个人简介

本人的工作以生态学理论为基础，综合运用机理模型、遥感观测、人工智能等大数据技术手段，研究农业生态系统对全球变化的响应机制，及其与其他环境系统之间的交互过程，为科学监测和管理农田生态系统的复杂过程提供理论依据和技术支撑，最终实现农业生产和生态环境保护的协同、可持续发展。相关论文发表在Science, 多个Nature子刊，Global Change Biology, Remote Sensing of Environment 等顶级期刊。曾获美国国家科学基金会职业生涯发展奖(NSF CAREER Award)。

教育经历

2011.8 – 2016.5 美国普渡大学，地球与大气科学，博士

2007.9 – 2011.7 bwin必赢国际官方网站，生态学，学士

工作经历

2024.9 – 至今  bwin必赢国际官方网站、生态研究中心，长聘副教授、研究员

2024.5 – 2024.8  美国明尼苏达大学，长聘副教授

2019.1 – 2024.5  美国明尼苏达大学，助理教授

2018.7 – 2019.1  美国Atlas AI公司，Lead Scientist

2016.7 – 2018.6  美国斯坦福大学，博士后

学术兼职

AGU期刊Earth's Future  副主编

Environmental Research：Food System  编委

SCIENCE CHINA Life Sciences  编委

欢迎对农业生态学、农业遥感、人工智能感兴趣的本科生、研究生和博士后加入研究组！

科学问题

如何在保障粮食安全的同时实现生态环境的可持续发展？

如何利用人工智能提升数值模型的预测精度和可迁移性？

科研项目

在美国期间主要项目(已全部于2024年9月回国入职前终止)：

【1】NSF CAREER: AI-enabled Integrated Nutrient, Streamflow, and Parcel sImulation for Resilient agroEcosystems (INSPIRE): a framework for climate-smart crop production and cleaner water  主持

【2】NSF III: Medium: Advancing Deep Learning for Inverse Modeling  参与

【3】USDA NIFA: National Artificial Intelligence Institute for Climate-Land Interactions, Mitigations, Adaption, Trade-offs and Economy (AI-CLIMATE)  参与

【4】USDA NIFA: High-resolution integrated assessments of tillage practice impacts on crop production and agroecosystem sustainability in the US Midwest - combining meta-analysis, airborne-satellite sensing, and process-based modeling  参与

【5】USDA FAS: ProsperCashew: Mapping cashew plantation and productivity in Cote d'Ivoire  主持

【6】NSF SCC-IRG Track 1: Co-Producing Community - An integrated approach to building smart and connected nutrient management communities in the US Corn Belt  参与

【7】USDA WinterTurf: A Holistic Approach to Understanding the Mechanisms and Mitigating the Effects of Winter Stress on Turfgrasses in Northern Climates  参与

【8】NSF SitS: Spatial and Temporal Patterns of Soil N and P Cycles Quantified by a Sensor-Model Fusion Framework: Implications for Sustainable Nutrient Management  主持

【9】DOE SMARTFARM: The System of Systems Solutions for Commercial Field-Level Quantification of Soil Organic Carbon and Nitrous Oxide Emission for Scalable Applications (SYMFONI)  参与

【10】USAID SIIL: Geospatial, Farming Systems, and Digital Tools Consortium Building a New Era of Predictive Agricultural Innovation to Improve the Livelihood of Small holder Farmers  参与

【11】USDA FAS: BeninCaju: Mapping cashew plantation and productivity in Benin  主持

【12】NASA LCLUC: Evaluating land use change and livelihood responses to large investments for high-value agriculture: managing risks in the era of Green Morocco Plan  主持

全部论文

https://scholar.google.com/citations?user=DghN-sAAAAAJ&hl=en

近5年代表性论文

#: 通讯作者，下划线: 指导学生

[1] Yang, Y., Tilman, D.^#, Jin, Z.^#, Smith, P., Barrett, C.B.^#, Zhu, Y.G., ... & Lobell, D.B.^# (2024). Climate change exacerbates the environmental impacts of agriculture. Science, 385(6713), eadn3747.

[2] Zhou, J., Zhu, P., Kluger, D. M., Lobell, D. B., & Jin, Z.^# (2024). Changes in the Yield Effect of the Preceding Crop in the US Corn Belt Under a Warming Climate. Global Change Biology, 30(11), e17556.

[3] Liu, L., Zhou, W., Guan, K.^#, Peng, B., Xu, S., Tang, J., Zhu, Q., Till, J., Jia, X., Jiang, C., Wang, S., ..., Kumar, V. & Jin, Z.^# (2024) Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems. Nature Communications, 15, 357.

[4] Yang, Q., Liu, L., Zhou, J., Ghosh, R., Peng, B., Guan, K., Tang, J., Zhou, W., Kumar, V., & 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, 113880.

[5] Yang, Y., Jin, Z.^#, Muller, N.D.^#, Driscoll, A., Hernandez, R.R., Grodsky, S., Sloat, L., …, Zhu, Y.G., & Lobell, D.B. (2023) Sustainable irrigation and climate feedbacks. Nature Food, 4, 654–663.

[6] Yin, L., Ghosh, R., Lin, C., Hale, D., Weigl, C., Obrowski, J., Zhou, J., Till, J., Jia, X., You, N., Mao, T., Kumar, V., & Jin, Z.^# (2023) Mapping smallholder cashew plantations to inform sustainable tree crop expansion in Benin. Remote Sensing of Environment, 295, 113695.

[7] Liu, L., Xu, S., Tang, J., Guan, K., Griffis, T.J., Erickson, M.D., Frie, A.L., Jia, X., Kim, T., Miller, L.T., Peng, B., ..., Kumar, V., & Jin, Z.^# (2022) KGML-ag: A Modeling Framework of Knowledge- Guided Machine Learning to Simulate Agroecosystems: A Case Study of Estimating N₂O Emission using Data from Mesocosm Experiments. Geoscientific Model Development, 15, 2839–2858. 

[8] Lin, C., Zhong, L., Song, X., Dong, J., Lobell, D.B., & Jin, Z.^# (2022) Early- and in-season crop type mapping without current-year ground truth: Generating labels from historical information via a topology-based approach. Remote Sensing of Environment, 274, 112994.

[9] Zhu, P., Kim, T., Jin, Z.^#, Lin, C., Wang, X., Ciais, P., Mueller, N.D., AghaKouchak, A., Huang, J., Mulla, D., & Makowski, D. (2022) The critical benefits of snowpack insulation and snowmelt for winter wheat productivity. Nature Climate Change, 12, 485–490.

[10] Benami, E.^#, Jin, Z.^#, Carter, M., Lobell, D.B., Kenduiywo, B., Ghosh, A., & Hijmans, R. (2021) Uniting remote sensing, crop modelling and economics for agricultural risk management. Nature Review Earth & Environment, 2, 140-159.