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Master of Science in Geographic Information Science
Research
My current research focuses on improving the Global Crop Water Model (GCWM) by incorporating dynamic land-use information and high-resolution climate data to more accurately simulate agricultural water use under changing environmental conditions. I update the model with multi-year crop-specific irrigated and rainfed area data from the MIRCA-OS dataset, allowing GCWM to capture interannual variability in cropping patterns and irrigation demand. In addition, I use the ECIRA dataset to provide daily, high-resolution climate inputs to better represent drought conditions and their influence on green and blue water use. This work involves calibrating and validating soil-water-balance processes, analyzing drought-driven irrigation dynamics, and comparing model behavior under static versus dynamic inputs. Through these efforts, I aim to enhance the model’s realism in representing irrigation practices and to improve large-scale assessments of water demand, crop productivity, and agricultural vulnerability.My previous research centered on evaluating crop production potential and climatic suitability using the Python Package of Agro-Ecological Zones (PyAEZ). I quantified yield potential, growing season length, and agro-climatic constraints for major crops such as maize and soybean across the North East Asia by integrating long-term climate observations, CMIP6 projections, soil and land-use data, and extensive field measurements. This work examined how shifts in temperature, hydrothermal conditions, and water–energy balances shape spatial patterns of crop productivity and suitability. By coupling PyAEZ simulations with remote sensing indicators, I provided a detailed assessment of historical and future changes in crop production potential, contributing to the understanding of climate-driven impacts on agroecosystems.
Expertise:
- GIS and Remote sensing
- Programming of Python
- Simulating the dynamic of agricultural ecosystem by crop growth model
- Remote sensing inversion for vegetation properties
Publications:
Bao, L., Li, X., Yu, J., Li, G., Chang, X., Yu, L., & Li, Y. (2024). Forecasting spring maize yield using vegetation indices and crop phenology metrics from UAV observations. Food and Energy Security, 13(1), e505. DOI: 10.2139/ssrn.4364604Chang, X., Yu, L., Li, G., Li, X., & Bao, L. (2023). Wetland vegetation cover changes and its response to
climate changes across Heilongjiang-Amur River Basin. Frontiers in Plant Science, 14, 1169898.DOI: 10.3389/fpls.2023.1169898
Wang, C., Ren, Z., Chang, X., Wang, G., Hong, X., Dong, Y., ... & Wang, W. (2023). Understanding the cooling capacity and its potential drivers in urban forests at the single tree and cluster scales. Sustainable Cities and Society, 93, 104531.DOI: 10.1016/j.scs.2023.104531
Education:
Sep. 2018 – Jun. 2022Bachelor of Science in Geographic Information Science
Yanbian University, Hunchun, China
Sep. 2022 – June 2025
Master of Science in Geographic Information Science
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China