Using Multi-Temporal Sentinel-1 and Sentinel-2 data for water bodies mapping
This work addresses water resource monitoring for climate change adaptation, but it is incremental as it builds on an existing dataset with new data integration.
The paper tackled water body mapping by extending the SEN2DWATER dataset with multi-temporal Sentinel-1 and Sentinel-2 data, achieving promising results through benchmarking with indices and an unsupervised ML classifier.
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.