Manuscript in progress

  • Zhou et al. Knowledge-based artificial intelligence for agroecosystem carbon budget and crop yield estimation.
  • Wang et al. Disproportionate nonlinear impact of coupled heat stress and soil arsenic on rice yields
  • Yang et al. A flexible and efficient knowledge-guided machine learning data assimilation (KGML-DA) framework for agroecosystem prediction in the US Midwest
  • Zhou et al. A data transfer learning framework for mapping high spatiotemporal resolution LAI


(black for group memebers, # for the corresponding author):


  • Yang Y, Jin Z#, Muller ND#, Driscoll A, Hernandez RR, Grodsky S, …, & Lobell DB. (2023). Sustainable irrigation and climate feedbacks. Nature Food. [link]
  • Yin L, Ghosh R, Lin C, Hale D, Weigl C, Obarowski J, … & Jin Z#. (2023). Mapping smallholder cashew plantations to inform sustainable tree crop expansion in Benin. Remote Sensing of Environment295, 113695. [link]
  • Ye L, Guan K, Qin Z, Wang S, Zhou W, Peng B, Grant R, Tang J, Hu T, Jin Z, Schaefer D. (2023). Improved quantification of cover crop biomass and ecosystem services through remote sensing-based model-data fusion. Environmental Research Letters. [link]
  • Guan K#, Jin Z#, Peng B#, Tang J#, DeLucia E H, West P, … & Yang, S J. (2023). A scalable framework for quantifying field-level agricultural carbon outcomes. Earth-Science Reviews, 104462. [link]
  • You N, Dong J, Li J, Huang J, Jin Z. (2023). Rapid early-season maize mapping without crop labels. Remote Sensing of Environment. 290, 113496. [link]
  • Yang T, Dong J, Huang L, Li Y, Yan H, Zhai J, Wang J, Jin Z, Zhang G. (2023). A large forage gap in forage availability in traditional pastoral regions in China. Fundamental Research. 3, 188-200. [link]
  • Qin Z, Guan, Zhou W, Peng B, Tang J, Jin Z, Grant R, et al. (2023). Assessing long-term impacts of cover crops on soil organic carbon in the central US Midwestern agroecosystems. Global Change Biology, 29(9), 2572-2590. [link]
  • Liu Z, Liu L, Xie Y, Jin Z, Jia X. (2023). Task-Adaptive Meta-Learning Framework for Advancing Spatial Generalizability. AAAI-2023. [link]
  • He E, Xie Y, Liu L, Chen W, Jin Z, Jia X. (2023). Physics Guided Neural Networks for Time-aware Fairness: An Application in Crop Yield Prediction. AAAI-2023.


  • Ghosh R, Jia X, Yin L, Lin C, Jin Z, & Kumar V. (2022). Clustering augmented self-supervised learning: an application to land cover mapping. In Proceedings of the 30th International Conference on Advances in Geographic Information Systems. ACM SIGSPATIAL’22. 3, 1-10. [link]
  • Wang Z, Guan K, Peng B, Margenot A, Lee D, Tang J, Jin Z, Grant R, DeLucia E, Qin Z, Wander M, & Wang S (2022). How does uncertainty of soil organic carbon stock affect the calculation of carbon budgets and soil carbon credits for croplands in the U.S. Midwest? Geoderma, 429, 116254. [link]
  • Wang H, Yu L, Chen L, Zhang Z, Li X, Jin Z,  Liang N, Peng C,  & He J. (2022). Carbon fluxes and soil carbon dynamics along a gradient of biogeomorphic succession in alpine wetlands of Tibetan Plateau. Fundamental Research. 3, 151-159. [link]
  • Zhang, T., He, Y., DePauw, R., Jin, Z., Gravin D, Yue X, Anderson W, Li T, Dong X, Zhang T, & Yang, X. (2022). Climate change may outpace current wheat breeding yield improvements in North America. Nature communications, 13, 5591. [link] [EurekAlert!]
  • Zhu P, Burney J, Chang J, Jin Z, Mueller N, Xin Q, Xu J, Yu L, Makowski D, & Ciais P. (2022). Warming reduces global agricultural production by decreasing cropping frequency and yields. Nature Climate Change, 12, 1016-1023. [link]
  • Khondakar A, Dong J, Li Z, Deng X, Singha M, Rahman M, Jin Z, Wang S, Zhen L, & Xiao X (2022) Spatiotemporal pattern of the dynamics in area, production, and yield of Aus rice in Bangladesh and its response to droughts from 1980 to 2018. Journal of Geographical Sciences32, 2069-2084. [link]
  • Oluoch, K. O. A., De Groote, H., Gitonga, Z. M., Jin, Z., & Davis, K. F. (2022). A suite of agronomic factors can offset the effects of climate variability on rainfed maize production in Kenya. Scientific Reports, 12, 16043. [link]
  • Qiu, B. et al, including Jin Z. (2022). From cropland to cropped field: A robust algorithm for national-scale mapping by fusing time series of Sentinel-1 and Sentinel-2. International Journal of Applied Earth Observation and Geoinformation, 113, 103006. [link]
  • Yuan K, Zhu Q, et al. including Jin Z & Liu L (2022). Causality guided machine learning model on wetland CH4 emissions across global wetlands. Agricultural and Forest Meteorology, 324, 109115. [link]
  • Yang Y, Liu L, Zhou W, Guan K, Tang J, Kim T, Grant R, Peng B, Zhu P, Li Z, Griffis TJ, Jin Z# (2022) Distinct driving mechanisms of non-growing season N2O emissions call for spatial-specific mitigation strategies in the US Midwest. Agricultural and Forest Meterology, 324, 109108. [link] [ScienceDaily]
  • Li, Z., Guan, K., Zhou, W., Peng, B., Jin, Z., Tang J, Grant R, Nafziger E, Margenot A, Gentry L, DeLucia E, Yang W, Cai Y, Qin Z, Archontoulis S, Fernández F, Yu Z, Lee D, & Yang, Y. (2022). Assessing the impacts of pre-growing-season weather conditions on soil nitrogen dynamics and corn productivity in the U.S. Midwest. Field Crops Research, 284, 108563. [link]
  • Liu L, Xu S, Tang J, Guan K, Griffis TJ, Erickson MD, Frie AL, Jia X, Kim T, Miller LT, Peng B, Wu S, Yang Y, Zhou W, 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 [link] [ScienceDaily]
  • Lin C, Zhong L, Song X, Dong J, Lobell DB, 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. [link] [UMN news] []
  • Zhu P, Kim T, Jin Z#, Lin C, Wang X, Ciais P, Mueller N, 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. [link] [UMN news] [EurekAlert!]


  • Wang C, Wang X, Jin Z, Müller C, Pugh TAM, Chen A, Wang T, Huang L, Zhang Y, Li L, Piao S (2021) Occurrence of crop pests and diseases has largely increased in China since 1970. Nature Food, 3, 57–65.[link]
  • Ghosh R, Ravirathinam P, Jia X, Lin C, Jin Z, Kumar V (2021) Attention-augmented Spatio-Temporal Segmentation for Land Cover Mapping. IEEE BigData 2021, regular paper. [link]
  • Qin Z, Guan K, Zhou W, Peng B, Villamil M, Jin Z, Tang J, Grant Robert, Gentry LE, Margenot AJ, Bollero G, Li Z (2021) Assessing the impacts of cover crops on maize and soybean yield in the U.S. Midwestern agroecosystems. Field Crops Research, 273, 108264. [PDF]
  • Ghosh R, Jia X, Lin C, Jin Z, Kumar V (2021) Clustering augmented Self-Supervised Learning: An Application to Land Cover Mapping. arXiv preprint. [DeepSpatial’21 Best Paper, August 15, 2021, Singapore]
  • Kim T, Jin Z#, Smith T, Liu L, Yang Y, Yang Y, Peng B, Phillips K, Guan K, Hunter L, Zhou W (2021) A metamodeling approach to identifying nitrogen loss hotspots and mitigation potential in the US Corn Belt. Environmental Research Letters, 16, 075008. [PDF] [EurekAlert] [UMN News]
  • Zhou W, Guan K, Peng B, Tang J, Jin Z, Jiang C, Grant R, Mezbahuddin S (2021) Quantifying carbon budget, crop yields and their responses to environmental variability using the ecosys model for U.S. Midwestern agroecosystems. Agricultural & Forest Meteorology, 307, 108521.[link]
  • 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. [link]
  • Benami E#, Jin Z#, Carter M, Ghosh A, Hijmans RJ, Hobbs A, Kenduiywo B, Lobell DB (2021) Uniting Remote Sensing, Crop Modelling and economics for agricultural risj management. Nature Review Earth & Environment, 2, 140–159. [link]


  • Lv Z, Li G, Jin Z#, Benediktsson JA, Foody GM (2020) Iterative training sample expansion to increase and balance the accuracy of land classification fromVHR imagery. IEEE-TGRS, 59, 139 – 150. [link]
  • Franz TE et al. including Jin Z (2020) The role of topography, soil, and remotely sensed vegetation condition towards predicting crop yield. Field Crops Research, 252, 107788. [link]
  • Peng B et al. including Jin Z (2020) Towards a multiscale crop modelling framework for climate change adaptation assessment. Nature Plants, 6, 338-348. [link]
  • Cai Y, Guan K, Nafziger E, Chowdhary G, Peng B, Jin Z, Wang S, Wang S (2020) Detecting in-season crop nitrogen stress of corn for field trials using UAV- and CubeSat-based multispectral sensing. IEEE-JSTARS. [link]


  • Lobell DB, Azzari G, Marshall B, Gourlay S, Jin Z, Talip K, Murray S (2019) Assessing the determinants of crop productivity in Uganda with field and satellite approaches. American Journal of Agricultural Economics. [link]
  • Jin Z#, Archontoulis SV, Lobell DB (2019) How much will precision nitrogen management pay off? An evaluation based on simulating thousands of corn fields over the US Corn-Belt. Field Crops Research, 240, 12-22. [link]
  • Jin Z*, Azzari G*, You C, Di Tommaso S, Aston S, Burke M, Lobell DB (2019) Smallholder maize area and yield mapping at national scales with Google Earth Engine. Remote Sensing of Environment, 228, 115-128. (*The two authors contributed equally) [link]
  • Leakey ADB, Ferguson JN, Pignon CP, Alex Wu, 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.

2018 and prior

  • 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, Marshall B, Aton S, Lobell DB (2017) Mapping and explaining smallholder yield heterogeneity in Eastern Africa. Remote Sensing, 9, 931; doi:10.3390/rs9090931.
  • 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 of 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, Jerry M. Melillo (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.
  • Jin Z#, Zhuang Q, Dukes JS, Chen M, Sokolov A, He J-S, Zhang T, Luo T (2016) Temporal variability in the thermal requirements for vegetation phenology on the Tibetan plateau and its implications for carbon dynamics. Climatic Change, 138, 617-632.
  • Hao G, Zhuang Q, Zhu Q, He Y, Jin Z, Shen W (2015) Quantifying microbial ecophysiological effects on the carbon fluxes of forest ecosystems over the conterminous United States. Climatic Change, 133, 695-708.
  • Jin Z#, Zhuang Q, He J-S, Zhu X, Song W (2015) Net exchanges of methane and carbon dioxide on the Qinghai-Tibetan Plateau from 1979 to 2100. Environmental Research Letters, 10(8), 085007.
  • Song W, Wang H, Wang G, Chen L, Jin Z, Zhuang Q, He J-S (2015) Methane emissions from an alpine wetland on the Tibetan Plateau: Neglected but vital contribution of non-growing season. J. Geophys. Res. Biogeosci., 120, 1475-1490.
  • Hao G, Zhuang Q, Pan J, Jin Z, Zhu X, Liu S (2014) Soil temperature trends from 1948 to 2008 in the contiguous United States: an analysis with a process-based soil physical model and AmeriFlux data. Climatic Change. 126, 135-150.
  • Jin Z#, Zhuang Q, He J-S, Luo T, Shi Y (2013) Phenology shift from 1989 to 2008 on the Tibetan Plateau: An analysis with a process-based soil physical model and remote sensing data. Climatic Change, 119, 435-449.