  Monitoring water is a complex task due to its dynamic nature, added
pollutants, and land build-up. The availability of high-resolu-tion data by
Sentinel-2 multispectral products makes implementing remote sensing
applications feasible. However, overutilizing or underutilizing multispectral
bands of the product can lead to inferior performance. In this work, we compare
the performances of ten out of the thirteen bands available in a Sentinel-2
product for water segmentation using eight machine learning algorithms. We find
that the shortwave infrared bands (B11 and B12) are the most superior for
segmenting water bodies. B11 achieves an overall accuracy of $71\%$ while B12
achieves $69\%$ across all algorithms on the test site. We also find that the
Support Vector Machine (SVM) algorithm is the most favourable for single-band
water segmentation. The SVM achieves an overall accuracy of $69\%$ across the
tested bands over the given test site. Finally, to demonstrate the
effectiveness of choosing the right amount of data, we use only B11 reflectance
data to train an artificial neural network, BandNet. Even with a basic
architecture, BandNet is proportionate to known architectures for semantic and
water segmentation, achieving a $92.47$ mIOU on the test site. BandNet requires
only a fraction of the time and resources to train and run inference, making it
suitable to be deployed on web applications to run and monitor water bodies in
localized regions. Our codebase is available at
https://github.com/IamShubhamGupto/BandNet.
