常年招聘对陆面数据开发、陆面过程和陆气相互作用研究感兴趣的博士后,待遇从优。同时,热烈欢迎大气科学、(地理)信息科学、地理学、数学、物理学等专业的同学报考硕士/博士研究生。
联系方式
通讯地址:广东省珠海市唐家湾中山大学珠海校区海琴二号 中山大学大气科学学院 (邮编519082)
Email: shgwei@mail.sysu.edu.cn
基本情况
教授、博士生导师。
科研方向
结合人工智能的陆面数据发展与陆面过程模拟、机器学习可解释性、数字土壤制图、土壤湿度\土壤碳等陆面关键气候变量的时空模拟与预测、农业干旱、地理信息科学等。
研究成果案例:http://ai.chinamsa.org/TechAchievement.html?id=74
Research network: https://www.researchgate.net/profile/Wei_Shangguan
合作博士后
1名(已出站)
指导学生
博士生:2019级1名(张叶),2021级1名(黄菲妮,获国家留学基金联合培养),2023级1名(石高松,校长奖学金,获国家留学基金联合培养),2024级1名(孙文烨),合作指导2名(2016级潘金晶、2018级李璐)
硕士生:2016级1名(鄢发鹏),2017级1名(李文耀),2019级1名(张如清,校级优秀毕业生,校级一等奖学金),2020级2名(毛韬宁,熊梓立),2021级2名(张咏焜,陈诗雨),2022级1名(孙文烨),2023级1名(李丹曦),2024级2名(李梦旸,安楠),2025级1名(朱德璋)
客座研究生:2022年3名(石高松、王子玉、朱禹衡),2023年3名(闫森,张程,洪健),2024年3名(张程,金小淳,肖琪昀),2025年3名(张程,金小淳,钟智超,柳增慧)
本科毕业论文:2013级1名,2014级1名,2015级3名(1名院级优秀),2016级5名(1名院级优秀),2017级1名,2018级1名,2019级1名
发布数据集
下载网址:http://globalchange.bnu.edu.cn/research/data
发展的数据全球注册使用已逾50,000人次(参见http://globalchange.bnu.edu.cn/user/users.jsp;国家青藏高原科学数据中心;其他数据下载网站的用户信息未收集或未公开),包括哈佛大学、MIT、耶鲁大学等国际知名大学,美国国家大气研究中心、德国马普气象研究所、USGS等国际知名研究机构、政府部门和国际组织。
论文列表
发表论文60余篇,含7篇ESI高被引论文。Google Scholar 引用次数9000余次;SCI引用6400余次,一作/通讯单篇最高引用为490、455和236次,h-index为26。入选爱思维尔2024中国高被引学者。所有论文参见谷歌学术http://scholar.google.com.cn/citations?hl=en&user=sWZZ984AAAAJ或Shangguan, Wei - Web of Science 核心合集
教育经历
1998年9月-2001年7月: 湖南省湘潭县一中,高中
2001年9月-2005年7月:中南大学,地理信息系统,本科
2005年9月-2010年6月:北京师范大学,全球环境变化,硕博连读,导师戴永久院士
2014年10月- 2015年10月: 国际土壤参考信息中心(ISRIC-World Soil Information),访问学者
工作经历
2010年8月- 2014年8月:北京师范大学全球变化与地球系统科学研究院,助理研究员(中级职称)
2014年9月- 2016年9月:北京师范大学全球变化与地球系统科学研究院,副教授,硕导
2016年9月- 2023年6月:中山大学大气科学学院,副教授,博导
2023年6月至今:中山大学大气科学学院,教授,博导
学术和社会兼职
欧洲地学联合会(EGU)终身会员;中国气象学会气象人工智能专业委员会委员;中国气象服务协会人工智能技术委员会会员(2022优秀会员);中国土壤学会终身会员;中国工业与应用数学学会终身会员;广东省气象学会象人工智能专业委员会副主任委员;广东省全国第三次土壤普查专家组成员;广东省气象学会气候和地球系统动力学专业委员会委员;湘潭县一中广东省校友会副会长和教育基金会副主席
《Scientific Data》Editorial Board
《Frontier in Soil Science》Associate Editor
《Big Earth Data》Topic Editor
AGU专刊(《Journal of Advances in Modeling Earth Systems》、《Geophysical Research Letters》、《Journal of Geophysical Research: Biogeosciences》)Special Collection Organizers
Advances in Modeling Soil System Science, Earth System Science, and Beyond(Until 31 May, 2025)
《热带气象学报》专刊召集人
专刊:人工智能+气象
《Advances in Atmospheric Sciences》(IF: 5.8, SCI) Guest Editor
Special issue (until Oct.2023): AI Applications in Atmospheric and Oceanic Science: Pioneering the Future
《Land》 (IF: 3.9, SSCI) Guest Editor
Special issue (guest editor, until Nov.2023): Soil Moisture and Land Surface Processes: Observation, Modeling and Coupling Analysis
《Atmosphere》 (IF: 2.9, SCI) Guest Editor
Special issue (guest editor, until Feb2024): Recent Advances in Earth Surface Processes: From Weathering to Climate Change
论著一览 (Google Scholar citations are given for publications with a significant contribution)
- Jiang, S., L.-b. Sweet, G. Blougouras, A. Brenning, W. Li, M. Reichstein, J. Denzler, W. Shangguan, G. Yu, F. Huang & J. Zscheischler. (2024). How Interpretable Machine Learning Can Benefit Process Understanding in the Geosciences. Earth's Future 12(7):e2024EF004540. https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2024EF004540 (42 Google Scholar citations)
- Li, Q., Shi, G., Shangguan, W.#, Li, J., Li, L., Huang, F., Zhang, Y., Wang, C., Wang, D., Qiu, J., Lu, X., and Dai, Y. 2022. A 1 km daily soil moisture dataset over China using in situ measurement and machine learning, Earth Syst. Sci. Data, 14, 5267–5286, https://doi.org/10.5194/essd-14-5267-2022. (128 Google Scholar citations). Data downloading
- Li, Q., Zhu, Y., Shangguan, W.#, Wang, X., Li, L., Yu, F., 2022. An attention-aware LSTM model for soil moisture and soil temperature prediction. Geoderma, 409: 115651. https://doi.org/10.1016/j.geoderma.2021.115651.(133 Google Scholar citations)
- Shangguan, W., T. Hengl, J. Mendes de Jesus, H. Yuan, and Y. Dai, 2017. Mapping the global depth to bedrock for land surface modeling. Journal of Advances in Modeling Earth Systems, 9:65-88, https://doi.org/10.1002/2016ms000686. (336 Google Scholar citations, top 2 most read among articles of JAMES). Data downloading. Hilighted by the Editor
- Hengl, T., M. d. J. J., G. B. M. Heuvelink, R. Gonzalez, K. M., M., A. Blagotic, W. Shangguan, M. N. Wright, X. Geng, B. Bauer-Marschallinger, M. A. Guevara, R. Vargas, R. A. MacMillan, N. H. Batjes, J. G. B. Leenaars, E. Ribeiro, I. Wheeler, S. Mantel, and B. Kempen, 2017. SoilGrids250m: global gridded soil information based on Machine Learning. PLOS One, 12: e0169748, https://doi.org/10.1371/journal.pone.0169748. (3822 Google Scholar citations). Data downloading
- Shangguan, W., Dai, Y., Duan, Q., Liu, B. and Yuan, H., 2014. A Global Soil Dataset for Earth System Modeling. Journal of Advances in Modeling Earth Systems, 6: 249-263, https://doi.org/10.1002/2013MS000293. (639 Google Scholar citations, Top 1 most read and top 10 most cited among articles of JAMES). Data downloading
- Shangguan, W., Y. Dai, B. Liu, A. Zhu, Q. Duan, L. Wu, D. Ji, A. Ye, H. Yuan, Q. Zhang, D. Chen, M. Chen, J. Chu, Y. Dou, J. Guo, H. Li, J. Li, L. Liang, X. Liang, H. Liu, S. Liu, C. Miao, and Y. Zhang, 2013, A China Data set of Soil Properties for Land Surface Modeling, Journal of Advances in Modeling Earth Systems, 5(2), 212-224, https://doi.org/10.1002/jame.20026. (555 Google Scholar citations, top 10 most cited among articles of JAMES). Data downloading
- Shi, G., Sun, W., Shangguan, W.#, Wei, Z., Yuan, H., Zhang, Y., Liang, H., Li, L., Sun, X., Li, D., Huang, F., Li, Q., Dai, Y. A China dataset of soil properties for land surface modeling (version 2, CSDLv2). 2025. Earth System Science Data,17(2): 517–543. https://doi.org/10.5194/essd-2024-299. Data downloading: TPDC or SCIDB (1 Google Scholar citations)
- Shi, G., Shangguan, W.#, Zhang, Y., Li, Q., Wang, C., Li, L.. (2024). Reducing Location Error of Legacy Soil Profiles Leads to Significant Improvement in Digital Soil Mapping. Gederma,447, 116912. . https://doi.org/10.1016/j.geoderma.2024.116912 (5 Google Scholar citations)
- Huang, F., Shangguan, W.#, Li, Q., Li, L., & Zhang, Y. (2023). Beyond prediction: An integrated post-hoc approach to interpret complex model in hydrometeorology. Environmental Modelling & Software, 167: 105762. https://doi.org/10.1016/j.envsoft.2023.105762 (15 Google Scholar citations)
- Huang, F., Zhang, Y., Zhang, Y., Shangguan, W.#, Nourani, V., Li, Q., & Li, L. (2023). Towards interpreting machine learning models for predicting soil moisture droughts. Environmental Research Letters,18: 074002. https://doi.org/10.1088/1748-9326/acdbe0 (18 Google Scholar citations)
- Li, Q., Wang, Z., Shangguan, W.#, Li, L., Yao, Y., Yu, F., 2021. Improved Daily SMAP Satellite Soil Moisture Prediction over China using deep learning model with transfer learning. Journal of Hydrology, 600: 126698. https://doi.org/10.1016/j.jhydrol.2021.126698. (134 Google Scholar citations)
- Yan, F., Shangguan, W.#, Zhang, J., and Hu, B., 2020. Depth-to-Bedrock Map of China at a Spatial Resolution of 100 Meters, Scientific Data,7:2, https://doi.org/10.1038/s41597-019-0345-6. (160 Google Scholar citations. A related post at Nature website: https://researchdata.springernature.com/users/338528-wei-shangguan/posts/57876-making-maps-for-china-and-the-world). Data downloading
- Dai, Y.#, Shangguan, W.#, Wei, N., Xin, Q., Yuan, H., Zhang, S., Liu, S., Lu, X., Wang, D., and Yan, F., 2019. A review of the global soil property maps for Earth system models, SOIL, 5, 137-158, https://doi.org/10.5194/soil-5-137-2019. (126 Google Scholar citations)
2019~2025
- Li, Q., X. Jin, C. Zhang, W. Shangguan, Z. Wei, L. Li, P. Liu & Y. Dai, 2025. Improving global soil moisture prediction based on Meta-Learning model leveraging Köppen-Geiger climate classification. CATENA 250:108743
- Huang, F., Jiang, S., Li, L., Zhang, Y., Zhang, Y., Zhang, R., Li, Q., Li, D., Shangguan, W.#, Dai, Y.. Applications of Explainable artificial intelligence in Earth system science. 2024. preprint. https://arxiv.org/abs/2406.11882 (4 Google Scholar citations)
- Zhang, R., Shangguan, W.#, Liu, J., Dong, W., & Wu, D. (2024). Assessing meteorological and agricultural drought characteristics and drought propagation in Guangdong, China. Journal of Hydrology: Regional Studies, 51, 101611. https://doi.org/10.1016/j.ejrh.2023.101611 (16 Google Scholar citations)
- Li, Q., Zhang, C., Shangguan, W., Wei, Z., Yuan, H., Zhu, J., Li, X., Li, L., Li, G., Liu, P., Dai, Y. (2024). LandBench 1.0: A benchmark dataset and evaluation metrics for data-driven land surface variables prediction. Expert Systems with Applications, 243, 122917. https://doi.org/10.1016/j.eswa.2023.122917 (8 Google Scholar citations)
- Li, L., Dai, Y., Wei, Z., Shangguan, W., Wei, N., Zhang, Y., Li, Q., Li, X. (2024). Enhancing Deep Learning Soil Moisture Forecasting Models with integrating Physical-based Models. Advances in Atmospheric Sciences. https://doi.org/10.1007/s00376-023-3181-8 (12 Google Scholar citations)
- Li, L., Dai, Y., Wei, Z., Shangguan, W., Zhang, Y., Wei, N., Li, Q. (2024). Enforcing Water Balance in Multitask Deep Learning Models for Hydrological Forecasting. Journal of Hydrometeorology, 25(1), 89-103. https://doi.org/10.1175/JHM-D-23-0073.1 (6 Google Scholar citations)
- Li, Q., C. Zhang, Z. Wei, X. Jin, W. Shangguan, H. Yuan, J. Zhu, L. Li, P. Liu, X. Chen, Y. Yan & Y. Dai, 2024. Advancing symbolic regression for earth science with a focus on evapotranspiration modeling. npj Climate and Atmospheric Science 7(1):321 doi:10.1038/s41612-024-00861-5.
- Li, Q., Q. Xiao, C. Zhang, J. Zhu, X. Chen, Y. Yan, P. Liu, W. Shangguan, Z. Wei, L. Li, W. Dong & Y. Dai. (2024). Improving global soil moisture prediction through cluster-averaged sampling strategy. Geoderma 449:116999
- Cai, K., J. He, Q. Li, W. Shangguan, L. Li, H. Hu. (2024). Meta-LSTM in hydrology: Advancing runoff predictions through model-agnostic meta-learning. Journal of Hydrology, 639, 131521.
- Lin, Z., Dai, Y., Mishra, U., Wang, G., Shangguan, W., Zhang, W., & Qin, Z. Global and regional soil organic carbon estimates: Magnitude and uncertainties. Pedosphere. 2024, 34(4): 685-698.
- Chen, S., Li, L., Wei, Z., Wei, N., Zhang, Y., Zhang, S., Yuan, H., Shangguan, W., Zhang, S., Li, Q., Dai, Y. (2024). Exploring Topography Downscaling Methods for Hyper‐Resolution Land Surface Modeling. Journal of Geophysical Research: Atmospheres 129 (20), e2024JD041338.
- Shangguan W#, Xiong Z, Nourani V, Li Q, Lu X, Li L, Huang F, Zhang Y, Sun W, Dai Y. A 1 km Global Carbon Flux Dataset Using In Situ Measurements and Deep Learning. Forests. 2023; 14(5):913. https://doi.org/10.3390/f14050913 (14 Google Scholar citations). Data downloading
- Huang, F., Zhang, Y., Zhang, Y., Shangguan, W.#, Li, Q., Li, L., Jiang, S. Interpreting Conv-LSTM for Spatio-Temporal Soil Moisture Prediction in China. Agriculture. 2023; 13(5):971. https://doi.org/10.3390/agriculture13050971 (18Google Scholar citations)
- Zhang, Y.; Huang, F.; Li, L.; Li, Q.; Zhang, Y.; Shangguan, W#. Real-Time Forecast of SMAP L3 Soil Moisture Using Spatial–Temporal Deep Learning Model with Data Integration. Remote Sens. 2023, 15, 366. https://doi.org/10.3390/rs15020366 (2 Google Scholar citations)
- Xiong, Z.; Shangguan, W.#; Nourani, V.; Li, Q.; Lu, X.; Li, L.; Huang, F.; Zhang, Y.; Sun, W.; Yuan, H.; Li, X. Assessing the Reliability of Global Carbon Flux Dataset Compared to Existing Datasets and Their Spatiotemporal Characteristics. Climate. 2023, 11, 205. https://doi.org/10.3390/cli11100205 (0 Google Scholar citations)
- 上官微. 土壤类型制图经验总结与建议.中国农业综合开发. 2023, 242 (8): 13-15.
- Li, Q., Zhang, C., Shangguan, W., Li, L., & Dai, Y. (2023). A novel local-global dependency deep learning model for soil mapping. Geoderma, 438, 116649. https://doi.org/10.1016/j.geoderma.2023.116649
- Lin, Y., Wang, D., Meng, Y., Sun, W., Qiu, J., Shangguan, W., Cai, J., Kim, Y., Dai, Y. Bias learning improves data driven models for streamflow prediction. Journal of Hydrology: Regional Studies. 2023. 50, 101557.
- Mao, T, Shangguan, W#, Li, Q, Li, L, Zhang, Y, Huang, F, Li, J, Liu, W, Zhang, R. A Spatial Downscaling Method for Remote Sensing Soil Moisture Based on Random Forest Considering Soil Moisture Memory and Mass Conservation. Remote Sensing. 2022; 14(16):3858. https://doi.org/10.3390/rs14163858 (32 Google Scholar citations)
- Shangguan, W.#, Zhang, R., Li, L., Zhang, S., Zhang, Y., Huang, F., Li, J., Liu, W. Assessment of Agricultural Drought Based on Reanalysis Soil Moisture in Southern China. Land 2022, 11, 502. https://doi.org/10.3390/land11040502 (14 Google Scholar citations)
- Li, Q., Li, Z., Shangguan, W.#, Wang, X., Li, L., Yu, F., 2022. Improving soil moisture prediction using a novel encoder-decoder model with residual learning. Computers and Electronics in Agriculture, 195: 106816. https://doi.org/10.1016/j.compag.2022.106816 (57 Google Scholar citations)
- Li, L., Dai, Y., Shangguan, W., Wei, N., Wei, Z., Gupta, S., 2022. Multistep Forecasting of Soil Moisture Using Spatiotemporal Deep Encoder–Decoder Networks. Journal of Hydrometeorology, 23(3): 337–350. https://doi.org/10.1175/JHM-D-21-0131.1 (18 Google Scholar citations)
- Li, L., Dai, Y., Shangguan, W., Wei, Z., Wei, N., and Li, Q., 2022. Causality-Structured Deep Learning for Soil Moisture Predictions, Journal of Hydrometeorology, 2022, https://doi.org/10.1175/JHM-D-21-0206.1 (16 Google Scholar citations)
- Qiu, J., Crow, W. T., Wang, S., Dong, J., Li, Y., Garcia, M., & Shangguan, W., 2022. Microwave-based soil moisture improves estimates of vegetation response to drought in China. Science of the Total Environment, 157535.
- Li, J., Miao, C., Zhang, G., Fang, Y.-H., Shangguan, W., & Niu, G.-Y., 2022. Global evaluation of the Noah-MP land surface model and suggestions for selecting parameterization schemes. Journal of Geophysical Research: Atmospheres, 127, e2021JD035753.
- Liu, W., Dong, S., Zheng, J., Liu, C., Wang, C., Shangguan, W., Zhang, Y., & Zhang, Y., 2022. Quantifying the Rainfall Cooling Effect: The Importance of Relative Humidity in Guangdong, South China, Journal of Hydrometeorology, 23(6), 875-889.
- Liu, X., Lu, X., Zhang, S., Wei, Z., Wei, N., Zhang, S., Yuan, H., Shangguan, W., Liu, S., Ye, X., Zhou, J., Hu, W., Dai, Y. (2022). Plant drought tolerance trait is the key parameter in improving the modeling of terrestrial transpiration in arid and semi-arid regions. Atmospheric and Oceanic Science Letters, 15(1), 100139.
- Zhang, R., Li, L., Zhang, Y., Huang, F., Li, J., Liu, W., Mao, T., Xiong, Z., Shangguan, W.#, 2021. Assessment of Agricultural Drought Using Soil Water Deficit Index Based on ERA5-Land Soil Moisture Data in Four Southern Provinces of China. Agriculture, 11, 411. https://doi.org/10.3390/agriculture11050411. (59 Google Scholar citations). Editor's choice
- Li, H., Lu, X., Wei, Z., Zhu, S., Wei, N., Zhang, S., Yuan, H., Shangguan, W., Liu, S., Zhang, S., Huang, J., Dai, Y, 2021. New Representation of Plant Hydraulics Improves the Estimates of Transpiration in Land Surface Model. Forests, 12(6):722.
- Lin, Y., Wang, D., Wang, G., Qiu, J., Long, K., Du, Y., Xie, H., Wei, Z., Shangguan, W. and Dai, Y., 2021. A hybrid deep learning algorithm and its application to streamflow prediction. Journal of Hydrology, 601: 126636.
- Liu, X., Lu, X., Zhang, S., Wei, Z., Wei, N., Zhang, S., Yuan, H. Shangguan, W., Liu, S., Huang, J., Li, L., Ye, X., Zhou, J., Hu, W., Dai Y, 2021. Plant drought tolerance trait is the key parameter in improving the modeling of terrestrial transpiration in arid and semi-arid regions. Atmospheric and Oceanic Science Letters. In press: 100139
- Zhu, S., Chen, H., Dai, Lu, X., Shangguan, W., Yuan, H., Wei, N., 2021. Evaluation of the effect of low soil temperature stress on the land surface energy fluxes simulation in the site and global offline experiments. Journal of Advances in Modeling Earth Systems, 13: e2020MS002403.
- Li, L., Shangguan, W.#, Deng, Y., Mao, J., Pan, J., Wei, N., Yuan, H., Zhang, S., Zhang, Y., Dai, Y., 2020. A causal-inference model based on Random Forest to identify the effect of soil moisture on precipitation. Journal of Hydrometeorology, 21: 1115-1131 https://doi.org/10.1175/JHM-D-19-0209.1. (32 Google Scholar citations)
- 李文耀,魏楠,黄丽娜,上官微#.2020.土壤数据集对全球陆面过程模拟的影响.气候与环境研究,25(5):555-574, https://doi.org/10.3878/j.issn.1006-9585.2020.20025. (3 Google Scholar citations)
- Pan, J., Shangguan, W.#, Li, L., Yuan, H., Zhang, S., Lu, X., Wei, N., and Dai, Y., 2019. Using data-driven methods to explore the predictability of surface soil moisture with FLUXNET site data, hydrological process,33:2978-2996, https://doi.org/10.1002/hyp.13540. (28 Google Scholar citations)
- Dai, Y., Yuan, H., Xin, Q., Wang, D., Shangguan, W., Zhang, S., Liu, S., and Wei, N., 2019. Different representations of canopy structure—A large source of uncertainty in global land surface modeling: Agricultural and Forest Meteorology, 269-270, 119-135.
- Dai, Y., Q. Xin, N. Wei, Y. Zhang, W. Shangguan, H. Yuan, S. Zhang, S. Liu, X. Lu, 2019. A global high-resolution dataset of soil hydraulic and thermal properties for land surface modeling. Journal of Advances in Modeling Earth Systems,11, 2996-3023.
- Dai, Y., N. Wei, H. Yuan, S. Zhang, W. Shangguan, S. Liu, and X. Lu, 2019. Evaluation of soil thermal conductivity schemes for use in land surface modelling, Journal of Advances in Modeling Earth Systems, 11, 3454-3473.
Before 2019
- Moeys, J., Wei Shangguan, 2018. Soil texture: Functions for soil texture plot, classification and transformation. http://cran.r-project.org/web/packages/soiltexture/. (75 Google Scholar citations)
- Dai, Y., Wei, N., Huang, A., Zhu, S., Shangguan, W., Yuan, H., Zhang, S., and Liu, S., 2018. The lake scheme of the Common Land Model and its performance evaluation. Chinese Science Bulletin, 63, 3002-3021.
- Shangguan, W., Yuan, H., Dai, Y., Hengl, T., & de Jesus, J. M., 2017. Mapping the global depth to bedrock combining soil profiles and boreholes. In GlobalSoilMap-Digital Soil Mapping from Country to Globe (pp. 63-68). CRC Press.
- Zhang, X., Y. Dai, H. Cui, R. E. Dickinson, S. Zhu, N. Wei, B. Yan, H. Yuan, W. Shangguan, and L. Wang, 2017: Evaluating common land model energy fluxes using FLUXNET data. Advances in Atmospheric Sciences, 34, 1035-1046.
- Yuan, H., Y. Dai, R. E. Dickinson, B. Pinty, W. Shangguan, S. Zhang, L. Wang, and S. Zhu, 2017: Reexamination and further development of two-stream canopy radiative transfer models for global land modeling. Journal of Advances in Modeling Earth Systems, 9, 113-129.
- Zhu, S., H. Chen, X. Zhang, N. Wei, W. Shangguan, H. Yuan, S. Zhang, L. Wang, L. Zhou, and Y. Dai, 2017: Incorporating root hydraulic redistribution and compensatory water uptake in the Common Land Model: Effects on site level and global land modeling. Journal of Geophysical Research: Atmospheres, 122, 7308-7322.
- Zhou, T., P. J. Shi, G. S. Jia, Y. J. Dai, X. Zhao, W. Shangguan, L. Du, H. Wu, and Y. Q. Luo, 2015: Age-dependent Forest carbon sink: Estimation via inverse modeling. Journal of Geophysical Research-Biogeosciences, 120, 2473-2492.
- Shangguan, W., P. Gong, L. Liang, Y. Dai, and K. Zhang, 2014. Soil Diversity as Affected by Land Use in China: Consequences for Soil Protection, The Scientific World Journal, 2014, https://doi.org/10.1155/2014/913852. (22 Google Scholar citations, reported by Science: http://www.sciencemagazinedigital.org/sciencemagazine/7_november_2014?folio=692#pg24)
- Shangguan, W., Y. Dai, C. García-Gutiérrez, and H. Yuan, 2014. Particle-size distribution models for the conversion of Chinese data to FAO/USDA system, The Scientific World Journal, 2014, Article ID 109310, 11 pages, https://doi.org/10.1155/2014/109310. (22 Google Scholar citations)
- Yuan, H., R. E. Dickinson, Y. Dai, M. J. Shaikh, L. Zhou, W. Shangguan, and D. Ji, 2014. A 3D Canopy Radiative Transfer Model for Global Climate Modeling: Description, Validation, and Application, Journal of Climate, 27, 1168-1192.
- Dai, Y., W. Shangguan, Q. Duan, B. Liu, S. Fu, and G. Niu, 2013. Development of a China dataset of soil hydraulic parameters using pedotransfer functions for land surface modeling. Journal of Hydrometeorology 14, 869–887, https://doi.org/10.1175/JHM-D-12-0149.1. (266 Google Scholar citations)
- Ren, D., L. M. Leslie, M. J. Lynch, Q. Duan, Y. Dai, and W. Shangguan, 2013. Why was the Auguest 2010 Zhouqu landslide so powerful. Geography, Environment, Sustainability, 6, 67-79.
- Zheng Y. M., Niu Z. G., Gong P., Dai Y. and Shangguan W., 2013. Preliminary estimation of the organic carbon pool in China’s wetlands. Chin Sci Bull, 58: 662-670.
- Shangguan, W., Y. Dai, B. Liu, A. Ye, and H. Yuan, 2012. A soil particle-size distribution dataset for regional land and climate modelling in China, Geoderma, 171-172, 85-91, https://doi.org/10.1016/j.geoderma.2011.01.013. (193 Google Scholar citations). Data downloading
- Yuan, H., Dai, Y., Xiao, Z., Ji, D., Shangguan, W., 2011. Reprocessing the MODIS Leaf Area Index products for land surface and climate modeling. Remote sensing of Environment 115, 1171-1187.
- 上官微, 戴永久, 2009. 几种土壤粒径分布参数模型在稀疏分级数据中的对比研究. 北京师范大学学报 (自然科学版), 45(3), 279-283. (0 Google Scholar citations)
专利
- 上官微,石高松,孙文烨,李丹曦,2025。一种基于机器学习的土壤典型样点采样方法。广东省:202510449624.X。申请时间:2025-7-18.(已公开)
- 上官微, 黄菲妮, 2025. 集成水文气象机器学习预报模型的可视化事后解释方法. 广东省:CN115099416A, 申请时间:2022-06-14.专利号:ZL202210664945.8,授权时间:2024-01-04(已授权)
- 李丹曦, 上官微。2024。水文气象深度学习预报模型的可解释性评价方法和系统。广东省:CN117556947A,申请时间:2024-08-20,专利号:ZL202411141699.3,授权时间:2024-11-19(已授权)
- 李清亮; 闫森; 张程; 上官微; 朱金龙; 李叶光; 金小淳; 陈霄,2024. 一种基于水平衡约束深度学习的土壤湿度预测方法。吉林省:CN117272813A。申请时间:2023.9.26,专利号:ZL 202311250109.6,授权时间:2024-4-30(已授权)
- 李清亮,张程,上官微,李叶光,肖祺昀,2024。一种基于局部-全局依存关系的土壤质地预测方法。吉林省:CN116307086A,申请时间:2023-07-31,专利号:ZL 202310950055.8,授权时间:2024-2-2(已授权)
- 王学智,李清亮,上官微,孙冲,李骐宇,于繁华,胡晏铭,2023. 基于EDC-LSTM模型的土壤湿度预测方法、装置及存储介质. 吉林省:CN114386332A,申请时间:2022-04-22,专利号:ZL202210050876.1,授权时间:2023-08-01.(已授权)
- 李清亮; 赵秀涛; 金小淳; 朱金龙; 李叶光; 陈霄; 晏俞光; 上官微; 魏忠旺; 李璐,2024.基于元学习的LSTM模型实现土壤湿度预测方法。吉林省:CN119066952A。申请时间:202412-3.(已公开)
- 李清亮; 肖祺昀; 张程; 朱金龙; 李叶光; 陈霄; 上官微; 魏忠旺; 李璐,2024. 基于平均聚类采样策略的全球土壤湿度预测方法。吉林省:CN118861725A,申请时间:2024-10-29。(已公开)
- 李清亮; 金小淳; 洪建; 祁彦龙; 武穆杰; 李叶光; 陈霄; 晏俞光; 上官微; 魏忠旺; 李璐,2024. 基于物理过程的注意力编码解码LSTM模型的土壤湿度预测方法。吉林省:CN202411153495.1,申请时间:2024-08-21。(已公开)
- 曾庆林; 周声圳; 上官微,2024.一种解析特征多元影响的分析方法、系统、设备及介质。广东省:CN118094166A,申请时间:2024-05-28。(已公开)
- 李清亮,上官微,李骐宇,朱金龙;胡晏铭;朱禹衡;崔欣怡,2022. 一种多变量注意的LSTM土壤温湿度预测模型及预测方法. 吉林省: CN114065638A,申请时间:2022-02-18.(已公开,公布后的撤回)
计算机软件著作权
- 张咏焜,上官微。2024。土壤有机碳空间分布绘制程序 V1.0。中山大学。2024SR1308662.
- 邱建秀,上官微,吾买尔江·吾布力卡斯木,潘於泓.。2023。基于变化检测方法的高分辨率土壤水分在线监测平台 V1.0. 中山大学:2023SR0161712。
- 上官微, 2012。土壤距离连接法系统(V1.0)。北京师范大学:2012SR099311,2012-10-22.
- 上官微, 2012。土壤类型连接法系统(V1.0)。北京师范大学:2012SR099219,2012-10-22.
- 上官微等,土壤湿度人工智能预报及数据产品开发。第二届水文过程变化与调控论坛。2025年4月。
- 上官微等,可解释人工智能在气象预报与气候预估中的研究。2024 年中国气象学会气象人工智能交流研讨会。2024年11月。
- 孙文烨,上官微等,用于陆面模式的土壤背景反照率数据的生成。2024 年中国气象学会气象人工智能交流研讨会。2024年11月。
- 石高松,上官微等,A China dataset of soil properties for land surface modeling (version 2)。2024 年中国气象学会气象人工智能交流研讨会。2024年11月。
- 上官微等,可解释人工智能在地球系统科学的若干应用。第二届“智能+气象海洋预报保障”论坛。2024年9月,长沙。
- Wei Shangguan et al., Explainable AI for water, energy and carbon cycle understanding. 9th Global Energy and Water Exchanges (GEWEX) Open Science Conference. July 2024, Sapporo.
- Wei Shangguan, Feini Huang. Development of soil, water and carbon datasets for land surface modeling. 9th Global Energy and Water Exchanges (GEWEX) Open Science Conference. July 2024, Sapporo.
- Wei Shangguan, Feini Huang. Making Machine Learning More Transparent Using Explainable AI for Hydrological and Climatological Understanding. Asia Oceania Geoscience Society 2024 Annual meeting. June, 2024, Pyeong Chang.
- Wei Shangguan, Yongkun Zhang, Feini Huang. Predicting Soil Organic Carbon of China in the Future, the Role of Carbon Flux and a 1 km Global Carbon Fluxes Dataset using machine learning. Asia Oceania Geoscience Society 2024 Annual meeting. June, 2024, Pyeong Chang.
- Wei Shangguan, Yongkun Zhang. Predicting Soil Organic Carbon of China in the Future and the Role of Carbon Flux. The 4th International Soil Modeling Consortium (ISMC) Conference, May, 2024. Tianjin.
- Wei Shangguan. Our recent advances in soil modeling and products using machine learning and explainable AI. The 4th International Soil Modeling Consortium (ISMC) Conference, May, 2024. Tianjin.
- Wei Shangguan, Feini Huang. Making Machine Learning More Transparent Using Explainable AI for soil modeling. The 4th International Soil Modeling Consortium (ISMC) Conference, May, 2024. Tianjin.
- Wei Shangguan, Gaosong Shi. Reducing location error of legacy soil profiles leads to significant improvement in digital soil mapping. European Geosciences Union General Assembly 2024, April 2024, Vienna.
- Wei Shangguan, Zili Xiong, and Feini Huang, Enhancing Carbon Cycle Understanding through Deep Learning: Development and Validation of the Global Carbon Fluxes Dataset (GCFD) (Invited). European Geosciences Union General Assembly 2024, April 2024, Vienna.
- Wei Shangguan et al., Our recent advances in soil modeling and products using machine learning and explainable AI, December 2023, American Geophysical Union 2023 Annual meeting, San Francisco.
- Wei Shangguan, Feini Huang, Shijie Jiang. Making machine learning for hydrologic modeling more transparent using explainable AI, December 2023, American Geophysical Union 2023 Annual meeting, San Francisco.
- Wei Shangguan, Zili Xiong. Unlocking the Potential of Deep Learning: Enhancing Global Carbon Flux Assessment with the Global Carbon Fluxes Dataset (GCFD), December 2023, American Geophysical Union 2023 Annual meeting, San Francisco.
- 上官微,无,第一届地球系统数值模拟科学大会,2023年11月,北京。
- 上官微,水文气象人工智能预报的可解释性(特邀报告),机器学习在地球系统研究中的应用主题研讨会,2023年11月,北京。
- 上官微,基于人工智能的陆面数据发展和陆面过程研究,气候模式与地气系统的辐射平衡研究学术研讨会,2023年8月,呼和浩特。
- 上官微,基于AI的数字土壤制图与土壤墒情预报,第五届光明新农科 · 智慧农业青年学者论坛,2023年6月,深圳。
- 上官微,无,风云际会・中国气象服务协会年会,2023年4月,无锡。
- 上官微,基于机器学习的土壤湿度预报与可解释性研究,水利部水文气象灾害机理与预警重点实验室第一届学术年会,2022年10月,南京。
- 上官微,陆面气象要素AI预报,人工智能技术在气象领域的应用之地球系统应用培训,2022年5月,北京。
- 上官微,陆面气象要素的机器学习预报,集智学园地球系统科学读书会,2022年1月,北京。https://campus.swarma.org/mobile/course/4284
- 分论坛主席:基于大数据的海陆气环境预报预警,第九届中国计算机学会大数据会议,2022年1月,广州。
- 上官微,陆面气象要素预报,人工智能技术委员会“AI+气象预测”专题研讨会,2021年8月,北京。
- Session co-covener: Advances in soil modeling through data analytics, machine learning and prediction. 3rd ISMC Conference: Advances in Modeling Soil Systems. May 2021. https://meetingorganizer.copernicus.org/ISMC2021/sessionprogramme
- 上官微等,地球系统模式中的全球土壤数据集。中国土壤学会土壤发生、分类与土壤地理专业委员会 ,土壤遥感与信息专业委员会2019年联合学术研讨会。西宁,2019。
- Shangguan, W. et al. (2019) A review of the global soil property maps for Earth system models. COAA 8th ICAOCC, Nanjing.
- Shangguan, W. et al. (2019) A review of the global soil property maps for Earth system models. AGU2019, San Francisco.
- 上官微等(2017)中国和世界的大尺度土壤制图。中国土壤学会土壤发生、分类与土壤地理专业委员会 ,土壤遥感与信息专业委员会2017年联合学术研讨会。上海。
- Shangguan, W., T. Hengl, J. Mendes de Jesus, H. Yuan, and Y. Dai (2017). Mapping the global depth to bedrock for land surface modeling. AGU2017. New Orleans, US.
- Shangguan, W. (2017) None. The 7th international workshop on catchment hydrological modeling and data assimilation. Xi’an, China.
- Shangguan, W. (2017) None. International workshop on open geographical modelling and simulation. Nanjing, China.
- Shangguan, W. (2017) Soil Grid China: a contribution to GlobalSoilMap. Globalsoilmap 2017. Moscow, Russia.
- Shangguan, W. (2017) Mapping the Global Depth to Bedrock. Globalsoilmap 2017. Moscow, Russia.
- Shangguan, W. (2017) Exploring extrapolation risks of spatial prediction models at global, continental and regional scales. Pedometric 2017. Wageningen, the Netherland.
- Chen, Z., Shangguan, W. et al. (2016) Optimization of terrestrial ecosystem model parameters using atmospheric CO2 concentration data with a global carbon assimilation system (GCAS). AGU Fall Meeting 2016: San Francisco, USA.
- Shangguan, W. (2015) Spatial prediction of depth to bedrock and saprolite using global dsm models. Pedometric 2015. Córdoba, Spain.
- Shangguan, W. (2015). Soil information for Earth system modelling in Wageningen conference on applied soil science, Wageningen, The Netherland.
- Shangguan, W. (2014). A comprehensive gridded global soil data sets, DAMES 2014, Milano, Italy.
- Shangguan, W., 2014. Comparison of aggregation ways on soil property maps, 20th World Congress of Soil Science, Jeju, Korea.
- Shangguan, W. et al., 2013. A Global Soil Dataset for Earth System Modeling, GlobalSoilMap Conference 2013, Orleans, France.
- Shangguan, W. 2012. An investigation of soil particle-size distribution models for the conversion of soil texture classification from ISSS and Katschinski’s to FAO/USDA System. Paper presented at PEDOFARACT VII Workshop on Scaling in Particulate and Porous Media: Modeling and Use in Predictions. A Coruña, Spain.
- Shangguan, W. Land use as a stress factor to soil diversity and its protection in China, in Wageningen conference on applied soil science, 2011: Wageningen, The Netherland.
- Shangguan, W. and Yongjiu Dai, A conterminous China sand, silt and clay dataset using soil family map and soil profile data for regional modeling, in Pedometric 2009. 2009: Beijing, China.
- Shangguan, W., Yongjiu Dai, and Aizhong Ye, Global pedodiversity and soil spatial pattern using Shannon’s entropy, in Soil Geography: New Horizons. 2009: Huatulco Santa Cruz, Oaxaca, Mexico.
- Dai, Y., Shangguan, W. and Duan, Q. A Conterminous China High Resolution Land Dataset for Regional Land Surface Modeling, in AGU Fall Meeting 2009: San Francisco, USA.
国家自然科学基金-面上项目:基于可解释人工智能的土壤湿度和径流预报研究及旱涝事件的归因分析(42375144), 负责人。 2024年1月到2027年12月。50万元。
广东省农业厅技术服务项目:广东省第三次全国土壤普查土壤分类鉴定、土壤志和土种志、土壤类型图成果编制(GPCGD242200FG131F),负责人。2024年10月到2025年9月30日。229.5万元。
信宜市农业农村局技术服务项目:信宜市第三次全国土壤普查项目(K24-76140-008 ),子课题负责人。2024年5月1日到2025年12月31日。100万元。
中国气象局青年创新团队:粤港澳大湾区人工智能强降水预报,骨干成员。2024年1月1日到2026年12月31日。
深圳市市场监督管理局技术服务项目:深圳市第三次全国土壤普查项目(非信息化)(K23-77000-006 ),主要成员。2023年6月27日到2025年6月30日。825.93万元。
广东省基础与应用基础研究重大项目:高分辨率陆面环境模拟与预测研究(2021B0301030007),主要成员。2022年1月到2025年12月。2200万元
主持:
广东省农业环境与耕地质量保护中心(广东省农业农村投资项目中心)技术服务项目:土壤墒情分析与智能预报预测应用服务(K23-74110-026),负责人。2023年9月1日到2024年7月31日。25万元。
国家自然科学基金-面上项目:基于数据驱动和机器学习模型的土壤湿度的预测研究(41975122), 负责人。 2020年1月到2023年12月。63万元。
高校基本科研业务费-青年教师培育项目: 基于大数据和机器学习的土壤湿度预测研究(19lgpy35), 负责人。2019年1月到2020年12月。14万。
中山大学“百人计划二期”科研启动费,负责人。2017年1月到2019年12月。30万。
国家自然科学基金面上项目:全球岩石深度的空间估计与其在陆面模拟中的实现(41575072), 负责人。 2016年1月到2019年12月。82万元。
高校基本科研业务费-一般项目:GlobalSoilMap标准下的中国土壤属性制图(259-105577GK),负责人。2014年1月到2015年12月。10万。
国家青年自然科学基金项目:用于陆面模拟的中国土壤水力参数集的建立(41205037),负责人。2013年1月至2015年12月。25万元。
参与:
高州市农业农村局技术服务项目:广东省高州市第三次全国土壤普查试点县项目(成果汇总与编制)-包组一:主要成员。2022年11月30日-2022年12月31日。42万。
广东省气候变化与自然灾害研究重点实验室(2019年度评估良好)(2020B1212060025),主要成员 2020年6月- 2023年5月。300万。
中国科学院计算机网络信息中心横向项目:陆面过程模式分系统软件开发(K20-74110-001), 子课题负责人。2020年1月 -2024年4月。300万。
国家自然科学基金-广东大数据科学中心项目:基于大数据的海陆气环境预警预报关键技术(U1811464),主要成员。2019年1月-2022年12月。2155万元。
国家自然科学基金重点项目:高分辨率陆面水文过程模式研制(41730962), 主要成员。2018年1月-2022年12月。310万元。
中山大学国家自然科学基金重大项目培育专项-创新研究群体项目:地球系统模拟研究,参与人。2018年1月到2020年12月。200万。
国家重点研发计划:高分辨率全球陆面过程模式研发与应用(2017YFA0604300),主要成员。2017年7月-2022年6月。1798万元。
公益性行业(气象)科研专项经费:GRAPES陆面数据同化系统建设(GYHY201206008),参与人。2012年1月2014年12月。385万元。
973计划生态和环境过程模式的研制与改进项目:全球生物地球化学模型及其元素循环过程研究,(2010CB951802),骨干。2010年6月2014年12月。592万元。
公益性行业(气象)科研专项子课题:中国土壤质地以及近地层气候资料的建立(GYHY200706005),参与人。2009年9月-2012年12月。
863计划陆面模拟与同化系统研究课题:全球陆表特征参量产品生成与应用研究(2009AA122104),子专题负责人。2009年1月到2011年12月。573万元。
本科课程:《气象大数据与人工智能》、《程序设计与实践》、《气象数据分析与应用》、《概率论与数理统计》、《地理空间信息技术与应用》(部分)
研究生课程:《人工智能与地球系统科学》、《地理信息系统及其在大气科学中的应用》、《遥感原理与应用》(部分)、《陆面过程模拟》(部分)
爱思维尔2024中国高被引学者,2024。
中国气象服务协会人工智能技术委员会优秀会员, 2022。
京师英才,北京师范大学,2014。
(更新时间:2025年5月)