NISAR Data for Flood Mapping in India: A New Era for Remote Sensing and GIS
India experiences destructive floods almost every monsoon. Assam, Bihar, Uttar Pradesh, West Bengal, Odisha, Kerala and several Himalayan states regularly face river flooding, flash floods, urban waterlogging and landslides.
Traditional flood monitoring depends on rainfall records, river gauges, field surveys and weather forecasts. These systems remain essential, but they cannot always show the complete geographical extent of a flood—particularly when roads are blocked, villages are inaccessible and thick clouds obscure the ground.
The NASA-ISRO Synthetic Aperture Radar mission, commonly known as NISAR, offers a powerful new source of radar imagery for mapping floods, monitoring surface changes and supporting GIS-based disaster management.
With NISAR’s L-band and S-band radar data now becoming publicly accessible, Indian researchers, government departments, GIS professionals and students can develop more detailed flood-monitoring applications.
What is NISAR?
NISAR is a joint Earth-observation mission developed by the Indian Space Research Organisation and NASA.
The satellite was launched on 30 July 2025. It carries two Synthetic Aperture Radar instruments:
L-band SAR, developed by NASA
S-band SAR, developed by ISRO
NISAR operates from a near-polar, sun-synchronous orbit and provides repeating global observations on a 12-day cycle. According to ISRO, the mission has an approximately 240-kilometre observation swath, with spatial resolution varying according to the selected acquisition mode and product. Read the official NISAR mission specifications from ISRO.
The mission is designed to monitor changes in:
Land and water surfaces
Forests and vegetation
Agricultural areas
Wetlands
Glaciers and ice sheets
Earthquake-affected regions
Landslides and land subsidence
Coastal areas
Floodplains
NISAR can measure certain changes in Earth’s surface at centimetre-scale precision using interferometric techniques. NASA’s official NISAR mission overview describes its role in tracking disasters, ecosystems, agriculture, glaciers and surface movement.
Why radar is important during floods
Optical satellites such as Sentinel-2 and Landsat produce images similar to photographs. They are extremely useful for mapping land, vegetation and water, but clouds can prevent them from seeing the surface.
This becomes a serious limitation during floods because major flooding often occurs at the same time as intense rainfall and dense cloud cover.
Synthetic Aperture Radar works differently. Instead of depending on sunlight, it transmits microwave signals towards Earth and records the signals reflected by the surface.
Radar imagery can therefore be collected:
During the day or night
Through most cloud cover
During rainfall events
Over large and inaccessible areas
This makes SAR one of the most dependable satellite technologies for flood mapping.
How does NISAR detect floodwater?
Smooth open water normally reflects much of the radar signal away from the satellite. Consequently, calm water often appears relatively dark in a SAR image.
Land, vegetation and buildings usually return more energy to the sensor and therefore appear brighter.
A flood-mapping system can compare radar imagery acquired before and after an event:
Obtain a pre-flood radar image.
Obtain an image during or immediately after flooding.
Correct and align the images.
identify significant reductions or changes in radar backscatter.
Remove permanent rivers, lakes and reservoirs.
Classify the remaining water as probable flood inundation.
Overlay the result with villages, roads and other exposure data.
The output is a GIS layer showing the probable geographical extent of floodwater.
However, dark pixels are not always water. Radar shadows, smooth roads, dry sand, low-backscatter soil and some agricultural fields may also appear dark. A reliable workflow must therefore combine radar imagery with terrain, permanent-water and land-cover information.
Advantages of NISAR’s dual-frequency radar
NISAR’s most distinctive feature is its combination of L-band and S-band radar observations.
L-band SAR
L-band uses a relatively long wavelength of approximately 24 centimetres. It can interact with larger vegetation structures and has a greater ability to penetrate through some vegetation canopies.
This can support:
Flood detection beneath vegetation
Forest and wetland monitoring
Biomass estimation
Ground-deformation analysis
Monitoring of agricultural landscapes
S-band SAR
S-band has a shorter wavelength of approximately 9.4 centimetres and responds differently to surface roughness, soil moisture and vegetation.
It can contribute to:
Surface-water mapping
Crop monitoring
Soil-moisture assessment
Wetland classification
Coastal monitoring
Flood-related surface-change detection
Combining the two frequencies may help analysts distinguish open water, flooded vegetation, crops, forests and built-up areas more effectively than using a single radar frequency alone.
NISAR data became available in 2026
NASA began the public release of provisional NISAR L-band products in July 2026. These products are being archived and distributed through NASA’s Alaska Satellite Facility Distributed Active Archive Center. NASA Earthdata explains the L-band data release and availability.
ISRO has also released daily processed NISAR S-band SAR products through Bhoonidhi, its Earth-observation data dissemination platform. Operational processing began with Cycle 25, which commenced on 8 July 2026. See ISRO’s official S-band data release announcement.
Available S-band product categories include:
Single Look Complex products
Geocoded SLC products
Polarimetric covariance products
Interferograms
Unwrapped interferograms
Geocoded interferometric products
Pixel-offset products
These datasets create opportunities for universities, government agencies and independent geospatial developers to build operational applications using authentic NISAR observations.
A practical NISAR flood-mapping workflow
A GIS-based NISAR flood application can be developed through the following stages.
1. Define the area and event
Select the state, district, watershed or river basin to be analysed. Record the approximate flood dates and identify suitable pre-flood and post-flood acquisitions.
2. Download NISAR data
Indian S-band products can be explored through the ISRO Bhoonidhi portal, while L-band products can be obtained through NASA Earthdata and the Alaska Satellite Facility.
The product level should be selected according to the intended analysis and the analyst’s SAR-processing experience.
3. Preprocess the radar images
Typical preprocessing may include:
Orbit correction
Radiometric calibration
Noise removal
Speckle filtering
Terrain correction
Image co-registration
Conversion to backscatter values
Geocoded products can reduce some of the initial processing required for GIS analysis.
4. Compare pre-flood and post-flood backscatter
Floodwater can be detected using:
Backscatter thresholds
Image differencing
Backscatter ratios
Change-detection algorithms
Machine-learning classification
Deep-learning segmentation
Using both pre-event and post-event images generally produces a more reliable result than classifying a single image.
5. Exclude permanent water
Permanent rivers, reservoirs, lakes and wetlands should be separated from newly inundated land.
This can be done using:
Historical water-occurrence maps
River and lake boundaries
Earlier satellite observations
Existing land-cover datasets
The final flood layer should represent newly flooded areas rather than all surface water.
6. Apply terrain constraints
Radar shadows and naturally dark surfaces can create false detections. A Digital Elevation Model can help remove steep terrain where riverine flooding is unlikely.
Drainage networks, flow direction and watershed boundaries can further improve the result.
7. Conduct GIS exposure analysis
The flood layer becomes more useful when combined with:
Village boundaries
Population grids
Agricultural fields
Roads and railways
Hospitals and schools
Electricity substations
Bridges
Administrative boundaries
Land-use and land-cover data
GIS can then calculate the number of settlements, people, roads, crops and public facilities potentially affected by the flood.
8. Validate the flood map
Flood classifications should be checked against:
Ground observations
River-gauge measurements
Drone surveys
News and field reports
Higher-resolution imagery
Maps issued by disaster-management agencies
Satellite-derived flood maps should be treated as analytical estimates until properly validated.
Applications for flood management in India
Near-real-time inundation mapping
Radar images can help identify where floodwater has expanded across districts and river basins, including regions hidden by cloud cover.
Village-level damage assessment
Flood layers can be intersected with settlement and population data to identify potentially affected villages and prioritise emergency assistance.
Agricultural-loss estimation
By overlaying floodwater with crop maps, analysts can calculate affected agricultural area and identify crops exposed during important growth stages.
Road and infrastructure monitoring
GIS analysis can locate flooded highways, railway corridors, bridges, hospitals, power infrastructure and evacuation routes.
Flood-frequency mapping
Multiple years of satellite-derived flood layers can reveal locations that are inundated repeatedly. These observations support flood-hazard zonation and long-term land-use planning.
India’s National Remote Sensing Centre already supports near-real-time flood and cyclone monitoring, hazard assessment and disaster-response mapping. ISRO’s Disaster Management Support Programme highlights operational flood monitoring among its major activities.
Wetland and river-change monitoring
Repeated observations can show changes in river channels, sandbars, wetlands and floodplain connectivity.
Himalayan hazard assessment
NISAR can support monitoring of landslides, glaciers, unstable slopes and surface deformation in Himalayan regions. However, detecting a hazard is not the same as predicting exactly when a flash flood or slope failure will occur.
Can NISAR measure flood depth?
Radar imagery primarily identifies surface characteristics and inundated extent. It does not automatically provide accurate flood depth everywhere.
Flood depth can be estimated by combining the flood boundary with:
A high-resolution Digital Elevation Model
River-gauge observations
Hydrodynamic models
Cross-section data
Field measurements
Water-surface elevation estimates
Therefore, NISAR should be integrated with hydrological and hydraulic models rather than used as a complete replacement for them.
Limitations of satellite flood mapping
Despite its capabilities, NISAR cannot solve every flood-monitoring problem.
Important limitations include:
Revisit time may not capture the maximum flood extent.
Rough water may return a strong radar signal.
Flooding beneath dense vegetation can be difficult to classify.
Urban buildings create complicated radar reflections.
Mountains can produce radar shadow and layover.
Satellite acquisition and processing are not identical to real-time river forecasting.
Flood depth requires additional elevation and hydrological information.
The strongest disaster-management system combines satellites, GIS, weather forecasts, river gauges, hydrological models and field observations.
NISAR and the future of GeoAI
NISAR’s large and regularly updated data archive will also support the development of GeoAI models.
Machine-learning systems can be trained to:
Automatically identify floodwater
Separate permanent and temporary water
Detect flooded vegetation
Estimate crop damage
Rank affected settlements
Monitor river-channel movement
Detect land deformation
Generate district-level flood statistics
A future flood dashboard could combine NISAR with rainfall data, river forecasts, Digital Elevation Models and administrative boundaries to produce interactive maps and downloadable reports.
Conclusion
NISAR represents an important development for remote sensing and GIS in India.
Its dual-frequency L-band and S-band radar system can observe Earth during the day, at night and through most cloud cover—conditions that make conventional optical flood mapping difficult.
For India, NISAR data can strengthen:
Flood-inundation mapping
Agricultural-damage assessment
Infrastructure monitoring
Wetland analysis
Surface-deformation studies
Flood-hazard zonation
GIS-based disaster planning
NISAR will not replace weather forecasts, river gauges or field surveys. Its real strength lies in providing consistent, wide-area radar observations that can be integrated with these systems.
With public NISAR data now available, Indian GIS professionals have an opportunity to develop a new generation of flood-monitoring, disaster-response and environmental applications.
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