唐荣林

中国科学院地理科学与资源研究所

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  • 唐荣林
  • 研究员

个人简况

男,1983年9月生,安徽怀远人,博士,研究员,博士生导师。2005年和2007年先后获得武汉大学学士和硕士学位,2011年获得中国科学院地理科学与资源研究所博士学位。2012/09-2014/11期间,任助理研究员。2014/12-2017/11,任副研究员。2017/12-至今,任研究员。2011年获中国科学院院长优秀奖及中国科学院地理资源所优秀博士学位论文,2012年获中国科学院优秀博士学位论文,2013年获中国科学院地理资源所“秉维”优秀青年人才基金,2014年获中国测绘地理学会科技进步一等奖,2015年入选中国科学院青促会会员、获科技部遥感中心“遥感青年科技人才创新资助计划”、北京市科学技术奖三等奖,2017年获教育部高等学校科学研究优秀成果奖自然科学奖二等奖。

科研业绩

长期从事区域地表蒸散发遥感定量反演的基础理论研究,创建了地表温度-植被指数特征空间的“干湿边自动确定法”、混合像元土壤蒸发/植被蒸腾分解的“两段分离法”、瞬时蒸散发日尺度扩展的“解耦因子不变法”以及蒸散发遥感反演空间尺度效应评估的解析模型,解决了制约地表蒸散发高精度反演的一系列关键问题,提高了地表蒸散发的遥感估算精度。至今,共发表论文70余篇(SCI论文38篇),其中以第一作者或通讯作者身份发表SCI论文19篇。已发表的SCI论文经Google Scholar检索总引用次数达1100余次,2篇第一作者SCI论文分别获《Remote Sensing of Environment》期刊2015年25篇“Most Cited Articles”之一和《Agricultural and Forest Meteorology》期刊“Top 25 Hottest Articles”之一。2篇SCI论文为ESI高被引论文。获得软件著作权6项、授权发明/实用新型专利6项。主持和参与数项国家自然科学基金及其他部委级项目。

研究领域和方向

主要从事地表能量平衡、蒸散发、土壤水分的遥感反演及验证研究

主要学术论著

[1] Tang Ronglin, Li Zhao-Liang, Huo Xing, Jiang Yazhen, Tang Bohui, Wu Hua. A re-examination of two methods for estimating daily evapotranspiration from remotely sensed instantaneous observations. International Journal of Remote Sensing, Accepted, 2018.

[2] Liu Meng, *Tang Ronglin, Li Zhao-Liang, Yao Yunjun, Yan Guangjian. Global Land Surface Evapotranspiration Estimation From Meteorological and Satellite Data Using the Support Vector Machine and Semiempirical Algorithm. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(2), 513-521, 2018.

[3] Jiang Yazhen, Jiang Xiaoguang, *Tang Ronglin, Li Zhao-Liang, Zhang Yuze, Liu Zhao-xia, Huang Cheng. Effect of Cloud Cover on Temporal Upscaling of Instantaneous Evapotranspiration. Journal of Hydrologic Engineering, 24(3), DOI: 10.1061/(ASCE)HE.1943-5584.0001635, 2018.

[4] Tang Ronglin, Li Zhao-Liang, Sun Xiaomin, Bi Yuyun. Temporal upscaling of instantaneous evapotranspiration clear-sky days using the constant reference evaporative fraction method with fixed or variable surface. Journal of Geophysical Research: Atmospheres,122, 784~801, 2017.

[5] Tang Ronglin, Li Zhao-Liang. An improved constant evaporative fraction method for estimating daily evapotranspiration from remotely sensed instantaneous observations. Geophysical Research Letters, 44(5), 2319-2326, 2017.

[6] Tang Ronglin, Li Zhao-Liang. An end-member-based two-source approach for estimating land surface evapotranspiration from remote sensing data. IEEE Transactions on Geoscience and Remote Sensing, 55(10), 5818-5832, 2017.

[7] Tang Ronglin, Li Zhao-Liang. Estimating daily evapotranspiration from remotely sensed instantaneous observations with simplified derivations of a theoretical model. Journal of Geophysical Research: Atmospheres, 122, 10177–10190, 2017.

[8] Tang Ronglin, Li Zhao-Liang. Evaluation of two end-member-based models for regional land surface evapotranspiration estimation from MODIS data. Agricultural and Forest Meteorology, 202, 69-82, 2015.

[9] Tang Ronglin, Shao Kun, *Li Zhao-Liang, Wu Hua, Tang Bohui, Zhou Guoqing, Zhang Li. Multiscale Validation of the 8-day MOD16 Evapotranspiration Product Using Flux Data Collected in China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(4), 1478-1486, 2015.

[10] Tang Ronglin, Tang Bohui, Wu Hua, *Li Zhao-Liang. On the feasibility of temporally upscaling instantaneous evapotranspiration using weather forecast information. International Journal of Remote Sensing, 36(19-20), 4918-4935, 2015.

[11] Zhang Dianjun, *Tang Ronglin, Tang Bohui, Wu Hua, Li Zhao-Liang. A Simple Method for Soil Moisture Determination From LST–VI Feature Space Using Nonlinear Interpolation Based on Thermal Infrared Remotely Sensed Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 99, 1-11, 2015.

[12] Zhang Dianjun, *Tang Ronglin, Zhao Wei, Tang Bohui, Wu Hua, Shao Kun, Li Zhao-Liang. Surface soil water content estimation from thermal remote sensing based on the temporal variation of land surface temperature. Remote Sensing, 6(4), 3170-3187, 2014.

[13] Tang Ronglin, Li Zhao-Liang, Sun Xiaomin. Temporal upscaling of instantaneous evapotranspiration: An intercomparison of four methods using eddy covariance measurements and MODIS data. Remote Sensing of Environment, 138, 102-118, 2013.

[14] Tang Ronglin, Li Zhao-Liang, Chen Kunshan, Jia Yuanyuan, Li Chuanrong, Sun Xiaomin. Spatial-scale effect on the SEBAL model for evapotranspiration estimation using remote sensing data. Agricultural and Forest Meteorology, 174, 28-42, 2013.

[15] Tang Ronglin, Li Zhao-Liang, Jia Yuanyuan, Li Chuanrong, Chen Kunshan, Sun Xiaomin, Lou Jinyong. Evaluating one-and two-source energy balance models in estimating surface evapotranspiration from Landsat-derived surface temperature and field measurements. International Journal of Remote Sensing, 34(9-10), 3299-3313, 2013.

[16] Tang Ronglin, Li Zhao-Liang, Chen Kunshan, Zhu Yuanjun, Liu Wenzhao. Verification of land surface evapotranspiration estimation from remote sensing spatial contextual information. Hydrological Processes, 26(15), 2283-2293, 2012.

[17] Tang Ronglin, Li Zhao-Liang, Jia Yuanyuan, Li Chuanrong, Sun Xiaomin, William P. Kustasf, Martha C. Andersonf. An intercomparison of three remote sensing-based energy balance models using Large Aperture Scintillometer measurements over a wheat–corn production region. Remote Sensing of Environment, 115(12), 3187-3202, 2011.

[18] Tang Ronglin, Li Zhao-Liang, Chen Kunshan. Validating MODIS‐derived land surface evapotranspiration with in situ measurements at two AmeriFlux sites in a semiarid region. Journal of Geophysical Research-Atmospheres, 116(D4), 2011.

[19] Tang Ronglin, Li Zhao-Liang, Tang Bohui. An application of the Ts–VI triangle method with enhanced edges determination for evapotranspiration estimation from MODIS data in arid and semi-arid regions: Implementation and validation. Remote Sensing of Environment, 114(3), 540-551, 2010.

[20] Li Zhao-Liang, Tang Ronglin, Wan Zhengming, Bi Yuyun, Zhou Chenghu, Tang Bohui, Yan Guangjian, Zhang Xiaoyu. A review of current methodologies for regional evapotranspiration estimation from remotely sensed data. Sensors, 9(5), 3801-3853, 2009.