Across Asia, new initiatives are showing how satellite Earth observation data and AI-powered technologies can turn fragmented water-related data into actionable insights for managers and policymakers in line ministries and local governments.

Only about 3% of global water quality measurements (around 60,000 out of 2 million) come from the world’s poorest regions, according to the United Nations, highlighting a persistent water data gap. Even where data exist, they are often scattered across agencies, with monitoring stations sparse and datasets rarely analysed together. Integrating satellite observations with cognitive digital technologies, including artificial intelligence, can bring these fragmented sources into a single data and analytical pipeline, turning environmental data into timely insights that strengthen water governance and accelerate progress toward SDG 6.

Guiding smarter water infrastructure investments

One example is from Cimanuk–Cisanggarung River Basin in West Java, Indonesia. Rapid urban growth, land-use change and climate variability are increasing flood risks during the rainy season and water shortages during the dry season.

Retention ponds or small reservoirs designed to capture and store excess rainwater are widely recognized as effective solutions because they can hold excess runoff during heavy rains and provide water for irrigation and communities during dry periods. The main policy challenge, however, is optimizing investments in retention ponds: quickly identifying the best locations and making site selection more systematic and less subjective. Conventionally, planning relies heavily on field surveys and fragmented datasets, making the process slow, costly and hard to scale.

An AI-powered tool developed by Indonesia’s National Research and Innovation Agency (BRIN) and the West Java Department of Water Resources demonstrates how a single data and analytical pipeline can guide infrastructure investment decisions. The tool combines satellite Earth observation data, including digital elevation maps, land cover maps and rainfall data, with georeferenced drainage networks and soil type information to identify locations where retention ponds can provide the most benefits for flood control and drought resilience. Socio-environmental filters exclude protected areas or sites that might cause social or legal conflicts.

To ensure the tool supports operational decision-making, the results were validated through field assessments and consultations with local stakeholders. Additionally, a mobile-based application is being developed to enable field technicians to access the outputs directly on site, improving the speed and practicality of retention pond planning.

Applying this tool shifts infrastructure planning from subjective judgment to transparent and evidence-based prioritization. Supported by capacity-building activities for local institutions, this approach enables governments to allocate resources more efficiently while enhancing the long-term resilience of water systems.

Monitoring lake ecosystems from space 

While the Indonesia example shows how digital technologies can guide infrastructure investments, similar approaches are also transforming how water ecosystems are monitored and protected.

Water quality monitoring in Songkhla Lake, Thailand’s largest lagoon system and a critical resource for fisheries and aquaculture, has traditionally relied on periodic sampling at fixed stations. Expanding the coverage and frequency of monitoring data could improve early warning for ecosystem management and aquaculture.

A project, implemented by Prince of Songkla University in collaboration with local authorities, is exploring this potential on Ko Yor Island in Songkhla Lake. The initiative combines multi-source satellite remote sensing data, historical monitoring records and machine learning models to estimate key water quality parameters, such as turbidity and biochemical oxygen demand. Satellite-based remote sensing expands the coverage and frequency of water-quality monitoring, enabling near-monthly maps rather than quarterly point measurements.

This effort draws on more than a decade of operational experience from Poyang Lake, the largest freshwater lake in China. There, Jiangxi Normal University developed a comprehensive monitoring and early warning platform integrating satellite Earth observation, drone, ground and lake-surface sources, combined with ecological data simulated by models to track the lake’s dynamic ecological security issues and overall health. The system supports water management and the conservation of flagship species and their habitats, including migratory birds and the Yangtze finless porpoise.

From pilots to regional transformation

These pilots highlight an important trend: many of the innovative technologies needed to address water data gaps are already available. Earth observation satellite-derived data can complement ground-based observations by expanding environmental monitoring, while cognitive technologies integrate datasets into decision-ready insights. Scaling these innovations is not only a technological challenge. As emphasized in ESCAP’s report Seizing the Opportunity: Digital Innovation for a Sustainable Future, digital innovation is a socio-technical transformation that requires the skills, institutions and partnerships to integrate technology into governance systems. 

Experiences from Indonesia and Thailand illustrate how integrating satellite-derived data, geospatial analysis and artificial intelligence can simultaneously strengthen climate resilience, livelihoods and water governance. With supportive policies, stronger digital capacities and sustained regional cooperation, such approaches could be adapted and replicated in suitable contexts. These pilots, along with exchanges of technical experience, including lessons from the Poyang Lake monitoring system, are supported through the Asia-Pacific Plan of Action on Space Applications for Sustainable Development (2018–2030).

The Asia-Pacific SDG Progress Report 2026 warns that progress across many Sustainable Development Goals remains off track, while data gaps continue to constrain effective policymaking. Strengthening water governance will depend not only on building infrastructure, but also on building the data systems and analytical capacities that guide where and how those investments are made. Scaling proven digital innovations could therefore help turn fragmented water data into the actionable intelligence needed to accelerate progress toward SDG 6 and the broader 2030 Agenda for Sustainable Development.---

Chaoyang Fang, Distinguished Professor, School of Geography and Environment and Chief Engineer, Key Laboratory of Poyang Lake Wetland and Watershed Research (Ministry of Education) of Jiangxi Normal University, also contributed insights to this piece.