Work

Multitemporal Classification of Water Bodies in the Lagoon Complexes of the State of Rio de Janeiro, Brazil, Using SAR Time Series

Year: 2026

Type: article

Abstract: Synthetic Aperture Radar (SAR) images offer significant advantages for monitoring the dynamics of water bodies in tropical regions, mainly due to their ability to acquire data under adverse weather conditions, which frequently limit optical sensors. However, the automated classification of water bodies using SAR data still faces methodological challenges, particularly regarding the selection of the most suitable parameters and polarizations. This study proposes a multitemporal classification methodology using Sentinel-1 data to map the flood regimes of lagoon complexes in the State of Rio de Janeiro (Brazil). The approach integrates SAR image time series with the Random Forest machine learning algorithm, evaluating the performance of different polarization configurations (VV, VH, and VV–VH). The results show that the combined use of single and cross polarizations (VV–VH) achieved excellent performance, with a Kappa index of 0.83, F-score of 0.90, and overall accuracy of 0.96, demonstrating methodological robustness. The multitemporal analysis identified approximately 294 km2 of permanently flooded areas, while seasonally flooded areas, associated with the seasonal variation in coastal lagoons, exhibited variations exceeding 30 km2 over the time series.

Source: Remote Sensing

DOI: 10.3390/rs18071005

Authors: Gabriel Carlos da Silva, Evelyn de Castro Porto Costa, Lino Augusto Sander de Carvalho

Institutions: Universidade Federal do Rio de Janeiro, Universidade do Estado do Rio de Janeiro

Language: English

Topic: Synthetic Aperture Radar (SAR) Applications and Techniques

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Clean water and sanitation, Climate action

Open Access status: gold

Estimated APC paid: $3,258