Hongtao Shi | Hydrology | Best Researcher Award

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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.

 

Xichao Gao | Hydrology | Best Researcher Award

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Dr. Xichao Gao | Hydrology | Best Researcher Award

Dr. Xichao Gao | Hydrology | Senior Engineer at China Institute of Water Resources and Hydropower Research | China

Gr. Xichao Gao is an eminent researcher specializing in hydrology, water resources, and climate-driven environmental modeling, with a strong focus on urban waterlogging, drought recovery, and real-time rainfall estimation using advanced computational and deep learning methods. He obtained his Ph.D. in Hydrology and Water Resources from a leading Chinese research university, which provided a solid foundation for his extensive contributions to the China Institute of Water Resources and Hydropower Research in Beijing, China, where he currently serves as a leading researcher. Over his career, Gr. Gao has published 30 high-impact documents, which have collectively received 403 citations from 356 documents, reflecting his scholarly influence and an h-index of 12. His professional experience encompasses leading and participating in multinational research projects addressing climate resilience, hydrological risk management, and sustainable urban water systems. Gr. Gao’s research interests include hydrological process modeling, environmental monitoring using video-based and remote-sensing techniques, AI-driven water resource management, and climate adaptation strategies. He possesses advanced research skills in machine learning applications in hydrology, statistical modeling, image-based waterlogging analysis, and integrated assessment of drought recovery processes, which have enabled him to produce innovative methodologies recognized internationally. His work has appeared in reputed journals such as Hydrological Processes, Environmental Modelling and Software, and Measurement: Journal of the International Measurement Confederation, highlighting his commitment to advancing high-quality scientific knowledge. In addition to his research output, Gr. Gao has demonstrated leadership through mentoring early-career researchers, contributing to community-based water resilience initiatives, and serving on professional committees within hydrology and environmental engineering societies. He has received multiple research honors and recognitions for his contributions to climate-adaptive hydrology and computational water modeling, reflecting his growing reputation as a leading scholar in his field.

Academic Profile: Scopus

Featured Publications:

  1. Gao, X. (2026). Measuring urban waterlogging depth from video images using human body models. Measurement: Journal of the International Measurement Confederation. 0 citations.

  2. Gao, X., et al. (2025). A framework to quantify drought recovery time accounting for the lagged effect. Hydrological Processes. 1 citation.

  3. Gao, X., et al. (2025). Real-time rainfall estimation using deep learning: Influence of background and rainfall intensity. Environmental Modelling and Software. 1 citation.