Using Multi-Temporal Sentinel-1 and Sentinel-2 data for water bodies
  mapping
Luigi Russo Francesco Mauro Babak Memar Alessandro Sebastianelli Paolo Gamba Silvia Liberata Ullo
Climate change is intensifying extreme weather events, causing both water
scarcity and severe rainfall unpredictability, and posing threats to
sustainable development, biodiversity, and access to water and sanitation. This
paper aims to provide valuable insights for comprehensive water resource
monitoring under diverse meteorological conditions. An extension of the
SEN2DWATER dataset is proposed to enhance its capabilities for water basin
segmentation. Through the integration of temporally and spatially aligned radar
information from Sentinel-1 data with the existing multispectral Sentinel-2
data, a novel multisource and multitemporal dataset is generated. Benchmarking
the enhanced dataset involves the application of indices such as the Soil Water
Index (SWI) and Normalized Difference Water Index (NDWI), along with an
unsupervised Machine Learning (ML) classifier (k-means clustering). Promising
results are obtained and potential future developments and applications arising
from this research are also explored.