Gender:Male
Alma Mater:Beijing Normal University
Education Level:With Certificate of Graduation for Doctorate Study
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Pei Zhan is now a lecturer and graduate advisor at Nanjing University of Information Science & Technology. His research mainly focus on agricultural & ecological remote sensing and the interaction between climate change and crops. He is the PI of three programs (including one National Natural Science Foundation of China). He has published over 15 papers on journals such as Remote Sensing of Environment, Earth's Future, Agricultural and Forest Meteorology, Science of The Total Environment (including one highly cited paper of ESI). His research is cited by AR6 of IPCC reports.
Area of Interests:
Agricultural remote sensing: Crop mapping, crop phenology, crop yield estimation
Ecological remote sensing: land surface phenology, net primary production
Climate change and crop: crop responses to climate change, adaptation and mitigation strategies
Degrees:
Ph. D., Cartography and Geographical Information System, Beijing Normal University, 2021
B. S., Geographical Information System, Northwest University, 2015
Publications (*Corresponding author):
- Han, X., Zhan, P.*, Tian, C. (2025). Characterization of spatial and temporal changes in net primary productivity of crop vegetation in Jiangsu Province during 2003-2023. Journa of Southern Agriculture (in Chinese).
- Bi, M., Zhan, P.*, He, Y., Fan, L. (2025). Research on Ruce Yield Estimation Method in Chongqing Based on Remote Sensing Data and Crop model. Jiangsu Journal of Agricultural Sciences (in Chinese).
- Li, N., Zhan, P.*, Pan, Y., Qiu, L., Wang, J., & Xu, W. (2024). Quantifying uncertainty: The benefits of removing snow cover from remote sensing time series on the extraction of climate-influenced grassland phenology on the Qinghai–Tibet Plateau. Agricultural and Forest Meteorology, 345, 109862.doi: https://doi.org/10.1016/j.agrformet.2023.109862 (IF: 6.2)
- Zhan, P., Zhu, W., Zhang, T., & Li, N. (2023). Regional inequalities of future climate change impact on rice (Oryza sativa L.) yield in China. Science of The Total Environment, 898, 165495. doi: https://doi.org/10.1016/j.scitotenv.2023.165495 (IF: 9.8)
- Shi, T., Zhan, P., Shen, Y., Wang, H., Wu, C., & Li, J. (2023). Using multi-technology to characterize transboundary Hg pollution in the largest presently active Hg deposit in China. Environmental Science and Pollution Research, 30(34), 82124-82141. doi: https://doi.org/10.1007/s11356-023-28080-0
- Xie, Z., Zhu, W., He, B., Qiao, K., Zhan, P., & Huang, X. (2022). A background-free phenology index for improved monitoring of vegetation phenology. Agricultural and Forest Meteorology, 315, 108826. doi:https://doi.org/10.1016/j.agrformet.2022.108826
- Zhan, P., Zhu, W., & Li, N. (2021). An automated rice mapping method based on flooding signals in synthetic aperture radar time series. Remote Sensing of Environment, 252, 112112. doi:https://doi.org/10.1016/j.rse.2020.112112 (IF: 10.164; ESI highly cited paper)
- Li, X., Zhu, W., Xie, Z., Zhan, P., Huang, X., Sun, L., & Duan, Z. (2021). Assessing the Effects of Time Interpolation of NDVI Composites on Phenology Trend Estimation. Remote Sensing, 13(24), 5018. doi:10.3390/rs13245018
- Huang, X., Zhu, W., Wang, X., Zhan, P., Liu, Q., Li, X., & Sun, L. (2020). A Method for Monitoring and Forecasting the Heading and Flowering Dates of Winter Wheat Combining Satellite-Derived Green-up Dates and Accumulated Temperature. Remote Sensing, 12(21), 3536. doi:10.3390/rs12213536
- Li, N., Zhan, P., Pan, Y., Zhu, X., Li, M., & Zhang, D. (2020). Comparison of Remote Sensing Time-Series Smoothing Methods for Grassland Spring Phenology Extraction on the Qinghai–Tibetan Plateau. Remote Sensing, 12(20), 3383. doi: doi:10.3390/rs12203383
- Zhan, P., Zhu, W., Zhang, T., Cui, X., & Li, N. (2019). Impacts of sulfate geoengineering on rice yield in China: Results from a multimodel ensemble. Earth's Future, 7(4), 395-410. doi:10.1029/2018ef001094 (IF: 7.495)
- Xie, Z., Zhu, W., Qiao, K., Zhan, P., & Li, P. (2019). Seasonal differences in relationships between changes in spring phenology and dynamics of carbon cycle in grasslands. Ecosphere, 10(5), e02733. doi:10.1002/ecs2.2733
- Tang, K., Zhu, W., Zhan, P., & Ding, S. (2018). An Identification Method for Spring Maize in Northeast China Based on Spectral and Phenological Features. Remote Sensing, 10(2), 193. doi:10.3390/rs10020193
Fundings:
- National Natural Science Foundation of China, Study on the automated extraction of fine rice cropping patterns with a combined use of optical and SAR remote sensing, 2024.01-2026.12
- Jiangsu Key Laboratory of Agricultural Meteorology Foundation, Study on the regional inequalities and countermeasures of the climate change impact on rice yield in China, 2024.01-2025.12
- The Startup Foundation for Introducing Talent of NUIST, The regional differences of climate change impact on rice yield in southern China and its countermeasures, 2022.03-2025.02