Dr. Hongtao Shi | Hydrology | Best Researcher Award
Dr. Hongtao Shi | Hydrology | Lecturer at China University of mining and technology | China
Dr. Hongtao Shi is a lecturer at the School of Environment and Spatial Informatics, China University of Mining and Technology (CUMT) in Xuzhou, China, where he focuses on high-resolution microwave remote sensing of soil moisture, polarimetric SAR scattering modelling, and agricultural-hydrological applications of remote sensing data. He holds a Ph.D. in Photogrammetry and Remote Sensing from Wuhan University, China, where his doctoral research concentrated on multisource SAR and passive microwave methods for soil moisture retrieval; he also undertook joint doctoral training abroad at the University of Alicante, Spain. Prior to his current position, he completed his earlier degrees with an M.Sc. in Surveying Science and Technology from China University of Petroleum (East China) and a B.Sc. in Geographic Information Systems from the same institution. His professional experience includes his appointment at CUMT from mid-2021 onwards in the Environmental & Surveying Institute, during which time he has led and participated in national-level and laboratory-level research grants addressing multi‐angle, multi‐polarization SAR retrieval of soil moisture, high-resolution microwave downscaling, and airborne/spaceborne sensor data integration. His research interests span soil moisture inversion, multisource remote sensing for agriculture and hydrology, SAR polarimetry, passive microwave monitoring, time‐series image analysis, and machine-learning‐enhanced Earth-surface parameter retrieval. He has developed research skills in polarimetric SAR decomposition, multiscale data fusion, processing of microwave and optical remote sensing datasets, Python/Matlab/IDL/C# programming, time‐series modelling of hydrological variables, and uncertainty quantification in soil moisture retrieval. His honours include his role as Guest Editor for a special issue on “Soil Moisture Observation Using Remote Sensing and Artificial Intelligence” in the journal Remote Sensing, his membership in IEEE and the Chinese Society for Agricultural Meteorology, and his reviewer service for more than ten international journals including RSE, TGRS, JSTARS and Journal of Hydrology.
Academic Profile: ORCID | Scopus
Featured Publications:
Shi, H., Zhao, L., Yang, J., Lopez-Sanchez, J. M., Jinqi, Z., Sun, W., Lei, S., & Li, P. (2021). Soil moisture retrieval over agricultural fields from L-band multi-incidence and multitemporal PolSAR observations using polarimetric decomposition techniques. Remote Sensing of Environment, 261, 112485. (Citation 42)
Lang, F., Zhang, M., Zhao, J., Zheng, N., & Shi, H. (2024). Semantic segmentation for multisource remote sensing images incorporating feature slice reconstruction and attention upsampling.
Lang, F., Zhu, J., Qian, J., Dou, Q., Shi, H., Liao, L., & Zhao, L. (2025). Soil organic carbon estimation and transfer framework in agricultural areas based on spatiotemporal constraint strategy combined with active and passive remote sensing.
Zhao, J., Wang, Z., Sun, W., Yang, J., Shi, H., & Li, P. (2025). DMCF-Net: Dilated multiscale context fusion network for SAR flood detection. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
Zhao, J., Zhang, M., Zhou, Z., Wang, Z., Wang, F., Shi, H., & Zheng, N. (2025). CFFormer: A cross-fusion transformer framework for the semantic segmentation of multisource remote sensing images. IEEE Transactions on Geoscience and Remote Sensing.