The assessment of surface water salinity is a long standing feature request for water quality assessment by remote sensing. The aim of the present work is to test an empirical method for surface water salinity retrieval by means of multispectral satellite images at medium resolution (30 m). For this purpose, two case studies were selected: the first is Lake Qarun (Egypt), the second is an on-shore tract of central Adriatic Sea, located between the mouths of Tronto and Salinello Rivers (Italy). For the experimentation ALI (Advanced Land Imager) and Landsat ETM imagery was collected. Field data were acquired at both sites by means of in situ conductivity measurements, for calibration purpose. The model applied to convert atmospherically corrected reflectance value in practical salinity units (PSU) has been developed analysing the correlation between field data and an expressly defined salinity index. First results show a promising overall correlation (R2 = 0.85), even if further work is required to provide a better validation.

Empirical model for salinity assessment on lacustrine and coastal waters by remote sensing

BITELLI, GABRIELE;CURZI, PIETRO;DINELLI, ENRICO;MANDANICI, EMANUELE
2011

Abstract

The assessment of surface water salinity is a long standing feature request for water quality assessment by remote sensing. The aim of the present work is to test an empirical method for surface water salinity retrieval by means of multispectral satellite images at medium resolution (30 m). For this purpose, two case studies were selected: the first is Lake Qarun (Egypt), the second is an on-shore tract of central Adriatic Sea, located between the mouths of Tronto and Salinello Rivers (Italy). For the experimentation ALI (Advanced Land Imager) and Landsat ETM imagery was collected. Field data were acquired at both sites by means of in situ conductivity measurements, for calibration purpose. The model applied to convert atmospherically corrected reflectance value in practical salinity units (PSU) has been developed analysing the correlation between field data and an expressly defined salinity index. First results show a promising overall correlation (R2 = 0.85), even if further work is required to provide a better validation.
Earth Resources and Environmental Remote Sensing/GIS Applications II
818119-1
818119-8
G. Bitelli; P. V. Curzi; E. Dinelli; E. Mandanici
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/108809
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