† Equally contributed, * Corresponding author.

1. Yang, Y., Guan, K., Peng, B., Liu, Y., Pan, M. (2024). Explicit consideration of plant xylem hydraulic transport improves the simulation of crop response to atmospheric dryness in the US Corn Belt. Water Resources Research.
2. Yang, Y., Peng, B.*, Guan, K.*, Pan, M., Franz, T.E., Cosh, M.H. , Bernacchi, C.J. (2024). Within-field soil moisture variability and temporal stability of agricultural fields in the US Midwest. Vadose Zone Journal.
3. Kimball, B. et al (including Peng, B.). (2024). Simulation of soil temperature under maize: An inter-comparison among 33 maize models. Agricultural and Forest Meteorology.
4. Liu, L., Zhou, W., Guan, K., Peng, B., Xu, S., Tang, J., Zhu, Q., Till, J., Jia, X., Jiang, C., Wang, S., Qin, Z., Kong, H., Grant, R., Mezbahuddin, S., Kumar, V., and Jin, Z. (2024). Knowledge-based artificial intelligence significantly improved agroecosystem carbon cycle quantification. Nature Communications.
5. 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.
6. 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.
7. Gomez-Casanovas, N., Mwebaze, P., Khanna, M., Branham, B., Time, A., DeLucia, E., Bernacchi, C., Knapp, A., Hoque, M., Du, X., Blanc-Betes, E., Barron-Gafford, G., Peng, B., Guan, K., Macknick, J., Miao, R., Miljkovic, N. (2023). Knowns, uncertainties, and challenges in agrivoltaics to sustainably intensify energy and food production. Cell Reports Physical Science.
8. Zhang, L., Zheng, H., Li, W., Olesen, J., Harrison, M., Bai, Z., Zou, J., Zheng, A., Bernacchi, C., Xu, X., Peng, B., Liu, K., Chen, F., Yin, X. (2023). Genetic progress battles climate variability: drivers of soybean yield gains in China from 2006 to 2020. Agronomy for Sustainable Development, 43:50.
9. Guan, K.*, Jin, Z.*, Peng, B.*, Tang, J.*, DeLucia, E., West, P., Jiang, C., Wang, S., Kim, T., Zhou, W., Griffis, T., Liu, L., Yang, W., Qin, Z., Yang, Q., Margenot, A., Stuchiner, E., Kumar, V., Bernacchi, C., Coppess, J., Novick, K., Gerber, J., Jahn, M., Khanna, M., Lee, D., Chen, Z., Yang, S. (2023). A scalable framework for quantifying field-level agricultural carbon outcomes. Earth-Science Reviews.
10. Zhang, J., Guan, K., Fu, R., Peng, B., Zhao, S., Zhuang, Y. (2023). Evaluating seasonal climate forecasts from dynamical models over South America. Journal of Hydrometeorology.
11. Kimball, B. et al (including Peng, B.). (2023). Simulation of evapotranspiration and yield of maize: An inter-comparison among 41 maize models. Agricultural and Forest Meteorology.
12. Zhang, J., Guan, K., Zhou, W., Jiang, C., Peng, B., Pan, M., Grant, R.,Franz, T., Suyker, A., Yang, Y., Chen, X., Lin, K., Ma, Z. (2023). Combining remotely sensed evapotranspiration and an agroecosystem model to estimate center-pivot irrigation water use at high spatio-temporal resolution. Water Resource Research.
13. Qin, Z., Guan, K., Zhou, W., Peng, B., Tang, J., Jin, Z., Grant, R., Hu, T., Villamil, M.B., DeLucia, E., Margenot, A.J., Mishra, U., Chen, Z., & Coppess, J. (2023). Assessing long-term impacts of cover crops on soil organic carbon in the central U.S. Midwestern agroecosystems. Global Change Biology.
14. Liu, K., Harrison, M.T., Yan, H., Liu, D.L., Meinke, H., Hoogenboom, G., Wang, B., Peng, B., Guan, K., Jaegermeyr, J., Wang, E., Zhang, F., Yin, X., Archontoulis, S., Nie, L., Badea, A., Man, J., Wallach, D., Zhao, J., Benjumea, A.B., Fahad, S., Tian, X., Wang, W., Tao, F., Zhang, Z., Rötter, R., Yuan, Y., Zhu, M., Dai, P., Nie, J., Yang, Y., Zhang, Y., & Zhou, M. (2023). Silver lining to a climate crisis in multiple prospects for alleviating crop waterlogging under future climates. Nature communications.
15. Zhou, Q., Wang, S., Liu, N., Townsend, P., Jiang, C., Peng, B., Verhoef, W., Guan, K. (2023). Towards operational atmospheric correction of airborne hyperspectral imaging spectroscopy: Algorithm evaluation, key parameter analysis, and machine learning emulators. Remote Sensing of Environment.
16. Zhou, W., Guan, K., Peng, B., Margenot, A., Lee, D.K., Tang, J., Jin, Z., Grant, R., DeLucia, E., Qin, Z., Wander, M., Wang, S. (2023). How does uncertainty of soil organic carbon stock affect the calculation of carbon budgets and soil carbon credits for croplands in the US Midwest? Geoderma.
17. Wang, S., Guan, K., Zhang, C., Zhou, Q., Wang, S., Wu, X., Jiang, C., Peng, B., Mei, W., Li, K., Li, Z., Yang, Y., Zhou, W., Huang, Y., Ma, Z. (2023). Cross-scale sensing of field-level crop residue cover: Integrating field photos, airborne hyperspectral imaging, and satellite data. Remote Sensing of Environment.
18. Burroughs, C., Montes, C., Moller, C., Mitchell, N., Michael, A., Peng, B., Kimm, H., Pederson, T., Lipka, A., Bernacchi, C., Guan, K., Ainsworth, E. (2022). Reductions in Leaf Area Index, Pod Production, Seed Size and Harvest Index Drive Yield Loss to High Temperatures in Soybean. Journal of Experimental Botany.
19. Ma, Z., Guan, K.*, Peng, B.*, Sivapalan, M., Li, L., Pan, M., Zhou, W., Warner, R., Zhang, J. (2022). Agricultural Nitrate Export Patterns Shaped by Crop Rotation and Tile Drainage. Water Research.
20. Zhou, Q., Guan, K., Wang, S., Jiang, C., Huang, Y., Peng, B., Chen, Z., Wang, S., Hipple, J., Schaefer, D., Qin, Z., Stroebel, S., Coppess, J., Khanna, M., Cai, Y. (2022). Recent rapid increase of cover crop adoption across the US Midwest detected by fusing multi‐source satellite data. Geophysical Research Letters.
21. Yang, Y., Liu, L., Zhou, W., Guan, K., Tang, J., Kim, T., Grant, R., Peng, B., Zhu, P., Li, Z., Griffis, T., 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 Meteorology.
22. Jong, M., Guan, K., Wang, S., Huang, Y., Peng, B. (2022). Improving field boundary delineation in ResUNets via adversarial deep learning . International Journal of Applied Earth Observation and Geoinformation.
23. 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.K., 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.
24. Liu, L., Xu, S., Jin, Z., Tang, J., Guan, K., Griffis, T., Erickson, M., Frie, A., Jia, X., Kim, T., Miller, L., Peng, B., Wu, S., Yang, Y., Zhou, W., Kumar, V. (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.
25. Xu, R., Li, Y., Guan, K., Zhao, L., Peng, B., Miao, C., Fu, B. (2021). Divergent responses of maize yield to precipitation in the United States. Environmental Research Letters.
26. Peng, B.*, Guan, K. (2021). Harmonizing climate-smart and sustainable agriculture. Nature Food.
27. Kumagai, E., Burroughs, C., Pederson, T., Montes, C., Peng, B., Kimm, H., Guan, K., Ainsworth, E., Bernacchi, C. (2021). Predicting biochemical acclimation of leaf photosynthesis in soybean under in-field canopy warming using hyperspectral reflectance. Plant, Cell & Environment.
28. Li, K., Guan, K., Jiang, C., Wang, S., Peng, B., and Cai, Y. (2021). Evaluation of four new land surface temperature (LST) products in the U.S. Corn Belt: ECOSTRESS, GOES-R, Landsat, and Sentinel-3. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
29. Zhang, J., Guan, K., Peng, B., Pan, M., Zhou, W., Grant, R., Franz, T., Rudnick, D., Heeren, D., Suyker, A., Yang, Y., Wu, G. (2021). Assessing different plant-centric water stress metrics for irrigation efficacy using soil-plant-atmosphere-continuum simulation. Water Resources Research.
30. Zhou, W.*, Guan, K.*, Peng, B.*, Wang, Z., Fu, R., Li, B., Ainsworth, E., DeLucia, E., Zhao, L., and Chen, Z. (2021). A generic risk assessment framework to evaluate historical and future climate-induced risk for rainfed corn and soybean yield in the U.S. Midwest. Weather and Climate Extremes.
31. Qin, Z., Guan, K., Zhou, W., Peng, B., Villamil, M., Jin, Z., Tang, J., Grant, R., Gentry, L., Margenot, A., 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.
32. Zhang, J.*, Guan, K.*, Peng, B.*, Pan, M., Zhou, W., Jiang, C., Kimm, H., Franz, T., Grant, R., Yang, Y., Rudnick, D., Heeren, D., Suyker, A., Bauerle, W., Miner, G. (2021). Sustainable irrigation based on co-regulation of soil water supply and atmospheric evaporative demand. Nature Communications.
33. Kim, T., Jin, Z., Smith, T., Liu, L., Yang, Y., Yang, Y., Peng, B., Phillips, K., Guan, K., Hunter, L., Zhou, W. (2021). Quantifying nitrogen loss hotspots and mitigation potential for individual fields in the US Corn Belt with a metamodeling approach. Environmental Research Letters.
34. 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 and Forest Meteorology.
35. Xu, T., Guan, K., Peng, B., Wei, S., Zhao, L. (2021). Machine Learning-Based Modeling of Spatio-Temporally Varying Responses of Rainfed Corn Yield to Climate, Soil, and Management in the U.S. Corn Belt. Frontiers in Artificial Intelligence, 4.
36. Kimm, H., Guan, K., Burroughs, C. H., Peng, B., Ainsworth, E. A., Bernacchi, C. J., Moore, C. E., Kumagai, E., Yang, X., Berry, J. A., Wu, G. (2021). Quantifying high-temperature stress on soybean canopy photosynthesis: The unique role of sun-induced chlorophyll fluorescence. Global Change Biology 27, 2403-2415.
37. Zhang, J., Guan, K., Peng, B., Jiang, C., Zhou, W., Yang, Y., Pan, M., Franz, T., Heeren, D., Rudnick, Peng Abimbola, O., Kimm, H., Caylor, K., Good, S., Khanna, M., Gates, J., Cai, Y. (2021). Challenges and opportunities in precision irrigation decision-support systems for center pivots. Environmental Research Letters.
38. Jiang, C., Guan, K., Wu, G., Peng, B., Wang, S. (2021). A daily, 250 m, and real-time gross primary productivity product (2000 – present) in the Contiguous United States. Earth System Science Data.
39. Yang, Y., Guan, K., Peng, B., Pan, M., Jiang, C., Franz, T. (2021). High-resolution spatially explicit land surface model calibration using field-scale satellite-based daily evapotranspiration product. Journal of Hydrology.
40. Peng, B.*, Guan, K.*, Tang, J., Ainsworth, E.A., Asseng, S., Bernacchi, C.J., Cooper, M., Delucia, E.H., Elliott, J.W., Ewert, F., Grant, R.F., Gustafson, D.I., Hammer, G.L., Jin, Z., Jones, J.W., Kimm, H., Lawrence, D.M., Li, Y., Lombardozzi, D.L., Marshall-Colon, A., Messina, C.D., Ort, D.R., Schnable, J.C., Vallejos, C.E., Wu, A., Yin, X., Zhou, W. (2020). Towards a multiscale crop modelling framework for climate change adaptation assessment. Nature Plants, 6, 338-348. (ESI Highly Cited Paper)
41. Peng, B.*, Guan, K.*, Zhou, W., Jiang, C., Frankenberg, C., Sun, Y., He, L., Köhler, P. (2020). Assessing the benefit of satellite-based Solar-Induced Chlorophyll Fluorescence in crop yield prediction. International Journal of Applied Earth Observation and Geoinformation, 90, 102126.
42. Zhou, W., Guan, K., Peng, B., Shi, J., Jiang, C., Wardlow, B., Pan, M., Kimball, J., Franz, T., Gentine, P., He, M., Zhang, J. (2020). Connections between hydrological cycle and crop yield in the rainfed U.S. Corn Belt. Journal of Hydrology.
43. Paul, R., Cai, Y., Peng, B., Yang, W., Guan, K., DeLucia, E. (2020). Spatiotemporal derivation of intermittent ponding in a maize-soybean landscape from Planet Labs CubeSat images. Remote Sensing.
44. He, L., Magney, T., Dutta, D., Yin, Y., Köhler, P., Grossmann, K., Stutz, J., Dold, C., Hatfield, J., Guan, K., Peng, B., Frankenberg, C. (2020). From the ground to space: Using solar-induced chlorophyll fluorescence to estimate crop productivity. Geophysical Research Letters, 47 (7), e2020GL087474.
45. Wang, C., Guan, K., Peng, B., Chen, M., Jiang, C., Zeng, Y., Wang, S., Wu, J., Yang, X.,Frankenberg, C., Köhler, P., Berry, J., Bernacchi, B., Zhu, K., Alden, C., Miao, G. (2020). Satellite footprint data from OCO-2 and TROPOMI reveal significant spatio-temporal and inter-vegetation type variabilities of solar-induced fluorescence yield in the U.S. Midwest. Remote Sensing of Environment. 241, 111728.
46. Jiang, C., Guan, K., Pan, M., Ryu, Y., Peng, B., Wang, S. (2020). BESS-STAIR: a framework to estimate daily, 30-meter, and all weather crop evapotranspiration using multi-source satellite data for the U.S. Corn Belt. Hydrology Earth System Science. 24, 1251–1273.
47. Benes, B.†, Guan, K.†, Lang, M.†, Long, S.†, Lynch, J.†, Marshall-Colón, C.†, Peng, B.†, Schnable, J.†, Sweetlove, L.†, Turk, M.† (2020). Multiscale computational models can guide experimentation and targeted measurements for crop improvement. The Plant Journal. (All authors contributed equally to the conception of the presented idea and the writing of the manuscript. Authors are listed alphabetically of their last names)
48. Wu, G., Guan, K., Jiang, C., Peng, B., Kimm, H., Chen, M., Yang, X., Wang, S., Suyker, A., Bernacchi, C., Moore, C., Zeng, Y., Berry, J., Cendrero-Mateo, M. P. (2020). Radiance-based NIRv as a proxy for GPP of corn and soybean. Environmental Research Letters. 15, 034009.
49. Li, Y., Guan, K., Peng, B., Franz, T., Wardlow, B., Pan, M. (2020). Quantifying irrigation cooling benefits to maize yield in the US Midwest. Global Change Biology, 26(5): 3065-3078.
50. Cheng, Y., Huang, M., Chen, M., Guan, K., Bernacchi, C., Peng, B., Tan, Z. (2020). Parameterizing perennial bioenergy crops in Version 5 of the Community Land Model based on site-level observations in the Central Midwestern United States. Journal of Advances in Modeling Earth Systems. 12: e2019MS001719.
51. Kimm, H., Guan, K., Jiang, C., Peng, B., Gentry, L., Wilkin, S., Wang, S., Cai, Y., Bernacchi, C., Peng, J., Luo, Y. (2020). Deriving high-spatiotemporal-resolution leaf area index for agroecosystems in the U.S. Corn Belt using Planet’s CubeSAT and STAIR fusion data. Remote Sensing of Environment. 239: 111615.
52. Cai, Y., Guan, K., Nafziger, E., Chowdhary, G., Peng, B., Jin, Z., Wang, S., Wang, S. (2019). Detecting in-season crop nitrogen stress of corn for field trials using UAV- and CubeSat-based multispectral sensing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 12(12): 5153-5166.
53. Zhou, W., Shi, J., Wang, T., Peng, B., Yu, Y., Zhao, R., Yao, R. (2019). New methods for deriving clear-sky surface longwave downward radiation based on remotely sensed data and ground measurements. Earth and Space Science. 6, 2071-2086.
54. DeLucia, E., Chen, S., Guan, K., Peng, B., Li, Y., Gomez-Casanovas, N., Kantola, I., Bernacchi, C., Long, S., Ort, D. (2019). Are We Approaching a Water Ceiling to Maize Yields in the United States? Ecosphere. 10(6):02773.
55. Zhu, P., Zhuang, Q., Welp, L., Ciais, P., Heimann, M., Peng, B., Li, W., Bernacchi, C., Rodenbeck, C., Keenan, T. (2019). Recent warming has resulted in smaller gains in net carbon uptake in northern high latitudes. Journal of Climate. 32, 5849-5863.
56. Li, Y., Guan, K., Schnitkey, G.D., DeLucia, E., Peng, B. (2019). Excessive rainfall leads to maize yield loss of a comparable magnitude to extreme drought in the United States. Global Change Biology, 25, 2325-2337.
57. Cai, Y., Guan, K., Lobell, D., Potgieter, A., Wang, S., Peng, J., Xu, T., Asseng, S., Zhang, Y., You, L., and Peng, B. (2019). Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches. Agricultural and Forest Meteorology. 274: 144-159. (ESI Highly Cited Paper)
58. Li, Y., Guan, K., Yu, A., Peng, B., Zhao, L., Li, B., & Peng, J. (2019). Toward building a transparent statistical model for improving crop yield prediction: Modeling rainfed corn in the U.S. Field Crops Research, 234, 55-65.
59. Peng, B.*, Guan, K.*, Pan, M., Li, Y. (2018). Benefits of seasonal climate prediction and satellite data for forecasting US maize yield. Geophysical Research Letters, 45, 9662-9671.
60. Peng, B.*, Guan, K.*, Chen, M., Lawrence, D.M., Pokhrel, Y., Suyker, A., Arkebauer, T., Lu, Y. (2018). Improving maize growth processes in the community land model: Implementation and evaluation. Agricultural and forest meteorology, 250–251, 64-89.
61. Zhou, W., Shi, J., Wang, T., Peng, B., Zhao, R., Yu, Y. (2018). Clear-Sky Longwave Downward Radiation Estimation by Integrating MODIS Data and Ground-Based Measurements. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(2), 450-459.
62. Xu, Z., Guan, K., Casler, N., Peng, B., Wang, S. (2018). A 3D convolutional neural network method for land cover classification using LiDAR and multi-temporal Landsat imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 144, 423-434.
63. Miao, G., Guan, K., Yang, X., Bernacchi, C., Berry, J., DeLucia, E., Wu, J., Moore, C., Meacham, K., Cai, Y., Peng, B., Kimm, H., Masters, M. (2018). Sun-Induced Chlorophyll Fluorescence, Photosynthesis, and Light Use Efficiency of a Soybean Field . Journal of Geophysical ResearchBiogeosciences, 123, 610–623. (ESI Highly Cited Paper)
64. Peng, B.*, Zhao, T.*, Shi, J., Lu, H., Mialon, A., Kerr, Y.H., Liang, X., Guan, K. (2017). Reappraisal of the roughness effect parameterization schemes for L-band radiometry over bare soil. Remote Sensing of Environment, 199, 63-77.
65. Zhou, W., Peng, B.*, Shi, J.* (2017). Reconstructing spatial-temporal continuous MODIS land surface temperature using the DINEOF method. Journal of Applied Remote Sensing, 11(4), 046016.
66. Zhou, W.†, Peng, B.†*, Shi, J.*, Wang, T., Dhital, Y.P., Yao, R., Yu, Y., Lei, Z., Zhao, R. (2017). Estimating high resolution daily air temperature based on remote sensing products and climate reanalysis datasets over glacierized basins: a case study in the Langtang Valley, Nepal. Remote Sensing, 9, 959.
67. Xiong, C., Shi, J., Cui, Y., Peng, B. (2017). Snowmelt Pattern Over High-Mountain Asia Detected From Active and Passive Microwave Remote Sensing. IEEE Geoscience and Remote Sensing Letters, 14, 1096-1100.
68. McColl, K.A., Wang, W., Peng, B., Akbar, R., Short Gianotti, D.J., Lu, H., Pan, M., Entekhabi, D. (2017). Global characterization of surface soil moisture drydowns. Geophysical Research Letters, 44, 3682-3690.
69. Cui, Y., Xiong, C., Lemmetyinen, J., Shi, J., Jiang, L., Peng, B., Li, H., Zhao, T., Ji, D., Hu, T. (2016). Estimating Snow Water Equivalent with Backscattering at X and Ku Band Based on Absorption Loss. Remote Sensing, 8, 505-522.
70. Wang, S., Liu, S., Mo, X., Peng, B., Qiu, J., Li, M., Liu, C., Wang, Z., Bauer-Gottwein, P. (2015). Evaluation of Remotely Sensed Precipitation and its Performance for Streamflow Simulations in Basins of the Southeast Tibetan Plateau. Journal of Hydrometeorology, 16, 2577-2594.
71. Li, D., Zhao, T., Shi, J., Bindlish, R., Jackson, T.J., Peng, B., An, M., Han, B. (2015). First Evaluation of Aquarius Soil Moisture Products Using In Situ Observations and GLDAS Model Simulations. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8, 5511-5525.
72. Peng, B., Shi, J., Ni-Meister, W., Zhao, T., Ji, D. (2014). Evaluation of TRMM Multi-satellite Precipitation Analysis (TMPA) Products and Their Potential Hydrological Application at an Arid and Semiarid Basin in China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7, 3915-3930.
73. Peng, B., Tian, J., Tian, Q. (2012). Preliminary Simulation Study of Lake Water Color Monitoring Oriented Satellite Remote Sensing System: Based on Hyperion Scene. Journal of Remote Sensing Information, 27, 91-98. (Full paper in Chinese and abstract in English).
74. Peng, B., Zhou, Y., Gao, P., Ju, W. (2011). Suitability Assessment of Different Interpolation Methods in the Gridding Process of Station Collected Air Temperature: a Case Study in Jiangsu Province, China. Journal of Geo-information Science, 13, 539-548. (Full paper in Chinese and abstract in English).

• Editorship

  • Associate Editor of the Agronomy Journal (IF=2.240) of American Society of Agronomy (ASA), 2020-present.
  • Associate Editor (2022-present) and Guest Associate Editor (2020-2022) of the Frontiers in Big Data.
  • Topic Editor and Review Board Member of Remote Sensing (IF=4.848), 2020-present.
  • Young Editorial Board member of Journal of Remote Sensing, 2022-present

• Grant Review Panelist

  • Panelist for NASA Soil Moisture Active-Passive (SMAP) Mission Science Team (2020)
  • Panelist for NASA Deep Space Climate Observatory (DSCOVR) Science Team (2021)
  • Proposal review for the Foundation for Food and Agriculture Research (FFAR) (2021)

• Service to Disciplinary and Professional Societies or Associations

  • NASA Science Team: Carbon Monitoring System Science Team, 2017-present.
  • Judge for AGU Fall Meeting Outstanding Student Paper Award (OSPA), 2017-2019.
  • Founding President of IEEE GRSS Student Branch Chapter at University of Chinese Academy of Sciences, 2013-2015.
  • Vice President of IEEE Student Branch at University of Chinese Academy of Sciences, 2013-2014

• Conference Session Organizer

  • Primary Convener and Session Chair of “Sustainable Agriculture and Climate Change: Monitoring and Modeling Soil Organic Carbon Dynamics and Greenhouse Gas Emissions of Agroecosystems”, AGU Fall Meeting in 2020 (2 online oral sessions, 1 online poster discussion session), 2021 (2 online oral sessions, and 1 online poster discussion session), 2022 (3 oral sessions, 1 poster session, and 1 online poster discussion session).
  • Primary Convener and Session Chair of “Carbon, Water, and Society: Adapting to Changes on Multiple Scales” (Oral and postal sessions), AGU Fall Meeting, 2019.

• Journal Peer Reviewer (>100 manuscripts for over 35 domain leading journals. Publons Top Reviewers for Geoscience awardee)

  • Nature Communications (1/2019; 1/2020; 1/2022), Nature Food (2/2019, 1/2021), Global Change Biology (1/2019, 2/2020, 1/2021), Global Change Biology-Bioenergy (1/2020), Earth-Science Reviews (1/2019), Remote Sensing of Environment (2/2017, 7/2018, 8/2019, 3/2020, 3/2021, 3/2022), ISPRS Journal of Photogrammetry and Remote Sensing (1/2019), Remote Sensing (3/2017, 6/2018, 5/2019, 2/2020), IEEE Trans. on Geoscience and Remote Sensing (2/2020), Agricultural and Forest Meteorology (7/2018, 3/2019, 3/2020, 1/2021), Geophysical Research Letters (1/2017, 2/2019), Water Resource Research (1/2019, 1/2020, 1/2021), Journal of Hydrometeorology (1/2016, 2/2017), Journal of Climate (2/2020), Climate Dynamics (1/2021), Hydrology and Earth System Sciences (2/2018, 1/2020), Geoscientific Model Development (1/2019, 1/2020), Journal of Geophysical Research-Atmospheres (1/2017, 2/2018), Journal of Geophysical Research-Biogeoscience (5/2018, 2/2020, 1/2021), Environmental Research Letters (2/2018, 3/2020), Earth’s Future (1/2018, 1/2019, 2/2022), Environmental Modelling and Software (1/2018), Advances in Atmospheric Sciences (1/2018, 2/2020), Frontiers in Plant Science (1/2019), Frontiers in Big Data (1/2020), Monthly Weather Review (2/2015), Journal of Atmospheric and Oceanic Technology (1/2014), Hydrological Processes (1/2019, 1/2020), Remote Sensing Applications: Society and Environment (1/2017), Water (1/2018), Regional Studies (1/2019), Communications Earth and Environment (1/2020), One Earth (2/2020, 1/2021), Computers and Electronics in Agriculture (2/2021), Agronomy Journal (1/2022), Geomatics, Natural Hazards and Risk (1/2020).

• Professional Membership

  • 2016-present Member of American Geophysical Union (AGU)
  • 2012-present Member of International Association of Hydrological Sciences (IAHS)
  • 2021-present Member of Sino-Ecologists Association Overseas
  • 2014-2016 Student Member of the Society of Photo-Optical Instrumentation Engineers (SPIE)
  • 2013-2016 Student Member of IEEE and IEEE Geoscience and Remote Sensing Society (IEEE GRSS)

• Class Teaching

  • UIUC CPSC 499: Agricultural Hydrology (Fall of 2024)
  • UIUC NRES 512: Ecosystem Biogeochemistry (Fall of 2022, Instructor for selected chapters)
  • UIUC NRES 598: Terrestrial Remote Sensing Applications (Spring of 2017, 2018, and 2019, Instructor for selected chapters and TA)
  • UIUC NRES 512: Biometeorology (Spring of 2022, Instructor for selected chapters)
  • UIUC NRES 499: Ecohydrology and Water Management (Fall of 2017, Instructor for selected chapters and TA)

• Mentoring

  • Postdoc: Mengqi Jia, Yaji Wang, Jie Yang
  • Graduate student: Qianyu Zhao, Huan Liu, Yuanxin Song, Haoyuan Yu
  • Graduate commitee: Kayla Vittore (PhD, CPSC), Qu Zhou (PhD, NRES), Kaiyuan Li (PhD, NRES), Zewei Ma (PhD, NRES), Ziyi Li (PhD, NRES), Lianlei Fu (MS, NRES)
  • Graduate interns: Nimisha Jasuja (Informatics, 2022)
  • Undergraduate interns: Bowen Song (2018, SPIN), Brain Yan (2019, SPIN), Junrui Ni (2019, SPIN), Yihong Jian (2021, SPIN), Chen Song (2021, SPIN), Isabelle Wagenvoord (2022, REU)

The Water, Agriculture, and Conservation Innovation Lab (WACI Lab) led by Dr. Bin Peng at University of Illinois Urbana-Champaign (UIUC) is recruiting multiple postdoctoral researchers and graduate students on the water-agriculture nexus from microscale to macroscale for 2025. We are looking for highly motivated and enthusiastic members who are interested in (1) improving plant traits and soil health by harnessing plant-soil-microbe interactions, and (2) unraveling the complexities of water, nutrient, and carbon cycles within diverse agricultural landscapes and their interconnectedness with downstream water quality and the broad environmental system. Please refer to the recruiting flyer for more details.

• Area 1: Plant-soil-microbe interaction modeling. This position is to build multi-scale plant-soil-microbe interaction models and further integrate these models with multi-omics and meta-omics data to investigate the synergies and tradeoffs of multiple plant or/and microbial traits and their impacts on crop water use efficiency (WUE) and nitrogen use efficiency (NUE). The work is expected to be fully integrated with advanced breeding and bioengineering programs. Candidates with strong research interests, background, and potential in computational systems biology, quantitative genetics, crop growth modeling, plant functional-structural modeling, root growth and rhizosphere modeling, soil hydrology and reactive transport modeling, microbial ecology and biogeochemistry modeling, earth system modeling are encouraged to apply.

• Area 2: Watershed hydrology and water quality modeling. This position is to build next-generation watershed hydrological and water quality models and use the systems modeling to advance our understanding of the complex two-way scaling from field-scale farming management to watershed-scale water quality. Candidates with strong research interests, background, and potential in agroecosystem modeling, biogeochemical modeling, coupled surface-subsurface hydrological modeling, watershed hydrological modeling, reactive transport modeling, river hydraulic modeling are particularly encouraged to apply.

• Area 3: Watershed hydrology and water quality monitoring and sensing. This position is to discover knowledge with high-frequency hydrological and water quality monitoring data from IoT sensor networks and to reveal spatial patterns of water quality variables through integrated sensing with unmanned aerial vehicles (UAVs) and satellites. Candidates with strong research interests, background, and potential in using high-frequency sensor data to investigate mechanisms and controls of nutrient export patterns and pathways from field to watershed scales or in water quality remote sensing (UAV and Satellite) are encouraged to apply for this position. Experiences in water quality sensor development, calibration, deployment, maintenance, data analytics and/or remote sensing are highly preferred.

• Area 4: Environmental data science for watershed hydrology and water quality. This position is to develop high-resolution environmental (such as field-scale soil moisture, evapotranspiration, and inundation dynamics) and management (such as conservation practices) data layers with advanced remote sensing and geospatial big data analytics and to further build data-driven or hybrid models linking environmental and management drivers with watershed-scale hydrological and water quality variables. Candidates with strong research interests, background, and potential in quantitative remote sensing (optical, thermal, and microwave), machine learning, deep learning, geospatial analytics are particularly encouraged to apply. Experiences in handling large-scale datasets are highly preferred.

• Area 5: Modeling agriculture in the Earth system. This position is to improve the representation of agriculture and human management in the Earth System Models for assessing different climate change adaptation and mitigation strategies related to agriculture and quantifying water (blue, green, and gray) and carbon footprints of agricultural production at regional to global scales. Candidates with strong research interests, background and potential in earth system modeling, large-scale hydrological modeling, ecosystem modeling, crop growth modeling, and geospatial data science are encouraged to apply.