The European Environment Agency data on nitrates levels gives us a ratio of 8:1 for nitrates in groundwater versus river water, when we analyse the data across 27 countries. The “missing” nitrate, at an average of about 89%, matches the levels of “missing” nitrate due to capture of nitrate on the river bottom, by microbes known as diatoms, which take up 65-95% of the water nitrate load.

Diatoms convert nitrate to ammonium in daily cycles, that are linked to sunlight and Oxygen abundance, encountered in typical river and lake conditions.

We can identify that the major pathway of nitrate into rivers and lakes is through groundwater feeds, which average 25% of surface waterway volumes worldwide -because their nitrate levels dwarf those from any other source. We can also identify that the main mechanism of nitrates removal in river bottoms is diatom capture, where diatoms take up the bulk loads of nitrate arriving in the groundwaters beneath.

Diatoms' virtual monopoly on nitrates conversion may allow us to control N2O and global warming levels, by intercepting the conveyor belt system of nitrates to diatoms in waterways. We can capture and repurpose the nutrient for use as farm fertilizer and harvest diatom ammonium as a carrier for Hydrogen fuel. Diatoms are already farmed commercially for fish food, showing they are amenable to farming, and they are already a source of soil conditioner for farms. Ammonium is harvested in wastewater plants for Hydrogen fuel purposes already, and diatoms offer a low carbon method of ammonium production.

The junction between the UN, EEA and microbial data also allows us to calculate the world processing levels of nitrate in terms of both natural and human produced components. We obtain a range around 300,000 kilotons per annum as being processed by surface waters worldwide from all sources. About 120,000 kilotons of the load comes from human produced sources.

Ammonium nutrient from diatom nitrate conversion is quickly absorbed by aquatic plants and riverside trees, but there is a risk of high levels on hot days in lowered Oxygen conditions. Trees draw up around 1000 litres a day of groundwaters in river basins, so that ammonium and nitrates consumption by trees is additionally a main mechanism of Nitrogen reduction around the riverbed.

How to cite: Hall, C., Smith, D., Munro, A., and Sgroi, A.: Microbial breakthroughs in 2022 now allow us to link United Nations water volumes with EEA nitrates data, to reveal world nitrates processing loads in kilotons, including how much nitrate is from natural sources, and how much is from human activity., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13318, https://doi.org/10.5194/egusphere-egu26-13318, 2026.

In recent years, the triple-oxygen isotopic composition (Δ′¹⁷O) of CO₂ has emerged as a powerful tracer of atmospheric carbon cycling. The Δ′¹⁷O signature of tropospheric CO₂ is controlled by key processes, including global biosphere-atmosphere CO₂ exchange, tropospheric residence times, and stratosphere-troposphere mixing, each of which modifies CO₂ composition through dynamic isotopic fractionation. High-precision measurement of Δ′¹⁷O is essential for constraining models that predict future changes in atmospheric CO₂, yet current datasets remain limited owing to extreme low abundance of ¹⁷O and the considerable analytical challenges involved for accurate and precise isotopic measurement. A further obstacle is the absence of a well-constrained global background Δ′¹⁷O value for atmospheric CO₂ that restricts proper evaluation of deviations arising from diverse source contributions. As a result, model predictions of tropospheric Δ′¹⁷O(CO₂) often diverge substantially from observational constraints.

To address this gap, we have initiated high-precision measurements of δ¹³C, δ¹⁸O, and Δ′¹⁷O in atmospheric CO₂ using TILDAS (Tunable Infrared Direct Laser Absorption Spectroscopy) at the Stable Light Isotope Laboratory in University of Cape Town, South Africa. Bi-monthly air samples have been collected at the Global Atmospheric Watch (GAW) Cape Point station, South Africa, since December 2024. Given the station’s location, sampling is preferentially conducted under south-easterly wind conditions to minimize local anthropogenic influence. CO₂ is extracted, purified, and analysed with a precision of ±10 ppm (1σ). We will present the Δ′¹⁷O record and evaluate its correspondence with existing predictive models. We will also discuss perturbation of local Δ′¹⁷O values by regional fluxes, such as anthropogenic inputs or seasonal biospheric exchange. This initiative aims to provide the first annual Δ′¹⁷O (CO₂) baseline from the Southern Hemisphere and improve the accuracy of predictive models of the carbon cycle.

How to cite: Ghoshmaulik, S., Labuschagne, C., and Hare, V.: Insights into global carbon cycling using bi-monthly measurements of triple oxygen isotopes in CO₂ from Cape Point, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-644, https://doi.org/10.5194/egusphere-egu26-644, 2026.

Soil-derived N₂O represents a critical climate feedback in semi-arid agriculture. With a global warming potential (GWP) 298 times greater than CO₂, 6.2 TgN₂O-N is emitted annually from agricultural soils. The atmospheric acceleration with a growth rate >1.0 nmol mol^-1 y^-1 remains unexplained by fertilizer use alone, suggesting climate change as a critical driver of enhanced emissions, particularly through extreme precipitation and droughts. Rajasthan’s 4.6 million hectares of climate-resilient millet cultivation experience intense droughts and severe monsoon variability. Both these factors lead to drought-rewetting cycles and impact N₂O emissions, which remain unquantified at regional scales.

This study integrates satellite-derived measurements coupled with multi-model ensemble projections to model N₂O emission hotspots in the millet croplands at the district level in this state, which is a major producer of millet in India. Published millet area datasets for spatial distribution and water-filled pore space (WFPS) thresholds (80 - 95%, for optimal denitrification) with soil moisture proxies (NDVI, LST) are integrated to quantify N₂O flux. Standard precipitation index from CMIP6 models (SSP2-4.5, SSP5-8.5) is applied to quantify temporal shifts in wet and dry frequencies. 

The results indicated that the denitrification-dominated pathways have dominated during rewetting phases, with N₂O peaks lagging behind soil moisture recovery by 48–72 hours, consistent with the Birch effect. Meta-analytical synthesis suggests rewetting pulses release 5 - 10 times higher N₂O flux than constant moisture conditions. CMIP6 scenarios project 20 - 35% intensification in drought frequency by 2050, driving 15 - 25% increases in cumulative annual N₂O emissions under high-emission scenarios. The regional assessment enables evidence-based fertilizer timing and supports India’s Nationally Determined Contributions (NDCs) by quantifying emissions, thereby paving the way for more effective mitigation strategies.

How to cite: Aarav, P., Burman, P., Phartiyal, G., and Sharma, S.: Quantifying N₂O Pulses from Millet Croplands: The Role of Drought-Rewetting Cycles Observed via Remote Sensing and CMIP6, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16677, https://doi.org/10.5194/egusphere-egu26-16677, 2026.

Since 2011 in Italy the surface area cultivated with pear tree (Pyrus communis L.) decreased from 35,400 ha to approximately 23,700 ha in 2023, driven by escalating production costs, stagnant market prices, and recurrent phytopathological challenges. Additionally, this decline has been hypothesized to be causally linked to progressively unfavorable climatic conditions, characterized by rising temperatures, frequent summer heatwaves exceeding 35°C, and reduced precipitation. The objective of this study is to identify the most effective soil–plant relationships across Emilia-Romagna, which is the leading Italian region for pear production. We aim at providing a scientific basis for adaptive management strategies linked to regional pedoclimatic variability, so as to support the future of the Italian periculture.
Three experimental orchards were located in the provinces of Ferrara, Modena and Ravenna, which are key districts in Italy for periculture were set up in 2023. Agro-meteorological conditions were continuously monitored through an online automated system. Soil samples were thoroughly characterized with respect to their texture, calcium carbonate content, pH, loss on ignition LOI, multi-element profiling by X-ray fluorescence (XRF). To link soil properties with the plant performance, fast chlorophyll a fluorescence was measured in leaves in June 2025 on BA-29 grafted trees and the PI_tot (total performance index) was calculated, which represents a synthetic indicator of photosystem II efficiency.

Soils at the Modena site were predominantly sandy, deriving from Apennine sediments, whereas soils from Ferrara and Ravenna sites exhibited a higher clay content resulting in greater, water-holding capacity. XRF analyses indicate that elemental concentrations at all sites were within expected background levels. LOI and calcimetric analyses were higher in Ravenna soils compared to those from Ferrara and Modena, indicating a greater organic matter and carbonate content. At the Modena site, trees exhibited significantly lower PItot values than those observed in  Ferrara and Ravenna sites, This pattern is attributable to the higher sand fraction, which promotes rapid water drainage and reduced nutrient retention in contrast to clay-rich soils where enhanced water and nutrient availability can support improved photosynthetic performance performance. A relationship could also be envisaged found between soil organic carbon content and PItot. Because meteorological conditions were comparable and the germoplasm was uniform across the three sites, the observed differences in photosynthetic performance can be primarily ascribed to soil properties. These location-specific soil–plant correlations can inform precision agriculture practices, rootstock-scion selection, and adaptation strategies to enhance the resilience of pear orchards under changing climate conditions.

Research funded by the European Union – NextGenerationEU, Ministero dell’Università e della Ricerca - Piano Nazionale di Ripresa e Resilienza, D.M. 630/2024.

How to cite: Bigoni, M., Marrocchino, E., Viola, I., Zaccarini, M., Farinelli, A., and Ferroni, L.: Linking soil texture and organic carbon to leaf chlorophyll fluorescence in Pyrus communis orchards: a multi-site study in Emilia-Romagna, Italy , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18092, https://doi.org/10.5194/egusphere-egu26-18092, 2026.

Stable carbon and nitrogen isotopes are widely applied to infer plant water-use efficiency and nutrient dynamics; however, their physiological interpretation remains uncertain when isotopic signals are not supported by functional measurements. Here, we combine elemental and isotopic analysis (EA-IRMS; δ¹³C, δ¹⁵N, %C, %N and C/N ratio) with fast chlorophyll a fluorescence assessed in vivo through the JIP-test (Handy-PEA) to resolve the physiological meaning of isotopic variability across two genotypes and three geographical contexts of typical Italian red chicory.
In December 2024, leaves and roots of two Cichorium intybus cultivars (“Rosso precoce di Chioggia” and “Rosso precoce di Treviso”) were sampled across three sites, resulting in four genotype–site combinations. Plants were collected at their areas of origin, as defined by Protected Geographical Indication (PGI, Chioggia and Treviso), and outside the PGI area in Massenzatica (Ferrara, Italy), where both cultivars are cultivated in a sandy coastal soil. Isotopic and elemental analyses revealed a pronounced site-dependent differentiation. Leaf and root δ¹³C values varied among sites, indicating substantial long-term differences in C discrimination, related to the balance between transpiration and CO₂ assimilation, which seemed more favourable in Chioggia. δ¹⁵N further discriminated sites and cultivars, highlighting marked differences in N dynamics and internal allocation, although without clear attribution to specific sources.
To assess whether isotopic shifts reflected adaptive regulation or functional impairment, chlorophyll fluorescence transients (OJIP) were analysed using JIP-test parameters. All samples exhibited the typical polyphasic OJIP pattern, yet clear differences emerged between cultivars and sites. Across the entire induction curve, the Treviso PGI transient were consistently higher at the J and I steps than the other samples. This pattern was associated with increased light absorption and energy dissipation per photosystem II reaction centre (ABS/RC, DI₀/RC), reduced electron transport efficiency (ET₀/RC), and lower probabilities of electron transfer to photosystem I end acceptors (ψ(RE₀), δ(RE₀)), indicating a tendency to over-reduce the electron transport chain, probably driven by downstream limitations in electron utilisation.
Overall, our results demonstrate that a more negative δ¹³C, while implicating a better water use efficiency, does not necessarily reflect an overall better plant adaptation, which could be affected by  some biochemical photosynthetic constraints. Integrating EA-IRMS with JIP-test fluorescence analysis provides a robust framework to discriminate between stomatal regulation and biochemical limitation, improving the mechanistic interpretation of isotopic markers for geographical fingerprinting and genotypic differentiation in climate-sensitive agroecosystems.

This research was allowed by phD fellowship granted by EUROPEAN SOCIAL FUND P L U S - The ESF+ 2021-2027
Programme of the Regione Emilia Romagna

How to cite: Martina, A., Ferroni, L., and Marrocchino, E.: From isotopic fingerprints to functional diagnosis in Italian red chicory: linking δ¹³C and δ¹⁵N to photosynthetic performance across geography and genotype, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8969, https://doi.org/10.5194/egusphere-egu26-8969, 2026.

Understanding the interactions (synergies and trade-offs) among ecosystem services (ESs) and their driving factors is crucial for sustainable ecosystem management under intensifying climate change and anthropogenic disturbances. In recent years, machine learning approaches have demonstrated strong potential in capturing nonlinear relationships and exploring the driving mechanisms of ES interactions. However, most existing studies provide unified explanations at the global scale and often overlook the spatial heterogeneity and spatial dependence inherent in geographic locations, thereby limiting the ability to reveal the differentiated effects of the same driving factors on ES synergies and trade-offs across regions. This gap becomes particularly critical in large river basins, where pronounced environmental gradients, spatial connectivity, and heterogeneous human activities jointly drive strong spatial differentiation in ecosystem processes and services.

In this study, we develop a geospatially explainable machine learning framework to more explicitly characterize the spatial variability of ES interactions and their formation mechanisms in the Yangtze River Basin, China. Specifically, six key ESs, including food supply (FS), water yield (WY), water purification (WP), soil conservation (SC), carbon sequestration (CS), and habitat quality (HQ), were quantitatively assessed for the period from 2000 to 2023. Spearman correlation analysis and geographically weighted regression (GWR) were then employed to identify the ES relationships and their spatial distribution patterns. Furthermore, the GeoShapley method was introduced to incorporate geographic location into the model interpretation process, thereby enhancing the transparency and interpretability of machine learning decisions. From a spatial interaction perspective, this approach enables the analysis and visualization of the differentiated driving effects of climate conditions, topography, land use, and human activities on ES synergies and trade-offs across different spatial locations.

This study shows that the geospatially explainable framework enhances insights into the formation mechanisms of ES interactions and provides scientific support for implementing zoned ecosystem management and targeted regulation strategies under ongoing global environmental change.

How to cite: Jiang, C., Yao, Y., and Ma, L.: Ecosystem service interactions and their driving factors based on a geospatially explainable framework: A case study in the Yangtze River Basin, China, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18238, https://doi.org/10.5194/egusphere-egu26-18238, 2026.

Cropland abandonment is a key land-use change process within the human-environment system, shaped by diverse environmental and socio-economic determinants. However, many studies overlook the complex interrelationships among these determinants, which may result in the reconfiguration of agricultural landscapes. Here, we developed an analytic framework based on social-ecological system theory to map cropland abandonment archetypes in Sichuan Province, Southwest China, using a combination of biophysical conditions, proximity characteristics, socio-economic determinants, and the extent, cumulative proportion, and spatial configuration of abandoned croplands. We implemented self-organizing feature maps using a nested clustering approach, which resulted in 25 sub-archetypes and 6 meta-archetypes. We used random forest regressions to quantify the relative importance of explanatory determinants influencing archetype geographies. Our results revealed diverse cropland abandonment archetypes, with meta-archetype area shares ranging from 4.4 % to 48.4 %. The most widespread archetype was characterized by favorable terrain, low cropland per capita, and low cumulative proportions of abandonment. Determinants of meta-archetypes varied in their importance but consistently highlighted the role of environmental determinants (i.e., topography, temperature), as well as productivity-related and socio-economic determinants (i.e., employee wages, pension insurance, high-value crops) as the most important determinants. Our findings argue against one-size-fits-all solutions and are highly relevant to nuance existing regional land-use policies addressing cropland abandonment. They further allow targeting key determinants of cropland abandonment and considering regional and local socio-ecological contexts in decision-making processes.

How to cite: Hong, C.: Revealing nested archetypes of cropland abandonment based on social-ecological system theory, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21572, https://doi.org/10.5194/egusphere-egu26-21572, 2026.

Bamboo expansion constitutes a significant process altering forest ecosystem structure and function. However, quantitative research remains scarce on how its long-term evolution influences the critical relationship between ecosystem productivity and biodiversity. This study aims to evaluate the long-term ecological effects of bamboo expansion in Wuyishan National Park (a biodiversity hotspot in China).

By analysing Landsat time-series imagery (1986–2020) and existing land cover dynamic classification maps, seven land cover types—including bamboo forests and broad-leaved forests—were identified. Analysis of surface classification results revealed an overall upward trend in bamboo forest area, with expansion primarily occurring at the expense of broad-leaved forests. Notably, 48% of the newly added bamboo forest area resulted from the conversion of broad-leaved forests.

To assess ecosystem responses to bamboo expansion, spectral diversity was quantified using Rao's Q index (functional diversity) and Shannon index (species diversity), calculated from NDVI and NDMVI. Ecosystem productivity was characterised via habitat indices (DHIs) derived from NDVI time series. Results indicate that regional ecosystem productivity has steadily increased, whereas spectral diversity has markedly declined, with both Rao's Q and Shannon indices showing significant downward trends. Specifically, bamboo forest patches exhibited higher Cumulative DHI (8.8 ± 0.65) than broadleaf forests, yet lower Rao's Q indices (0.010 ± 0.004), whereas broadleaf forests recorded (8.7 ± 0.69) and (0.011 ± 0.003), respectively. Moreover, farmland and tea plantations exhibited abnormally high Rao's Q values, likely attributable to fragmentation and edge effects (small patches embedded within forest backgrounds) rather than genuine species richness.

The study employed Theil–Sen trend estimation and Mann–Kendall significance testing to investigate correlations between bamboo forest area changes and biodiversity. Results revealed a significant negative correlation between bamboo forest expansion and spectral diversity indices (R²≈−0.36), suggesting bamboo encroachment may diminish biodiversity.

The observed trend of increasing productivity coupled with declining spectral diversity warrants further analysis to elucidate underlying drivers. Future research should integrate additional vegetation indices and morphological parameters for diversity calculations. Furthermore, long-term assessments of animal habitat suitability and ecosystem stability require combined ground-truthing and modelling approaches.

How to cite: Shi, W., Zhang, Q., and Qiu, F.: Bamboo Expansion Drives Divergence in Productivity and Spectral Diversity in Wuyishan National Park over Nearly Four Decades, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3212, https://doi.org/10.5194/egusphere-egu26-3212, 2026.

The Cumbrian Wildlife Trust (CWT) is undertaking the most ambitious attempt to revive Britain’s lost temperate rainforest in Skiddaw, northern Lake District, over the next 100 years. This involves the restoration of native Atlantic tree and treeless communities, including peatland in the area. There are ongoing ecological surveys to map the current vegetation and assess peatland status to establish baseline for detailed framework to restore degraded bogs. In collaboration with the CWT, this study employs different lines of palaeoecological evidence to investigate Skiddaw bog’s ecological history, the degree of its degradation over time, and the role of climatic and anthropogenic factors in shaping the landscape. This long-term perspective complements ongoing ecological appraisals by establishing a comprehensive baseline to predict changes in the bog and develop robust restoration and conservation frameworks against future warming climates.

How to cite: Adeleye, M., Essell, H., Handley, J., and Arizpe, A.: Long-term peatland ecological assessment in England’s largest national park (Lake District) for restoration under changing climates, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-257, https://doi.org/10.5194/egusphere-egu26-257, 2026.

Support Vector Machine (SVM) classifiers are widely used for satellite-based crop mapping, yet hyperparameter tuning is often treated as a black-box process, with limited insight into how individual parameters influence classification performance. This limitation becomes critical when deploying SVM models across heterogeneous agricultural landscapes, where robustness and transferability are required. This study systematically investigates the sensitivity of SVM hyperparameters for crop type discrimination using Sentinel-2 NDVI time series over the Al Haouz plain in central Morocco, a heterogeneous irrigated agricultural region comprising winter cereals and perennial orchards. An exhaustive grid search was conducted across multiple orders of magnitude for the regularization parameter C (0.01–1000) and the RBF kernel coefficient γ (0.001–10). Model performance was evaluated using F1-score, Recall, and Overall Accuracy for six crop classes with contrasting phenological patterns.

Results reveal a pronounced asymmetry in hyperparameter influence. The regularization parameter C exhibits a high degree of robustness: once a moderate threshold is reached (C ≥ 1), classification performance stabilizes and remains insensitive to further increases. In contrast, γ shows a narrow optimal range (0.1–1.0), beyond which performance rapidly deteriorates. High γ values induce overfitting, particularly among crops with similar seasonal dynamics, as evidenced by persistent confusion between citrus and olive classes. The optimal configuration (C = 1, γ = 1) achieved an F1-score of 0.80 and an Overall Accuracy of 81%. More importantly, sensitivity analysis demonstrates that γ plays a dominant role in model calibration. These findings provide practical guidance for deploying robust SVM classifiers in data-limited agricultural contexts, where extensive hyperparameter tuning is often impractical.

How to cite: Ben zhair, F., Elyoussfi, H., Alami Machichi, M., Azamz, R., El Kasri, J., Boufous, B., and Belaqziz, S.: Hyperparameter Sensitivity Analysis of Support Vector Machine for Crop Type Classification Using Sentinel-2 NDVI Time Series, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12087, https://doi.org/10.5194/egusphere-egu26-12087, 2026.

Continuous, spatially explicit estimates of environmental attributes are increasingly provided as gridded data. The accuracy of gridded data, including classifications derived from remotely-sensed data, is typically evaluated using measures based on confusion matrices with site-specific class allocations; however, these measures are defined for categorical variables and are therefore not applicable to ratio-scale attribute estimates representing quantities, such as canopy height or population abundance.

We present an approach that extends commonly used agreement measures, i.e. the Jaccard index, Precision, Recall, and F-score, to non-negative, continuous ratio-scale attributes. The extended measures (cJaccard, cPrecision, cRecall, and cF-score) are viable equivalents to their binary counterparts, invariant to data imbalance and suitable for evaluating the agreement of various types of data representing ratio-scale attribute estimates. The cJaccard measure has proven useful for a range of applications in the geospatial domain, illustrating the broader potential of these measures for evaluating large-scale environmental gridded data products and beyond.

The aim of this contribution is to showcase and discuss the practical application of these continuous agreement measures to real-world gridded datasets representing spatial-environmental variables. Through applied examples, we demonstrate how cPrecision and cRecall enable a directional interpretation of disagreement, disentangling commission and omission errors in the total proportion of misallocated magnitudes. We further illustrate how cJaccard provides a bounded, scale-independent measure of agreement that complements typically used error-based measures (such as Mean Absolute Error or Root Mean Square Error) in the data comparison process.

How to cite: Krasnodębska, K., Goch, W., Uhl, J. H., Verstegen, J. A., and Pesaresi, M.: Agreement measures for continuous, ratio-scale data: cJaccard, cPrecision, cRecall and cF-score, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20818, https://doi.org/10.5194/egusphere-egu26-20818, 2026.

The rapid technological developments of recent years have enabled new methods for acquiring aerial photographs and high-spectral-resolution imagery. In this context, unmanned aerial vehicles (UAVs) offer significant potential for high-resolution Land Use/Land Cover (LULC) mapping, allowing clear distinction between natural and human-made features. UAV-based approaches provide high accuracy, faster data acquisition, and cost-effective solutions for detailed LULC analyses. However, there is a fertile ground in evaluating different methodological approaches and testing different algorithms for obtaining robust and transferable results. To this end, the present study aims at comparing two advanced classification techniques for mapping agricultural areas using multispectral UAV data over a typical agricultural site. The area selected for the study consists of crops and agricultural land located near the town of Amygdales, in the regional unit of Grevena. The two techniques are SVM (Support Vector Machines) and MLC (Maximum Likelihood Classification). In overall, results showed that the SVM proved to be more accurate with an overall accuracy of 79.45% compared to 78.91% for MLC, while both methods achieved a Kappa coefficient of 0.72. The statistical significance of the findings was further confirmed from the Mc-Nemar statistical significance results which were also computed. The results evidenced the capability of both methods obtaining LULC maps at very high spatial resolution. All in all, the methodological approach presented herein provides potentially a low-cost solution in mapping agricultural areas at very high spatial resolution which may be also fully transferable and reproducible to other locations too, which offer potentially important pathways to be used in precision agriculture applications. Such information can be of practical value to both farmers and decision-makers in reaching the most appropriate decisions for field management.

Keywords: Precision Agriculture, Mapping, UAVs, Classification, Machine Learning, Support Vector Machine, Maximum Likelihood

Acknowledgement

The participation of George P. Petropoulos study is financial supported by supported by the ACCELERATE MSCA SE program of the European Union’s Horizon research and innovation program under grant agreement No. 101182930.

How to cite: Charisoulis, P., Petropoulos, G. P., Detsikas, S. E., Volianaki, E., and Litke, A.: Evaluating different methodological approaches for very high spatial resolution mapping of agricultural areas exploiting UAV data: a case study from Greek agricultural site, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1693, https://doi.org/10.5194/egusphere-egu26-1693, 2026.

Monitoring the fractional cover of vegetation and bare soil is essential for sustainable land management, soil erosion control, and precision agriculture. However, accurately estimating these fractions from conventional satellite imagery is challenging due to mixed ground cover and limitations such as cloud contamination and coarse spatiotemporal resolution. High-resolution UAV imagery provides an effective solution by capturing fine-scale heterogeneity, enabling the application of spectral mixture modeling techniques to decompose each pixel into proportions of vegetation, bare soil, and other components. Understanding how GSD influences the performance of such mixture models is critical for optimizing UAV-based monitoring strategies and ensuring reliable, quantitative estimates of soil and vegetation fractions for informed land management decisions.

The objective of this study is to evaluate the sensitivity of fractional vegetation estimates to different ground sampling distances (GSDs) derived from unmanned aerial vehicle (UAV) imagery. Multispectral data were acquired using a UAV equipped with RGB, near-infrared, red, and red-edge sensors, flown at altitudes of 40 m, 80 m, and 120 m above ground level. The study area is a heterogeneous vineyard located in Drama, Macedonia, northern Greece. Image acquisition took place on 30 July 2025, under stable atmospheric and illumination conditions.

An object-based image analysis (OBIA) approach was applied to the UAV imagery, and the data were classified into three main land cover classes: photosynthetic vegetation, non-photosynthetic vegetation, and bare soil. Fractional vegetation cover estimates derived at each flight altitude were compared in order to assess the influence of spatial resolution on classification performance and vegetation fraction retrieval. Validation of the classification results was performed using an independent dataset generated through direct photo interpretation, allowing for an objective assessment of accuracy across the different GSDs.

This contribution aims to evaluate the effects of different ground sampling distances (GSDs) on the estimation of fractional vegetation cover (FVC) using multispectral UAV imagery over commercial vineyards in Northern Greece. The study highlights the influence of spatial resolution on canopy representation, particularly in young or sparsely developed vineyards, and supports the development of robust UAV-based tools for precision viticulture

Keywords: UAV, Vineyard, Fractional Vegetation Cover , ACCELERATE

Acknowledgement: This study is supported by ACCELERATE research project which has received funding from the European Union’s Horizon  research and innovation program under grant agreement No.101182930.

How to cite: Tselos, G.-N., Detsikas, S. E., Petropoulos, G. P., Grigoriadis, K., Polychronos, V., Mamagiannou, E.-M., Balomenou, P., Ramnalis, D., and Masouridis, P.: Assessing the effect of different ground sampling distances for drone-based mapping of fractional cover: a case study from a vineyard field in Northern Greece , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3144, https://doi.org/10.5194/egusphere-egu26-3144, 2026.

Stubble burning after harvest is known to degrade soil organic carbon (SOC). However, research on its long-term impacts on SOC is scarce and inconclusive. To address this gap, we introduce a data-driven modeling approach for SOC quantification by integrating remote sensing data with machine learning models to quantify changes in SOC during 2004-2021 across burnt rice areas in Punjab, India. This involved synthesizing literature to obtain SOC values pre- and post-burning, as well as intersecting MODIS burnt areas with rice crop maps to identify stubble burning areas in Punjab from 2004 to 2021. Post synthesis and identification, MODIS satellite band values were extracted for the synthesized experimental plots on pre- and post-burning dates. Further, remote sensing indices, which are sensitive to SOC changes such as NDVI, NBR, RECI, and BSI, were calculated for the pre- and post-burning dates. Using these indices and band values as predictors and literature-derived observed SOC values as response variables, multiple machine learning models were trained, whereby an R² value of 0.3 was obtained. While further efforts are required to improve model accuracy, our study revealed a significant decline in SOC from 2004 to 2018, ranging from 0.1  to 12.5 %,  whereas from 2019 to 2021, SOC increased by 0.7 to 7 % in various districts in Punjab. More specifically, these districts-Sangrur, Ludhiana, and Kapurthala have had the most significant decline from 2014 to 2018, whereas Rupnagar, Patiala, and Fatehgarh Sahib exhibit the highest increase in SOC from 2019 to 2021. The decline in SOC could be attributed to accelerated mineralization driven by combustion and the loss of SOC in the form of CO₂ emissions. Whereas the increase in SOC could be attributed to a reduction in stubble burning and incomplete combustion of residue, leading to the return of unburnt organic matter to the soil. These findings highlight the efficacy of integrating remote sensing frameworks with data-driven machine learning models in monitoring SOC and other aspects of soil health.

How to cite: Hoojon, J., Narayanan, M., and Ilampooranan, I.: Data-driven modelling to quantify soil organic carbon in burnt croplands: An integration of remote sensing and machine learning , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13704, https://doi.org/10.5194/egusphere-egu26-13704, 2026.

Improving olive yield in Moroccan agro-ecosystems requires a better understanding of the interactions between water availability, soil properties, and management practices. The complexity and non-linear nature of these interactions limit the effectiveness of conventional analytical approaches. This study applies machine learning methods to predict olive yield and to assess how the importance of yield determinants varies under contrasting water regimes. A multi-site dataset from Moroccan olive groves, including more than 2,000 observations, was analyzed. Machine learning models showed high predictive accuracy across water regimes. Under rainfed conditions, CatBoost achieved the best performance (R² = 0.845), indicating that yield variability is mainly driven by soil properties and spatial context. Under irrigated conditions, XGBoost provided the highest accuracy (R² = 0.855), highlighting the increasing role of management practices such as planting density and nitrogen fertilization. Under intensive irrigation, fruit-related variables, particularly 100-fruit weight, became the dominant predictors, while the influence of edaphic constraints decreased.

Overall, the results demonstrate that irrigation does not simply increase olive yield but fundamentally alters the hierarchy of factors controlling production. These findings emphasize the need for data-driven, site-specific management strategies to enhance the sustainability and efficiency of olive production in Morocco.

How to cite: Azamz, R., Elyoussfi, H., Benzhair, F., El Mousadik, R., and Belaqziz, S.: Deciphering Olive Yield Determinants under Contrasting Water Regimes: A Multi-Site Machine Learning Approach in Morocco Agro-Ecosystems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21818, https://doi.org/10.5194/egusphere-egu26-21818, 2026.

Twenty years of MODIS satellite data (2002-2022), TROPOMI glyoxal observations (2018-2022), and ground-based isoprene measurements were used to examine vegetation greenness (NDVI) and atmospheric glyoxal over Houston, Texas. Biogenically produced glyoxal grew by 51% between 2018 and 2022, despite a 2% per decade decrease in summer vegetation greenness and continued urbanization. Ambient mixing ratios of isoprene, the main biogenic glyoxal precursor, paradoxically dropped by 14% within the same time frame. Temperature (+0.68°C/year), ozone (+28%), and photochemical oxidants all significantly increased over this time, according to analysis of concurrent environmental data. The results indicate that higher temperature-driven isoprene emissions (+35%) and accelerated photochemical oxidation (+10%) overcame the declining vegetation signal, resulting in net increases in atmospheric glyoxal. This suggests that Houston's remaining flora is experiencing temperature-driven changes in biogenic volatile organic compound (VOC) emissions per unit area, even while its greenness has reduced.

Keywords: MODIS NDVI; TROPOMI glyoxal; Isoprene emissions; Photochemical oxidation

How to cite: Bibi, S. and Rappenglück, B.: Vegetation Dynamics and Atmospheric Glyoxal in Houston, Texas (2018-2022), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13501, https://doi.org/10.5194/egusphere-egu26-13501, 2026.

Kathmandu has undergone significant urbanization over the past decade, resulting in consistently warmer peri-urban regions compared to nearby rural landscapes due to the urban heat island effect. This study examines how baseline warming interacts with monsoon droughts to affect grassland ecosystems.

Using the Kathmandu Valley as a case study, we analyzed monsoon-season (June–October) data from 2000 to 2022, comparing land surface temperature (LST) and Normalized Difference Vegetation Index (NDVI) from MODIS, volumetric soil moisture from ERA5-Land, and reference evapotranspiration (ET₀) from Terra-Climate between peri-urban and rural grasslands. Grasslands were considered instead of agricultural regions to avoid the effects from irrigation. The premises of Tribhuvan University were chosen as the peri-urban location for their closeness to the core-city region and Changunarayan (Bhaktapur) was chosen as a rural location. The Standardized Precipitation Index (SPI-3) was derived from historical precipitation records (1980–2020) and drought years were identified by negative monsoon-mean SPI values.

The results reveal a persistent peri-urban heat penalty throughout the study period. On average, peri-urban grasslands were 0.94°C warmer than their rural counterparts. This contrast increased to 1.15°C during non-drought years but narrowed to 0.5°C during drought years, as rural grasslands experienced sharper warming related to soil moisture depletion and reduced evaporative cooling. Despite the partial thermal convergence, the peri-urban zone experienced greater ecological stress during droughts, with NDVI declining by approximately 4% relative to rural areas as soil in peri-urban region are 1.12% drier during droughts compared to rural grasslands. An average potential evapotranspiration difference of 23.6 mm exists between the region, and during droughts, the evapotranspiration is 2.66% higher in peri-urban region.

These findings demonstrate that monsoon drought reduces spatial thermal contrasts but does not eliminate peri-urban vulnerability. Persistent background heating in peri-urban landscapes results in elevated vegetation stress even when meteorological drought conditions are similar. These results highlight the importance of peri-urban land management and thermal mitigation strategies in reducing ecological stress under increasing climate variability in rapidly growing cities.

How to cite: Dahal, P. and Lamichhane, S.: Peri-urban heat amplification of monsoon drought impacts on grasslands in the Kathmandu Valley, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15799, https://doi.org/10.5194/egusphere-egu26-15799, 2026.

Estuarine systems are crucial in deciphering coastal ocean dynamics and biogeochemistry, including the vital role they play as ecological sequesters of greenhouse gases. We present a modelling framework that combines the Estuary Box Model (EBM) with the Biogeochemical Flux Model (BFM) to simulate the interplay between physical dynamics and biogeochemical processes. The EBM is a robust, yet simplified model that represents estuarine hydrodynamics, addressing salinity, temperature, and freshwater discharge variations. The BFM simulates nutrient cycling, microbial interactions, phytoplankton dynamics, organic matter mineralization and particulate sedimentation across chemical functional families and living functional groups. To realistically simulate estuarine scenarios, the passive tracer transport equation was adapted to include explicit biogeochemical reaction terms within a time-varying estuarine simplified control volume, furthermore, accounting for riverine nutrient inputs, vertical mixing, tidal exchange and various biological feedback. Additional alterations were made to accommodate burial and sequestration parameters better representing estuarine zones.
The coupled framework was applied to the Po di Goro estuary in northern Italy, and the simulations were conducted for the period 2010 to 2023. The results were validated by comparing the Chlorophyll concentration outputs against satellite and in-situ buoy observations. The outcomes show a strong correlation between phytoplankton biomass and residence time during periods of algal blooms, whereas a rapid shift to zooplankton propelled top-down grazing control during prolonged periods of stable conditions. The model effectively replicates the organic matter sedimentation dynamics typical of deltaic environments, offering insights into the scale and factors controlling the burial and sequestration of organic matter in these ecosystems. The coupled EBM-BFM system is a highly computationally efficient and scalable framework for understanding the estuarine ecosystem drivers, with important potential applications in biogeochemical variability, nutrient retention, and climate-driven changes in coastal zones.

How to cite: Santhoshkumar, S., Verri, G., Vigiak, O., Niroumand, M., Riminucci, F., Silvestri, S., and Mentaschi, L.: A computationally efficient framework for modelling estuarine biogeochemistry, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12306, https://doi.org/10.5194/egusphere-egu26-12306, 2026.

The increasing demand for reliable food authentication highlights the need for scalable and innovative tools to link geochemical fingerprints in food products with their geographic provenance. Food authentication is not only essential for preventing fraud but also offers a unique opportunity to relate agricultural products to their underlying geochemical signatures. Here we present a unified framework that combines stable isotopes (e.g. ⁸⁷Sr/⁸⁶Sr) and trace-element fingerprints measured in food products with explainable and cost-aware machine learning to support provenance verification.

We first develop a cost-aware binary classification model for French sparkling wines, demonstrating how high-precision ⁸⁷Sr/⁸⁶Sr ratios can be partially substituted by low-cost elemental proxies (e.g. Rb) while maintaining strong discriminative power. To address scalability constraints, we extend this approach to a multiclass setting using cost-sensitive logistic regression to classify wines from multiple Portuguese and Chilean regions, explicitly handling class imbalance and feature redundancy. Finally, we introduce TeaPrint, an unsupervised multimodal clustering framework that jointly integrates isotopic, elemental and volatile organic compound data to uncover coherent regional geochemical patterns in international tea samples without requiring prior labels.

Across these case studies, we show that food products carry integrated geochemical signatures that can be exploited for robust provenance authentication across heterogeneous datasets. By bridging forensic geochemistry and explainable machine learning, our approach offers a cost-efficient and scalable pathway towards robust provenance authentication and transparent food supply chains.

How to cite: Lu, Y., Doerr, C., and Sebilo, M.: From Geochemical Fingerprints to Food Authentication: Integrating Explainable and Cost-Aware Machine Learning for Provenance Analysis , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21380, https://doi.org/10.5194/egusphere-egu26-21380, 2026.