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王聪
发布者：管理员发布时间：2023-07-11作者：来源：点击量：
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 Sensingof 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。
电子邮箱：wangcong01@caas.cn
教育背景：
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
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 improvingphenology 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 Sensing15: 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 DatesUsing 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 tundraand 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 Letters16: 1185-1189.
[10] R. Cao, Y. Chen, J. Chen, M. Shen, J. Zhou, C. Wangand 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 studyRemote Sensing of Environment211: 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 Sensing11: 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. Wangand L. Ma (2014) Earlier vegetation green-up has reduced spring dust storms. Scientific Reports, 4: 6749.
部分获奖奖励：
2017，北京师范大学优秀博士学位论文
