Delimitation of flooded areas based on Sentinel-1 SAR data processed through machine learning

A study case from Central Amazon, Brazil

Abstract

Delimitation of areas subject to flooding is crucial to understand water
dynamics and fluvial changes. This study analyzed the potential of C-band Synthetic Aperture
Radar (SAR) images acquired by the Sentinel-1 satellite in 2017, 2018, and 2019 to
delineate flooded areas in the Central Amazon. The images were processed by the Artificial
Neural Network Multi-Layer Perceptron (ANN-MLP) and two K-Nearest Neighbor (KNN-7
and KNN-11) machine learning (ML) classifiers. Pre-processing of Single Look Complex
(SLC) SAR images involved the following methodological steps: orbit-file application; radiometric
calibration (σ0); Range-Doppler terrain correction; speckle noise filtering; and conversion
of linear data to backscattering coefficients (units in dB). We applied the Lee filter,
with a window size of 3x3, for speckle filtering. A set of 6000 randomly distributed samples
for training (70%), validation (20%), and test (10%) was obtained based on visual interpretation
of Sentinel-2 optical satellite image acquired in the same years of SAR images. We
found the largest flooded areas in 2019 in the study area (municipality of Parintins and
Urucará, Amazonas River, Brazil): 6244km2 by the ANN-MLP classifier; 6268km2 by KNN-
7; and 6290km2 by KNN-11, while the smallest flooded areas were found in 2018: 5364km2
by ANN-MLP; 5412km2 by KNN-7; and 5535km2 by KNN-11. The three classifiers presented
Kappa coefficients between 0.77 and 0.91. ANN-MLP showed the best accuracy. The
presence of shadow effects in the SAR images increased the commission errors.