[Quantitative estimation of suspended solid concentration in the lower Min River based on multi-source synchronal data].
Three synchronal data collected on 2006-09-18 have been used in the study of the suspended solid concentration (SSC) of the lower Min River, which are in situ sampled water data, field-spectrometer measured spectral data and Landsat TM spectral data. Two models for predicting SSC have been proposed, one of which is based on field-spectrometer measured data and the other is on Landsat TM data. The statistical analysis of the field-spectroreter measured data has revealed that the reflectance of the SSC at the 690 nm has the strongest correlation with the in situ-sampled SSC data. The regression model can be expressed as SS = 116.2 (R690/R530) - 33.4. Furthermore, the model built upon the ratio of the reflectance at 690 nm to 530 nm has the best fitness with the in situ sampled SSC data. While the best predicting model for the Landsat TM data is achieved using the band combination of (TM2 + TM3)2 and is defined as SS = 3793.7 (R(TM3) + R(TM2)2 - 16.5. The assessment of the two models shows that the model on the field-spectrometer data has higher accuracy than that on the Landsat TM data but the difference is not big. This suggests that the Landsat TM data are still valuable in the prediction of the SSC if the field-spectrometer data are not available. Consequently, the predicting model based on the Landsat data has been applied in the study of the SSC of the lower Min River. The result shows that the model can efficiently reveal the SSC with its spatial distributional pattern features.
Wen
Xiao-le
XL
Key Laboratory of Spatial Data Mining and Information Sharing, Ministry of Education, College of Environment and Resources, Fuzhou University, Fuzhou 350002, China. wenxiaole@sina.com
Xu
Han-qiu
HQ
D002681:China; D004784:Environmental Monitoring / Q000379:methods*; D005618:Fresh Water / Q000032:analysis*; D008962:Models, Theoretical; D010316:Particle Size; D045483:Rivers; D017215:Satellite Communications; D014873:Water Pollutants / Q000032:analysis* / Q000737:chemistry
D014873:Water Pollutants