Bin Peng

Publications

100 journal publications, listed in reverse chronological order.

* Corresponding author · † Equal contribution · bold italic underline indicates group members supervised by Dr. Peng · Citation count and Dr. Peng's full publication record are on Google Scholar.

2027

  1. Chen, Q., Peng, Y., Peng, B., Guan, K., Wang, L. (2027). Global synthesis of no-tillage and reduced tillage on soil nitrogen loss. Soil and Tillage Research.

2026

  1. Wang, Z., Timlin, D., Wu, X., Ebrahimi, E., Peng, B., Paff, K., Chang, C., Han, E., Fultz, L., Sadeghpour, A., Ahn, E., Fleisher, D., Hassan, M., Mitra, A., Beegum, S., Reddy, V., Horton, R., Tully, K. (2026). WheatSim: A process-based wheat model linking organ development, plant architecture, and field-scale responses under environmental variability. Computers and Electronics in Agriculture.
  2. Rateb, A., Scanlon, B.R., Pokhrel, Y., Shrestha, A., Jia, M., Peng, B. (2026). Freshwater availability in the Mississippi River Basin and adjacent Texas aquifers under human and climate pressures. Earth's Future.
  3. Gibson, C., Sible, C., Mashange, G., Becker, T., Gucker, D., Peng, B., Ford, T., Ngumbi, E. (2026). Keeping Pace With Intensifying Agricultural Field Inundation Events: A Framework for Testing the Mitigative Capacity of Current Best Management Practices. Global Change Biology.
  4. Zhang, Z., Guan, K., Grant, R.F., Zhou, W., Peng, B., Qin, R., Hu, T., Asseng, S., Zhang, J., Li, Z., Kidwell, K.K., Zhang, J. (2026). Observation-Constrained Agroecosystem Model Inversion Reveals Continental-Scale Variation of Winter Wheat Traits. Global Change Biology.
  5. Tang, R., Li, B., Moreno-García, B., Tajfar, E., Reba, M., Guan, K., Peng, B., Oikawa, P., Runkle, B. (2026). Depth-dependent hydrological and substrate dynamics enhance methane modeling and inform water management in rice systems. Agricultural and Forest Meteorology.
  6. Derner, J.D., Gonzalo Irisarri, J., Raynor, E.J., Ritten, J.P., Lents, C.A., Guan, K., Peng, B., Ye, L., Thoma, G., Porensky, L.M., Augustine, D.J. (2026). From data to decisions: the potential of real-time precision technologies to enhance adaptive grazing management for livestock ranchers. African Journal of Range & Forage Science.
  7. Jia, M.*, Peng, B.*, Guan, K.*, Lawrence, D.M., DeLucia, E.H., Knapp, A.K., Barron-Gafford, G.A., Khanna, M., Lombardozzi, D.L., Sturchio, M.A., Kannenberg, S.A., Zhao, L., McCall, J., Tang, J., Bernacchi, C.J., Mwebaze, P., Majeed, F., Lee, D., Time, A. (2026). Climate-driven divergence in biophysical and economic impacts of agrivoltaics. Proceedings of the National Academy of Sciences.
  8. Yang, J., Peng, B.*, Wang, Y., Ma, Z., Zhao, Q., Liu, L., Jia, X., Kumar, V., Pan, M., Jia, M., Li, X., Nieber, J., Jin, Z., Guan, K.* (2026). Knowledge-guided graph machine learning for spatially distributed prediction of daily discharge and nitrogen export dynamics. Water Research.
  9. Yang, W., Guan, K., DeLucia, E., Wagner-Riddle, C., Bernacchi, C., Yu, Z., Peng, B., Li, Z., Butterbach-Bahl, K., Griffis, T., Groffman, P., Hall, S., Johnson, J., Lee, D., Liu, L., Moore, D., Nevison, C., Novick, K., Ogle, S., Pelster, D., Silver, W., Tang, T., Weintraub-Leff, S., Zondlo, M., Brown, S., Eddy, W., Kantola, I., Vittore, K. (2026). N2Onet: a global collaborative network facilitating advances in measurement, modeling, and mitigation of agricultural soil nitrous oxide emissions. Environmental Research Letters.
  10. Jia, M.*, Peng, B.*, Guan, K.*, Lawrence, D.M., DeLucia, E.H., Lombardozzi, D.L., Sturchio, M.A., Kannenberg, S.A., Knapp, A.K., Du, X., Time, A., Bernacchi, C.J., Lee, D., Miljkovic, N., Branham, B., Khanna, M. (2026). Assessing the impact of agrivoltaics on water, energy, and carbon cycles using the Community Land Model version 5. Journal of Advances in Modeling Earth Systems.
  11. Li, Z., Sun, K., Guan, K., Wang, S., Peng, B., Clarisse, L., Van Damme, M., Coheur, P.F., Cady-Pereira, K., Shephard, M.W., Zondlo, M. (2026). Ammonia emissions and depositions over the contiguous United States derived from IASI and CrIS using the directional derivative approach. Atmospheric Chemistry and Physics.
  12. Furuta, D.C., Yang, J., Liu, L., Jin, Z., Guan, K., Peng, B., Li, J. (2026). Design and Test of a Lower-Cost Water-Quality Sensor for Nitrate. ACS ES&T Water.
  13. Wu, X., Zhou, Q., Guan, K., Wang, S., Hipple, J., Peng, B., Chen, Z., Qin, R. (2026). A framework to detect tillage practices from space: A demonstration in the US Midwest. Remote Sensing of Environment.
  14. Yang, J., Liu, L., Yang, Q., Jia, X., Peng, B., Guan, K., Jin, Z. (2026). Knowledge-guided graph machine learning improves corn yield mapping in the US Midwest. Remote Sensing of Environment.

2025

  1. Zhang, J., Guan, K., Chen, Z., Huang, Y., Zhao, K., Peng, B., Wang, S., Wu, X., Wang, S., Banerjee, A., Vergopolan, N., Fu, R., Zhao, S., Colussi, J. (2025). Transfer learning for improved crop yield predictions in a cross-scale pathway: a case study for Brazilian national soybean. International Journal of Applied Earth Observation and Geoinformation.
  2. Zhao, Q., Peng, B.*, Ma, Z., Jia, M., McIsaac, G.F., Robertson, D.M., Saad, D.A., Warner, R.E., Wu, X., Zhou, Q., Guan, K.* (2025). How do Hydrological Variability and Human Activities Control the Spatiotemporal Changes of Riverine Nitrogen Export in the Upper Mississippi River Basin?. Environmental Science & Technology.
  3. Ma, Z., Guan, K.*, Peng, B.*, Zhou, W., Grant, R., Tang, J., Sivapalan, M., Pan, M., Li, L., Jin, Z. (2025). Soil oxygen dynamics: a key mediator of tile drainage impacts on coupled hydrological, biogeochemical, and crop systems. Hydrology and Earth System Sciences.
  4. Li, Z., Guan, K., Zhou, W., Peng, B., Nafziger, E., Grant, R., Jin, Z., Tang, J., Margenot, A., Lee, D., Bernacchi, C., DeLucia, E., Ciampitti, I., Hu, T., Ye, L., Till, J., Jia, M. (2025). Comparing continuous-corn and soybean-corn rotation cropping systems in the US central Midwest: Trade-offs among crop yield, nutrient losses, and change in soil organic carbon. Agriculture, Ecosystems & Environment.
  5. Deng, Y., Peng, B.*, Guan, K.*, Runkle, B., Moreno-García, B., Wu, X., Wang, S., Zhou, Q., Reba, M. (2025). Detecting the onset of rice field inundation in the Lower Mississippi River Basin via Harmonized Landsat Sentinel-2 (HLS) satellite time series. ISPRS Journal of Photogrammetry and Remote Sensing.
  6. Zhang, J., Guan, K., Sivapalan, M., Jiang, C., Pan, M., Peng, B., Zhou, W., Franz, T., Chen, X., Lin, K., Li, Z. (2025). An upscaling approach for estimating field-level irrigation water use through the Budyko framework. Journal of Hydrology.
  7. Zhang, J., Guan, K., Chen, Z., Hipple, J., Huang, Y., Peng, B., Wang, S., Xu, X., Jin, Z., Zhao, K., Jong, M. (2025). Aligning satellite-based phenology in a deep learning model for improved crop yield estimates over large regions. Agricultural and Forest Meteorology.
  8. Ma, Z., Peng, B.*, Yue, Z., Zeng, H., Pan, M., Wu, X., Yang, J., Mai, L., Guan, K.* (2025). Embracing large language model (LLM) technologies in hydrology research. Environmental Research: Water.
  9. Nand, V. et al (including Peng, B.) (2025). Evaluation of multimodel averaging approaches for ensembling evapotranspiration and yield simulations from maize models. Journal of Hydrology.
  10. Yang, Y., Guan, K.*, Peng, B.*, Feng, X., Xu, X., Pan, M., Sloan, B.P., Zhang, J., Zhou, W., Li, L. and Sivapalan, M., Ainsworth, E., Novick, K., Yang, Z., Wang, S. (2025). A unified framework to reconcile different approaches of modeling transpiration response to water stress: Plant hydraulics, supply demand balance, and empirical soil water stress function. Journal of Advances in Modeling Earth Systems.
  11. Qin, R., Guan, K., Peng, B., Zhang, F., Zhou, W., Tang, J., Hu, T., Grant, R., Runkle, B.R., Reba, M. and Wu, X. (2025). A model-data fusion approach for quantifying the carbon budget in cotton agroecosystems across the United States. Agricultural and Forest Meteorology.

2024

  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.

2023

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. 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.

2022

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.

2021

  1. 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.
  2. Peng, B.*, Guan, K. (2021). Harmonizing climate-smart and sustainable agriculture. Nature Food.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. 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.
  15. 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.

2020

  1. 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).
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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).
  9. 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.
  10. 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.
  11. 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.
  12. 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.

2019

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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).
  7. 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.

2018

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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).

2017

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.

2016

  1. 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.

2015

  1. 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.
  2. 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.

2014

  1. 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.

2012

  1. 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).

2011

  1. 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).