effect during erosion significantly undermines sediment source tracing accuracy. This study proposes an innovative strategy integrating particle size information to enhance traditional methods. Laboratory experiments using sediments with fine-particle enrichment ratios showed that under strong sorting, traditional methods incurred high errors (MAE: 29.3%), while the new strategy reduced MAE to 6.8% by optimizing fingerprint matching. Validation via simulated rainfall across slopes (5°–25°) and intensities (60–120 mm/h) confirmed broad applicability. MAEs for geochemical and mid-infrared spectral tracing remained below 9.7% and 8.3%, respectively, significantly outperforming conventional methods. In high-sorting scenarios (e.g., 5°/120 mm/h), accuracy gains ranged from 31.3% to 64.4%. Mechanistically, the strategy isolates sorting interference, preserving source-like fingerprint characteristics. It also reveals spectral tracing's superior sensitivity to mineralogical differences in fine fractions, supporting multi-dimensional tracing systems. This research provides a breakthrough in overcoming the long-standing constraint of particle size sorting, with practical implications for understanding erosion and enabling precision source management.
How to cite: Ni, L., Fang, N., Niu, H., Zhang, J., and Zeng, Y.: Improving Sediment Source Tracing Accuracy by Coupling Particle-Size Information, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2472, https://doi.org/10.5194/egusphere-egu26-2472, 2026.
Soil moisture is a key component of the Earth system and hydrological cycle. Accurate soil moisture estimates are critical for many applications. Global soil moisture measurements are primarily derived from microwave remote sensing (RS); however, their spatial resolution is typically coarse, often on the order of kilometers, and is impacted by various factors. Therefore, in situ ground measurements should be used to improve the spatial and temporal representation of soil moisture in RS. The current study presents a comparative analysis of soil moisture data retrieved from Time Domain Reflectometry (TDR), Electromagnetic Induction (EMI), Cosmic-Ray Soil Moisture Observation System (COSMOS), and satellite remote sensing soil moisture derived using the OPTical TRApezoid Model (OPTRAM). The study site is located in a semi-arid environment, with a mean annual rainfall of 150 mm that falls between October and May. EMI measurements were conducted manually during the dry summer and wet winter seasons. Concurrently, TDR at depths of 10 and 20 cm and COSMOS continuously monitored and collected soil moisture data, respectively. Satellite information for the dates of the EMI surveys was retrieved from Sentinel-2 images.
Various correlation analyses were performed. The spatial and seasonal relationships between apparent electrical conductivity (ECa) and remote sensing soil moisture (RSSM) were also tested. At the beginning of the winter season, after a long dry spell, the ECa values correlated negatively with the RSSM. The best positive correlation occurred only after a long period of water percolation. The correlation between TDR and RSSM was the strongest among the methods. Meanwhile, COSMOS soil moisture also showed a strong positive correlation with RSSM, stronger than with ECa.
Concerning EMI measurements, soil moisture variability was minimal after five months of a dry, hot summer. Following several rain events, the ECa values exhibited high variability, which was related to increases in soil moisture. The RSSM showed a corresponding phenomenon: during the dry period, a narrow distribution of values was observed, and after a number of rain events, the distribution expanded. Thus, the ground-based EMI method and RSSM indicated the same spatiotemporal dynamics of soil moisture in the subsurface of dryland.
It is concluded that the RSSM represents the spatiotemporal conditions of the top-soil moisture conditions, but only after sufficient time for water percolation and distribution. TDR and COSMOS provide reliable soil moisture data to correct RSSM across time and space, whereas EMI is seasonally dependent (positive correlation during very wet periods and negative correlation after long dry spells).
How to cite: Kabenla, R., Karnieli, A., and Turkeltuab, T.: Validate satellite remote sensing soil moisture with ground-based methods in dryland, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4538, 2026.
Precision management of medicinal plant resources is critical for the sustainability of traditional medicine industries. However, accurate identification of genetically similar herbs in heterogeneous environments remains challenging due to high spectral similarity and the "curse of dimensionality" in UAV hyperspectral data. To address these issues, this study challenges the conventional "global optimization" paradigm by proposing a hierarchical Class-Specific Feature Selection (CSFS) strategy. Integrating SPA, CARS, GA, and RFE, this strategy extracts parsimonious diagnostic features tailored to the unique separability of each species, rather than a uniform subset. Furthermore, a probability-calibrated Stacking Ensemble model (RF-LR) was constructed to resolve decision ambiguity in transition zones. The results demonstrate that the CSFS strategy successfully mitigated data redundancy, achieving a dimensionality reduction rate of 96%–98% (reducing 321 features to 5–14 key variables). Mechanistic analysis revealed distinct bio-optical drivers for separability: Melicope pteleifolia is distinguished by pigment-induced spectral shifts in visible bands, Murraya exotica by chlorophyll-sensitive red-edge traits, and Zanthoxylum nitidum by morphology-driven canopy textures. Consequently, the RF-LR model achieved an Overall Accuracy of 97% and a Kappa of 0.96, significantly outperforming traditional classifiers (RF, XGBoost, SAM) in terms of stability and generalization. This study validates the effectiveness of coupling class-specific optimization with decision-level fusion, providing a robust, interpretable, and lightweight technical solution for the operational monitoring of medicinal plant resources.
How to cite: Chang, X.: Precise Mapping of Medicinal Crops Using UAV Hyperspectral Image: A Strategy Driven by Crop-Specific Feature Selection and Decision-Level Fusion Classifier, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6388, 2026.
Diameter at Breast Height (DBH) is a critical parameter for global carbon cycle modeling and forest biomass estimation. Conventionally, Individual Tree Segmentation (ITS) serves as the necessary prerequisite for DBH extraction. However, in high-density natural forests, this dependency becomes a bottleneck: severe canopy overlap and understory occlusion often cause traditional ITS algorithms to fail, severely limiting the accuracy of subsequent DBH retrieval.
To address this challenge using handheld LiDAR, this study proposes an "Interference Rejection" strategy, shifting the focus from the challenging full-tree segmentation to targeted "stem semantic extraction." We argue that for DBH retrieval, separating the entire tree structure is unnecessary. Therefore, based on the PointNet++ framework, our method actively identifies and filters out non-essential interference (e.g., canopy foliage and shrub noise) from the LiDAR point clouds, isolating clean stem points directly.
We validated this framework in the boreal forests of Mohe, China. Experimental results demonstrate the significant advantage of our approach. While recent mobile laser scanning studies typically report DBH estimation RMSEs ranging from 1.5 to 3.0 cm due to segmentation errors in complex environments, our "stem-focused" strategy achieved a superior RMSE of 1.26 cm. This workflow effectively bypasses the limitations of traditional segmentation, providing a highly automated and precise solution for forest inventory.
How to cite: JiaLiang, C.: From Tree Segmentation to Stem Extraction: A Robust DBH Estimation Framework for Complex Forests using Handheld LiDAR and PointNet++, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6392, 2026.
Alpine soil cover and water resources face compounding pressures from climate extremes and land-use intensification. Yet, monitoring degradation remains challenging as coarse satellite data often mask localized hydrological failures. We address this by integrating high-resolution UAV imagery, historical aerial surveys, and Sentinel-2 time-series with meteorological networks in an alpine silvopastoral system. Using Machine Learning to fuse topographic, climatic, and spectral datasets, we reveal a critical divergence in detection: while satellites suggest landscape greening, sub-meter UAV sensing unmasks degradation hotspots hidden by sub-pixel heterogeneity. Crucially, these hotspots are not driven by steep-slope erosion, but by a "climate-accessibility mismatch" concentrated on gentle, convergent slopes. Here, livestock congregation overwhelms physical soil resilience, particularly during pulse drought events. Coupling this framework with CMIP6 projections further demonstrates that future degradation risk is structurally constrained by topographic accessibility rather than linearly coupled with warming. This implies a "saturation" trajectory defined by landscape morphology. Our findings highlight how fusing high-resolution topography with AI modeling precisely identifies "tipping point" zones, translating complex sensing data into targeted decision support for sustainable alpine management.
How to cite: Na, M., Bettella, F., Zuecco, G., and Tarolli, P.: Multi-scale sensing and ML fusion reveal the accessibility paradox driving soil degradation in alpine pastures, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10086, 2026.
Accurately predicting the intermittent and highly fluctuating hypoxia at the estuary of a small river, particularly in urbanized areas where the upstream water is notably affected by human activities, presents a significant challenge. The dissolved oxygen concentration (DO) in tidal estuaries is affected by multiple driving factors, and the dynamic changes in the impacts of these factors make it difficult to develop an hourly forecasting model. However, accurate prediction of DO is of utmost importance for water ecological security and aquaculture. To tackle this issue, this study combined an "end - to - end" deep learning model and incorporated an encoder - decoder architecture based on the latest time series decomposition forecasting model, TimeMixer. It realized dynamic feature selection via multi - scale time encoding and set up an attention mechanism at both the time step level and across components to emphasize the dynamic contributions of driving factors. In hourly forecasting, TimeMixer outperformed the enhanced long short - term memory (LSTM) model, and the enhanced Fusion Attention - TimeMixer (FAT) model enhanced the overall prediction by an average of 17% across all indicators. Specifically, the Nash - Sutcliffe Efficiency (NSE) increased by 24.74%, and the Mean Absolute Error (MAE) decreased by 14.83%. The F1 Score for hypoxia prediction went up by 3.24%, and the forecast error for peak values dropped by 12.56%. Additionally, this study explored the impact of adjusting the input and output windows and integrating multi - source monitoring data on the prediction accuracy of the FAT model. Specifically, when predicting DO for the next 48 hours, a 48 - hour historical window input is optimal for the model's prediction performance. If the prediction length is extended, a longer historical data window than the future window should be used as input. During the model improvement process, it was discovered that the introduction of feature - level attention mechanisms is not advantageous for DO forecasting during rainfall periods. Nevertheless, conducting attention calculations at the time - step dimension and realizing dynamic feature selection through multi - scale time encoding can respectively improve the root mean square error (RMSE) and mean absolute error (MAE) by 7.87% and 12.15% for DO forecasting during rainfall periods. Zero - shot prediction experiments were carried out on the improved deep learning models. Although the prediction performance of most models declined, the FAT model still maintained satisfactory performance and achieved acceptable prediction results even with a reduced number of features. It was discovered that when only tide level and meteorological indicators were used as inputs, the NSE of the FAT model's prediction could reach 0.82. Finally, the prediction effectiveness of the FAT model for DO in five typical scenarios at the estuary was tested to provide references for different station establishment schemes, and the results were presented via a web interface to allow users to predict and evaluate the model. Overall, this study validated the feasibility of predicting non - stationary DO sequences at estuaries using multi - source monitoring data, offering crucial methodological support for real - time prediction and systematic management of estuaries.
How to cite: Jian, Z., Xu, Y., and Qin, H.: A high-frequency time-series prediction model for dissolved oxygen based on multi-source data in estuarine areas, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16124, 2026.
Climate change is intensifying drought risk and hydroclimatic variability in many rainfed agricultural regions, posing increasing challenges for smallholder farming systems. Access to reliable on-farm water storage is essential, particularly during short-term droughts affecting critical stages of crop growth. Small agricultural ponds are a low-cost and widely adopted solution for buffering rainfall variability, yet their adequacy is rarely evaluated through integrated monitoring frameworks that link climate drought indicators with spatial observations of land use and water storage at the local scale. Despite increasing availability of climate data and remotely sensed land and water information, a clear gap remains in systematically connecting drought monitoring with on-farm water storage requirements under future climate scenarios in rice-based systems. This study assesses how climate change may alter agricultural pond requirements in the rice-growing region of Palakkad, Kerala (India) by integrating multi-temporal climatic drought indicators with spatially explicit land-use and water storage datasets. Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) were derived for historical (1984–2014) and future periods (2020–2050 and 2070–2100; CMIP6) under SSP4.5 and SSP8.5 scenarios. These indicators were combined with spatial rice cultivation maps of Palakkad and georeferenced pond distribution data to construct a composite agricultural water stress index. By comparing present and future conditions, the study identifies areas where water stress is projected to intensify and where existing pond availability may become insufficient. The results highlight priority zones where additional pond infrastructure will be critical to sustain rainfed rice farming systems in Palakkad. By bridging climate drought monitoring and spatial water storage assessment, this research advances a transferable, monitoring-based decision-support framework for climate-resilient agricultural water management.
How to cite: Wang, W., Straffelini, E., Sreenivasan, R., and Tarolli, P.: Linking climate drought indices with spatial monitoring data to project agricultural water storage requirements, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9299, 2026.
Posters virtual: Thu, 7 May, 14:00–18:00 | vPoster spot 1a
EGU26-17269 | ECS | Posters virtual | VPS15
Interactive effects of warming and biochar addition on photosynthesis and greenhouse gas emissions in a paddy systemThu, 07 May, 16:15–18:00 (CEST) vPoster Discussions
Global warming and rice cultivation are both significant drivers of greenhouse gas emissions, with methane (CH₄) representing a potent short lived climate forcer. Understand the interactive effects of rising temperatures and soil management practices in regulating carbon fixation and emissions is essential for developing climate-smart rice agroecosystems. Biochar amendment has been proposed to improve soil fertility and mitigate greenhouse gas emissions, yet its interactive effects with warming remain insufficiently understood. A synergistic assessment of warming and biochar application is therefore necessary to evaluate their integrated potential for climate mitigation and sustainable rice production.
A controlled pot experiment using a water bath warming system was established to investigate the interactive effects of warming and biochar amendment. Four treatments were implemented: (1) conventional fertilization (NPK, control), (2) warming (NPK + H), (3) biochar addition (NPK + BC), and (4) combined warming and biochar (NPK + BC + H). Throughout the growing season, key environmental variables, including soil temperature, moisture, and electrical conductivity were continuously monitored. In parallel, rice growth traits and photosynthetic parameters were measured periodically. Greenhouse gas fluxes (CO₂, CH₄, and N₂O) were regularly quantified to assess treatment effects on emissions dynamics.
The experiment revealed critical interactions between warming and biochar. Their effects were often divergent when applied singly but convergent in combination. Specifically, while biochar alone stimulated CO₂ and CH₄ fluxes, and warming independently raised soil temperature, their combined application did not yield additive outcomes. Instead, it suppressed the biochar-induced increase in CO₂ and CH₄, demonstrating a clear interactive mitigation effect. Furthermore, this combination synergistically promoted rice photosynthesis and growth, and all amendment treatments reduced N₂O emissions relative to the NPK control.
Our findings demonstrate that warming and biochar amendment interactively regulate soil-plant processes and greenhouse gas fluxes in rice paddies, primarily through an antagonistic interaction that reverses the sole effect of biochar on CH₄ and CO₂ emissions. This shift indicates a fundamental change in microbial activity and carbon metabolism under combined treatment. Moreover, the synergy between warming and biochar enhanced photosynthetic carbon fixation, illustrating a dual mechanism that simultaneously optimizes carbon gain and attenuates carbon loss. These results provide mechanistic insight into how integrated management can reconcile productivity with climate mitigation, supporting the development of climate-smart strategies for rice agroecosystems under future warming scenarios.
How to cite: Chen, X., Ma, L., Mao, Q., and Cui, N.: Interactive effects of warming and biochar addition on photosynthesis and greenhouse gas emissions in a paddy system, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17269, 2026.
