NISAR: All Eyes on Open-Source, High-Resolution SAR Dataset

The upcoming NASA-ISRO Synthetic Aperture Radar (NISAR) mission, scheduled for launch in March 2025 from India’s Satish Dhawan Space Centre, is poised to revolutionize Earth observation by measuring changes in the planet’s ecosystems, dynamic surfaces, and ice masses. Operating from an orbit with an altitude of 747 km and an inclination of 98.4°, NISAR will provide global coverage with a 12-day repeat cycle, effectively sampling Earth’s surface every 6 days over its baseline 3-year mission—with consumables available for up to 5 years. The mission employs dual-frequency radar sensors in the L-band (24 cm) and S-band (9 cm), achieving a mode-dependent resolution of 3 to 10 m while maintaining precise orbit and pointing controls.

After addressing pre-launch challenges—including thermal issues with its 12-meter deployable reflector, the spacecraft is now fully prepared to commence science operations approximately three months post-launch. With its advanced dual-frequency capabilities, NISAR will offer significant advantages over existing SAR missions such as the Copernicus Sentinel-1, notably through enhanced vegetation and soil penetration via L-band and improved monitoring of surface deformations and ice dynamics via S-band. The mission’s free and open data policy will further empower the Remote Sensing and GIS Laboratory community to advance research in environmental monitoring, natural hazard assessment, and resource management worldwide.

NISAR’s sophisticated imaging technology holds transformative potential for agriculture by enabling precise, timely monitoring of crop health, soil moisture, and biomass changes. The L-band sensor, with its superior penetration of vegetation canopies, will deliver critical insights into crop structure and vigor, while the high revisit frequency of just 12 days allows for near real-time assessment of plant growth and stress indicators. By integrating NISAR’s rich datasets with existing datasets, researchers and agricultural stakeholders can develop predictive models that enhance decision-making, promote sustainable farming practices, and ultimately improve crop yields on both regional and global scales.