• Spectroscopy and Spectral Analysis
  • Vol. 38, Issue 8, 2536 (2018)
LIU Zhen-yu1、2、*, CUI Ting-wei3, ZHANG Sheng-hua1, and ZHAO Wen-jing4
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
  • 3[in Chinese]
  • 4[in Chinese]
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    DOI: 10.3964/j.issn.1000-0593(2018)08-2536-06 Cite this Article
    LIU Zhen-yu, CUI Ting-wei, ZHANG Sheng-hua, ZHAO Wen-jing. Piecewise Linear Retrieval Suspended Particulate Matter for the Yellow River Estuary Based on Landsat8 OLI[J]. Spectroscopy and Spectral Analysis, 2018, 38(8): 2536 Copy Citation Text show less

    Abstract

    Much sediment is transported into Bohai by Yellow River every year. Therefore the study on suspended matter concentration(SPM) in Yellow River estuary is significant to the environmental monitoring and sediment transport of surrounding ocean. The piecewise linear retrieval model was proposed based on the remote sensing reflectance and suspended matter concentration synchronous sampled in summer and winter in 2011. The results showed: the sensitive bands combination for suspended matter concentration inversion was different under different concentration ranges, (600~700 nm) /(400~600 nm) and (750~900 nm)/(420~720 nm) respectively to the concentration range below and above 50 mg·L-1, and the corresponding bands for Landsat 8 OLI were B4/B2 and B5/B3 respectively. The R2, RMSE and APD of the Piecewise model were 0.873 5, 4.08 mg·L-1 and 22.81%(SPM≤50 mg·L-1), 0.969 3, 102.96 mg·L-1, and 17.51%(SPM>50 mg·L-1) respectively, and 0.975 3, 67.03 mg·L-1and 20.45% for the overall concentration, which were better than parameters of common single model under entire concentration range. In summary, the piecewise linear retrieval model is more suitable for suspended matter concentration inversion of the Yellow River estuary with large variation of concentration.
    LIU Zhen-yu, CUI Ting-wei, ZHANG Sheng-hua, ZHAO Wen-jing. Piecewise Linear Retrieval Suspended Particulate Matter for the Yellow River Estuary Based on Landsat8 OLI[J]. Spectroscopy and Spectral Analysis, 2018, 38(8): 2536
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