Conference Agenda
Overview and details of the sessions for this conference. Please select a date and a session for detailed view (with abstracts and downloads if available).
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Daily Overview |
| Date: Tuesday, 15/July/2025 | |
| 09:00 - 12:00 | 2025 DRAGON 6 SYMPOSIUM OPENING Room: Conference Center Session Chair: Eric Doyle Session Chair: Xiaowei Zhang |
| 12:00 - 14:00 | Lunch |
| 14:00 | DRAGON 6 SCIENTIFIC SESSIONS |
| 14:00 - 15:30 | S.1.1: ATMOSPHERE Room: No.3 Conference Room (2nd floor) Session Chair: Dr. Jieying Ding Session Chair: Prof. Wuke Wang ID. 95381 ID. 95395 |
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14:00 - 14:45
Oral ID: 170 / S.1.1: 1 Dragon 6 Oral Presentation ATMOSPHERE: 95381 - Air Quality Monitoring and Analysis in Populous areas in China (AQMAP) All Authors Contribute to the Air Quality Monitoring and Analysis in Populous areas in China (AQMAP) Project. 1: China University of Geosciences, Wuhan, China; 2: Royal Netherlands Meteorological Institute (KNMI), The Netherlands; 3: LATMOS, France; 4: National Satellite Meteorological Centre (NSMC), China; 5: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China Using satellite observations of trace gases and aerosols, this project studies the air pollution in the most populous regions in China: the North China Plain (NCP) and along the Yangtze River. With a total population of about 700 million, these regions are amongst the most densely populated regions in the world. Human activities, traffic, transportation and heavy industry in these areas result in high anthropogenic emissions with a variety of adverse effects. Monitoring and analysis of air quality is essential to evaluate different factors (emissions and emission reduction policy, meteorological factors, atmospheric chemistry and physics) contributing to the pollutant concentrations. We have focused on the important components of air quality (AQ). The results of the work from last year will be presented. We have derived the NOx and NH3 emissions over the populous areas in China. The effects of emission reduction to improve AQ and competing meteorological processes will be evaluated. The evaluation of proxies to detect the occurrence of new particle formation using ground-based remote sensing and in situ data was successfully completed using data from the Beijing-RADI site during the winter of 2022. The study on the evolution of AOD over China and the competition between anthropogenic and meteorological influences will use satellite data in combination with model simulations. FY-4A data are used for the study of cloud detection and aerosol retrieval. We also examine nocturnal ozone surge events in Wuhan between 2016 and 2023 and employs self-organizing maps (SOM) to analyze the prevailing synoptic weather patterns associated with these events. 14:45 - 15:30
Oral ID: 125 / S.1.1: 2 Dragon 6 Oral Presentation ATMOSPHERE: 95395 - Towards understanding aerosol cloud radiation interactions using 3D satellite observations (TACT) Evaluation of EarthCARE Atlid Aerosol Products 1: Royal Netherlands Meteorological Institute (KNMI), The Netherlands, Netherlands, The; 2: Institute of Atmospheric Physics, Chinese Academy of Sciences, China; 3: National Satellite Meteorological Centre (NSMC), China The Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) has been launched on 28 May 2024 which is a satellite mission implemented by the European Space Agency (ESA), in cooperation with the Japan Aerospace Exploration Agency (JAXA). The EarthCARE satellite includes four scientific instruments: an atmospheric lidar (ATLID), a Doppler cloud radar, a multi-spectral imager and a broadband radiometer. EarthCARE provides simultaneous measurements of the vertical structure and horizontal distribution of cloud and aerosol fields, together with outgoing radiation. The EarthCARE products can be used to evaluate their representation in weather forecasting and climate models. The ATLID L2a processor chain starts with the A-FM (ATLID Feature mask) processor which detects features to help facilitate the appropriate averaging of the data. The A-PRO (ATLID-profile) processor retrieves the aerosol and cloud optical property profiles and classifies the detected targets. The ATLID level 2a products include feature mask, particle extinction coefficient, backscatter coefficient, lidar ratio, particle linear depolarization ratio profiles and some aerosol/cloud layer properties derived from the profiles. In the Dragon 6 TACT project we will use the EarthCARE multi-instrument synergy products to study the aerosol, cloud, radiation interaction for some selected cases. In the first year, we focused on the evaluation of the EarthCARE products. We have been evaluating the ATLID products with the ground-based EARLINET-ACTRIS measurements since August 2024 in the commissioning phase. The ATLID aerosol products are also compared with CAMS and ACDL aerosol data. We will report the progress in the first year of our project. |
| 14:00 - 15:30 | S.2.1: OCEAN & COASTAL ZONES Room: No.2 Conference Room (3rd floor) Session Chair: Dr. Roshin P. Raj Session Chair: Dr. Jungang Yang ID. 95258 ID. 95315 |
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14:00 - 14:45
Oral ID: 204 / S.2.1: 1 Dragon 6 Oral Presentation OCEAN & COASTAL ZONES: 95258 - marinE added-ValuE pRoducts Generated by Remotely sEnsed microwavE measuremeNts (EVERGREEN) Marine Added-Value Products Generated by Remotely Sensed Microwave Measurements 1: University of Rome "La Sapienza", Italy, Italy; 2: Nanjing University, China The project is to propose novel and effective methods to generate marine added-value products starting from remotely sensed measurements mainly coming from satellite radar sensors. Hence, the proposed piece of research is framed into the “Ocean & coastal zone” Dragon-6 thematic area. The various sub-topics addressed within this domain include the understanding of “marine dynamic environment”, the analysis of “sea surface characteristics”, the Earth Observation (EO) support for “marine disasters” management, the exploitation of radar measurements to observe “algae and phytoplankton” blooms. These applications are of paramount importance for both the scientific and the end-user communities especially in the management of coastal areas. The proposed added-value products are generated mainly from Synthetic Aperture Radar (SAR) satellite measurements (but include also microwave radiometer and scattereometer and ancillary optical measurements) and are devoted to open sea and coastal environments, even under extreme weather conditions. The proposed piece of research will involve the development of tailored models of the processes under study combined with Artificial Intelligence (AI) methodologies that allow the interpretation and the processing of SAR measurements, collected under different imaging modes (including measurements acquired using SAR conventional and compact- polarimetric modes), to derive the above-mentioned user friendly added-value products in the following thematic domains: The latter include, but are not limited to, maps of targets at sea (including aquacultures, ships, wind farms, algae and aggregated of plastics) wetland coastal erosion/accretion trends due to both anthropogenic and natural phenomena, mapping marine pollutants, modelling, tracking, and forecasting extreme weather events as cyclones/typhoons Water pollution Macroalgae water pollution is addressed using L- and C-band multi-polarization SAR measurements along with optical imagery. A model is developed to explain the scattering mechanisms that are expected to rule algae-free and algae-covered backscatter. Plastic pollution is addressed using X-band SAR imagery along with drone-based optical measurements through controlled experiments where plastic targets are deployed at sea and observed by satellite and drone measurements. Target detection The polSAR backscatter from PAZ imagery acquired over the Robin Riggs wind farm is analysed to estimate blade rotation using sub-aperture analysis. Intertidal area monitoring Coastal areas calling for intertidal flat are analysed using multi-frequency and multi-polarization SAR imagery to perform land classification. Iceberg detection and tracking The C33 iceberg which, calved from the Antarctica continental ice, drifted in the Terra Nova Bay for more than 1 month has been observed using dual-polarimetric SAR measurements. All this matter will be detailed in the proposed piece of study. 14:45 - 15:30
Oral ID: 135 / S.2.1: 2 Dragon 6 Oral Presentation OCEAN & COASTAL ZONES: 95315 - Synergistic Monitoring and Prediction of Ocean Dynamic Environment from Multi-satellite Data First Year Results of Synergistic Monitoring and Prediction of Ocean Dynamic Environment from Multi-satellite Data 1: Second Institute of Oceanography, MNR, China; 2: Nanjing University of Information Science and Technology, China; 3: National Ocean Technology Center, MNR, China; 4: Collecte Localisation Satellites, France; 5: Laboratoire d’Océanographie Physique et Spatiale (LOPS), IFREMER, France It is presented in this paper the first year results of ESA-MOST China Dragon 6 Cooperation Program “Synergistic Monitoring and Prediction of Ocean Dynamic Environment from Multi-satellite Data (ID. 95315)” covering following main scientific objectives: (1) assimilation studies of wind, waves and sea level in the context of hurricanes forecasts; (2) the influence of swell on the studies of coastal extremes; (3) studies of vortex Rossby waves, asymmetric TC structures, rain bands, and sub-scale circulations by using high spatial resolution ocean wind data; (4) analysis of relationship between the above internal dynamical processes and TC intensity changes; (5) consistent analysis and prediction of winds, waves and storm surges in the context of hurricanes; and (6) consistent monitoring and prediction of ocean surface current and internal waves using multi-source satellite data. |
| 14:00 - 15:30 | S.3.1: URBAN & DATA ANALYSIS Room: No.1 Conference Room (3rd floor) Session Chair: Dr. Rui Song Session Chair: Prof. Linlin Lu ID. 95235 ID. 95393 |
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14:00 - 14:45
Oral ID: 249 / S.3.1: 1 Dragon 6 Oral Presentation URBANISATION & ENVIRONMENT: 95235 - EO-AI4ResilientCities: Enhancing Urban Resilience with Earth Observation and AI-Powered Insights EO-AI4ResilientCities: Advancing Urban and Environmental Resilience with AI-Driven Earth Observation 1: KTH Royal Institute of Technology, Sweden; 2: Nanjing University, China; 3: University of Pavia, Italy; 4: International Research Center of Big Data for Sustainable Development Goals (CBAS), China; 5: East China Normal University, China; 6: University of Science and Technology of China, China This project aims to advance the application of artificial intelligence (AI) and Earth observation (EO) technologies to address critical urban and environmental challenges. As cities face increasing pressures from rapid urbanization and climate change, the need for high-resolution, timely, and actionable geospatial intelligence has become critical. This project brings together complementary expertise in AI, remote sensing, urban analytics, and environmental resilience to develop novel methods for understanding and managing complex urban-ecological systems. To tackle the challenge of producing fine-scale thermal data, a conditional diffusion probabilistic model (DDPM) was developed to generate daily 30-meter resolution Land Surface Temperature (LST) maps. By fusing high-temporal-resolution VIIRS data with high-spatial-resolution SDGSat-1 imagery, and leveraging a U-Net-based transformer-enhanced architecture, this model significantly outperformed traditional interpolation methods. The resulting LST products provide a scalable solution for urban heat island (UHI) monitoring and climate adaptation planning in data-sparse environments (Brune et al., 2025). Urban heat risk was further examined in Karachi using SDGSat-1 thermal infrared imagery in combination with local climate Zone (LCZ) classifications and socioeconomic vulnerability indicators. The study applied Crichton’s Risk Triangle framework to produce a Heat Health Risk Index (HHRI), revealing that the highest risk was concentrated in LCZs with high population density and informal settlements. The results support tailored urban planning responses in heat-vulnerable areas and contribute directly to SDGs 11, 13, and 10 (Baqa et al., 2025). A deep learning-based approach was also developed to monitor 3D structural changes in urban areas using synthetic aperture radar (SAR) data. A CycleGAN-based image translation framework was applied to convert Sentinel-1 and COSMO-SkyMed SAR images into a common representation, enabling high-frequency monitoring of building construction and demolition. Applied over Shanghai, the model significantly improved spatial consistency and signal alignment across sensors. This approach was further extended to support 2D and 3D urban structure extraction in Shenzhen, providing a strong foundation for SAR-based urban monitoring and resilience applications (Memar et al., 2025). Complementing this, a novel generative AI model was introduced for building damage assessment, focusing on the transferability of high-resolution satellite imagery models to lower-resolution datasets. The diffusion-based model is trained on the xView2 Wildfire Building Damage Benchmark, a dataset specifically designed for wildfire-induced building damage detection. The model is further evaluated on real-world wildfire incidents in Lahaina, Hawaii, and Athens, Greece, demonstrating its effectiveness in damage localization across varying spatial resolutions (Shibli et al., 2025). To support urban infrastructure monitoring, the Poly-BRBLE framework was proposed for individual building localization and extraction. Combining boundary refinement, deformable convolution feature fusion, and cross-layer regularization, the model achieved state-of-the-art accuracy on multiple building datasets including SH, WHU, and Inria. It is particularly suited to complex urban environments and enables scalable footprint extraction for resilience assessment (Tan, 2025). For urban land use monitoring, developed to detect functional zone changes. By fusing satellite remote sensing and street view imagery using attention mechanisms, the model provided high-precision classification of commercial, residential, and public service zones. Tested in Nanjing, MSTFM outperformed traditional CNN-based approaches in spatial detail and temporal coherence (Yang et al., 2025). In arid urban regions, ecological sensitivity to anthropogenic activity was analyzed using a combination of Morphological Spatial Pattern Analysis (MSPA), InVEST habitat quality modeling, and omnidirectional connectivity assessment. Structural equation modeling and Geodetector analyses revealed that cultivated and bare lands are the primary drivers of ecological fragmentation, with habitat quality playing a crucial mediating role. Even modest improvements in habitat conditions yielded measurable gains in ecological connectivity (Mu et al., 2025). To evaluate long-term ecological vulnerability, a Dryland Ecological Vulnerability Index (DEVI) was developed for the Hohhot-Baotou-Ordos-Yulin urban agglomeration. Incorporating six remote sensing-based indicators, DEVI mapped 40 years of ecological change. Using XGBoost and SHAP for model interpretation, the study identified high-risk areas, thresholds of vulnerability, and zones where ecological restoration has been effective (Li et al., under review). A comprehensive multi-hazard risk framework, termed “risk source–risk exposure–mitigation force,” was implemented for urban disaster simulation. This framework integrated satellite, street view, and video data with deep learning and physical models (e.g., CFD) to assess vulnerability to wind, seismic, and fire hazards. Applications included city-scale wind damage analysis through multi-view image fusion and a spatiotemporal casualty assessment method for earthquake-induced debris and fire (Gu et al., 2025; Xu et al., 2025). By combining diverse datasets, advanced deep learning techniques, and validated case studies across multiple regions, this project delivers innovative, scalable solutions for urban heat mitigation, land use management, ecological conservation, and disaster resilience. The methodologies developed offer practical tools for urban planners, ecologists, and risk managers committed to building sustainable and adaptive cities. Brune et al. (2025). Generating Daily High-Resolution Urban Land Surface Temperature Maps using a Conditional Diffusion Model with SDGSat-1 and VIIRS Data. (Abstract). Baqa et al. (2025). Investigating Heat-Related Health Risks Related to Local Climate Zones Using SDGSAT-1 High-Resolution Thermal Infrared Imagery in an Arid Megacity. International Journal of Applied Earth Observation and Geoinformation, 136, 104334. Memar et al. (2025). Building Height Estimation from COSMO-SkyMed Imagery through Deep Learning Methods. Proceedings of JURSE 2025. Shibli et al. (2025). Very High- to High-Resolution Imagery Transferability for Building Damage Detection Using Generative AI. Proceedings of JURSE 2025. Tan (2025). Poly-BRBLE: A Boundary Refinement-Based Individual Building Localization and Extraction Model Combined with Regularization. IEEE Transactions on Geoscience and Remote Sensing, Early Access. Yang et al. (Under Review). Urban Functional Zone Change Detection via Multi-Source Temporal Fusion of Street View and Remote Sensing Imagery. IEEE Transactions on Geoscience and Remote Sensing. Mu et al. (2025). Quantifying the Anthropogenic Sensitivity of Ecological Patterns in Arid Urban Agglomeration. Applied Geography, 178, 103595. Li et al. (Under Review). Dynamic Monitoring of Fine-Grained Ecological Vulnerability in Dryland Urban Agglomerations Integrating Remote Sensing Index and Explainable Machine Learning. GIScience & Remote Sensing. Gu et al. (2025). Multi‐View Street View Image Fusion for City‐Scale Assessment of Wind Damage to Building Clusters. Computer‐Aided Civil and Infrastructure Engineering, 40(2), 198–214. Xu et al. (2025). A Spatiotemporal Casualty Assessment Method Caused by Earthquake Falling Debris Considering Human Emergency Behaviors. International Journal of Disaster Risk Reduction, 105206. 14:45 - 15:30
Oral ID: 186 / S.3.1: 2 Dragon 6 Oral Presentation URBANISATION & ENVIRONMENT: 95393 - Use of Earth Observation for Urban Security: addressing heat risk and geological hazards Earth Observation in Support of Urban Security: New Insights into Urban Heat Risk and Land Subsidence Evolution 1: National and Kapodistrian University of Athens, Greece; 2: Capital Normal University, China, People's Republic In Dragon 6, urban security research primarily focuses on utilizing earth observation to address two distinct themes: urban thermal risk, and geological disasters related to land subsidence. (a) Urban Heat Risk The complex interplay between heat dynamics and urban form and urban fabric was examined in view of recognizing relationships that need to be considered as far as the scientific basis of urban heat risk is concerned. Results demonstrate the importance of both urban form and urban fabric to the state of the urban thermal environment and consequently to urban heat risk. Furthermore, a multiparameter architecture, combining downscaling methods and machine learning, has been shaped to unravel the spatial and temporal dimension of EO data, quantify the urban heat dynamics also with respect to urbanization trends and finally develop a classification scheme of urban heat risk on the basis of urban typologies. The architecture addresses the exploitation of such parameters as Land Surface Temperature (LST), vegetation, proportion of ground surface with building cover, proportion of impervious surface (paved, rock), proportion of pervious surface (bare soil, vegetation, water), distance from green area/park, etc. as well as derived indices such as LST Anomaly, NDVI Anomaly, combined pervious surface/NDVI anomaly, compound heat and drought events, etc. Special attention was given to the assessment of the impact of urban greenery on urban heat dynamics. (b) Urban land subsidence: Addressing the complex evolution and mechanism of land subsidence in Beijing-Tianjin-Hebei urban area under the combined impact of climate change and human activities, a comprehensive interdisciplinary approach integrating remote sensing, geophysical exploration, hydrogeology, and artificial intelligence has been employed. Advanced technologies such as InSAR, GRACE, peridynamics, and physical information neural networks (PINNs) have been integrated to analyze the differential evolution patterns of urban groundwater driven by the natural-human dualistic water cycle and the response of land subsidence. (1) The ERA-5 reanalysis dataset, water resources bulletin dataset and GLDAS hydrological model were combined to obtain the temporal characteristics of key water cycle elements including precipitation, runoff, evaporation, and groundwater extraction in recent twenty years. What’s more, During the process, Theil-Sen trend analysis method, wavelet analysis method variational modal decomposition, time-lag correlation analysis, and random forest methods were used to analyze the response of groundwater storage change to precipitation and groundwater extraction, and it’s seasonal and long-term trends characteristics in the North China Plain. (2) PINNs (Physics-Informed Neural Networks) groundwater level simulation model with physical mechanisms was constructed by integrating long-term observation data from the groundwater level monitoring network, hydrogeological data, and precipitation data. Under the prior constraint of simulating groundwater level using the MLP (Multilayer Perceptron) model, the groundwater field partial differential equations were incorporated into the network. The weights of different loss function components were optimized determined. The model was successfully applied in simulating the karst and porous medium groundwater level. This study provides a powerful and reference-worthy approach for exploring the response to groundwater level. (3) The ordinary state-based peridynamics, employing equilibrium equations based on integral formulations, was modified to simulate the evolution from continuous land subsidence to discontinuous fissure due to groundwater over-pumping. The approach is used to investigate fissure, associated to pre-existing faults and excessive exploitation of aquifer systems, occurrences in the subsiding North China Plain around Beijing. The relations between the typical hydrogeological setting and the fissures were quantified. Result highlights that wider and deeper fissures develop with increasing fault dip, aquifer thickness, piezometric decline, and stiffness of surface deposits. |
| 14:00 - 15:30 | S.4.1: SUSTAINABLE AGRICULTURE Room: 2nd Floor Meeting Room Session Chair: Dr. Patrick Matgen Session Chair: Prof. Yelu Zeng ID. 95177 ID. 95250 |
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14:00 - 14:45
Oral ID: 109 / S.4.1: 1 Dragon 6 Oral Presentation SUSTAINABLE AGRICULTURE & WATER RESOURCES: 95177 - Monitoring Crop Growth with Big Earth Observation Satellite Data in Support of Agricultural Management Monitoring Crop Growth with Big Earth Observation Satellite Data in Support of Agricultural Management 1: Beijing Normal University, China, China, People's Republic of; 2: Université catholique de Louvain, Belgium In recent years, 10 to 30 meter resolution optical multispectral and hyperspectral satellites data and SAR data from Europe and China became available and is encouraging the Remote sensing community to explore the new technology to harness all advantages of all these satellites to achieve best crop monitoring at various scale. The capability of agricultural monitoring in general is being enhanced and improved with these diverse satellite data in term of the monitoring spatial extent and the quality of the retrieved crop growth information. However, the agricultural cultivation is diverse in China and the rest of the world. There are existing large fields with mono crop and small fields with multiple strips of various crop types. It has to take this situation into account when the crop monitoring is being conducted. What are the performance differences between multispectral time series or hyperspectral imagery from both side for crop type mapping, in particular between similar crop types? What is the transferability from one year to another for crop type classification models based on multispectral time series on one hand, and hyperspectral data on the other hand? In this project, 5 study sites, 4 from China and 1 from Belgium, are selected representing the major cropping systems, including winter wheat, maize, rice, sugarcane in China and winter cereals, potato, sprint cereals and maize in Europe. These sites also will be representing the agricultural systems in the flat area or in hilly area, irrigated or rainfed, in the North and the South. The field campaigns will be organized and the data collection will be following the JECAM guideline. The Sentinal1/2, EnMAP, from Europe and GF1/6, Obita and BNUE from China as well as other satellite data will be mainly investigated to support this study. The crop classification algorithm will be evaluated with various satellite data, like optical, SAR and hyperspectral data, either alone or combination to make best crop type maps, but in particular with a focus on hyperspectral data. Through this joint project and the heavy involvement of young scientists from Europe and China, the satellite data finely processing and information retrieval algorithm will be exchanged and the objective of this project will be fulfilled as the task team brings a step forward to support agricultural monitoring at fine scale. The collaboration will also make great technological contributions to GEO Global Agricultural Monitoring Imitative, GEOGLAM. 14:45 - 15:30
Oral ID: 220 / S.4.1: 2 Dragon 6 Oral Presentation SUSTAINABLE AGRICULTURE & WATER RESOURCES: 95250 - Optical and Thermal Copernicus-Chinese EO Data for Analyzing the Driving Factors, Impacting on Food Security and Quality Optical and Thermal Data for Analyzing Agricultural Components for Farmland Applications 1: Institute Of Methodologies For Environmental analysis (IMAA)- Italian National Research Council (CNR), C. da S.Loja, 85050 Tito Scalo, Italy; 2: Aerospace Information Research Institute, Chinese Academy of Sciences, No.9 Dengzhuang South Road, Haidian District,Beijing 100094, P.R.China; 3: Department of Agriculture and Forestry Sciences (DAFNE), University of Tuscia, Via San Camillo de Lellis, 01100 Viterbo, Italy; 4: Scuola Ingegneria Aereospaziale (SIA), University of Rome "La Sapienza", Via Eudossiana 18, 00184 Roma, Italy; 5: National Engineering Research Center for Information Technology in Agriculture, No.11 Shuguanghuayuan Middle Road, Haidian District, P.R. China; 6: School of Surveying, Mapping and Spatial Information, Shandong University of Science and Technology 579 Qianwan Harbour Road, Huangdao District, Qingdao 266590, Shandong Province, P.R. China Climate change and anthropogenic pressures increasingly threaten food security and quality, necessitating advanced monitoring and analytical tools. The activity conducted since now in the project ID# 95250 aims at evaluating the effectiveness of synergistically combining EU and Chinese EO data for retrieving key parameters for soil and crop conditions. Specifically, for soil topic, our objective is to assess the efficacy of a combined use of visible and thermal sensor data to retrieve soil texture and properties, such as Soil Organic Carbon (SOC), Calcium Carbonate (CaCO3), and pH. The primary objective is to evaluate the potential of an approach combining visible and thermal data in view of the future ESA missions (i.e. CHIME and LST) as well as the NASA missions (i.e. SBG and SBG-TIR). While current satellite missions such as PRISMA and EnMAP, have demonstrated their capabilities in soil monitoring, the TIR spectra range has not been fully investigated, Moreover, PRISMA and EnMAP acquire images only on demand, while HYTES, being an airborne mission, covers only limited and localized areas. This makes it difficult to obtain simultaneous VSWIR and TIR images on the same area, hindering the study of a combined approach. However, with the new ESA missions CHIME and LSTM, featuring larger swaths of 130 km and 670 km, respectively, and high revisit times of 11 and 2 days, the likelihood of obtaining simultaneous thermal and visible images increases significantly. This study investigates the Jolanda di Savoia site, located in the agriculturally significant Po Valley of Northern Italy (centered44.87°N, 11.97°E). This region, a very fertile land, is a prominent producer of high-quality rice. The soil composition exhibits considerable variability, primarily attributed to the area's historical landscape evolution. Notably, the presence of buried paleo-channels, remnants of former marshlands, contributes to the observed soil heterogeneity. A time series of PRISMA and EnMAP imagery, from 2020 to 2025 has been used as well as a HYTES airborne survey was performed in 2023 and data, acquired within a joint ESA-ASI campaign, were obtained for this site. The ongoing research, at present, has integrated in-situ soil wet lab measurements with 0.4-2.5 µm (VSWIR) and 7.5-11.6 µm (LWIR). Various preprocessing techniques and machine learning algorithms, including Gaussian Process Regression (GPR) and Partial Least Squares Regression (PLSR), have been employed to develop robust models for retrieving soil properties. The efficacy of these models will be assessed based on their accuracy and their capacity to effectively take advantage of combined visible and thermal data. Results indicate that SOC, by using VSWIR time series, could be predicted within an RMSE of 2.79, an R² of 0.54, and an RPD of 1.47, while with LWIR spectral range, an RMSE of 1.22, an R² of 0.54, and an RPD of 1.42 has been obtained. Future research will be concentrated to the combined use of VSWIR and LWIR mimic the future CHIME-LSTM tandem. In China, the demand for accurate and up to date information on wheat Grain Protein Content (GPC) has gained increased urgency, driven by the rising demands in the food consumption market and intensifying international market competition. To address this challenge and the data gap, the first 500-meter spatial resolution of GPC, long-term winter wheat dataset covering major planting regions in China (CNWheatGPC-500), was created by integrating multi-source data from ERA5/MODIS and Hierarchical Linear Model (HLM), established by deciphering the spatial hierarchical relationships among temporal phenological, meteorological, and remote sensing data. CNWheatGPC-500 provides valuable insights for improving wheat production, enhancing quality control, and supporting decision-making in the agricultural sector. The CNWheatGPC-500 product is freely accessible at https://doi.org/10.5281/zenodo.10066544. For what concerns crop threats, the core of the system aiming at detecting yellow rust outbreaks in maize and wheat crops, will be built on PRISMA satellite. Several VIs (NDVI, SIPI, PRI, PSRI, MSR) computed by using hyperspectral images will be used to implement a Diseases Infection Index. The impact of spatial resolution on the capability to detect yellow rust in crops will be one of the results of the activity. For desert locust presence risk forecasting, we have proposed a dynamic prediction method of desert locust presence risk at Somalia-Ethiopia-Kenya. Monthly prediction experiments until now were conducted, extracting high, medium and low risk areas of desert locust occurrence in the study area. Results demonstrated that the overall accuracy was 77.46%, and the model enables daily dynamic forecasting of desert locust risk up to 16 days in advance, providing early warning and decision support for preventive ground control measures for the desert locust. In addition, we have established the remote sensing-based monitoring and risk assessment methods for biological disaster of forest and grassland. For pine wilt disease monitoring, we proposed a spatiotemporal change detection method to reduce false detections in tree-scale PWD monitoring under a complex landscape. The results showed that the proposed method effectively distinguished wilted pine trees from other easily confused objects. For grasshopper, we took the two steppe types as the research object and established a remote sensing monitoring model for grasshopper potential habitat. The results demonstrated that the most suitable and moderately suitable areas were distributed mainly in the southern part of the meadow steppe and the eastern and southern parts of the typical steppe. We also found that the soil temperature in the egg stage, the vegetation type, the soil type, and the precipitation amount in the nymph stage were significant factors both in the meadow and typical steppes. |
| 15:30 - 16:00 | Coffee Break |
| 16:00 - 18:00 | DRAGON 6 ADJUDICATED YOUNG SCIENTIST POSTER SESSION Room: Conference Center (Back Hall) |
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ID: 199
Dragon 6 Poster Presentation OCEAN & COASTAL ZONES: 95258 - marinE added-ValuE pRoducts Generated by Remotely sEnsed microwavE measuremeNts (EVERGREEN) Detecting Coastal Aggregation Of Macro-Plastic Litter Using Satellite X-Band SAR Imagery Università di Napoli Parthenope, Italy, Italy ID: 205
Dragon 6 Poster Presentation OCEAN & COASTAL ZONES: 95258 - marinE added-ValuE pRoducts Generated by Remotely sEnsed microwavE measuremeNts (EVERGREEN) Multi-frequency and Multi-polarization SAR Backscattering of Green Tide 1: Institute of Oceanology, Chinese Academy of Sciences, China; 2: University of Rome "La Sapienza", Italy; 3: Università di Napoli Parthenope, Italy ID: 108
Dragon 6 Poster Presentation OCEAN & COASTAL ZONES: 95315 - Synergistic Monitoring and Prediction of Ocean Dynamic Environment from Multi-satellite Data Evaluating and Comparing ASCAT and Sentinel-1 SAR Winds Against NDBC Buoys 1: National Ocean Technology Center, MNR, China; 2: Second Institute of Oceanography, MNR, China; 3: CLS, France; 4: IFREMER, France ID: 182
Dragon 6 Poster Presentation OCEAN & COASTAL ZONES: 95316 - PeRcEiving natural and anthropogenic Disaster conditions and assessing risks In Coastal regions Through artificial intelligence, traditional and nOvel synthetic aperture RADAR technologies (PREDICTOR) Assessment of the Comprehensive Risk of Disaster-Bearing Bodies and Economic Losses in Megacities Based on Future Disaster Scenarios and Input-Output Models 1: Key Laboratory of Geographical Information Science, Ministry of Education, East China Normal University, Shanghai 200241, China; 2: School of Geographic Sciences, East China Normal University, Shanghai 200241, China; 3: Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities, Ministry of Natural Resources, Shanghai 200241, China ID: 154
Dragon 6 Poster Presentation OCEAN & COASTAL ZONES: 95373 - Marine dynamic environment monitoring combining conventional and new generation radar altimeters over the coastal and polar ocean Inversion Of The 3-D Subsurface Temperature Field In The Northwest Pacific Based On The Spatial-temporal Fusion Model 1: Qingdao Innovation and Development Base of Harbin Engineering University，Qingdao, China.; 2: First Insititute of Oceanography, Ministry of Natural Resources, Qingdao, China. ID: 257
Dragon 6 Poster Presentation SUSTAINABLE AGRICULTURE & WATER RESOURCES: 95177 - Monitoring Crop Growth with Big Earth Observation Satellite Data in Support of Agricultural Management To What Extent Can EnMAP Data Differentiate Between 6 Similar Winter Cereal Species? A Case Study in Wallonia (Belgium) Université catholique de Louvain, Belgium, Belgium ID: 228
Dragon 6 Poster Presentation SUSTAINABLE AGRICULTURE & WATER RESOURCES: 95177 - Monitoring Crop Growth with Big Earth Observation Satellite Data in Support of Agricultural Management A Case Study on Estimation of Soil NOx Emissions in East China Based on Satellite Data 1: Chinese Academy of Meteorological Sciences; 2: National Satellite Meteorological Center; 3: Beijing Normal University ID: 232
Dragon 6 Poster Presentation SUSTAINABLE AGRICULTURE & WATER RESOURCES: 95177 - Monitoring Crop Growth with Big Earth Observation Satellite Data in Support of Agricultural Management Study on Retrieving the Crop Cultivation Practices Using High Resolution Satellite Data 1: Taiyuan University of Technology; 2: Beijing Normal University; 3: Beidahuang Group Jiansanjiang Branch Office, Agriculture development department ID: 143
Dragon 6 Poster Presentation SUSTAINABLE AGRICULTURE & WATER RESOURCES: 95338 - Quantifying the impacts of compound hot-dry extremes on agriculture and water resources from Earth observation (AgriWATER) Synergies of Cosmic-Ray Neutron Sensors and Remote Sensing Products for Spatial Mapping of Soil Moisture and Vegetation 1: Helmholtz Centre for Environmental Research (UFZ), Germany, Germany; 2: University of Potsdam, Germany ID: 184
Dragon 6 Poster Presentation SUSTAINABLE AGRICULTURE & WATER RESOURCES: 95338 - Quantifying the impacts of compound hot-dry extremes on agriculture and water resources from Earth observation (AgriWATER) Soil Hot Extremes Are Increasing Faster Than Air Hot Extremes: Possible Implications for Agriculture 1: Helmholtz Centre for Environmental Research (UFZ), Germany, Germany; 2: Remote Sensing Centre for Earth System Research, Leipzig University, Germany. ID: 229
Dragon 6 Poster Presentation SUSTAINABLE AGRICULTURE & WATER RESOURCES: 95424 - Satellite data applicability and accuracy at different spatiotemporal scales for sustainable agricultural water management (SAA4Water) Assimilation Of Satellite LST and SSM Retrievals For Irrigation Detection And Evapotranspiration Impact Over Heterogeneous Agricultural Areas Politecnico di Milano, Italy, Italy ID: 221
Dragon 6 Poster Presentation SUSTAINABLE AGRICULTURE & WATER RESOURCES: 95441 - Synergy of Thermal and Solar-induced Fluorescence Remote Sensing for Crop Water Stress Monitoring over North China Plain, Iberian Peninsula, and Luxembourg Evapotranspiration Estimation by the Combined Use of an Energy-Water Balance Model and Geostationary Land Surface Temperature Data 1: Politecnico di Milano, Milan, Italy; 2: Luxembourg Institute of Science and Technology, Belvaux, Luxembourg ID: 218
Dragon 6 Poster Presentation SUSTAINABLE AGRICULTURE & WATER RESOURCES: 95424 - Satellite data applicability and accuracy at different spatiotemporal scales for sustainable agricultural water management (SAA4Water) Assessing Irrigation Performance Using Remote Sensing Data and the Budyko Hypothesis: A Case Study in Northwest China 1: State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences; 2: University of Chinese Academy of Sciences; 3: Faculty of Civil Engineering and Geosciences, Delft University of Technology; 4: Institute of Tibetan Plateau Research, Chinese Academy of Sciences ID: 180
Dragon 6 Poster Presentation SUSTAINABLE AGRICULTURE & WATER RESOURCES: 95441 - Synergy of Thermal and Solar-induced Fluorescence Remote Sensing for Crop Water Stress Monitoring over North China Plain, Iberian Peninsula, and Luxembourg Enhancing Soybean LAI Estimation Across Scales with Transfer Learning and Multi-Source Data China Agricultural University, China. ID: 266
Dragon 6 Poster Presentation SUSTAINABLE AGRICULTURE & WATER RESOURCES: 95441 - Synergy of Thermal and Solar-induced Fluorescence Remote Sensing for Crop Water Stress Monitoring over North China Plain, Iberian Peninsula, and Luxembourg Normalized Solar-Induced Chlorophyll Fluorescence (SIFn) Reveals Drought-Induced Vegetation Physiological-Structural Changes in the North China Plain 1: College of Land Science and Technology, China Agricultural University, Beijing 100083, China; 2: Department of Environment Research and Innovation, Luxembourg Institute of Science and Technology, Belvaux, Luxembourg ID: 267
Dragon 6 Poster Presentation SUSTAINABLE AGRICULTURE & WATER RESOURCES: 95441 - Synergy of Thermal and Solar-induced Fluorescence Remote Sensing for Crop Water Stress Monitoring over North China Plain, Iberian Peninsula, and Luxembourg Integrating UAV-Derived Enhanced Disease Detection Index and Texture Features for Monitoring Southern Corn Rust Severity College of Land Science and Technology, China Agricultural University, Beijing 100083, China ID: 208
Dragon 6 Poster Presentation SUSTAINABLE AGRICULTURE & WATER RESOURCES: 95424 - Satellite data applicability and accuracy at different spatiotemporal scales for sustainable agricultural water management (SAA4Water) Monitoring Actual Irrigated Cropland Area in the Hexi Corridor Based on Blue/Green Evapotranspiration using Remote Sensing Observations 1: China University of Mining and Technology, Xuzhou 221116, China; 2: State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China; 3: University of Chinese Academy of Sciences, Beijing 100049, China ID: 227
Dragon 6 Poster Presentation SOLID EARTH & DISASTER REDUCTION: 95348 - Collaborative detection of surface deformations associated to natural phenomena and anthropogenic activities with multi-source remote sensing data Debris Flow Risk Assessment via Numerical Simulation: A Case Study in Northeast China 1: School of Resources and Civil Engineering, Northeastern University, Shenyang, 110819, PR China; 2: Istituto Nazionale di Geofisica e Vulcanologia, Rome, 00143, Italy; 3: Schmid College of Science and Technology, Chapman University, Orange, CA, 92866, USA ID: 131
Dragon 6 Poster Presentation SOLID EARTH & DISASTER REDUCTION: 95355 - REmote SEnsing for Landslide Monitoring and impact Assessment on Infrastructure (RESELMAIN) Land Subsidence and Its Potential Impact on Infrastructure in the Caudete-Villena Aquifer Area (SE Spain): Insights from EGMS Data 1: University of Alicante, Alicante, Spain; 2: Capital Normal University, Beijng, China ID: 225
Dragon 6 Poster Presentation SOLID EARTH & DISASTER REDUCTION: 95348 - Collaborative detection of surface deformations associated to natural phenomena and anthropogenic activities with multi-source remote sensing data Phase Gradient Rate-Constrained Minimum Cost Flow Method for Accurate Phase Unwrapping in Large Gradient Deformation Fields 1: School of Resources and Civil Engineering, Northeastern University, Shenyang, 110819, China; 2: Istituto Nazionale di Geofisica e Vulcanologia, Rome, 00143, Italy ID: 166
Dragon 6 Poster Presentation SOLID EARTH & DISASTER REDUCTION: 95436 - Dynamic deformation monitoring and health diagnosis of infrastructures and surrounding geologic environments with multi-source earth observation data Effective Quantification of Bridge Temperature Effects in PS-InSAR Time-Series Deformation Analysis 1: State Key Laboratory of Intelligent Geotechnics and Tunnelling (Shenzhen University); 2: Shenzhen Technology Institute of Urban Public Safety, and Key Laboratory of Urban Safety Risk Monitoring and Early Warning ID: 226
Dragon 6 Poster Presentation SOLID EARTH & DISASTER REDUCTION: 95473 - Multi-Sensor InSAR Railway Structure Monitoring: Towards Generating Product-Level Deformation Results Railway Deformation Zone Mapping via InSAR Phase Gradient Stacking: Chinese Section of China-Laos Railway 1: Yunnan University, China, China, People's Republic of; 2: Delft University of Technology，The Netherlands ID: 187
Dragon 6 Poster Presentation SOLID EARTH & DISASTER REDUCTION: 95473 - Multi-Sensor InSAR Railway Structure Monitoring: Towards Generating Product-Level Deformation Results Taking INSAR Point Scatterers to the Max: Optimizing the Interpretation of a Single Scatterer in Terms of Information 1: Delft University of Technology, The Netherlands, Netherlands, The; 2: Yunnan Normal University (YNU), China; 3: University Roma Tre, Italy ID: 146
Dragon 6 Poster Presentation SOLID EARTH & DISASTER REDUCTION: 95436 - Dynamic deformation monitoring and health diagnosis of infrastructures and surrounding geologic environments with multi-source earth observation data A Two-Stage Method Using SegFormer and Convolutional Networks for Noise-Resistant InSAR Phase Unwrapping 1: Southeast University(SEU), Nanjing, China; 2: Sun Yat-sen University(SYSU), Zhuhai, China ID: 164
Dragon 6 Poster Presentation SOLID EARTH & DISASTER REDUCTION: 95436 - Dynamic deformation monitoring and health diagnosis of infrastructures and surrounding geologic environments with multi-source earth observation data Temporal and Spatial Subsidence Monitoring and Prediction of Reclamation Airports Based on Time-series InSAR Measurements Shenzhen University (SZU), China, China, People's Republic of ID: 188
Dragon 6 Poster Presentation SOLID EARTH & DISASTER REDUCTION: 95358 - Geophysical and geodetics retrieval from SAR data stacks over natural scenarios Calibrating Mono-and Bi-static TomoSAR Data from the TomoSense Campaign 1: National Space Science Center, CAS, China; 2: Politecnico di Milano, Italy; 3: Wuhan University (WHU), China ID: 156
Dragon 6 Poster Presentation SOLID EARTH & DISASTER REDUCTION: 95436 - Dynamic deformation monitoring and health diagnosis of infrastructures and surrounding geologic environments with multi-source earth observation data Forest Fire Scar Detection From Time-series Sentinel-1 Observations Based On Coherence Prediction Using Recurrent Neural Network 1: Wuhan University, Wuhan 430072, China; 2: The 9th System Design Department of China Areospace Science Industry Corporation, Wuhan 430000, China; ID: 126
Dragon 6 Poster Presentation SOLID EARTH & DISASTER REDUCTION: 95436 - Dynamic deformation monitoring and health diagnosis of infrastructures and surrounding geologic environments with multi-source earth observation data Research Progress of Phase Linking Method in Time-Series InSAR Data Processing 1: School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China; 2: State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China; 3: School of Geoscience and Info-Physics, Central South University, Changsha 410083, China; 4: State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China ID: 195
Dragon 6 Poster Presentation OCEAN & COASTAL ZONES: 95316 - PeRcEiving natural and anthropogenic Disaster conditions and assessing risks In Coastal regions Through artificial intelligence, traditional and nOvel synthetic aperture RADAR technologies (PREDICTOR) Patterns analysis of ground deformation field in Huaibei Plain based on time-series InSAR and Independent Component Analysis 1: Key Laboratory of Geographical Information Science, Ministry of Education, East China Normal University, Shanghai 200241, China; 2: School of Geographic Sciences, East China Normal University, Shanghai 200241, China; 3: Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities, Ministry of Natural Resources, Shanghai 200241, China; 4: Key Laboratory of Land Subsidence Monitoring and Prevention, Ministry of Land and Resources, Shanghai 200072, China; 5: Shanghai Institute of Geological Survey, Shanghai 200072, China; 6: Fuyang Geo-environmental Monitoring Center, Fuyang, Anhui 236000, China ID: 202
Dragon 6 Poster Presentation CALIBRATION & VALIDATION: 95376 - Calibration, validation and data assessment for Chinese and European spaceborne high spectral resolution lidars: ACDL/DQ-1, ATLID/EarthCARE and ALADIN/Aeolus PANGEA Observatory for the Validation of Aeolus, EarthCARE, and DQ1 Aerosol Products 1: National Observatory of Athens, Greece; 2: Department of Physics, Aristotle University of Thessaloniki, Greece; 3: Department of Physics, Section of Environmental Physics-Meteorology, National and Kapodistrian University of Athens; 4: Meteorological Institute, Ludwig Maximilian Universität München (LMU), 80539 Munich, Germany ID: 230
Dragon 6 Poster Presentation CALIBRATION & VALIDATION: 95376 - Calibration, validation and data assessment for Chinese and European spaceborne high spectral resolution lidars: ACDL/DQ-1, ATLID/EarthCARE and ALADIN/Aeolus Cross-validation For Chinese And European Spaceborne High Spectral Resolution Lidars: ACDL/DQ-1, ATLID/EarthCARE And ALADIN/Aeolus 1: College of Marine Technology, Ocean University of China, Qingdao, China; 2: Leibniz Institute for Tropospheric Research, Leipzig, Germany ID: 239
Dragon 6 Poster Presentation CALIBRATION & VALIDATION: 95376 - Calibration, validation and data assessment for Chinese and European spaceborne high spectral resolution lidars: ACDL/DQ-1, ATLID/EarthCARE and ALADIN/Aeolus First Validation Results for EarthCARE‘s ATLID using Ground-based Lidar Observations 1: Leibniz Institute for Tropospheric Research, Leipzig, Germany; 2: Instituto Nacional de Meteorologia e Geografia, Mindelo, Cabo Verde; 3: Physical Technical Institute, National Academy of Sciences of Tajikistan, Dushanbe, Tajikistan; 4: Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands ID: 247
Dragon 6 Poster Presentation CALIBRATION & VALIDATION: 95376 - Calibration, validation and data assessment for Chinese and European spaceborne high spectral resolution lidars: ACDL/DQ-1, ATLID/EarthCARE and ALADIN/Aeolus Aerosol and Cloud Classification and Comparison Based on Chinese and European Spaceborne High Spectral Resolution Lidar: ACDL/DQ-1 and ATLID/EarthCARE 1: Ocean University of China, China, China, People's Republic of; 2: Leibniz Institute for Tropospheric Research, Germany; 3: Chinese Academy of Sciences - Shanghai Institute of Optics and Fine Mechanics, China ID: 245
Dragon 6 Poster Presentation CLIMATE CHANGE: 95387 - Multi-Sensor Remote Sensing for Cultural Heritage Climate Change Resilience Sar-Optical Image Matching Enhancement Using Conditional Generative Adversarial Networks LIESMARS, Wuhan University (WHU), China ID: 145
Dragon 6 Poster Presentation CLIMATE CHANGE: 95445 - Integrating Multisource Data for Precision, Fine-Scale Monitoring of Climate Induced Floods and Droughts Memory-based Multimodal Change Detection 1: Shanghai Jiao Tong University (SJTU), China, People's Republic of China; 2: Tongji University, China, People's Republic of China ID: 161
Dragon 6 Poster Presentation CLIMATE CHANGE: 95387 - Multi-Sensor Remote Sensing for Cultural Heritage Climate Change Resilience Experiment, Simulation And Verification Of Looting Holes With SAR Wuhan University, State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan 430079, China ID: 115
Dragon 6 Poster Presentation CLIMATE CHANGE: 95387 - Multi-Sensor Remote Sensing for Cultural Heritage Climate Change Resilience Robust SAR Coherence Estimation for Low-Coherence Regions Using Machine Learning State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing (LIESMARS), Wuhan University, China ID: 254
Dragon 6 Poster Presentation ATMOSPHERE: 95395 - Towards understanding aerosol cloud radiation interactions using 3D satellite observations (TACT) Study on FY4B/AGRI Aerosol Retrieval Algorithm and Uncertainty Analysis Based on hourly surface reflectance model National Satellite Meteorological Center ID: 196
Dragon 6 Poster Presentation ATMOSPHERE: 95381 - Air Quality Monitoring and Analysis in Populous areas in China (AQMAP) Improvements In The TROPOMI Ozone Profile Retrieval To Enhance Tropospheric Ozone Detection Sensitivity 1: KNMI - Royal Netherlands Meterological Institute, de Bilt, The Netherlands; 2: Delf University of Technology, Delft, The Netherlands; 3: Royal Belgian Institute for Space Aeronomy (BIRA-IASB), Brussels, Belgium ID: 134
Dragon 6 Poster Presentation ATMOSPHERE: 95395 - Towards understanding aerosol cloud radiation interactions using 3D satellite observations (TACT) Retrieval of Aerosol Optical Depth and Vertical Extinction Profiles from OCO-2 Oxygen A-band Observations over China 1: Chengdu University of Information Technology (CUIT), Chengdu, China; 2: Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands; 3: Institute of Atmospheric Physics,Chinese Academy of Sciences (IAP), Beijing, China ID: 235
Dragon 6 Poster Presentation ATMOSPHERE: 95396 - Monitoring Greenhouse Gases from Space Observing And Quantifying Wetland Emissions Using Drone AirCore Finnish Meteorological Institute, Finland ID: 121
Dragon 6 Poster Presentation ATMOSPHERE: 95400 - Assessing Effect of Greenhouse Gases Emission Reduction with Variable Renewable Energy Implementation in Marine Climate Islands Active and Passive Collaborative Remote Sensing CO2 based on DQ-1/DQ-2 China Meteorological Administration-National Satellite Meteorological Center, China, China, People's Republic of ID: 167
Dragon 6 Poster Presentation CRYOSPHERE & HYDROLOGY: 95461 - Seasonal changes of glaciers in High Mountain Asia 2016- 2026 and their fate until 2100 Investigating Seasonal Velocity Variations Of Debris-covered And Debris-free Glaciers In High Mountain Asia 1: Graz University of Technology, Austria, Austria; 2: National Central University, Taiwan; 3: Aerospace Information Research Institute, Chinese Academy of Sciences ID: 112
Dragon 6 Poster Presentation CRYOSPHERE & HYDROLOGY: 95462 - Inverting mountain meteorology from cryospheric remote sensing and ecohydrological modelling (IMMERSE) Machine Learning-Based Methods for Precipitation Phase Discrimination in China Department of Earth System Science,Ministry of Education Key Laboratory for Earth System Modeling,Global Change Research Insititute,Tsinghua University,Beijing 100084,China ID: 158
Dragon 6 Poster Presentation CRYOSPHERE & HYDROLOGY: 95462 - Inverting mountain meteorology from cryospheric remote sensing and ecohydrological modelling (IMMERSE) An Improved Method for Estimating Glacier Velocity: A Case Study of the Yanong Glacier in the southeastern Tibetan Plateau 1: State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences; 2: University of Chinese Academy of Sciences; 3: Faculty of Civil Engineering and Geosciences, Delft University of Technology ID: 238
Dragon 6 Poster Presentation CRYOSPHERE & HYDROLOGY: 95462 - Inverting mountain meteorology from cryospheric remote sensing and ecohydrological modelling (IMMERSE) Modeling the water balance of selected glacierized catchments in High Mountain Asia from 1970 to 2100 1: Institute for Science and Technology of Austria, Austria; 2: Institute of Environmental Engineering, ETH Zurich, Switzerland; 3: Swiss Federal Research Institute WSL, Birmensdorf, Switzerland; 4: University of Zurich, Department of Geography, Glaciology and Geomorphodynamics Group, Zürich, Switzerland; 5: Department of Geosciences, University of Fribourg, Switzerland; 6: Institut des Géosciences de l’Environnement, Université Grenoble-Alpes, CNRS, IRD, Grenoble, France; 7: Department of Atmospheric and Cryospheric Sciences, University of Innsbruck, Innsbruck, Austria; 8: Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokohama, Japan; 9: Graduate School of Environmental Studies, Nagoya University, Nagoya, Japan ID: 191
Dragon 6 Poster Presentation DATA ANALYSIS: 95341 - Exploring Earth’s magnetic field using Swarm and MSS-1 data Estimating Curie Point Depth Beneath The Tibetan Plateau And Surrounding Regions Using Bayesian Inference And Wavelet Analysis China University of Geosciences, China, China, People's Republic of ID: 201
Dragon 6 Poster Presentation DATA ANALYSIS: 95341 - Exploring Earth’s magnetic field using Swarm and MSS-1 data Compiled Aeromagnetic Anomaly Data Improving The Spatial Resolution Of The Lithospheric Magnetic Field Model Of China And Surroundings 1: Hubei Subsurface Multi-scale Imaging Key Laboratory, School of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China; 2: Key Laboratory of Geological Survey and Evaluation of Ministry of Education, China University of Geosciences, Wuhan 430074, China; 3: State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan 430074, China ID: 185
Dragon 6 Poster Presentation DATA ANALYSIS: 95374 - STAI4CH: Spatio-Temporal AI-based EO data mining to assess anthropogenic impacts and sustainability measures on Cultural Heritage along ancient and modern waterways A Clustering-based Pre-training Method for Remote Sensing Semantic Segmentation Wuhan University (WHU), China, China, People's Republic of ID: 200
Dragon 6 Poster Presentation DATA ANALYSIS: 95452 - FUCEO: Exploring synergies between Chinese and European EO mission using data fusion Validation of Reference Forward Model Simulation of Atmospheric Radiative Transfer by Comparison with IASI Satellite Observations Atmospheric, Oceanic, and Planetary Physics, University of Oxford, Clarendon Laboratory, Parks Road, Oxford, OX1 3PU, UK ID: 222
Dragon 6 Poster Presentation URBANISATION & ENVIRONMENT: 95393 - Use of Earth Observation for Urban Security: addressing heat risk and geological hazards Groundwater Level Modeling with Physics-Informed Neural Networks: A Novel Physics-Constrained and Transferable Approach 1: Capital Normal University, China, China, People's Republic of; 2: Key Laboratory of Mechanism, Prevention and Mitigation of Land Subsidence, China, People's Republic of; 3: Beijing Laboratory of Water Resources Security, China, People's Republic of; 4: Cangzhou Groundwater and Land Subsidence National Observation and Research Station, China, People's Republic of ID: 240
Dragon 6 Poster Presentation URBANISATION & ENVIRONMENT: 95235 - EO-AI4ResilientCities: Enhancing Urban Resilience with Earth Observation and AI-Powered Insights Generating Daily High-Resolution Urban Land Surface Temperature Maps using a Conditional Diffusion Model with SDGSat-1 and VIIRS Data KTH Royal Institute of Technology, Sweden, Sweden ID: 183
Dragon 6 Poster Presentation OCEAN & COASTAL ZONES: 95316 - PeRcEiving natural and anthropogenic Disaster conditions and assessing risks In Coastal regions Through artificial intelligence, traditional and nOvel synthetic aperture RADAR technologies (PREDICTOR) Building Change Detection Based on a Dual-Stream Network with Adaptive Feature Fusion and Cross-Attention Guidance Using SAR and Optical Remote Sensing Imagery 1: Key Laboratory of Geographical Information Science, Ministry of Education, East China Normal University, Shanghai 200241, China; 2: School of Geographic Sciences, East China Normal University, Shanghai 200241, China; 3: Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities, Ministry of Natural Resources, Shanghai 200241, China ID: 172
Dragon 6 Poster Presentation ECOSYSTEMS: 95458 - Microwave and Optical Remote Sensing of Salt Lakes from Space A Classification Method For The Sediment Thickness Of Salt Ponds Based On Sentinel-2 Data 1: College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, P.R. China; 2: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, P.R. China ID: 216
Dragon 6 Poster Presentation ECOSYSTEMS: 95458 - Microwave and Optical Remote Sensing of Salt Lakes from Space Time-series Detection Of Water Depth In Darbeson Salt Lake Based On Joint Multispectral Data And LiDAR Data College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, P.R. China ID: 155
Dragon 6 Poster Presentation ECOSYSTEMS: 95469 - Towards forest quality assessment using remote sensing Progressively Synergizing Individual-tree and Area‑based Approaches across Generalized Low-Density Point Cloud for Regional Biomass Estimation Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China; Key Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration, Beijing 100091, China ID: 252
Dragon 6 Poster Presentation ECOSYSTEMS: 95469 - Towards forest quality assessment using remote sensing Monitoring Growth of Regenerated Saplings Through Stratifying Forest Overstory Using Airborne LiDAR Data Chinese Academy of Forestry - Institute of Forest Resource Information Techniques (CAF-IFRIT), China, People's Republic of ID: 265
Dragon 6 Poster Presentation ECOSYSTEMS: 95458 - Microwave and Optical Remote Sensing of Salt Lakes from Space Optical Remote Sensing of Salt Lakes from Space: Case study of Aigues Mortes salted field (South France) University of Strasbourg, France, France ID: 171
Dragon 6 Poster Presentation SOLID EARTH & DISASTER REDUCTION: 95358 - Geophysical and geodetics retrieval from SAR data stacks over natural scenarios Forest Parameters Inversion Based on Airborne P-Band SAR Tomography: A Case Study of the Saihanba Forest 1: State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Luoyu Road 129, Wuhan, 430079, China; 2: School of Remote Sensing and Information Engineering, Wuhan University, Luoyu Road 129, Wuhan, 430079, China; 3: Hubei Luojia Laboratory, Luoyu Road 129, Wuhan, 430079, China; 4: National Space Science Center, Chinese Academy Sciences, Beijing, 100190, China ID: 147
Dragon 6 Poster Presentation SOLID EARTH & DISASTER REDUCTION: 95358 - Geophysical and geodetics retrieval from SAR data stacks over natural scenarios Forest Above Ground Biomass Inversion Based on Baseline-Optimized Phase Histogram Technique 1: State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China; 2: School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China; 3: Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy |
| 16:00 - 18:00 | DRAGON 6 NON-ADJUDICATED POSTERS Room: Conference Center (Back Hall) |
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Poster no adjudication
ID: 237 Dragon 6 Poster Presentation CRYOSPHERE & HYDROLOGY: 95462 - Inverting mountain meteorology from cryospheric remote sensing and ecohydrological modelling (IMMERSE) Quantifying Glacier Mass Change in the Western Pamirs (2003–2024): Insights from High-Resolution DEM Time Series 1: Institute of Science and Technology Austria, Klosterneuburg, Austria; 2: Glaciology and Geomorphodynamics Group, Institute of Geography, University of Zurich, Zurich, Switzerland; 3: Mountain Hydrology and Mass Movements, Swiss Federal Institute for Snow, Forest, and Landscape Research, Birmensdorf, Switzerland; 4: Department of Geosciences, University of Fribourg, Fribourg, Switzerland; 5: Institute of Environmental Engineering, ETH Zurich, Zurich, Switzerland Poster no adjudication
ID: 236 Dragon 6 Poster Presentation CRYOSPHERE & HYDROLOGY: 95462 - Inverting mountain meteorology from cryospheric remote sensing and ecohydrological modelling (IMMERSE) Recent Response Of Pamir Glaciers To Intensified Global Warming 1: Institute for Science and Technology of Austria, Austria, Austria; 2: Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Birmensdorf, Switzerland; 3: Institute of Geography, University of Zurich, Zurich, Switzerland; 4: Department of Geosciences, University of Fribourg, Switzerland; 5: Institute of Environmental Engineering & Laboratory of Hydraulics, Hydrology and Glaciology, ETH Zurich, Zurich, Switzerland; 6: Geophysical Institute, University of Alaska Fairbanks, Fairbanks, USA; 7: Institut des Géosciences de l’Environnement, Université Grenoble-Alpes, CNRS, IRD, Grenoble, France Poster no adjudication
ID: 110 Dragon 6 Poster Presentation CLIMATE CHANGE: 95387 - Multi-Sensor Remote Sensing for Cultural Heritage Climate Change Resilience A Sample Spatiotemporal Generation Method Using Optical-SAR Fusion For Crop Classification In Cloudy And Rainy Regions State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, China Poster no adjudication
ID: 264 Dragon 6 Poster Presentation CLIMATE CHANGE: 95357 - DTE-CLIMATE: Digital Twin Earth Approach for Monitoring and Modelling Climate Change in Water, Energy and Carbon Cycles in Eurasia Generation of a Continuous Bias-Free Land Surface Reanalysis Dataset Over EURO-CORDEX for 2000 to 2022 1: Forschungszentrum Jülich GmbH, Jülich, Germany; 2: European Centre for Medium-Range Weather Forecasts (ECMWF), Bonn, Germany Poster no adjudication
ID: 162 Dragon 6 Poster Presentation CLIMATE CHANGE: 95357 - DTE-CLIMATE: Digital Twin Earth Approach for Monitoring and Modelling Climate Change in Water, Energy and Carbon Cycles in Eurasia Mapping flood susceptibility using Random Forest Exploiting Satellite Observations and Geomorphic Features University of Naples Federico II, Italy, Italy Poster no adjudication
ID: 129 Dragon 6 Poster Presentation OCEAN & COASTAL ZONES: 95373 - Marine dynamic environment monitoring combining conventional and new generation radar altimeters over the coastal and polar ocean Along‐Track Marine Geoid Resolution Enhancement With SWOT 1: Northeastern University, China, China, People's Republic of; 2: Technical University of Denmark, Denmark; 3: National Satellite Ocean Application Service, China Poster no adjudication
ID: 246 Dragon 6 Poster Presentation OCEAN & COASTAL ZONES: 95258 - marinE added-ValuE pRoducts Generated by Remotely sEnsed microwavE measuremeNts (EVERGREEN) Performances in Observations of Tropical Cyclones from Simulations of Pencil- and Fan-beam Rotating Scatterometer Systems 1: National Space Science Center, CAS-NSSC, China, China, People's Republic of; 2: School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences Poster no adjudication
ID: 120 Dragon 6 Poster Presentation ATMOSPHERE: 95395 - Towards understanding aerosol cloud radiation interactions using 3D satellite observations (TACT) Cloud Top Height from a Lidar Perspective: Cirrus, Multi-Layer Clouds, and other Complex Scenarios 1: National Satellite Meteorological Centre (NSMC), China; 2: Shanghai Institute of Satellite Engineering, China Poster no adjudication
ID: 116 Dragon 6 Poster Presentation ATMOSPHERE: 95395 - Towards understanding aerosol cloud radiation interactions using 3D satellite observations (TACT) Exploring Aerosol Optical Properties: Validation of EarthCARE ATLID Products with Observations and CAMS Forecast Royal Netherlands Meteorological Institute (KNMI), The Netherlands, Netherlands, The Poster no adjudication
ID: 203 Dragon 6 Poster Presentation SOLID EARTH & DISASTER REDUCTION: 95407 - Electromagnetism Anomaly Detection and Deformation Monitoring by Generative and Predictive AI Approaches Reconstructing Incomplete Geomagnetic Data from SWARM Satellites using GANs University of Ulster, UK, Ireland Poster no adjudication
ID: 142 Dragon 6 Poster Presentation SOLID EARTH & DISASTER REDUCTION: 95436 - Dynamic deformation monitoring and health diagnosis of infrastructures and surrounding geologic environments with multi-source earth observation data Multi-Temporal InSAR Analysis of Karst-Induced Subsidence and Its Effects on Structural Stability at Gharbanyiat Area, Alexandria, Egypt State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing Poster no adjudication
ID: 144 Dragon 6 Poster Presentation SOLID EARTH & DISASTER REDUCTION: 95436 - Dynamic deformation monitoring and health diagnosis of infrastructures and surrounding geologic environments with multi-source earth observation data PSSformer: A Persistent Scatters Selection Method for SAR Interferometry based on Temporal-Spatial Vision Transformers 1: CommSensLab, Dept. of Signal Theory and Communications, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain; 2: School of Electrical Engineering, Naval University of Engineering, Wuhan, China; 3: Department of Information Engineering, Harbin Institute of Technology, Harbin, China; 4: School of Geosciences and Info-Physics, Central South University, Changsha, China; 5: School of Environment Science and Spatial Informatics, China University of Mining and Technology, China; 6: Department of Geography, University of Manchester, Manchester, United Kingdom Poster no adjudication
ID: 190 Dragon 6 Poster Presentation SOLID EARTH & DISASTER REDUCTION: 95358 - Geophysical and geodetics retrieval from SAR data stacks over natural scenarios Biomass Interferometric Stack Phase Calibration: Methods and Simulated Results Politecnico di Milano, Italy Poster no adjudication
ID: 139 Dragon 6 Poster Presentation SOLID EARTH & DISASTER REDUCTION: 95369 - Synergizing Space Technologies for Comprehensive Earth Surface Monitoring: Detecting Multi-Types of Deformation and Optimizing Water Usage in Agriculture Monitoring Selective Logging Impacts in Tropical Forests Using Sentinel-1 SAR: A Case Study in the Jamari Forest 1: Universidade de Trás-os-Montes e Alto Douro (UTAD), Portugal, Portugal; 2: Institute for Systems and Computer Engineering, Technology and Science Porto, Portugal; 3: Centre for the Research and Technology of Agro-Environmental and Biological Sciences, Vila Real, Portugal; 4: University of Presov (UNIPO) Presov, Slovakia; 5: insar.sk Ltd, Presov, Slovakia; 6: Fundacao Coa Parque, Rua Museu Vila Nova De Foz Coa, Portugal Poster no adjudication
ID: 140 Dragon 6 Poster Presentation SOLID EARTH & DISASTER REDUCTION: 95369 - Synergizing Space Technologies for Comprehensive Earth Surface Monitoring: Detecting Multi-Types of Deformation and Optimizing Water Usage in Agriculture Enhancing Subsidence Detection in Low-Coherence Zones: QPS-InSAR Analysis of Maceió’s Salt Mine Collapse 1: Universidade de Trás-os-Montes e Alto Douro (UTAD), Portugal, Portugal; 2: Institute for Systems and Computer Engineering, Technology and Science (INESC-TEC), 4200-465 Porto, Portugal; 3: insar.sk Ltd., Konstantinova 3, 08001 Presov, Slovakia; 4: Department of Finance, Accounting and Mathematical Methods, Faculty of Management and Business, University of Presov, Konstantinova 16, 08001 Presov, Slovakia; 5: RASER Limited, Unit 609, 9Wing Hong Street, Lai Chi Kok, Hong Kong, China Poster no adjudication
ID: 136 Dragon 6 Poster Presentation SOLID EARTH & DISASTER REDUCTION: 95369 - Synergizing Space Technologies for Comprehensive Earth Surface Monitoring: Detecting Multi-Types of Deformation and Optimizing Water Usage in Agriculture Soil Moisture Estimation in Olive Orchards Using Sentinel-1 SAR and Neural Networks 1: Universidade de Trás-os-Montes e Alto Douro (UTAD), Portugal, Portugal; 2: Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal; 3: Agronomy Department, School of Agrarian and Veterinary Sciences, University of Trás-os-Montes e Alto Douro, Vila Real, Portugal; 4: Centre for the Research and Technology of Agro-Environmental and Biological Sciences, Vila Real, Portugal; 5: Department of Environmental Economy and Management, Faculty of Management and Business, University of Presov (UNIPO), Presov, Slovakia; 6: insar.sk Ltd, Presov, Slovakia; 7: Department of Forestry Sciences and Landscape Architecture, University of Trás-os-Montes e Alto Douro, Vila Real, Portugal; 8: Fundacao Coa Parque, Rua Museu, Vila Nova De Foz Coa, Portugal Poster no adjudication
ID: 269 Dragon 6 Poster Presentation SOLID EARTH & DISASTER REDUCTION: 95473 - Multi-Sensor InSAR Railway Structure Monitoring: Towards Generating Product-Level Deformation Results Dynamic InSAR Time Series Analysis for Near Real-time Monitoring of the BuiltEnvironment Delft University of Technology, The Netherlands Poster no adjudication
ID: 128 Dragon 6 Poster Presentation DATA ANALYSIS: 95341 - Exploring Earth’s magnetic field using Swarm and MSS-1 data Ionospheric Response to the May 2024 Geomagnetic Storm within the SAA Region: Analysis with MSS-1, COSMIC-2, and ground-based GNSS Data Tongji University, China, China, People's Republic of Poster no adjudication
ID: 117 Dragon 6 Poster Presentation DATA ANALYSIS: 95374 - STAI4CH: Spatio-Temporal AI-based EO data mining to assess anthropogenic impacts and sustainability measures on Cultural Heritage along ancient and modern waterways Mapping Ancient Irrigation Systems in Southern Iraq using High-Resolution Satellite Imagery 1: School of Engineering, Newcastle University, NE1 7RU, Newcastle upon Tyne, UK; 2: School of History, Classics and Archaeology, Newcastle University, NE1 7RU, Newcastle upon Tyne, UK; 3: Department of Archaeology, University of Al-Qadisiyah, Iraq; 4: United Nations Satellite Centre (UNOSAT), Geneva, Switzerland; 5: Italian Space Agency (ASI), 00133, Rome, Italy; 6: Institute of Atmospheric Sciences and Climate (ISAC), National Research Council (CNR), 00133, Rome, Italy Poster no adjudication
ID: 258 Dragon 6 Poster Presentation DATA ANALYSIS: 95497 - Research and application of deep learning for the improvement of wave remote sensing from Multi-missions On the Combined Assimiation of Swath SWH and Directional Wave Spectra in the Model MFWAM : Update with Sentinel-1C Data 1: Meteo France-CNRM, France, France; 2: Sun-Yat Sen University Poster no adjudication
ID: 194 Dragon 6 Poster Presentation SUSTAINABLE AGRICULTURE & WATER RESOURCES: 95338 - Quantifying the impacts of compound hot-dry extremes on agriculture and water resources from Earth observation (AgriWATER) Ground Heat Flux Estimates From Machine Learning Techniques 1: Department of Remote Sensing, Helmholtz Centre for Environmental Research (UFZ), Leipzig, Germany; 2: Institute for Earth System Science, Univsersitat Leipzig, Leipzig, Germany Poster no adjudication
ID: 260 Dragon 6 Poster Presentation SUSTAINABLE AGRICULTURE & WATER RESOURCES: 95250 - Optical and Thermal Copernicus-Chinese EO Data for Analyzing the Driving Factors, Impacting on Food Security and Quality ISAC and TES Algorithms Optimization for Topsoil Characterization in View of the LSTM Mission 1: Institute Of Methodologies For Environmental analysis (Imaa)- Italian National Research Council (CNR), C. Da S.Loja, 85050 Tito Scalo, Italy; 2: Department Of Agriculture And Forestry Sciences (Dafne), University Of Tuscia, Via San Camillo De Lellis, 01100 Viterbo, Italy; 3: Scuola Ingegneria Aereospaziale (SIA), University Of Rome "La Sapienza", Via Eudossiana 18, 00184 Roma, Italy; 4: Shandong University of Science and Technology (SDUST), P.R.China; 5: Aerospace Information Research Institute, Chinese Academy of Sciences, No.9 Dengzhuang South Road, Haidian District,Beijing 100094, P.R.China Poster no adjudication
ID: 177 Dragon 6 Poster Presentation ECOSYSTEMS: 95469 - Towards forest quality assessment using remote sensing Forest Type Classification Based on Combined Sentinel -1/-2 Data Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Switzerland, Switzerland Poster no adjudication
ID: 212 Dragon 6 Poster Presentation URBANISATION & ENVIRONMENT: 95235 - EO-AI4ResilientCities: Enhancing Urban Resilience with Earth Observation and AI-Powered Insights Urban Functional Zone Change Detection via Multi-Source Temporal Fusion of Street View and Remote Sensing Imagery Nanjing University, China, China, People's Republic of Poster no adjudication
ID: 217 Dragon 6 Poster Presentation URBANISATION & ENVIRONMENT: 95235 - EO-AI4ResilientCities: Enhancing Urban Resilience with Earth Observation and AI-Powered Insights Dynamic Monitoring of Long-term, Fine-grained Ecological Vulnerability in the Hohhot-Baotou-Ordos-Yulin Urban Agglomeration Nanjing University, China Poster no adjudication
ID: 193 Dragon 6 Poster Presentation URBANISATION & ENVIRONMENT: 95235 - EO-AI4ResilientCities: Enhancing Urban Resilience with Earth Observation and AI-Powered Insights Deep Learning Advancements for Urban Change Detection Using Multitemporal SAR Images from Heterogeneous Sensors 1: University of Pavia, Pavia, Italy; 2: University of Sannio, Benevento, Italy; 3: AIRCAS, China |
| 18:30 | Gala Dinner Room: 2nd floor Zhonghua Restaurant |
