Volume XLII-3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 349-354, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-349-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 349-354, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-349-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Apr 2018

30 Apr 2018

ESTIMATION CODMN IN GUANGZHOU SECTION OF PEARL RIVER BASED ON GF-1 IMAGES

Y. B. Feng1,2, Y. Q. He1,2, Q. H. Fu1,2, C. Q. Liu1,2, H. Z. Pan1,2, and B. Yin1,2 Y. B. Feng et al.
  • 1Key Laboratory of dynamics and associated process of the Pearl River Estuary, Ministry of water resources, China
  • 2Pearl River Hydraulic Research Institute, Guangzhou, China

Keywords: COD_Mn, GF-1, water color remote sensing, Pearl River, Guangzhou

Abstract. Due to the way that remote sensing works, it has natural advantage to detect optical constituents in waters. And many kinds of inversion models were constructed based on the three main optical constituents, namely chlorophyll-a (Chl-a), suspended particulate matter (SPM), colored dissolved organic matter (CDOM). Except Chl-a used as an indicator of eutrophication, however, the public generally cares less about other two parameters and is more familiar with Grade I ~ V scheme for utilization and protection purposes. Notice the three main optical constituents are also organic-related to some extent. It offers a possible way to estimate CODMn via remote sensing. According to field measurement conducted along the Guangzhou section of Pearl River (GPR for short), the spatial variation of CODMn in GPR shows some kinds of geographical feature, so does the correlation between CODMn and water color constituents. It indicated the complicated contribution of CODMn in GPR or some other urban rivers. Based on the band setting of GF-1 satellite, two kinds of inversion model of CODMn in GPR were finally constructed. One directly achieved CODMn from regression models of which predictors were different band combinations in different channels of GPR. To make the study more practical, the other one first provided empirical models of the three optical constituents, and then estimated CODMn of GPR based on its relationship with optical constituents. After all, Chl-a, SPM and CDOM could be distinguished optically, and remote sensing models of these three constituents in other studies may also be available.