期刊文献+

Evaluation of Ordinary Least Square(OLS) and Geographically Weighted Regression(GWR) for Water Quality Monitoring:A Case Study for the Estimation of Salinity 被引量:1

Evaluation of Ordinary Least Square(OLS) and Geographically Weighted Regression(GWR) for Water Quality Monitoring:A Case Study for the Estimation of Salinity
下载PDF
导出
摘要 Landsat-5 Thematic Mapper(TM) dataset have been used to estimate salinity in the coastal area of Hong Kong. Four adjacent Landsat TM images were used in this study, which was atmospherically corrected using the Second Simulation of the Satellite Signal in the Solar Spectrum(6S) radiative transfer code. The atmospherically corrected images were further used to develop models for salinity using Ordinary Least Square(OLS) regression and Geographically Weighted Regression(GWR) based on in situ data of October 2009. Results show that the coefficient of determination(R^2) of 0.42 between the OLS estimated and in situ measured salinity is much lower than that of the GWR model, which is two times higher(R^2 = 0.86). It indicates that the GWR model has more ability than the OLS regression model to predict salinity and show its spatial heterogeneity better. It was observed that the salinity was high in Deep Bay(north-western part of Hong Kong) which might be due to the industrial waste disposal, whereas the salinity was estimated to be constant(32 practical salinity units) towards the open sea. Landsat-5 Thematic Mapper(TM) dataset have been used to estimate salinity in the coastal area of Hong Kong. Four adjacent Landsat TM images were used in this study, which was atmospherically corrected using the Second Simulation of the Satellite Signal in the Solar Spectrum(6S) radiative transfer code. The atmospherically corrected images were further used to develop models for salinity using Ordinary Least Square(OLS) regression and Geographically Weighted Regression(GWR) based on in situ data of October 2009. Results show that the coefficient of determination(R^2) of 0.42 between the OLS estimated and in situ measured salinity is much lower than that of the GWR model, which is two times higher(R^2 = 0.86). It indicates that the GWR model has more ability than the OLS regression model to predict salinity and show its spatial heterogeneity better. It was observed that the salinity was high in Deep Bay(north-western part of Hong Kong) which might be due to the industrial waste disposal, whereas the salinity was estimated to be constant(32 practical salinity units) towards the open sea.
出处 《Journal of Ocean University of China》 SCIE CAS CSCD 2018年第2期305-310,共6页 中国海洋大学学报(英文版)
基金 The National Key Research and Development Program of China (No.2016YFC1400901)
关键词 Landsat THEMATIC MAPPER WATER quality SALINITY remote sensing coastal WATER Landsat Thematic Mapper water quality salinity remote sensing coastal water
  • 相关文献

参考文献2

二级参考文献27

  • 1李志,魏恩泊,田纪伟.一个L波段海表盐度遥感反演的新经验模式[J].物理学报,2007,56(5):3028-3030. 被引量:8
  • 2Boutin, J., and Martin, N., 2006. ARGO upper salinity meas- urements: Perspectives for L-band radiometers calibration and retrieved sea surface salinity validation. IEEE Geoscience and Remote Sensing Letters, 3 (2): 202-206.
  • 3Boutin, J., Martin, N., Reverdin, G., Yin, X., and Gaillard, F., 2013. Sea surface freshening inferred from SMOS and ARGO salinity: Impact of rain. Ocean Science, 9:183-192.
  • 4Boutin, J., Martin, N., Yin, X., Font, J., Reul, N., and Spurgeon, P., 2012. First assessment of SMOS data over open ocean: Part II-Sea surface salinity. 1EEE Transactions on Geoscience andRemote Sensing, 50 (5): 1662-1675.
  • 5Camps, A., Vall-llossera, M., Duffo, N., Torres, F., and Corbella, I., 2005. Performance of sea surface salinity and soil moisture retrieval algorithms with different auxiliary datasets in 2-D L-Band aperture synthesis interferometric radiometers. 1EEE Transactions on Geoscience and Remote Sensing, 43 (5): 1189-1200.
  • 6Camps, A., Vall-llossera, M., Miranda, J., and Font, J., 2004. Sea surface brightness temperature at L-band: Impact of sur- face currents. Geoscience and Remote Sensing Symposium, 5: 3481-3484.
  • 7Feng, S. Z., Li, F. Q., and Li, S. J., 1999. Introduction to Marine Science. Higher Education Press, Beijing, 503pp.
  • 8Font, J., Camps, A., Borges, A., Martin-Neira, M., Boutin, J., Reul, N., Kerr, Y., Hahne, A., and Mecklenburg, S., 2010.SMOS: The challenging measurement of sea surface salinity from space. Proceedings of the IEEE, 98 (5): 649-665.
  • 9Gabarr6, C., Portabella, M., Talone, M,, and Font, J., 2009. Toward an optimal SMOS ocean salinity inversion algorithm. IEEE Geoscience and Remote Sensing Letters, 6 (3): 509-513.
  • 10Kerr, Y. H., Waldteufel, P., Wigneron, J. P., Delwart, S., Cabot, F., Boutin, J., Escorihuela, M. J., Font, J., Reul, N., Gruhier, C., Juglea, S. E., Drinkwater, M. R., Hahne, A., Martin-Neira, M., and Mecklenburg, S., 2010. The SMOS mission: New tool for monitoring key elements of the global water cycle. Proceedings of the IEEE, 98: 666-687.

共引文献7

同被引文献13

引证文献1

二级引证文献3

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部