Introducing NISAR Data Support

• Vegetation and land cover changes
• Biomass and wetland dynamics
• Forest ecosystems
• Soil moisture variation
• Tectonics and earthquake effects
• Landslides
• Glacier movement
• Human-induced terrain alterations

NISAR will further improve our understanding of environmental change, as well as natural and anthropogenic hazards on land and ice.

NISAR satellite. Image credit: NASA.

ENVI SARscape 6.3 provides an Import NISAR tool for importing the NISAR data types mentioned above and saving them to a dedicated SLC format for further processing.

An exciting aspect of NISAR is the inclusion of an L-band sensor, which allows us to observe unique phenomena that we can’t achieve with shorter-wavelength sensors such as X-band and C-band. For example, L-band sensors allow deeper penetration into vegetation canopy layers, interacting with trunks, branches, and even partially with the ground. L-band wavelengths are sensitive to biomass and woody components, which facilitates soil moisture estimation in sparsely vegetated and moderately vegetated areas.

Complex SAR data can be used to analyze vegetation using the intensity part of the radar signal, the phase part, or both. Examples are shown below for the Everglades Agricultural Area south of Lake Okeechobee in Florida, USA. A set of five NISAR HH+HV images between November 2025 and January 2026 were combined for a statistical analysis of the intensity of reflected radar signals.

The resulting RGB composite map presents the coefficient of variation (red channel), minimum value (green channel), and the gradient (blue channel). The coefficient of variation is the ratio between the standard variation and the mean value of SAR backscatter intensity, showing changes in crops. Green pixels show the minimum backscattered value extracted from all input data or minimal changes on the surface. The gradient (blue) pixels represent the maximum absolute variation between consecutive acquisition dates for tracking rapid changes over time. The combinations of these statistical parameters reveal where changes appeared during the observation time—the red, blue and violet areas, and where the states remained stable over the time—the green areas.

Color composite image, courtesy of sarmap.

An intensity time series can reveal exactly when the changes happened:

Intensity time series for three selected regions of interest (ROIs), courtesy of sarmap.

A series of NISAR L-Band images can be used to derive the Enhanced Dual Polarization SAR Vegetation Index (EDPSVI) using HH+HV or VV+VH polarized data. This index demonstrates land cover diversity and vegetation extent based on the intensity information from a set of single L-Band images. The additional estimation of coherence in the EDPSVI method provides a threshold to distinguish between urban and vegetated areas. This helps to avoid false calculations of biomass components over non-vegetated areas.

Enhanced-DPSVI (Dual Polarization SAR Vegetation Index, with integration of the interferometric coherence). Image courtesy of sarmap.

Finally, a coherence time series can be used to generate a temporal profile, shown below.

Coherence time series image and temporal profile, courtesy of sarmap.

The following example shows an analysis of orchards in the Telangana region in India during the dry season (December 2025 to January 2026). For this study, a set of NISAR L-Band images were collected between November 2025 and January 2026. L-Band radar signals can penetrate through the tree canopy and reach the trunks with strong backscattering. In this way, the coherence stays stable and strong over time, which manifests as brighter parcels of land in multi-temporal coherence maps. In contrast, the surrounding crop fields exhibit variable coherence in time, which are visible as darker patches.

A joint analysis was performed using E-DPSVI and NDVI vegetation indexes calculated from a one-year series of Sentinel-1 images.

Agricultural case study, courtesy of sarmap.

NISAR’s L-band radar is highly effective for flood mapping because it can detect water using strong radar contrast and identify inundation beneath forest canopies. Open water appears dark because the radar signal bounces off the water and away from the sensor. Flooded forests appear bright because the radar signal bounces between tree trunks and water in a process known as dihedral (or double bounce) scattering.

Kruger National Park in South Africa experienced significant flooding on January 12, 2026. The images below show NISAR GSLC HH-polarized scenes before and after the flood.

NISAR pre-flood image, January 5, 2026.

NISAR post-flood image, January 11, 2026. Notice the increase in dark areas associated with open water.

Given these two images, we used the ENVI SARscape Flooding Classification tool to create a flood classification map of the overall region.

Even with the limited availability of NISAR data, a preliminary demonstration shows a Small Baseline Subset (SBAS) approach for surface deformation monitoring. In this example, six NISAR images were collected around Mexico City between October 2025 and January 2026. To verify the results, the observation data set was extended with 30 Sentinel-1 images collected between January 2024 and December 2024, as well as 18 Capella images from June to August 2024.

NISAR-determined velocity in the area of Mexico City for the period October 2025–January 2026. Images courtesy of sarmap.

LOS velocity comparison for Mexico City determined by NISAR (L-Band), Sentinel-1 (C-Band) and Capella (X-Band) images, courtesy of sarmap.

L-band wavelengths can penetrate snow layers and dry ice surfaces, allowing us to monitor glacier dynamics and estimate the velocity of moving ice sheets.

NISAR geocoded, HH-polarized, GSLC image of Russian sea ice, displayed in ENVI.

NISAR RSLC, HH-polarized, image of Malaspina Glacier in Alaska, displayed in ENVI.

ENVI SARscape and ENVI Agent

NV5 is currently working with sarmap to integrate ENVI SARscape tools into the ENVI Agent environment. This enables users to quickly run SAR processing tasks while gaining additional insights into their analyses. For example, asking ENVI Agent to estimate the quality of the NISAR Malaspina Glacier HH- and HV-polarized images resulted in the synopsis shown here.

Looking Ahead

Additional NISAR support is planned for an ENVI SARscape 6.3.1 release this summer. That release will enable the use of ‘B’ frequency, coarse-resolution data to correct ionospheric time delays in single-image geocoding and to remove ionospheric phase distortion when generating interferogram pairs.

Additional functionality will include time-based analysis of amplitude changes and phase displacement, as well as potential integration with other public datasets of similar resolution but different wavelengths (e.g., Sentinel-1).

The Import NISAR tool will also support the following polarization schemes:

• Both co-polarized data (HH and VV for A Band, HH for B Band) and cross-polarized data (HV and VH for A Band, HV for B Band)
• Only co-polarized data (for interferometry purposes)

• Only cross-polarized data (for polarimetric purposes)

• Single, Dual and Quad Pol

Finally, the next ENVI SARscape software release will include a tool for directly downloading NISAR data.

These updates will further enhance its compatibility with NISAR data, broadening the scope of applications and improving user experience. As these tools evolve, they will empower researchers and professionals to derive deeper insights and make informed decisions based on precise and timely data.

• NISAR Science User’s Handbook

• NISAR Mission Observation Plan

• NISAR Observation Plan