A New Spectral Index for Mapping Water Surfaces in Urban Contexts

This paper proposes a modification of the Modified Normalized Difference Water Index (MNDWI) called Rescaled Water Index (RWI), aiming to enhance the delineation and performance of water body mapping in urban contexts. To evaluate the proposed index, we compare its performance in mapping Water Surfaces (WSs) in different cities in South America with the results obtained by other well-known water indices, namely, Normalized Difference Water Index (NDWI), MNDWI, Automated Water Extraction Index (AWEIsh, and AWEInsh)). We utilized images from the Sentinel satellites (2A and 2B), all acquired in June 2021, focusing on cities located south of the Tropic of Capricorn to obtain images with the highest incidence of shading, whose spectral response complicates the mapping of WSs. In this scenario, the present study revealed that selecting the best single index for mapping all WSs is an arduous task, as their performances varied across the analyzed locations. Overall, the performance of the indices, evaluated by the partial Receiver Operating Characteristic (pROC) curve and non-water misclassification point restrictions, revealed quite close results, especially considering the RWI, NDWI, and MNDWI. In the six cities analyzed, the RWI outperformed in three. Additionally, when considering the results for all the cities, the RWI outperformed all the other indices. Our study also rainforced the importance of the complementarity of the indices to improve the performance of the mappings. Such results, therefore, were interpreted as an essential contribution to water body mapping, considering its practical applications in environmental monitoring and water resource management in urbanized areas.

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Publication Details

Journal
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Published
2025-01-01
DOI
https://doi.org/10.1109/jstars.2025.3562089
Citations
3
Primary Topic
Remote-Sensing Image Classification
Type
article
Field-Weighted Citation Impact
3.41

Funders

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article

A New Spectral Index for Mapping Water Surfaces in Urban Contexts

Júlio César Pedrassoli, Rúbia Gomes Morato, Eduardo Felix Justiniano, Marcel Fantin et al.
3 citations
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Remote-Sensing Image Classification
3.41
article

A New Spectral Index for Mapping Water Surfaces in Urban Contexts

Júlio César Pedrassoli, Rúbia Gomes Morato, Eduardo Felix Justiniano, Marcel Fantin, Edimilson Rodrigues dos Santos, Breno Malheiros de Melo, Gustavo Paixão Menezes, Fernando Shinjí Kawakubo, Marcos Roberto Martines
article en
3 citations

Abstract

This paper proposes a modification of the Modified Normalized Difference Water Index (MNDWI) called Rescaled Water Index (RWI), aiming to enhance the delineation and performance of water body mapping in urban contexts. To evaluate the proposed index, we compare its performance in mapping Water Surfaces (WSs) in different cities in South America with the results obtained by other well-known water indices, namely, Normalized Difference Water Index (NDWI), MNDWI, Automated Water Extraction Index (AWEIsh, and AWEInsh)). We utilized images from the Sentinel satellites (2A and 2B), all acquired in June 2021, focusing on cities located south of the Tropic of Capricorn to obtain images with the highest incidence of shading, whose spectral response complicates the mapping of WSs. In this scenario, the present study revealed that selecting the best single index for mapping all WSs is an arduous task, as their performances varied across the analyzed locations. Overall, the performance of the indices, evaluated by the partial Receiver Operating Characteristic (pROC) curve and non-water misclassification point restrictions, revealed quite close results, especially considering the RWI, NDWI, and MNDWI. In the six cities analyzed, the RWI outperformed in three. Additionally, when considering the results for all the cities, the RWI outperformed all the other indices. Our study also rainforced the importance of the complementarity of the indices to improve the performance of the mappings. Such results, therefore, were interpreted as an essential contribution to water body mapping, considering its practical applications in environmental monitoring and water resource management in urbanized areas.

IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingVol. 18
Western Michigan University (US), Universidade Federal de São Carlos (BR), Universidade de São Paulo (BR), Institute of Mathematics and Computer Science (MD)
Conselho Nacional de Desenvolvimento Científico e Tecnológico
Sustainable cities and communities
Openalex Percentile: Top 8%
Remote-Sensing Image Classification
3.41
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