Hello!
I am a Ph.D. candidate in the Department of Geographical Sciences at the University of Maryland (UMD), where I am working with Dr. Xiaopeng Song and Dr. Matthew Hansen at the Global Land Analysis and Discovery (GLAD) team.
My research interests focus on agricultural sustainability monitoring using satellite-based and statistical approaches, including large-scale satellite data processing, near real-time crop type mapping and crop yield forecasting, long-term crop extent and phenology monitoring, and assessment of climate impacts on crop production and agricultural adaptation. Currently, I am working on national-scale high-resolution crop type mapping by combining machine learning, sample-based field surveys, and multi-source satellite data.
Before joining UMD in 2022, I studied for two years in the Geosciences Doctoral program at Texas Tech University. Prior to my Ph.D. journeys abroad, I worked for three years as a senior software engineer at NetEase, Inc. in China. I obtained my M.S. in Cartography and Geography Information System in 2017, from the State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, where I worked with Dr. Xiaoling Chen on environmental remote sensing, especially in water environments. I received my B.S. in Geographic Information System in 2014 from the School of Resource and Environmental Sciences (SRES), Wuhan University.
Updates
Dec. 2025: I presented my poster about oil and gas infrastructure monitoring from remote sensing in AUG 2025 in New Orleans, LA, USA. “Estimating Long-Term Fractional Cover of Oil and Gas Well Pads in the Permian Basin Using Landsat Time Series (2000–2023)”
Dec. 2025: I presented my poster about within-season crop mapping in AUG 2025 in New Orleans, LA, USA. “Progressive Early-Season Crop Mapping over the Contiguous United States Using Sentinel-2 Time Series and Historical Ground Data”
Dec. 2025: I successfully defended my Ph.D. dissertation! “Advancing National-scale High-resolution Crop Mapping Using Remote Sensing”.
Nov. 2025: I gave an oral presentation on “Quantifying the Benefits of 10-m Crop Mapping for Industrial Agriculture Over the United States” at the MAD-AAG 2025 conference in Arnold, MD, USA.
Oct. 2025: I am honored to receive the Jacob K. Goldhaber Travel Grant from the Graduate School at the University of Maryland.
Feb. 2025: My co-authored paper about machine learnning in crop mapping is published online on Remote Sensing of Environment. “Accounting for spatial variability with geo-aware random forest: A case study for US major crop mapping”
Dec. 2024: My poster about 10-m crop mapping over the United States was presented in AUG 2024 in Washington, D.C., USA. “Advancing 10-m Crop Mapping Using All Sentinel-2 Observations over the Contiguous United States”
Oct. 2024: I successfully defended my dissertation proposal.
May. 2024: I am honored to receive the Excellence in Graduate Research Award (2nd place) for outstanding performance as a graduate student in the Department of Geographical Sciences at UMD.
Dec. 2023: I presented my poster about continental-scal high-resolution crop mapping over the North America in AUG 2023 in San Franscico, CA, USA. “10-m Crop Mapping Using Satellite Data, Field Survey and Machine Learning over North America”
May. 2023: My first-author paper about smallholder crop mapping is published online on Remote Sensing of Environment. “Development of a 10-m resolution maize and soybean map over China: Matching satellite-based crop classification with sample-based area estimation”
Dec. 2022: I gave an oral presentation about 10-m crop mapping in China in AUG 2022 in Chicago, IL, USA. “Development of a 10 m Resolution Maize and Soybean Map Over China”
Nov. 2022: My co-authored paper about long-terrm soybean yield modeling is published online on Agricultural and Forest Meteorology. “Annual 30 m soybean yield mapping in Brazil using long-term satellite observations, climate data and machine learning”