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
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. https://doi.org/10.1016/j.isprsjprs.2024.03.018
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