Evaluation of SAR and Optical Data for Flood Delineation Using Supervised and Unsupervised Classification

Fatemeh Foroughnia, S.M. Alfieri, M. Menenti*, R.C. Lindenbergh

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Precise and accurate delineation of flooding areas with synthetic aperture radar (SAR) and multi-spectral (MS) data is challenging because flooded areas are inherently heterogeneous as emergent vegetation (EV) and turbid water (TW) are common. We addressed these challenges by developing and applying a new stepwise sequence of unsupervised and supervised classification methods using both SAR and MS data. The MS and SAR signatures of land and water targets in the study area were evaluated prior to the classification to identify the land and water classes that could be delineated. The delineation based on a simple thresholding method provided a satisfactory estimate of the total flooded area but did not perform well on heterogeneous surface water. To deal with the heterogeneity and fragmentation of water patches, a new unsupervised classification approach based on a combination of thresholding and segmentation (CThS) was developed. Since sandy areas and emergent vegetation could not be classified by the SAR-based unsupervised methods, supervised random forest (RF) classification was applied to a time series of SAR and co-event MS data, both combined and separated. The new stepwise approach was tested for determining the flood extent of two events in Italy. The results showed that all the classification methods applied to MS data outperformed the ones applied to SAR data. Although the supervised RF classification may lead to better accuracies, the CThS (unsupervised) method achieved precision and accuracy comparable to the RF, making it more appropriate for rapid flood mapping due to its ease of implementation.
Original languageEnglish
Number of pages29
JournalRemote Sensing
Issue number15
Publication statusPublished - 2022


  • SAR
  • optical
  • flood mapping
  • random forest
  • Otsu thresholding
  • unsupervised classification
  • supervised classification


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