👋 About Me
Hi, I am a Ph.D. candidate, advised by Dr. Yiqun Xie, in the Department of Geographical Sciences at the University of Maryland, College Park.
My primary research interests focus on remote sensing and knowledge-guided machine learning, with expertise in land surface variable retrieval, data downscaling and multi-source data fusion, all-weather product development, knowledge-guided machine learning, and tackling related scientific challenges.
I have authored over 11 papers in top journals and international AI conferences with a total of
Google Scholar citations , and serve as a reviewer for 14 international journals, including Remote Sensing of Environment (RSE) (IF = 11.4).
🔥 News
- 2025.09: 🎉🎉 Paper accepted by NeurIPS 2025!
- 2025.01: 🎉🎉 RSE paper ranked top 1% most-cited (Environment/Ecology 2021), dataset accessed 46,000+ times worldwide.
- 2024.12: 🎉🎉 Paper published in STOTEN (IF = 8.0)!
- 2024.12: 🎉🎉 Paper published in IEEE MGRS (IF = 16.4)!
- 2024.03: 🎉🎉 Paper published by AAAI 2024!
🔬 Research Interests
- Remote Sensing and Multi-Source Data Fusion
- Knowledge-guided Machine Learning and Quantitative Analysis
- Time Series Prediction and Analysis
🛠 Technical Skills
- Programming & Analysis: Python, MATLAB, IDL; High Performance Computing for Large-Scale Datasets
- Geospatial Tools: Google Earth Engine, ArcPro, ENVI, ERDAS
- Machine Learning & Frameworks: PyTorch, TensorFlow; Data-Driven and Knowledge-Guided Machine Learning
- Specialties: Temporal Prediction, Spatial and Temporal Data Analysis, and Ecosystem Modeling
📝 Peer-review Publications
Journal Paper Conference Paper
Shuo Xu, Jie Cheng.
- Ranked among the top 1% most-cited Environment/Ecology articles published in 2021.
- Method adopted to generate a widely used temperature dataset, accessed 46,000+ times worldwide.
Shuo Xu, Dongdong Wang, Shunlin Liang, Aolin Jia, Ruohan Li, Zhihao Wang, Yuling Liu.
- Publicly released the algorithm on Google Earth Engine to support broader applications.
Shuo Xu, Dongdong Wang, Shunlin Liang, Yuling Liu, Aolin Jia.
Assessing the reliability of the MODIS LST product to detect temporal variability
Shuo Xu, Dongdong Wang, Shunlin Liang, Yuling Liu, Aolin Jia.
Shuo Xu, Jie Cheng, Qiang Zhang.
Aolin Jia, Shunlin Liang, Dongdong Wang, Kanishka Mallick, Shugui Zhou, Tian Hu, Shuo Xu.
Aolin Jia, Shunlin Liang, Dongdong Wang, Lei Ma, Zhihao Wang, Shuo Xu.
Qiang Zhang, Ning Wang, Jie Cheng, Shuo Xu.
Zhihao Wang, Yiqun Xie, Lei Ma, George Hurtt, Xiaowei Jia, Yanhua Li, Ruohan Li, Zhili Li, Shuo Xu.
SimFair: Physics-Guided Fairness-Aware Learning with Simulation Models
Zhihao Wang, Yiqun Xie, Zhili Li, Xiaowei Jia, Zhe Jiang, Aolin Jia, Shuo Xu.
🏅 Honors and Awards
- 2025 🏆 Ann G. Wylie Dissertation Fellowship, University of Maryland
- 2025 🏆 Excellence in Graduate Research Award (Second Place), University of Maryland
- 2025 🏆 GIS Summer Research Fellowship, University of Maryland
- 2024 🏆 Outstanding Graduate Research Assistant Award, University of Maryland
- 2024 🏆 Excellence in Graduate Research Award (Third Place), University of Maryland
- 2024 🏆 Jingli Yang Summer Research Fellowship, University of Maryland
- 2024 🏆 Graduate Travel Fellowship, University of Maryland
- 2022 🏆 Jacob K. Goldhaber Travel Grant, University of Maryland
- 2021 🏆 Outstanding Graduate, Beijing Normal University
- 2021 🏆 Zhou Tingru Scholarship, Beijing Normal University
- 2020 🏆 National Scholarship, Beijing Normal University
- 2019 🏆 First-class Scholarship, Beijing Normal University
📖 Educations
- 2021.09 – Present Ph.D. candidate, Geographic Information Science and Cartography, University of Maryland, College Park, MD, USA
- 2018.09 – 2021.06 M.S., Cartography and Geography Information System, Beijing Normal University, Beijing, China
- 2014.09 – 2018.06 B.S., Remote Sensing Science and Technology, Shandong University of Science and Technology, Qingdao, China
💼 Work Experience
Research Assistant — University of Maryland
Sep 2021 – Present
NSF-Funded Project: Improving the Ecosystem Demography (ED) Model with AI
- Developed transformer-based ML frameworks integrating satellite, model, and in-situ data.
- Generated time series of carbon stocks, fluxes, and vegetation dynamics.
- Applied spatial and temporal analyses.
NOAA-Funded Project: Enhancing NOAA VIIRS Land Surface Temperature Product
- Designed multi-source fusion algorithms for all-weather VIIRS LST product.
- Built data pipelines (Python/TensorFlow, MATLAB) to produce global LST time series and anomalies.
- Presented findings at AGU and AMS conferences.
🎙️ Conference Presentations
- Shuo Xu, Yuling Liu. All-weather VIIRS LST: Machine Learning Based Methodology and Experiment. 105th American Meteorological Society (AMS) Annual Meeting, New Orleans, Jan 2025.
- Shuo Xu, Yuling Liu, Yunyue Yu, Peng Yu. All-weather LST: Methodology and Experiment on JPSS/VIIRS LST. American Geophysical Union (AGU) Fall Meeting, Washington DC, Dec 2024.
- Shuo Xu, Yuling Liu, Yunyue Yu, Peng Yu. All-weather LST: Methodology and Experiment. National Oceanic and Atmospheric Administration (NOAA) Cooperative Research Programs (CoRP) Science Symposium, Madison, WI, July 2023.
- Shuo Xu, Dongdong Wang. Validation of gridded datasets of near-surface temperature variables: uncertainties, spatial heterogeneity and clear-sky bias. American Geophysical Union (AGU) Fall Meeting, Chicago, IL, Dec 2022.
- Yuling Liu, Yunyue Yu, Peng Yu, Shuo Xu, Heshun Wang. Validation and Performance Evaluation of NOAA-21 VIIRS LST Product. American Geophysical Union (AGU) Fall Meeting, Washington DC, Dec 2024.
🤝 Peer Review & Scientific Committee Service
- IGARSS 2025 Scientific Committee
- GeoIndustry Workshop at ACM SIGSPATIAL 2025 Program Reviewer
- Reviewer for 14 International Journals, including:
- Remote Sensing of Environment
- Earth System Science Data
- IEEE Transactions on Geoscience and Remote Sensing
- Scientific Data
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- GPS Solutions
- Scientific Reports
- Journal of Geophysical Research: Atmospheres
- Environmental Monitoring and Assessment
- Earth Science Informatics
- Photogrammetric Engineering and Remote Sensing
- Discover Cities
- Environmental Earth Sciences
- Theoretical and Applied Climatology