Publication

Manuscript in progress:

Liu L, Xu S, Jin Z*, Tang J, Guan K, Griffis T, Erickson MD, Frie AL, Jia X, Kim T, Miler LT, Peng B, Wu S, Yang Y, Zhou W, Kumar V (under review) 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.

Zhu et al. including Jin Z (under review) Decreased cropping frequency exacerbates global caloric production losses from climate warming.

Zhou et al. including Jin Z (under review) How does the uncertainty of soil organic carbon stock affect cropland carbon budget calculation for the U.S. Midwest?

Ghosh R, Jia X, Lin C, Jin Z, Kumar V (2021) Clustering augmented Self-Supervised Learning: An Application to Land Cover Mapping. arXiv preprint arXiv:2108.07323. [DeepSpatial'21 Best Paper Award, August 15, 2021, Singapore]

Guan K*, Jin Z*, West P, Margenot A, DeLucia E, Griffis T, Peng B, Bernacchi C, Coppess J, Gerber J, Jahn M, Jiang C, Khanna M, Kim T, Liu L, Qin Z, Tang J, Wang S, Yang S-J, Zhou W (under review) A roadmap toward quantifying field-level agricultural carbon credits.

Ghosh R, Ravirathinam P, Jia X, Lin C, Jin Z, Kumar V (2021) Attention-augmented Spatio-Temporal Segmentation for Land Cover Mapping. arXiv preprint arXiv:2105.02963

Yang Y, Jin Z*, Mueller ND*, Hernandez RR, Grodsky S, Sloat L, Chester M, Zhu YG, Lobell DB (under review) Climate feedbacks from irrigation inform sustainable adaptation.

Lin C, Zhong L, Lobell DB, Jin Z*(under review) Using a topology-based classification framework to ensure temporal transferability in mapping the US Corn and Soybean.

Dong J et al. including Jin Z (under review) Satellite observations reveal stagnant rice production in North Korea.

Wang C et al. including Jin Z (under review) Large increase in crop pests and diseases over China driven by nighttime warming since 1970.

Xu X, Guan K, Peng B, Li Y, Jin Z, Wang S (under review) Assessing the drivers of spatio-temporal variations of corn yield in Illinois by evaluating A Satellite-based, scalable, and data-driven PredIctive framework for cRop yiEld (ASPIRE).

Published:

[26] 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]

[25] 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]

[24] 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. [PDF]

[23] 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 [PDF]

[22] Benami E*, Jin Z*, Carter M, Ghosh A, Hijmans RJ, Hobbs A, Kenduiywo B, Lobell DB (2021) Uniting Advances in Remote Sensing, Crop Modeling, & Economics for Understanding and Managing Weather Risk in Agriculture. Nature Review Earth & Environment, 2, 140–159. [PDF] [EurekAlert]

[21] 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. [PDF]

[20] 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.

[19] Peng B et al. including Jin Z (2020) Advancing multi-scale crop modeling for agricultural climate change adaptation assessment. Nature Plants, 6, 338-348.

[18] 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. doi: 10.1109/JSTARS.2019.2953489

[17] 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. doi: 10.1093/ajae/aaz051

[16] 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.

[15] 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)

[14] 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.

[13] 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.

[12] 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.

[11] 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.

[10] 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.

[9] 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.

[8] 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.

[7] 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.

[6] 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.

[5] 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.

[4] 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.

[3] 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.

[2] 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.

[1] 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.