Next Article in Journal
A Hybrid Multi-Step Rolling Forecasting Model Based on SSA and Simulated Annealing—Adaptive Particle Swarm Optimization for Wind Speed
Previous Article in Journal
Analyzing the Effects of Spatial Interaction among City Clusters on Urban Growth—Case of Wuhan Urban Agglomeration
Article Menu

Export Article

Open AccessArticle
Sustainability 2016, 8(8), 758; doi:10.3390/su8080758

Study on Retrieval of Chlorophyll-a Concentration Based on Landsat OLI Imagery in the Haihe River, China

School of Geology and Geomatics, Tianjin Chengjian University, Jinjing Road, Tianjin 300384, China
College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China
Author to whom correspondence should be addressed.
Academic Editor: Vincenzo Torretta
Received: 27 April 2016 / Revised: 13 July 2016 / Accepted: 29 July 2016 / Published: 6 August 2016
(This article belongs to the Section Sustainable Use of the Environment and Resources)
View Full-Text   |   Download PDF [5770 KB, uploaded 6 August 2016]   |  


The optical complexity of urban waters makes the remote retrieval of chlorophyll-a (Chl-a) concentration a challenging task. In this study, Chl-a concentration was retrieved using reflectance data of Landsat OLI images. Chl-a concentration in the Haihe River of China was obtained using mathematical regression analysis (MRA) and an artificial neural network (ANN). A regression model was built based on an analysis of the spectral reflectance and water quality sampling data. Remote sensing inversion results of Chl-a concentration were obtained and analyzed based on a verification of the algorithm and application of the models to the images. The analysis results revealed that the two models satisfactorily reproduced the temporal variation based on the input variables. In particular, the ANN model showed better performance than the MRA model, which was reflected in its higher accuracy in the validation. This study demonstrated that Landsat Operational Land Imager (OLI) images are suitable for remote sensing monitoring of water quality and that they can produce high-accuracy inversion results. View Full-Text
Keywords: chlorophyll-a; Landsat OLI; remote sensing retrieval; artificial neural network chlorophyll-a; Landsat OLI; remote sensing retrieval; artificial neural network

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Guo, Q.; Wu, X.; Bing, Q.; Pan, Y.; Wang, Z.; Fu, Y.; Wang, D.; Liu, J. Study on Retrieval of Chlorophyll-a Concentration Based on Landsat OLI Imagery in the Haihe River, China. Sustainability 2016, 8, 758.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sustainability EISSN 2071-1050 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top