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彭漪
2015-03-03 09:21:50  发布者:00030665  来源:  评论:0 点击:

彭漪,副教授。研究方向:定量环境遥感。办公地址:武汉大学遥感信息工程学院 5-427。E-mail:ypeng@whu.edu.cn




教育经历


2002 – 2006,武汉大学,遥感信息工程学院,学士
2006 – 2008,武汉大学,遥感信息工程学院,硕士
2008 – 2012,美国内布拉斯加大学,自然资源环境学院,博士



 
工作经历

2012 – 2013,美国马里兰大学,地理科学系,初级研究员 (博士后)
2013 – 至今, 武汉大学,遥感信息工程学院,副教授


研究方向

定量环境遥感:基于高光谱的近景及卫星遥感数据,反演植被及水环境生态系统的各类典型生化参数,以达到生态环境监测的目的。


主持参与项目

美国NASA资助项目:农作物初级生产力的遥感估测模型研究,2008 - 2012
美国NASA资助项目:湿地森林生长变化的遥感监测模型研究,2012 – 2013
国家青年自然基金资助项目:适应光照环境变化的多种农作物的GPP遥感估测模型研究,2014 – 至今



发表主要论文

[1] Nguy-Robertson, A.L., Peng, Y., Gitelson, A.A., Arkebauer, T., Scoby, D., & Schepers, J. (2015). Using a simple leaf color chart to estimate leaf and canopy chlorophyll a content in maize (Zea mays). Communications in Soil Science and Plant Analysis, In press. 
[2] Gitelson, A.A., Peng, Y., T.J. Arkebauer, A.E. Suyker. (2015). Productivity, absorbed photosynthetically active radiation, and light use efficiency in crops: Implications for remote sensing of crop primary production, Journal of Plant Physiology, 177, 100-109.
[3] Gitelson, A. A., Peng, Y., K. F. Huemmrich. (2014). Relationship between fraction of radiation absorbed by photosynthesizing maize and soybean canopies and NDVI from remotely sensed data taken at close range and from MODIS 250 m resolution data. Remote Sensing of Environment, 147: 108–120.
[4] Gitelson, A.A., Peng, Y., Schepers, J. & Arkebauer, T. (2014). Relationships between gross primary production, green LAI, and canopy chlorophyll content: Implications for remote sensing of primary production. Remote Sensing of Environment, 144, 65-72.
[5]   Huang, C., Peng, Y., Lang, M. & Yeo, I.Y. (2014). Wetland inundation mapping and change monitoring using Landsat and airborne LiDAR data. Remote Sensing of Environment, 141, 231-242.
[6]   Nguy-Robertson, A.L., Peng, Y., Gitelson, A.A., Arkebauer, T.J., Pimstein, A., Herrmann, I., Karnieli, A., Rundquist, D.C. & Bonfil, D.J. (2014). Estimating green LAI in four crops: Potential of determining optimal spectral bands for a universal algorithm. Agricultural and Forest Meteorology, 192-193: 140-148.
[7] Peng, Y. Sakamoto, T., & Gitelson, A.A. (2013). Remote estimation of gross primary productivity in crops using MODIS 250 m data. Remote Sensing of Environment, 128, 186-196.
[8] Gitelson, A.A., Peng, Y., Masek, J., Verma, S.B., Suyer, A., Baker, J.M., Hatfield, J.L. & Meyers, T. (2013). Remote estimation of gross primary productivity in crops with Landsat data. Remote Sensing of Environment, 121, 404-414.
[9] Nguy-Robertson, A., Gitelson,A.A., Peng, Y., Walter-Shea, E., Leavitt, B., & T. Arkebauer. (2013). Continuous Monitoring of Crop Reflectance, Vegetation Fraction, and Identification of Developmental Stages Using a Four Band Radiometer, Agronomy Journal, 105: 1769–1779.
[10] Schlemmer, M., Gitelson, A.A., Schepers, J., Ferguson R., Peng Y., Shanahan, J., Rundquist, D.C. (2013). Remote estimation of nitrogen and chlorophyll contents in maize at leaf and canopy levels, International Journal of Applied Earth Observation and Geoinformation, 25: 47–54.
[11] Peng, Y. & Gitelson, A.A. (2012). Remote estimation of gross primary productivity in soybean and maize based on total crop chlorophyll content. Remote Sensing of Environment, 117, 440-448.
[12] Nguy-Robertson, A.L., Gitelson, A.A., Peng, Y., Vina, A., Arkebauer, T. & Rundquist, D. (2012). Combining vegetation indices to increase their sensitivity to green leaf area index of crops along its entire dynamic range. Agronomy Journal, 104, 5, 1336 – 1347.
[13] Peng, Y. & Gitelson, A.A.(2011). Application of chlorophyll-related vegetation indices for remote estimation of maize productivity. Agricultural and Forest Meteorology, 151, 1267 – 1276.
[14] Peng, Y., Gitelson, A.A., Keydan, G., Rundquist, D.C. & Moses, W. (2011). Remote estimation of gross primary production in maize and support for a new paradigm based on total crop chlorophyll content. Remote Sensing of Environment, 115, 978-989.
[15] Vina, A., Gitelson, A.A., Nguy-Robertson, A.L. & Peng, Y. (2011). Comparison of different vegetation indices for the remote assessment of green leaf area index of crops. Remote Sensing of Environment, 115, 3468 – 3478.




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