Abstract
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.
Highlights
Chlorophyll-a (Chl-a) concentration can be used as a direct indicator of the ecological state of a water body
Using ENVI 5.1 (Exelis Visual Information Solutions, Boulder, CO, USA, 2014), the Landsat-8 Operational Land Imager (OLI) Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) processing tool was used for the atmospheric correction and the georeferencing tool was used for the geometric correction of the images
The section of the Haihe River in the Binhai New Area of Tianjin was chosen as the water study area
Summary
Chlorophyll-a (Chl-a) concentration can be used as a direct indicator of the ecological state of a water body. Inversion of water quality parameters using remote sensing technology can improve the monitoring of surface water quality and derive dynamic water quality information in real-time. This technique represents an important complement to regular water quality monitoring, and it can provide a robust scientific basis for governmental decision-making in relation to the ecological economic zone. The lack of in situ real-time monitoring data on the quality and optical properties of inland water bodies renders it difficult to determine the spatiotemporal variation of Chl-a concentration. Improvements of the geometry and the spectral resolution associated with remote sensing technology have presented new possibilities for the evaluation of water resources via the monitoring of Chl-a concentration.
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