Peer-Reviewed Journal Articles (* corresponding author)

  1. Ma, Y. and Zhang, Z.*. (2022). A Bayesian Domain Adversarial Neural Network for Corn Yield Prediction. IEEE Geoscience and Remote Sensing Letters, 19, 5513705.
  2. Zhou, J., Wang, B., Fan, J., Ma, Y., Wang, Y. and Zhang, Z.* (2022). A systematic study of estimating potato N concentrations using UAV-based hyper- and multi-spectral imagery. Agronomy, 12(10), 2533.
  3. Chung, H., Zhang, X., Jung, S., Zhang, Z., Choi, C*. (2022). Application of machine-learned metadata-driven model for dairy barn ventilation simulation. Computers and Electronics in Agriculture, 202, 107350.
  4. Chen, S., Liu, W., Feng, P., Ye, T.*, Ma, Y., and Zhang, Z. (2022). Improving Spatial Disaggregation of Crop Yield by Incorporating Machine Learning with Multisource Data: A Case Study of Chinese Maize Yield. Remote Sensing, 14(10), 2340.
  5. Fan, J., Zhou, J., Wang, B., de Leon, N., Kaeppler, S. M., Lima, D. C., and Zhang, Z.* (2022). Estimation of Maize Yield and Flowering Time Using Multi-Temporal UAV-Based Hyperspectral Data. Remote Sensing, 14(13), 3052.
  6. Liu, W., Zhou, J., Wang, B., Costa, M., Kaeppler, S., Zhang, Z.* (2022). IntegrateNet: A Deep learning Network for maize Stand Counting from UAV Imagery by Integrating Density and Local Count Maps. IEEE Geoscience and Remote Sensing Letters, 19, 6512605.
  7. Sun, C., Zhou, J., Ma, Y., Xu, Y., Pan, B., Zhang, Z.* (2022). A Review of Remote Sensing for Potato Traits Characterization in Precision Agriculture. Frontiers in Plant Science, 2556.
  8. Shi, L., Liang, N., Xu, X.*, Li, T., and Zhang, Z. (2021) SA-JSTN: Self-attention joint spatiotemporal network for temperature forecasting. IEEE Journal of Selected Topics on Earth Observation and Remote Sensing,14, 9475-9485.
  9. Ma, Y., Zhang, Z.*, Yang, H. L., & Yang, Z. (2021). An adaptive adversarial domain adaptation approach for corn yield prediction. Computers and Electronics in Agriculture187, 106314.
  10. Feng, L., Zhang, Z. *, Ma, Y., Sun, Y., Du, Q., Williams, P., Drewry, J., and Luck, B. (2021). Multitask Learning of Alfalfa Nutritive Value From UAV-Based Hyperspectral Images. IEEE Geoscience and Remote Sensing Letters, 19, 1-5.
  11. Ma, Y., Zhang, Z.*, Kang, Y., and Özdoğan, M.. Corn Yield Prediction and Uncertainty Analysis based on Remotely Sensed Variables using a Bayesian Neural Network Approach. Remote Sensing of Environment, 2021, 259, 112408.
  12. Wang, Y., Zhang, Z.*, Feng, L., Ma, Y., and Du, Q.. A new attention-based CNN approach for crop mapping using time series Sentinel-2 images. Computers and Electronics in Agriculture, 2021, 184, 106090.
  13. Feng, L., Wang, Y., Zhang, Z. *, and Du, Q. Geographically and temporally weighted neural network for winter wheat yield prediction. Remote Sensing of Environment, 2021, 262, 112514.
  14. Sun, C., Feng, L., Zhang, Z.*, Ma, Y., Crosby, T., Naber, M., and Wang, Y., Prediction of end-of-season tuber yield and tuber set in potatoes using in-season UAV-based hyperspectral imagery and machine learning. Sensors, 2020, 20(18), 5293.
  15. Feng, L., Zhang, Z.*, Ma, Y., Du, Q., Williams, P., Drewry, J., and Luck, B., Alfalfa Yield Prediction Using UAV-Based Hyperspectral Imagery and Ensemble Learning. Remote Sensing, 2020, 12, 2028.
  16. Wang, Y., Zhang, Z.*, Feng, L., Du, Q., and Runge, T., Combining multi-source data and machine learning approaches to predict winter wheat yield in the conterminous united states. Remote Sensing, 2020, 12, pp.275-295.
  17. Huang, J., Desai, A. R., Zhu, J., Hartemink, A. E., Stoy, P., Loheide II, S. P., Zhang, Y., Zhang, Z., and Arriaga, F. J.. Retrieving Heterogeneous Surface Soil Moisture at 100 m across the Globe via Synergistic Fusion of Remote Sensing and Land Surface Parameters. Frontiers in Water, 2020.
  18. Zou, Z., Shi, T., Li, W., Zhang, Z. and Shi, Z., Do game data generalize well for remote sensing image segmentation? Remote Sensing, 2020, 12, pp.275-293.
  19. Z. Zhang, E. Pasolli, M. M. Crawford, “An Adaptive Multiview Active Learning Approach for Spectral-Spatial Classification of Hyperspectral Images,” IEEE Transactions on Geoscience and Remote Sensing, 2019, 58, pp.2557-2570.
  20. Z. Zhang, Y. Jin, B. Chen, P. Brown, “Almond Yield Prediction at the Orchard Level with a Machine Learning Approach,”  Frontiers in Plant Science, 10, 809, 2019.
  21. Z. Lv, G. Li, J.A. Benediktsson, Z. Zhang, and J. Yan, 2019. Training sample refining method using an adaptive neighbor to improve the classification performance of very high-spatial resolution remote sensing images. Journal of Applied Remote Sensing, vol. 13, no. 3, 2019.
  22. R. Zhao, Z. Shi, Z. Zou, and Z. Zhang. Ensemble-Based Cascaded Constrained Energy Minimization for Hyperspectral Target Detection. Remote Sensing, vol. 11, no. 11, 2019.
  23. C. Bai and Z. Zhang*, “A least mean square based active disturbance rejection control for an inertially stabilized platform,” Optik, International Journal for Light and Electron Optics, vol. 174, pp. 609-622, 2018.
  24. A. Habib, T. Zhou, A. Masjedi, Z. Zhang, J. Flatt and M. M. Crawford, “Boresight calibration of GNSS/INS-assisted push-broom hyperspectral scanners on UAV platforms,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 11, no. 5, pp. 1734-1749, 2018.
  25. ​ Z. Zhang and M. M. Crawford, “A batch-mode regularized multimetric active learning framework for classification of hyperspectral images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 11, pp. 6594-6609, 2017.
  26. ​A. Habib, Y. Han, W. Xiong, F. He, Z. Zhang and M. M.  Crawford, “Automated ortho-rectification of UAV-based hyperspectral data over an agricultural field using frame RGB imagery,” Remote Sensing, vol. 8, no. 10, pp. 796-817, 2016.
  27. Z. Zhang, E. Pasolli, H. L. Yang and M. M. Crawford, “Multimetric active learning for classification of remote sensing data,” IEEE Geoscience and Remote Sensing Letters, vol. 13, no. 7, pp. 1007-1011, 2016.
  28.  Z. Zhang, E. Pasolli, M. M. Crawford and J. C. Tilton, “An active learning framework for hyperspectral image classification using hierarchical segmentation,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 9, no. 2, pp. 640-653, 2016.
  29. Zhang, Z. and Shi, Z., Nonnegative matrix factorization-based hyperspectral and panchromatic image fusion. Neural Computing and Applications, 2013, 23, pp.895-905.
  30. Zhang, Z., Shi, Z. and An, Z., Hyperspectral and panchromatic image fusion using unmixing-based constrained nonnegative matrix factorization. Optik, 2013, 124, pp.1601-1608.
  31. Fang, J. C., Zhang, Z. and Gong, X.L., Modeling and simulation of transfer alignment for distributed POS. Journal of Chinese Inertial Technology, 2012, 20, pp.379-385.
  32. Changrui, B., Zhang, Z. and Yan, Z., NTD based measurement method of the relative angular velocity of ISP gimbal. Chinese Journal of Scientific Instrument, 2012, 33, pp.1945-1951.

Publications list is available at  Google Scholar