Selected Publications
2026
Yang, J., Liu, L., Yang, Q., Jia, X., Peng, B., Guan, K. and Jin, Z.*, 2026. Knowledge-guided graph machine learning improves corn yield mapping in the US Midwest. Remote Sensing of Environment, 335, p.115287. PDF
Jin, Z.*, Liu, L., Yang, Q., Jia, X., Tao, S., Guo, Y., Ghosh, R., Wang, S., Zhu, Q., Jung, M. and Guan, K., 2026. Knowledge‐Guided Machine Learning for Global Change Ecology Research. Global Change Biology, 32(2), p.e70742. PDF
2025
Yang, Q., Zhou, J., Zhao, L. and Jin, Z.*, 2025. NeRF-LAI: A hybrid method combining neural radiance field and gap-fraction theory for deriving effective leaf area index of corn and soybean using multi-angle UAV images. Remote Sensing of Environment, 328, p.114844. PDF Loading...
Zhao, L., Wu, J.*, Yang, Q., Mao, J. and Gobin, A., 2025. A hybrid method for improving groundwater evapotranspiration modeling in saline areas using remote sensing data. Journal of Hydrology, p.133939. PDF
Han, J., Shi, L.*, Yang, Q., Yu, J. and Athanasiadis, I.N., 2025. Knowledge-guided machine learning with multivariate sparse data for crop growth modelling. Field Crops Research, 328, p.109912. PDF
2024
Yang, Q., Liu, L., Zhou, J., Rogers, M. and Jin, Z.*, 2024. Predicting the growth trajectory and yield of greenhouse strawberries based on knowledge-guided computer vision. Computers and Electronics in Agriculture, 220, p.108911. PDF Code Dataset Loading...
2023
Yang, Q., Liu, L., Zhou, J., Ghosh, R., Peng, B., Guan, K., Tang, J., Zhou, W., Kumar, V. and Jin, Z.*, 2023. A flexible and efficient knowledge-guided machine learning data assimilation (KGML-DA) framework for agroecosystem prediction in the US Midwest. Remote sensing of environment, 299, p.113880. PDF Code Loading...
Yang, Q., Shi, L.*, Han, J., Zha, Y., Yu, J., Wu, W. and Huang, K., 2023. Regulating the time of the crop model clock: A data assimilation framework for regions with high phenological heterogeneity. Field Crops Research, 293, p.108847. PDF Code Loading...
Zhou, J., Yang, Q., Liu, L., Kang, Y., Jia, X., Chen, M., Ghosh, R., Xu, S., Jiang, C., Guan, K., Kumar, V. and Jin, Z.*, 2023. A deep transfer learning framework for mapping high spatiotemporal resolution LAI. ISPRS Journal of Photogrammetry and Remote Sensing, 206, pp.30-48. PDF Code
2022
Yang, Q., Shi, L.*, Han, J., Chen, Z. and Yu, J., 2022. A VI-based phenology adaptation approach for rice crop monitoring using UAV multispectral images. Field Crops Research, 277, p.108419. PDF Loading...
Han, J., Shi, L.*, Yang, Q., Chen, Z., Yu, J. and Zha, Y., 2022. Rice yield estimation using a CNN-based image-driven data assimilation framework. Field Crops Research, 288, p.108693. PDF
Before 2022
Han, J., Shi, L.*, Yang, Q., Huang, K., Zha, Y. and Yu, J., 2021. Real-time detection of rice phenology through convolutional neural network using handheld camera images. Precision Agriculture, 22(1), pp.154-178. PDF
Zhao, L., Yang, Q., Zhao, Q. and Wu, J.*, 2021. Assessing the long-term evolution of abandoned salinized farmland via temporal remote sensing data. Remote Sensing, 13(20), p.4057. PDF
Yang, Q., Shi, L.*, Han, J., Yu, J. and Huang, K., 2020. A near real-time deep learning approach for detecting rice phenology based on UAV images. Agricultural and Forest Meteorology, 287, p.107938. PDF Loading...
Yang, Q., Shi, L.*, Han, J., Zha, Y. and Zhu, P., 2019. Deep convolutional neural networks for rice grain yield estimation at the ripening stage using UAV-based remotely sensed images. Field Crops Research, 235, pp.142-153. PDF Loading...
Yang, Q., Shi, L.* and Lin, L., 2019, July. Plot-scale rice grain yield estimation using UAV-based remotely sensed images via CNN with time-invariant deep features decomposition. IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium (pp. 7180-7183). IEEE. PDF Loading...