Professor Tianxiang Yue works at Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences. The research group led by him has innovatively developed the high accuracy and speed method (HASM) for surface modelling and HASM quantum machine learning algorithms to give solutions to error, multi-resolution, and slow-computational-speed problems which long troubled surface modelling. HASM has been successfully applied to simulating surfaces of elevation, soil properties, nature (including biodiversity and ecosystems), ecosystem services (including food provision and carbon stocks), and drivers of changes (including carbon dioxide concentrations, climate change, land use/cover change, and population distribution) as well as COVID-19 transmission dynamics. In all these applications, HASM produced more accurate results than other methods.Professor Na Zhao is mainly engaged in the research of high-accuracy surface modelling of eco-environmental factors, especially in the spatial interpolation, downscaling, and multisource data fusion of climate elements, proposing a series of innovative methods. She has developed a high-precision mixed interpolation method for climate elements based on spatial stationarity and non-stationarity, effectively addressing the challenges brought by spatial heterogeneity. She has also established a variable-coefficient downscaling method that considers spatial scale effects, solving the core key issues affecting downscaling effects and providing new high-precision methodological support for meeting the needs of local scales and fine simulation under future scenarios. From the perspective of image restoration, she has originated a high-dimensional data fusion method, pioneering new approaches for data fusion and providing core foundational guarantees for accurately revealing the mechanisms of geographical environmental changes under the interaction of multiple factors.Professor Wenjiao Shi has developed a high-accuracy surface modelling (HASM) for regional agricultural parameters, such as soil attributes, which integrates macro-geological information and micro-sampling data to achieve accurate predictions that align more closely with the spatial distribution of parameters. This innovative approach provides a novel methodology for accurately characterizing regional agricultural parameters in terms of their spatial representation.Professor Zemeng Fan has mainly focused on research fields of ecological modelling and system simulation, including surface modelling of land cover changes and scenarios, trend and scenario of biodiversity change, ecosystem service trade-off/collaborative quantification, and their response to climate change. He has improved the Holdridge life zone model and established a surface modelling method for simulating the types and patterns of terrestrial ecosystem change in multi-scale, innovatively developed a surface modelling method of land cover change driven by the coupling of natural and human factors, a spatiotemporal recognition model of ecotone, and a surface modelling biodiversity method.
