王聪

王聪，浙江宁波人，博士，现为中国农业科学院农业资源与农业区划研究所研究员。主要从事生态遥感与农业遥感领域研究，特别是针对植被物候，日光诱导叶绿素荧光（SIF）等植被/生态系统参数遥感反演算法的开发与应用。国际农业与生物科学研究中心（CABI）旗下官方期刊CABI Agriculture and Bioscience副主编（Associate Editor），《Remote Sensing》专刊编委。在遥感领域顶级期刊-环境遥感（Remote Sensing of Environment）等期刊发表SCI论文10余篇，在NASA地球观测系统数据与信息系统（EOSDIS）数据中心ORNL DAAC发表数据一套。

教育经历：

2011.09-2016.07：北京师范大学地理科学学部，理学博士（导师：陈晋教授），北京

2014.09-2015.09: 日本国立环境研究所，交换留学生，日本

2007.08-2011.07: 北京师范大学物理学，理学学士，北京

工作经历：

2020.05- 至今: 中国农业科学院农业资源与农业区划研究所，研究员，北京

2018.02-2020.02: 伊利诺伊大学香槟分校自然资源与环境科学系，博士后研究员，美国

2017.07-2018.02: 加州大学圣塔克鲁兹分校环境系，博士后研究员，美国

2017.03-2017.07: 德州大学阿灵顿分校生物系，博士后研究员，美国

2016.07-2017.02: 北京师范大学减灾与应急管理研究院，研究助理，北京

数据产品：

Wang, C., K. Guan, B. Peng, C. Jiang, J. Peng, G. Wu, C. Frankenberg et al., (2021). High Resolution Land Cover-Specific Solar-Induced Fluorescence, Midwestern USA, 2018. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1813

获奖及荣誉：

2017，北京师范大学优秀博士学位论文

学术论文：

[1] S. Wang, J. Chen, M. Shen, T. Shi, L. liu; L. Zhang, Q. Dong, C. Wang* (2022) Characterizing spatiotemporal patterns of winter wheat phenology from 1981 to 2016 in North China by improving phenology estimation,  remote sensing,  2022, 14(19).

[2] Y. Liu,Q. Yu, Q. Zhou, C. Wang, et al. (2022) Mapping the Complex Crop Rotation Systems in Southern China Considering Cropping Intensity, Crop Diversity, and Their Seasonal Dynamics.  IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  15: 9584-9598.

[3] S. Ge, Q. Yu, Q. Zhou, C. Wang, W. Wu, (2022) From multiple cropping frequency to multiple cropping system: A new perspective for the characterization of cropland use intensity.  Agricultural Systems.  204, 103535.

[4] L. Zhao, W. Guo, J. Wang, H. Wang, Y. Duan, C. Wang, W. Wu (2021) An Efficient Method for Estimating Wheat Heading Dates Using UAV Images.  Remote Sens.  13, 3067.

[5] D. Xu, C. Wang, J. Chen; M. Shen, B. Shen, et al. (2021) The superiority of the normalized difference phenology index (NDPI) for estimating grassland aboveground fresh biomass.  Remote Sens. Environ.   264, 112578.

[6] C. Wang, K. Guan, B. Peng, C. Jiang, J. Peng, G. Wu, C. Frankenberg et al., (2021). High Resolution Land Cover-Specific Solar-Induced Fluorescence, Midwestern USA, 2018. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1813

[7] C. Wang*, K. Guan*, B. Peng, M. Chen, C. Jiang, Y. Zeng et al., (2020) Satellite footprint data from OCO-2 and TROPOMI reveal significant spatio-temporal and inter-vegetation type variabilities of solar-induced fluorescence yield in the U.S. Midwest.  Remote Sensing of Environment  241:111728.

[8] W. Yang, H. Kobayashi, C. Wang, M. Shen, J. Chen, B. et al., (2019) A semi-analytical snow-free vegetation index for improving estimation of plant phenology in tundra and grassland ecosystems.  Remote Sensing of Environment  228: 31-44.

[9] C. Wang and K Zhu (2019) Misestimation of Growing Season Length Due to Inaccurate Construction of Satellite Vegetation Index Time Series.  IEEE Geoscience and Remote Sensing Letters  16: 1185-1189.

[10] R. Cao, Y. Chen, J. Chen, M. Shen, J. Zhou, C. Wang and W. Yang (2018) A simple method to improve the quality of NDVI time-series data by integrating spatiotemporal information with the Savitzky-Golay filter.  Remote Sensing of Environment  217: 244-257

[11] X. Chen, D. Wang, J. Chen, C. Wang and M. Shen (2018) The mixed pixel effect in land surface phenology: A simulation study Remote Sensing of Environment 211: 338-344.

[12] C. Wang, J. Chen, Y. Tang, T.A. Black and K. Zhu (2018) A novel method for removing snow melting-induced fluctuation in GIMMS NDVI3g data for vegetation phenology monitoring.  IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  11: 800-807.

[13] Z. Yang, M. Shen, S. Jia, W. Yang, C. Wang, X. Chen, J. Chen and L. Guo (2017) Asymmetric responses of the end of growing season to daily maximum and minimum temperatures on the Tibetan Plateau.  Journal of Geophysical Research: Atmospheres  122: 13,278-13,287.

[14] C. Wang, J. Chen, J. Wu, Y. Tang, P. Shi, T.A. Black and K. Zhu (2017) A snow-free vegetation index for improved monitoring of vegetation spring green-up date in deciduous ecosystems.  Remote Sensing of Environment  196: 1-12.

[15] C. Wang, J. Chen and Y. Tang (2016) Plant phenological synchrony increases under rapid within-spring warming.  Scientific Reports , 6: 25460.

[16] J. Chen, Y. Rao, M. Shen, C. Wang, Y. Zhou, L. Ma, Y. Tang and X. Yang (2016) A simple method for detecting phenological change from time series of vegetation index.  IEEE transactions on geoscience and remote sensing.  54: 3436-3449.

[17] C. Wang, R. Cao, J. Chen, Y. Rao and Y. Tang (2015) Temperature sensitivity of spring vegetation phenology correlates to within-spring warming speed over the Northern Hemisphere.  Ecological Indicators , 50: 62-68.

[18] M. Shen, Y. Tang, J. Chen, X. Yang, C. Wang, X. Cui, Y. Yang, L. Han, L. Li, J. Du, G. Zhang and N. Cong (2014) Earlier-Season Vegetation Has Greater Temperature Sensitivity of Spring Phenology in Northern Hemisphere.  PLoS One,  9: e88178.

[19] B. Fan, L. Guo, N. Li, J. Chen, H. Lin, X. Zhang, M. Shen, Y. Rao, C. Wang and L. Ma (2014) Earlier vegetation green-up has reduced spring dust storms.  Scientific Reports , 4: 6749.

联系方式：

电子邮箱：wangcong01@caas.cn