Fangni Lei

Overview

My research focuses on enhancing the quantification of global hydrological parameters and advancing the understanding of land-atmosphere interactions by integrating remote sensing big data, physical models, statistical methods, and artificial intelligence techniques. My work encompasses a range of topics, including soil moisture quantification from microwave remote sensing, land surface water-energy balance modeling, watershed-scale hydrologic modeling, hydrologic data assimilation, flood mapping, and agricultural water management. I am currently an assistant research professor at the Eversource Energy Center and the Department of Civil and Environmental Engineering at the University of Connecticut. I earned my BS, MS, and PhD degrees from Wuhan University and was a visiting student at the Hydrology and Remote Sensing Laboratory of the USDA Agricultural Research Service. Following my PhD, I continued my research as a post-doctoral associate at USDA and then as a research assistant professor at Mississippi State University.

Research Interests

Remote sensing; hydrology; agricultural water management; land-atmosphere interaction; climate change

Education

• BS, Geographic Information Systems, Wuhan University 2011
• MS, Cartography and Geographic Information Systems, Wuhan University 2013
• PhD, Cartography and Geographic Information Engineering, Wuhan University 2016

Selected Publications

• M.M. Nabi, V. Senyurek, F. Lei, M. Kurum, and A.C. Gurbuz, "Quasi-Global Assessment of Deep Learning-Based CYGNSS Soil Moisture Retrieval," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 16, pp. 5629-5644, 2023.
• F. Lei, V. Senyurek, M. Kurum, A. Gurbuz, D. Boyd, W. T. Crow, and R. Moorhead, "Quasi-Global Machine Learning-based Soil Moisture Estimates at High Spatio-Temporal Scalesusing CYGNSS and SMAP Observations," Remote Sensing of Environment, 276, 113041, 2022.
• J. Dong, F. Lei, and W. T. Crow, "Land transpiration-evaporation partitioning errors responsible for modeled summertime warm bias in the central United States," Nature Communications, 13(1): 1-8, 2022.
• F. Chen, F. Lei, K. Knipper, and et al., "Application of the vineyard data assimilation (VIDA) system to vineyard root-zone soil moisture monitoring in the California Central Valley," Irrigation Science, 1-21, 2022.
• F. Lei, W. T. Crow, W. P. Kustas, J. Dong, Y. Yang, K. R. Knipper, M. C. Anderson, F. Gao, C. Nortarnicola, F. Gerifeneder, L. M. McKee, J. G. Alfieri, C.r Hain, and N. Dokoozlian, "Data assimilation of high-resolution thermal and radar remote sensing retrievals for soil moisture monitoring in a drip-irrigated vineyard," Remote Sensing of Environment, 239, 111622, 2020.
• V. Senyurek, F. Lei, D. Boyd, A. C. Gurbuz, M. Kurum, R. Moorhead, ”Evaluations of a Machine Learning-Based CYGNSS Soil Moisture Estimates against SMAP Observations,” Remote Sensing, 12(21), 3503, 2020.
• V. Senyurek, F. Lei, D. Boyd, M. Kurum, A. C. Gurbuz, R. Moorhead, ”Machine learning-based CYGNSS soil moisture estimates over ISMN sites in CONUS,” Remote Sensing, 12(7), 1168, 2020.
• J. Dong, P. A. Dirmeyer, F. Lei, M. C. Anderson, T. R. Holmes, C. Hain, and W. T. Crow, ”Soil Evaporation Stress Determines Soil Moisture-Evapotranspiration Coupling Strength in Land Surface Modeling,” Geophysical Research Letters, 47(21), 2020.
• J. Dong, W. Crow, R. Reichle, Q. Liu, F. Lei, and M. Cosh, "A global assessment of added value in the SMAP Level 4 soil moisture product relative to its baseline land surface model," Geophysical Research Letters, 46, 6604–6613, 2019.
• H. Jiang, H. Shen, X. Li, C. Zeng, H. Liu, and F. Lei, "Extending the SMAP 9-km soil mois-ture product using a spatio-temporal fusion model," Remote Sensing of Environment, 231, pp.111224, 2019.
• F. Lei, W. T. Crow, T. R. H. Holmes, C. Hain, and M. C. Anderson, "Global investigation of soil moisture and latent heat flux coupling strength," Water Resources Research, 54(10): 8196-8215, 2018.
• F. Lei, W. T. Crow, H. Shen, C.-H. Su, T. R. H. Holmes, R. M. Parinussa, and G. Wang, "Assessment of the impact of spatial heterogeneity on microwave satellite soil moisture periodic error," Remote Sensing of Environment, 205: 85-99, 2018.