Mapping agriculture features using high-resolution satellite imagery, forecasting crop yields, integrating crop models with remote sensing for precision nitrogen management, impacts of climate change on agroecosystem
Zhenong Jin’s research integrates remote sensing, computational modeling, and machine learning to address agricultural and environmental sustainability under a changing climate. Before joining UMN, he received Postdoc training at Stanford University and was the Lead Crop Scientist at AtlasAI, where he directed product development of high-resolution maps of crop types and yield in multiple African countries.
Current research focuses are:
Remote sensing and deep learning for agroecosystem management
Process-based modeling and physics-guided machine learning in agroecosystem systems
Field-Level quantification of SOC and GHG Emission for large-scale applications
Climate change impacts and adaptation
Lin C, Jin Z, Mulla D, Ghosh R, Guan K, Kumar V, Cai Y (2021) Towards large-scale mapping of tree crops with high-resolution satellite imagery and deep learning algorithms: a case study of olive orchards in Morocco. Remote Sensing, 13, 1740. doi.org/10.3390/rs13091740
Benami E*, Jin Z*, Carter M, Lobell DB, 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. (*joint-lead authors)
Lv Z, Li G, Jin Z, Benediktsson JA, Foody GM (2020) Iterative training sample expansion to increase and balance the accuracy of land classification from VHR Imagery. IEEE Transactions on Geoscience and Remote Sensing, 59, 139-150.
Jin Z, Azzari G, You C, Di Tommaso S, Burke M, David B. Lobell (2019) Smallholder maize area and yield mapping at national scales with Google Earth Engine. Remote Sensing of Environment, 228, 115-128.
Jin Z, Archontoulis SV, Lobell DB (2019) Heterogeneous benefit of variable rate nitrogen technology over the Midwestern US: an assessment based on satellite imagery and crop modeling.Field Crops Research, 240, 12-22.
Leakey ADB, Ferguson J, Pignon CP, Wu A, Jin Z, Hammer GL, Lobell DB (2019) Water Use Efficiency – a key constraint and opportunity for improvement of future plant productivity. Annual Review of Plant Biology, 70, 781-808.
Zhu P, Jin Z, Zhuang Q, Ciais P, Bernacchi C, Wang X, Makowski D, Lobell DB (2018) The important but weakening maize yield benefit of grain filling prolongation in the US Midwest. Global Change Biology, 24, 4718-4730.
Jin Z, Ainsworth, E, Leakey ADB, Lobell DB (2018) Increasing drought and diminishing benefits of elevated carbon dioxide for soybean yields across the US Midwest. Global Change Biology, 24, e522-e533.
Jin Z, Azzari G, Lobell DB (2017) Improving the accuracy of satellite-based high-resolution yield estimation: a test of multiple scalable approaches. Agricultural & Forest Meteorology, 247, 207-220.
Jin Z, Zhuang Q, Wang J, Archontoulis SV, Zobel Z, Kotamarthi VR (2017) The combined and separate impacts of climate extremes on the current and future US rainfed maize and soybean production under elevated CO2. Global Change Biology, 23, 2687-2704.
Jin Z, Prasad R, Shriver J, Zhuang Q (2017) Crop model- and satellite imagery-based recommendation tool for variable rate N fertilizer application for the US Corn system. Precision Agriculture, 18, 779-800.
Jin Z, Zhuang Q, Tan Z, Dukes JS, Bangyou Zheng, Melillo JM (2016) Do maize models capture the impacts of heat and drought stresses on yield? Using algorithm ensembles to identify successful approaches. Global Change Biology, 22, 3112-3126.