個人簡介

本人的工作以生態(tài)學理論為基礎，綜合運用機理模型、遙感觀測、人工智能等大數(shù)據(jù)技術手段，研究農(nóng)業(yè)生態(tài)系統(tǒng)對全球變化的響應機制，及其與其他環(huán)境系統(tǒng)之間的交互過程，為科學監(jiān)測和管理農(nóng)田生態(tài)系統(tǒng)的復雜過程提供理論依據(jù)和技術支撐，最終實現(xiàn)農(nóng)業(yè)生產(chǎn)和生態(tài)環(huán)境保護的協(xié)同、可持續(xù)發(fā)展。相關論文發(fā)表在Science, 多個Nature子刊，Global Change Biology, Remote Sensing of Environment 等頂級期刊。曾獲美國國家科學基金會職業(yè)生涯發(fā)展獎(NSF CAREER Award)。

教育經(jīng)歷

2011.8 – 2016.5 美國普渡大學，地球與大氣科學，博士

2007.9 – 2011.7 北京大學，生態(tài)學，學士

工作經(jīng)歷

2024.9 – 至今  北京大學城市與環(huán)境學院、生態(tài)研究中心，長聘副教授、研究員

2024.5 – 2024.8  美國明尼蘇達大學，長聘副教授

2019.1 – 2024.5  美國明尼蘇達大學，助理教授

2018.7 – 2019.1  美國Atlas AI公司，Lead Scientist

2016.7 – 2018.6  美國斯坦福大學，博士后

學術兼職

AGU期刊Earth's Future  副主編

Environmental Research：Food System  編委

SCIENCE CHINA Life Sciences  編委

歡迎對農(nóng)業(yè)生態(tài)學、農(nóng)業(yè)遙感、人工智能感興趣的本科生、研究生和博士后加入研究組！

科學問題

如何在保障糧食安全的同時實現(xiàn)生態(tài)環(huán)境的可持續(xù)發(fā)展？

如何利用人工智能提升數(shù)值模型的預測精度和可遷移性？

科研項目

在美國期間主要項目(已全部于2024年9月回國入職前終止)：

【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  參與

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

【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  參與

【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  參與

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

【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)  參與

【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  參與

【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

全部論文

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

近5年代表性論文

#: 通訊作者，下劃線: 指導學生

[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.