Technology – Page 19 – Air Marshal's Perspective

June 7, 2025

677: NISAR: MAPPING THE FUTURE AND REVOLUTIONISING CLIMATE AND DISASTER INTELLIGENCE

My article was published in the Jun edition of the

News Analytics Journal

In an era where climate change, natural disasters, and ecological degradation are becoming more pressing global concerns, advanced space-based Earth observation has emerged as a vital tool. The NASA-ISRO Synthetic Aperture Radar (NISAR) mission is a landmark collaboration between the National Aeronautics and Space Administration (NASA) and the Indian Space Research Organisation (ISRO).

NISAR represents the most advanced dual-frequency radar satellite ever developed for civilian use. Once operational, NISAR will monitor Earth’s land and ice surfaces with high precision. It will capture surface movements down to fractions of an inch, aiding in studying tectonic shifts, glacier dynamics, forest health, and infrastructure stability. It can transform how we understand and respond to changes on Earth’s surface, ranging from glacial movements to forest biomass, from seismic activity to urban land subsidence.

The latest update on the NASA-ISRO Synthetic Aperture Radar (NISAR) mission indicates that the launch is scheduled for late May to June 2025, a shift from the anticipated March 2025 timeline. This delay, caused by thermal coating issues with the 12-meter radar antenna reflector, was resolved by October 2024. Despite the delay, the mission’s objectives and timeline remain intact. Final integration and testing are underway at ISRO’s facilities in Bengaluru. The satellite is expected to be transported to the Satish Dhawan Space Centre in the coming weeks to prepare for its launch aboard a GSLV Mark II rocket.

NISAR Project: Collaborative Effort

Genesis. The NISAR mission concept emerged from NASA’s 2007 Decadal Survey, which called for advanced SAR data to address gaps in Earth science. Formalised in 2014 with a partnership agreement, the project has progressed through rigorous design, testing, and integration phases. NASA’s Jet Propulsion Laboratory (JPL) and ISRO’s Space Applications Centre have worked closely to refine the mission’s science plan and hardware.

Project Details. The NISAR mission is designed to provide unprecedented global radar imagery using L-band and S-band synthetic aperture radars. NASA has provided the L-band radar system, high-rate communication subsystem, GPS receivers, and payload data systems. ISRO is contributing the S-band radar, satellite bus, and launch services via the GSLV Mk II from the Satish Dhawan Space Centre. The satellite will be placed in a sun-synchronous polar orbit at about 747 kilometres and revisit the exact location on Earth every 12 days. The SAR payload will produce radar images with a resolution of 5–10 meters and a swath of 240 kilometres, enabling wide-area monitoring of Earth’s surface with high precision. The unique dual-band system of NISAR allows it to penetrate vegetation, ice, and soil more accurately than single-frequency satellites, making it a game-changer in Earth observation. The L-band is particularly effective for tracking subsurface movement and biomass, while the S-band is more sensitive to finer surface features.

Collaboration. The NISAR partnership exemplifies international cooperation in space exploration. NASA’s investment, estimated at $1.118 billion, covers the L-band radar and critical subsystems, while ISRO’s contribution, approximately ₹788 crore ($92 million), includes the S-band radar, spacecraft bus, and launch services. This division of responsibilities optimises costs and expertise, building on NASA’s legacy of SAR missions (e.g., SEASAT in 1978) and ISRO’s advancements in satellite technology (e.g., the Chandrayaan missions). The collaboration extends beyond hardware. Joint workshops, working groups, and the NISAR Utilisation Programme announced by ISRO in July 2023 engage the global scientific community, fostering data analysis and application development. The mission’s open data policy aligns with the principles of transparency and collaboration, setting a precedent for future NASA-ISRO endeavours, including potential Mars exploration missions.

Mission Objectives and Scientific Impact

NISAR’s primary goal is to make global measurements of land surface changes, detecting movements as small as a centimeter. By mapping the globe every 12 days, the satellite will generate spatially and temporally consistent data, offering insights into complex Earth processes. Its objectives span three key domains: deformation, ecosystem structure, and ice dynamics. NISAR will monitor seismic zones, volcanic activity, and landslide-prone areas for deformation, providing early warning signs for natural disasters. In ecosystem studies, it will track forest extent, vegetation biomass, and agricultural patterns, aiding sustainable resource management. NISAR will measure glacier flow rates and ice-sheet stability for ice dynamics, contributing to our understanding of climate change and sea level rise.

All NISAR data will be freely available within one to two days of observation or hours for emergencies like natural disasters. This accessibility and NISAR’s high-resolution imagery (5-10 meters) will empower scientists, policymakers, and disaster response teams worldwide. The data can enhance infrastructure monitoring, improve agricultural management, and inform rapid disaster response, potentially saving lives and property. The open data policy also encourages collaboration and innovation, allowing for the development of new applications and tools to further leverage NISAR’s capabilities.

Applications

Natural Disaster Monitoring and Response. NISAR will be critical in mapping the aftermath and precursors of earthquakes, floods, volcanic eruptions, and landslides. The radar’s ability to detect minute ground deformations will help forecast and emergency response, reducing the human and economic cost of such events.

Climate Change Observation. The satellite will track ice sheet movement in Antarctica and Greenland, glacial retreat in the Himalayas, and coastal subsidence, all critical indicators of global climate change. NISAR data will also assist in modelling sea level rise and understanding the behaviour of the permafrost regions, which store vast amounts of greenhouse gases.

Agriculture and Forestry. NISAR’s radar can estimate biomass and crop yield, making it invaluable for food security planning and carbon stock assessment. It will monitor deforestation, forest degradation, and land-use changes, helping countries meet international commitments such as those under the Paris Agreement and REDD+ initiatives.

Urban Infrastructure Monitoring. Urban planners and disaster mitigation agencies can use NISAR to monitor growing cities’ subsidence, groundwater depletion, and infrastructure stress. Its precise deformation measurements can help predict building collapses, dam failures, and roadbed weaknesses.

Scientific and Tectonic Research. Scientists will use NISAR to understand better plate tectonics, fault line dynamics, and volcano formation. The L-band radar, in particular, is ideal for detecting ground movements as small as a few millimetres, critical for early warnings in earthquake-prone regions.

Strategic Significance

The NISAR mission is a scientific milestone and a strategic symbol of the growing India-US partnership in space technology. It reflects significant technological trust and collaborative capacity-building, especially as China expands its space and Earth observation programs.

For India, the mission provides access to advanced radar imaging technology, enhances its global space diplomacy profile, and contributes to developing disaster management and environmental monitoring capacity. For the U.S., NISAR extends Earth observation to low-latitude and tropical regions, which are difficult to monitor from NASA’s polar-focused satellites.

Conclusion

NISAR stands at the intersection of science, diplomacy, and strategic policy. As the world’s most advanced Earth-observing radar satellite, it will provide a detailed, dynamic picture of the planet’s changing surface. Whether helping farmers optimise irrigation, supporting relief efforts after natural disasters, or aiding climate scientists in tracking global warming, NISAR will become an indispensable part of humanity’s Earth-monitoring infrastructure.

By combining ISRO’s cost-effective engineering and operational expertise with NASA’s deep technological experience, NISAR heralds a new era in Earth observation and exemplifies the international collaboration required to tackle global challenges.

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Information and data included in the blog are for educational & non-commercial purposes only and have been carefully adapted, excerpted, or edited from reliable and accurate sources. All copyrighted material belongs to respective owners and is provided only for wider dissemination.

References: –

Indian Space Research Organisation. (2024). NASA-ISRO SAR (NISAR) Mission Overview. Retrieved from https://www.isro.gov.in

NASA Earth Science Division. (2023). NISAR Mission Overview. https://nisar.jpl.nasa.gov

NASA Jet Propulsion Laboratory. (2024). NISAR: NASA-ISRO Synthetic Aperture Radar. https://nisar.jpl.nasa.gov

ESA Earth Observation Portal. (2023). Synthetic Aperture Radar Applications in Climate and Disaster Monitoring.

United Nations Office for Disaster Risk Reduction (UNDRR). (2023). Role of Earth Observation in Risk Reduction.

Sharma, A. & Kumar, R. (2022). “India-US Space Cooperation: Strategic Implications.” ORF Occasional Paper, Observer Research Foundation.

Ray, P. (2023). “Climate Resilience through Satellite Monitoring in South Asia.” Nature Climate Policy, 15(3), 410-417.

Rosen, P. A. (2021). The NASA-ISRO Synthetic Aperture Radar (NISAR) Mission – Technologies and Techniques for Earth Science. NASA Technical Reports Server. https://ntrs.nasa.gov

Ramachandran, R. (2024). “Thermal coating issue fixed on NASA-ISRO NISAR mission.” The Hindu Science & Tech. https://www.thehindu.com

Nayak, A., & Kumar, P. (2023). “SAR Technology for Earth Observation: Advances with the NISAR Mission.” Current Science, 125(9), 1463–1471.

Prasad, S., & Mehta, K. (2022). “Earth Observation and Indian Disaster Management.” Journal of Geospatial Technologies, 14(2), 91–104.

June 3, 2025

676: COUNTER-STEALTH TECHNOLOGIES: EVOLVING DEFENCES AGAINST INVISIBLE THREATS

My article was published in the “Life of Soldier” Journal Jun 25 Issue.

Stealth technology, a marvel of engineering designed to render aircraft, ships, and other military assets nearly invisible to radar, infrared, and other detection systems, has been a cornerstone of modern warfare since the late 20th century. Stealth platforms have provided significant tactical advantages by reducing radar cross-sections (RCS), absorbing radar waves, and minimising heat signatures. However, as stealth technology has proliferated, so too have counter-stealth technologies aimed at detecting, tracking, and neutralising these elusive targets. The race to detect the undetectable has intensified as stealth platforms proliferate in modern arsenals. Counter-stealth technologies—once niche and experimental—are now at the forefront of 21st-century defence strategy.

Principles of Stealth Technology. To understand counter-stealth technologies, it’s essential to grasp how stealth works. Counter-stealth technologies aim to exploit weaknesses in these principles, leveraging advanced sensors, signal processing, and innovative detection methods to uncover hidden assets. Stealth platforms rely on several key principles:-

- Radar Cross-Section Reduction. Stealth vehicles are designed with smooth, angular shapes to deflect radar waves from the source, minimising the energy returned to the radar receiver. Materials like radar-absorbent coatings further reduce reflectivity.

- Infrared Signature Suppression. Engines and exhaust systems are engineered to minimise heat emissions, making it harder for infrared sensors to detect the platform.

- Electronic Emission Control. Stealth systems limit or disguise electromagnetic emissions, such as radio or radar signals, to avoid detection by electronic support measures (ESM).

- Acoustic and Visual Camouflage. Submarines and some aircraft reduce noise and visual signatures to evade sonar and optical detection.

Evolution of Counter-Stealth Technologies. The quest to counter stealth began shortly after the introduction of stealth aircraft like the F-117 Nighthawk in the 1980s. Early efforts focused on improving existing radar systems and exploring alternative detection methods. For example, during the 1999 Kosovo War, Serbian forces reportedly used outdated but modified low-frequency radars to detect and shoot down an F-117, highlighting vulnerabilities in stealth designs optimised against high-frequency X-band radars used in most modern systems. Low-frequency radars became an early counter-stealth tool operating in the VHF and UHF bands. Though less precise, these radars can detect stealth aircraft because their longer wavelengths are less affected by radar-absorbent materials and angular designs. However, their large size and limited resolution initially restricted their battlefield utility.

Modern Counter-Stealth Advancements.

Counter-stealth technologies have become more sophisticated in the 21st century, driven by advancements in computing, sensor fusion, and materials science.

Advanced Radar Systems.

- Low-Frequency and Bistatic Radars. Modern low-frequency radars, such as Russia’s Nebo-M and China’s JY-26, combine improved signal processing with mobility, overcoming earlier limitations. Bistatic and multistatic radar systems, which separate the transmitter and receiver, further complicate stealth designs by detecting scattered radar waves that stealth platforms cannot entirely suppress.

- Over-the-Horizon (OTH) Radars. OTH radars bounce signals off the ionosphere to detect targets thousands of kilometers away. Systems like Australia’s Jindalee Operational Radar Network can track stealth aircraft over vast distances, though their resolution remains coarse.

- Passive Radar Systems. These systems detect stealth platforms by analysing ambient electromagnetic signals from FM radio, TV broadcasts, or cellular networks. Because stealth platforms cannot eliminate all reflections, passive radars can exploit these signals to identify anomalies. The Czech Republic’s Vera-NG is a notable example.

Infrared and Electro-Optical Systems. Infrared search and track (IRST) systems have become a powerful counter-stealth tool. By detecting heat signatures from engines, exhausts, or even aerodynamic friction, IRST systems bypass radar stealth entirely. Modern IRST systems, like those on the Russian Su-57 or the Eurofighter Typhoon, use advanced focal plane arrays and image processing to distinguish stealth platforms from background noise. Electro-optical systems, combining high-resolution cameras with machine learning, can also identify visual anomalies, such as aircraft silhouettes against the sky.

Acoustic and Seismic Detection. Acoustic and seismic sensors offer detection capabilities for ground-based or naval stealth assets. Despite their stealth, submarines produce low-frequency noise that hydrophones and sonar arrays can detect. Similarly, seismic sensors can detect vibrations from stealth vehicles or aircraft, particularly during takeoff or landing. Signal processing advances have improved these systems’ sensitivity, enabling detection over greater distances.

Quantum and Photonic Technologies. Emerging quantum radar systems promise to revolutionise counter-stealth detection. Using entangled photons, quantum radars can detect objects with unprecedented sensitivity, even through radar-absorbent materials. China has claimed progress in this area, though practical deployment remains years away. Photonic radars, which use laser-based systems, also show potential for high-resolution detection of stealth platforms.

Sensor Fusion and Artificial Intelligence. Perhaps the most significant advancement in counter-stealth technology is integrating multiple sensor types through sensor fusion. By combining radar, IRST, acoustic, and passive systems data, militaries can create a comprehensive picture of the battlefield. The role of AI in counter-stealth technologies is crucial, as it enhances the detection process and aids in developing more sophisticated and adaptive stealth designs. Artificial intelligence (AI) enhances this process by analysing vast datasets in real time, identifying patterns, and filtering out noise. For example, AI can distinguish a stealth aircraft’s faint radar return from environmental clutter, improving detection accuracy.

Challenges in Counter-Stealth Development

Despite these advancements, counter-stealth technologies face significant challenges:-

- Signal-to-Noise Ratio. Stealth platforms are designed to produce minimal detectable signatures, making it difficult for sensors to distinguish them from background noise.

- Cost and Complexity. Advanced counter-stealth systems, such as quantum radars or multistatic arrays, are expensive and require significant infrastructure. Deploying and maintaining these systems can strain defence budgets.

- Adaptability of Stealth. As counter-stealth technologies evolve, so do stealth designs. Newer platforms, like the B-21 Raider, incorporate lessons from past vulnerabilities, making them harder to detect.

- Electronic Warfare. Stealth platforms often employ electronic countermeasures, such as jamming or decoys, to confuse or overwhelm counter-stealth systems.

Case Studies

Russia’s S-400 and S-500 Systems. Russia’s S-400 and S-500 air defence systems exemplify modern counter-stealth capabilities. These systems integrate low-frequency radars, IRST, and advanced signal processing to detect and track stealth aircraft. For instance, the S-400’s 91N6E radar operates across multiple frequency bands, making it effective against low-RCS targets. The S-500, with its reported ability to engage hypersonic and stealth targets, underscores Russia’s investment in counter-stealth technology.

China’s Anti-Stealth Efforts. China has prioritised counter-stealth development, deploying systems like the Type 055 destroyer’s integrated sensor suite and the Divine Eagle UAV, which uses low-frequency radar for long-range detection. China’s quantum radar and AI-driven sensor fusion advancements further position it as a leader in this field.

NATO’s Integrated Air Defence. NATO countries have focused on networked counter-stealth solutions. For example, the U.S.’s Aegis Combat System integrates radar, IRST, and passive sensors across ships and aircraft, creating a layered defence against stealth threats. Through projects like the Future Combat Air System (FCAS), European nations are developing AI-enhanced counter-stealth capabilities for next-generation warfare.

Future of Counter-Stealth Technologies

Looking ahead, counter-stealth technologies will likely focus on three key areas:-

Hyperspectral and Multispectral Sensing. Hyperspectral sensors can detect subtle signatures that stealth platforms cannot entirely suppress by analysing targets across a broader range of wavelengths. These systems, already used for satellite reconnaissance, could be adapted for real-time battlefield detection.

Distributed Sensor Networks. Future counter-stealth systems will rely on vast networks of small, low-cost sensors deployed across air, sea, and land. These networks, linked by AI, will create a resilient detection grid that stealth platforms find difficult to evade.

Directed Energy and Electronic Warfare. Counter-stealth technologies may integrate directed energy weapons, such as lasers or microwaves, to neutralise stealth platforms as detection improves. Advanced electronic warfare systems could also disrupt stealth platforms’ onboard systems, rendering them vulnerable.

Strategic Implications

The development of counter-stealth technologies raises profound questions about the future of warfare. On one hand, these technologies enhance defensive capabilities, potentially deterring aggression by neutralising the advantages of stealth. On the other hand, they risk escalating arms races, as nations invest in ever-more advanced stealth and counter-stealth systems.

Conclusion

Counter-stealth technologies represent a dynamic and rapidly evolving field, driven by the need to counter one of the most transformative innovations in modern warfare. From low-frequency radars to quantum sensors, these technologies leverage cutting-edge science to pierce the veil of invisibility. However, as the technological race between stealth and counter-stealth intensifies, militaries must balance innovation with strategic stability. The future of warfare will likely be defined not by the dominance of one technology but by the interplay of offence and defence in an increasingly complex battlespace.

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References:-

Sweetman, B. (2013). The Stealth Fighter: How the F-117 Changed Warfare. Zenith Press.

Roblin, S. (2019). “How Russia’s S-400 and S-500 Missile Systems Plan to Take on Stealth Aircraft.” The National Interest.

Majumdar, D. (2016). “China’s Quantum Radar: The Next Big Thing in Stealth Detection?” The Diplomat.

Easton, I., & Hsiao, L. C. (2017). The Chinese People’s Liberation Army’s Anti-Stealth Strategy. Jamestown Foundation.

Gilli, A., & Gilli, M. (2019). “The Diffusion of Stealth Technology and the Challenges for Air Defence.” Journal of Strategic Studies, 42(3-4), 451-479.

Hammes, T. X. (2020). Technological Change and the Future of Warfare. Brookings Institution Press.

Rogoway, T. (2021). “Passive Radar Systems Are Becoming a Bigger Threat to Stealth Aircraft.” The Drive.

Kopp, C. (2010). “Counter-Stealth Radar Technologies: An Analysis of Low Frequency and Bistatic Systems.” Air Power Australia.

Perrett, B. (2018). “Australia’s Jindalee Over-the-Horizon Radar: A Strategic Asset for Stealth Detection.” Aviation Week & Space Technology.

Fulghum, D. A. (2014). “Infrared Search and Track Systems: The Next Generation of Counter-Stealth.” Aerospace America.

Singer, P. W., & Cole, A. (2022). Ghost Fleet: A Novel of the Next World War. Houghton Mifflin Harcourt.

Zikidis, K. C., Skondras, A., & Tokas, C. (2014). “Low Observable Principles, Stealth Aircraft and Anti-Stealth Technologies.” Journal of Computations & Modelling, 4(1), 129-165.