
Research Areas
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
Courses Taught
External Links
Recent Publications
Diao, C., & Li, G. (2022). Near-Surface and High-Resolution Satellite Time Series for Detecting Crop Phenology. Remote Sensing, 14(9). https://doi.org/10.3390/rs14091957
Diao, C., Yang, Z., Gao, F., Zhang, X., & Yang, Z. (2021). Hybrid phenology matching model for robust crop phenological retrieval. ISPRS Journal of Photogrammetry and Remote Sensing, 181, 308-326. https://doi.org/10.1016/j.isprsjprs.2021.09.011
Gao, F., Anderson, M. C., Johnson, D. M., Seffrin, R., Wardlow, B., Suyker, A., Diao, C., & Browning, D. M. (2021). Towards Routine Mapping of Crop Emergence within the Season Using the Harmonized Landsat and Sentinel-2 Dataset. Remote Sensing, 13(24), [5074]. https://doi.org/10.3390/rs13245074
Lv, X., Shao, Z., Ming, D., Diao, C., Zhou, K., & Tong, C. (2021). Improved object-based convolutional neural network (IOCNN) to classify very high-resolution remote sensing images. International Journal of Remote Sensing, 42(21), 8318-8344. https://doi.org/10.1080/01431161.2021.1951879
Yang, Z., Diao, C., & Li, B. (2021). A Robust Hybrid Deep Learning Model for Spatiotemporal Image Fusion. Remote Sensing, 13(24), [5005]. https://doi.org/10.3390/rs13245005