个人简况：

博士，现任生态环境部南京环境科学研究所助理研究员，主要从事全球变化对生态系统碳循环的影响研究。近年来，聚焦植被物候与碳循环的相互作用机制，在遥感监测、数据融合与模型分析等方面积累了丰富经验。参与中国科学院A类战略性先导科技专项“地球大数据科学工程”、国家重点研发计划“近地面植被光谱监测与多源物候数据融合”、国家自然基金面上“地面观测植被春季叶物候升尺度转换方法研究”和“融合多源遥感数据的植被物候变化反演与生态系统碳循环研究”等。以第一作者或通讯作者在Agricultural and Forest Meteorology、ISPRS Journal of Photogrammetry and Remote Sensing、Forest Ecology and Management、Global and Planetary Change等SCI期刊发表论文9篇。

近期代表成果包括：

1. Yuan, H., Zhang, J., Zhang, H., et al. Monitoring Autumn Phenology in Understory Plants with a Fine-Resolution Camera. Remote Sensing, 2025, 17, 1025.

2. Yuan, H., Yan, J., Liu, Y., et al. Influence of vegetation phenological carryover effects on plant autumn phenology under climate change. Agricultural and Forest Meteorology, 2024, 359, 110284.

3. Yuan, H., Zhang, J., Wang, Z., et al. Multi-Temporal Change of LULC and Its Impact on Carbon Storage in Jiangsu Coastal, China. Land, 2023, 12, 1943.

4. Yuan, H., Wang, X., Jassal, R., et al. Remote Sensing of Autumn Phenology by Including Surface Soil Temperature: Algorithm Development, Calibration, and Validation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, 15, 1-10.

5. Yuan, H., Wang, X., Wu, C., et al. Satellite Observed Strong Relationship Between Nighttime Surface Temperature and Leaf Coloring Dates of Terrestrial Ecosystems in East China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13, 717-725.

6. Yuan, H., Wu, C., Gu, C., et al. Evidence for satellite observed changes in the relative influence of climate indicators on autumn phenology over the Northern Hemisphere. Global and Planetary Change, 2020, 187.

7. Yuan, H., Wu, C., Lu, L., et al. A new algorithm predicting the end of growth at five evergreen conifer forests based on nighttime temperature and the enhanced vegetation index. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 144, 390-399.

8. Yuan, H., Yang, G., Li, C., et al. Retrieving Soybean Leaf Area Index from Unmanned Aerial Vehicle Hyperspectral Remote Sensing: Analysis of RF, ANN, and SVM Regression Models. Remote Sensing, 2017, 9(4).

9. Zhang, H., Yuan, H*., & Wang, Z*. Positive interactions in shaping neighborhood diversity during secondary forests recovery: Revisiting the classical paradigm. Forest Ecology and Management, 2024, 552, 121586.