Conference Agenda

Earth’s climate is influenced profoundly by anthropogenic greenhouse gas (GHG) emissions. The lack of available global CO₂ and CH₄ measurements makes it difficult to estimate their emissions accurately. Satellite measurements would be very helpful for understanding the global CO₂ and CH₄ flux distribution if CO₂ and CH₄ column-averaged dry air mole fractions (XCO₂ and XCH₄) could be measured with a precision of better than 2 ppm. To this point, one of our main objectives of this research is to use a combination of ground-based measurements of CO₂ and CH₄ and data from current satellite observations (TanSat, GOSAT/-2, OCO-2/-3 and TROPOMI) to validate and evaluate satellite retrievals with retrieval inter-comparisons, to assess them against model calculations and to ingest them into inverse methods to assess surface flux estimates of CO₂ and CH₄.

The aim of Global Stocktakes requires more accurate and larger coverage measurement in future missions. The next generation of TanSat mission, known as TanSat-2 mission kicked-off two years ago. To improve the measurement coverage and repeat frequency, TanSat-2 satellite will sample atmosphere at 1500 km wide across-track swaths, with a footprint size of 3 km ×3 km and hence the measurement covers the global in daily flight. The NIR/SWIR hyperspectral measurement on backscattered sunlight by the spectrometers onboard TanSat-2 includes 0.4 um (ultraviolet), 0.76 um (O₂ A), 1.61 um (CO₂) and 2.0 um (CO₂) bands. There will also be an aerosol optical property to improve the XCO₂ and XCH₄ accuracy. The TanSat-2 XCO₂ measurement precision is designed to be better than 1 ppm for each pixel.

The preliminary OSSE on global and regional scale indicates high efficiency for the TanSat 2 emission in error reduction for top-down flux estimation. We also develop a new method to separate the ecosystem and anthropogenic emission which will be helpful for atmospheric inversion method toward the Global Stocktake. The TanSat mission has been used in city carbon emission signature investigation, which show the capability of TanSat-2 to detect the anthropogenic emission signals.

To evaluate TanSat-2 potential for estimating surface CH₄ fluxes at a weekly scale, we designed a series of observing system simulation experiments (OSSEs) using an existing Ensemble Kalman Filter framework. These experiments focused on the sensitivity of flux estimates to systematic errors (μ) and random errors (σ) in XCH₄ measurements, as well as the impact of satellite swath width. Our findings indicate that with a target precision of 8 ppb, the globally inverted CH₄ flux exhibits an accuracy of 5.1 ± 1.7%, with a reduction of 86.6 ± 11.2% in the a priori uncertainties. The accuracy in the Northern Hemisphere mid-latitudes reaches at 1.9 ± 1.6%. Increasing temporal resolution from 1-month to 1-week reduces flux discrepancies in most regions, except Africa, which is also highly sensitive to XCH₄ bias. In particular, the accuracy of inverted CH₄ fluxes in North Africa declines by 20.4% for each ppb increase in global μ levels. It leads to a 2.3% rise (~11 Tg/yr) in the global total discrepancy. The σ value affects both the accuracy and precision of optimized fluxes, especially in North American Boreal and Tropical Asia, but these effects can be mitigated by expanding the satellite swath width from 1,000 to 3,000 pixels. CH₄ fluxes in Eurasia Temperate and North America Temperate show higher reliability and resilience to variations in swath width and measurement uncertainties. For μ-sensitive regions like Africa, expanding swath width alone offers limited improvements, highlighting the need for complementary observations from other platforms.

To understand the hot-spot emission measurement capability of TanSat-2 mission, we developed a parametric model and a database to swift assess the quantification capability of satellites and configure satellite technique characteristic for certain detection goal. we get the detection thresholds of several satellites and find that the high spatial resolution is much more important to monitor CH₄ emissions than measurement precision. For quantifying emissions, improving measurement precision is a more effective way. A more accurate simulation model and estimation method are needed for the assessment of the quantified capacity of satellites for low-intensity emissions.

The Climate Change Act (Northern Ireland) 2022 (Act) sets a target of an at least 100% reduction in net zero greenhouse gas (GHG) emissions by 2050 (i.e., net zero emissions by 2050) for Northern Ireland compared to baseline, along with interim targets including an at least 48% reduction in net emissions by 2030. The greenhouse gases include carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulphur hexafluoride (SF6) and nitrogen trifluoride (NF3). There is greater difference around emissions in Northern Ireland / Ireland compared to other parts of the United Kingdom due to the relative importance of methane and nitrous oxide emissions in the agriculture sector (Figure 1). In the Northern Ireland, the GHG emissions are mainly from agriculture, buildings, transport and power generation (Figure 2). The fossil fuel has been replaced by renewable energy eventually for the carbon emission reduction.

Figure 1. Composition of emissions between Northern Ireland and UK in 2022.

Figure 2. Greenhouse gas emissions by sector in Northern Ireland, 2022

Figure 3. Change in emissions by sector from base year -2022 in Northern Ireland

Comparing with the other sectors the GHG emissions from the agricuture has increased 15% with the baseline in 1990 (Figure 3). The GHG emissions from the agricultural takes 29.1% compared to the other sector and methane (26%) is mainly produced by livestock (Figure 3). It needs to find out the GHG emissions in agricultural sector in more detailed.

Globally, methane is the second most important greenhouse gas (GHG). Its contribution to global warming is estimated at 27 times that of carbon dioxide, over a 100 year period. Biogenic methane (methane produced by animals and plants) is one of the predominant GHG emissions emitted from global agricultural, the majority of which is originates from ruminant livestock as enteric or manure methane. The agricultural sector has a target to reduce its greenhouse gas emissions by 25% by the year 2030, relative to 2018 levels, developing strategies to reduce enteric methane will be crucial to meeting Ireland’s agricultural climate targets. A 500 kg beef animal on a high concentrate diet produces 230 g methane per day and a 550 kg dairy cow grazing on pasture emits about 320-330 grams of methane per day. Methane associated with ruminant livestock production accounts for three-quarters (74%) of Irish agricultural GHG emissions. As a result, reducing the volume of methane produced by ruminant livestock, will be critical to achieving the agricultural sectors 2030 GHG emissions reduction target.
Figure 4. Methane emission through the year from 2009 to 2022 in Ireland

Emissions of methane are more difficult to estimate than carbon dioxide, and the agriculture sector makes up a larger share of Northern Ireland’s emissions than in other parts of the UK. This greenhouse gas emission estimates are based on a wide range of data sources and sources of uncertainty include statistical differences, assumptions, proxy datasets and expert judgement. In addition, the natural variability in the processes that are being modelled introduce uncertainty. The uncertainties presented are a close approximation of the 95% confidence interval. For the percentage reduction between the base year and 2022, the uncertainty interval ranges from 20% to 37%. Therefore, data from satellite will be used to support the analysis with verified accuracy. The GHG production monitored by the Satellites are retrieved and validated by the project partner from China from the year 1990 to 2022 on the geolocation of Ireland, N. Ireland and Island (Figure 4). From the data analysis, it has found that the differential between them is less than 2%. Detailed emission on CO2 and CH4 will be analysed along with the emission through the agriculture applications.

In our era of global climate change and rapid urban expansion, escalating natural and human activities contribute to a wide range of geohazard phenomena worldwide. These hazards include earthquakes, volcanic eruptions, landslides, urban subsidence, floods, tsunamis, droughts, and forest fires. Effectively addressing such threats from global to local scales necessitates advanced, efficient, and precise monitoring tools and techniques. Satellite-based Synthetic Aperture Radar (SAR) remote sensing has fundamentally transformed the monitoring of Earth’s environments and geohazards, providing high-resolution data on inundation, land movement, and risk exposure essential for disaster response and planning. Several multi-temporal Interferometric synthetic aperture radar (MT-InSAR) techniques [1]-[3] have been developed and successfully applied in such context. Several noise-filtering methods have been proposed to mitigate the noise effects in the differential SAR interferograms, most working independently on single interferograms. Subsequently, new efforts have been made to extend these methods to include space-time information [4], [5], [6].

This work shows the results of some recent technological and applicative advancements of the Mt-InSAR technologies through dedicated experiments with Sentinel-1 SAR data. The experiments concern the test-site areas of the active “Campi Flegrei” Caldera (Italy), the zone in Turkey-Syria hit by the Mw 7.9 earthquake on February 6, 2023, and the city of Hanoi. These experiments give the Dragon 6 audience a clue about the state-of-the-art and recent development trends of the used radar technologies. The efforts realised to retrieve the three-dimensional field of deformation in areas subjected to severe earthquakes and/or volcanic activities are evidenced by the problems of phase unwrapping, co-registration, burst-overlapped interferometry and large-swath datasets processing. Concerning the applications in urban zones, the focus is on the region of the Red River Delta, which represents one of the most crucial economic zones of Vietnam, and its capital, the city of Hanoi. In this work, a comprehensive analysis relying on the exploitation of the interferometric SAR technologies and the use of multi-track SAR datasets acquired from the European Copernicus Sentinel-1 and TerraSAR-X was carried out to generate and interpret the 2017-2024 dynamics of the RRD terrain deformations, by generating 2-D maps, along the vertical and horizontal directions, and ground deformation time series. As a result, the megacities Hanoi, Hai Duong, and Nam Dinh of the delta are found to be affected by significant subsidence. Overall, our study analyses the state-of-the-art of Mt-InSAR technologies and provides some insights into the relationships between population, land-cover changes, the distribution of built-up areas, the role of over-pumping groundwater, geological setting, and ground subsidence.

References

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4. P. Berardino, G. Fornaro, R. Lanari, and E. Sansosti, «A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms», IEEE Transactions on Geoscience and Remote Sensing, vol. 40, n. 11, pp. 2375–2383, nov. 2002, doi: 10.1109/TGRS.2002.803792.
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Driven by multiple factors, including tides, wind fields, ocean currents, and seafloor topography, the marginal seas exhibit abundant dynamic processes. Among these, sub-mesoscale oceanic eddies are the key driver in the mixing of the upper ocean layer and the transport of heat, salt, and nutrients. Spaceborne synthetic aperture radar (SAR), with its high spatial resolution and wide coverage, is the superior sensor for studying ocean sub-mesoscale eddies.

During the first year of DRAGON 6, we have been focusing on the detection and identification of multi-scale eddies on SAR imagery and on their spatio-temporal variation and coverage. Ten years of Sentinel-1 SAR-C data of the Western Mediterranean Sea have been used for an automated detection of oceanic eddies by the EOLO system, an AI technique developed during DRAGON 5. A comparison with results of visual inspections of SAR-C data of the same region reveals that the results achieved by EOLO show the same seasonalities and spatial distributions.

Given the robustness of the EOLO on the large volume of SAR data acquired in the Mediterranean Sea, the EOLO is further applied to multiple sources of SAR-C data acquired in the global ocean, including Sentinel-1 and Envisat SAR imagery, with a total of 3,192,002 scenes. The detected 82,750 eddies, 97% of which have a diameter less than 40 km, are compared with the concurrent mesoscale eddies detected by radar altimeter. The spatial comparison results show a high consistency of sub-mesoscale and mesoscale eddies. Quantitative analysis indicates about 50% of sub-mesoscale eddies are located within twice the radius of mesoscale eddies, where sub-mesoscale eddies in the core area of mesoscale eddies only account for approx. 9%.

Using the spatial variation of the mesoscale strain rate (MSR) in the normalized mesoscale eddy-center coordinate system, we found that the high MSR band is located within a ring-shaped area, i.e., 0.5 to 1.5 times the normalized radius of mesoscale eddies around the eddy center. The radial distribution further shows that the MSR is positive in the ring-shaped area, while it is negative in the core area. This quantitative analysis shows that about 50% of the global sub-mesoscale eddies are generated by shear instabilities along mesoscale eddies.

The MSS-1 (Macau Science Satellites-1) twin satellites were successfully launched on 21 May 2023 into a nearly circular low-inclination (41 degree) orbit at an altitude of about 450 km: Satellite A carries a payload of high-precision magnetometers to accurately measure Earth's magnetic field while Satellite B is equipped with other sensors such as a solar X-ray instrument to obtain space environmental data.

The MSS-2 twin satellites, marked by strongly eccentric polar orbits forming the MSS constellation of geomagnetic survey satellites, are scheduled to launch in the year of 2028.

The geomagnetic field measured by the satellites is closely associated with the whole Earth's system because the measured field consists of contribution from various sources: the core convection-driven dynamo, the induced field from the electrically conducting ocean and mantle, the crustal magnetization, and the ionosphere/magnetosphere currents.

I will report progress in the MSS constellation of geomagnetic survey satellites and its initial scientific results.

The main goal of the recently kicked-off Dragon-6 STAI4CH project is to demonstrate the significant capabilities of optical and radar EO data to provide essential information for archaeological and cultural heritage mapping and monitoring applications, by building upon first achievements by Dragon-5 research collaborations. In particular, STAI4CH aims to develop novel Artificial Intelligence (AI) and Deep learning (DL) methods to process medium and high resolution EO data to efficiently map waterways and waterscapes of ancient Empires in China, Italy and Iraq, and detect physical transformations and land cover changes due to modern development and urbanisation that might impact the heritage assets.

During its first months of activity, STAI4CH’s team has started to design and develop the heritage asset mapping and change detection approaches, which exploit semantic segmentation Neural Network (NN) based on EO self-supervised learning and change detection NN based on multi-source EO data, respectively. Medium resolution EO datasets acquired by Copernicus Sentinel-1 SAR and Sentinel-2 optical missions, and the Chinese sustainable development science satellite SDGSAT-1, have started to be collected for a transect of the UNESCO World Heritage Site (WHS) of the Grand Canal of China, which has been selected as the testing ground to train and validate the newly developed methodologies. In parallel, preparatory work on EO data interpretation approaches to map heritage features in archaeological landscapes of Southern Iraq has been carried out, with the aim to start to generate a knowledgebase to validate the methods when exported to the waterscapes of the UNESCO WHS of the Ahwar marshes. Pivotal has been the role of the Chinese and European Young Scientists in both the algorithmic development and initial experiments in China, and the archaeological mapping in Iraq.

The first results of the AI/DL-based experiments on the Grand Canal transect will be showcased during this presentation, including a discussion on how the methods performed to detect the heritage features and land cover changes, their limitations and pathway to technical improvement. Future work will focus on the enhancement of the performance of the developed methods, their implementation across the whole site of the Grand Canal, and then the testing of replicability through geographical exporting of the AI/DL-based methods on the Ahwar marshes in Iraq and the waterways of the Tiber river in Italy, part of the Historic Centre of Rome UNESCO WHS. Further algorithmic development will include tailoring of the methods to the processing of high resolution imagery from SkySat optical and ICEYE SAR missions, including fine tuning to the local scale for precise mapping and change detection, and also validation of the results based on medium resolution imagery.

With an increasingly warming climate globally, hydro-climate extremes such as heatwaves and droughts have surged, challenging the stability and resilience of social-ecological systems. In particular, the frequency, intensity, and duration of compound heatwave and drought extremes (compound hot-dry extremes) largely increased in recent decades, which intensifies the spatio-temporal mismatch between water supply and demand in the agricultural water community. Furthermore, compound hot-dry extremes are foreseen to be more frequent and intense in the coming decades under a projected warming scenario. This underscores the imperative need for well-informed and effective adaptation strategies within the agricultural water system to address the disasters induced by compound hot-dry extremes. The AgriWATER project encompasses three key aspects: (1) detection and mechanism analysis of compound hot-dry extremes in both Europe and China for the past two decades; (2) quantifying impacts of compound hot-dry extremes on agriculture and water resources across the major crop areas of Europe and China; and (3) developing adaptative strategies to reduce the vulnerability of agricultural water system to compound hot-dry extremes. Using multi-source remote sensing retrievals, machine learning methods, and advanced hydrological and crop models, the AgriWATER project aims to improve the understanding of interactions between compound hot-dry extremes and agricultural water systems over the core food production areas in Europe and China. This provides an important reference into water resources management and food security for both Europe and China, and delivers valuable insights for other agricultural areas globally.

The objective of the project is to evaluate the role of satellite earth observation (EOs), and in particular of optical-thermal-microwave data, in support of water management at different spatial scales (field, regional, and global). Satellite data can play an important role in assessing water availability as well as crop water needs and stress conditions, either as a stand-alone source of information or in combination with hydro-meteorological modelling tools. In this context, satellite data represent an optimal compromise between the need to monitor large areas in a cost-effective manner, and the need for detailed local estimations akin to ground data.

A large suite of satellite-based products is available for water balance modelling at multiple spatial scales, which are relevant to climate, meteorology, basin-scale water management models and field-scale precision irrigation. Nevertheless, there exists a need to understand how the accuracy of such data products affects the reliability of hydrological model simulations when used for operational applications at multiple spatial scales. These needs are exacerbated by the increasing expansion of areas devoted to water-intensive crops and the concurrent impact of climate change on water availability.

Examples of Chinese and Italian / European case studies will be presented on several practical topics to support the discussion on the SAA4Water project.

Chinese case studies will be presented and discussed in relation to the following application:

• Evaluating the Irrigation Performance Using Remote Sensing Data and the Budyko Hypothesis at a regional scale. A novel metric of consumptive irrigation water use was developed after partitioning the total actual evapotranspiration (ET) into green ET and blue ET, and it was applied to the irrigated areas in northwest China to evaluate the performance of irrigation. The irrigation water use efficiency, estimated as the ratio of BET to net irrigation water, was evaluated in detail. The results show that the irrigation water use efficiency has improved in the last 10 years, although some regions still face severe net irrigation water deficits.
• Monitoring cropland water use efficiency (WUE) at global scale. By combining the improved EF-LUE model with the ETMonitor model, a 20-year (2001–2020) global cropland WUE dataset was developed with a spatial resolution of 1-km. It is driven by consistent forcing data and considers the coupling relationship between the carbon and water exchange. This dataset can help to assess and explore the changes in the global cropland WUE and support the monitoring and assessment of the SDG 6.4.
• ET and precipitation derive from remote sensing observations in supporting drought’s impact evaluation at continental scale. We investigated the impact of drought on water availability in the Belt and Road region using high-resolution remote sensing data from 2001 to 2020. An overall decreasing trend in water availability is observed with an expanding water deficit area and increasing of the water deficit intensity associated with drought frequency. The critical role of ET drived from remote sensing observations in assessing seasonal variability of water availability evaluation is also highlighted.

European / Italian case studies will be presented and discussed in relation to the following application:

• Precision irrigation supported by satellite information was analysed, providing a discussion on the required satellite spatial resolution and temporal frequency.
• Estimation of evapotranspiration using observations from several satellites, ground data and hydrological models at different spatial and temporal resolutions.
• Water balance analysis using a suite of satellites observations, ground data and hydrological modelling for operational water availability detection at river cross sections in basins of different sizes.

The results reinforced the synergistic use of satellite data in water and energy balance models is a robust approach for irrigation.

The emergence of wide swath radar altimeter provides a new observation method for ocean dynamic environment monitoring, which can achieve two-dimensional synchronous observation of sea surface height, wave height and sea surface wind speed. The SWOT satellite launched in December 2022 is equipped with Ka-band Radar Interferometer (KaRIn), which can achieve two-dimensional observation of marine dynamic environment. Based on SWOT ocean observation data, the spectral analysis method is used to evaluate the observation ability of ocean sub-mesoscale processes in sea surface height data, and buoy data is used to evaluate the accuracy of significant wave height and sea surface wind speed.

The spatial and temporal distribution of satellite altimeter along track observation data is sparse, making it difficult to comprehensively capture the spatial and temporal structure of ocean circulation. The fusion of multi-source altimetry data can compensate for the shortcomings of single satellite altimetry observation data and obtain more comprehensive sea surface height information. Based on ocean reanalysis data, the spatiotemporal correlation scale of global ocean dynamic processes is calculated and the variable spatiotemporal scale optimal interpolation method is developed for multi-source satellite altimetry data fusion. Research on multi-source satellite altimetry data fusion based on two-dimensional variational method, efficiently fusing multi-source satellite altimetry data by fully utilizing the error characteristics and covariance information of background field and observation data, optimizing the accuracy of fusion results and improving their spatial resolution. Temporal dynamic interpolation of sea surface height anomalies based on quasi geostrophic models is developed by introducing SST data as physical constraints to more accurately describe SLA temporal evolution and improve the temporal resolution of fusion results. The Northwest Pacific Ocean is taken as study area to conduct data fusion experiments and evaluate the developed data fusion method.

The Lofoten Basin (LB) is located along the advective path of Atlantic Water (AW) is the most eddy active part of the Nordic Seas. A main feature of the LB circulation is the spinning up off eddies from the Norwegian Atlantic Current, which in turn transports warm AW into the basin. The AW transported into the basin interior is found to penetrate deep down to 1000 m, and not surprisingly, the LB is the largest heat reservoir in the Nordic Seas. The eddy shedding from the slope current to the deeper areas of the basin, transports heat to the basin interior and thus mediates the gradual cooling of the slope current on its way toward the Arctic. This is of great importance since AW transported to the Arctic Ocean through the Nordic Seas plays a major role in the global climate system. Furthermore, this also links to the “Atlantification”, a commonly used term for the increasing influence of AW in the Arctic Ocean, having a large impact on the Arctic marine ecosystem.

The overall objective of LoWa is to quantify the impact of ocean eddies on the heat transport across the LB affecting Atlantification. To address the overall objective LoWa uses a holistic approach, thanks to the outstanding opportunities offered by the: available satellite constellations with multi-sensor covariability/synergies; in-situ observational networks, gliders and ARGO drifters; co-location of in-situ and remote-sensed data and data driven Artificial Intelligence (AI) methods. Such an approach also extends the possibility to not only investigate ocean mesoscale eddies but also the sub-mesoscale eddies that are even less studied in comparison to mesoscale eddies.

We will report ongoing activities associated with mesoscale eddies in the Lofoten Basin. In addition, we will also present results of a recent study (Dong et al., 2025, manuscript in revision, Nature Communication), focusing on the Warm Rings in Mesoscale Eddies in a Cold Straining Ocean. In here we show the role of submesoscale heat transport as the primary mechanism for substantial heat loss from AW in the Lofoten Basin.

Objective and methods:

With the increasing availability of different medium and high-resolution remote sensing data, the fusion of multiple data sources to generate high-temporal-spatial- resolution data for grassland monitoring has been widely used, among which the fusion of Landsat 8 with Sentinel-2 and MODIS is the most common. Data fusion processors have been developed in the EO community for a longer time already. Now, Harmonized Landsat Sentinel-2(HLS) could be download from the Earthdata Search and Land Processes Distributed Active Archive Center. However, there is currently limited research that considers fusing Chinese high-resolution data(such as GF-1 and GF-6 WFV) with foreign medium and high-resolution data(Such as Sentinel-2 and Landsat) and does not introduce low spatial resolution data information.

Sandy land is one of the results of land desertification, which refers to land mostly covered by sand or sandy soil (sand-covered land), including desert. Knowledge of the spatial distribution and variation in sandy land is important to better understand desertification processes, land resource management, and environmental research. The emissivity of sandy soil in the TIR bands has remarkable characteristics. In addition, the green transition increasing attention is paid to the thermal insulation efficiency of building. Several thermal mission can support related information needs such as Landsat TIRS, Ecostress and at coarser resolution also Sentinel-3. The upcoming Copernicus LSTM mission will provide systematic global TIR observations at high spatial resolution. Certainly, synergies between such mission need to be explored. Here the Chinese SDG-Sat mission is highly relevant. It provides 30m TIR observation yet over a small spatial footprint. Therefore, complementary use need to be explored and potential for fusion these sources need be assessed.

Based on the background above, the objectives of this proposal are to focus on the following aspects:

• Evaluate complementary EO data sources from Chinese and European missions in terms of their characteristics for integrated or fused applications. Special focus will be paid to Sentinel2 and Geofen6. Also thermal complementarities between SDG Sat and other LST data sources will be explored.
• Explore the value of different harmonization and fusion processors such as STARFM, sen2like and others.
• Perform a prototype implementation of a fusion processor in a cloud environment
• Evaluate fused data stream in different application contexts

The deliverables include：

(1) Image Processing Algorithms: A prototype of a fusion processor will be developed and transformed into an cloud based on demand service prototype.

(2) Geospatial Products: Fused China-EU EO data of study area. Spatial distribution of heat emission of study area. Spatial distribution of sandy land in Xilin Gol League, China

(3) Technical Reports: Comprehensive technical reports detailing the methods, processes, and techniques used in the project.

(4) Scientific Publications: 3-5 research papers or scientific publications presenting the project's findings, methodologies, and significant results.

(5) Young scientist training: 3-5 young scientist exchange and training

Three-dimensional ocean dynamic prediction holds significant implications for maritime activities, marine economy, and meteorological-climatic systems. Traditional numerical forecast models for ocean dynamics require substantial computational resources and time consumption. With advancements in artificial intelligence (AI) methodologies, data-driven AI-based ocean models can now achieve superior prediction accuracy compared to numerical models while demanding quite limited computational time and resources. However, current AI ocean models remain constrained by numerical forecasting systems as they continue to rely on numerical analysis fields for forecast initials. This study proposes an end-to-end AI-based ocean forecasting framework that directly utilizes satellite remote sensing observations for three-dimensional ocean dynamic prediction. The model architecture connects satellite observations (input) with three-dimensional oceanic parameter forecasts (output). Input parameters include sea surface temperature(SST), sea level anomalies (SLA), and sea surface current derived from satellite remote sensing. The model generates 10-day forecasts of three-dimensional parameters across 0-200 meters depth with 20 vertical layers. By eliminating the dependence on numerical model outputs, this AI framework enables near real-time prediction updates through instantaneous computation (seconds-level processing) upon acquisition of new satellite observations. This innovation facilitates rolling updates of three-dimensional forecasts, thereby fundamentally transforming the conventional role of satellite remote sensing from merely providing observational references to enabling predictive capabilities for future oceanic states. This advancement establishes a new operational framework that satellite remote sensing can transcend its traditional function as a diagnostic tool, emerging as a proactive predictive foundation for marine environmental forecasting.

Effective and timely monitoring of crop water stress is critical for achieving agricultural sustainability. In our Dragon 6 project, we aim to synergistically utilise thermal infrared (TIR) and solar-induced fluorescence (SIF) observations from ESA and Chinese satellites to monitor crop water stress The key scientific objectives of this project are: 1) refining the land surface temperature (LST) retrieval method, 2) advancing evapotranspiration partitioning into soil evaporation and plant transpiration using an analytical model driven by LST, 3) developing models for estimating plant transpiration using SIF observations, 4) developing downscaling methods for LST and SIF data to fine spatial resolutions, and 5) enhancing crop water stress monitoring capability based on plant transpiration and SIF estimates. The progress in these five work packages (WP) for the last year is described as follows:

WP1. LST retrieval algorithm.

A new LST retrieval algorithm was developed by integrating the physical mechanisms of the split window (SW) and temperature emissivity separation (TES) algorithms into the deep learning (DL) model, constructing the DL-SW-TES framework. This new framework directly retrieves LST from easily accessible parameters without requiring any prior knowledge of LSE information and atmospheric profiles.

WP2. ET partitioning.

An advancement of the analytical surface energy balance (SEB) model STIC is envisaged. A “top-down” decomposition approach will be adopted based on the SW85 model. Such a method requires lumped estimates of energy fluxes (latent heat flux (ET), sensible heat flux, net radiation, and ground heat flux) in conjunction with air and dewpoint temperatures, LST, canopy-air vapor pressure, and aerodynamic conductance (g_A). The energy fluxes, canopy-air vapor pressure, and g_A are retrievable from STIC.

WP3. Modelling transpiration from SIF.

Two modelling approaches are planned. The first one is based on the connection of plant transpiration and photosynthesis through stomatal conductance. The second method is based on the FEST-2X2-EWB model. The FEST-2X2-EWB model computes continuously in time and distributes in space the total ET as well as the soil and vegetation components, and soil moisture at multiple soil layers. FEST-EWB computes LST as an internal variable and has physically-based non-residual formulations for the energy balance components (and can be used for model calibration or for updating the model state through data assimilation.

WP4. LST and SIF downscaling

The LST downscaling is planned to use a deep-learning based method. The estimated LST using the developed LST retrieval algorithm in WP1 will be input into the deep-learning framework. High spatial resolution LST will be obtained by combining ECOSTRESS and SEVIRI LST estimates.

To address systematic errors in SIF downscaling caused by canopy shadow, we implemented BRDF correction for vegetation indices and utilized SIF_n to minimize angular and PAR effects, generating a 500m SIF product. Subsequently, a Random Forest model was developed using VPD, LAI, and the generated SIF to estimate evapotranspiration, achieving high fitting accuracy across cropland, broadleaf forests, and needleleaf forests.

WP5. Crop water stress indicator development

we developed a novel crop water stress indicator, SIF_n, by minimizing the impact of angular effects and PAR variations on satellite-derived solar-induced fluorescence (SIF). During the 2019 North China Plain drought event, SIF_n exhibited an earlier decline and greater sensitivity in detecting the onset of water stress compared to traditional vegetation indices, raw SIF, and fluorescence quantum yield (ΦF). Furthermore, SIF_n anomalies showed a notable correlation with rainfall and meteorological factors, revealing the dynamic contributions of both canopy structure and vegetation physiology to SIF across different drought phases. These findings underscore SIF_n's potential to enhance the accuracy and timeliness of crop water stress monitoring.

Overall, these advancements in LST and plant transpiration retrieval, SIF downscaling will support the crop water stress monitoring with soil-independent information at high spatial resolutions.

Aerosols and clouds are important factors affecting radiative forcing and climate change, and improving the ability to observe high-precision profiles of their optical properties will reduce uncertainties in climate model simulations and forecasting systems. High spectral atmospheric detection lidar is capable of wide-area, continuous, dynamic, and full-time integrated monitoring of aerosols and clouds. Three-dimensional wind field data with high spatial and temporal resolution is an important observational basis for an in-depth understanding of the structure and evolutionary characteristics of the atmospheric boundary layer, and the heat-momentum-matter exchange and equilibrium process between the upper and lower troposphere. In order to achieve global high-precision wind field and aerosol profile observations by satellite-borne Doppler wind measurement lidar and high spectral resolution lidar, it is necessary to carry out long-term and rigorous calibration and verification.

The Chinese atmospheric environment monitoring satellite DQ-1 has been successfully launched on 16 April 2022. As an integrated detection scientific research satellite, it will serve as an important part of Chinese atmospheric environment monitoring system. The DQ-1 equips five sensors including an a Particulate Observing Scanning Polarimeter (POSP), a Directional Polarization Camera (DPC), an Environmental trace gas Monitoring Instrument (EMI) and a Wide Swath Imaging system (WSI). As the primary payload, Aerosol and Carbon Detection Lidar (ACDL) is a high spectral resolution lidar (HSRL) with two-wavelength polarization detection, that can be utilized to derive the aerosol optical properties. In this study, we develop radiation calibration and optical parameter retrieval algorithms for ACDL channels in daytime and nighttime scenarios, and carry out cloud phase classification and aerosol classification studies through layer identification and scene classification algorithms. The aerosol and cloud optical properties products of the ACDL include total depolarization ratio, backscatter coefficient, extinction coefficient, lidar ratio and color ratio. After the calibration and comparison iterations, the high-resolution standardized data products of the ACDL aerosol channel developed in this study are widely used in joint multi-sensor observations and meteorological and environmental monitoring.

The ESA’s EarthCARE satellite was launched in May 2024. The HSRL payload ATLID will provide vertical profiles of aerosols and thin clouds. It will operate at a wavelength of 355 nm and have a high-spectral resolution receiver and depolarization channel. In this study, after temporal and spatial matching and quality control of the ACDL and ATLID on-orbit observation data products, synchronized observations are carried out for the scenarios of dust, cirrus clouds and polar stratospheric clouds. Through the establishment of a worldwide comparison and validation process for satellite-ground and satellite-satellite observations, the two lidars are jointly compared on optical parameters such as attenuation backscatter coefficient and depolarization ratio. The observation capability and data accuracy of the spaceborne high spectral resolution lidar are verified, and iterative optimization directions are provided for the calibration program and data processing algorithms.

Aeolus was the first satellite mission to acquire profiles of Earth’s wind on a global scale. It was launched on 22 August 2018 and finished its nominal lifetime on 30 April 2023 before an assisted re-entry on 28 July 2023. The Aeolus satellite’s only payload is a direct detection DWL ALADIN. During its lifetime, it has measured the global wind profiles and aerosol profiles simultaneously and continuously for more than 4 years. In order to guarantee the data quality and improve the inversion algorithm, the reprocessing of the Aeolus data products to improve data quality will continue in the next 5 years during phase F. This research utilizes the China Coherent Doppler Lidar Network (CDLnet) to assess the reprocessed data from Aeolus, and evaluating and analyzing the data quality.

In this study, we performed a detailed analysis of the ionospheric zonal currents from equator to middle latitudes during the recent intense geomagnetic storm on 10-12 May 2024. Magnetic measurements from two ground stations in the America sector, as well as that from the Swarm satellites have been used. Extreme intensified eastward and westward EEJ values, reaching 300 mA/m and -400 mA/m have been observed during the storm main and recovery phases, respectively. Such intense EEJ values have never been observed during the past 11-year flying period of Swarm mission. In addition, the storm responses of zonal currents at low and middle latitudes have been analyzed, by using the vertical magnetic field component from Swarm. These zonal currents showed quite prominent dependences on magnetic local time. In the noon sector, eastward currents were dominated under both quiet and storm conditions, with slightly intensification during storm. Conversely, the zonal currents in the dawn and dusk sectors displayed abrupt current reversals within 30 minutes after the sudden storm commencement, characterized by sustained eastward (dawn) and westward (dusk) perturbations persisting for the rest one and a half days. Most interestingly, two eastward zonal current jets were found located at ±25º magnetic latitudes at the dusk sector, emerged with westward zonal currents at other low and middle latitudes. To our knowledge, this is the first time to report such narrow current jets at middle latitudes during storms.

The objectives of project 95549 are that we take advantage of the multiple satellites such as CFOSAT, FY series, Sentinel series, to provide the combined satellite monitoring of marine environment disasters and a better understanding of the associate marine environment dynamics. So far, we have carried out the following work：

1.Validation of CFOSAT and multiple satellite data

CFOSAT and HY-2 series satellites can measure the sea surface wind speed (WS) and significant wave height (SWH) at nadir points. By comparing and verifying with the calibrated sea surface WS and SWH such as Jason-3 and sentinel-3, we have carried out unified benchmark calibration of the observation data of multiple satellites. Besides, previously result shows that the non-fully-developed-sea (NFDS) wind-wave relation is robust for different satellite observations. For example, such relation derived from the Jason series satellite is comparable with the buoy observation and is also valid for the CFOSAT data. It is thus useful for wind-wave mutual correction since they might be underestimated by the satellite for the high wind condition with wave breakings. So we carried out a reconstruction study of the SWH based on the 10 WS measurements.

2. Multiscale description of the W²CI

To understand better the W²CI in typical regions and events, the multiscale scale-to-scale flux analysis is being performed. The scale-to-scale flux is the cornerstone of the W²CI. However, due to the lack of observation data, the understanding is incomplete. It is thus one of the major uncertainties of climate models. With high temporal and spatial observation, this issue will be overcome. More precisely, we would like to explore the multiscale description and analysis of the W²CI. Such analysis will be applied to two typical regions. They are the western Pacific with one of the strongest ocean currents, namely Kuroshio current, mesoscale eddies and typhoon; and ACC with strong ocean circulation. The analysis result will enrich our understanding of the underlying dynamics.

3. Machine-Learning-Based Optical Flow

For the high temporal resolution observation, such as the geostationary satellite, the optical flow is a potential technique to estimate the ocean surface velocity. With aid of the machine learning, the Machine-Learning-Based Optical Flow (MLBOF) is being developed for geostationary satellite, such as FengYun-4, to retrieve the ocean surface velocity. Jointly combined with other satellite measurements, we expect to carry out a comparison. Moreover, MLBOF will be applied to the archived data provided by other geostationary satellites (e.g., FY-2, Sentinel-4, Himawari-8, etc) to construct a historical ocean surface velocity field. The historical data will be useful for climate change study.

In the framework of the ESA-MOST Dragon-6 project (id.95348), the Istituto Nazionale di Geofisica e Vulcanologia (INGV) from Italy, the Northeastern University (NEU), the Jilin Earthquake Agency and Jilin University from China are carrying out collaborative research using time-series radar and optical images on different geohazards in Northeast China.

Stemming from the experience of the previous Dragon-4 and Dragon-5 projects, the joint research team from Italy and China collaborated to monitor the deformation due to industrial activities and volcanic dynamics. In particular, we are continuing to measure and analyze the ground motion at the Fushun open pit mine and Changbaishan Tianchi volcanic complex to support local hazard assessment. In addition, we analyze the ground deformation at a new study site, the Songliao Basin. Songliao is a Mesozoic-Cenozoic continental basin characterized by significant oil and gas extraction activities and water exploitation. To investigate the surface motion affecting the basin, we mainly use InSAR data from Sentinel-1. The Songliao Basin appears to be affected by ground motion (subsidence and uplift) caused by natural phenomena and anthropogenic activities.

Our main goal is to identify and characterize the processes responsible for the deformation (fluid migration, slope mass movement, volcano dynamics, and human activities), hence quantifying the deformation caused by each source. To this aim, we model the degassing and magmatic activity at Changbaishan Tianchi by using the detected surface deformation and explore the correlation between deformation and the amount of oil/gas extraction or fluid pumping in deep wells in the Songliao Basin.

In detail, the Changbaishan Tianchi intraplate volcano is one of the most active and hazardous volcanoes of Northeast Asia, characterized by a summit caldera formed after the 946 CE ‘Millennium’ Plinian eruption. From December 2020 to June 2021, the frequency and magnitude of earthquakes were significantly higher than during background periods with hundreds of earthquakes (46 events per month on average) reaching a local maximum magnitude of ML 3.1. Our study reports a comprehensive deformation analysis and a geophysical inversion scheme aimed at unveiling the volcano dynamics in this period. Multi-temporal InSAR analysis results of 32 ALOS-2 images from 2018 to 2022 show that the surface deformation is a combination of seasonal fluctuations (± 25 mm in average, with a maximum ± 45 mm) and a long-term positive component. The least squares linear regression of the deformation time series and temperature data isolates the seasonal fluctuations, revealing a clear uplift and subsidence process from June 2020 to July 2021 in the caldera area. To constrain the Tianchi plumbing system dynamics, a combined inversion scheme consisting of three deformation sources is proposed. The inversion results and the seismic records indicate that the Tianchi volcano experienced a low-level unrest episode from December 2020 to June 2021. The shallower plumbing system, located at about 5–9 km depth and modeled by pressurized spheroids, underwent a cumulative volume increase of 26 × 10⁶ m³ from November 2018 to April 2021, followed by a volume decrease of 9 × 10⁶ m³ from April to July 2021. This suggests magma rising from a 14 km deep storage zone to the shallower plumbing system, followed by depressurization due to the escape of fluids. In the next years of the project, we will continue to monitor the Tianchi volcano complex using InSAR techniques and also considering images acquired by the ALOS-2 sensor operating in the L-Band, thus allowing a larger spatial coverage due to a lower sensitivity to vegetation.

Aiming at testing new monitoring procedures based on SAR and optical satellite data, the NEU and INGV teams have also investigated the Hunga Tonga-Hunga Ha’apai (HTHH) submarine volcano, providing an exhaustive analysis able to interpret the disastrous eruptive event that destroyed the island in January 2021. HTHH in the Tonga-Kermadec island arc initiated a new eruptive phase lasting approximately 1 month after 7 years of dormancy. This eruptive phase culminated in the January 15, 2022, Plinian eruption and the associated destructive tsunami. To analyze the complete eruption sequence from 2021 to 2022, satellite optical images and synthetic aperture radar images were analyzed, revealing the morphological changes of the volcanic island(s) during the time. The preparatory phase preceding the first eruption on December 19, 2021, was studied by considering descending Sentinel-1A images from 2020 to 2021. The obtained surface deformation before the December 2021 eruption shows an uplift of nearly 6.4 cm on the line of sight. Results reveal that a possible intrusion of magma started in May 2020 and gradually increased until December 2021, leading to a 19-month-long deformation phase before the eruption. The observed deformation is possibly explained by (a) a NNW-SSE striking, magma-filled dike upraising since the beginning of 2020 from a spheroid-like source located at 5000 m depth.

As far as the Songliao basin is concerned, we have analyzed Sentinel 1-A/B images belonging to different tracks to obtain the largest possible spatial coverage for such an area. To achieve this goal, we have selected SAR data along the descending orbit in the temporal interval between 2021-2024 for Sentinel-1A data for the northern part and Sentinel-1B data for 2017-2021. The latter was because the southern part of the basin was only covered by Sentinel-1B data, which was unfortunately discontinued in 2022. We processed more than 600 SAR images using the Small Baseline Subset technique and obtained the mean ground velocity and the displacement time series. Some very interesting phenomena were detected from these products, highlighting different behaviors, like subsidence and uplifting, for contiguous areas. In the Daqing area, where one of the larger oil fields of the Songliao basin is located, we have detected an uplift in the oil extraction area and a subsidence in the neighboring water network. Data show that for every ton of crude oil produced in the oil field area, 2-3 tons of water collected from the neighboring areas (the water basins and the city) need to be re-injected, and there are 36,879 m³/yr of oil-polluted wastewater. The detected ground deformation shows a subsidence in the water network area and uplift due to water injection in the oil field.

The resilience of vulnerable communities and agricultural systems against natural hazards—particularly landslides and land subsidence—needs to be strengthened. These events cause numerous fatalities and significant economic losses each year. Landslides primarily affect linear infrastructure and urban areas in mountainous regions, resulting in serious communication disruptions and loss of life. Meanwhile, land subsidence is emerging as an increasingly severe geohazard, expected to impact millions of people globally in the coming years by raising flood risks in urbanized areas and damaging critical infrastructure. In this contribution, we present the results obtained after one year of the ReSeLMAIN project (ID: 95355 – Remote Sensing for Landslide Monitoring and Impact Assessment on Infrastructure), developed within the framework of the Dragon-6 cooperation program—an initiative between the European Space Agency (ESA) and the Chinese Ministry of Science and Technology (MOST). The ReSeLMAIN project aims to employ remote sensing techniques, particularly Synthetic Aperture Radar Interferometry (InSAR), to identify, map, and monitor landslides and land subsidence, as well as assess their impacts on infrastructure.

We are working in various study areas across China and Spain. In Beijing, the capital of China, high-resolution TerraSAR-X data was used to analyze land subsidence induced by groundwater withdrawal, revealing a maximum mean displacement rate of over 110 mm/year between 2010 and 2019. A methodology based on slope gradient, vertical radius of curvature, and angular distortion calculated from InSAR-derived displacements has been developed to assess potential damage along the Beijing–Tianjin high-speed railway. In the eastern Tibetan Plateau, China, multi-platform SAR images from 2007 to 2022 were used to identify and characterize landslide groups, which pose grave peril to community members and critical construction along the Jinsha River. A novel InSAR-based method was developed to infer the slip surface of active landslides, along which the maximum displacement reached 1.5 m. We also found that in our study area, the 2018 Baige landslide failure had caused persistent acceleration to downstream wading landslides, highlighting the prolonged cascading impact of landslides on the watershed’s geological environment. In Monóvar, Spain, an Active Deformation Area (ADA) was identified using data from the European Ground Motion Service (EGMS). This area shows maximum vertical and east–west displacement rates of 64.6 mm/year and 23.8 mm/year, respectively, during the period from 2019 to 2024, affecting more than 6 hectares of partially urbanized land. While the deformation was initially attributed to a landslide, subsequent investigations have linked it to a subsurface dissolution process. Supporting evidence includes the concentration of structural damage in buildings located over gypsiferous substrata, the concentric pattern of surface cracking, and the identification of a characteristic subsidence bowl. The findings indicate that a significant number of buildings, roads, and other infrastructure in Monóvar are affected by these ground deformations, underscoring the need for improved monitoring and mitigation strategies.

Since the project launch last year, our project focuses on two critical challenges in current degradation monitoring, including large-scale quantitative assessment of shrub encroachment, and grassland degradation assessment with very high spatial resolution image. Through sensitivity analysis of optical and SAR features against shrub coverage, optimal monitoring periods and feature combinations were identified. By developing a feature fusion algorithm to amplify shrub signals, the team successfully constructed a shrub coverage index. The team conducted visual interpretation of Google Earth imagery in Hexigten Banner, Inner Mongolia. This enabled high-precision mapping of land cover and spatial patterns of vegetation/soil degradation, establishing a grassland degradation classification framework. The findings demonstrate that VHR image analysis coupled with cross-scale multidisciplinary frameworks serves as an effective tool for policy-making and land management decisions.

Aiming at information extraction of wetland environment factors, a signal photon extraction method based on convolutional neural network and a hyperspectral vegetation feature band selection method based on quantum genetic spectral angle classification algorithm were proposed based on ICESat-2 and measured hyperspectral data respectively. At the basin scale, statistical analysis and model simulation method were used to analyze the characteristics and driving factors of water and sediment change, and separate the contribution of natural and human factors based on the long-term hydro-meteorological, natural and socio-economic factor and remote sensing data. The digital twin technology was used to construct more than 8 types of migratory bird models and 12 types of wetland vegetation models in Poyang Lake wetland, dynamically display the habitat activities of migratory birds and simulate the vegetation growth process synchronously, so as to realize the accurate restoration of the seasonal growth and decline of the virtual Poyang Lake water area, the inter-annual change of vegetation growth and the migratory activities of migratory birds. Above works are benefit for evaluation of wetlands resilience and reveal the human-water relationship in watersheds.

The aim of this project focuses on the development and application of processing methodologies to address the 3D characterization of sub-surface targets using stacks of spaceborne SAR data acquired over natural scenarios. The investigated applications will include 3D imagery of forests, ice sheets, and desert areas, and are therefore mapped into Dragon topic Solid Earth - Subsurface target detection. The topics above are of fundamental importance in the context of present and future spaceborne SAR missions, which will allow increasingly more systematic use of multiple acquisitions thanks to improved hardware stability and more strict orbital control. Specifically, the proposed activities are intended to support use of multi-pass data stacks from:

• the upcoming P-Band mission BIOMASS.
• in-orbit L-Band missions, such as the Argentinian SAOCOM constellation, the Chinese missions LuTan-1 & LuTan-4, and potentially ALOS-4 and NISAR.

Research activities will consider SAR data stacks acquired by P- and L-band spaceborne SARs over dense tropical forests, ice sheets, and desert areas, as well as campaign data from ESA campaigns such as TomoSense, AfriSAR, AlpTomoSAR, IceSAR and the Second Tibetan Plateau Scientific Expedition and Research led by the Chinese Academy of Sciences (CAS). The activities will be concentrated on processing SAR image stacks to extract information about vertical vegetation structure and sub-surface terrain topography in forested areas, and also about the internal structure of sand dunes in desert area as well as snow-ice volume in glacier area. Estimation and compensation of ionospheric and tropospheric propagation effects will be investigated as well. Leveraging the unprecedented availability of P-Band spaceborne data from the BIOMASS mission, the research will as well be extend to investigating the 3D of the ionosphere. Whenever possible, validation activities will exploit the availability of reference data gathered at campaign sites, for which we plan to analyze spaceborne acquisitions at the same sites

The first year of the Dragon 6 project focused on the application of satellite remote sensing methods to monitor geohazards and support agricultural water use assessment. Activities were carried out in Brazil, Portugal China, and Pakistan, combining Synthetic Aperture Radar (SAR), optical data, ground measurements, and artificial intelligence models.

In Maceió, Brazil, ground deformation due to underground salt mining was analyzed using 145 Sentinel-1A images acquired between June 2019 and April 2024. Initial Persistent Scatterer Interferometry (PSI) identified general deformation trends but showed limitations in low-coherence areas. To address this, a Quasi-Persistent Scatterer (QPS-InSAR) approach was applied, increasing point density by over 400% (from 4,494 to 23,460 scatterers) and revealing cumulative vertical displacements ranging from −1,750 mm to +10 mm. Notably, 17 scatterers exhibited a loss of amplitude stability in 2023, with 11 of these changes occurring in December—correlating with the collapse of Mine 18. These results support the use of amplitude time series as an indicator of pre-failure instability.

In the Jamari National Forest, also in Brazil, Sentinel-1 SAR data were used to detect the impact of selective logging in tropical forest areas. Backscatter-based change detection methods were applied to areas logged in 2017 and 2020 and a control area. The detected canopy loss was 6.90% and 4.11% in the logged plots, respectively, while the undisturbed area showed only 0.16% change. Spatial overlap with mapped logging infrastructure reached 60.94% and 51.65%, confirming the method’s suitability for operational-scale forest monitoring.

In Portugal, a study in the Vilariça Valley addressed soil moisture estimation for irrigation management in olive orchards. Sentinel-1 SAR imagery (VV and VH polarizations) was combined with field measurements taken every 30 minutes from July 2020 to December 2021 at 10 cm depth. Two Artificial Neural Network (ANN) models were trained: one using 161 ascending images (D1), and another combining 246 ascending and descending images (D2). The D1 model achieved higher performance (RMSE: 2.78%, R²: 0.69, MAPE: 8.26%) compared to D2 (RMSE: 3.96%, R²: 0.59, MAPE: 12.41%), highlighting the importance of acquisition geometry consistency.

Collaborative work conducted by the Chinese team included surface deformation analysis in the Gilgit section of the China–Pakistan Highway and in the North China Plain (NCP). Multi-temporal InSAR, GNSS, and hydraulic head data from 2015–2019 were used to characterize aquifer behavior and land subsidence in NCP. Seasonal vertical displacements of up to 25 mm were identified, with long-term subsidence averaging 29 mm/year and localized peaks reaching 120 mm/year. Variability across regions was linked to differences in groundwater management and the influence of the South-to-North Water Diversion (SNWD) infrastructure.

These results demonstrate the effectiveness of combining EO data, ground observations, and AI models to monitor dynamic environmental processes. The activities developed during this first year contribute to building operational workflows for deformation risk detection, support tools for irrigation optimization, and future developments such as early warning systems and integration of heterogeneous data sources.