Comparisons of algorithms to estimate water turbidity in the coastal areas of China. Issue 24 (16th December 2016)
- Record Type:
- Journal Article
- Title:
- Comparisons of algorithms to estimate water turbidity in the coastal areas of China. Issue 24 (16th December 2016)
- Main Title:
- Comparisons of algorithms to estimate water turbidity in the coastal areas of China
- Authors:
- Zheng, Lufei
Qiu, Zhongfeng
Zhou, Yan
Sun, Deyong
Wang, Shengqiang
Wu, Wei
Perrie, William - Abstract:
- ABSTRACT: Turbidity is an important indicator of water environments and water-quality conditions. Ocean colour remote sensing has proved to be an efficient way of monitoring water turbidity because of its wide synoptic coverage and repeated regular sampling. However, operational tasks are still challenging in high-turbidity waters, especially in estuaries and the coastal regions of China. In these areas, the existing algorithms derived from remote-sensing reflectance ( R rs ) are usually invalid because it is difficult to correctly estimate the reflectance R rs from satellite data such as Moderate Resolution Imaging Spectroradiometer (MODIS) data. A new algorithm that uses Rayleigh-corrected reflectance ( R rc ) instead of R rs has been recently introduced and was used to estimate water turbidity in Zhejiang (ZJ) coastal areas from Geostationary Ocean Color Imager (GOCI) data. The R rc algorithm has previously shown a capability to estimate water turbidity. However, its performance still requires careful evaluation. In this article, we compared the new R rc algorithm with two other existing algorithms. Differences among the three algorithms were assessed by comparing the results from using R rc data and R rs reflectance data derived from both GOCI and MODIS imagery data. The capability of the new R rc algorithm to estimate water turbidity in larger areas and extended seasons in the coastal seas of China was also estimated. The results showed that the new R rc algorithm isABSTRACT: Turbidity is an important indicator of water environments and water-quality conditions. Ocean colour remote sensing has proved to be an efficient way of monitoring water turbidity because of its wide synoptic coverage and repeated regular sampling. However, operational tasks are still challenging in high-turbidity waters, especially in estuaries and the coastal regions of China. In these areas, the existing algorithms derived from remote-sensing reflectance ( R rs ) are usually invalid because it is difficult to correctly estimate the reflectance R rs from satellite data such as Moderate Resolution Imaging Spectroradiometer (MODIS) data. A new algorithm that uses Rayleigh-corrected reflectance ( R rc ) instead of R rs has been recently introduced and was used to estimate water turbidity in Zhejiang (ZJ) coastal areas from Geostationary Ocean Color Imager (GOCI) data. The R rc algorithm has previously shown a capability to estimate water turbidity. However, its performance still requires careful evaluation. In this article, we compared the new R rc algorithm with two other existing algorithms. Differences among the three algorithms were assessed by comparing the results from using R rc data and R rs reflectance data derived from both GOCI and MODIS imagery data. The capability of the new R rc algorithm to estimate water turbidity in larger areas and extended seasons in the coastal seas of China was also estimated. The results showed that the new R rc algorithm is suitable for the coastal waters of China, especially for highly turbid waters. … (more)
- Is Part Of:
- International journal of remote sensing. Volume 37:Issue 24(2016)
- Journal:
- International journal of remote sensing
- Issue:
- Volume 37:Issue 24(2016)
- Issue Display:
- Volume 37, Issue 24 (2016)
- Year:
- 2016
- Volume:
- 37
- Issue:
- 24
- Issue Sort Value:
- 2016-0037-0024-0000
- Page Start:
- 6165
- Page End:
- 6186
- Publication Date:
- 2016-12-16
- Subjects:
- Remote sensing -- Periodicals
Télédétection -- Périodiques
621.3678 - Journal URLs:
- http://www.tandfonline.com/toc/tres20/current ↗
http://www.tandfonline.com/ ↗ - DOI:
- 10.1080/01431161.2016.1256510 ↗
- Languages:
- English
- ISSNs:
- 0143-1161
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4542.528000
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 7337.xml