Finding Water with Satellite Data & AI

Updated July 2026 · ~6 min read

You cannot "see" groundwater from space directly — but you can read the surface and subsurface signals that reliably indicate it. Here is how modern apps turn free satellite data into a groundwater probability, with no field gear.

The data layers

• NDVI (vegetation index): healthy, persistent vegetation often marks accessible moisture.
• NDWI (water/moisture index): highlights surface and near-surface water content.
• Terrain elevation (DEM): reveals valleys, slopes and where water concentrates.
• Drainage networks: show recharge paths and convergence zones.
• Regional geology & aquifer maps: tell you what can store water and how deep.

How AI combines them

Each layer is a weak signal alone. A model weights and combines them — calibrated against known well and hydrogeology data — to output a probability score for a specific point, an estimated depth band, and a likely water-quality indication. This is essentially the desktop-study phase of a geophysical survey, automated and instant.

JalDrishti runs this analysis on-device from a map pin and returns a report in seconds, in 33 languages — free, anywhere on Earth.

What it can and cannot do

It can: rank candidate points, save you from obviously poor spots, and focus expensive surveys where they matter. It cannot replace an on-site geophysical survey for final confirmation on high-value drilling — nothing beats measuring the actual subsurface. Use AI to shortlist, then confirm.

Why "phone sensor" detectors are a myth

Apps that claim your phone's magnetometer or camera can sense underground water are not credible — the physics does not support it. Trustworthy tools rely on geospatial data and transparent methodology, and they are honest about uncertainty.