Professor | Xu Shan | Asia Growth Partners

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Name	Xu Shan
Contact Information	xushan@njau.edu.cn
Language ability	English, Chinese
Province	Jiangsu

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Name

Xu Shan

Contact Information

xushan@njau.edu.cn

Language ability

English, Chinese

Province

Jiangsu

University/Lab Affiliation and Title	Nanjing Agricultural University
Faculty	academy for adyanced interdisciplinary studies
Academic Background & Achievements	2015.9-2021.6: Ph.D. in Cartography and Geographic Information System, Beijing Normal University 2019.1-2020.1: Joint Ph.D. student, University of Helsinki 2008.9-2012.6: B.Sc. in Geographic Information System, Huazhong Agricultural University

University/Lab Affiliation and Title

Nanjing Agricultural University

Faculty

academy for adyanced interdisciplinary studies

Academic Background & Achievements

2015.9-2021.6: Ph.D. in Cartography and Geographic Information System, Beijing Normal University
2019.1-2020.1: Joint Ph.D. student, University of Helsinki
2008.9-2012.6: B.Sc. in Geographic Information System, Huazhong Agricultural University

Areas of Focus	Quantitative remote sensing of vegetation and crop growth monitoring Spectral-based crop physiological phenotype extraction
Publications	Exploring the Sensitivity of Solar-Induced Chlorophyll Fluorescence at Different Wavelengths in Response to Drought, Xu, S., Liu, Z., Han, S., Chen, Z., He, X., Zhao, H., & Ren, S., 2023 Structural and photosynthetic dynamics mediate the response of SIF to water stress in a potato crop, Xu, S., Atherton, J., Riikonen, A., Zhang, C., Oivukkamäki, J., MacArthur, A., ... & Porcar-Castell, A., 2021 On the estimation of the leaf angle distribution from drone based photogrammetry, Xu, S., Zaidan, M. A., Honkavaara, E., Hakala, T., Viljanen, N., Porcar-Castell, A., ... & Atherton, J., 2020 Diurnal response of sun-induced fluorescence and PRI to water stress in maize using a near-surface remote sensing platform, Xu, S., Liu, Z., Zhao, L., Zhao, H., & Ren, S., 2018 Proximal and remote sensing in plant phenomics: Twenty years of progress, challenges and perspectives, Tao, H., Xu, S., Tian, Y., Li, Z., Ge, Y., Zhang, J., ... & Jin, S., 2022 Retrieving the diurnal FPAR of a maize canopy from the jointing stage to the tasseling stage with vegetation indices under different water stresses and light conditions, Zhao, L., Liu, Z., Xu, S., He, X., Ni, Z., Zhao, H., & Ren, S., 2018 An automated comparative observation system for sun-induced chlorophyll fluorescence of vegetation canopies, Zhou, X., Liu, Z., Xu, S., Zhang, W., & Wu, J., 2016 Is satellite Sun-Induced Chlorophyll Fluorescence more indicative than vegetation indices under drought condition?, Cao, J., An, Q., Zhang, X., Xu, S., Si, T., & Niyogi, D., 2021 Using High-Frequency PAR Measurements to Assess the Quality of the SIF Derived from Continuous Field Observations, Han, S., Liu, Z., Chen, Z., Jiang, H., Xu, S., Zhao, H., & Ren, S., 2022 Using the diurnal variation characteristics of effective quantum yield of PSII photochemistry for drought stress detection in maize, Chen, Z., Liu, Z., Han, S., Jiang, H., Xu, S., Zhao, H., & Ren, S., 2022 Simultaneous prediction of wheat yield and grain protein content using multitask deep learning from time-series proximal sensing, Sun, Z., Li, Q., Jin, S., Song, Y., Xu, S., Wang, X., ... & Jiang, D., 2022 What does the NDVI really tell us about crops? Insight from proximal spectral field sensors, Atherton, J., Zhang, C., Oivukkamäki, J., Kulmala, L., Xu, S., Hakala, T., ... & Porcar-Castell, A., 2022 Deciphering the contributions of spectral and structural data to wheat yield estimation from proximal sensing, Li, Q., Jin, S., Zang, J., Wang, X., Sun, Z., Li, Z., Xu, S., Ma, Q., … & Jiang, D., 2022 Assessing the response of satellite sun-induced chlorophyll fluorescence and MODIS vegetation products to soil moisture from 2010 to 2017: a case in Yunnan Province of China, Ni, Z., Huo, H., Tang, S., Li, Z. L., Liu, Z., Xu, S., & Chen, B., 2019

Areas of Focus

Quantitative remote sensing of vegetation and crop growth monitoring
Spectral-based crop physiological phenotype extraction

Publications

Exploring the Sensitivity of Solar-Induced Chlorophyll Fluorescence at Different Wavelengths in Response to Drought, Xu, S., Liu, Z., Han, S., Chen, Z., He, X., Zhao, H., & Ren, S., 2023
Structural and photosynthetic dynamics mediate the response of SIF to water stress in a potato crop, Xu, S., Atherton, J., Riikonen, A., Zhang, C., Oivukkamäki, J., MacArthur, A., ... & Porcar-Castell, A., 2021
On the estimation of the leaf angle distribution from drone based photogrammetry, Xu, S., Zaidan, M. A., Honkavaara, E., Hakala, T., Viljanen, N., Porcar-Castell, A., ... & Atherton, J., 2020
Diurnal response of sun-induced fluorescence and PRI to water stress in maize using a near-surface remote sensing platform, Xu, S., Liu, Z., Zhao, L., Zhao, H., & Ren, S., 2018
Proximal and remote sensing in plant phenomics: Twenty years of progress, challenges and perspectives, Tao, H., Xu, S., Tian, Y., Li, Z., Ge, Y., Zhang, J., ... & Jin, S., 2022
Retrieving the diurnal FPAR of a maize canopy from the jointing stage to the tasseling stage with vegetation indices under different water stresses and light conditions, Zhao, L., Liu, Z., Xu, S., He, X., Ni, Z., Zhao, H., & Ren, S., 2018
An automated comparative observation system for sun-induced chlorophyll fluorescence of vegetation canopies, Zhou, X., Liu, Z., Xu, S., Zhang, W., & Wu, J., 2016
Is satellite Sun-Induced Chlorophyll Fluorescence more indicative than vegetation indices under drought condition?, Cao, J., An, Q., Zhang, X., Xu, S., Si, T., & Niyogi, D., 2021
Using High-Frequency PAR Measurements to Assess the Quality of the SIF Derived from Continuous Field Observations, Han, S., Liu, Z., Chen, Z., Jiang, H., Xu, S., Zhao, H., & Ren, S., 2022
Using the diurnal variation characteristics of effective quantum yield of PSII photochemistry for drought stress detection in maize, Chen, Z., Liu, Z., Han, S., Jiang, H., Xu, S., Zhao, H., & Ren, S., 2022
Simultaneous prediction of wheat yield and grain protein content using multitask deep learning from time-series proximal sensing, Sun, Z., Li, Q., Jin, S., Song, Y., Xu, S., Wang, X., ... & Jiang, D., 2022
What does the NDVI really tell us about crops? Insight from proximal spectral field sensors, Atherton, J., Zhang, C., Oivukkamäki, J., Kulmala, L., Xu, S., Hakala, T., ... & Porcar-Castell, A., 2022
Deciphering the contributions of spectral and structural data to wheat yield estimation from proximal sensing, Li, Q., Jin, S., Zang, J., Wang, X., Sun, Z., Li, Z., Xu, S., Ma, Q., … & Jiang, D., 2022
Assessing the response of satellite sun-induced chlorophyll fluorescence and MODIS vegetation products to soil moisture from 2010 to 2017: a case in Yunnan Province of China, Ni, Z., Huo, H., Tang, S., Li, Z. L., Liu, Z., Xu, S., & Chen, B., 2019

Remote Sensing Vegetation Crop Growth Monitoring Spectral Analysis Phenotype Extraction Physiological Quantitative Agriculture Sensing Technology

Ji Zhou - Nanjing Agricultural University

Shouyang Liu - Nanjing Agricultural University

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