姓名：杨昊轩
性别：男
职称：助理教授
籍贯：黑龙江牡丹江
邮箱：yhx965665334@163.com
haoxuan_yang@tongji.edu.cn
简介
主要研究土壤湿度遥感数据重建与应用，发表同行评审学术论文30余篇，担任ESSD/RSE/ISPRS J/IEEE TGRS/JOH/WRR/Geoderma/ERL等30余本SCI期刊审稿人。
工作经历
2026.08-至今 同济大学 测绘与地理信息学院 助理教授
2024.08-2026.07 美国俄克拉荷马州立大学 自然资源生态管理系 博士后研究员
2016.07-2017.07 西藏昌都市第二高级中学 教师
教育经历
2020.09-2024.06 同济大学 测绘与地理信息学院 博士
2017.09-2020.06 东北农业大学 资源与环境学院 硕士
2012.09-2016.06 东北农业大学 资源与环境学院 本科
科研项目
Application of prescribed fire to enhance water resource resilience in the Cross-Timbers ecoregion, U.S. Bureau of Land Management, 2024-2028 (参与)
Rural Confluence: Communities and Academic Partners Uniting to Drive Discovery and Build Capacity for Climate Resilience, U.S. National Science Foundation, 2023-2027 (参与)
Megafire Risk Evaluation System (MERES) for the Southern Great Plains, U.S. Geological Survey South Central Climate Adaptation Science Center grant, 2023-2025 (参与)
高分辨率遥感数据融合重建, 国家自然科学优秀青年基金项目, 2023-2025 (参与)
公开发布数据集
1. A 3 h, 1 km surface soil moisture dataset for the contiguous United States from 2015 to 2023. 发布于美国地质调查局科学数据中心 (https://doi.org/10.5066/P13CCN69)
2. A spatially seamless daily SMAP surface soil moisture dataset. 发布于国家生态科学数据中心 (https://doi.org/10.6084/m9.figshare.20279514)
3. A global 9 km, 8-day SMAP surface soil moisture dataset during 2015-2020. 发布于国家生态科学数据中心 (https://doi.org/10.6084/m9.figshare.14634276)
学术论文
1. Yang, H*., Yang, J., Ochsner, T. E., Zhang, Q., Jiao, X., Fang, S., Zhou, Y., and Zou, C. B.: Increasing irrigation water requirements across croplands in the Contiguous United States throughout the 21st century, Agricultural Water Management, 323, 110044, https://doi.org/10.1016/j.agwat.2025.110044, 2026.
2. Yang, H*., Yang, J., Ochsner, T. E., Krueger, E. S., Xu, M., and Zou, C. B.: A 3 h, 1 km surface soil moisture dataset for the contiguous United States from 2015 to 2023. Earth System Science Data, 17, 3391–3409, 2025.
3. Yang, H., Wang, Q*., Yang, J., Krueger, E. S., Xi, H., Liu, W., Ding, X., Zhou, Y., and Ochsner, T. E.: Generating a downscaled hourly surface soil moisture dataset for wildfire prediction. Journal of Hydrology, 661, 133795, 2025.
4. Yang, H., Wang, Q*., & Liu, W.: A stepwise method for downscaling SMAP soil moisture dataset in the CONUS during 2015-2019. International Journal of Applied Earth Observation and Geoinformation, 130, 103912, 2024.
5. Yang, H., Wang, Q*., Zhao, W., & Atkinson, P. M.: Reconstruction of Historical SMAP Soil Moisture Dataset From 1979 to 2015 Using CCI Time-Series. IEEE Transactions on Geoscience and Remote Sensing, 62, 1-19, 2024.
6. Yang, H. & Wang, Q*.: Reconstruction of a spatially seamless, daily SMAP (SSD_SMAP) surface soil moisture dataset from 2015 to 2021. Journal of Hydrology, 621, 2023.
7. Yang, H., Wang, Q*., Ma, X., Liu, W., & Liu, H.: Digital Soil Mapping Based on Fine Temporal Resolution Landsat Data Produced by Spatiotemporal Fusion. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, 3905-3914, 2023.
8. Yang, H., Wang, Q*., Zhao, W., Tong, X., & Atkinson, P. M.: Reconstruction of a Global 9 km, 8-Day SMAP Surface Soil Moisture Dataset during 2015–2020 by Spatiotemporal Fusion. Journal of Remote Sensing, 2022, 1-23, 2022.
9. Yang, H., Zhang, X., Xu, M., Shao, S., Wang, X., Liu, W., Wu, D., Ma, Y., Bao, Y., Zhang, X., & Liu, H*.: Hyper-temporal remote sensing data in bare soil period and terrain attributes for digital soil mapping in the Black soil regions of China. Catena, 184, 2020.
