The structural health of manmade infrastructures such as buildings, highway, railways, bridges, tunnels, subways, airports, dams, and reservoirs is of great significance for the sustainable development of human society and economy. Infrastructure stability is not only determined by its internal structural degradation, but also affected by potential geological hazards in the surrounding environment as well as anthropogenic disturbances. Therefore, it is an essential task to perform infrastructure health surveillance to ensure its long-term operation safety, for which surface deformation is regarded as an effective indicator of structural health status and thus should be monitored regularly. As a cutting-edge Earth observation (EO) technology, the interferometric synthetic aperture radar (InSAR) technology can obtain surface deformation through interferometric processing of two radar images acquired in repeat-pass mode. The Sino-European Dragon Program provides an opportunity for the joint exploitation of multi-source EO data to facilitate deformation monitoring and health diagnosis of infrastructures and surrounding geologic environments. However, it is still a challenge to comprehensively use the abundant EO data to achieve high-precision intelligent monitoring of infrastructure deformation and reliable health diagnosis. In this project, we plan to take InSAR technology as the main means to develop accurate health status monitoring tools for infrastructure. For this purpose, the monitoring will include not only the infrastructure itself but also the surrounding geological environment as well as modeling the deformation rules, all with the help of multi-source EO data. The term multi-source consists of multiple aspects including diversity of observation geometry, frequency or bands, polarization, image resolution, and ancillary data acquired in-field. The primary outcome will be more reliable sequential dynamic monitoring and infrastructure health degradation diagnosis. This project will facilitate win-win cooperation between both Chinese and European teams, promote satellite utilization and data sharing, and provide technical training and international exchange for young scientists in multi-source EO-based surface deformation monitoring. It is believed that the completion of this project will boost technical development for the engineering application of EO-based infrastructure health monitoring technology, which can ensure the safety of people’s lives and property and identify the potential geological hazards that pose significant threats to human lives.

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.

In this Dragon-6 project Northeastern University (China) and National Institute of Geophysics and Volcanology (INGV, Italy ) research teams jointly plan to monitor the deformation associated to industrial activities and volcano dynamics in Northeast China. These objectives are the natural follow-up of the successful research activities of the previous DARAGON-4/5 projects. In this proposal, a new study site will be considered, the Songliao Basin, where ground subsidence caused by both natural phenomena and anthropogenic activities are occurring. In fact, the Songliao Basin, consisting in a Mesozoic-Cenozoic continental basin characterized by relevant oil and gas extraction activities, is a very interesting site to be deeply investigated by means of remote sensed data. The main goal for the selected sites is to identify and characterize the processes responsible for the ground deformation (volcano dynamics, fluid migration, slope mass movement and human activities). The results will be relevant for the assessment of the hazard connected to the different natural and/or anthropogenic phenomena, providing the base for risk prevention and early warning. The Earth Observation data will be integrated with ground based data, to provide a comprehensive view of the relationships between surface deformation and sub-surface dynamics. The main research objectives are to detect, quantify, and study the temporal evolution of ground deformation measured through remote sensing data, and characterize the processes and the hazards mainly due to: anthropogenic activities at Fushun and Songliao Basin (mining and oil/gas extraction); volcano dynamics at the Changbaishan volcano; The areas exposed to natural and anthropogenic ground deformation will be defined, providing the fundamental information for the prevention and damage mitigation actions to be planned from Local Authorities. Probably the most useful methodology to achieve such objectives is represented by the joint analysis of multi-source EO and in situ data: InSAR time-series (MT-InSAR), VNIR optical data series, seismic data, geochemical data, oil/gas extraction data, and the subsequent modeling. Moreover, the retrieved deformation patterns will be validated with leveling and GNSS data (if available), and through cross-comparison between ascending and descending MT-InSAR results. Tailored for diverse requirements, we could monitor the phenomena using SAR images acquired from sensors operating in different frequency bands (e.g. C-band and L-band). Based on the detected surface deformation and the possible correlation between volcanic and seismic activity, we will model the degassing and magmatic activity in the volcanic areas along with exploring the correlation between deformation and amount of oil/gas extraction or fluid pumping in deep wells. Finally, the proposed research plans to carry out training for both the European and Chinese team researchers, and experience exchange for the Young Scientists granted by the project’s fundings.

The United Nations (UN) Sustainable Development Goals (SDGs) highlights the crucial need for boosting resilience in at-risk populations and agricultural systems to withstand extreme climate-related events, specifically landslides and land subsidence. These disasters carry grave implications, leading to numerous deaths and significant economic damages every year. Mountainous regions, particularly susceptible to landslides, experience sudden and disastrous events that present considerable hurdles in mitigation measures. Furthermore, landslides serve as additional hazards in continent-wide earthquakes, intensifying the severity of the disasters. On the other hand, land subsidence is rapidly developing into a serious geohazard, with forecasts predicting it will affect millions of people globally by 2040, impacting the global population substantially. Against this backdrop, the joint European Space Agency (ESA) and the Chinese Ministry of Science and Technology (MOST) Dragon-5 initiative cooperation project ReSeLMAIN project (ID: 95355, REmote SEnsing for Landslide Monitoring and impact Assessment on INfrastructure) aims to use remote sensing techniques, like Synthetic Aperture Radar Interferometry (InSAR), to identify and map landslides and land subsidence. It will monitor these geohazards and assess their impact on infrastructure. The main objective is to combine various remote sensing data and techniques. This integrated approach will enhance understanding and management of landslides and land subsidence, and will help mitigate disasters and make infrastructure more resilient. Four key components make up the methodology: First, InSAR automatically will be used to find and map geohazards. Secondly, different sensors like LiDAR, SAR, and optical remote sensing will be combined. Thirdly, data from in-situ and remote sensing will be merged. Finally, early warning systems will be set up for proactive risk control. Consequently, this comprehensive approach will contribute significantly to improving landslide and subsidence risk management, facilitating proactive measures for disaster resilience and infrastructure protection.

It is presented in this paper the main scientific objectives and some progresses of ESA-MOST China Dragon 6 Cooperation Program “Synergistic Monitoring and Prediction of Ocean Dynamic Environment from Multi-satellite Data (ID. 95315)” including: (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.

Atlantic Water (AW) transported to the Arctic Ocean through the Nordic Seas plays a major role in the global climate system. “Atlantification”, a commonly used term for the increasing influence of AW in the Arctic Ocean,has been found as one of the main climate-change related factors associated with the changes in the Arctic marine ecosystem. For example, Atlantification has been linked to the poleward expansion of temperate phytoplankton and boreal species, and the northward retreat of the Arctic marine ecosystem. Quantifying the role of ocean eddies has been categorized as an important “missing puzzle piece” yet to be studied in detail to better understand Atlantification. The Lofoten Basin (LB) which is the most eddy active region in the Nordic Seas, is a natural laborotory to study the role of the eddies on Atlantification. Located along the advective path of AW in the Nordic Seas, a main feature of the LB circulation is the spinning up off eddies from the Norwegian Atlantic Current, which transports heat to the basin interior and thus mediates the gradual cooling of the slope current on its way toward the Arctic. The overall objective of the project LoWa (Lofoten Basin eddies and its impact on Atlantic Water heat transport towards the Arctic) is to quantify the impact of ocean eddies on the heat transport across the LB affecting Atlantification. To address the overall objective we will use 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.

Marine disasters, such as typhoons, algae and phytoplankton blooms pose a serious threat to coastal areas, aquaculture and maritime transportation. As global climate change intensifies, the impact of these marine disasters is becoming increasingly severe. Marine dynamics is the foundation of various marine disaster studies, and the combination of remote sensing monitoring and ocean dynamics is used here to analyze marine disasters. Through the implementation of the Dragon 5 project 59310, progress has been made in remote sensing data validation, improvement of monitoring technology and enhancement of applications. We used the China’s Ocean Satellites (HY-1 C/D satellites) and the China-France Oceanography Satellite (CFOSAT), combined with Sentinel satellites data from the European Space Agency to carry out three aspects of work: (1)    We carried out remote sensing monitoring research and data application for typical marine ecological disasters in China's offshore areas (such as the green tide of Enteromorpha prolifera and oil spills). (2)    We compared and analyzed the wave spectrum and significant wave height observation results of CFOSAT with buoys, SAR satellite observations and numerical model results, and propose an innovative comparison method. (3)    Based on the geometric relationship between shadow length and satellite observation angle information, we got the iceberg freeboard and its changes in Prydz Bay. With the approved Dragon Plan Phase 6 project (ID 95549), we plan to further advance the previous Dragon 5 project 59310. More precisely, the new goal is to use multiple satellites such as CFOSAT, FY series, Sentinel series, etc. to jointly monitor marine environmental disasters and better understand relevant marine environmental dynamics. This study will be summarized as follows: (1)    To perform consistency verification on the wind and wave fields provided by CFOSAT and multiple satellites for other missions, such as SWOT (Surface Water and Ocean Topography), HY series, and Sentinel series. (2)    To combine multiple satellite data to monitor and track the development trend of marine environmental disasters such as algae blooms. Meanwhile, multi-scale dynamic analysis will be introduced to characterize the related Wind, Wave and Current Interactions (W2CI) and better understand their potential mechanisms. (3)    To extract ocean surface currents with development of machine learning based optical flow. This technology will be validated using Lagrangian velocities extracted from monitored algal blooms and provide a high spatial resolution velocity field. In the Dragon Plan Phase 6 project, we have strengthened our team members from both China and France. The French PI is Dr. François G. Schmitt. His main research area is turbulence, multi-scale data analysis and modelling, and physics-biology coupling in the ocean. The French members are from two units, CNRS-LOG and CNRS-LATMOS. The Chinese PI is Professor Ying Xu. His main research area is ocean satellite processing and applications including waves, sea level rising and marine disasters. The Chinese members are from five units, NSOAS, XMU, NUIST, GDOU and UCAS The professional field of Chinese and French members cover ocean satellite remote sensing and ocean dynamics. The professional background of personnel and previous joint work experience will be very beneficial for the implementation of the Dragon 6 project 95549.

Remote sensing technologies hold great promise for archaeology and heritage conservation. In particular, satellite imagery provides an efficient means of surveying large areas for the detection and monitoring of archaeological sites and features. In this Dragon-6 project, a collaboration between the University of Bern (Switzerland), the National Research Council (Italy), Wuhan University (China), the Aerospace Information Research Institute of the Chinese Academy of Sciences (China), and Guizhou University (China), we aim to use multi-sensor remote sensing to address critical issues in archaeology and heritage conservation. Our project focuses on test sites in China, like the Great Wall, as well as several other sites, e.g., in Italy and Kazakhstan. Mainly, we consider the following: Cultural Heritage Protection, including looting detection and deformation monitoring Detection of targets of archaeological interest using AI-based approaches and sub-surface sensing Contextual landscape analysis For looting detection, we build upon our work from Dragon-5 to identify the wide range of looting activities, from organized industrial-scale looting to small-scale illicit excavations by individuals. Although large-scale looting can be identified in remote sensing images, detecting small looting holes (1-2 meters in size) is more challenging and requires automated methods. Deformation monitoring using Persistent Scatterer Interferometry (PSInSAR) and SqueeSAR techniques can provide valuable information for heritage conservation. PSInSAR is widely used but has limitations in cultural heritage sites with few persistent scatterers. SqueeSAR overcomes this by analyzing statistically homogeneously distributed scatterers (DS). We will refine the DS phase estimation and selection to better handle deformation gradients and identify the finer-scale deformation patterns relevant to heritage sites. Seasonal deformation patterns over a multi-year time series can also serve as indicators of subsurface archaeological features, acting as crop marks. Extremely small differences in deformation associated with buried structures require advanced time series analysis. Using phase-preserving DS approaches, we aim to process large areas over long time spans to identify time-series outliers indicative of subsurface features. Machine learning will be applied to detect both generic outliers and patterns associated with specific types of buried archaeological remains (e.g., walls, roads, and tombs) based on training data. To detect archaeological sites and features exposed at the surface, we will develop AI-based methods for automated site and feature detection using optical and SAR data. Landscape archaeological approaches will also be tested to understand the sites within their broader geomorphological and environmental contexts. Multi-sensor fusion will be explored to synergistically combine these different threads and leverage the complementary strengths of optical, SAR, multispectral, and other remote-sensing data for more comprehensive archaeological mapping and heritage monitoring. Field work will be conducted to validate remote-sensing-based detections and to iteratively refine our methods based on ground truth data. This collaboration, building upon the unique strengths and expertise of each team, will develop innovative Earth observation methods to help protect cultural heritage and enable new archaeological discoveries.

Amidst the global population explosion, food security has emerged as a pressing issue in the 21st century. In this context, global warming and extended drought periods are of major concern – with agricultural droughts increasingly challenging sustainable food supply. Timely and accurate monitoring of crop water stress stands as a promising way forward for mitigating the detrimental impacts on crop yield caused by droughts. 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 over North China Plain, Iberian Peninsula, and Luxembourg. The key scientific objectives of this project are: 1) advancing evapotranspiration partitioning into soil evaporation and plant transpiration using TIR observations based on an analytical model, 2) developing a method for estimating plant transpiration using SIF observations, 3) constructing downscaling algorithms for land surface temperature and SIF data to fine spatial resolutions, and 4) enhancing crop water stress monitoring capability based on plant transpiration and SIF estimates. The project will advance our understanding and modelling skills in plant water and carbon cycles by synthesizing the expertise in TIR remote sensing (European colleagues) and SIF remote sensing (Chinese colleagues), thereby strengthening the scientific exchanges between the Sino-European teams. This collaboration is expected to take full advantage of the European and Chinese satellite data to achieve the project’s scientific goals and develop a crop water stress monitoring system. The dissemination of the project results will be performed through submissions to scientific journals and ESA and NRSCC joint reports, along with presentations at Dragon Annual Symposia, thereby promoting the visibility of the expected achievements. Additionally, the young scientists involved in the project will benefit from a unique portfolio of highly innovative training, fostering their professional knowledge growth and transferable skill development that is crucial for successful career advancement, including research management, knowledge utilisation, and scientific writing.

Wetlands, known as the "kidneys of the Earth," are formed by the interaction between water and land, and characterized by seasonal and annual waterlogged. Meanwhile, they are unique and resilient ecosystem with biodiversity. Wetlands and water are the most important resources for human and other living things. The harmony of human-water relations is the core issue of global sustainable development. The contradiction between human and water is more prominent in high-quality development stage of China. However, water is not scarce but uneven and pollution under the condition of global climate change. We face several challenges for resilient wetlands and harmony of human-water relations, such as, the critical points of resilient wetland at drought and flooding conditions, simulation and estimation the non-point source pollution load of studying watershed with the influence of global climate changes and human activities, harmony of human-water relations in watershed and so on. Advanced satellite remote sensing technology provide timely and accurate scientific data, technical support, effective valuation and management system for dynamic monitoring and comprehensive management of resilient wetlands and human-water relationships in watersheds, including spatiotemporal changes of water resources, environmental changes in watershed, and the impact of human activities, dynamic monitoring of water and drought disasters, watershed ecological restoration, and non-point source pollution estimation in watershed and so on. However, raw remote sensing data is not enough to support the evaluation of resilient wetlands and human-water relationship in watersheds. Effective wetland and watershed ecological indicators and their the dynamic monitoring are prominent for the coordination of resilient wetlands and human-water relationships. An effective and reliable wetland and watershed dynamic monitoring and management platform should be developed urgently to monitor resilient wetland and human-water relations based on advanced remote sensing. Poyang Lake watershed is selected as one of the study area in this proposal. This proposal includes three works, such as, 1) evaluation of resilient wetlands at drought and flooding conditions, 2) watershed runoff and non-point pollution modeling under impacts of human activities and climate change and 3) virtual and real collaborative geographic experiments for the human-water relationship.

Salt lake monitoring and information retrieval is important in ecological protection, water resource management, economic development, and tourism culture. Understanding the ecosystem and biodiversity of the salt lake helps to preserve its natural environment. Understanding the hydrological characteristics and water quality of salt lakes is helpful for the scientific management and utilization of water resources. Salt lakes also provide salt resources, which contribute to economic growth, and have unique natural landscape and cultural values, attracting tourists and promoting tourism. Salt lake monitoring from space has great advantages in efficiency and continuity, which is mainly reflected in the following aspects: Firstly, salt lakes are often located in remote areas, which are difficult to reach and monitor directly. Remote sensing satellites can cover a wide surface area and provide high-resolution data, making it easier and more comprehensive to monitor and study salt lakes. Secondly, remote sensing satellites can obtain data over a long period of time, which can help us understand the salt crust changes, water volume and quality status, surrounding vegetation coverage and other information of salt lakes, and provide a scientific basis for natural resources management and ecological protection. In addition, remote sensing satellites can also provide multi-frequency and multi-polarimetric data, which can be used for the exploration of salt resources in related economic and scientific research. Microwave remote sensing is capable of earth observation in full-time and full climate condition, especially polarimetric SAR can reflect the geometrical and bio-physical information of salt lakes, so as to reveal the interaction process of salt crystal precipitation and typical environmental elements, and finally realize the fine and quantitative acquisition of salt lake areas. On the other hand, optical remote sensing has significant advantages in salt lake monitoring as water change in terms of geochemistry components, associated with modification of alga and bacteria’s populations involving change in water colors. By analyzing the multispectral and/or hyperspectral data, the physical and chemical information of the salt lake water body and the surrounding environment can be obtained. The objectives of this project is to observe salt lakes, an ecosystem that evolves from wet to dry, over the year/year meteorological and exploitation conditions, using microwave and optical remote sensing EO data, including: 1) To establish the feature mapping of polarimetric SAR data associated with terrain types, and develop water segmentation and fine classification methods based on the physical scattering mechanism of salt lake; 2) To analyze the optical reflectivity and absorption characteristics of different wavelength bands, and to obtain the physical and chemical information of the salt lake water body and the surrounding environment; 3)To fuse the mapping and classification results of joint microwave and optical satellite remote sensing data. A synergistic use of multi-source EO sensors will be developed in order to accurately monitor the temporal evolution of salt lakes. Typical salt lakes located in China, France and Spain will be investigated for the methodology demonstration. For microwave satellite data, Sentinel 1A&1B, GF3, LT1, as well as ALOS2 and Radarsat2 SLC data in the test sites will be collected. While for optical satellite data, Sentinel 2, GF1, Landsat8&9 multispectral data, as well as optical Jilin-1 and the hyperspectral GF 5 (01A) and if possible EnMap/Prisma data in the test sites will be applied. The deliverables of the project consist in joint reporting during Dragon 6 meetings, joint publications in international conferences and peer-reviewed journals, student exchanges, jointly organized tutorials, and dissemination of processing methods.

For centuries, waterways were strategic infrastructure for communication, transportation, agriculture, mobility and trade of ancient Empires, both in Western and Eastern countries. These waterways have endured up to present and are now embedded in modern cities and landscapes wherein, while they remain water infrastructures, they also represent a cultural heritage (CH) resource and landscape asset to safeguard from modern challenges, e.g. urbanization, development, mass tourism. These processes induce a multitude of physical transformations and changes at surface that, in a relatively short time, can sum up and cause a complete modification of land use and skyline in the buffer area surrounding the waterways and the heritage assets (e.g. historical buildings, green spaces, archaeological areas) distributed along them. Given the high temporal dynamics of these processes and the spatial scale of the waterways, Earth Observation (EO) data are crucial to detect changes at the rate of the transformations, and long and dense time series need to be processed and analysed. While the existing Copernicus and Chinese missions offer the opportunity to exploit a plethora of data to achieve this scientific purpose, Artificial Intelligence (AI) and Deep Learning (DL) methods are required to make the change detection task more cost-effective and replicable, and generate value-added maps. The new Dragon-6 STAI4CH project will follow on from successful achievements by Dragon-5 projects such as SARchaeology in demonstrating the capabilities of optical and SAR data to provide crucial information for archaeological and CH mapping and monitoring applications. Building upon those achievements, STAI4CH will make a step change towards the development and implementation of novel AI and DL methods to assess anthropogenic impacts on CH sites. In particular, STAI4CH will aim to demonstrate that AI and DL can be effectively implemented on medium to very high resolution optical and radar EO imagery acquired by operating ESA, TPM and Chinese missions (e.g. Sentinel-1/2, SDGSAT-1, SkySat, ICEYE), in order to detect recent and current human-induced impacts on CH and, as such, be used to support decision-making for CH preservation. The achievable benefits will be showcased by focusing on waterways and waterscapes of natural or manmade origin, which have been recognized as UNESCO World Heritage Sites (WHS): the Tiber river in Italy, part of the Historic Centre of Rome UNESCO WHS; the Grand Canal WHS in China, the longest artificial river in the world; and the Ahwar (marshes) of Southern Iraq WHS, refuge of biodiversity and the relict landscape of the Mesopotamian cities. The developed algorithms will improve the change detection performance compared to conventional single-source methods, and provide evidence of the direct correlation between detected Land Cover Changes (LCC) and ongoing anthropogenic activities. Through DL models, combinations of optical/radar EO data will be processed to extract LCCs as objective proxies to depict human activities of potential threat for local CH. The spatio-temporal mining method will enable the generation of LCC maps and geodatabases highlighting “preserved” and “at-risk” areas, acting as AI-based prototypes that heritage bodies may use for planning and mitigation purposes. In this respect, the comparison between the three use-cases will allow an evaluation of the commonalities and differences in conservation challenges, and adaptability of AI-based methods to suit CH applications at different spatial and temporal scales, and in different types of environment. The cases will be developed on the Open Geospatial Engine (OGE), which is a cloud computing platform for EO data analysis.

On the 21st of May 2023, the Macau Science Satellite (MSS-1) was successfully launched at the Jiuquan satellite launch centre in the Gansu province of China and is currently operating in orbit in good condition. The MSS-1 satellite is equipped with over ten pieces of state-of-the-art scientific equipment and is designed to monitor Earth’s magnetic field in the medium and low latitudes in both scalar and vector forms with a special focus on the South Atlantic magnetic anomalous region with unprecedented resolutions; simultaneously it measures the solar activities and the interplanetary magnetic field for detecting the variation of the space weather. All scientific payloads have undergone precise calibrations and begun to produce high-quality magnetic observational data that will continue for the next five to ten years. The geomagnetic field, which is generated by Earth’s dynamo action within Earth’s outer core region and interacts and balances with solar wind at approximately eleven Earth’s radii, provides the ultimate protection of Earth’s ecosystem and the sustainability of the civilizations of human beings against the harmful radiations from the deep space. The variation of the geomagnetic field reveals the interactions of a series of physical and chemical processes occurring in Earth’s dynamical systems, i.e., the dynamics of the geodynamo system, the space electromagnetic environments, the mantel/ocean magnetic induction and the crustal magnetic field induction and magnetisation processes. Therefore, the geomagnetic field is a crucial physical quantity to study for understanding the inner workings of Earth’s interior and the surrounding near-Earth environment. Given the highly accurate geomagnetic observations on a global scale obtained by MSS-1 and other sources, e.g., the Swarm mission by ESA, a series of challenging but rewarding research projects may be carried out in the studies of geomagnetism. We plan to divide the big scientific project into a few smaller ones and carry out them in a parallel manner on an international basis within the Dragon6 program. We will focus our studies on 1) the short-term and secular variation of the geodynamo system, 2) the mantel conductivity and induction process 3) the ocean tide & current induced magnetic field 4) the crustal magnetisation and the induced magnetic field and 5) the magnetic field generated by the space currents. A physically constrained geomagnetic model, namely the Macau Magnetic Model (M3), will be created for accurately describing the variation of the geomagnetic field in a broad range of spatiotemporal scales. In comparison with the conventional geomagnetic models, the new approach combines a set of significant physical processes as the constraint for modelling the geomagnetic field. It is expected to be accurate up to O(0.1)～O(1) nT with a spatial resolution of up to a hundred kilometres.