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章钊颖，betway西汉姆联/国际地球系统科学研究所副教授（准聘，Tenure-Track）、特聘研究员、博士生导师。主要研究方向为全球变化与生态学、植被遥感、叶绿素荧光遥感、人工智能+遥感。
主要学术贡献包括：①发展了基于辐射传输模型的叶片总激发荧光遥感理论与方法，提升了荧光信号对植被光合作用的表征能力；②构建了植被光合作用多时间尺度荧光遥感普适性模型，推进了区域至全球植被生产力的精细估算；③揭示了全球植被光合作用时空格局的新特征，阐明了大气干燥对光合作用下午下降的主导作用及亚马逊森林光合作用的日内补偿机制。此外，担任国产陆地生态系统碳监测卫星“句芒号”荧光反演负责人，完成该卫星首个全球荧光数据产品的研制，建立涵盖反演算法、误差分析及“星—空—地”协同验证的技术流程，推动数据产品公开发布与持续更新。
入选国家级青年人才计划、全球前2%顶尖科学家榜单、第十届中国科协青年人才托举工程、江苏省杰青、betway西汉姆联毓秀青年学者计划。兼任国际数字地球学会中国国家委员会青年科学家工作委员会委员、美国地球物理学会（AGU）年会荧光遥感专题召集人、《Journal of Plant Ecology》、《遥感科学与技术》青年编委及《Remote Sensing》客座编辑。
主持国家自然科学基金青年科学基金项目（B类）、面上项目、江苏省杰出青年科学基金项目、国家重点研发计划青年科学家项目专题、中国博士后科学基金站中特别资助及面上项目等。获全国高校GIS新锐奖、江苏省青年遥感与地理信息科技奖、《遥感学报》优秀论文奖、betway西汉姆联优秀博士学位论文、优秀毕业生等奖励与荣誉。
诚挚欢迎在遥感科学、生态学、气候变化等相关领域拥有扎实背景的本科生和研究生与我们联系 （zhaoying_zhang@nju.edu.cn）。优先考虑遥感理论基础扎实、计算机编程技术熟练，中英文写作能力出色的同学。目前团队聚焦于以下三个研究方向：
全球变化与生态学：聚焦气候变化、极端事件及土地利用变化对植被生长和生态系统功能的影响。结合多尺度观测、遥感技术与生态系统模型，研究陆地生态系统碳循环、光合作用、植被物候的时空变化，揭示生态系统对环境变化的响应与适应机制，为评估陆地生态系统碳汇及其稳定性、服务碳中和目标提供科学依据。
植被遥感：利用卫星、无人机与地面观测等技术手段，监测植被分布、生长动态及生理状态。通过光谱信息分析与定量遥感模型，解析植被结构与功能，评估植被生产力及固碳能力的时空演变，为生态环境监测、自然资源管理及气候变化影响评估提供技术与数据支撑。
人工智能+遥感：结合人工智能与遥感观测，挖掘多源数据中的植被结构、功能及环境变化信息。融合辐射传输理论、生态系统过程与机器学习方法，发展物理机制约束的遥感反演、时空重建及生态预测模型，提升植被参数与碳通量估算的精度和可解释性，推动生态监测向精细化与智能化发展。
I am Zhaoying Zhang, a tenure-track Associate Professor, Distinguished Research Fellow, and PhD supervisor at the School of Geography and Ocean Science / International Institute for Earth System Science, Nanjing University. My research focuses on global change and ecology, vegetation remote sensing, chlorophyll fluorescence remote sensing, and artificial intelligence for remote sensing.
My major scientific contributions include: (1) developing radiative transfer–based theory and methods for retrieving total leaf fluorescence emission, improving the ability of fluorescence observations to characterize vegetation photosynthesis; (2) establishing generalizable fluorescence-based models of vegetation photosynthesis across multiple temporal scales, advancing detailed estimates of vegetation productivity from regional to global scales; and (3) revealing previously unrecognized features of the spatiotemporal patterns of global vegetation photosynthesis, including the dominant role of atmospheric dryness in afternoon photosynthetic depression and the mechanisms underlying diurnal compensation in Amazon forest photosynthesis. I also lead fluorescence retrieval for China’s Terrestrial Ecosystem Carbon Monitoring Satellite, Goumang. In this role, I developed the satellite’s first global fluorescence data product and established a workflow encompassing retrieval algorithms, error analysis, and coordinated satellite, airborne, and ground-based validation, supporting the public release and continued updating of the products.
I have been selected for a national young talent program, the World’s Top 2% Scientists list, the 10th cohort of the Young Elite Scientists Sponsorship Program of the China Association for Science and Technology, the Jiangsu Distinguished Young Scholars Program, and Nanjing University’s Yuxiu Young Scholars Program. I serve on the Young Scientists Committee of the Chinese National Committee for the International Society for Digital Earth, convene fluorescence remote sensing sessions at the American Geophysical Union (AGU) Annual Meeting, and serve as a Young Editorial Board Member of the Journal of Plant Ecology and Remote Sensing Science and Technology , as well as a Guest Editor for Remote Sensing.
I have led projects supported by the National Natural Science Foundation of China’s Young Scientists Fund (Category B) and General Program, the Jiangsu Distinguished Young Scholars Fund, and the Special and General Programs of the China Postdoctoral Science Foundation. I have also led a research task under a Young Scientists Project of the National Key R&D Program of China. My awards and honors include the National University GIS Rising Star Award, the Jiangsu Young Scientist Award in Remote Sensing and Geographic Information Science, an Outstanding Paper Award from the National Remote Sensing Bulletin, and Nanjing University’s Outstanding Doctoral Dissertation and Outstanding Graduate honors.
We warmly welcome inquiries from undergraduate and graduate students with strong backgrounds in remote sensing, ecology, climate change, or related fields. Please contact us at zhaoying_zhang@nju.edu.cn. Particular consideration will be given to students with a solid foundation in remote sensing theory, strong programming skills, and excellent writing skills in both Chinese and English. Our team currently focuses on three research areas:
Global Change and Ecology: We investigate how climate change, extreme events, and land-use change affect vegetation growth and ecosystem functioning. By integrating observations across scales, remote sensing, and ecosystem models, we examine spatiotemporal changes in terrestrial carbon cycling, photosynthesis, and vegetation phenology. Our research seeks to uncover ecosystem responses and adaptation to environmental change, providing a scientific basis for assessing terrestrial carbon sinks and their stability and supporting carbon neutrality goals.
Vegetation Remote Sensing: We use satellite, UAV, and ground-based observations to monitor vegetation distribution, growth dynamics, and physiological conditions. Through spectral analysis and quantitative remote sensing models, we characterize vegetation structure and function and assess spatiotemporal changes in productivity and carbon sequestration capacity. This work provides methods and data for ecological monitoring, natural resource management, and assessments of climate change impacts.
Artificial Intelligence for Remote Sensing: We combine artificial intelligence with remote sensing to extract information on vegetation structure, ecosystem functioning, and environmental change from multiple data sources. By integrating radiative transfer theory, ecosystem processes, and machine learning, we develop physically constrained methods for remote sensing retrieval, spatiotemporal reconstruction, and ecological prediction. These approaches aim to improve the accuracy and interpretability of vegetation parameter and carbon flux estimates and enable more detailed and intelligent ecological monitoring.
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专著：
Zhang, Y., & Zhang, Z. (2025). Solar induced chlorophyll fluorescence: Origins and applications, relation to photosynthesis and retrieval. In Comprehensive Remote Sensing (pp. 1–33). Elsevier.
Zhang, Y., & Zhang, Z. (2025). Global hourly total canopy SIF emission and SIF-based GPP products (2000–2022). Terrestrial ecosystem and carbon cycle. Chapter 51. Elsevier.
主要学术论文（* 表示通讯作者）：
Zhang, X., Zhang, Z.*, Wu, L., & Zhang, Y*. (2026). Developing the optimal observation scale model for maize canopy solar-induced chlorophyll fluorescence ground measurements based on UAV system and DART model. Remote Sensing of Environment, 346, 115638.
Liu, Y., Zhang, Z.*, Johnson, J. E., Berry, J. A., Ju, W., Chen, J. M., & Zhang, Y*. (2026). Improving GPP and SIF simulation with a mechanistic photosynthesis model integrated into the BEPS framework. Global Change Biology, 32, e70802.
Chen, S., Wu, M., Wang, P., Liu, Y., Wu, L., Zhang, Y., & Zhang, Z.* (2026). Deriving optimal air temperature from light use efficiency to improve ecosystem productivity estimates. Journal of Plant Ecology, rtag153.
陶诗语, 陈镜明, 张永光, 吴霖升, & 章钊颖* (2025). 面向空天地遥感的日光诱导叶绿素荧光反演算法综述. 遥感学报, 29(12), 3395–3434.
Liu, Y., Zhao, D., Zhang, Y., & Zhang, Z.* (2025). Global 0.1-degree monthly mean hourly total canopy solar-induced chlorophyll fluorescence dataset derived from random forest. Remote Sensing, 17(20), 3429.
Zhang, Z., , , , , & (2025). First global retrievals of solar induced chlorophyll fluorescence from the SIFIS instrument onboard the Chinese goumang satellite. Geophysical Research Letters, 52, e2025GL118327.
Zhang, Z.*, & Zhang, Y. (2025). Fluorescence efficiency-derived physiological optimal temperature improving the estimation of photosynthesis. Geophysical Research Letters, 52, e2025GL116420.
张永光,吴霖升,章钊颖*(2025). 卫星日光诱导叶绿素荧光遥感十年研究进展.遥感学报
Wu Y., Zhang Z.*, Wu L., Zhang Y. (2024). Solar-induced chlorophyll fluorescence and its relationship with photosynthesis during waterlogging in a maize field. Agricultural and Forest Meteorology, 363, 110404
Liu, Y., Peñuelas, J., Cescatti, A., Zhang, Y., & Zhang, Z*. (2024). Atmospheric Dryness Dominates Afternoon Depression of Global Terrestrial Photosynthesis. Geophysical Research Letters, 51, e2024GL110954.
Tao, S., Chen, J.M.*, Zhang, Z.*, Zhang, Y., Ju, W., Zhu, T., Wu, L., Wu, Y., & Kang, X. (2024). A high-resolution satellite-based solar-induced chlorophyll fluorescence dataset for China from 2000 to 2022. Scientific Data, 11, 1286.
Zhang, Z.*,& Zhang, Y. (2024). Mitigating the directional retrieval error of solar-induced chlorophyll fluorescence in the red band. Remote Sensing of Environment, 316, 114496.
Wu, Y., Zhang, Z.*, Wu, L., & Zhang, Y. (2024). Solar-induced chlorophyll fluorescence tracks canopy photosynthesis under dry conditions in a semi-arid grassland. Agricultural and Forest Meteorology, 356, 110174.
Zhang, Z., Cescatti, A., Wang, Y.-P., Gentine, P., Xiao, J., Guanter, L., Huete, A.R., Wu, J., Chen, J.M., Ju, W., Peñuelas, J., & Zhang, Y. (2023). Large diurnal compensatory effects mitigate the response of Amazonian forests to atmospheric warming and drying. Science Advances, 9, eabq4974.
Zhang, Z.*, & Zhang, Y. (2023). Solar angle matters: Diurnal pattern of solar-induced chlorophyll fluorescence from OCO-3 and TROPOMI. Remote Sensing of Environment, 285, 113380.
Zhang, Z.*, Zhang, Y., & Zhang, Y. (2023). Generating high-resolution total canopy SIF emission from TROPOMI data: Algorithm and application. Remote Sensing of Environment, 295, 113699.
Zhang, Z.*, Chen, J.M., Zhang, Y., & Li, M. (2023). Improving the ability of solar-induced chlorophyll fluorescence to track gross primary production through differentiating sunlit and shaded leaves. Agricultural and Forest Meteorology, 341, 109658.
Zhang, Z., Guanter, L., Porcar-Castell, A., Rossini, M., Pacheco-Labrador, J., & Zhang, Y. (2023). Global modeling diurnal gross primary production from OCO-3 solar-induced chlorophyll fluorescence. Remote Sensing of Environment, 285, 113383.
Zhang, Z., Zhang, X., Porcar-Castell, A., Chen, J.M., Ju, W., Wu, L., Wu, Y., & Zhang, Y. (2022). Sun-induced chlorophyll fluorescence is more strongly related to photosynthesis with hemispherical than nadir measurements: Evidence from field observations and model simulations. Remote Sensing of Environment, 279, 113118.
Wu, L., Zhang, X., Rossini, M., Wu, Y., Zhang, Z.*, & Zhang, Y. (2022). Physiological dynamics dominate the response of canopy far-red solar-induced fluorescence to herbicide treatment. Agricultural and Forest Meteorology, 323, 109063.
Wu, Y., Zhang, Z.*, Zhang, X., Wu, L., & Zhang, Y. (2022). How do sky conditions affect the relationships between ground-based solar-induced chlorophyll fluorescence and gross primary productivity across different plant types? Journal of Geophysical Research: Biogeosciences, 127, e2022JG006865.
Zhang, X., Zhang, Z. *, Zhang, Y. *, Zhang, Q., Liu, X., Chen, J., Wu, Y., & Wu, L. (2022). Influences of fractional vegetation cover on the spatial variability of canopy SIF from unmanned aerial vehicle observations. International Journal of Applied Earth Observation and Geoinformation, 107, 102712.
Zhang, Z., Zhang, Y., Chen, J.M., Ju, W., Migliavacca, M., & El-Madany, T.S. (2021). Sensitivity of estimated total canopy SIF emission to remotely sensed LAI and BRDF products. Journal of Remote Sensing, 9795837.
Zhang, Z., Zhang, Y., Porcar-Castell, A., Joiner, J., Guanter, L., Yang, X., Migliavacca, M., Ju, W., Sun, Z., Chen, S., Martini, D., Zhang, Q., Li, Z., Cleverly, J., Wang, H., & Goulas, Y. (2020). Reduction of structural impacts and distinction of photosynthetic pathways in a global estimation of GPP from space-borne solar-induced chlorophyll fluorescence. Remote Sensing of Environment, 240, 111722.
Zhang, Z., Zhang, Y., Zhang, Y., Gobron, N., Frankenberg, C., Wang, S., & Li, Z. (2020). The potential of satellite FPAR product for GPP estimation: An indirect evaluation using solar-induced chlorophyll fluorescence. Remote Sensing of Environment, 240, 111686.
Zhang, Z., Zhang, Y., Zhang, Q., Chen, J.M., Porcar-Castell, A., Guanter, L., Wu, Y., Zhang, X., Wang, H., Ding, D., & Li, Z. (2020). Assessing bi-directional effects on the diurnal cycle of measured solar-induced chlorophyll fluorescence in crop canopies. Agricultural and Forest Meteorology, 295, 108147.
Zhang, Z., Zhang, Y., Zhang, Y., & Chen, J.M. (2020). Correcting clear-sky bias in gross primary production modeling from satellite solar-induced chlorophyll fluorescence data. Journal of Geophysical Research: Biogeosciences, 125, e2020JG005822.
Zhang, Z., Chen, J.M., Guanter, L., He, L., & Zhang, Y. (2019). From canopy-leaving to total canopy far-red fluorescence emission for remote sensing of photosynthesis: first results from TROPOMI. Geophysical Research Letters, 46, 12030-12040.
Liu, Y.#, Zhang, Z. #, Tong, L., Khalifa, M., Wang, Q., Gang, C., Wang, Z., Li, J., & Sun, Z. (2019). Assessing the effects of climate variation and human activities on grassland degradation and restoration across the globe. Ecological Indicators, 106, 105504.
章钊颖, 王松寒, 邱博, 宋练 & 张永光* (2019). 日光诱导叶绿素荧光遥感反演及碳循环应用进展.遥感学报 (pp. 37-52)
Zhang, Z., Zhang, Y., Joiner, J., & Migliavacca, M. (2018). Angle matters: bidirectional effects impact the slope of relationship between gross primary productivity and sun-induced chlorophyll fluorescence from Orbiting Carbon Observatory-2 across biomes. Global Change Biology, 24, 5017-5020.
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