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Chunyuan Diao

Assistant Professor


My research interests lie at the confluence of remote sensing, GIScience, and biogeography. My current research focuses on computational remote sensing of terrestrial ecosystem dynamics at local to global spatial scales, and daily to decadal temporal scales. I am particularly interested in advancing computational remote sensing paradigms in characterizing land surface patterns and processes, underlying mechanisms, and subsequent feedbacks to the atmosphere. My work combines remote sensing, process-based models, field observations, artificial intelligence, high-performance and cloud computing, to study ecosystem structures, functions, and responses to climate change and human activities. My ongoing research traverses varying types of ecosystems, including natural (e.g., forest), human-dominated (e.g., agriculture), and disturbed (e.g., species invasion) ecosystems. Current research foci include computational remote sensing, multi-scale land surface phenology, intelligent agriculture, and invasive species and biodiversity.


  • Ph.D., Geography, State University of New York at Buffalo
  • M.A., Biostatistics, State University of New York at Buffalo
  • B.S., Beijing Normal University

Awards and Honors

2024  AAG Fellow
2023  UCGIS Early/Mid-Career Research Award
2023  UIUC Teacher Ranked as Excellent
2022  CPGIS Young Scholar Award
2021  NSF CAREER Award
2021  NASA Early Career Investigator Award
2020  AAG Early Career Scholars in Remote Sensing Award
2019  Microsoft AI for Earth Award
2019  UIUC Teacher Ranked as Excellent
2018  UIUC Arnold O. Beckman Research Award
2018  UIUC Teacher Ranked as Excellent
2017  ASPRS Robert N. Colwell Memorial Fellowship

Courses Taught

GGIS 477 - Intro to Remote Sensing
GGIS 478 - Techniques of Remote Sensing
GGIS 489 - Programming for GIS

Additional Campus Affiliations

Assistant Professor, Geography and Geographic Information Science

Recent Publications

Diao, C., Augspurger, C. K., Zhao, Y., & Salk, C. F. (2024). A satellite-field phenological bridging framework for characterizing community-level spring forest phenology using multi-scale satellite imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 211, 83-103.

Liu, Y., Diao, C., & Yang, Z. (2023). CropSow: An integrative remotely sensed crop modeling framework for field-level crop planting date estimation. ISPRS Journal of Photogrammetry and Remote Sensing, 202, 334-355.

Yang, Z., Diao, C., & Gao, F. (2023). Towards Scalable Within-Season Crop Mapping With Phenology Normalization and Deep Learning. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, 1390-1402.

Zhang, C., & Diao, C. (2023). A Phenology-guided Bayesian-CNN (PB-CNN) framework for soybean yield estimation and uncertainty analysis. ISPRS Journal of Photogrammetry and Remote Sensing, 205, 50-73.

Zhang, C., Diao, C., & Guo, T. (2023). GeoAI for Agriculture. In Handbook of Geospatial Artificial Intelligence (pp. 330-350). CRC Press.

View all publications on Illinois Experts