Editorial: Achieving SDG 6: Remote Sensing Applications in Sustainable Water Management

This collection 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., (2024) 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., (2025) 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., (2025) demonstrate the power of highresolution 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., (2025) 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 collection 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 collection 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 issues 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 collection. 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: Changes in vegetation cover, wetland dynamics, primary production, sedimentation, or algal blooms can be detected through EO-data analysis, enabling rapid intervention to protect and restore critical ecosystems under threat from anthropogenic pressures or natural hazards.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 nearsurface 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 collection include:- Saranathan et al., (2024) 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 topic of uncertainty quantification for EO-derived water quality variables has been the subject of extensive research (Hammond et al., 2020;Mélin, 2021Mélin, , 2019Mélin, , 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., (2024) 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., (2025) 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 longterm 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.-Atton Beckmann et al., (2025) demonstrate the feasibility of quantifying phytoplankton in small lakes using high-resolution imagery from Planet SuperDoves and Sentinel-2. This capability is particularly valuable for mapping water quality in poorly monitored environments, such as small ponds and streams. Early demonstrations (e.g., Kwon et al., 2023;Mishra et al., 2020) established the feasibility of deriving water quality variables from meter-level satellite imagery. Building on this foundation, the study by Atton Beckmann et al., ( 2025) demonstrate that such imagery can accurately detect localized phenomena, such as algal scums and sediment plumes, with accuracy comparable to in-situ observations. As these high-resolution data sources become more routinely available, we can anticipate a significant expansion in the number of water bodies monitored globally, along with more granular integration of water quality data into urban management systems, e.g., smart city dashboards for water (Chen et al., 2023;Okoli and Kabaso, 2024).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 issues are especially pronounced when using Unmanned Aerial Vehicles (De Keukelaere et al., 2023;Windle and Silsbe, 2021).- Wilson et al., (2025) Despite progress in technical skill development, the transition toward sustained institutional capacity remains underdeveloped, limiting long-term and systemwide 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. 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.With the rapid depletion of groundwater resources and increasing hydrological variability driven by climate change, small surface water bodies are becoming vital sources of drinking water, particularly for rural, peri-urban, and marginalized communities (Jasechko et al., 2024). These lakes, ponds, and reservoirs, often less than 1 km² in area, remain among the least monitored yet most vulnerable to contamination and seasonal fluctuations. Historically overlooked by traditional water management systems due to their small spatial footprint and remoteness, their importance in local water supply is steadily growing.The use of sub-meter high-resolution EO platforms (such as Pléiades, WorldView, and UAVs) has the potential to substantially improve monitoring of small water surfaces by providing data at scales relevant for local water management and policy implementation.Netherlands, where floating solar panels have been installed. This pond, currently being explored for its potential to complement the local drinking water supply while also serving as a site for green energy production, exemplifies the integration of renewable infrastructure with small water bodies. This approach reflects the growing importance of multi-functional water resource management, particularly in regions facing increasing demand and water stress. High-resolution EO data, such as the image presented in Figure ( 1), enable detailed assessments of how floating solar installations influence water quality like photosynthetically available radiation and light attenuation coefficients, which in turn affect lake stratification and lake-atmosphere heat exchange (Heiskanen et al., 2015). Meanwhile, high-resolution thermal EO capabilities are being actively developed under missions such as the ESA Land Surface Temperature Monitoring (LSTM, Koetz et al., 2019) and NASA's Surface Biology and Geology (SBG, Stavros et al., 2023). These missions aim to provide thermal data at spatial resolutions fine enough (30-100 m) to detect water surface temperature anomalies. In parallel, commercial satellite constellations are emerging with even finer Thermal InfraRed capabilities. For instance, HotSat1 by SatVu captures detailed heat variations across the Earth's surface with a resolution of up to 3.5 meters, both day and night. These systems offer potential for fine-scale assessment of surface temperature gradients, particularly in the context of floating infrastructure and climate-sensitive inland waters. For example Prandini et al., (2025) showed, using detailed in-situ measurements, that floating solar modules created a microclimate above them with temperatures approximately 12% higher than those recorded by weather stations in the surrounding area. When coupled with biophysical lake models, EO data can support the quantification of energy fluxes (e.g., latent heat) and like exchange to algal primary et al., the potential of future research the development of EO algorithms and integrating them with biophysical and ecological models designed for small, optically complex water these water bodies into national SDG and strengthen in regions, ensuring that these resources are effectively and of the most yet in remote sensing is the detection of emerging in systems. These (e.g., dissolved products and these pollutants using satellite or platforms remains due to their concentrations and the of or by EO they are to traditional These are often to sources such as wastewater, and urban significant ecological and human especially in regions with sanitation However, in remote sensing technologies, particularly offer new its capacity to high-resolution across remote sensing for that be associated with these has in both and environmental enabling pixel-level of on their et al., et al., et al., et al., directly concentrations or surface are or through such as water or algal while detection of remains et al., optical sensing technologies such as and sensors have been to on their (Chen et al., et al., a for future and contamination can also be through remote For instance, studies have used data in with environmental to and in systems on variables such as and et al., While these approaches not detect they systems on and dissolved have more optical in and Infrared and when integrated with machine learning models, have been used to estimate concentrations of such as and in inland et al., 2024). in and high-resolution EO data are enabling assessments of nutrient dissolved organic and related water quality indicators et al., et al., 2024). Despite this detection of and remains in the early of remote sensing research on in-situ optical and platforms to the and of these et al., toward operational monitoring, future the integration of and satellite missions (e.g., with in-situ and machine learning significantly capacity to monitor water quality beyond optical into the and of SDG 6 requires monitoring systems that are not only and but also and in local promising in the integration of sensing EO, proximal sensing, and This to and water quality in that are and in this collection have how EO and large-scale of surface water bodies, for the monitoring of key water quality However, EO-derived water quality information is primarily to the surface and can be significantly by et al., as as by its inability to directly detect optically and sensing technologies, including in-situ and platforms, critical gaps by providing high-resolution at local scales (e.g., et al., sensing is particularly effective in fine-scale spatial and enabling rapid in small or water bodies. When as of a they offer valuable in both urban and where EO data be limited or (e.g., the et al., both by communities in data collection and through and applications such as and or et al., in water data offer promising opportunities for expanding water quality monitoring, they critical challenges related to data and reliability et al., 2023). in and can introduce significant For instance, and (2024) in measurements, particularly in optically complex where such tools are often address these and as from sensors or essential for and improving et al., this at it is essential to ensure data of consistent and across observations by proximal and EO data can data while institutional support is for integrating data alongside datasets. When this approach has to potential of and supporting more inclusive, and evidence-based governance in with SDG digital twins have progress in particularly for dynamics, and et al., 2024), their application to water quality remains relatively et al., 2024). digital for water quality a of systems, with data to and spatial and temporal in key such as nutrient algal blooms, and dissolved this the integration of sensing with and models is EO and surface proximal sensors offer localized in and critical data gaps through When into models, these enable of water quality dynamics, of supporting analysis, and policy EO data with models is a approach for improving (e.g., et al., the of EO-derived water quality products remains relatively and is still not in operational systems et al., developing a global digital for water quality remains due to the and nature of systems. lake or coastal and that require approaches and For et al., in their that a challenge in EO data into water quality models in the uncertainty of EO observations and the scales and the and time Addressing these requires not only methodological (e.g., et al., but also a toward more integrated frameworks. As digital future the of sensing data with models at regional and global scales. will enable a new of predictive tools that beyond or monitoring, supporting early systems, and adaptive water quality governance. This a critical step toward more and water management systems with the targets of SDG ensure broader and sustained remote water quality products beyond research toward full operational For that a is not only mature but has been under and established monitoring This transition more than algorithm it requires integration into regulatory or institutional development of and with in observations, and the of support and readiness also on sustained with and a from individual capacity-building to institutional This longterm the of technical and operational and with standards for data quality, and While platforms such as Earth Earth and have advanced the technical of EO broader and adoption requires water quality information that is not only but also for use regulatory and significant in EO, a persistent challenge in the the data availability and its effective integration into decision-making et al., remote sensing products be integrated into institutional policy and regulatory This EO with water and to ensure and with EO into actionable indicators, or risk that local water and EO-derived products into early systems, environmental impact assessments water and SDG the of EO by quantifying how monitoring or particularly in Building institutional through capacity development, long-term and that clarify and uncertainties of EO Earth Observation (EO) and remote sensing technologies are powerful for SDG 6. integration into water governance systems data availability and while supporting more decision-making in the of climate change and increasing demand for clean However, adoption requires expanding technical capabilities to fine-scale water bodies and emerging the integration of sensors with models, and advancing explainable AI to improve the biophysical of water quality important is the of institutional capacity to pace with rapid change. While emerging tools like digital virtual and generative AI offer knowledge systems remain EO products with national SDG 6 expanding particularly in the Global and long-term These be by open data and strategic capacity then can EO beyond its current technical role to become a of and water quality management