商荣

个人履历 

教  育：

2013.09-2018.06 中国科学院大学地理科学与资源研究所，理学博士

2009.09-2013.06 武汉大学资源与环境科学学院，理学学士

工  作：

2020.12-至今 福建师范大学地理科学学院，副教授

2019.01-2020.08 美国康涅狄格大学资源与环境系，博士后

2018.06-2019.01 美国德州理工大学地理系，博士后

个人简介

商荣，福建师范大学地理科学学院、碳中和未来技术学院副教授，主要从事植被遥感与碳中和、遥感大数据、地表变化动态监测等方面的研究，近期工作主要聚焦在森林动态监测、参数反演与碳汇模拟等方面。开发了多套全球适用的遥感数据处理和参数反演算法，生产了多套全球500–1000米分辨率和中国30米分辨率遥感数据产品。主持了国家自然科学基金、福建省自然科学基金等项目4项，在The Innovation、RSE（5篇）、ISPRS P&RS、AFM、JAG、IEEE TGRS等杂志上发表论文20多篇，其中1篇入选ESI高被引论文。

主要获奖成果

2018年 中国科学院 地理科学与资源研究所 优秀博士毕业生

2018年 中国科学院 地理科学与资源研究所 所长奖学金

2017年 博士国家奖学金

科研项目

1. 海南热带雨林的碳汇稳定性及其调控机制研究，国家自然科学基金联合项目课题，2024.01-2027.12，65万，主持

2. 融合高分辨率遥感数据的森林扰动近实时监测算法研究，国家自然科学基金青年项目，2022.01-2024.12，30万，主持

3. 基于Landsat数据的森林扰动后碳汇恢复力评估与分析：以福建龙岩为例，福建省自然科学基金青创项目，2021.08-2024.08，8万，主持

4. 基于InTEC模型的福建省森林碳汇计算与预测，福建省林业科技攻关项目课题，2022.01-2024.12，35万，主持

5. 基于林龄结构的福建森林固碳潜力预测及风险评估，福建师范大学碳中和研究院开放基金项目，2022.09-2024.08，25万，主持

6. Toward Near Real-time Monitoring and Characterization of Land Surface Change for the Conterminous US，USGS-NASA项目，2018.06-2020.08，参加

7. 基于多源卫星遥感的高分辨率全球碳同化系统研究，国家重点研发计划, 2016.07-2018.06，参加

8. 基于现有知识的历史遥感数据回溯反演研究, 国家自然科学基金面上项目, 2013.06-2015.12，参加

代表性论文

第一或通讯作者：

Shang, R., Chen, J.M., Xu, M., et. al. (2023). China’s current forest age structure will lead to weakened carbon sinks in the near future. The Innovation, 4(6), 100515.

Shang, R.*, Zhu, Z., Zhang, J., et. al. (2022). Near-real-time monitoring of land disturbance with harmonized Landsats 7–8 and Sentinel-2 data. Remote Sensing of Environment. 278, 113073.

Shang, R.*, Zhu, Z. (2019). Harmonizing Landsat 8 and Sentinel-2: A Time-series-based Reflectance Adjustment Approach. Remote Sensing of Environment, 235, 111439.

Shang, R., Liu, R., Xu, M., et. al. (2017). The relationship between the threshold-based and the inflexion-based approaches in extraction of land surface phenology. Remote Sensing of Environment, 199, 167-170.

Lin, X., Shang, R.*, Chen, J.M., et. al. (2023). High-resolution forest age mapping based on forest height maps derived from GEDI and ICESat-2 space-borne lidar data. Agricultural and Forest Meteorology, 339, 109592.

Li, W., Niu, Z., Shang, R.*, et. al. (2020). High-resolution mapping of forest canopy height using machine learning by coupling ICESat-2 LiDAR with Sentinel-1, Sentinel-2 and Landsat-8 data. International Journal of Applied Earth Observations and Geoinformation, 92,102163.  

Zhang, J., Shang, R.*, Rittenhouse, C., et. al. (2021). Evaluating the impacts of models, data density and irregularity on reconstructing and forecasting dense Landsat time series. Science of Remote Sensing. 4, 100023.

Liang, L., Shang, R.*, Chen, J. M., et. al. (2023) Improved estimation of the underestimated GEDI footprint LAI in dense forests. Geo-spatial Information Science, https://doi.org/10.1080/10095020.2023.2286377.

Xu, M., Shang, R.*, Chen, J. M., et. al. (2023) LACC2.0: Improving the LACC Algorithm for Reconstructing Satellite-Derived Time Series of Vegetation Biochemical Parameters. Remote Sensing, 15, 3277.

Qiu, D., Liang, Y., Shang, R.*, et. al. (2023) Improving LandTrendr Forest Disturbance Mapping in China Using Multi-Season Observations and Multispectral Indices. Remote Sensing, 15, 2381.

Qiu, S., Lin, Y., Shang, R.*, et. al. (2019). Making Landsat Time Series Consistent: Evaluating and Improving Landsat Analysis Ready Data. Remote Sensing, 11, 51.

Shang, R., Liu, R., Xu, M., et. al. (2018). Determining the Start of the Growing Season from MODIS Data in the Indian Monsoon Region: Identifying Available Data in the Rainy Season and Modeling the Varied Vegetation Growth Trajectories. Remote Sensing, 10, 122.

合作者：

Liu, R., Shang, R., Liu, Y., et. al. (2017). Global evaluation of gap-filling approaches for seasonal NDVI with considering vegetation growth trajectory, protection of key point, noise resistance and curve stability. Remote Sensing of Environment, 189, 164-179.

Xu, M., Liu, R., Chen, J., Liu, Y., Shang, R., et. al. (2019). Retrieving leaf chlorophyll content using a matrix-based vegetation index combination approach. Remote Sensing of Environment, 224, 560-73.

Liu, Yang, Liu, R., Shang, R. (2022). GLOBMAP SWF: a global annual surface water cover frequency dataset during 2000–2020. Earth System Science Data. 14, 4505–4523.

Xu, M., Liu, R., Chen, J.M., Shang, R., et. al. (2022). Retrieving global leaf chlorophyll content from MERIS data using a neural network method. ISPRS Journal of Photogrammetry and Remote Sensing. 192, 66–82.

Liu, Y., Wu, C., …, Shang, R. (2022). Satellite Observed Land Surface Greening in Summer Controlled by the Precipitation Frequency Rather Than Its Total Over Tibetan Plateau. Earth’s Future. 10.

Xu, M., Liu, R., Chen, J.M., …, Shang, R., et. al. (2022). A 21-year time-series of global leaf chlorophyll content maps from MODIS imagery. IEEE Transactions on Geoscience and Remote Sensing. 60, 1–13.

Qiu, S., Zhu, Z., Shang, R., Crawford, C.J. (2021). Can Landsat 7 preserve its science capability with a drifting orbit? Science of Remote Sensing. 4, 100026.