(* denotes student advisees)

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.

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.

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.

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.

Diao, C. and G. Li*. (2022). Near-surface and high-resolution satellite time series for detecting crop phenology. Remote Sensing, 14(9), 1957.

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.

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.

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).

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.

Yang, Z.*, Diao, C., and B. Li. (2021). A robust hybrid deep learning model for spatiotemporal image fusion. Remote Sensing, 13(24), 5005.

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.

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.

Diao, C. (2020). Remote sensing phenological monitoring framework to characterize corn and soybean physiological growing stages. Remote Sensing of Environment, 248, 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.

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.

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.

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.

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.

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.

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.

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.

Diao, C. and L. Wang. (2016). Temporal partial unmixing of exotic saltcedar using Landsat time series. Remote Sensing Letters, 7(5), 466-475.

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.

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.

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.

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.

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.

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.

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.

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.

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.