RISAT: use radar to observe through cloud and darkness

RISAT identifies Indian radar-imaging satellites, whose instruments send microwave signals and measure returned energy. This active approach does not depend on reflected sunlight, making observations possible at night. Microwave imaging also supplies observations under cloud conditions that obstruct many optical cameras. Those advantages expand the observing toolkit without making radar an unlimited view through every material.

RISAT-1 launched in April 2012 with a C-band synthetic aperture radar. Later missions include other configurations: RISAT-2B used X-band radar. The program name therefore does not identify one universal wavelength, observing mode or spatial resolution. A comparison should name the spacecraft and payload configuration rather than infer specifications from the shared RISAT label.

A radar image records how signals interact with the scene and return toward the instrument. Surface roughness, moisture, vegetation and viewing geometry can influence the response. Radar brightness is therefore not the same as visible-light color. A dark region does not automatically mean one particular material, and a bright region does not automatically mean dry ground.

Synthetic aperture processing combines measurements along the spacecraft’s motion to form an image. Calibration and geometric processing help make those measurements useful for comparison. When observations differ, ask whether the ground changed, the viewing conditions changed or the processing changed. Multiple explanations can be consistent with a simple visual difference until additional evidence separates them.

RISAT: from radar return to a useful estimate

In February 2024, ISRO described a soil-moisture product derived from EOS-04, also called RISAT-1A, using C-band radar observations and a retrieval algorithm — open definition. The published product had 500-meter spatial sampling and used field measurements for validation. Its map is an estimate constructed from radar data, not a photograph in which soil water can simply be read without interpretation.

For a fictional flood comparison, examine acquisition time, geometry, terrain and relevant reference observations before classifying changed areas. Keep uncertainty and unobserved conditions with the result. Radar can fill important gaps in optical coverage, especially during cloudy periods, while field information and other sensors can strengthen the interpretation. Complementary observations are more useful than assuming that any single technology answers every question.