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Chunyuan Diao

Assistant Professor

Biography

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.

Education

  • 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

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. https://doi.org/10.1016/j.isprsjprs.2023.06.012

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. https://doi.org/10.1109/JSTARS.2023.3237500

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. https://doi.org/10.1016/j.isprsjprs.2023.09.025

Zhao, Y., Diao, C., Augspurger, C. K., & Yang, Z. (2023). Monitoring spring leaf phenology of individual trees in a temperate forest fragment with multi-scale satellite time series. Remote Sensing of Environment, 297, Article 113790. https://doi.org/10.1016/j.rse.2023.113790

Diao, C., & Li, G. (2022). Near-Surface and High-Resolution Satellite Time Series for Detecting Crop Phenology. Remote Sensing, 14(9), Article 1957. https://doi.org/10.3390/rs14091957

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