From Earth Observation to Water Intelligence: Remote Sensing for Integrated and Sustainable Water Resource Management

1. Introduction

Sustainable water resource management requires actionable information on water availability, water quality, water-related hazards, and water use [1,2]. Achieving an integrated understanding of these interconnected components increasingly relies on remote sensing technologies that provide consistent, scalable, and timely information across spatial and temporal scales [3,4,5]. Furthermore, the integration of statistical methods, machine learning, and environmental modeling with Earth observation data is enabling the transformation of observations into actionable intelligence, supporting enhanced assessment, prediction, forecasting, and evidence-based decision-making [6].

While significant progress has been made in both Earth observation technologies and analytical methodologies, challenges remain in integrating the resulting insights into operational management frameworks [7,8]. Although numerous studies have demonstrated the value of these approaches across diverse water-related applications, the resulting knowledge remains dispersed across different research domains, methodologies, and management contexts [5,8]. Consequently, there is a growing need to consolidate emerging knowledge to better understand how Earth observation technologies and analytical methodologies can be integrated to support sustainable water resource management.

In response to this need, the Special Issue “Use of Remote Sensing Technologies for Water Resource Management” was established to provide a platform for showcasing recent advances in Earth observation technologies, analytical methodologies, and their applications to water resource management. The Special Issue emphasizes the combination of traditional and emerging remote sensing approaches, including satellite observations, laser altimetry, digital elevation models, precipitation products, and multi-source Earth observation datasets, with statistical methods, machine learning, deep learning, hydrological modeling, and environmental analytics. Its overarching objective is to highlight innovative approaches that not only improve our understanding of water systems but also facilitate the translation of Earth observation data into actionable decision-support information for planning, management, and policy development.

The eight papers published in this Special Issue demonstrate the growing potential of integrating Earth observation data with advanced analytical approaches to support sustainable water resource management. Spanning diverse environmental settings and geographic regions, the contributions address challenges related to hydrological monitoring, water quality assessment, environmental hazards, ecosystem management, and agricultural water use. Collectively, they showcase how remote sensing and data analytics are enhancing our ability to understand, monitor, and manage water systems across multiple scales and application domains.

2. Main Contribution of This Special Issue

The eight papers included in this Special Issue demonstrate how remote sensing technologies and advanced analytical approaches are being applied across the key dimensions of sustainable water resource management. Three contributions focus on water availability through precipitation estimation, runoff modeling, and inland water-level monitoring [Contributions 1–3]. Three contributions address water quality and ecosystem health through assessments of water clarity, lake water quality dynamics, and harmful algal blooms [Contributions 4–6]. One contribution examines water-related hazards through flash-flood risk assessment [Contribution 7], while another addresses water use and management through agricultural water monitoring and irrigation decision support [Contributions 8].

Water availability is addressed through contributions focused on precipitation estimation, runoff modeling, and inland water-level monitoring. Ewnetu et al. (Contribution 1) evaluated six satellite and reanalysis precipitation products (CHIRPS v2, ERA5, ERA5-Land, IMERG v07, MSWEP v2.8, and TRMM 3B42) across the mountainous Lake Tana Basin in Ethiopia. Using statistical, categorical, and distributional performance metrics, the study assessed product reliability across temporal scales, rainfall intensities, and terrain conditions, providing guidance for the selection of precipitation datasets in data-scarce hydrological applications. Similarly, Bekić and Leskovar (Contribution 2) assessed the performance of CHIRPS and ERA5 precipitation datasets for long-term runoff modeling in Alpine headwaters, emphasizing the importance of selecting appropriate Earth observation products for hydrological simulations in complex terrain. Complementing these contributions, Kaya (Contribution 3) evaluated the performance of ICESat-2 laser altimetry for monitoring inland water levels across 182 Canadian lakes, illustrating the potential of satellite altimetry to support large-scale assessments of freshwater resources.

Water quality and ecosystem health are addressed through three complementary contributions focused on water clarity assessment, lake water quality dynamics, and harmful algal bloom monitoring. Salas et al. (Contribution 4) integrated satellite imagery and machine learning techniques to estimate water clarity through Secchi Disk Depth prediction, demonstrating the potential of data-driven approaches for water quality assessment. Tiwari et al. (Contribution 5) employed Sentinel-2 imagery together with breakpoint detection, trend analysis, clustering, and dimensionality-reduction techniques to evaluate spatiotemporal water quality dynamics in Horseshoe Lake, Illinois, USA, identifying pollution hotspots and ecological stress patterns. Extending this focus to ecological risk assessment, Wang et al. (Contribution 6) investigated two decades of algal bloom dynamics in Lake Bosten using multi-source remote sensing data and deep learning techniques.

Water-related hazards are represented by Bashir and Alsalman (Contribution 7), who utilized ALOS PALSAR digital elevation models, morphometric characterization, and geospatial analysis to evaluate flash-flood susceptibility in the Wadi Al-Lith watershed of Saudi Arabia. Their work demonstrates the value of Earth observation data and geospatial analysis for identifying vulnerable areas, supporting disaster preparedness, and informing risk-reduction strategies in flood-prone environments.

Water use and management are addressed by Silva et al. (Contribution 8), who developed a smart agro-modeling framework that integrates Sentinel-2 observations, thermal time modeling, and yield estimation approaches to assess maize growth, crop water use, and productivity under Mediterranean climatic conditions. The study demonstrates how remote sensing can support irrigation management and agricultural decision-making, highlighting the broader role of Earth observation technologies in promoting efficient and sustainable water use.

Beyond their individual applications, the contributions collectively highlight the diversity of contemporary Earth observation approaches for water resource management. The papers span mountainous watersheds, inland lakes, arid environments, and agricultural landscapes, demonstrating the adaptability of remote sensing across diverse environmental conditions. They also draw upon multiple Earth observation datasets and platforms, including satellite-derived precipitation products, optical imagery, laser altimetry, and digital elevation models, illustrating the expanding toolbox available for water resource applications. Furthermore, the contributions employ a diverse range of analytical approaches, including hydrological modeling, machine learning, deep learning, statistical trend analysis, clustering, dimensionality reduction, morphometric assessment, and agro-hydrological modeling. This methodological diversity illustrates the increasingly interdisciplinary nature of modern water resources research.

3. Conclusions

The papers included in this Special Issue highlight several important developments in the application of remote sensing technologies for water resource management. First, Earth observation technologies are increasingly supporting multiple dimensions of water resource management, including water availability, water quality, water-related hazards, and water use. This reflects the growing capacity of Earth observation technologies to support a more integrated understanding of complex water systems. Second, the contributions demonstrate the increasing integration of Earth observation data with advanced analytical methodologies, including hydrological modeling, machine learning, deep learning, statistical analysis, and geospatial modeling. This convergence of Earth observation and analytical approaches is expanding the range of questions that can be addressed across water resource applications. Third, the collection reflects a broader evolution in the role of remote sensing, from environmental monitoring toward more integrated assessment and decision-support applications. Rather than serving solely as observational tools, Earth observation technologies are increasingly being used to support forecasting, risk assessment, resource planning, ecosystem management, and operational decision-making. Finally, the contributions illustrate an ongoing transition from Earth observation to water intelligence, where environmental observations are transformed into actionable information for planning and management. As Earth observation capabilities continue to evolve and become increasingly integrated with advanced analytical, modeling, and artificial intelligence approaches, they are expected to play an even greater role in supporting adaptive, data-driven, and evidence-based water resource management.