Land use change is a significant source of anthropogenic carbon emissions, making it a critical yet often underrepresented component in climate projections. As next-generation Earth System Models move toward kilometer-scale resolutions to capture fine-scale land-atmosphere interactions, existing land use projections (typically provided at ≈30 km resolution) are insufficient to represent the spatial heterogeneity these models require.

Relying on coarse datasets can result in a loss of 31–54% of spatial information, introducing substantial biases in simulated terrestrial carbon sequestration and surface fluxes. To address this, we present a deep learning framework designed to downscale coarse Land-Use Harmonization 2 (LUH2) data into high-resolution 1 km mosaics covering the historical and future period from 1850 to 2100.

Our methodology employs a U-Net architecture to integrate transient anthropogenic drivers from LUH2, high-resolution environmental conditions using Köppen-Geiger climate classifications, and high-resolution population density with a suite of high-resolution static geophysical features (elevation, 2D depth-weighted soil composition, terrain characteristics). 

A key technical advancement is our distributed inference pipeline using Gaussian-weighted patch aggregation. By normalizing overlapping predictions, this approach eliminates blockiness and edge artifacts, ensuring seamless global transitions across the 1 km mosaic. Validation against the HILDA+ dataset demonstrates high fidelity, achieving a global accuracy of 94.5% and a mean Intersection over Union (mIoU) of 0.799 for primary land use classes. These results provide a continuous boundary condition that enhances the realism of carbon, water, and energy fluxes in next-generation climate simulations and digital twin infrastructures.

How to cite: Castaño, M., Mozaffari, A., Materia, S., and Duarte, A.: Global 1 km Reconstruction of Historical and Future Land Use with Machine Learning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10039, https://doi.org/10.5194/egusphere-egu26-10039, 2026.

Robust carbon cycle science and effective carbon market governance depend on accurate monitoring, transparent modelling and credible representation of climate–economic feedbacks. Integrated Assessment Models (IAMs) such as RICE provide a long-standing framework for linking carbon emissions, climate dynamics and economic development and are widely used to inform mitigation pathways, carbon pricing and international climate policy. However, traditional IAMs rely on hand-calibrated parameters, simplified damage functions and fixed ethical assumptions, limiting their ability to integrate observational data, quantify uncertainty and support evidence-based carbon management. We build on recent advances in machine learning for climate policy and introduce RICE-N-JAX, a fully differentiable implementation of the multi-region RICE-N model (Zhang et al., 2025). RICE-N extends classical IAMs with multi-agent reinforcement learning to model strategic interactions and international climate negotiations. Our JAX-based reimplementation makes the entire climate–economic simulation fast and differentiable, including carbon emissions, climate response, production, trade, mitigation decisions and negotiation dynamics. Differentiability enables a new class of hybrid, data-driven climate–economic models. Our current research focuses on two key directions. First, we develop non-parametric hybrid damage functions in which the traditional analytical damage formulation is replaced by neural or spline-based surrogates trained on empirical and scenario data. This allows the damage–temperature relationship to be learned directly from data. Second, we perform inverse modelling of ethical and behavioural parameters, such as regional risk aversion, time preferences and mitigation bias, by calibrating the model against emissions, GDP and temperature trajectories from the Shared Socioeconomic Pathways (SSPs). This enables the recovery of latent normative assumptions embedded in scenario narratives and provides a data-informed basis for policy analysis. Finally, differentiability supports gradient-based calibration, uncertainty quantification, and sensitivity analysis of carbon price trajectories, mitigation pathways, and long-term climate impacts. We demonstrate a proof-of-concept end-to-end calibration of climate damage functions and show how parameter uncertainty propagates into future economic and emissions outcomes. By bridging process-based climate–economic theory with hybrid, knowledge-guided machine learning, RICE-N-JAX provides a foundation for fast and data-driven carbon-cycle modelling. The framework supports policy-relevant applications ranging from carbon pricing and climate clubs to carbon market design, illustrating how hybrid ML can strengthen the scientific basis of carbon management and climate mitigation.

References: Zhang, T., Williams, A. R., Wozny, P., Cohrs, K.-H., Ponse, K., Jiralerspong, M., Phade, S. R., Srinivasa, S., Li, L., Zhang, Y., Gupta, P., Acar, E., Rish, I., Bengio, Y., and Zheng, S.: AI for global climate cooperation: Modeling global climate negotiations, agreements, and long-term cooperation in RICE-N, Proceedings of the 42nd International Conference on Machine Learning (ICML 2025), 2025

How to cite: Ponse, K., Cohrs, K.-H., Wozny, P., Williams, A. R., Zhang, T., Acar, E., Bengio, Y., Plaat, A., Moerland, T., Gentine, P., and Camps-Valls, G.: Leveraging Differentiable Climate-Economy Models for Hybrid Modeling and Inverse Problems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11690, https://doi.org/10.5194/egusphere-egu26-11690, 2026.

Carbon farming can deliver climate mitigation and improved soil health, but credible deployment requires scalable MRV that supports additionality assessment and remains operational at farm scale. We present an EO-driven pipeline that integrates heterogeneous Earth-system data with hybrid modelling (machine learning + process-based physics) to estimate crop yield trajectories, soil organic carbon (SOC) evolution, and economic viability under baseline and regenerative management. A case study illustrates how a crop system can transition toward regenerative farming, demonstrating alignment with EU carbon farming policy. Results show how integrated, data-driven approaches can support quantification of both environmental and financial outcomes, enabling credible carbon accounting and guiding targeted investment in sustainable agriculture.

Multi-sensor satellite time series provide indicators of vegetation dynamics, and management proxies relevant to practice adoption (e.g., seasonal soil cover and surface condition). SoilGrids data provide spatially detailed soil information that helps us capture how soil conditions vary across and within fields, and how sensitive each site is. Climate forcing relies on high-resolution CMCC climate projections, enabling stress-testing of productivity and SOC outcomes under plausible future conditions.

A Random Forest model learns non-linear relationships between yield, EO indicators, soil attributes, and climate predictors to generate baseline yield projections. These projections are translated into carbon input assumptions (e.g., residue returns) and coupled to a RothC-class SOC model to simulate SOC evolution under regenerative scenarios such as cover crops.

Farm-level decision metrics integrate transition costs, yield impacts, potential carbon revenues, and land value appreciation to estimate break-even time and NPV, supporting project design and investment appraisal.

How to cite: Galli, G., Zamboni, M., Ricciardelli, A., Quarta, M. L., and Folegani, M.: EO-driven carbon farming MRV: linking crop yield prediction to SOC change, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20977, https://doi.org/10.5194/egusphere-egu26-20977, 2026.

Quantifying carbon exchanges in natural grasslands is crucial for improving management practices, estimating carbon budgets, and supporting climate mitigation policies. However, direct measurements of net ecosystem CO₂ exchange (NEE) using flux towers are spatially limited, particularly in heterogeneous biomes such as the Brazilian Pampa. This study presents a machine learning framework to upscale carbon exchange observations based on flux towers in natural grasslands used for extensive cattle production in southern Brazil. Continuous CO₂ flux measurements were obtained from multiple flux towers installed across four ecological regions representative of the Brazilian Pampa biome, encompassing different combinations of soil types, vegetation structure, climatic conditions, and grassland management. These long-term observations capture pronounced seasonal and interannual variability in NEE, driven primarily by climate variability and grazing management. Artificial neural networks (ANNs) were trained using eddy covariance flux data, meteorological variables (solar radiation, precipitation, air temperature, and humidity) derived from reanalysis products, and vegetation indicators obtained from satellite remote sensing. The trained models were applied to estimate daily NEE in other regions of the Pampa with different edaphoclimatic and vegetation characteristics where flux towers were installed. Model performance was evaluated using independent subsets of eddy covariance observations, with accuracy assessed using standard statistical metrics for this type of model. The results demonstrate that the machine learning approach successfully reproduces observed seasonal patterns and interannual variability of carbon exchanges, enabling spatially explicit estimation of carbon uptake and emissions in natural grasslands. This framework provides a scalable tool for regional carbon accounting in natural grasslands and for deriving regional emission and uptake factors. The approach contributes to improving monitoring, reporting, and verification (MRV) of nature-based climate solutions and supports policies aimed at low-carbon livestock production and conservation of the Pampa biome.

How to cite: Mergen, A., Sehnem, J., Pinheiro, M., Roberti, D., and Jacques, R.: Integrating eddy covariance and machine learning for the spatial estimation ofcarbon exchanges in natural grasslands of the Pampa biome, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13867, https://doi.org/10.5194/egusphere-egu26-13867, 2026.

The precise Measurement, Reporting, and Verification (MRV) of carbon stocks in small-scale afforestation and restoration forests can be served as the  foundation for subsequent carbon sink monitoring and benefit assessment.  Satellite remote sensing method, on the other hand, often  faces insufficient spatial resolution comparing to Unmanned Aerial Vehicle (UAV) imagery. UAV can capture fine details, but often results in "scale mismatch" and systematic estimation bias due to canopy shadows, background soil noise, and spectral saturation effects while applying estimation models directly. To address this technical bottleneck, this study aims to establish an automated carbon stock estimation workflow based on UAV multispectral imagery and to optimize estimation accuracy by identifying the optimal observational resolution through multi-scale analysis. 

The research methodology synchronizes field surveys with remote sensing modeling. First, a comprehensive tree-by-tree biomass inventory was conducted in sample plots. Allometric equations were used to calculate stand biomass, which was then converted into measured carbon stock to serve as Ground Truth for model validation. Subsequently, UAV multispectral images were acquired to calculate vegetation indices (e.g., NDVI) and establish regression models between spectral features and carbon stock. Furthermore, image resampling techniques were adopted to simulate multi-level spatial resolutions ranging from 0.03 to 5 m, systematically analyzing the impact of resolution changes on the Root Mean Square Error (RMSE) and the coefficient of determination (R²). This study clarifies the interference mechanism of spatial scale on canopy spectral signals and identifies the optimal aggregation scale to mitigate background noise. Ultimately, this research provides practical prediction formulas and a Standard Operating Procedure (SOP). In the future, applying this model to UAV-acquired imagery in similar restoration forests will enable rapid, automated carbon estimation without the need for time-consuming field surveys, significantly enhancing the efficiency and economic viability of carbon asset inventories.

Keywords

Aboveground Biomass (AGB), Multispectral UAV, NDVI, Allometric Biomass Model, Scale Effect, Restoration Forest, Carbon Sink Estimation

How to cite: Lee, C.-I. and Ho, H.-C.: Optimizing Aboveground Carbon Stock Estimation in Restoration Forests: A Multi-Scale Analysis of UAV Multispectral Imagery, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16064, https://doi.org/10.5194/egusphere-egu26-16064, 2026.

Abstract
More accurate local estimates of biomass and other forest attributes translate
into more accurate national-level estimates, improving forest monitoring and
informing forest policy. Higher-resolution local estimates facilitate more precise
monitoring of forest growth and harvest, allowing for better forest management
planning, and can also be used for verification of forest carbon storage, such as
for tree-based carbon credit programs and afforestation projects.

We present the first time series of high-resolution national maps of tree biomass,
carbon, volume, canopy height, and basal area produced using deep learning
methods applied to 3D point cloud LiDAR data. With hexagonal tiles of a 30
m diameter, the maps enable direct observation of stock change of aboveground
biomass, carbon, and other forest attributes at high resolution, in contrast to
inventory based estimates or coarser resolution remote sensing-based products.
We verify that our approach provides reliable estimates at the national and local
scales by comparing it to additional ground truth plot data from a time series
of local inventories.

The model was trained and validated on ground-truth data from the Danish Na-
tional Forest Inventory (DNFI) by combining field measurements aligned with
more than 20,000 sample plots extracted from two complete national LiDAR
scans. Based on [1], we apply a 3D convolutional neural network (CNN) using
the SENet50 architecture. We extended the approach to perform quantile re-
gression for uncertainty quantification. Our best model achieves an R2 of 0.83
for biomass and carbon, 0.84 for volume, 0.91 for canopy height, and 0.78 for
basal area on validation data.

We find that our model outperforms other state-of-the-art methods, which are
either based on passive 2D imagery or depend on using point cloud data indi-
rectly by extracting summary statistics. By using active LiDAR, we can derive
information from beneath tree canopies, and using the full point cloud enables
the model to learn from detailed information on forest structure, which may be
a key advantage.

The high resolution and accuracy of our method offer unprecedented potential
for time series analysis. The model is sensitive to changes at the individual tree
level, allowing for the detection of individual tree removals or growth. While
large scale forest cover change is easily detected with aerial imagery, thinnings
or partial removals are more difficult to uncover with most methods; however,
our analysis of independent repeated local inventory plots shows that our model
successfully detects smaller scale thinnings and tree growth.

References
[1] Stefan Oehmcke et al. “Deep point cloud regression for above-ground forest
biomass estimation from airborne LiDAR”. In: Remote Sensing of Environ-
ment 302 (2024).

How to cite: Brownell, H., Oehmcke, S., Nord-Larsen, T., and Igel, C.: Time series of national biomass maps from deep learning applied to airborne laser scanning point cloud data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16682, https://doi.org/10.5194/egusphere-egu26-16682, 2026.

Accurate estimation of global gross primary productivity (GPP) is fundamental for understanding terrestrial carbon cycling. Eddy covariance (EC) flux observations provide reliable site-scale GPP estimates, but the spatially sparse distribution limits their applicability at large scales. Satellite-based solar-induced chlorophyll fluorescence (SIF) has emerged as a promising proxy for large-scale GPP estimation; however, current satellite SIF observations also suffer from limited spatiotemporal coverage, and uncertainties remain in the SIF–GPP conversion. Moreover, conventional machine learning models trained solely on EC observations often exhibit limited spatial generalization due to the scarcity of spatially representative training samples.

To address these challenges, this study proposes a satellite–ground jointly constrained framework that integrates EC flux measurements and satellite SIF observations using transfer learning and multi-task learning techniques to exploit the complementary strengths of both data sources for global GPP estimation. First, for TROPOMI SIF data that has global spatial coverage but short temporal records, SIF is treated as a source domain to pre-train the model, which is then fine-tuned using long-term EC-derived GPP data as a target domain. This transfer learning-based model (SIFTML) demonstrates improved spatial generalization compared to models trained solely on SIF or EC data, effectively reducing systematic underestimation and overestimation at high and low GPP levels, respectively, while remaining insensitive to the magnitude scaling of source-domain SIF inputs.

Second, for the spatially sparse and track-like distributed OCO-2 SIF observations, a multi-task learning framework based on a mixture-of-experts architecture is developed. A physically constrained loss function derived from the SIF–GPP relationship is introduced to simultaneously achieve seamless SIF reconstruction and high-accuracy GPP estimation by jointly leveraging SIF and EC constraints. Results indicate that the multi-task model outperforms traditional single-task approaches in both GPP estimation and SIF reconstruction.

Overall, this study provides a new paradigm for long-term, high-accuracy global GPP estimation by alleviating limitations associated with the spatiotemporal coverage of ground EC and satellite SIF observations, as well as the uncertainties in SIF–GPP conversion, thereby offering improved support for global carbon cycle research.

How to cite: Guan, X., Ma, Y., Zeng, C., and Lin, L.: Satellite SIF and Ground EC Observation Jointly Constrained Estimation of Global Gross Primary Productivity, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21697, https://doi.org/10.5194/egusphere-egu26-21697, 2026.

Land degradation, deforestation and climate change have exacerbated droughts in Ethiopia, severely threatening its agriculture dependent economy. This led to large-scale restoration initiatives such as Sustainable Land Management Program (SLMP), Reduction of Emission from Deforestation and Forest Degradation Plus (REDD⁺) and the Green Legacy Initiative (GLI). GLI reported planting 32 billion trees since 2019, yet evidence remains limited. Here, we developed a deep learning framework robust to geolocation errors to monitor nationwide canopy height dynamic at 10m resolution to conduct intervention specific outcome assessments. We found a net gain of 23,537 km² in tree cover with trees above 8m height over the period 2019-2024. The large gain in young trees offsetting loss of tall trees is attributed to recent tree planting initiatives such as the GLI, REDD⁺, SLMP and expansion of commercial plantation by the small landholder farmers. SLMP and REDD⁺ interventions yielded the largest mean canopy height gains albeit in smaller areas.  Our results demonstrate measurable evidence that large-scale restoration interventions in Ethiopia are reversing the long-standing deforestation trends in the country.

How to cite: Workie, T., Brandt, M., Ciais, P., Gaber, M., Pellikka, P., and Gonsamo, A.: Satellite observations reveal large-scale restoration interventions reversing deforestation in Ethiopia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2837, https://doi.org/10.5194/egusphere-egu26-2837, 2026.

Robust carbon monitoring is fundamental to the credibility of climate mitigation strategies, including carbon markets, nature-based solutions, and ecosystem restoration initiatives. Soil organic carbon (SOC), as a major and dynamic component of the carbon cycle, is traditionally quantified through soil sampling and laboratory analyses. Although accurate at local scales, these methods are costly, time-consuming, and spatially sparse, limiting their suitability for large-scale monitoring, underscoring the need for scalable and robust alternatives.

Recent advances in machine learning (ML), and particularly deep learning (DL), offer substantial potential to integrate heterogeneous data streams and reinforce the scientific basis of carbon accounting. However, the application of DL to soil carbon studies remains limited, with most existing work confined to small spatial domains and relatively modest datasets. This limitation reflects the intrinsic complexity of environmental systems, the scarcity of high-quality reference observations, and persistent challenges in multimodal data integration and model interpretability.

Using the pan-European Land Use/Cover Area Frame Survey (LUCAS) soil dataset, this study presents a multimodal deep learning framework for large-scale prediction of SOC stocks. In addition to SOC, the framework estimates texture-related proxies and ancillary soil attributes relevant to carbon stock assessment. The approach integrates a comprehensive suite of data sources, including multispectral Sentinel-2 imagery, climate time series variables, and land-cover information, to jointly exploit spectral and spatio-temporal dependencies.

The proposed architecture integrates modality-specific components tailored to each data type, enabling a coherent spatio-temporal representation of SOC dynamics. Convolutional neural networks (CNNs) are used to extract spatial patterns and vegetation–soil spectral signatures from multispectral imagery, while recurrent architectures, including long short-term memory (LSTM) networks, encode seasonal to interannual variability driven by climatic conditions. Multiple deep learning encoders are systematically compared, ranging from conventional CNN–LSTM architectures to state-of-the-art transformer and vision transformer models, in order to assess their ability to capture long-range dependencies, cross-modal interactions, and complex non-linear relationships underlying SOC distribution.

A comparative analysis further benchmarks the proposed deep learning framework against widely used machine learning methods in soil science, including Random Forest (RF), Extreme Gradient Boosting (XGB), and Multiple Linear Regression (MLR). Model performance is assessed not only in terms of predictive accuracy, but also with respect to implementation complexity and interpretability, highlighting practical trade-offs for operational deployment.

By integrating heterogeneous data sources, this work demonstrates how artificial intelligence can bridge the gap between point-based field measurements and policy-relevant carbon assessments, while supporting state-of-the-art monitoring, reporting, and verification (MRV) frameworks. This analysis contributes to ongoing efforts to develop transparent, scalable, and evidence-based carbon monitoring tools, while explicitly highlighting persistent challenges related to data bias, spatial transferability, and model interpretability.

How to cite: Mercier, M., Marinoni, A., and Selvakumaran, S.: Multimodal Machine and Deep Learning Frameworks for Soil Organic Carbon Monitoring , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13805, https://doi.org/10.5194/egusphere-egu26-13805, 2026.

Accurate estimates of the spatiotemporal distribution of atmospheric carbon dioxide (CO₂) are essential to evaluate and enforce international climate agreements as well as to infer fluxes of the greenhouse gas. However, current observations are spatially sparse, with satellite and in-situ measurements providing only partial coverage of the Earth’s surface and atmosphere. Atmospheric transport models are often used to infer CO₂ concentrations across unobserved regions by simulating how gases move and mix in the atmosphere. While physically grounded, these models are computationally intensive and notoriously difficult to calibrate with observational data, due to the complexity of atmospheric dynamics and the sparsity of available measurements.

This study investigates the use of generative machine learning for inpainting of CO₂. More specifically, we apply flow matching, an approach that generates samples from an unknown target distribution by iteratively transforming samples from a simple known noise distribution with a deep neural network. In a first step, we train a flow matching model on assimilation data from CarbonTracker (CT2022). This trains the model to respect the physical patterns of atmospheric CO₂ fields, turning it into an effective prior for data assimilation. In a second step, we test the trained flow matching model on conditional generation that is, reconstruction of atmospheric CO₂ from partial observations. For this, we artificially mask parts of the CT2022’s CO₂ in a way that emulates the availability of satellite measurements. In a third step, we infer global CO₂ by conditioning on the total column average CO₂ (XCO₂) measurements from NASA’s Orbiting Carbon Observatory-2 (OCO-2), comparable to other inversions from the OCO-2 v11 MIP, but using a novel approach.

Extensive evaluation against independent and held-out test-sets from in-situ and satellite measurements show physical consistency and decent agreement of the reconstructed global CO₂ fields from OCO-2 measurements. However, challenges remain: specifically, future research needs to alleviate spurious artifacts from the employed posterior conditioning method in both the artificial mask and particularly the conditioning on XCO₂ before the approach can become operational.

Our presented flow matching approach opens up new avenues of research. The prior parameterized by the flow matching model can be investigated itself. For instance, it is possible to perform feature extraction inside the latent space and hence purposefully explore counterfactual scenarios of CO₂ distributions by carefully tracing out paths in the noise distribution and analyzing the corresponding generated CO₂ samples.

How to cite: Groß, J., Benson, V., Lima, M., Winkler, A., and Reimers, C.: Reconstructing Atmospheric CO2 with Flow Matching Models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15228, https://doi.org/10.5194/egusphere-egu26-15228, 2026.

The 3D-ABC project, developed within the Helmholtz Foundation Model Initiative, aims to create a foundation model for accurate mapping of global terrestrial above- and below-ground carbon stocks in vegetation and soils at high spatial resolution. The model integrates multimodal remote sensing data including Harmonized Landsat-Sentinel-2 (HLS) imagery, TanDEM-X InSAR coherence, and will also integrate climatic, topographic, and space-borne 3D lidar data. The architecture employs a multi-modal input processor, FM encoder, adaptive fusion neck, and task-specific prediction heads, trained via masked autoencoder pretraining followed by supervised fine-tuning. Training leverages JSC's JUWELS Booster and the forthcoming JUPITER exascale system.

BioMassters, a dataset that encompasses satellite imagery and associated forest biomass estimates for large-scale above-ground biomass mapping, provides an ideal initial evaluation framework for 3D-ABC for several compelling reasons.

Above Ground Biomass (AGB) estimation represents a core downstream task for carbon monitoring. BioMassters specifically targets this capability using Sentinel-1 SAR and Sentinel-2 MSI time series, modalities that overlap substantially with 3D-ABC's input data streams. This alignment allows direct assessment of whether 3D-ABC's learned representations capture vegetation structure and biomass-relevant features.

The dataset derives AGB labels from Finnish Forest Centre airborne LiDAR campaigns at 5 points per square meter density, combined with field measurements and calibrated allometric equations. This produces reference data with approximately 8% RMSE for key tree attributes, far more reliable than existing global products and essential for meaningful foundation model evaluation.

With 310,000 patches of size 224x224 covering 8 million hectares across five years, BioMassters offers the statistical power needed to assess foundation model generalization. The temporal dimension, 12 monthly observations per sample, tests whether 3D-ABC effectively captures phenological dynamics crucial for vegetation monitoring. Beyond its scale and temporal richness, BioMassters also benefits from a strong benchmarking ecosystem.

The NeurIPS 2023 competition produced well-documented baseline performance: U-TAE achieved 27.49 t/px RMSE overall, with results stratified by biomass density (15.24 t/px for low density, 37.59 t/px for high density). These benchmarks enable rigorous comparison of 3D-ABC against state-of-the-art task-specific models.

Current global biomass products operate at 100m resolution with RMSE values of 30-50 t/px. BioMassters operates at 10m resolution, allowing assessment of whether 3D-ABC's multimodal fusion can advance both accuracy and spatial detail simultaneously.

The dataset reveals where current approaches struggle, accuracy degrades with increasing forest density due to SAR backscatter and MSI reflectance saturation. This provides a specific challenge for 3D-ABC's multi-modal fusion architecture, and in future work we will be testing whether incorporating additional modalities (particularly 3D space-borne lidar) addresses these saturation effects.

While BioMassters covers boreal forests exclusively, it establishes whether 3D-ABC's pretrained representations provide a foundation for fine-tuning to other biomes, a critical test of foundation model utility before deploying resources on global-scale evaluation, e.g. in the arctic region. 

How to cite: Hashim, S., Mandal, S., Sedona, R., Zandi, E., and Cavallaro, G. and the 3D-ABC Team: BioMassters as Initial Benchmark for 3D-ABC, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22441, https://doi.org/10.5194/egusphere-egu26-22441, 2026.

Limited data and high sampling costs challenge soil carbon modeling. While previous generative AI methods, such as Generative Adversarial Networks (GANs) and Variational Auto-Encoders (VAEs), are commonly used, this study benchmarks Flow Matching's effectiveness for modeling complex soil data distributions. We introduce an Unconditional Flow Matching framework using the LUCAS soil dataset. Our procedures encompass: (a) training models without labels; (b) generating synthetic data, and (c) applying identical clustering protocols to the datasets generated in (a) and (b). Model performance is assessed through statistical divergence and cluster consistency between observed and synthetic data distributions. The goal is to determine if Flow Matching provides a more robust and accurate method for generating realistic soil carbon datasets.

How to cite: do Carmo Melicio, V., Mourão, V. H. M., Barioni, L. G., and Gois, J. P.: Unsupervised Manifold Learning: Validating Unconditional Flow Matching for Soil Carbon Data Topology, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21845, https://doi.org/10.5194/egusphere-egu26-21845, 2026.