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AIPROPX ReportFortune · 6h ago
SpaceAI could be Southeast Asia’s edge against El Niño—if governments actually use it
In June, the U.S. National Oceanic and Atmospheric Administration warned that there is a 63% chance of a very strong El Niño developing before the end of 2026, potentially rivaling the most severe episodes since records began in 1950. The 1997-98 El Niño , one of the strongest on record, triggered severe floods and droughts across Africa, Latin America, North America, and Southeast Asia, resulting in an estimated 22,000 deaths and more than $36 billion in economic losses.
Other major El Niño episodes brought crop failures, devastating peatland fires, and prolonged droughts. The effects of weather disruptions ripple through regional supply chains, affecting everything from aviation and manufacturing to insurance and public health.
Southeast Asia does not have a lack of climate information. It instead suffers from a lack of action.
The region already possesses satellites, weather observations, sophisticated climate models, and regional monitoring mechanisms. International agencies can predict the onset of El Niño months in advance, while institutions such as Singapore’s ASEAN Specialized Meteorological Centre (ASMC) continuously monitor haze and environmental conditions.
Yet governments and businesses still struggle to answer the questions that matter. Which communities will be hit by extreme weather first? Which peatlands are becoming most vulnerable? Which supply chains face the greatest disruption? And what should be done before environmental stress becomes economic crisis?
The region needs to turn data into timely, trusted decisions, and not just collect more unstructured information. This is where SpaceAI —the convergence of artificial intelligence (AI) and space technologies—occupies the gap between data and decision-making, between strings of numbers and outcomes like saved lives and households.
What is SpaceAI?
SpaceAI combines satellite-based Earth observation, large language models, cloud computing and advanced analytics to transform enormous volumes of environmental data into predictive, decision-ready intelligence.
Normally, Earth observation is retrospective. Satellites capture images, analysts interpret them–and then governments respond once the damage has already been done.
But with AI, governments can act first. AI models can combine satellite imagery with weather forecasts, soil moisture, vegetation health, and other environmental indicators to identify which areas are at risk.
This is not hypothetical: Researchers have already combined peat depth, elevation, slope, vegetation type, rainfall and distance to infrastructure with satellite data and machine learning to map fire susceptibility in Indonesian peatlands . A more recent study in Riau Province, located on the east-central coast of Sumatra, Indonesia, used spaceborne data and machine learning to reveal that groundwater level was the major driver of fire risk. Governments can then make decisions based on the resulting risk maps, such as prioritising patrols and fire bans in high-risk areas, and blocking drainage canals to rewet peatlands and raise groundwater levels before a fire starts.
Satellites are becoming more than cameras in space. Instead of transmitting large volumes of data down to Earth (which overcrowds bandwidth and delays analysis), AI can process observations onboard the satellite itself, selecting only the data that is relevant, so decision-makers get useful information faster than traditional analysis allows.
Even a small improvement in lead time can have an outsized economic return. Governments can restore water levels in vulnerable peatlands before fires spread. Firefighting assets can be put in position before a fire happens, instead of deployed once a fire has begun. Farmers and logistics companies can alter their operations before disruption occurs. Insurers can more accurately model their exposure to weather risks.
Prediction is not the same as prevention
It’s true that just having actionable intelligence isn’t very helpful if governments aren’t willing, or able, to act on it. Acting on a risk assessment before that risk has materialized requires political willpower; just having better data, or even better analyses, doesn’t wholly solve that problem.
Still, the value of SpaceAI comes in reducing the uncertainty that gives policymakers the excuse to kick the can down the road.
To get there, Southeast Asia needs an integrated ecosystem that connects Earth observation, AI, scientific expertise and trusted public institutions. Satellites generate the data, AI turns it into predictive intelligence, and governments, emergency responders and businesses convert those insights into coordinated action.
Singapore illustrates one piece of what such an ecosystem could look like in practice. Since April 2026, its newly established National Space Agency of Singapore (NSAS) has consolidated the country’s space functions under one roof, with a mandate that spans regulation, industry development and building a domestic space and AI talent pipeline. The government has already committed more than 200 million Singapore dollars ($155 million) to space research and development since 2022, and initiatives such as the upcoming NeuSAR-2 synthetic aperture radar constellation (which will strengthen day-and-night, all-weather Earth observation over the region) show how that investment is translating into sharper eyes in orbit.
The lesson for the rest of Southeast Asia is less about any single satellite than about the institutional plumbing behind it: a dedicated agency, sustained funding, and a workforce trained to turn that data into decisions.
Once this infrastructure is in place, Southeast Asia’s governments can turn to the next problem: building the interdisciplinary workforce and cross-border trust needed to translate sophisticated analytics into actionable decisions.
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