@article{fdi:010092070,
  title = {{A}pplication of {S}entinel-2 {L}evel-2{A} images for monitoring water surface in reservoirs in the semiarid region of {P}ernambuco-{B}razil},
  author = {de {S}ouza, {J}. {F}. {S}. and {N}eto, {A}. {R}. and {P}eña-{L}uque, {S}. and {G}osset, {M}arielle},
  editor = {},
  language = {{ENG}},
  abstract = {{R}emote sensing techniques offer effective and efficient alternatives for observing the spatiotemporal dynamics of surface water in reservoirs. {T}his paper aimed to analyze the applicability of {S}entinel-2 {L}evel-2{A} satellite images from 2016 to 2024 for mapping and monitoring the extent of water surfaces in reservoirs in the {S}ertao region of {P}ernambuco state. {A}n automatic, unsupervised, and non-parametric algorithm was employed, combining water indices with reflectance bands of optical images to identify water pixels. {T}he results were compared with two datasets: in situ monitoring and {M}ap{B}iomas. {I}ssues with optical images affected by clouds over the reservoir and errors in classifying water pixels were noted. {G}enerally, the algorithm tended to underestimate the extent of the water surface due to difficulty detecting water pixels at the edges of the reservoirs. {T}o mitigate this issue, an artificial neural network ({ANN}) was applied to correct the underestimation bias. {T}he 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.},
  keywords = {optical images ; water indices ; semiarid ; artificial neural network ; {BRESIL} ; {PERNAMBUCO} ; {ZONE} {SEMIARIDE}},
  booktitle = {},
  journal = {{R}evista {B}rasileira de {C}iencias {A}mbientais},
  volume = {59},
  numero = {},
  pages = {e1927 [9 p.]},
  ISSN = {1808-4524},
  year = {2024},
  DOI = {10.5327/z2176-94781927},
  URL = {https://www.documentation.ird.fr/hor/fdi:010092070},
}