Premium
What is the minimum river width for the estimation of water clarity using medium‐resolution remote sensing images?
Author(s) -
Zhao Dehua,
Lv Meiting,
Zou Xiangxu,
Wang Penghe,
Yang Tangwu,
An Shuqing
Publication year - 2014
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1002/2013wr015068
Subject(s) - remote sensing , image resolution , pixel , environmental science , water quality , hydrology (agriculture) , geology , computer science , artificial intelligence , biology , ecology , geotechnical engineering
Numerous studies have demonstrated the feasibility of remotely sensing water clarity of lakes, reservoirs, and larger rivers using medium‐resolution spatial images. However, addressing relatively small rivers or river sections is very challenging due to the adjacency effect from the riverbanks. The objectives of this study are to quantify the minimum river width for water quality remote sensing and to validate the feasibility of using medium‐resolution spatial images for estimating the Secchi disk depth (SDD). A methodology was developed to quantify the minimum river width for water quality remote sensing using high‐resolution spatial images from WorldView‐2 and Pléiades. Our results suggest that the influential distance of the adjacency effect from the riverbank is 17.3 m, i.e., water pixels with a distance of more than 17.3 m from the shoreline experienced a minimal disturbance from the riverbank. For the 30 m spatial resolution HJ‐1A image (one of Chinese civilian satellites launched in 2008), the minimum river width is 64.6–98.5 m (the variation was determined according to the river flow direction and the pixel position relative to the shoreline). Using the sections that satisfied the minimum river width requirement, a significant estimation model was established between the spectral reflectance and the SDD (R 2 = 0.94), demonstrating that the minimum river width recommended in this study is practical. This work is the first study to quantify the minimum river width for water quality remote sensing and thus provides a valuable reference for remote sensing of relatively small rivers.