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 Peng   Fu

Peng Fu Assistant Professor of Geospatial Science and Environment

Peng Fu is an Assistant Professor of Geospatial Data Science and a lead research scientist with the Center for Environment, Energy, and Economy at HU. Before working at HU, he was a research fellow at University of Illinois at Urbana-Champaign, Illinois, USA, where he was financially supported by the Bill & Melinda Gates Foundation to conduct research in phenotyping of photosynthesis for improved crop production in the U.S. Midwest and worldwide. He obtained his Ph.D. degree in Spatial and Earth Sciences at Indiana State University in 2018. His research interests cover a wide range of remote sensing and geospatial applications in environment, plant physiology, natural resources, and urbanization. He has been the chair (2019-2020) of the early-career professional council and works as a scholarship coordinator (2019-present) for the American Society for Photogrammetry and Remote Sensing. Peng currently also serves as an editorial board member/special issue editor for a few journals such as Remote Sensing and Frontiers in Plant Sciences. Peng is also the recipient of the ASPRS Presidential Citation Award (2021) for helping manage and oversee ASPRS scholarship programs.

Research Interests


  • GIS/Remote Sensing: GIS/Remote sensing for agriculture, cities, and ecology
  • Environmental Applications: digital agriculture, urban climate/ecology, soil mapping, and environmental health
  • Geospatial data analytics: data fusion, downscaling, deep learning, geospatial programming (e.g., GEE and computer clusters)
  • Earth System Modeling: e.g., Weather and Research Forecasting Model, Terrestrial Biosphere Models, and process-based crop models such as APSIM

Selected Awards & Scholarships

ESIP-EarthCube Scholarship06/2021
Best Reviewer Award for Remote Sensing of Environment in 202004/2021
ASPRS Presidential Citation Award03/2021
NSF Travel Award, USGIS-AAG Summer School07/2019
Phenome 2019 Travel Award, American Society of Plant Biologists02/2019
Benjamin Moulton Geography Scholarship, Indiana State University05/2018
Research & Creativity Award (Highest Doctoral Research Honor at Indiana State University)03/2018


Postdoctoral Research Fellow in GIS/Remote sensing in Ecophysiology, University of Illinois at Urbana-Champaign,  Urbana, IL

Ph.D. in Spatial and Earth Sciences and M.A. in Geography, Indiana State University (Department of Earth and Environmental Systems), Terre Haute, IN

B.S. with honors in Geographic Information System (GIS), Huazhong Agricultural University (College of Resources and the Environment), Wuhan, China


  1. Guo, Q., Zhang, K., Wang, B., Cao, S., Xue, T., Zhang, Q., Tian, H., Fu, P., Zhang, J.J. and Duan, X., 2022. Chemical constituents of ambient fine particulate matter and obesity among school-aged children: A representative national study in China. Science of The Total Environment, 157742.
  2. Huang, S., Zhang, X., Chen, N., Ma, H., Zeng, J., Fu, P., & Niyogi, D. (2022). Generating high-accuracy and cloud-free surface soil moisture at 1 km resolution by point-surface data fusion over the Southwestern US. Agricultural and Forest Meteorology, 321, 108985.
  3. Huang, S., Zhang, X., Chen, N., Ma, H., Fu, P., Dong, J., et al. (2022). A novel fusion method for generating surface soil moisture data with high accuracy, high spatial resolution, and high spatio-temporal continuity. Water Resources Research, 58, e2021WR030827.
  4. Fu, H., Shao, Z., Fu, P., Huang, X., Cheng, T., & Fan, Y. (2022). Combining ATC and 3D-CNN for reconstructing spatially and temporally continuous land surface temperature. International Journal of Applied Earth Observation and Geoinformation, 108, 102733.
  5. Fu P, Montes CM, Siebers MH, Gomez-Casanovas N, McGrath JM, Ainsworth EA, Bernacchi CJ. Advances in field-based high-throughput photosynthetic phenotyping, Journal of Experimental Botany, 2022. erac077.
  6. Fu P.*, Jaiswal D., McGrath J., Wang S., Long S., Bernacchi C. 2022. Drought imprints on crops can reduce yield loss: Nature’s insights for food security. Food and Energy Security, 11(1), e332.
  7. Fu P.*, Hu L, Ainsworth EA, Tai X, Myint SW, Zhan W, Blakely BJ, Bernacchi CJ. Enhanced drought resistance of vegetation growth in cities due to urban heat, CO2 domes and O3 Environmental Research Letters, 16(12), 124052.
  8. Fu H., Shao Z., Fu P.*, Zhan W., Xie Y., Cheng T. 2021. Reconciling the inconsistency of annual temperature cycles modeled from Landsat and MODIS LSTs through a percentile approach. International Journal of Remote Sensing, 42(20), 7907-7930.
  9. Fu P., Meacham-Hensold K, Siebers M., Bernacchi C. 2021. The inverse relationship between solar-induced fluorescence yield and photosynthetic capacity: benefits for field phenotyping. Journal of Experimental Botany, 72(4), 1295-1306.
  10. Meacham-Hensold K.+, Fu P.+, Wu J., Serbin S., Montes C., Ainsworth E., Guan K., Dracup E., Pederson T., Bernacchi C. 2020. Plot level rapid screening for photosynthetic parameters using proximal hyperspectral imaging. Journal of Experimental Botany, 71(7), 2312-2328. (+ Joint First Author)
  11. Fu P., Meacham-Hensold K., Guan K., Wu J., Bernacchi C. 2020. Estimating photosynthetic traits from hyperspectral imagery: A synthesis of spectral indices, numerical inversion, and partial least square regression. Plant, Cell & Environment, 43, 1241-1258.
  12. Shao Z., Cai J., Fu P.*, Hu L., Liu T. 2019. Deep learning-based fusion of Landsat-8 and Sentinel-2 images for a harmonized surface reflectance product. Remote Sensing of Environment, 235, ‡
  13. Fu P.*, Xie Y., Moore C., Bernacchi C. 2019. A comparative analysis of anthropogenic CO2 emissions at city level using OCO-2 observations: A global perspective. Earth’s Future, 7(9), 1058-1070.
  14. Fu P., Meacham-Hensold K., Guan K., Bernacchi C. 2019. Hyperspectral leaf reflectance as proxy for photosynthetic capacities: an ensemble approach based on multiple machine learning algorithms. Frontiers in Plant Science, 10, 730. (IF: 4.402)
  15. Fu P.*, Xie Y., Weng Q., Myint S., Meacham-Hensold K., Bernacchi C. 2019. A physical model-based method for retrieving LSTs under cloudy skies in urban areas. Remote Sensing of Environment, 230, 111191.
  16. Fu P.* Responses of Vegetation Productivity to Temperature Trends over Continental United States from MODIS Imagery. IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing, 12(4), 1085-1090, 10.1109/JSTARS.2019.2903080.
  17. Fu P., Weng Q. 2018. Variability in Annual Temperature Cycle in the Urban Areas of the United States as Revealed by MODIS Imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 146, 65-73.

(* indicates corresponding authorship)

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