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Proceedings Paper

Estimation of turbidity along the Guadalquivir estuary using Landsat TM and ETM+ images
Author(s): M. Carpintero; E. Contreras; A. Millares; M. J. Polo
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Paper Abstract

Estuarine water in Mediterranean basins has high concentrations of suspended sediment. In order to study the temporal and spatial distribution of turbidity, a monitoring network with sufficient temporal and spatial resolution is needed to monitor water quality, and this is not always available. Thus, over the last few years, satellite images have been used as an alternative way to estimate water quality parameters, such as turbidity. The Guadalquivir River estuary in south-west Spain extends for 105 km and is one of the world’s most turbid estuaries. The sediments present are of a very fine texture due to the great length of the river but, mainly, to the extreme trapping efficiency of the dense reservoir system upstream. This work shows the relationship between turbidity patterns along the Guadalquivir river estuary and the data from Landsat ETM+ images from August 2008 to 2010, and the suitability of the algorithms previously used in this estuary environment, with the ultimate goal of obtaining turbidity maps. The results of this study show that the use of previously developed algorithms underestimate turbidity values measured by the monitoring network used, which proves that one single algorithm for the entire period of study does not provide a reliable reproduction of the real situation. The wide variability in turbidity data along the estuary has enabled us to develop specific expressions for each day, which allow us to obtain turbidity maps.

Paper Details

Date Published: 16 October 2013
PDF: 15 pages
Proc. SPIE 8887, Remote Sensing for Agriculture, Ecosystems, and Hydrology XV, 88870B (16 October 2013); doi: 10.1117/12.2029183
Show Author Affiliations
M. Carpintero, Univ. de Córdoba (Spain)
E. Contreras, Univ. de Córdoba (Spain)
A. Millares, Univ. de Granada (Spain)
M. J. Polo, Univ. de Córdoba (Spain)


Published in SPIE Proceedings Vol. 8887:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XV
Christopher M. U. Neale; Antonino Maltese, Editor(s)

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