(* denotes student advisees)
Zhou, Z.*, Liu, Y.* and C. Diao (2026). CropSight-US: An object-based crop type ground truth dataset using street view and Sentinel-2 satellite imagery across the contiguous United States, 2013–2023. Earth System Science Data, 18, 3069-3097. https://doi.org/10.5194/essd-18-3069-2026 
Liu, Y.* and C. Diao (2026). CropPlantHarvest: a 500 m annual dataset of crop planting and harvesting dates (2001–2024) of the U.S. Midwest. Earth System Science Data, 18, 4373–4392. https://doi.org/10.5194/essd-18-4373-2026
Yang, Z.*, Diao, C., and Y. Liu* (2026). Monitoring crop phenology with remote sensing, in eds. Peng, F. Agricultural Applications of Earth Observation. Elsevier. https://doi.org/10.1016/B978-0-443-38345-8.00002-8
Zhang, C.* and C. Diao. (2026). Phenology-guided deep learning with uncertainty quantification for soybean yield prediction, in eds. Mayer, T., Bhandari , B. & Saah, D. Applied Artificial Intelligence and Deep Learning Book, NASA EarthRISE. https://doi.org/10.5281/zenodo.20547807
Liu, Y.*, Diao, C., Yang, Z.*, Mei, W. and T. Guo* (2025). A novel Normalized Harvest Phenology Index (NHPI) for corn and soybean harvesting date detection using Landsat and Sentinel-2 imagery on Google Earth Engine. Remote Sensing of Environment, 331, 115016. https://doi.org/10.1016/j.rse.2025.115016
Zhang, C.*, Li, X., Mieno, T., Diao, C., and D. Bullock (2025). Quadratic-Plateau Geographically Weighted Regression model for estimating site-specific economically optimal input rates. Computers and Electronics in Agriculture, 237, 110655. https://doi.org/10.1016/j.compag.2025.110655
Lyu, F.*, Yang, Z.*, Diao, C., and S. Wang (2025). Multi-stream STGAN: A spatiotemporal image fusion model with improved temporal transferability. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,18, 1562-1576. https://doi.org/10.1109/JSTARS.2024.3506879
Tian, J., Wang, L., Diao, C., Zhang, Y., Jia, M., Zhu, L., Xu, M., Li, X., and H. Gong (2025). National scale sub-meter mangrove mapping using an augmented border training sample method. ISPRS Journal of Photogrammetry and Remote Sensing, 220, 156-171. https://doi.org/10.1016/j.isprsjprs.2024.12.009
Zhao, Y.*, Diao, C. and Z. Zhou* (2025). Invasive species, in eds. Huang, X., Wang, S., Kalogeropoulos, K. & Tsatsaris A. Data-Driven Earth Observation for Disaster Management: From Theory to Practical Applications. Elsevier. https://doi.org/10.1016/C2024-0-00164-6
Diao, C., Augspurger, C., Zhao, Y.*, and C. Salk (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., Mei, W. and C. Zhang* (2024). CropSight: Towards a large-scale operational framework for object-based crop type ground truth retrieval using street view and PlanetScope satellite imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 216, 66-89. https://doi.org/10.1016/j.isprsjprs.2024.07.025
Yang, Z.*, Diao, C., Gao, F. and B. Li (2024). EMET: An emergence-based thermal phenological framework for near real-time crop type mapping. ISPRS Journal of Photogrammetry and Remote Sensing, 215, 271-291. https://doi.org/10.1016/j.isprsjprs.2024.07.007
Wang, L., Diao, C., and L. Ying (2024). The role of remote sensing in species distribution models: a review. International Journal of Remote Sensing, 1–25. https://doi.org/10.1080/01431161.2024.2421949
Zhang, C.* and C. Diao. (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. https://doi.org/10.1016/j.isprsjprs.2023.09.025
Zhao, Y.*, Diao, C., Augspurger, C. and Z. Yang*. (2023). Monitoring spring leaf phenology of individual trees in a temperate forest fragment with multi-scale satellite time series. Remote Sensing of Environment, 297, 113790. https://doi.org/10.1016/j.rse.2023.113790
Liu, Y.*, Diao, C. and Z. Yang*. (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. and F. Gao. (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. and T. Guo*. (2023). GeoAI for agriculture, in eds. Gao, S., Hu, Y. & Li, W. Handbook of Geospatial Artificial Intelligence, CRC Press/Taylor & Francis Group. https://doi.org/10.1201/9781003308423-16
Diao, C. and G. Li*. (2022). Near-surface and high-resolution satellite time series for detecting crop phenology. Remote Sensing, 14(9), 1957. https://doi.org/10.3390/rs14091957
Kang, B. and C. Diao. (2022). Walking school bus program feasibility in a suburban setting. Journal of Planning Education and Research. 42(3), 365-374. https://doi.org/10.1177/0739456X18817353
Li, X., Tian, J., Li, X., Wang, L., Gong, H., Shi, C., Nie, S., Zhu, L., Chen, B., Pan, Y., He, J., Ni, R., and C. Diao. (2022). Developing a sub-meter phenological spectral feature for mapping Poplars and Willows in urban environment. ISPRS Journal of Photogrammetry and Remote Sensing, 193, 77-89. https://doi.org/10.1016/j.isprsjprs.2022.09.002
Lyu, F.*, Yang, Z.*, Xiao, Z., Diao, C., Park, J., and S. Wang. (2022). CyberGIS for scalable remote sensing data fusion. In, Practice and Experience in Advanced Research Computing (pp. 1-4). https://doi.org/10.1145/3491418.3535145
Diao, C., Yang, Z.*, Gao, F., Zhang, X., and Z. Yang. (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
Yang, Z.*, Diao, C., and B. Li. (2021). A robust hybrid deep learning model for spatiotemporal image fusion. Remote Sensing, 13(24), 5005. https://doi.org/10.3390/rs13245005
Gao, F., Anderson, M.C., Johnson, D.M., Seffrin, R., Wardlow, B.; Suyker, A., Diao, C. and D.M. Browning. (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., and C. Tong. (2021). Improved object-based convolutional neural network (IOCNN) to classify very high-resolution remote sensing images. International Journal of Remote Sensing, 42, 8318-8344. https://doi.org/10.1080/01431161.2021.1951879
Diao, C. (2020). Remote sensing phenological monitoring framework to characterize corn and soybean physiological growing stages. Remote Sensing of Environment, 248, 111960. https://doi.org/10.1016/j.rse.2020.111960
Wang, L., Diao, C., Xian, G., Yin, D., Lu, Y., Zou, S., and T.A. Erickson. (2020). A summary of the special issue on remote sensing of land change science with Google earth engine. Remote Sensing of Environment, 248, 112002. https://doi.org/10.1016/j.rse.2020.112002
Tian, J., Wang, L., Yin, D., Li, X., Diao, C., Gong, H., Shi, C., Menenti, M., Ge, Y., Nie, S., Ou, Y, Song, X. and X. Liu. (2020). Development of spectral-phenological features for deep learning to understand Spartina alterniflora invasion. Remote Sensing of Environment, 242, 111745. https://doi.org/10.1016/j.rse.2020.111745
Diao, C. (2019). Complex network-based time series remote sensing model in monitoring the fall foliage transition date for peak coloration. Remote Sensing of Environment, 229, 179-192. https://doi.org/10.1016/j.rse.2019.05.003
Diao, C. (2019). Innovative pheno-network model in estimating crop phenological stages with satellite time series. ISPRS Journal of Photogrammetry and Remote Sensing, 153, 96-109. https://doi.org/10.1016/j.isprsjprs.2019.04.012
Shao, Z., Pan, Y., Diao, C., and J. Cai. (2019). Cloud detection in remote sensing images based on multiscale features-convolutional neural network. IEEE Transactions on Geoscience and Remote Sensing. 1-15. https://doi.org/10.1109/TGRS.2018.2889677
Diao, C. and L. Wang. (2018). Landsat time series-based multiyear spectral angle clustering (MSAC) model to monitor the inter-annual leaf senescence of exotic saltcedar. Remote Sensing of Environment, 209, 581-593. https://doi.org/10.1016/j.rse.2018.02.036
Zhou, L., Wu, J., Mo, X., Zhou, H., Diao, C., Wang, Q., Chen, Y., and F. Zhang. (2017). Quantitative and detailed spatiotemporal patterns of drought in China during 2001-2013. Science of the Total Environment, 589, 136-145. https://doi.org/10.1016/j.scitotenv.2017.02.202
Diao, C. and L. Wang. (2016). Incorporating plant phenological trajectory in exotic saltcedar detection with monthly time series of Landsat imagery. Remote Sensing of Environment, 182, 60-71. https://doi.org/10.1016/j.rse.2016.04.029
Diao, C. and L. Wang. (2016). Temporal partial unmixing of exotic saltcedar using Landsat time series. Remote Sensing Letters, 7(5), 466-475. https://doi.org/10.1080/2150704X.2016.1149250
Yoo, E.-H., Chen, D., Diao, C., and C. Russell. (2016). The effects of weather and environmental factors on West Nile virus mosquito abundance in Greater Toronto Area. Earth Interactions, 20, 1-22. https://doi.org/10.1175/EI-D-15-0003.1
Wang, L., Shi, C., Diao, C., Ji, W., and D. Yin. (2016). A survey of methods incorporating spatial information in image classification and spectral unmixing. International Journal of Remote Sensing, 37(16), 3870-3910. https://doi.org/10.1080/01431161.2016.1204032
Zhou, W., Shao, Z., Diao, C., and Q. Cheng. (2015). High-resolution remote-sensing imagery retrieval using sparse features by auto-encoder. Remote Sensing Letters, 6(10), 775-783. https://doi.org/10.1080/2150704X.2015.1074756
Zhang, L., Shao, Z., and C. Diao. (2015). Synergistic retrieval model of forest biomass using the integration of optical and microwave remote sensing. Journal of Applied Remote Sensing, 9(1), 096069. https://doi.org/10.1117/1.JRS.9.096069
Shao, Z., Zhou, W., Cheng, Q., Diao, C., and L. Zhang. (2015). An effective hyperspectral image retrieval method using integrated spectral and textural features. Sensor Review, 35(3), 274-281. https://doi.org/10.1108/SR-10-2014-0716
Diao, C. and L. Wang. (2014). Development of an invasive species distribution model with fine-resolution remote sensing. International Journal of Applied Earth Observation and Geoinformation, 30, 65-75. https://doi.org/10.1016/j.jag.2014.01.015
Wang, L. and C. Diao. Automated individual tree-crown delineation and treetop detection with very-high-resolution aerial imagery, in eds. Wang, G. & Weng, Q. Remote Sensing of Natural Resources, CRC Press, 2013. https://doi.org/10.1201/b15159
Wu, J., Zhou, L., Liu, M., Zhang, J., Leng, S., and C. Diao. (2013). Establishing and assessing the Integrated Surface Drought Index (ISDI) for agricultural drought monitoring in mid-eastern China. International Journal of Applied Earth Observation and Geoinformation, 23, 397-410. https://doi.org/10.1016/j.jag.2012.11.003
Zhou, L., Wu, J., Leng, S., Liu, M., Zhang, J., Zhao, L., Diao, C., Zhang, J., Luo, H., Zhang, F., and Y. Shi. (2012). Using a new integrated drought monitoring index to improve drought detection in mid-eastern China. In, Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International (pp. 883-886): IEEE. https://doi.org/10.1109/IGARSS.2012.6351417