Remote Sensing of Vegetation

       We aims to improve the understanding and detection capabilities of vegetation photosynthesis and global climate change, by investigating the vegetation parameters inversion mechanisms of hyperspectral remote sensing and hyperspectral intelligent observation modes, developing advanced inversion models and methods as well as hyperspectral based product algorithms and quality control specifications for key vegetation parameters, and ultimately addressing the key scientific problems and technical bottlenecks in multiple applications using the novel SPAtial-Temporal-Spectral (SPATS) data., including agricultural, forestry, ecological environment and climate change.



           Changping Huang, Dongjie Fu, Xiaojun She, Yao Li, Wenzhe Jiao, Guibin Hao, Hao Chen, Yongguang Zhai, Siheng Wang, Na Qiao.

Research Projects:

1、 National Natural Science Fund of China for Young Scholars (Grant No. 41501394): Mechanism and Method for Remote Retrieval of Solar-induced Vegetation Chlorophyll Fluorescence Spectra. 2016-2018.

2、 National Natural Science Fund of China for Young Scholars (Grant No. 41501394): Cropland GPP estimation and up-scaling research blended with multi-angle remote sensing data and eddy-covariance measurements. 2016-2018.

3、 Carbon Monitoring Satellite Plan for Terrestrial Ecosystem (Grant No. Y5H0630034): Retrieval of Vegetation Fluorescence from Satellite Observations. 2015-2016.

4、 Open Fund of State Key Laboratory of Remote Sensing Science (Grant No. Y4Y00100KZ): Quantitative Remotely Retrieval of Solar-induced Full-band Chlorophyll Fluorescence of Vegetation .2014-2015.

5、Research grant of the Direct Youth Foundation in Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences (grant No. Y5SJ2000CX): Estimation of sunlit/shaded light-use efficiency of cropland using tower-based multi-angle remote sensing data and eddy covariance flux measurements.

6、 Research grant funded by China Postdoctoral Science Foundation (grant No. 2014M550871): Estimating gross primary production of maize using tower-based multi-angle remote sensing. 




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