Application of Sentinel-2 Level-2A images for monitoring water surface in reservoirs in the semiarid region of Pernambuco — Brazil

Jonas Felipe Santos de Souza; Alfredo Ribeiro Neto; Santiago Peña-Luque; Marielle Gosset

doi:10.5327/Z2176-94781927

Authors

Jonas Felipe Santos de Souza Federal University of Pernambuco (UFPE) - Brazil https://orcid.org/0009-0008-6201-8122
Alfredo Ribeiro Neto Federal University of Pernambuco (UFPE) - Brazil https://orcid.org/0000-0002-9411-0651
Santiago Peña-Luque Centre National d’Etudes Spatiales (CNES) - France https://orcid.org/0000-0002-7729-1198
Marielle Gosset Institut de Recherche pour le Développement, Géosciences Environment Toulouse (IRD) - France https://orcid.org/0000-0003-1064-7003

DOI:

https://doi.org/10.5327/Z2176-94781927

Keywords:

optical images; water indices; semiarid; artificial neural network

Abstract

Remote sensing techniques offer effective and efficient alternatives for observing the spatiotemporal dynamics of surface water in reservoirs. This paper aimed to analyze the applicability of Sentinel-2 Level-2A satellite images from 2016 to 2024 for mapping and monitoring the extent of water surfaces in reservoirs in the Sertão region of Pernambuco state. An automatic, unsupervised, and non-parametric algorithm was employed, combining water indices with reflectance bands of optical images to identify water pixels. The results were compared with two datasets: in situ monitoring and MapBiomas. Issues with optical images affected by clouds over the reservoir and errors in classifying water pixels were noted. Generally, the algorithm tended to underestimate the extent of the water surface due to difficulty detecting water pixels at the edges of the reservoirs. To mitigate this issue, an artificial neural network (ANN) was applied to correct the underestimation bias. The bias correction improved the performance of the metrics when the size and representativeness of the calibration sample were sufficient for training and building the ANN model.

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