Differently from conventional procedures, the proposed solution advocates for a groundbreaking paradigm in water quality monitoring through the integration of satellite Remote Sensing (RS) data, Artificial Intelligence (AI) techniques, and onboard processing. The objective is to offer nearly real-time detection of contaminants in coastal waters addressing a significant gap in the existing literature. Moreover, the expected outcomes include substantial advancements in environmental monitoring, public health protection, and resource conservation. The specific focus of our study is on the estimation of Turbidity and pH parameters, for their implications on human and aquatic health. Nevertheless, the designed framework can be extended to include other parameters of interest in the water environment and beyond. Originating from our participation in the European Space Agency (ESA) OrbitalAI Challenge, this article describes the distinctive opportunities and issues for the contaminants monitoring on the Phisat-2 mission. The specific characteristics of this mission, with the tools made available, will be presented, with the methodology proposed by the authors for the onboard monitoring of water contaminants in near real-time. Preliminary promising results are discussed and in progress and future work introduced.

翻译：与常规方法不同，本方案倡导通过融合卫星遥感数据、人工智能技术及星上处理，构建一种突破性的水质监测范式。其目标是通过近实时检测沿海水域污染物，填补现有文献中的显著空白。预期成果包括在环境监测、公共卫生保护及资源保护领域的重大进展。本研究聚焦于浊度与pH参数的估算，因其对人类及水生健康具有重要影响。但所设计的框架可扩展至水环境及其他领域中的其他关注参数。源自我们参与欧洲空间局轨道人工智能挑战赛的经历，本文阐述了Phisat-2任务中污染物监测的特殊机遇与挑战。将介绍该任务的独特特征及可用工具，并展示作者提出的星上近实时水体污染物监测方法。本文讨论了初步的令人鼓舞的成果，并介绍了正在进行的及未来的工作。