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.

通过将雷达数据与现有的多光谱数据相结合，生成了一种新颖的多源多时期数据集，用于拓展 SEN2DWATER 数据集的能力以进行水资源监测，取得了有希望的结果，并探讨了未来可能的发展和应用。

利用多时期 Sentinel-1 和 Sentinel-2 数据进行水体绘制

Using Multi-Temporal Sentinel-1 and Sentinel-2 data for water bodies  mapping

Monitoring and patrolling large water resources is a major challenge for
conservation. The problem of acquiring data of an underlying environment that
usually changes within time involves a proper formulation of the information.
The use of Autonomous Surface Vehicles equipped with water quality sensor
modules can serve as an early-warning system agents for contamination
peak-detection, algae blooms monitoring, or oil-spill scenarios. In addition to
information gathering, the vehicle must plan routes that are free of obstacles
on non-convex maps. This work proposes a framework to obtain a collision-free
policy that addresses the patrolling task for static and dynamic scenarios.
Using information gain as a measure of the uncertainty reduction over data, it
is proposed a Deep Q-Learning algorithm improved by a Q-Censoring mechanism for
model-based obstacle avoidance. The obtained results demonstrate the usefulness
of the proposed algorithm for water resource monitoring for static and dynamic
scenarios. Simulations showed the use of noise-networks are a good choice for
enhanced exploration, with 3 times less redundancy in the paths. Previous
coverage strategies are also outperformed both in the accuracy of the obtained
contamination model by a 13% on average and by a 37% in the detection of
dangerous contamination peaks. Finally, these results indicate the
appropriateness of the proposed framework for monitoring scenarios with
autonomous vehicles.

本论文提出一种使用水质传感器装备的自主水面船只的深度 Q-Learning 算法框架，解决它在静态和动态环境下的监控任务中的障碍物避免以及路径规划问题，结合信息增益来衡量不确定性，模拟实验表明噪声网络是增强探测的良好选择，该框架成功应用于自主监控下的水资源监测。