专家人才
 
  您现在的位置:首页 > 研究队伍
  专家人才
姓 名:
 宋开山
性 别:
 男
职 称:
 研究员
学 历:
 博士研究生
电 话:
 0431-85542364
传 真:
 
电子邮件:
 songks@iga.ac.cn; songks@126.com
个人主页:
 http://people.gucas.ac.cn/~songkaishan
通讯地址:
 长春市高新北区盛北大街4888号 130102
简历:

教育经历:

1995-1999 吉林师范大学地理系,学士学位
1999-2002东北师范大学城市与环境科学学院,硕士学位
2002-2005中科院东北地理与农业生态研究所,博士学位
2007-2008 澳大利亚CSIRO水土研究中心,访问学者
2009-2013 印第安纳大学,博士后

工作经历:

2005.01 - 2006.06 中科院东北地理与农业生态所 助理研究员
2006.07 – 2011.11 中科院东北地理与农业生态所 副研究员
2011.12 – 至今 中科院东北地理与农业生态所 研究员

主持的在研项目:

1.国家自然科学基金重点项目,41730104,咸水湖泊固有光学-偏振-介电特性研究,2018/01-2022/12,316万元.

2.国家科技部重点研发项目子课题,2016YFB0501502,全球湿地动态变化检测及环境变化研究,2016/07-2020/09,100万元.

3.国家自然科学基金面上项目,41471293,湖冰生物光学特性研究,2015/01-2018/12,90万元.

4.国防科工局高分系统重大专项-课题,41-Y20A31-9003-15/17,GF-5高光谱载荷的内陆水体参量反演技术-东北共性技术,2016/01-2018/09,25 万元.

5.吉林省科技厅领军人才及创新团队项目,20150519006JH,吉林省城市及饮用水体水质遥感创新团队,2015/01-2018/08,20万元.

主持的结题项目:

1.国家973计划项目子课题,2013CB430401,滨海湿地生态系统类型与格局演变规律研究,2013/01-2017/09,150万元.

2.国家自然基金面上项目,41471290,内陆浑浊水体叶绿素a与藻清蛋白(PC)遥感反演研究,2012/01-2015/12,60万元.

3.国家自然科学基金重点项目子课题,41074233,松嫩平原LUCC与湖沼生态系统演化耦合机理,2011/01-2014/12, 40万元.

4.中国科学院重点部署项目之课题,KZZD-EW-TZ-16-1,土壤理化性质遥感模型研究,2013/05-2016/07,120万元.

5.中国科学院青年人才项目,KZCX2-YW-QN305,基于星空一体化的农作物生理参数遥感反演研究,2009/01-2012/12,50万元.

6.环境保护部环境友好湖泊基线调查项目-课题,JXZFCG2013-XXK21-03,兴凯湖饮用水基线调查与湖滨带制图,2014/05-2016/08,25 万元.

7.吉林省与中国科学院合作项目,2006SYHZ0026,吉林省重要地表饮用水源地水质遥感监测与示范,2007/05-2009/05,50万元(技术总负责).


研究方向:

水体生物光学特性与水质遥感;水体富营养化与湿地参数遥感;内陆水体碳组分与储量遥感估算。


专家类别:
研究员
职务:

社会任职:

获奖及荣誉:
 
代表论著:
 

1.   Song, K.S*., Wen, Z.D., Shang, Y.X., Yang, H., Lyu, L.L., Liu, G., Fang, C., Du, J., Zhao, Y., 2018. Quantification of dissolved organic carbon (DOC) storage in lakes and reservoirs of mainland China. Journal of environmental management, 217: 319-402.

2.   Fang, C., Song, K.S*., Li, L., Wen, Z.D., Liu, G., Du, J., Zhao, Y., 2018. Spatial variability and temporal dynamics of HABs in Northeast China. Ecological Indicators, 90: 280-294.

3.   Song, K.S*., Ma, J.H., Wen, Z.D., Fang, C., Shang, Y.X., 2017. Remote estimation of Kd (PAR) using MODIS and Landsat imagery for turbid inland waters in Northeast China. ISPRS Journal of Photogrammetry and Remote Sensing, 123: 159~172.

4.   Jin, X.L., Song, K.S*., Du, J., Liu, H.J., Wen, Z.D., 2017. Soil organic matter estimation based on simulated spectral configuration of different satellite sensors: optimal three band algorithm versus the PSO-SVM model. Agricultural and Forest Meteorology, 244-245: 57-71.

5.   Song, K.S*., Zhao, Y., Wen, Z.D., Fang, C., Shang, Y.X., 2017. A systematic examination of the relationships between CDOM and DOC in inland waters in China. Hydrology and Earth System Sciences, 21: 5127-5141.

6.   Zhao, Y., Song, K.S*., Wen, Z.D., Fang, C., Shang, Y.X., Lyu, L.L., 2017. Evaluation of CDOM sources and their links with water quality in the lakes of Northeast China using fluorescence spectroscopy. Journal of Hydrology, 550: 80-91.

7.   Zhao, Y., Song, K.S*., Shang, Y.X., Shao, T.T., Wen, Z.D., Lyu, L.L., 2017. Characterization of CDOM of river waters in China using fluorescence excitation-emission matrix and regional integration techniques. Journal of Geophysical Research: Biogeosciences, 122: 1940–1953.

8.   Wen, Z.D., Song, K.S*., Shang, Y.X., Fang, C., Li, L., Lyu, L.L., Lyu, X.G., Chen, L.J., 2017. Carbon dioxide emissions from lakes and reservoirs of China: Aregional estimate based on the calculated pCO2. Atmospheric Environment, 170: 71-81.

9.   Wen, Z.D., Song, K.S*., Zhao, Y., Du, J., Ma, J.H., 2016. Influence of environmental factors on spectral characteristic of chromophoric dissolved organic matter(CDOM) in Inner Mongolia Plateau, China. Hydrology and Earth System Sciences, 20: 787-801.

10. Zhao, Y., Song, K.S*., Wen, Z.D., Li., L., Zang, S.Y., Shao, T.T., Li, S.J., Du, J. 2016. Seasonal characterization of CDOM for lakes in semiarid regions of Northeast China using excitation–emission matrix fluorescence and parallel factor analysis (EEM–PARAFAC). Biogeosciences, 13: 1-11.

11. Jin, X.L., Du, J., Liu, H.J., Wang, Z.M., Song, K.S*., 2016. Remote estimation of soil organic matter content in the Sanjiang Plain, Northest China: the optimal band algorithm versus the GRA-ANN model. Agricultural and Forest Meteorology, 218-219: 250-260.

12. Song, K.S*., Li, L., Tedesco, L., Li, S., Hall, B., Du, J., 2014. Remote quantification of phycocyanin in potable water sources through an adaptive model. ISPRS Journal of Photogrammetry and Remote Sensing, 95: 68-80.

13. Song, K. S*., Li, L., Li, S., Tedesco, L., Duan, H. T., Li, Z. C., Shi, K., Du, J., Zhao, Y., Shao, T. T. 2014. Using Partial Least Squares-Artificial Neural Network for Inversion of Inland Water Chlorophyll-a. IEEE Transactions on Geoscience and Remote Sensing, 52: 1502-1517.

14. Song, K.S*., Li, L., Duan, H. T., Tedesco, L., Li, L. H., Du, J., 2014. Remote quantification of total suspended matter through empirical approaches for inland waters. Journal of Environmental Informatics, 23(1): 23-36.

15. Song, K.S*., Wang, Z.M., Du, J., Liu, L., Zeng, L.H., Ren, C.Y., 2014. Wetland degradation, driving forces and its environmental impacts in the Sanjiang Plain, China. Environmental Management, 54:255-271.

16. Song, K.S*., Li, L., Lenore, L. P., Li, S., Duan, H. T., Liu, D. W., Hall, B. E., Du, J., Li, Z. C., Shi, K., Zhao, Y., 2013. Remote estimation of chlorophyll-a in turbid inland waters: Three-band model versus GA-PLS model. Remote Sensing of Environment, 136: 342-357.

17. Song, K.S*., Zang, S. Y., Zhao, Y., Li, L., Du, J., Zhang, N. N., Wang, X. D., Shao, T. T., Liu, L., Guan, Y., 2013. Spatiotemporal characterization of dissolved Carbon for inland waters in semi-humid/semiarid region, China. Hydrology and Earth System Science, 17: 4269-4281.

18. Song, K.S*., Wang, Z.M., Li, L., Tedesco, L., Li, F., Jin, C., Du, J., 2012. Wetlands shrinkage, fragmentation and their links to agriculture in the Muleng-Xingkai Plain, China. Journal of Environmental Management, 111: 120-132.

19. Song, K.S*., Li, L., Li, S., Tedesco, L., Hall, B., Li, Z.C., 2012. Hyperspectral retrieval of phycocyanin in potable water sources using genetic algorithm-partial least squares (GA–PLS) modeling. International Journal of Applied Earth Observation and Geoinformation, 18: 368-385.

20. Song, K.S*., Li, L., Tedesco, L.P., Li, S., Clercin, A.N., Hall, B., Li, Z.C., Shi, K. 2012. Hyperspectral determination of eutrophication for a water supply source via genetic algorithm-partial least squares (GA–PLS) modeling. Science of the Total Environment, 426: 220-232.