This session invites contributions that advance our understanding of the biosphere through strategies, methods, and applications of remote sensing. We welcome studies on:
• Integrating RS data across spectral regions, angular configurations, and fluorescence signals
• Combining RS with in-situ measurements for modelling carbon, water and nutrients cycles
• Applications in climate change mitigation and adaptation, food production and security, sustainable development, land and nature conservation and protection, biodiversity, epidemiology, and public health (e.g., pollen-related impacts)
• Air pollution from both natural and anthropogenic sources (e.g., fires emissions, GHG emissions form land-sector, VOCs)
• Data assimilation of RS and in-situ observations in bio-geophysical, land surface models and atmospheric models
• Innovative RS signal extraction and processing techniques
We encourage submissions showcasing novel approaches, interdisciplinary applications, and case studies that highlight the growing potential of remote sensing for addressing urgent environmental and societal challenges.
Orals: Thu, 7 May, 08:30–12:30 | Room 1.14
This study will focus on the joint assimilation of multiple Earth Observation (EO) data streams – FAPAR, SIF, L-VOD, ASCAT slope, and SM – with eddy-covariance flux measurements of NEE and GPP at various FLUXNET sites in a developed data assimilation framework with TCCAS land surface model. The framework will be used to address the following questions. First, the consistency of assimilation results across sites and data streams will be tested to assess if the addition of EO variables is leading to significant improvements in the NEE/GPP estimates, and to measure how much posterior parameters are converging towards physically consistent values and their spread. The combination of diverse EO and flux will explain how multi-stream model-data can better constrain ecosystem fluxes and parameters, thus improving the terrestrial biosphere models used for the evaluation of the resilience of the land carbon sink under climate change.
How to cite: Shende, P.: Site-Level Coupled Assimilation of FLUXNET and Earth Observation (EO) Data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6342, https://doi.org/10.5194/egusphere-egu26-6342, 2026.
Accurate representation of terrestrial carbon–water dynamics remains a challenge in Earth system modelling, particularly under extreme hydro-climatic conditions such as droughts and heatwaves. While data assimilation (DA) of satellite-based brightness temperature (BT) and soil moisture (SM) retrievals improves near-surface moisture estimates, its impact on evapotranspiration (ET) and carbon fluxes is limited. Recent studies demonstrate that the joint assimilation of multiple Earth observation streams, such as vegetation indices, can improve estimates of both hydrological and biogeochemical state variables.
In this study, we developed a DA framework coupled to the Encore Community Land Model (eCLM), a fork of the Community Land Model version 5.0, with some extensions. The framework is applied over the EURO-CORDEX domain at 0.11-degree resolution. Assimilation is performed using the Ensemble Smoother with Multiple Data Assimilation (ES-MDA) with 64 ensemble members, allowing for the joint updating of key land surface parameters governing both soil hydrology and vegetation physiology. Assimilation observations include satellite-derived SM retrievals from the Soil Moisture Active Passive (SMAP) mission, ET from the Integrated Carbon Observation System (ICOS) eddy covariance (ET) flux towers, and leaf area index (LAI) from the Moderate Resolution Imaging Spectroradiometer (MODIS). Experiments are performed for the period 2018-2020, which include several recent European hydro-climatic extremes. Model performance is evaluated against in situ eddy covariance (EC) flux tower measurements of SM, ET, and net ecosystem exchange (NEE) across multiple European sites. We demonstrate that joint assimilation enhances the model’s ability to reproduce observed water–carbon fluxes and improves representation of land surface responses under recent extreme drought conditions.
How to cite: Zhao, H. and Hendricks Franssen, H.-J.: Improving water and carbon flux simulations through multi-source earth observation data assimilation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18239, https://doi.org/10.5194/egusphere-egu26-18239, 2026.
Understanding the spatial variability of tree water stress in complex terrain remains challenging due to strong heterogeneity in subsurface hydrology, plant water availability, and species-specific physiological responses. Along hillslopes, topographic gradients influence soil moisture redistribution, while tree species differ in their hydraulic regulation strategies and sensitivity to drought. Although field-based measurements provide detailed insights into individual-tree water relations, scaling these observations to heterogeneous landscapes remains limited.
In this study, we investigate how hillslope position, associated with spatial heterogeneity in water stress exposure driven by slope and soil depth, and species identity, modulate tree water stress across two forested sites in Valais, Switzerland: Saillon and Lens. Saillon is a mixed Quercus robur-Fagus sylvatica forested hillslope ranging from 645 to 1110 m a.s.l., with study trees located between 830 and 960 m a.s.l. Along this hillslope, soil depth varies strongly, with deep loess deposits upslope and shallower soils downslope, providing pronounced spatial heterogeneity in water stress exposure. The Lens site is a Pinus sylvestris-dominated forest with a south-facing hillslope ranging from 1057-1197 m.a.s.l. Tree water deficit was monitored using stem dendrometers installed on individual trees (7 oak, 8 beech, and 2 representative pine trees), complemented by in situ measurements of soil water potential and meteorological variables. To capture spatial and temporal variability in canopy conditions and link point-scale physiological measurements to landscape-scale patterns, unmanned aerial vehicles (UAV)-based multispectral and thermal imagery were acquired repeatedly over two consecutive growing seasons (2024 and 2025).
Preliminary analyses from the 2024 season indicate clear spatial and temporal patterns in tree water status across species and hillslope positions at the Saillon site, with downslope trees generally exhibiting higher water deficit and reduced canopy greenness compared to upslope trees, particularly during mid to late summer, reflecting the site-specific soil depth gradient, with shallower soils downslope and deeper loess deposits upslope. Relationships among UAV-derived spectral and thermal metrics, tree water deficit, and soil water potential were examined across species and hillslope positions. This integrated, multi-scale observational framework aims to improve the interpretation and spatial scaling of plant water stress across heterogeneous landscapes and complex terrain.
How to cite: El-Mejjaouy, Y., Hufkens, K., Walthert, L., and Stocker, B. D.: Understanding spatial variations in tree water stress across species and hillslope gradients , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18437, https://doi.org/10.5194/egusphere-egu26-18437, 2026.
An improved representation of the carbon and water cycle dynamics in terrestrial ecosystems underpins a large uncertainty reduction in modeling Earth system dynamics. The climate sensitivity of ecosystem processes controls land-atmosphere interactions and the overall atmospheric carbon uptake and release dynamics across scales. Local and Earth observations of vegetation dynamics are key for the evaluation of our understanding and support the quantification of process representation in model development. Previous research has shown the importance in undermining equifinality using multi-variate observation constraints, focusing water and carbon fluxes and stocks.
Long-wavelength radar backscatter provides unique insights into the dynamics of plant water and carbon dynamics when compared to optical EO products, as such, embeds the potential for constraining various parameters controlling local climate vegetation responses. In this study, we present an approach for assimilating L-band ALOS PALSAR backscatter data along with carbon and water fluxes measured at FLUXNET sites into a terrestrial ecosystem model to improve estimates of vegetation parameters turnover rates. A semi-empirical radiative transfer model, the Water Cloud Model (WCM), is employed as the observation operator linking modeled plant water content to L-band backscatter. Multiple model–data integration experiments are conducted to assess the added value of radar constraints across different model structures, including configurations with and without plant hydraulic schemes, and across temporal scales ranging from sub-daily to monthly.
Our results indicate that assimilating L-band backscatter observations improves estimates of aboveground biomass and strengthens constraints on foliage and woody turnover rates. However, persistent equifinality between plant water and carbon cycle processes remains, highlighting the need for improved estimates of the WCM parameters. Ultimately, this study highlights the potential of L-band backscatter to enhance vegetation carbon cycle modeling, emphasizes the added value of the newly launched ESA BIOMASS mission, and underscores the importance of integrating vegetation water dynamics into carbon models.
How to cite: Shan, X., Koirala, S., Zehner, M., De, R., Alonso, L., Papathanassiou, K., and Carvalhais, N.: Constraining vegetation turnover rates in Terrestrial Biosphere Model using L-band ALOS PALSAR backscatter, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6889, https://doi.org/10.5194/egusphere-egu26-6889, 2026.
Heterogeneous Mediterranean ecosystems are landscapes with scattered trees dispersed throughout a grassy matrix, and long-time series of land surface temperature (LST) images with high spatial and high temporal resolutions are needed to model their dynamics of the vegetation. Data assimilation systems have been developed for guiding the model with observations towards optimal solutions, and can be useful in the case of operational prediction approaches for reducing the uncertain model parameterization. For operational data assimilation approaches in heterogenous ecosystems there is the need of long-time series of LST images with high spatial and high temporal resolutions. This is currently difficult because of the spatial-temporal trade-off associated with satellite-based observations of LST, and there is a need of obtaining long time series of high-spatial and high-temporal resolution thermal data. This prompted us to propose a novel downscaling procedure that used MOD11A1 and MYD11A1 (~1000 m spatial resolution) as source data, and a coarse (~ 1000 m) and a fine (~30 m) annual estimation of the NDVI as ancillary. The approach, tested in a ecosystem in Sardinia (Italy), supplied by an eddy-covariance station, led to the creation of ~7700 maps of LST (30 m) covering the years 2000-2022. A first validation was done by comparing 19 years of ground-based data with the LST estimates from satellites, while the second validation was performed spatially by comparing MODIS downscaled maps with ASTER (90 m) and LANDSAT (100 m) scenes. The approach was able to reduce the spatial scale of MODIS LST observations by maintaining their original time frequency. The use of the LST observations from MODIS using the downscaling approach allowed merging the LST data from remote sensors and the LSM optimally for predicting accurately grass and tree LST in the assimilation approach. A sensitivity analysis of the data assimilation approach to assimilation time interval demonstrated that the use of the MODIS time interval of acquisition (i.e., ~12 hours) allowed to obtain accurate results.
How to cite: Montaldo, N. and Sirigu, S.: The Estimate of Land Surface Temperature Components for Soil and Vegetation Using the MODIS Dataset and an Ensemble Kalman Filter – Based Assimilation Approach in a Heterogeneous Mediterranean Ecosystem, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21289, https://doi.org/10.5194/egusphere-egu26-21289, 2026.
Above Ground Biomass (AGB) and its temporal and spatial dynamics are key to monitor carbon budgets of land forest ecosystems, especially under climate change, but they are currently poorly represented in land surface models (LSMs). However recent advances in LSMs allow to have more explicit representations of stand dynamics, although key parameters associated to C allocation, mortality and recruitment processes are largely uncertain. Data assimilation methods can help to better parameterize these processes, but few studies focused on the use of AGB, compared to fast-varying fluxes like Gross Primary Productivity (GPP). This study aims to bridge this gap in the last version of the ORCHIDEE land surface model, focusing on the African tropical forest which was much less studied than other biomes. We investigate the optimal strategy to assimilate AGB products from remote sensing observations in combination with other classical C flux products in order to improve ORCHIDEE’s representations of C fluxes and stocks of African ecosystems. We assimilate the ESA-CCI AGB product along with the FLUXCOM GPP data to optimize key model parameters for two Plant Functional Types in Africa linked to photosynthesis, C allocation and mortality, using either a Genetic Algorithm or a variational approach. The fast processes are first constrained with GPP (FLUXCOM data) while the slow processes are optimized with AGB (ESA-CCI data). We select potential maximum AGB for each model pixel (~50km), using the upper quartile of the high-resolution data (~30m), which represents the likely AGB of an undisturbed ecosystem. This choice reflects the fact that the current ORCHIDEE version is more suitable to represent ecosystem response to climate drivers rather than to disturbances. The final objective will be to use raw AGB to define an additional regional or pixel-based disturbance layer to ORCHIDEE. Key parameters involved either in fast (GPP) or slow (AGB) processes are selected by sensibility analysis. This two-steps assimilation allows us to significantly reduce the RMSD against the observations, for both GPP and AGB. This study highlights the potential of remote sensing AGB to constrain slow processes of LSM to better capture the dynamic of AGB in African tropical forests. While requiring a specific methodology, the assimilation of AGB induces significant changes in the C allocation, mortality and regrowth simulation by the ORCHIDEE model, thus impacting the carbon budgets of African tropical forests as well as increasing the overall confidence in future projections.
How to cite: Poinssot, A., Marie, G., Luyssaert, S., Viovy, N., and Peylin, P.: Potential Above-Ground Biomass data assimilation to constrain slow processes in ORCHIDEE (v4.3) land surface model. , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9332, https://doi.org/10.5194/egusphere-egu26-9332, 2026.
The cooling efficiency of the land surface, i.e. its ability to dissipate absorbed radiation and moderate temperature rise, is manifested by its apparent heat capacity, a property of the land surface that varies throughout the day in response to the intensities of the sensible and latent heat fluxes. Under clear sky conditions, the daytime increase in apparent heat capacity can be reliably estimated from geostationary satellite data and is used to define the Cooling Efficiency Factor Index (CEFI), which uniquely characterizes the temperature response to radiation at a given location on a given day. At longer time scales, the temporal variability of CEFI is modulated by several factors associated with changes in land surface state, including land cover, soil moisture availability, as well as the structure and dynamics of the atmospheric boundary layer. These relationships can be exploited to derive proxies for variables and processes that are otherwise difficult to observe, especially in real time. Here, we recall the definition of CEFI and we also present several applications. As already demonstrated, the CEFI index can serve as an indicator of vegetation drought stress, the condition when plants close their stomata due to soil water limitation and excessive atmospheric moisture demand, and vegetation productivity. In addition, the CEFI can act as a proxy for surface wind stress over arid regions with sparse vegetation. Consequently, CEFI can be involved in the detection of flash drought and to estimate fire risk in natural ecosystems, crop production losses in agricultural areas, and dust formation in desert regions. The CEFI can also be applied to quantify the cooling efficiency of urban areas. Finally we introduce the estimation of the apparent heat capacity over the sea surface, with potential implications for estimating wind over the sea and mixed layer depth from a purely observed perspective. Given its broad range of applications, our next step is to compute CEFI using multiple geostationary satellites to extend its spatial coverage as well as to further demonstrate its applicability range.
How to cite: Zampieri, M., Girardello, M., Checcherini, G., Saharwardi, M. S., Migliavacca, M., Massaro, E., Kalas, M., Hoteit, I., and Cescatti, A.: The Cooling Efficiency Factor Index (CEFI): A New Satellite-Based Indicator for Research and Operational Monitoring of Land Surface Processes and Beyond, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11868, https://doi.org/10.5194/egusphere-egu26-11868, 2026.
Satellite observations reveal a widespread afternoon depression of photosynthesis globally.Utilizing satellite observations and eddy covariance tower‐based observations worldwide, we investigated the impact of climate factors on the diurnal patterns of ecosystem gross primary production (GPP). Our analysis revealed that the increase in vapor pressure deficit (VPD) shifts the diurnal peak of GPP activity to earlier morning hours, particularly in drylands and areas with short vegetation. After disentangling the strong correlations among VPD, temperature, and soil moisture, we unraveled that VPD emerges as the dominant driver contributing to the widespread afternoon depression of photosynthesis in terrestrial vegetation globally. However, Earth System Models (ESMs) systematically underestimate the significant role of VPD in regulating photosynthesis. Eight out of 10 ESMs exhibited a clear afternoon increase in photosynthesis, which was attributed to temperature. Our findings emphasize the need to enhance the negative effects of VPD on diurnal photosynthesis in ESMs
How to cite: Liu, Y., Peñuelas, J., Cescatti, A., Zhang, Y., and Zhang, Z.: Atmospheric Dryness Dominates Afternoon Depression of Global Terrestrial Photosynthesis, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4870, https://doi.org/10.5194/egusphere-egu26-4870, 2026.
Tropical Dry Forests (TDFs) exhibit strong couplings between precipitation and vegetation phenology. TDFs are broadly defined as ecological formations characterized by -but not limited to- the presence of deciduous tree species, occurring in tropical regions where precipitation is highly seasonal, with a distinct dry season lasting several consecutive months (Miles et al., 2006), and mean annual rainfall ranging from 250 to 2000 mm (Murphy, 1986; Holdridge, 1969). This broad definition covers multiple bioclimatic types depending on authors, ranging from woodland savannah to moist semi-deciduous forests. A final agreement on their extent and definition may remain unattainable (Murphy, 1986; Blasco, 2000), and areas locally recognized as TDFs sometimes defy the most commonly used thresholds (Pando-Ocon, 2021).
TDFs provide numerous critical ecosystem services including carbon sequestration, support for local livelihoods, maintenance of biodiversity through habitat provision, high levels of floristic endemism, and a buffering effect against desertification (Siyum, 2020; Mendes, 2025). Despite these vital services, TDFs have been described as one of the most threatened biomes worldwide, experiencing extensive loss, fragmentation, and degradation driven by agricultural conversion, fire, and other anthropic pressures, with less than one-third of original forest area remaining (Stan et al., 2024). However, these valuable ecosystems have long suffered from a lack of public interest and from limited attention in conservation policies and research (Santos et al., 2011).
In this context, our study aims to assess and compare the dynamics of vegetation phenology, precipitation and their relationships across multiple TDF hotspots: Santa Rosa national park (Costa Rica), the Caatinga ecoregion (Brazil), Bandipur and Mudumalai national parks (Southwestern Ghats, India), and the Chizarira and Sijarira national parks within the Miombo and Mopane woodlands (Zimbabwe). These sites collectively span more or less pronounced gradients in rainfall seasonality, topography, edaphic conditions, tree density and forest composition. Using two decades (2003-2023) of MODIS NDVI time-series and CHIRPS precipitation data, we investigate inter and intra-site variability based on the visual interpretation of weekly mean NDVI and precipitation time-series over multiple points along a rainfall gradient, and compare metrics characterizing both phenological dynamics (amplitude of the vegetation index time-series and temporal phenometrics) and rainfall regimes (precipitation values and temporality of the rainy season). Additionally, we cross both times-series to assess their relationship (lag time between rainy season onset and vegetation response).
Other drivers of phenology are mentioned as TDF definitions are not limited by the presence of deciduous vegetation, and phenological dynamics in these systems may be impacted by other factors such as access to groundwater or atmospheric moisture, floristic composition and stand age (Hasselquist et al., 2010; Cuba et al., 2017; Siyum, 2020; Parthasarathy et al., 2008).
Overall, understanding and spatializing the links between phenology, environmental drivers and the associated plant functional traits in TDFs has important implications for the assessment of carbon flux and storage, the projection of ecosystem resilience and redistribution under climate change as well as accurate description of land-cover, ecotones and habitat connectivity (Li et al., 2024; Pereira Dos Santos et al., 2025; Ribeiro et al., 2025).
How to cite: Loizeau-Woollgar, L., Corgne, S., Villavicencio, D., Mutti, P., Betbeder, J., and Arvor, D.: Assessing vegetation seasonality in Tropical Dry Forests: Multi-site comparison of MODIS NDVI and CHIRPS Precipitation Time Series., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19907, https://doi.org/10.5194/egusphere-egu26-19907, 2026.
Biomass burning has become an increasingly important driver of air quality degradation in Minas Gerais, southeastern Brazil, particularly during the dry season, when fire activity intensifies, and atmospheric dispersion is suppressed. This work addresses recent evidence on the role of regional biomass burning in shaping fine particulate matter (PM₂.₅) concentrations, with a particular focus on the extreme air pollution episodes observed during 2024. The analysis integrates long-term records of fire hotspots, ground-based air quality monitoring, and key meteorological variables to elucidate the coupled processes linking fire activity, atmospheric dynamics, and urban air quality.
Minas Gerais encompasses the third largest metropolitan region in Brazil and hosts major local atmospheric emission sources, including intensive mining activities, steelmaking, and a dense urban–industrial infrastructure. Despite the persistent contribution of these structural sources to baseline air pollution levels, fire occurrence in the state exhibits pronounced seasonality, with approximately two-thirds of annual hotspots concentrated between August and October. The year 2024 stands out as one of the most critical periods of the last decade, characterized by prolonged drought, anomalously high temperatures, and persistently low relative humidity. These conditions not only favored the ignition and spread of fires but also created a meteorological environment highly unfavorable to pollutant dispersion.
Time-series analyses indicate that peaks in PM₂.₅ concentrations closely coincided with periods of increased fire frequency and intensity, particularly in the Metropolitan Region of Belo Horizonte. Statistical analyses reveal a moderate-to-strong positive association between PM₂.₅ levels and fire hotspot counts, and a consistent negative association with relative humidity. Notably, even in a region with significant local emissions from mining, steel production, and vehicular traffic, biomass burning emerged in 2024 as the dominant driver of exceedances of the national PM₂.₅ air quality standards established by CONAMA Resolution No. 506/2024. These findings demonstrate that regional-scale transport and accumulation of biomass-burning emissions can outweigh the influence of traditional urban and industrial sources during extreme events.
Several monitoring stations recorded historically high PM₂.₅ concentrations in 2024, leading to recurrent violations of national air quality thresholds. The compounded effects of extreme meteorological conditions and biomass burning led to short-lived but severe pollution episodes that substantially deteriorated air quality across the metropolitan area. Beyond local emission sources, regional fire activity therefore represents a critical and recurrent contributor to urban particulate pollution, with direct implications for public health and regulatory compliance.
Overall, the results reinforce the need for integrated air quality management strategies that combine fire prevention and control, regional-scale monitoring, and meteorological forecasting. Such approaches are particularly relevant under a changing climate, in which the frequency and severity of droughts, heatwaves, and associated biomass-burning events are expected to increase, thereby amplifying their impacts on air quality even in heavily industrialized urban regions.
How to cite: Albuquerque, T., Lopes, C., Ribeiro, A., Rudke, A., Sobrinho, O., Damasceno, A., Queiroz, R., Gonçalves, J., Andrade, M. D. F., Pedruzzi, R., Martins, L., and Hoinasky, L.: Beyond Industrial Emissions: How Biomass Burning Drives Extreme Urban Air Pollution, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2861, https://doi.org/10.5194/egusphere-egu26-2861, 2026.
The world’s forests take up ca. 30% of anthropogenic carbon emissions. However, despite the huge importance of this sink, we lack any direct method to measure it at large scales. All routes to estimate the forest carbon sink involve modelling of some kind. Dynamic vegetation models have been widely used in such estimations. These models, especially when they consider forest demography, have a clear advantage in providing a fully self-consistent digital representation of the forest, allowing all fluxes and stocks to be interrogated and drivers to be directly diagnosed. However, they also suffer from biases due to missing or simplified process representations or the use of coarse-scale or global parameters, which fail to capture the local-scale heterogeneity. These biases are particularly marked when it comes to the rates of tree growth and mortality, which are central to the forest carbon sink. Forest Inventory data offer a unique opportunity to constrain these parameters, as they provide repeated, spatially extensive observations of forest structure, growth and mortality across large regions. Here we present an approach to use forest inventory data to bias correct dynamic vegetation model simulations, to generate a hybrid product which combines the advantages of both methods.
The proposed framework uses multi-census forest inventory data to refine the performance of the latest version of the LPJ-GUESS dynamic vegetation model across temporal scales. Firstly, LPJ-GUESS employs a newly developed state initialisation method to initialise the simulated forest stands with the observed forest structures from the earliest available forest inventory census. Secondly, we integrate the Land Variational Ensemble Data Assimilation Framework (LAVENDAR) with LPJ-GUESS to assimilate observed growth rates and mortality, thereby calibrating two sets of model parameters that constrain the growth and mortality processes of the model. Specifically, we adopt a two-stage assimilation approach that not only maximises the utilisation of forest inventory data to reduce overall model bias, but also better captures temporal forest dynamics.
We apply this framework to repeated forest inventory data from Sweden and evaluate its impact on simulated forest growth and mortality. The results show that this framework improves the agreement between simulated and observed growth increments and mortality rates, while also enhancing the temporal responsiveness of the model to interannual variability. Compared to the original LPJ-GUESS configuration, the proposed framework enables a regionally and temporally adaptive parameterisation, leading to more realistic calculations of forest dynamics and carbon fluxes.
This study demonstrates the potential of combining repeated forest inventory data with advanced data assimilation techniques to provide assessments of forest carbon dynamics. The proposed two-stage framework is generic and can be extended to other regions and inventory systems, as well as integrated with complementary information from remote sensing, offering a promising pathway towards data-constrained, temporally adaptive and rapidly updatable assessments of forest carbon dynamics across large scales.
How to cite: Dong, W., Mu, M., Olin, S., Lindeskog, M., Zhong, H., and Pugh, T.: Constraining forest dynamics in LPJ-GUESS through data assimilation of forest inventory data using LAVENDAR, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19603, https://doi.org/10.5194/egusphere-egu26-19603, 2026.
Accurate representation of soil and plant hydraulic processes is crucial for predicting forest water and carbon fluxes under variable climate conditions. At the Forest Research sites across Germany, we applied an Ensemble Smoother with Multiple Data Assimilation (ESMDA) coupled with the Community Land Surface Model (eCLM; https://github.com/HPSCTerrSys/eCLM) to optimize soil and vegetation parameters. Site-specific observations included soil moisture, evapotranspiration (ET), and dynamic canopy conductance, with the latter derived from sap-flow measurements. Across all sites, ensemble simulations were performed for 2012 – 2024 using 120 ensemble members in which key parameters controlling soil hydraulics, photosynthesis, stomatal behavior, and plant hydraulics were perturbed.
We tested several data assimilation configurations. Assimilating soil moisture alone improved simulated soil water content, reducing RMSE by 5–50% across soil depths, but had limited impact on ET, gross primary production (GPP), or net ecosystem exchange (NEE). In contrast, assimilating both soil moisture and ET further constrained vegetation parameters, resulting in modest improvements in ET, GPP and NEE, while maintaining a large ensemble spread that captures a high percentage of observations. Optimized soil and plant hydraulic parameters also enhanced the representation of seasonal plant water stress, capturing summer stress dynamics more realistically in both wet and dry years, with stronger hydraulic limitation during dry years.
These results indicate that correcting soil water availability alone is insufficient to improve plant water use and photosynthesis. Including ET and canopy conductance observations provides additional constraints, strengthening the interactions between soil moisture, transpiration, and carbon uptake. This demonstrates that multi-variable data assimilation is needed to effectively reduce uncertainty in both soil and plant hydraulics, and that direct physiological measurements, such as sap-flow, can further enhance model predictions of both water and carbon fluxes.
How to cite: Naz, B. S., Bogena, H., Eloundou, F. B., Cabrera, J. B., Graf, A., and Hendricks-Franssen, H.-J.: Optimizing soil and plant hydraulic parameters in eCLM using multi-variable data assimilation of soil and vegetation observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6485, https://doi.org/10.5194/egusphere-egu26-6485, 2026.
In our changing climate and the green transition movement, it is important to verify national greenhouse gas emission inventories. By using prior emission estimates from inventories and process models as well as atmospheric greenhouse gas observations, we are able to assess emissions and their changes from different sources with inversion modeling. However, the scarcity of measuring stations affect the uncertainty of the inversion models.
In this study, we assess our ability to monitor the green transition in Finland. Currently atmospheric greenhouse gas concentrations are measured in 6 locations in Finland. Including new observation towers would fill existing gaps in the observation network. We aim to see how new atmospheric concentration measurement towers with different sensor accuracies could improve greenhouse gas detection in the area. We carry out an observing system simulation experiment (OSSE), using sensitivity tests with the Community Inversion Framework (CIF) using the transport model FLEXPART with a 0.1º x 0.1º spatial resolution on a nested domain, and observing how the model reacts to changes in the prior emissions and synthetic observations.
Addition of new stations in Finland could improve greenhouse gas detection and emission inventory assessment, and using lower accuracy sensors could help improve detection with a lower cost. Development of the OSSE experiment is still in progress and emissions from different source sectors, like wetlands and anthropogenic emissions will be optimized for multiple years. Our final inversion products will give improved estimates of inversion model sensitivity and show how effective a new observing system in Finland will be at detecting emissions.
How to cite: Hyvärinen, S., Tenkanen, M. K., Mengistu, A., Pietarila, M., Tsuruta, A., Ward, R., and Aalto, T.: Using atmospheric inverse model CIF-FLEXPART to estimate the capability to monitor CO2 and CH4 emission changes in Finland, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12429, https://doi.org/10.5194/egusphere-egu26-12429, 2026.
Solar-induced chlorophyll fluorescence (SIF) is a plant signal that can currently be retrieved from satellites at regional to global scale. Since SIF originates from the pool of excitation energy absorbed by chlorophyll molecules that also provides the energy for the photosynthetic CO2 assimilation, it has potential for diagnosing vegetation stress, particularly before the stress becomes apparent by optical decreases in greenness. However, the interpretation of satellite-observed SIF (SIFobs) remains challenging because it integrates multiple confounding factors beyond plant physiology, including variations in illumination conditions, canopy structure, and observation geometry. For applications aiming to detect early stress signals, it is essential to disentangle the physiological component, i.e., fluorescence efficiency (ΦF). SIFobs are strongly influenced by illumination conditions which change with actual differences in overpass times that can occur from one day to the next. Also, the canopy structure determines the fraction (fesc) of fluorescence that escapes the canopy to the sensor. Consequently, SIFobs are affected by the spatial heterogeneity of vegetation elements within the satellite footprint. A common practice to account for these effects is to apply corrections after multiple instantaneous SIFobs have been aggregated onto a regular grid of a geographic coordinate system, which may underestimate the uncertainty from spatio-temporal mismatches. We propose that these procedures should be applied prior to spatial gridding to ensure they are done over the correct spatio-temporal supports. We hypothesize that doing so will ensure consistency within the same support of all contributing variables and reduce uncertainties arising from spatial and temporal mismatches.
Here we derive ΦF from TROPOMI observations by normalizing SIFobs with radiation and canopy features prior to gridding. We normalize SIFobs by photosynthetically active radiation (PAR) and near-infrared reflectance of vegetation (NIRv), where NIRv serves as a proxy for canopy structure and vegetation greenness status. We explore ΦF using multi-source NIRv and PAR datasets in combination with TROPOMI SIF from three independent retrieval products. PAR is approximated using downward shortwave radiation products with multiple spatio-temporal resolutions (e.g., MSG, ERA5, TROPOMI estimation of radiance). NIRv, derived from other sources (e.g., MODIS, Sentinel-3, and Sentinel-2), is aggregated to the TROPOMI footprint and compared against the native TROPOMI top-of-atmosphere reflectance product. To evaluate the performance of ΦF derived at the individual footprint level, we compare it against flux tower observations from the Austro-SIF dataset. Austro-SIF is a fluorescence-specific dataset that integrates both active and passive measurement approaches from multiple European sites collected over different time periods between 2018 and 2022. It combines meteorological data with photosynthetic measurements of vegetation at both leaf and canopy scales, capturing comprehensive ecosystem responses to environmental variation. Using this dataset, we further assess the cross-scale consistency and uncertainty of ΦF across ecosystems spanning diverse biomes.
How to cite: Deng, Q., Pabon Moreno, D., Zhang, Z., Wohlfahrt, G., and Duveiller, G.: Extracting fluorescence efficiency from TROPOMI satellite observations: is it better to work on individual observations before gridding into data cubes?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18352, https://doi.org/10.5194/egusphere-egu26-18352, 2026.
Remote sensing of solar-induced chlorophyll fluorescence (SIF) can provide non-invasive in-situ insight into plant physiology in real-time. Ground-based SIF measurements have advanced our understanding of ecosystem behavior and biosphere-atmosphere interactions and offer new approaches to crop and ecosystem monitoring. However, current SIF measurement techniques rely on cumbersome instrumentation and excessively complex signal retrieval algorithms. This fundamentally limits the scalability of SIF measurements, and thus their widespread application in research and agriculture.
We present a novel approach for proximal SIF remote sensing, in which SIF is measured directly, without the need for post-processing of the signal: The solar-blind optical radiometer for the quantification of SIF (SBR-SIF) measures the light intensity in a narrow spectral window (ca. 10 picometres width) inside a strong oxygen absorption line of the O2A-band. In this spectral window, SIF is the only natural light source, because all sunlight is absorbed by atmospheric oxygen before reaching the surface. SBR-SIF uses a Fabry-Pérot interferometer to achieve the required high spectral resolution and contrast in a compact and robust instrument [1].
Proof-of-concept measurements with a first SBR-SIF prototype under real-world conditions demonstrate the feasibility of accurate, scalable, and real-time SIF quantification.
[1] Kuhn, J., Bobrowski, N., Wagner, T., and Platt, U.: Mobile and high-spectral-resolution Fabry–Pérot interferometer spectrographs for atmospheric remote sensing, Atmos. Meas. Tech., 14, 7873–7892, https://doi.org/10.5194/amt-14-7873-2021, 2021.
How to cite: Kuhn, J. and Stutz, J.: Direct quantification of solar-induced chlorophyll fluorescence by solar-blind optical radiometry, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6011, https://doi.org/10.5194/egusphere-egu26-6011, 2026.
The expansion of satellite-based sensor constellations offer game-changing opportunities for observation-driven monitoring with unprecedented temporal resolution and timeliness. However, distributed sensor observations are not typically directly interoperable, and require significant preparation to add value and enable actionable and impactful insights.
The Harmonized Landsat, Sentinel-2, and PlanetScope (HLSP) surface reflectance (SR) product adopts an innovative data fusion methodology and brings enhancements in temporal resolution, quality, and interoperability to help unlock the full potential of time-series remote sensing data from both public and commercial sources.
HLSP leverages the comprehensive harmonization methodology developed as part of Planet’s flagship high resolution (3 m) analysis ready data products, which adopt an implementation of the CubeSat-Enabled Spatio-Temporal Enhancement Method (CESTEM) to leverage near-daily PlanetScope observations in synergy with public mission sources. HLSP is produced with a 30 m pixel size and consists of a traceable mixture of 30 m downsampled PlanetScope, Sentinel-2, and Landsat 8/9 clear-sky observations that have been directly fused and harmonized into a seamless sensor-agnostic data stream consistent with FORCE (The Framework for Operational Radiometric Correction for Environmental Monitoring) SR data.
HLSP moves towards fully unlocking the value of the multi-year (~2017 to present) near-daily PlanetScope archive, complemented and enhanced by Sentinel-2 and Landsat observations. As a near-daily, multi-year, analysis-ready product, HLSP provides the foundational characteristics required for use cases across many industries via high quality AI-driven insights and analytics. This presentation will showcase the impacts of HLSP processing on multi-constellation sensor interoperability, cross-sensor radiometric consistency, and temporal resolution, and highlight relevant use cases. Highlighted use cases include application of time-series HLSP data for daily phenological monitoring, land cover change and deforestation detection and mapping, and multi-modal (optical and passive/active microwave) data fusion.
The preliminary results support HLSP as a core demonstration of how public missions can be used to complement private Earth Observation efforts to produce analysis-ready SR datasets with significantly enhanced quality, temporal resolution, interoperability, and usability. The HLSP SR product is envisioned as a foundational analysis ready data building block to support effective and accurate monitoring and mitigation of impacts from critical environmental challenges.
How to cite: Houborg, R. and Nutini, J.: Applications of a Harmonized Landsat, Sentinel-2, and PlanetScope (HLSP) Surface Reflectance Product, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6284, https://doi.org/10.5194/egusphere-egu26-6284, 2026.
This study presents a scalable framework for retrieving sub-canopy terrain elevations and forest canopy heights based on phase-height histograms constructed from few-look L-band bistatic InSAR data acquired by China’s Lutan-1 mission, with forest canopy height directly derived from the uppermost position of the histogram. The proposed method was evaluated over several representative forested regions in China, including Jianfengling National Forest Park (Hainan Province), Saihanba National Forest Park (Hebei Province), and the Northeast China Tiger and Leopard National Park (Jilin Province). The approach classifies phase-height histograms into four distinct types based on their statistical and morphological characteristics, corresponding to different scattering scenarios. For each type, type-specific strategies are applied to extract ground-related features, enabling robust estimation of the digital terrain model (DTM), while forest canopy height is derived from the vertical distribution of scattering. To improve accuracy in areas with complex scatterers, such as wetlands or water bodies, a supplementary regression based on backscatter intensity is employed to correct anomalously low height estimates. Validation against spaceborne LiDAR (GEDI and ICESat-2/ATLAS) demonstrates that the method produces reliable terrain and canopy height products across diverse forest types, ranging from tropical montane forests to temperate plantations and mixed natural forests. These results demonstrate that the proposed phase-height histogram-based approach can reliably and automatically retrieve forest canopy height and DTM without requiring any additional auxiliary data (e.g., LiDAR). This highlights that phase-height histograms provide a practical, reproducible, and scalable tool for large-scale forest monitoring, offering a complementary approach to PolInSAR and TomoSAR techniques for ecological applications.
How to cite: Jinting, Z., Yang, L., Yuxiao, Q., Guoqing, L., and Weiliang, L.: Underlying Terrain and Forest Height Retrieval based on Lutan-1 L-Band Bistatic InSAR Phase-Height Histograms, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15600, https://doi.org/10.5194/egusphere-egu26-15600, 2026.
Strip-intercropping systems present challenges for radiative transfer modeling due to the complex mutual shadowing between alternating tall and short crops. To address this, we developed an analytical Radiative Transfer model for Strip-Intercropping (RTSI), validated against UAV observations and 3D simulations in a maize-soybean system. The model demonstrated high accuracy in capturing canopy reflectance (R²≥0.94, RMSE<0.0251). Sensitivity analysis confirmed RTSI's robustness (RRMSE<8%) across varying configurations, significantly outperforming the Spectral Linear Mixture (SLM) model, which produced large errors in heterogeneous scenarios. Furthermore, the study elucidated the role of multiple scattering in compensating for energy in shadowed regions, effectively reshaping bidirectional reflectance anisotropy. RTSI serves as a vital tool for analyzing light transport and supporting the precise retrieval of biophysical parameters in complex intercropping systems.
How to cite: Zhong, R., Li, Z., Hao, D., and Zeng, Y.: RTSI: An Analytical Radiative Transfer Model for Strip-Intercropping Systems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12012, https://doi.org/10.5194/egusphere-egu26-12012, 2026.
The Amazon has a significant role in regional and global climate and carbon cycles. Having a robust method to determine and understand representative climatological sub-regions, and being able to sample these regions would help improve modelling the land surface and carbon cycle. The regions would also inform understanding of the representativeness of in-situ observations thereby correctly interpolating and bias correcting results as well as informing locations of future sites. Here the regions are calculated using a machine learning algorithm called k-means clustering, which groups datapoints which are close in variable space, hence have similar climatological characteristics. Various combinations of input variables and number of clusters (k values) were explored but the final results used annual average temperature, annual average precipitation and soil phosphorus as inputs, which produced contiguous regions which were easy to interpret. These regions were then evaluated using marginal distributions of the input variales and by exploring the above-ground-biomass distribution. This was performed for both present day observational data inputs and for projected climate data using ISI-MIP bias correct climate projections, exploring how the regions may change in future climates. This showcases the Amazon as an example, but more importantly highlights a robust technique for determining eco-regions which can be applied to different locations and climate scenarios.
How to cite: Wright, E., Economou, T., Argles, A., Robertson, E., Gibbs, L., and Bennett, A.: Using k-means clustering as a robust and repeatable method to determine representative climate/eco-regions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12523, https://doi.org/10.5194/egusphere-egu26-12523, 2026.
According to Whitehead (2007), nature is apprehended by the human mind as a network of events. Each event has factors that possess intrinsic and extrinsic characteristics, of which only the extrinsic aspects are observable. Observability requires extension and duration in space and time, both of which are implied by mass. Mass, however, is not understood as a simple substrate of inertial or linear momentum or energy. Instead, it functions as a dynamic and hidden factor inherent to events and is inseparably linked to another hidden factor which is gravitation.
Building on this framework, we have integrated Whitehead’s concepts of space, time, and mass into Verlinde’s emergent theory of gravity. Verlinde (2017) conceptualizes gravitation not as a fundamental force but as a memory effect arising from changes in quantum information and the competition between short- and long-range entanglement entropy within de Sitter space. In this model, mass acts as a hidden variable that alters quantum entanglement, thereby contributing to the emergence of spacetime and gravity. The gravitational response to baryonic matter redistribution minimizes the memory effects of external perturbations in condensed matter systems. Verlinde interprets this response as an apparent positive dark energy and describes gravity as a pressureless fluid, revealing its nature as an intrinsic elastic property of spacetime characterized by stress and strain.
If gravitation is understood as an elastic response, then entropy production depends on the balance between strain and stress. In elastic systems, including Earth, entropy decreases under stress and increases under strain. Furthermore, biological life plays a significant role in Earth’s entropy dynamics. As argued by Penrose, living organisms contribute to a reduction in planetary entropy by organizing matter and energy, thereby reinforcing entropy reduction when stress dominates over strain.
To examine biodiversity within this thermodynamic framework, we adopt Hubbell’s Unified Neutral Theory of Biodiversity and Biogeography. This theory treats species as functionally equivalent and explains biodiversity patterns through stochastic processes such as reproduction, immigration, and emigration. Species abundance follows a lognormal distribution, allowing biodiversity to be quantified using Shannon entropy, with the standard deviation serving as a key parameter.
We estimated entropy density production across multiple ecosystems by combining satellite-derived monthly land surface temperature data (LST) from MODIS & SENTINEL (1 x1 km grid) with energy balance calculations based on the Stefan–Boltzmann law using latent heat flux data from FLUXCOM-X (1 x 1 km), and linking these results to ecosystem-specific Shannon entropy values globally over the period 2003-2020. Our analysis includes 11 ecosystems worldwide, eight located within national parks with minimal human impact and three adjacent control areas subjected to anthropogenic activity, enabling comparative assessment of natural and human-influenced systems.
How to cite: Verstraeten, G. J. M. and Verstraeten, W. W.: Earth's biodiversity balances the Universal's gravitational response of mass creation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4533, https://doi.org/10.5194/egusphere-egu26-4533, 2026.
Climate change impacts on Earth’s ecosystems have become increasingly evident in recent years. Europe is currently the fastest-warming continent, with temperatures rising at approximately twice the global average rate. Climatic extremes, rising temperatures, and altered precipitation patterns are expected to increase the frequency and severity of droughts, especially in southern Europe. Vegetation is particularly sensitive to climatic conditions; consequently, substantial impacts of climate change on vegetation are expected in the future.
In the presented study, a novel product is developed that combines near-real-time Earth observation data with short- to medium-range weather forecasts to monitor water stress dynamics and anticipate drought impacts. Earth observation data and derived products are utilized to deliver a regular monitoring of water stress and drought conditions. The application is based on a Combined Drought Indicator (CDI) approach, which enables the differentiation of drought severity levels. The CDI is based on anomaly detection using Copernicus Sentinel-2 and Sentinel-3–derived Normalized Difference Vegetation Index (NDVI), Sentinel-3–derived Land Surface Temperature (LST), Surface Soil Moisture (SSM) from the Copernicus Land Monitoring Service (CLMS), and the Standardized Precipitation Index (SPI) derived from Climate Hazards Center Infrared Precipitation with Stations (CHIRPS) data. Short-range to seasonal forecasts are integrated using the ECMWF HRES product. This information is exploited to generate spatially explicit early warnings of potential drought impacts. The derived product is computed at five-day intervals. To assess the accuracy of the product, forecasts for recent past years are generated and evaluated against existing drought maps and agricultural datasets.
The presented approach moves beyond classical drought indices by focusing on drought impacts, such as agricultural stress, water availability and hydrological deficits. The resulting product can assist in anticipating and managing drought impacts for stakeholders from agriculture, water management, civil protection, and other drought-affected areas.
How to cite: Eisfelder, C., Huth, J., and Bachofer, F.: Remote sensing based water stress dynamics monitoring and drought impact prediction, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16501, https://doi.org/10.5194/egusphere-egu26-16501, 2026.
Droughts across India exhibit variability arising from the complex interaction between atmospheric forcing and terrestrial hydrological processes. Although precipitation deficits are generally considered the primary trigger for drought, changes in terrestrial water storage dictate how drought evolves and recovers. It is therefore essential to understand how surplus and deficits in water balance governs not only the drought periods across different hydroclimatic zones of India but also the subsequent influence on vegetation health. In this study, we analyze how water balance components regulate vegetation by assessing the elasticity of vegetation to climatic and catchment storage variables. A dominant driver approach is used to evaluate whether vegetation response is mainly controlled by meteorological or terrestrial variability. Furthermore, we analyzed the influence of key drought attributes, including severity, duration, development and recovery speeds, on vegetation elasticity with respect to climate and catchment variables. The results show a shift from precipitation-dominated vegetation control during mild drought conditions to storage-driven regulation under extreme droughts. These findings highlight the role of subsurface water storage in buffering vegetation against severe drought stress across India. Overall, this analysis offers valuable insights into the processes controlling vegetation resilience and susceptibility, allowing for a more refined understanding of vegetation-catchment-climate interactions across diverse drought conditions.
How to cite: Bilal, S. B. and Gupta, V.: Role of Water Balance Components in Regulating Vegetation Response to Drought , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18248, https://doi.org/10.5194/egusphere-egu26-18248, 2026.
Extractivism driven by licit and illicit economic activities has generated negative effects on natural land-covers and biodiversity, increasing ecosystem vulnerability as a consequence of local hydroclimatic fluxes alterations, as well as those transcending geographic boundaries. One of the main current drivers is deforestation, which in tropical regions has altered ecosystem carbon storage capacity, however, it's relationship with local hydroclimatic alterations has been poorly addressed. This study evaluates the relationship between land-cover changes and hydroclimatic dynamics through the analysis of actual [AET] and potential [PET] evapotranspiration, precipitation [P], and runoff [R], as well as their implications for terrestrial carbon uptake in the Upper Putumayo river basin, located in Colombia within the Amazon region and in an ecosystem convergence zone with the Andean region. The analysis covers the 2000–2022 period and is based on remote sensing tools and in situ data under the Budyko framework, assessing water deficit in relation to biomass production in remaining forest ecosystems. Results show that deforested areas exhibit increased runoff, associated with reduced vegetation interception and greater exposure of bare soils, leading into diminished hydrological regulation capacity. Additionally, the basin maintains low evaporative ratios (AET/P = 0.346) despite a decrease in the aridity index (ΔPET/P = −0.023), evidencing an ecohydrological decoupling, which can be attributed to vegetation control loss. These changes reflect an increase in effective water deficit and coincide with reduced primary productivity [GPP and NPP], suggesting forest's lower capacity for terrestrial carbon sequestration.
How to cite: Garcia Salazar, A., Zamora Avila, D., and Belalcazar, L. C.: Land-Cover Changes effects on Water-Carbon fluxes in a Colombian Amazonian basin, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21978, https://doi.org/10.5194/egusphere-egu26-21978, 2026.
Tropical forests take up a significant fraction of human carbon emissions, thereby contributing to slowing down global warming. In the context of El Niño-Southern Oscillation (ENSO), the El Niño and La Niña phenomena can cause anomalously weather patterns across tropical regions. However, it is largely unknown to what extent ENSO-induced climatic anomalies can alter the response of ecosystem function, for example, by inducing water-limited conditions as a consequence of dry and warm weather.
To address this question, we (i) quantify the degree of vegetation water limitation using the ecosystem limitation index (ELI), and (ii) correlate the local ELI with prior sea surface temperature in the Niño3.4 region, which represents the strength of ENSO. Gridded observation-based evapotranspiration data and near-infrared reflectance of vegetation are used to represent vegetation functioning in the ELI calculation. First results show that there are significant ELI-El Niño relationships in northern Amazon forests, while for La Niña conditions we find such relationships more widespread across the Amazon, Africa, Southeast Asia, and northern Australia. Despite these relationships, in most tropical ecosystems energy is still the main driver of vegetation functioning, while actual water limitation only occurs in a few regions including eastern South America and northern Australia, likely due to decreased precipitation. Thereby, water limitation as a consequence of ENSO impacts could be more prominent during the dry season in the tropics. Finally, for near-neutral ENSO conditions we find generally weak impacts on ELI in the tropics. Understanding the ENSO-induced shift in ecosystem limitation is crucial for better understanding the interannual variability of the land carbon sink, as well as because ENSO variability is expected to increase with more frequent extreme El Niño and subsequently more occurrences of consecutive La Niña, likely enhancing its influence on ecosystems.
How to cite: Huang, H. and Orth, R.: ENSO-induced regime shift from energy to water limitation in tropical ecosystems?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11052, https://doi.org/10.5194/egusphere-egu26-11052, 2026.
Arctic landscapes are very sensitive to warming with changes happening much faster than in other regions. The investigation on circumarctic Arctic vegetation change is carried out in the framework of the Federal Ministry of Research, Technology and Space (BMFTR) funded project SQUEEZE (Protection of the Disappearing Arctic Tundra: Potential, Planning, and Communication) in a large consortium. This study presented here focusses on the region close to Inuvik in the Mackenzie delta area in northwest Canada, which holds many different habitat types important to Indigenous peoples. The habitat diversity is important for ecosystem health and should be monitored as well as protected. In the region north of Inuvik, habitats range from tundra with low shrub structure, over forest tundra with sparse spruce forests, to taiga with dense needleleaf forests south of Inuvik and wetlands, lakes and river floodplains distributed over the area. These environments can be used for hunting, fishing, foraging of food, medicinal plants, firewood and construction material or as grazing grounds for caribou. However, those regions are facing changes due to climate change. Most dominant processes are increased permafrost thaw, shrubification of the tundra, northward shift of the treeline, more fires and pests in forests and changed waterways.
Remote sensing offers valuable insights into the current state of this region and can help to track changes. Airborne remote sensing provides high resolution and allows to cover large areas. The airborne data used in this work was acquired with the AWI Perma-X flight campaign in the summers 2023 and 2025. We use the Modular Airborne Camera System-Polar (MACS-Polar) optical data. The MACS-Polar camera was developed by the German Aerospace Centre (DLR, Adlershof) specifically for challenging, contrasting light conditions in the polar region. MACS images were processed to four-band (visible and near-infrared, VNIR) orthomosaics and digital surface models with spatial resolution of 15 cm and 3D point clouds with point densities of up to 25 points per m2. Features of the VNIR images as well as structural features of the surface will be used to classify the habitat types. The analysis of the data for the years 2023 and 2025 in this work allows for tracking of changes between the years. The outcomes are classified maps of habitats, such as wetland, tundra, forest tundra and different forest types, in the area around Inuvik. Those will be made publicly available to the Indigenous communities in northwest Canada. MACS optical orthomosaics can be challenging because of changing illumination during flight times and the data derivation from Structure from Motion can hold inaccuracies. However, the resulting maps of the current state of vegetation structure are valuable products. Future work can build upon those by looking at longer timescales and upscaling with Sentinel-2 satellite data.
How to cite: Riegel, E., Heim, B., Schulz, L., Herzschuh, U., Lisovski, S., Nitze, I., Grosse, G., Stadie, C. C., Bartsch, A., von Baeckmann, C., Feilhauer, H., Heim, R., Ludwig, A., and Kruse, S.: Vegetation Mapping at the Tundra-Taiga Region in the Northwest Territories, Canada, and Indigenous Use, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8049, https://doi.org/10.5194/egusphere-egu26-8049, 2026.
Agriculture is a cornerstone of national security, underpinning human survival and economic stability. However, the combined pressures of climate change, population growth, and recent global disruptions—most notably the COVID-19 pandemic—have intensified vulnerabilities within agricultural systems and food supply chains. These compounded challenges have led to socio-economic insecurities and health disparities, particularly among marginalized communities. Farmers now face the dual crises of climatic variability and pandemic-induced uncertainties, underscoring the urgent need for a transition from incremental adaptation to transformative agricultural strategies that emphasize human health, nutrition, and environmental sustainability.
Developing climate-resilient agricultural systems requires the cultivation of crops that can withstand diverse and extreme environmental conditions. Millets, often referred to as “climate-smart crops,” offer a promising solution due to their inherent resilience to biotic and abiotic stresses, ability to thrive on marginal lands, and superior nutritional profile compared to major cereals. To advance millet improvement and identify stress-resilient genotypes, high-throughput phenotyping (HTP) technologies provide a powerful approach for rapid, quantitative, and automated evaluation of physiological performance under controlled and field conditions.
In this study, we applied HTP to assess water-use traits in two pearl millet hybrid lines (COH9 and COH10) grown under well-watered (WW) and water-stress (WS) regimes. Environmental monitoring revealed characteristic diurnal variations in vapor pressure deficit (VPD) and photosynthetically active radiation (PAR), both peaking around midday. The two hybrids exhibited distinct transpiration dynamics in response to stress. COH9 maintained higher transpiration rates during midday hours under WW conditions and demonstrated faster transpiration recovery in the mornings following water stress, indicating superior water-use efficiency and regulatory capacity. Over the entire experimental period, COH9 showed greater cumulative transpiration and soil water extraction efficiency relative to COH10. These physiological advantages were reflected in significantly higher field yields for COH9 under both irrigated and rain-fed conditions.
Our findings confirm the effectiveness of HTP for identifying genotypic variation in water utilization and stress adaptation. The integration of HTP with genomic sequencing and bioinformatic analysis presents a promising pathway to accelerate millet breeding programs. This combined approach enables precise, data-driven selection of drought-tolerant and water-efficient genotypes, reducing both time and cost associated with conventional breeding methods.
Overall, this study highlights the critical role of climate-resilient crops such as millets and the transformative potential of advanced phenotyping technologies in ensuring sustainable food production under changing global conditions.
How to cite: Sade, N., Yehoshua Zach, Y., and Selvaraj, K. S. V.: Automatically tracking down dynamic physiological traits in Millet plants as a possible physiological pre-breeding system, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2, https://doi.org/10.5194/egusphere-egu26-2, 2026.
Solar-induced chlorophyll fluorescence (SIF) provides a direct optical signature of photosynthetic light reactions and is used to assess vegetation physiological function across spatial scales. However, top-of-canopy SIF measured from airborne platforms can be strongly modulated by canopy structure, and sun–shade geometry. As part of a larger effort to diagnose structural versus physiological controls on SIF at Alice Holt Forest, we report preliminary findings from high-resolution IBIS airborne SIF and Fenix hyperspectral data acquired over mixed oak-dominated woodland and adjacent pasture in southern England.
Initial analysis confirmed that pasture showed higher mean SIF than the neighbouring forest, despite the forest having higher NDVI, EVI and NIRv. Given the dense, vertically complex canopy at Alice Holt, we hypothesize that shading and within-canopy radiative transfer reduce the apparent SIF emitted from the forest canopy, whereas the pasture—being uniformly sunlit—preserves a higher top-of-canopy signal.
To test this hypothesis, we combine multi-method FLD analyses with targeted radiative-transfer experiments (DART) to quantify how much of the observed pasture–forest SIF contrast arises from canopy geometry rather than physiology.
This study provides high-resolution evaluations of structural biases in airborne SIF over temperate woodland and highlights the need to account for canopy shading when interpreting SIF–photosynthesis relationships.
How to cite: Onkaew, K.: Disentangling Structural Controls on Airborne SIF: Unexpected Pasture–Forest Contrasts at Alice Holt Forest, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5607, https://doi.org/10.5194/egusphere-egu26-5607, 2026.
Geostationary meteorological satellites such as Himawari-8/9 provide unprecedented high-frequency observations that enable detailed monitoring of land surface reflectance and albedo, which are key variables for terrestrial ecosystem and surface energy/carbon cycle studies. In this study, we will introduce our current status of development, validation, and application of land surface reflectance and albedo dataset derived from Himawari-8/9 observations. Surface reflectance was estimated from Himawari-8/9 measurements using the publicly available 6SV radiative transfer model and evaluated across low-mid-high latitudes using MODIS data. Surface albedo was derived using a standard broadband albedo algorithm, and its performance was assessed using a large number of validation sites distributed across full-disk spatial domains. The results demonstrate stable and physically consistent retrievals over diverse land cover and atmospheric conditions. We further present several application examples using the established datasets, including monitoring of vegetation dynamics in tropical rainforests, phenology monitoring as the detection of leaf flushing and senescence, and the detection of rapid albedo changes associated with anthropogenic disturbances in cropland areas. These examples highlight the advantages of geostationary satellite observations for capturing diurnal to sub-seasonal variability in land surface properties. The presented datasets provide a valuable foundation for advancing studies of terrestrial ecosystem processes, land–atmosphere interactions, and surface energy and carbon exchanges using geostationary satellite observations.
How to cite: Ichii, K., Li, W., Yamamoto, Y., Yang, W., Sasagawa, T., Zhang, B., and Hase, M.: Development, validation, and application of a land surface reflectance and albedo dataset derived from Himawari-8/9, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20694, https://doi.org/10.5194/egusphere-egu26-20694, 2026.
Land surface albedo, the ratio of reflected to incoming solar radiation at the Earth’s surface, is a fundamental parameter in the global energy budget. As an essential climate variable, it plays a key role in climate monitoring, environmental studies, and operational numerical weather prediction. Accurate, high-resolution albedo products are therefore critical for capturing land surface processes and their rapid changes, which in turn influence atmospheric dynamics and climate feedbacks.
Satellite remote sensing from Low Earth Orbit (LEO) provides a consistent means of estimating land surface albedo in the visible and near-infrared globally across the planet. The recent launch of the first platform of the EUMETSAT Polar System - Second Generation A series (Metop-SG-A1) marks a significant leap in satellite observation capabilities in comparison to the previous generation. In particular, Metop-SG-A payload includes two advanced multi-spectral imaging radiometers: METimage, offering enhanced spatial resolution down to 500 meters across a wide range of spectral channels, and the Multi-viewing, Multi-channel, Multi-polarisation Imager (3MI), which provides unique multi-angular information for characterising clouds, aerosols properties and surface directional reflectance.
Within the EUMETSAT Satellite Application Facility for Land Surface Analysis (LSA SAF), led by the Portuguese Institute for Sea and Atmosphere (IPMA), efforts are currently underway to develop, validate, and operationally and freely distribute surface albedo products derived from METimage and 3MI observations separately. These will ensure continuity with existing LSA SAF albedo products based on previous-generation sensors (Metop/AVHRR). The next step will be to exploit the complementarity between Metop-SG-A instruments, by combining METimage’s high spatial and spectral detail with 3MI’s angular diversity and related aerosol retrievals (e.g. from future EUMETSAT operational aerosol products). This joint retrieval is expected to mitigate existing atmospheric correction biases and enhance the overall accuracy of the current LSA SAF albedo products, as well as their temporal responsiveness in front of sudden land surface changes. All these aspects will be discussed in our presentation.
How to cite: Vigerie, V., Juncu, D., Ceamanos, X., Dutra, E., Gomes, S., and Trigo, I.: Next-Generation Land Surface Albedo Products from Metop-SG: Towards a Synergetic Use of METimage and 3MI Observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9504, https://doi.org/10.5194/egusphere-egu26-9504, 2026.
The human population in sub-Saharan Africa is growing the fastest in the world, which causes pressure on land resource together with climate change. More cropland needs to be cleared to maintain food security, but decreasing woody vegetation has climatic impacts. Loss of forests and woody vegetation decreases carbon sequestration from the air and carbon stocks of the above ground vegetation, while management of the land cleared for agriculture releases greenhouse gas emissions from soil. Loss of forest cover also leads to decreased soil organic carbon stocks. Loss of woody vegetation and trees in general causes increased land surface temperature, and consequently, increased air temperature. In the highlands, the decreasing forest cover also decreases the ability of the trees to capture atmospheric moisture by fog deposit, which also decreases the ability of water to infiltrate to the soil. Fog deposit is also decreased by increased land surface temperature, which causes the cloud base height to be at a higher level and out of the reach of forest canopy. While conservation and protected areas are typically considered to be positive, too high elephant populations overseeding the carrying capacity of the environment are decreasing the woody vegetation, thus having climatic impacts, too. This is because elephants tend to eat leaves and bark from the trees while not having grass to eat during the dry spells.
University of Helsinki has been studying climatic impacts of land cover change in Africa using Taita Taveta County in Kenya as a test site and model for whole sub-Saharan Africa applying remote sensing data and environmental sensing network since 2009. Currently, we are developing climate-smart agriculture and livestock management to mitigate climate change but improving food security.
How to cite: Pellikka, P., Abera, T., Vuorinne, I., Adler, I., Adhikari, H., Heikinheimo, V., Wachiye, S., Kopecny, J., Autio, A., and Heiskanen, J.: Climatic impacts of decreased tree cover in sub-Saharan Africa, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2424, https://doi.org/10.5194/egusphere-egu26-2424, 2026.
Mountain forests play a key role in the terrestrial carbon cycle, yet their contribution as carbon sinks remains highly uncertain, particularly in alpine regions. Steep elevation gradients, complex topography, and highly heterogeneous forest structure generate strong spatial variability in meteorological conditions and ecosystem processes, making high spatial resolution essential for both observation and modeling. While process-based forest models provide valuable insight into carbon and water fluxes, their application in mountain environments is often constrained by sparse observations and difficulties in scaling plot-level processes to the landscape. Integrating plot-scale modeling with high-resolution spatial information is therefore critical to better constrain model estimates in these systems. In our study, we developed a model–data integration framework for the Italian Alps (~52,000 km²), in which the 3D-CMCC-FEM process-based forest model was parametrized and run at National Forest Inventory (NFI) plot level. Plot-scale simulations of Gross Primary Production (GPP), Net Primary Production (NPP), and Evapotranspiration (ET) were then spatialized to continuous 30 m resolution maps using machine learning. The spatialization combined NFI-derived forest structural variables with high-resolution meteorological data, topographic predictors, and satellite-based vegetation indices. Four machine learning algorithms—Random Forest, Artificial Neural Networks, Extreme Gradient Boosting, and Support Vector Machines—were evaluated to extend plot-scale model outputs across the landscape. Model performance was assessed using k-fold cross-validation. Random Forest consistently achieved the highest predictive accuracy for all target variables, explaining approximately 27–47% of the variance in GPP, NPP, and ET across k-fold cross-validation and showing 3–15% lower prediction errors compared to the other machine learning methods. Variable importance analyses indicated that forest structural attributes derived from NFI data, elevation-related topographic metrics, and temperature- and precipitation-based meteorological predictors together accounted for the majority of the explained variance, emphasizing their dominant control on the spatial variability of forest carbon and water fluxes in alpine terrain. The resulting maps show clear spatial patterns in productivity and water use across alpine forest types and elevational gradients, providing spatially continuous, wall-to-wall information that complements plot-based National Forest Inventories. By linking plot-scale forest processes to landscape-scale patterns, this approach supports improved estimation, spatial consistency, and upscaling of forest carbon fluxes and stocks for measurement, reporting, and verification activities in heterogeneous mountain landscapes under ongoing climate change.
How to cite: Saponaro, V., Vangi, E., Candotti, A., Dalmonech, D., Galvagno, M., Filippa, G., Collalti, A., and Tomelleri, E.: Constraining Forest Carbon and Water Fluxes in the Italian Alps by Coupling National Forest Inventory Data, Process-Based Modeling, and Earth Observation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10895, https://doi.org/10.5194/egusphere-egu26-10895, 2026.
Degradation of the Amazon rainforest is increasing by expanding human activities, especially unregulated extractivism. Particularly gold mining is a major driver of environmental change, causing large-scale deforestation, river fragmentation and increased sediment loads. These pressures have intensified over the past decade due to rising global gold prices and policy shifts, most notably the 2016 decision by the Ecuadorian government to open approximately 13% of the national territory to mining exploration, including areas that were previously under protection [1, 2].
This study assesses the suitability of different remote sensing datasets for detecting unregulated mining and investigates the spatio-temporal dynamics of mining expansion in the Ecuadorian Amazon. The analysis focuses on three mining hotspots in eastern Ecuador (further called Punino, Napo, and Shaime) where unregulated activities have been widely reported. Given the sensitivity of the topic and the need for transparent and reproducible information, the study relies exclusively on remote sensing data, including Sentinel-1 synthetic aperture radar (SAR) data and PlanetScope optical imagery, as well as the Satellite Embedding Datatset V1 (SED). All datasets are processed mainly in Google Earth Engine (GEE) with dataset-specific methodologies applied. Supervised classification approaches were used, employing a k-NN classifier for the SED dataset and a random forest classifier for PlanetScope imagery, covering the period from 2017 to 2024. For Sentinel-1 data, a Sequential Change Detection (SCD) approach was implemented, evaluating multi-temporal polarimetric SAR time series to detect statistically significant changes throughout the specified observation period, with a revisit interval of approximately 12 days.
Results show a pronounced increase in mining extent and associated deforestation across all study areas, with particularly strong expansion during 2023 and 2024. In the Punino region, several sub-areas exhibited mining coverage approaching 10 % of the total AOI in 2024, while one sub-AOI exceeded 20 %, corresponding to approximately 13.2 km² of mining area. Comparison of classification results indicates that persistent cloud cover and temporal inconsistencies limit the effectiveness of optical PlanetScope data, whereas the SED dataset provides a reliable and efficient alternative for annual assessments with minimal preprocessing requirements. The SCD analysis revealed detailed expansion dynamics, demonstrating that mining typically initiates along major rivers and progressively expands toward tributaries and surrounding forest areas. The multi-method approach further enables cross-validation of results, which are consistent with independent reports documenting similar spatial patterns and trends.
The severe environmental consequences of unregulated mining, including deforestation, water pollution, and threats to indigenous communities, emphasize the importance of systematic and transferable remote sensing-based monitoring frameworks to support environmental protection in the Ecuadorian Amazon and enable timely, accessible reporting for environmental governance and decision-making.
[1] Albert, J. S., Carnaval, A. C., Flantua, S. G., Lohmann, L. G., Ribas, C. C., Riff, D., ... & Nobre, C. A. (2023). Human impacts outpace natural processes in the Amazon. Science, 379(6630), eabo5003.
[2] Roy, B. A., Zorrilla, M., Endara, L., Thomas, D. C., Vandegrift, R., Rubenstein, J. M., ... & Read, M. (2018). New mining concessions could severely decrease biodiversity and ecosystem services in Ecuador. Tropical Conservation Science, 11, 1940082918780427.
How to cite: Lammers, I., Jara-Alvear, J., Geiß, C., and Graw, V.: Remote sensing capabilities of detecting spatio-temporal dynamics in unregulated extractivism hotspots in Ecuador , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12972, https://doi.org/10.5194/egusphere-egu26-12972, 2026.
The 2017–2027 National Academies Earth Science Decadal Survey identified Surface Topography and Vegetation (STV) as a targeted observable, calling for high-resolution, global measurements to improve understanding of the evolution of Earth’s landscape. The Concurrent Artificially-intelligent Spectrometry and Adaptive Lidar System (CASALS) is a novel airborne swath-mapping altimetry lidar that employs a novel transmitter and grating method in combination with a state-of-the-art, high-speed, photon-sensitive detector array. CASALS is designed to exceed current lidar capabilities by providing 3D swath mapping of Earth surface heights and vegetation waveforms with fine cross-track spatial resolution that are especially useful for characterizing the structure of forests.
The current airborne CASALS instrument flew onboard the NASA B-200 King Air aircraft at a typical cruise altitude of 4.5 km for 4 flights completed from 07-18 November 2024 targeting vegetated surfaces and forests over Virginia and North Carolina. The full-waveform lidar collected a total of 256 individual waveforms with ~21 cm horizontal resolution, creating a swath of 55 m across the aircraft flight line during these flights. This work focuses on describing the CASALS algorithm development to identify ground and vegetation features within CASALS waveform data, with a goal of demonstrating progress toward full waveform–to–3D point cloud generation and applicability to a future spaceborne NASA STV mission.
How to cite: Midzak, N., Yorks, J., Yang, G., Lee, J., Chen, J., Selmer, P., and Harding, D.: Airborne Full-Waveform Lidar Swath Mapping of Surface Topography and Vegetation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14508, https://doi.org/10.5194/egusphere-egu26-14508, 2026.
Multi-variable analyses combining several Earth Observation (EO) Essential Climate Variables (ECVs) are increasingly used to investigate biosphere-land surface interactions under climate variability and anthropogenic pressure, including drought dynamics, vegetation responses, irrigation and land management practices, and land-atmosphere exchanges. By integrating optical, thermal, and microwave EO observations, large interdisciplinary initiatives such as the ESA Agricultural Land Abandonment and Climate Change (GLANCE) project aim to characterise coupled biosphere-hydrosphere processes at regional scales. These applications implicitly assume that EO-based ECVs provide a consistent and physically meaningful representation of the Earth system when jointly analysed. However, this assumption has rarely been evaluated explicitly.
Here, we present a systematic multi-annual (approximately two decades) cross-variable consistency analysis of key ESA Climate Change Initiative (CCI) ECVs over the Mediterranean region, conducted in the framework of the GLANCE project. The analysis focuses on soil moisture, precipitation, land surface temperature, vegetation parameters, biomass, and land cover, spanning multiple components of the biosphere-soil-water-atmosphere continuum.
We first assess each ECV by analysing its spatial patterns, seasonal and interannual variability, associated uncertainty, and response to drought events, and by comparing CCI products with external reference datasets. This single-variable assessment reveals substantial differences in some cases between CCI and non-CCI products, with particularly pronounced discrepancies for land cover. Building on this assessment, we investigate cross-variable consistency by jointly analysing the different CCI ECVs, focusing on the spatial correspondence of long-term mean patterns, correlations of temporal anomalies, and joint responses during drought conditions. Precipitation, land surface temperature, vegetation parameters, and soil moisture generally exhibit consistent behaviour, although sometimes with pronounced spatial and/or temporal differences, while biomass and land cover show substantially lower cross-variable consistency.
By explicitly evaluating the consistency of EO-based ECVs across biosphere-relevant variables, this work demonstrates that multi-variable EO analyses cannot assume coherence a priori. The results provide critical guidance for the interpretation and integration of multi-sensor EO datasets in biosphere and land surface studies, and help identify strengths and limitations of individual ECV products for Earth system research.
How to cite: Laluet, P., Corbari, C., Massari, C., Ciabatta, L., Adeniyi, O. D., Tariq, M., Oxoli, D., Brovelli, M. A., Wappis, A., Hebden, S., and Dorigo, W.: Do Earth Observation Essential Climate Variables (ECVs) provide a consistent picture of land surface dynamics? Insights from a multi-variable analysis over the Mediterranean region, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19219, https://doi.org/10.5194/egusphere-egu26-19219, 2026.
Peatlands are a valuable environmental resource on Earth due to their large carbon storage capacity and role as a breeding ground for biodiversity. However, many of them are currently at risk of decline due to land development and climate change. We investigated the effectiveness of satellite-based Interferometric Synthetic Aperture Radar (InSAR) technique to support peatland conservation and restoration projects by monitoring the degree of peatland degradation and restoration. Sarobetsu Mire is the largest peatland in Japan, however its natural vegetation has been significantly disturbed by pasture development, drainage channels, and peat mining. We collected C-band SAR images from the Sentinel-1 satellite from 2015 to 2025. We created a series of surface displacement maps with a spatial resolution of 20 m using interferometry, Short Baseline Subset (SBAS), and phase unwrapping procedures. We then analyzed seasonal surface elevation changes during a snow-free period (207 days in total). Relative surface elevation change predicted by InSAR was highly correlated with ground observations (mean bias = -0.001 m, RMSE = 0.019 m, r = 0.58). Seasonal surface displacement clearly responded to changes in groundwater table. The amplitude of seasonal surface displacement differed significantly between natural vegetation and peat-mining ruin, and among the dominant vegetation classes (Sphagnum, Moliniopsis, and Phragmites). This differential dynamics indicates the amount and physical properties of subsurface peat deposits and potentially be a useful indicator of peatland health.
How to cite: Machimura, T., Zhang, H., Sugita, S., Tsuyuzaki, S., and Hotes, S.: Spatiotemporal analysis of ground surface displacement using InSAR technique for monitoring peatland health in Sarobetsu Mire, Japan, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18162, https://doi.org/10.5194/egusphere-egu26-18162, 2026.
Tropical Asia is influenced by large-scale climate modes, notably the El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD), yet their impacts on vegetation in tropical Asia remain uncertain. A key challenge is the scarcity of clear-sky observations from satellites in this persistently cloudy region. The Advanced Himawari Imager (AHI) onboard Himawari-8/9 geostationary satellites provides observations every 10 minutes, substantially increasing clear-sky samplings and enabling more robust monitoring of vegetation responses to climate anomalies. Here, we focus on two recent large climate anomalies, the positive IOD (pIOD) event in 2019 and the compound pIOD and El Niño event in 2023/24, to characterize how vegetation responses evolve within events.
Based on ENSO and IOD indices, we defined the event periods as May 2019–November 2019 for the 2019 pIOD event, and April 2023–March 2024 for the 2023/24 compound pIOD and El Niño event. We used the two-band Enhanced Vegetation Index (EVI2) derived from Himawari-8/9 AHI surface reflectance (Li et al., 2025; Zhang et al., 2025) for 2016–2024, and monthly climate variables (i.e., shortwave radiation (SWR) and vapor pressure deficit (VPD)) over the same period. Monthly anomalies were computed relative to the 2016–2024 average. To capture intra-event evolution, we further assessed anomalies by sub-seasonal phases within each event.
We found that the anomalies in EVI2 showed clear phase-dependent responses during these events. For example, EVI2 decreased over continental Southeast Asia in phase 1 (April–June 2023) of the 2023/24 compound pIOD and El Niño, but increased in the same region in phase 3 (September–December 2023). These changes in EVI2 were consistently associated with a trade-off between atmospheric dryness (higher VPD) and enhanced light availability (higher SWR). Our results highlight that vegetation dynamics during extreme climate anomalies are strongly modulated by phase-specific light-dryness regimes, while the causal impact is unclear when examining these pIOD and El Niño events as a whole.
How to cite: Hase, M., Luo, X., and Ichii, K.: Tracking phase-specific vegetation responses to extreme climate anomalies in Tropical Asia using Himawari-8/9 AHI, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21075, https://doi.org/10.5194/egusphere-egu26-21075, 2026.
In recent years, Earth observation using geostationary satellites has experienced remarkable development. In particular, third-generation geostationary satellites, beginning with Japan’s Himawari-8, have enabled sub-hourly measurements of the Earth. Following Himawari-8, the U.S. GOES-R series, China’s FY-4A, Korea’s GK-2A, and Europe’s MTG-1 have successively begun observations, making it possible to observe nearly the entire globe at sub-hourly temporal resolution. Along with these advances, research targeting terrestrial ecosystems such as forests, grasslands, and croplands has rapidly expanded beyond traditional meteorological applications. However, current studies on land-surface ecosystem observation with geostationary satellites still overlook an important but critical issue: topographic effects arising from the relatively large viewing angles of geostationary satellites compared with polar-orbiting satellites. As the distance from the satellite sub-satellite point increases, discrepancies grow between the latitude and longitude coordinates on the reference ellipsoid to which geostationary satellite data are projected and the actual geographic coordinates of the land surface. In addition, topographic features such as high mountains create invisible areas that are partially or entirely invisible to geostationary satellites. Despite these effects, few studies have considered topographic effects when comparing with in situ observations or polar-orbiting satellite data. In this study, we address this issue by using high-resolution digital surface model (DSM) data that are sufficiently detailed to represent sub-pixel topographic variability within individual geostationary satellite pixels. Specifically, we simulated topographic effects on geostationary satellite observations using a 30 m resolution DSM provided by the Japan Aerospace Exploration Agency (JAXA). Our results show that, due to topographic effects, the correspondence between ellipsoidal latitude–longitude coordinates and actual surface coordinates can be shifted by more than one pixel in some regions. We further confirmed that this spatial mismatch leads to differences in the seasonal variation patterns of vegetation indices. In addition, when attempting ray-matching with polar-orbiting satellite observations, we found significant differences between geostationary and polar-orbiting satellite data when topographic effects were not considered. These findings demonstrate that accounting for topographic effects is essential for accurate land-surface observation using geostationary satellites. Our results provide valuable guidance for future studies that aim to compare geostationary satellite data with in-situ observations or to perform data fusion with polar-orbiting satellites, and they will contribute to achieving more precise and reliable land-surface monitoring.
How to cite: Sasagawa, T., Ichii, K., Yoshioka, H., Matsuoka, M., Miura, T., Yamamoto, Y., and Yang, W.: How Does Topography Affect Land-Surface Observations from Geostationary Satellites?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21288, https://doi.org/10.5194/egusphere-egu26-21288, 2026.
Geostationary satellites allow continuous monitoring of the Earth including land surfaces and aerosols, which can now benefit from the advanced measuring capabilities of the new Meteosat Third Generation-Imager and its Flexible Combined Imager (FCI) on board. FCI offers many opportunities to improve the near real time (NRT) clear-sky retrieval of shortwave surface albedo as it is operationally conducted in the EUMETSAT Land Surface Analysis Satellite Application Facility (LSA-SAF) project (Juncu et al. 2022). For instance, FCI's new VIS04 measuring channel centered at 444 nm was found to enable better aerosol characterization (Georgeot et al. 2025) compared to what can be achieved with the previous generation GEO multi-spectral imager SEVIRI, which is essential to achieve high-quality estimation of surface properties. In addition, FCI's high spatio-temporal resolution (10-minute full-disk scan frequency and 1 km in most visible and near-infrared channels) enables enhanced surface monitoring overall. Currently, the atmospheric correction scheme used to retrieve surface albedo from FCI in the LSA-SAF is being improved to exploit all the relevant data provided by FCI while meeting the time constraints of near-real-time processing.
To achieve this goal, we use fast radiative transfer (RT) codes that make assumptions for the sake of computational constraints. This includes the plane-parallel and scalar approximations, which respectively neglect the Earth's sphericity and light polarization effects. Based on accurate top-of-atmosphere (TOA) reflectance simulations from the SMART-G Monte-Carlo RT code, we assess the errors resulting from these two simplifications in the case of FCI data processing. First, the plane-parallel approximation is found to impact significantly 36\% of FCI observations over the year, including errors larger than 10\% in some cases (e.g., at the beginning and end of each day) (preprint by Klein et al. 2025). Second, neglecting light polarization is found to lead to errors up to 6 \% in TOA reflectance simulations, especially in short visible wavelengths. Based on this study, we propose a simple approach that compensates fast RT simulations for the errors coming from these two assumptions by using pre-calculated look-up-tables of accurate Rayleigh reflectance accounting for Earth's sphericity and light polarization. According to our results, this simple approach leads to a significant error reduction overall, especially in FCI's VIS04 channel where error is divided by 4.
In addition to presenting the results above, we will discuss the upcoming integration of this simple approach in the atmospheric correction scheme of the algorithm iAERUS-GEO, which jointly retrieves aerosol optical depth and surface albedo from FCI (Ceamanos et al. 2023). Finally, we will present a first assessment of the benefits offered by this method when used to process real FCI data corresponding to relevant case studies.
How to cite: Klein, G., Ceamanos, X., Vidot, J., Es-Sayeh, M., Ramon, D., and Moulana, M.: Enhancing surface albedo and aerosol retrieval from MTG-I/FCI by accounting for Earth's sphericity and light polarization effects using a simple near-real-time-compatible approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7562, https://doi.org/10.5194/egusphere-egu26-7562, 2026.
Vegetation phenology is a primary indicator of ecosystem functioning and climate sensitivity, yet robust monitoring at landscape scales remains challenging in land-use heterogeneous regions such as England. The Copernicus High-Resolution Vegetation Phenology and Productivity product (HR-VPP), derived from Sentinel-2, provides 10 m phenological metrics, including start of season (SOS), end of season (EOS), and length of season (LOS), enabling unprecedented fine-scale mapping of vegetation dynamics.
In this contribution, we present a workflow to characterise phenological timing across England using HR-VPP time series from 2017 to 2024. We quantify spatial patterns and interannual variability in key phenometrics and summarise trends across major land cover types.
This study provides: (i) an England-scale baseline of phenological timing at 10 m resolution; (ii) an assessment of recent anomalies and climate-driven variability within the HR-VPP record; and (iii) practical guidance for integrating high-resolution Copernicus products into national ecological monitoring. This work supports the broader application of HR-VPP for assessing vegetation resilience to climate and land-use pressures at the national level.
How to cite: Salgado-Castillo, F., Peppa, M., Mills, J., and Boyd, D.: Monitoring of vegetation phenology across England: Assessing interannual variability and land-cover sensitivity using Copernicus HR-VPP, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15547, https://doi.org/10.5194/egusphere-egu26-15547, 2026.
Posters virtual: Wed, 6 May, 14:00–18:00 | vPoster spot 2
EGU26-18862 | ECS | Posters virtual | VPS6
Beyond Risk: Predicting Tropical Deforestation Intensity Patterns with Regression-Based Fully Convolutional Neural NetworksThu, 07 May, 14:45–14:48 (CEST) vPoster spot 2
Tropical forests provide vital ecological, economic, cultural and climate-regulating services to local and global communities. However, these ecosystems are threatened by deforestation, often driven by complex and region-specific factors. Numerous studies have been conducted to predict the spatial distribution of deforestation risk, yet little research has explored the possible advantages of predicting deforestation intensity patterns. To support more effective forest management and conservation planning, this study examines the use of deep learning for predicting the spatial patterns of deforestation intensity.
This research develops and evaluates a regression-based ResUNet architecture for predicting deforestation intensity patterns.
The deforestation datasets are, in most cases, highly skewed and zero-dominated, which poses the first challenge since this can significantly affect the predictive performance of the regression model. Several loss functions have been evaluated to mitigate this effect. The results illustrate how the Tweedie loss performs best. Furthermore, with a Root Mean Squared Error (RMSE) of 0.00494 on all values and 0.0169 on non-zero values, the Tweedie ResUnet model consistently outperforms the baseline XGBoost regression model.
To test the model's cross-regional generalizability, four tropical regions were selected, each located on a different continent and characterised by varying deforestation drivers and dynamics. The Tweedie-ResUNet architecture was trained and tested on each study area. The differences in performance could be explained by regional characteristics such as data quality, topography, and seasonal cloud cover. However, the results still demonstrate a strong potential for the model's applicability to other tropical regions.
The overall findings of this study suggest that deep learning models can be utilised to offer valuable insight into spatial patterns of deforestation intensity.
How to cite: Sillem, K. and Cue la rosa, L.: Beyond Risk: Predicting Tropical Deforestation Intensity Patterns with Regression-Based Fully Convolutional Neural Networks, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18862, https://doi.org/10.5194/egusphere-egu26-18862, 2026.
EGU26-12474 | ECS | Posters virtual | VPS6
Beyond Static Fluxes: Constraining parameters of a wetland methane model in a new fully coupled CH4DAS using Satellite Concentrations and In-Situ FluxesThu, 07 May, 14:51–14:54 (CEST) vPoster spot 2
Methane emissions from natural wetlands are the largest and most uncertain component in the global methane budget. Conventionally they are estimated using two main methods. Top-Down methods work by inverting atmospheric concentrations of methane into fluxes, using a chemistry transport model. This is often done by optimizing the scaling factors of the inventory flux maps, but this approach decouples the fluxes from their physics and has limited predictive capabilities. Conversely, Bottom-up methods use biogeochemical models to directly estimate the fluxes, but they are severely affected by the scarcity of in-situ flux observations to calibrate them. To bridge this gap, we present the development and validation of a new fully coupled Methane Data Assimilation System (CH4DAS). This system integrates these two techniques, and provides constraints to the bottom-up model (ie., optimizing its main parameters) from both satellite concentrations and site-level fluxes. Such integration ensures that flux estimates remain consistent with physical drivers, simultaneously addressing data scarcity and enabling predictive capability.
CH4DAS is developed within the Community Inversion Framework (Berchet et al., 2021) and couples SatWetCH4 (Bernard et al., 2025), a simple bottom-up wetland methane model with the LMDz-SACS chemistry-transport model. This system can simultaneously assimilate both satellite concentrations (GOSAT) and site-level in-situ fluxes (FLUXNET-CH4) within a variational assimilation scheme to constrain the model parameters. To address the scale challenges while simultaneously assimilating observations of different streams, we run two instances of SatWetCH4. The first, driven by global forcing is coupled with LMDz-SACS and constrained by Satellite observations. While the second instance driven by site-level forcing is constrained by in-situ fluxes. This way, the shared internal temperature sensitivity parameter Q100 is jointly constrained by two data streams, while site-level and regional base rate parameter K account for data-specific variability.
We mathematically validate the system using an Identical Twin Observing System Simulation Experiment (OSSE), demonstrating its capacity to constrain the control variables. Further, we apply the system to real-world data to demonstrate that the system can successfully reduce the mismatches in the prior to match the spatiotemporal gradients observed by GOSAT, enabling insights on regional CH4 budgets.
How to cite: Vetriselvan, R., Rayner, P., Berchet, A., Peylin, P., Salmon, E., Saunois, M., Bernard, J., and Singh, A.: Beyond Static Fluxes: Constraining parameters of a wetland methane model in a new fully coupled CH4DAS using Satellite Concentrations and In-Situ Fluxes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12474, https://doi.org/10.5194/egusphere-egu26-12474, 2026.
