Sustainable Development Goal 6 (SDG 6, Clean Water and Sanitation) is a foundational pillar of the 2030 Agenda, aiming to ensure universal access to safe and affordable drinking water, sanitation, and hygiene, while promoting the sustainable management of water resources. It encompasses critical targets including the elimination of open defecation, improvements in water quality, enhanced water-use efficiency, and protection of aquatic ecosystems. Achieving SDG 6 is vital not only for public health but also for reducing poverty, ensuring educational attainment, and enabling economic development.

Despite notable progress, significant disparities persist in water service coverage, with billions of people still lacking access to basic sanitation and safe drinking water. Many regions face compounding pressures from water scarcity, pollution, climate change, and inadequate infrastructure. In response, remote sensing technologies, particularly satellite-based sensors, are increasingly used to support large-scale, cost-effective monitoring of water quantity and quality, offering spatial and temporal coverage that complements traditional field-based methods.

This Research Topic in Frontiers in Remote Sensing highlights innovative applications of remote sensing and Earth Observation (EO) that support the realization of SDG 6. We present four articles that span a diverse spectrum of technologies, methodologies, and geographic contexts, collectively showcasing the growing maturity and impact of remote sensing in sustainable water management. While the broader discourse around SDG 6 has often emphasized the quantitative aspects of water (such as availability and access) these contributions notably centre on the quality dimension of SDG 6. This focus is both timely and essential, as water quality has historically received comparatively less attention despite its critical role in achieving sustainable water outcomes. The first contribution, by Saranathan et al. introduce probabilistic neural networks to retrieve water quality indicators with associated uncertainty estimates, enabling confidence-based decision-making in data-scarce environments. Whereas, Balasubramanian et al. leverage machine learning and multi-mission satellite archives to reconstruct long-term water quality trends, facilitating retrospective assessments, essential for monitoring policy effectiveness and environmental change. On the other hand, Atton Beckmann et al. demonstrate the power of high-resolution satellite imagery to monitor algal blooms in small, under-monitored inland lakes, significantly expanding the scope of water bodies that can be routinely assessed from space. Finally, Wilson et al. address systemic barriers to EO adoption, offering inclusive, action-oriented pathways to empower data-poor regions, particularly in the Global South.

Together, these contributions underscore the critical role that remote sensing technologies and EO play in advancing the objectives of SDG 6. These technologies have matured to the point where they are actively enhancing water quality monitoring, expanding spatial and temporal data coverage, informing evidence-based policy, and enabling the early detection of ecological threats. Yet, realizing the full potential of EO requires urgent attention to persistent challenges: the difficulty of integrating EO products into national regulatory and decision-making frameworks; the limited availability of robust, transferable algorithms for optically complex water bodies; the current inability of EO to detect emerging pollutants or non-optically active water quality constituents; and the lack of integration with predictive water quality models and complementary sensing technologies, particularly in the Global South. Addressing these challenges will require sustained investment in EO infrastructure, open-data platforms, algorithm innovation, capacity-building, and interdisciplinary collaboration.

In the sections that follow, we first define the scope of the articles included in this Research Topic and clarify the central focus of this editorial essay. We then outline the role of remote sensing and EO in advancing SDG 6, with particular attention to the specific targets where these technologies offer the greatest impact. Next, we summarize the key contributions of the featured papers, highlighting their methodological innovations and practical insights. This is followed by a discussion of emerging directions aimed at enhancing the utility, adoption, and influence of EO in driving progress toward SDG 6. We conclude with reflections on the broader implications of these findings and offer recommendations for future research, policy, and implementation.

The articles in this Research Topic primarily focus on the remote sensing of water quality, reflecting a key dimension of ensuring clean water for all. While water quality is a central concern, it is important to recognize SDG 6 encompasses a broader and more integrated vision. The goal aims to ensure availability and sustainable management of water and sanitation for all by 2030, covering a wide range of targets related to water governance and resource management.

Beyond improving water quality, SDG 6 addresses critical objectives such as equitable access to safe and affordable drinking water, universal access to sanitation and hygiene, and increased water-use efficiency across sectors. It also calls for the sustainable management of freshwater resources and ecosystems, the safe treatment and reuse of wastewater, and the creation of enabling environments through policies and institutional frameworks. Moreover, SDG 6 emphasizes the importance of international cooperation and capacity-building, particularly for developing countries, to strengthen water and sanitation-related programs and activities.

These broader Research Topic will be briefly addressed in the following section, “Setting the Scene” to provide context on how remote sensing aligns with the full scope of SDG 6. However, the main body of this editorial will primarily focus on water quality, reflecting the central theme of the contributions in this Research Topic. The final section on Future Directions will explore opportunities to expand and deepen the role of EO technologies in advancing the remote sensing of water quality as a key component of SDG 6 implementation.

Throughout this paper, the terms “remote sensing technologies” and “Earth Observation (EO)” are used interchangeably to reflect the integrated nature of EO systems, which encompass satellite, aerial, UAV-based, and in-situ sensing platforms.

As regulatory agencies, national governments, and international organizations work to implement SDG 6, remote sensing and EO are playing an increasingly important role in supporting both policy development and regulatory compliance. In this context, EO helps close critical data gaps that hinder effective water governance. Its capabilities directly support the assessment of progress toward environmental standards and several SDG 6 targets, including:

1. Target 6.1 – Safe and Affordable Drinking Water: EO supports the identification and assessment of surface water bodies that can serve as potential sources of safe and affordable drinking water. By detecting seasonal fluctuations, water extent, and potential sources of contamination, EO helps decision-makers prioritize investments in water supply infrastructure and track service delivery in underserved or remote regions.

Remote sensing technologies, from satellites to Unmanned Aerial Vehicles (UAVs), have emerged as powerful tools for tracking water quality indicators across large spatial and temporal scales. Optical sensors aboard Earth-observing satellites can detect near-surface concentrations of chlorophyll-a, coloured dissolved organic matter, total suspended solids, and other optically active substances, enabling regional to global assessments. This capability is increasingly critical as climate change and human activities intensify pressures on inland lakes and urban water systems. The integration of multi-mission satellite data streams has significantly improved the temporal resolution of observations, supporting more timely and actionable water management decisions.

Over the past decade, remote sensing applications for water quality management have advanced considerably. Satellite-based EO, when combined with in-situ measurements, proximal sensing, and machine learning techniques, now enables monitoring from global scales down to small, individual water bodies.

Key contributions to this Research Topic include:

- Saranathan et al. demonstrate that Mixture Density Networks and Bayesian Neural Networks with Monte Carlo Dropout can generate pixel-level probability distributions for key indicators such as chlorophyll-a. By quantifying uncertainty alongside the estimates, these models enable more informed decision-making and risk assessment. The broader Research Topic of uncertainty quantification for EO-derived water quality variables has been the subject of extensive research (Hammond et al., 2020; Mélin, 2021; 2019; 2010; Zhang et al., 2022). However, the use of neural networks models to estimate heteroscedastic and epistemic uncertainties at a pixel-level is relatively new. This development represents an important advancement in generating EO products that provide not only quantitative estimates of water quality indicators but also associated measures of uncertainty, enhancing their reliability for operational monitoring and decision-making.

While machine learning models are rapidly advancing, their opaque internal structures remain a challenge for transparency and interpretability. The proposed uncertainty-aware approach by Saranathan et al. represents a step forward toward developing more explainable and biophysically-informed AI algorithms for retrieving water quality variables (Jiang et al., 2020; Roy et al., 2023).

- Balasubramanian et al. reconstruct long-term time series of water quality indicators using historical EO datasets. The study harmonizes data from multiple satellite missions, including the Moderate Resolution Imaging Spectroradiometer (MODIS), Medium Resolution Imaging Spectrometer (MERIS), and Visible Infrared Imaging Radiometer Suite (VIIRS). It also applies machine learning models to the 40+ year Landsat archive. This integration enables the generation of multi-decadal records of inland and coastal water quality indicators, such as water clarity, algal blooms, and nutrient status. Crucially, the researchers went beyond algorithm development to operationalize EO-derived indicators across archived datasets, demonstrating their readiness for large-scale monitoring and policy support (Chen et al., 2022; Wilkinson et al., 2024).

Although such reconstruction approaches are essential for assessing the long-term effectiveness of environmental policies (such as nutrient reduction strategies) they still require further development to effectively integrate with current and forthcoming observations from a wide range of EO missions.

While high-resolution data hold great potential for mapping previously unmonitored water bodies, their application is often challenged by the need to correct for variable scene illumination, surface reflectance, sun glint and geolocation. These Research Topic are especially pronounced when using Unmanned Aerial Vehicles (De Keukelaere et al., 2023; Windle and Silsbe, 2021).

Despite progress in technical skill development, the transition toward sustained institutional capacity remains underdeveloped, limiting long-term and system-wide adoption (Pritchard et al., 2022; Thapa et al., 2019). As we enter the Fifth Industrial Revolution, characterized by the explosion of information and the integration of generative AI, new questions emerge around how to build adaptive and enduring capacity (Marino and Monaca, 2025; Ziatdinov et al., 2024; Gunderson et al., 2020; Orion, 2019). How can pedagogical approaches be designed to retain institutional knowledge while keeping pace with rapidly evolving technologies? Emerging learning tools such as virtual reality (Makransky and Petersen, 2021), digital twins (Hazeleger et al., 2024), serious games (Sajjadi et al., 2022), gamification and generative AI tools (Patra et al., 2024) offer promising avenues. Yet, these questions remain open and are the subject of ongoing inquiry as the landscape of AI-driven capacity-building continues to evolve.

Across these contributions, a consistent set of technologies and data infrastructures emphasize the advancement of remote sensing for SDG 6. The Sentinel constellation (Sentinel-2 and Sentinel-3) and the Landsat series remain the backbone of optical water quality monitoring, widely applied in both regional case studies and global assessments. Complementing these are legacy ocean-colour missions such as Moderate Resolution Imaging Spectroradiometer (MODIS), Medium Resolution Imaging Spectrometer (MERIS), Visible Infrared Imaging Radiometer Suite (VIIRS), and Ocean and Land Colour Instrument (OLCI), which support long-term trend analysis across large water bodies. More recently, hyperspectral satellite missions—including Plankton, Aerosol, Cloud, ocean Ecosystem, PACE (Werdell et al., 2019), PRecursore IperSpettrale della Missione Applicativa, PRISMA (Candela et al., 2016) and Environmental Mapping and Analysis Program, EnMAP (Storch et al., 2023) —have begun to demonstrate their enhanced ability to resolve optically complex waters, such as those rich in suspended matter or algal pigments, though their integration into operational workflows is still in its early stages. On the ground, the development of extensive in situ datasets—such as the GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments, GLORIA (Lehmann et al., 2023), Lake Bio-optical Measurements and Matchup Data for Remote Sensing, LIMNADES (Carrea et al., 2015), SeaWiFS Bio-optical Archive and Storage System, SeaBASS (Werdell et al., 2003) and various regional monitoring networks, has proven essential for training and validating analytical and machine learning algorithms. These datasets enable robust generalization across diverse water types and geographies. In parallel, cloud-based platforms and pre-processed EO datasets, such as atmospherically corrected surface reflectance, are lowering technical barriers by streamlining data access, processing, and integration. These advances are enabling more consistent, scalable, and regionally transferable approaches to EO-based water quality monitoring.

As remote sensing technologies and EO continue to mature and diversify, the scope of remote sensing applications for sustainable water management is rapidly expanding. Looking ahead, six strategic priorities emerge that can further strengthen the role of remote sensing in improving water quality targets of SDG 6.

In summary, Earth Observation (EO) and remote sensing technologies are powerful enablers for operationalizing SDG 6. Their integration into water governance systems enhances data availability and transparency, while supporting more adaptive, evidence-based decision-making in the face of climate change and increasing demand for clean water. However, system-wide adoption requires expanding technical capabilities to include fine-scale water bodies and emerging pollutants, strengthening the integration of multi-modal sensors with process-based models, and advancing explainable AI techniques to improve the biophysical relevance of water quality retrievals. Equally important is the evolution of institutional capacity to keep pace with rapid technological change. While emerging tools like digital twins, virtual reality, and generative AI offer promise, durable knowledge systems remain essential.

Decisive next steps include aligning EO products with national SDG 6 reporting frameworks, expanding validation networks, particularly in the Global South, and securing long-term funding. These efforts must be supported by cross-sectoral alliances, open data practices, and strategic capacity building. Only then can EO move beyond its current technical role to become a foundational element of inclusive and resilient water quality management system.