Profile:

Position: Associate Professor

Address: Room 581, Lihua Building, Qianwei North Campus

Email: gyin@jlu.edu.cn

Research Interests:

(1) GRACE and GNSS applications for hydrology

(2) Multivairate land surface data assimilation 

(3) Machine learning applications in Earth Science

(4) Floods and Droughts

Research Projects:

(1) Estimation and Attribution of terrestrial water storage variation in northeast China via multivariate data assimilation, National Natural Science Foundation of China (NSFC), 2024-2026, PI.

(2) Prediction of climate change impact on terrestrial water storage and hydrological extremes based on data- and model-driven approaches, International Cooperation and Exchange of the NSFC, 2024-2026, PI.

(3) High-resolution Groundwater Storage Monitoring in Northeast China Based on GRACE Gravity Satellite Data, Department of Education of Jilin Province (Project for Outstanding Young Sholars), 2025-2026, PI.

(4) AI-based extraction of errosion gullies in the Northeast black soil region driven by multi-source data, Natural Science Foundation of Jilin Province, 2025-2027, PI.

(5) Deep learning-based high-resolution groundwater storage prediction in Heihe River Basin, Water Cycle Field Station of the Heihe River Basin of China Geological Survey, 2024-2026, PI.

(6) Improving flood and drought prediction using downscaled GRACE terrestrial water storage, Japan Society for the Promotion of Science (JSPS) Grants-in-Aid for Research Activity Start-up, 2021-2023, PI.

(7) Development and evaluation of the land surface hydrological model system in Japan and examination of its application to global models, JAXA, 2020-present, Participant

(8) Hydrology mascon solution from the combined reduction of GRACE inter-satellite ranging data and GPS observations of surface displacement, NASA, 2016-2019, Participant

Honors and Awards

2025: Best Presentation Award, 14th International Symposium on Digital Earth

2024: First Prize for Teaching Competition, College of New Energy and Environment, Jilin University 

2022: Female Researcher Funding Support, The University of Tokyo

2022: Land Surface Modeling Summit Early Career Scientist Travel Grant, Oxford University 

2021: Best Student Poster Prize Supervisor (Mentoring Master student won the Best Student Poster Prize over the engineering college)

2019: David Miller Award, 76th Eastern Snow Conference

2019: Snow Measurement Field School Travel Award, CUAHSI

2019: Future Faculty Fellow, UMD

2017: The Jacob K. Goldhaber Travel Award, UMD

2017; Dean’s Fellowship, UMD, 2016

2015: Best Student Paper Prize, MSSANZ MODSIM Congress

Publications

[12] Yin, G.*, Park, J., & Kei, Y. (2025). Spatial Downscaling of GRACE Terrestrial Water Storage Anomalies for Drought and Flood Potential Assessment. Journal of Hydrology. (Accepted)

[11] Hua, X., Bian, J., & Yin, G.* (2025). Satellite-Based Assessment of Snow Dynamics and Climatic Drivers in the Changbai Mountain Region (2001–2022). Remote Sensing, 17(3), 442. 

[10] Yin, G., Zhang, Y., Cao, Y., Park, J*. (2024). Improving Daily Precipitation Estimates by Merging Satellite and Reanalysis Data in Northeast China. Remote Sensing , 16(24), 4703. 

[9] Yin, G.*, Yoshikane, T., Kaneko, R., Yoshimura, K. (2023). Improving Global Subseasonal to Seasonal Precipitation Forecasts Using a Support Vector Machine-Based Method. Journal of Geophysical Research: Atmospheres, 128(17), e2023JD038929.

[8] Yin, G.*, Yoshikane, T., Yamamoto K., Kubota, T., Yoshimura, K*. (2022). A support vector machine-based method for improving real-time hourly precipitation forecast in Japan. Journal of Hydrology. 612, 128125.

[7] Yin, G., Baik, J., Park, J*. (2022). Comprehensive analysis of GEO-KOMPSAT-2A and FengYun satellite-based precipitation estimates across Northeast Asia. GIScience & Remote Sensing, 59(1), 782-800. https://doi.org/10.1080/15481603.2022.2067970

[6] Yin, G., Park, J*. (2021). The use of triple collocation approach to merge satellite- and model-based terrestrial water storage for flood potential analysis. Journal of Hydrology, 603, 127197. https://doi.org/10.1016/j.jhydrol.2021.127197

[5] Yin, G.*, Forman, B. A., Wang, J. (2021).  Assimilation of ground-based GPS observations of vertical displacement into a land surface model to improve terrestrial water storage estimates. Water Resources Research, 57(2), e2020WR028763. 

[4] Yin, G.*, Forman, B. A., Loomis, B. D., Luthcke, S. B. (2020). Comparison of vertical surface deformation estimates derived from space-based gravimetry, ground-based GPS, and model-based hydrologic loading over snow-dominated watersheds in the United States. Journal of Geophysical Research: Solid Earth, 125, e2020JB019432. https://doi.org/10.1029/2020JB019432

[3] Yin, G.*, Mariethoz, G, Sun, Y., McCabe, M. F. (2017).  A comparison of gap-filling approaches for Landsat 7 satellite data.  International Journal of Remote Sensing, 38(23), 6653-6679. https://doi.org/10.1080/01431161.2017.1363432 

[2] Yin, G.*, McCabe, M. F., Mariethoz, G. (2017). Gap-filling of Landsat 7 imagery using the Direct Sampling method. Remote Sensing, 9(1), 12. https://doi.org/10.3390/rs9010012. 

[1] Yin, G.*, Mariethoz, G., McCabe, M. F. (2015). A Multiple-point Geostatistics Method for filling gaps in Landsat ETM+ SLC-off images. In Weber, T., McPhee, M.J. and Anderssen, R.S. (eds) MODSIM2015, 21st International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, December 2015, pp. 180-186. (invited)

Research Group

[1]Name of Research Group:HERA@JLU

Description of Research Group:Hydrology, Earth Observation, and AI