徐山, 南京农业大学前沿交叉研究院讲师
邮箱：xushan@njau.edu.cn
办公室地址：第三实验楼306
研究方向：
（1）植被定量遥感与农作物生长监测
（2）基于光谱的农作物生理表型提取
学习经历：
2015.9-2021.6：北京师范大学，地图学与地理信息系统专业，理学博士；
2019.1-2020.1：赫尔辛基大学，联合培养博士生；
2008.9-2012.6：华中农业大学，地理信息系统专业，理学学士；
科研项目：
(1) 国家自然科学基金委员会, 基于日光诱导叶绿素荧光的光合电子传递速率近地遥感估算，2023-01至2025-12，在研，主持。
(2) 中央高校基本科研业务费，基于高通量表型技术的小麦优异种质产量性状精准鉴定与育种应用，2022-01 至2024-12，在研，主持。
(3) 海南省崖州湾种子实验室,崖州湾水稻和大豆高通量表型监测与精准育种决策支持系统研发, 2021-12 至 2024-12，在研, 参与。
(4) 国家自然科学基金委员会,利用多源观测数据和辐射传输模型解耦干旱胁迫下冠层SIF的生理与非生理信息, 2021-01-01 至 2024-12-31, 在研, 参与。
发表论文：
1. Xu, S., Liu, Z., Han, S., Chen, Z., He, X., Zhao, H., & Ren, S. (2023). Exploring the Sensitivity of Solar-Induced Chlorophyll Fluorescence at Different Wavelengths in Response to Drought. Remote Sensing, 15(4), 1077.
2. Xu, S., Atherton, J., Riikonen, A., Zhang, C., Oivukkamäki, J., MacArthur, A., ... & Porcar-Castell, A. (2021). Structural and photosynthetic dynamics mediate the response of SIF to water stress in a potato crop. Remote Sensing of Environment, 263, 112555.
3. Xu, S., Zaidan, M. A., Honkavaara, E., Hakala, T., Viljanen, N., Porcar-Castell, A., ... & Atherton, J. (2020, September). On the estimation of the leaf angle distribution from drone based photogrammetry. In IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium (pp. 4379-4382). IEEE.
4. Xu, S., Liu, Z., Zhao, L., Zhao, H., & Ren, S. (2018). Diurnal response of sun-induced fluorescence and PRI to water stress in maize using a near-surface remote sensing platform. Remote Sensing, 10(10), 1510.
5. Tao, H., Xu, S., Tian, Y., Li, Z., Ge, Y., Zhang, J., ... & Jin, S. (2022). Proximal and remote sensing in plant phenomics: Twenty years of progress, challenges and perspectives. Plant Communications, 100344.
6. Zhao, L., Liu, Z., Xu, S., He, X., Ni, Z., Zhao, H., & Ren, S. (2018). 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. Sensors, 18(11), 3965.
7. Zhou, X., Liu, Z., Xu, S., Zhang, W., & Wu, J. (2016). An automated comparative observation system for sun-induced chlorophyll fluorescence of vegetation canopies. Sensors, 16(6), 775.
8. Cao, J., An, Q., Zhang, X., Xu, S., Si, T., & Niyogi, D. (2021). Is satellite Sun-Induced Chlorophyll Fluorescence more indicative than vegetation indices under drought condition?. Science of The Total Environment, 792, 148396.
9. Han, S., Liu, Z., Chen, Z., Jiang, H., Xu, S., Zhao, H., & Ren, S. (2022). Using High-Frequency PAR Measurements to Assess the Quality of the SIF Derived from Continuous Field Observations. Remote Sensing, 14(9), 2083.
10. Chen, Z., Liu, Z., Han, S., 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. Ecological Indicators, 138, 108842.
11. Sun, Z., Li, Q., Jin, S., Song, Y., Xu, S., Wang, X., ... & Jiang, D. (2022). Simultaneous prediction of wheat yield and grain protein content using multitask deep learning from time-series proximal sensing. Plant Phenomics, 2022.
12. Atherton, J., Zhang, C., Oivukkamäki, J., Kulmala, L., Xu, S., Hakala, T., ... & Porcar-Castell, A. (2022). What does the NDVI really tell us about crops? Insight from proximal spectral field sensors. In Information and Communication Technologies for Agriculture—Theme I: Sensors (pp. 251-265). Cham: Springer International Publishing.
13. Li, Q., Jin, S., Zang, J., Wang, X., Sun, Z., Li, Z., Xu, S., Ma, Q., … & Jiang, D. (2022). Deciphering the contributions of spectral and structural data to wheat yield estimation from proximal sensing. The Crop Journal, 10(5), 1334-1345.
14. Ni, Z., Huo, H., Tang, S., Li, Z. L., Liu, Z., Xu, S., & Chen, B. (2019). 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. International Journal of Remote Sensing, 40(5-6), 2278-2295.
