Comparing the Capability of Sentinel-2 and Landsat 9 Imagery for Mapping Water and Sandbars in the River Bed of the Lower Tagus River (Portugal)

Author:

Gerardo Romeu123,de Lima Isabel P.14ORCID

Affiliation:

1. Department of Civil Engineering, Faculty of Sciences and Technology, University of Coimbra, Rua Luís Reis Santos, 3030-788 Coimbra, Portugal

2. CERIS, University of Coimbra, Rua Pedro Hispano s/n, 3030-289 Coimbra, Portugal

3. Itecons—Institute of Research and Technological Development in Construction, Energy, Environment and Sustainability, Rua Pedro Hispano, 3030-289 Coimbra, Portugal

4. MARE—Marine and Environmental Sciences Centre/ARNET–Aquatic Research Network, Pole MARE–UCoimbra, Polo II University of Coimbra, Rua Sílvio Lima, 3030-790 Coimbra, Portugal

Abstract

Mapping river beds to identify water and sandbars is a crucial task for understanding the morphology and hydrodynamics of rivers and their ecological conditions. The main difficulties of this task so far have been the limitations of conventional approaches, which are generally costly (e.g., equipment, time- and human resource-demanding) and have poor flexibility to deal with all river conditions. Currently, alternative approaches rely on remote sensing techniques, which offer innovative tools for mapping water bodies in a quick and cost-effective manner based on relevant spectral indices. This study aimed to compare the capability of using imagery from the Sentinel-2 and newly launched Landsat 9 satellite to achieve this goal. For a segment of the Lower Tagus River (Portugal) with conditions of very low river discharge, comparison of the Normalized Difference Water Index, Modified Normalized Difference Water Index, Augmented Normalized Difference Water Index, and Automated Water Extraction Index calculated from the imagery of the two satellites shows that the two satellites’ datasets and mapping were consistent and therefore could be used complementarily. However, the results highlighted the need to classify satellite imagery based on index-specific classification decision values, which is an important factor in the quality of the information extracted.

Funder

Portuguese Fundação para a Ciência e a Tecnologia

MARE–Marine and Environmental Research Center

Associate Laboratory ARNET–Aquatic Research Network

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

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