Conference Agenda

Earth’s climate is influenced profoundly by anthropogenic greenhouse gas (GHG) emissions. Climate forecasts are needed so that we can prepare, mitigate and adapt to the changing climate. The forecasts require accurate information about the sources and sinks of natural and anthropogenic GHGs, in particular, carbon dioxide (CO₂) and methane (CH₄). Presently, GHG concentrations are observed using ground-based and satellite observations. While local sources can be observed using accurate in-situ measurements, remote sensing methods from satellites are needed to obtain global and regional coverage, which are important for climate research. A number of studies have indicated that uncertainties in regional CO₂ and CH₄ surface fluxes can be significantly reduced with global, unbiased, precise space-borne measurements which can lead to a more complete understanding of the CO₂ and CH₄ budget. The accuracy requirements of satellite remote sensing of atmospheric composition and, in particular, GHGs are challenging. Validation of measurements and their uncertainties and continuous development of retrieval methods are important for the success of satellite remote sensing systems, especially for GHGs where error requirements are demanding. Furthermore, sophisticated data assimilation methods and atmospheric transport models are needed to link atmospheric concentration to the underlying surface fluxes.

In this project we use a combination of ground-based measurements of CO₂ and CH₄ and data from current satellite observations (TanSat/-2, GOSAT/-2, OCO-2/-3, TROPOMI, MicroCarb and CO2M) to validate and evaluate satellite retrievals with retrieval intercomparisons, to assess them against model calculations and to ingest them into inverse methods to assess surface flux estimates of CO₂ and CH₄. The main geographic focus is China but we will also take advantage of the global view provided by the space-borne data.

We will show validation results from the TCCON network and Chinese ground-based measurements complemented with AirCore profile observations of GHGs at Sodankylä. We will also present the outcome of an intercomparison of two independent retrieval algorithms available at University of Leicester and IAP that have been applied to TanSat and will be applied to TanSat-2 and CO2M. Furthermore, we discuss CO2 and CH4 surface flux results obtained with the GEOS-Chem atmospheric transport model combined with Ensemble Kalman Filter. We will conclude the presentation with an outlook towards future satellite missions for greenhouse gases. Finally, we will compare the inversed fluxes with the Emission inventories generated through surface activity measurements, which are reported annually or bi-annually and evaluated periodically through a “Global Stocktake”

The project aims to enhance dynamic National Forest Inventories (NFIs) by integrating close-range and remote sensing technologies. The project primarily focuses on temperate and sub-tropical forests in China and the United Kingdom, with the goal of accurately quantifying carbon stocks and predicting future trends in forested areas. The project involves constructing high-precision carbon inventories using spaceborne LiDAR and radar technologies. Time-series satellite data, such as Sentinel-1 and 2, Gaofen, and TECIS, will be used to monitor forest cover changes, pests, diseases, and succession stages. The study includes field measurements, drones, and Airborne Laser Scanning (ALS) for data collection in the first year. In the second year, the project will use time-series satellite imagery to model phenological cycles and monitor growth trends. In the third year, the team will link baseline and projected inventories with spaceborne sensors to reduce uncertainty in future trend estimates. Deliverables include papers on forest inventory methodology, forest resilience applications, satellite imagery analysis, and validation of spaceborne LiDAR and radar. The project aims to provide a robust framework for dynamic NFIs, addressing questions about model-data integration and other ecosystem services. The team consists of experts from Chinese and UK institutions with extensive experience in time-series optical satellite data analysis, LiDAR applications, radiative transfer modeling, and remote sensing. The project aligns with the Dragon 6 cooperation proposal call document and focuses on the applications of remote sensing for change detection, yield and forest carbon sequestration, and forest health monitoring.

Land degradation is a major global environmental problem. By reducing the depth, fertility and organic matter content of soil, and the density, structure, species composition and productivity of vegetation, it has significant economic, social and environmental consequences. Grasslands account for the majority of dry areas and are very vulnerable to degradation: about half of all grasslands have been degraded to some extent.

Comprehensive monitoring of grassland degradation is necessary to understand the extent of degradation and the factors that influence it. Restoring degraded grasslands to achieve 'land degradation neutrality' is a complex process, and monitoring it enables restoration measures to be made more effective.

Ground surveys provide high-precision degradation information for small areas, but applying them to larger regions is time-consuming and inefficient. Remote sensing technology has the advantages of wide coverage, short revisit periods, and low costs, but also has limitations, e.g. some indicators are inaccessible to remote sensing while others are difficult to quantify. Numerous remote sensing studies of grassland degradation have focused on indicators such as coverage, height, aboveground biomass, grass yield, and net primary productivity. But they lack the spatial and temporal resolution to inform effective grassland protection and restoration; mainly characterize common vegetation parameters; have limited coverage of both vegetation and soil degradation, and often neglect human drivers.

Yet recent improvements in the temporal, spatial, and spectral resolutions of sensors now make reliable long-term and large-scale grassland degradation monitoring practical. To exploit these, this project will focus Essential Grassland Degradation Variables (EGDV) mapping, namely Photosynthetic Vegetation (PV) and Non-Photosynthetic Vegetation (NPV) coverage, shrubification, rodent hole density and grazing intensity. It will study a typical Chinese grassland using multiple Chinese and European satellite datasets.

For PV and NPV coverage, we will integrate spectral decomposition and polarization decomposition techniques to propose a new mixed pixel decomposition model based on Sentinel-2/GF-6 and Sentinel-1 datasets. For grassland shrubification, we will segment Very High Resolution data to get samples, then develop a machine learning model with multiple optical and SAR inputs. For rodent hole density, we will utilize unmanned aerial vehicle data to acquire ground samples, then employ machine learning and deep learning algorithms to detect rodent holes. For grazing intensity, we will fuse Sentinel-2, SDGSAT-1, and GF-6 WFV data to generate dense and consistent time series satellite images and use the proportional difference between grasslands with different grazing intensities to map utilization intensity.

The project aims to comprehensively assess the quality and health of forest ecosystems using advanced remote sensing technology based on multiple source remote sensing data. This project will implement innovative methods for forest type classification, develop assessment techniques for forest growth to evaluate carbon sequestration, detect forest gap and estimate the light regime, analyze forest vertical structure complexity, quantify and characterize forest edge, and finally assess forest quality. To achieve this, a wide range of light detection and ranging (LiDAR) and optical data from Chinese satellite (Gaofen-1/2/6/7, Terrestrial Ecosystem Carbon Inventory Satellite) and European satellite data (Sentinel-1, Sentinel-2), ESA BIOMASS mission, high resolution aerial images and airborne laser scanning (ALS) data from Swisstopo will be used. New methods including machine learning, data fusion, forest growth models and deep learning will be developed and implemented. In particular, a novel workflow that integrates diverse remote sensing data, enabling robust, cost-efficient, and highly automated forest type classification will be established. The stereo images and calculated stand level LiDAR biomass index (LBI) will be used to model forest growth assessment. Height, spectral, and textural data extracted from satellite images will be integrated to automatically detect forest canopy gaps. And sky-view fraction from synthetic hemispheric images will be calculated by using ALS point clouds and digital terrain models. Quantification and characterization of forest edge effects will be assessed by the proposed edge metrics demonstrated the ability to capture ecological characteristics. Forest quality assessment based on forest structure and growth thematic dataset by advanced machine learning and artificial intelligence algorithms will be applied for large-scale forest quality mapping. The project will contribute to provide a thorough assessment of forest quality using remote sensing technology.

There are two participates from China (Chinese Academy of Forestry) and Europe (Swiss Federal Institute for Forest, Snow and Landscape Research WSL) of this project. This project will be conducted in typical temperate forest in China (Genhe in Inner Mongolia) and Europe (Canton Aargau in Switzerland). Some funding sources in this joint project includes China Gaofen Forest Application Project, Regulation mechanisms of ecosystem resilience and adaptive forest management (eco2adapt). They will support the effective progressing of the Dragon 6 cooperation and the participating of the annual symposium.