Advanced SAR Training

Explore how electromagnetic scattering shapes SAR data, from sensor design and polarization choices to processor decisions, metadata, calibration, and information extraction for end-user applications.

Explore how electromagnetic scattering produces constructive and destructive interference, explains single and double bounce returns, and uses multi-band radar (S-band and C-band) to map biomass and canopy structure.

Explore how wavelength choice, polarization, and a-mode SAR affect ground return, coherence, and resolution in sensor design. Assess tradeoffs between pixel counts, downlink time, and temporal resolution.

Explore how processors and image products use look-up tables and spectral weighting, and assess the accuracy and availability of orbital information and state vectors for precise mapping.

Examine how a coherent sar processor uses matched filtering and range-doppler concepts to generate image products, including slc and quad-polarization data, with calibration and slope corrections.

Explore how sar products deliver surface displacement, topography, and mean velocity across multiple channels, and how vegetation and tomography with biomass sensing enable three-dimensional canopy height estimation.

Identify the commercial demand for satellite data and show how frequent data every few days enables agriculture, wetlands, forestry, and glacial monitoring through constellation SaaS.

Explore SAR acquisition modes including standard strip map, sliding spotlight, staring spotlight, and Topps, with emphasis on Doppler corrections, burst metadata, and coherence improvements from tandem sensors.

Explore X-band SAR data from TerraSAR-X and Cosmos SkyMed, including calibration coefficients, high-quality metadata, and rapid four-hour delivery with archived data access.

Learn about C-band sensors, their free and open data policies, preplanned acquisitions, multi-mode options, resolutions from 20 to 60 meters, wide swath and incidence angle variety, and left/right look capability.

Explore L-band sensors for soil moisture mapping and forest biomass. Review single to quad mode with H and V polarizations and online calibration lookups for day-to-day data.

Explore dual frequency sensors in a NASA-ISRO collaboration delivering L-band data with 5 m by 5 m resolution and 3.5 mm global displacement accuracy, with free open data.

Explain how InSAR data is captured as in-phase and quadrature components, not amplitude and phase, and how to derive amplitude and phase from I and Q.

Combine two coherent signals with a complex conjugate to extract phase difference and coherence from interferograms, revealing stability and elevation clues.

Generate interferograms by using a common wavelength and polarization, and by keeping the view geometry similar, defined by the orbital revisit time, to preserve phase alignment.

Understand how baselines between sensors, 200 to 500 meters for differential interferograms and near zero for subsidence maps, influence coherence and fringe patterns.

Learn to correct interferometric data step by step: optimize baseline geometry, ensure precise image-to-image co-registration, apply an earth model, and remove topography, orbital, atmospheric, and systematic effects to isolate phase.

Transition to a 3D vector approach for baseline geometry in SAR, replacing geometry with unit vectors and a flexible earth model, simplifying Doppler-based ground-point solutions and enabling image co-registration.

Describes automatic image to image coregistration in four steps: tile into 128 by 128 blocks, use spectral domain shape matching, derive a transformation, and obtain a core-aligned pair.

Learn how phase changes depend on slant-range pixel differences, delta r, lambda, and incidence angle, and apply the flat-earth correction using perpendicular baseline to unwrap fringes.

We cannot control target scattering; coherence fades over time, so keep acquisition intervals short to preserve interference ramps and good data, with constellations enabling frequent monitoring.

Learn to predict radar phase from range and wavelength, convert distance to cycles and radians, and derive phase difference from delta r using the interferogram concept.

Perform topographic phase correction by projecting height changes onto the reference line, compute the angle with unit vectors via dot product, and derive phase from delta h over lambda.

Measure surface deformation using line-of-sight distance changes. Apply delta D equals the change in phase times lambda divided by four pi, and use a second satellite to resolve line-of-sight ambiguity.

Identify baseline and drift errors from interferograms to model orbit accurately, then iteratively correct horizontal and vertical bending with phase-based normalization.