What RISAT and Cartosat Actually Watch and Why India Runs Two Separate Imaging Fleets

For roughly four months every year, a thick unbroken ceiling of cloud sits over large parts of India. It defeats optical cameras completely. A satellite carrying a conventional camera, the kind that works on reflected sunlight, sees nothing below a monsoon cloud deck. For a country whose borders, coastlines, and agricultural belts all fall under that ceiling at the same time, that is an intelligence and planning gap too large to ignore.

This is why ISRO runs two distinct imaging fleets. The answer is not administrative convenience. It is physics. Optical sensors and radar sensors answer different questions about the same ground, and no single satellite design resolves that tension.

The Cartosat series solves the daylight, clear-sky problem. The RISAT series solves everything else.

RISAT stands for Radar Imaging Satellite. Each satellite in the series carries a Synthetic Aperture Radar, or SAR, which transmits its own microwave pulses toward the ground and reads the energy that bounces back. Because the satellite generates its own signal rather than depending on sunlight, it works equally at 2 PM and 2 AM. Because microwave energy passes through water vapour and cloud cover, it works through the entire monsoon season.

RISAT-2, launched in 2009, carried an X-band SAR built in collaboration with Israel Aerospace Industries. X-band radar offers finer resolution, which is why RISAT-2 was placed primarily in a reconnaissance role. RISAT-1, launched in 2012, carried a C-band SAR and was designed with a wider aperture of applications: flood mapping, agricultural monitoring, and disaster assessment. RISAT-1A, launched in April 2022, is the current operational C-band satellite, with improved resolution and faster revisit times over the subcontinent.

SAR imagery reads differently from a photograph. Water surfaces appear very dark because they reflect microwave pulses away from the satellite. Rough terrain and man-made structures, buildings, bridges, military hardware, appear bright because they scatter energy back strongly. A trained analyst reads a SAR image the way a radiologist reads an X-ray: the logic of contrast is inverted from what the eye expects, but the information is precise. After the 2014 Jammu and Kashmir floods, RISAT-1 data was used to map inundated areas under cloud cover that grounded aircraft and blocked optical satellites entirely.

The Cartosat series does what radar cannot: it produces images that look like photographs, with the tonal and textural detail that makes urban mapping, land-use classification, and infrastructure monitoring tractable at scale.

Cartosat-1, launched in 2005, carried a panchromatic camera with 2.5-metre ground resolution, enough to distinguish a large vehicle from a building. Each successive generation tightened that number. Cartosat-2, launched in 2007, reached 1-metre resolution. Cartosat-3, launched in November 2019, achieved 0.25-metre ground resolution, placing it among the sharpest civilian Earth-observation satellites operating anywhere in the world at the time of its launch.

At 0.25 metres, Cartosat-3 can image a parked car clearly enough to distinguish its roof from its shadow. That resolution makes the satellite directly useful for cadastral mapping, the precise delineation of land parcels that underpins property records across Indian states. It is also used for monitoring encroachments along forest boundaries, tracking urban sprawl in cities like Bengaluru and Pune where the built edge moves measurably between seasons, and updating road and drainage maps in areas where ground surveys are slow or dangerous.

Cartosat satellites operate in sun-synchronous orbits, crossing the equator at the same local solar time on each pass. This keeps lighting conditions consistent across images taken weeks apart, which matters enormously when analysts are comparing two images to detect change.

The most demanding applications pull data from both series simultaneously. Border surveillance along the Line of Control in Jammu and Kashmir cannot afford four months of cloud-blind gaps. RISAT satellites maintain watch through the monsoon. When skies clear, Cartosat imagery fills in the optical detail that radar cannot supply: the texture of construction, the colour contrast of disturbed earth, the geometry of new structures.

Crop acreage estimation, which feeds into the government's agricultural planning and the minimum support price mechanism, uses RISAT SAR data to track the extent of paddy cultivation under cloud cover during the kharif season, then cross-checks it against Cartosat optical data once the skies open. The two datasets correct each other. SAR can misread waterlogged fields as water bodies; optical imagery disambiguates. Optical imagery cannot see through haze over the Indo-Gangetic Plain in winter; SAR cuts through it.

After Cyclone Fani struck Odisha in May 2019, both satellite types were tasked within hours. RISAT data gave disaster managers a damage map before any aircraft could fly. Cartosat imagery, acquired as the storm cleared, provided the structural detail needed to prioritise which roads and bridges required immediate repair.

The engineering case for a combined SAR-optical satellite exists. Several commercial operators have explored dual-payload designs. The tradeoffs are severe. SAR antennas are large, power-hungry, and generate significant heat. High-resolution optical telescopes require long focal lengths and precise thermal stability. Combining both on a single platform at the performance levels India requires from each would produce a satellite too heavy, too expensive, and too complex to be worth the integration. Separate, optimised platforms outperform any compromise design at this resolution class.

ISRO's decision to run parallel fleets is therefore an engineering conclusion, not a duplication of effort. The RISAT series and the Cartosat series are not doing the same job from different angles. They are measuring different physical properties of the same ground, and the ground requires both measurements to be understood completely.

The monsoon made that non-negotiable. A country that needs to watch its borders, map its crops, and respond to its disasters through four months of solid cloud cover cannot afford a fleet that goes blind when it rains. India's geography wrote the specification. ISRO built to it.