InSAR Landslide & Cloudburst Modeling

Calculate radar received power using the foundational radar range equation.

Evaluate the physical significance of different radar bands (e.g., X, C, L) for surface penetration and atmospheric effects.

Distinguish between surface, volume, and double-bounce scattering mechanisms based on target properties.

Describe how polarization of electromagnetic waves is used in radar to characterize surface properties.

Calculate the achievable range resolution based on the bandwidth of a transmitted chirp signal.

Explain how the Doppler effect on the signal's phase history enables high azimuth resolution.

Derive the theoretical improvement in azimuth resolution achieved through synthetic aperture processing.

Calculate the geometric distortions (foreshortening, layover, shadow) induced by SAR's side-looking geometry and local topography.

Formulate the interferometric phase from two co-registered SAR acquisitions.

Decompose the total interferometric phase into its primary contributors: topography, surface deformation, and atmospheric delay.

Formulate the creation of a differential interferogram by mathematically removing the topographic phase using a Digital Elevation Model (DEM).

Describe the causes and effects of spatial and temporal decorrelation on interferogram quality.

Explain the mathematical challenge of 2D phase unwrapping to resolve integer-cycle ambiguities.

Convert an unwrapped interferometric phase value into a line-of-sight (LOS) displacement measurement.

Distinguish between Single Look Complex (SLC) and Ground Range Detected (GRD) SAR data formats and their use cases.

Search for a suitable pair of SLC images over an area of interest using the Alaska Satellite Facility (ASF) search API.

Submit an InSAR processing job to the ASF HyP3 service via its Python API to generate a differential interferogram.

Retrieve and interpret the key products of an InSAR job, including the unwrapped phase and coherence maps.

Apply a geocoding correction to the InSAR products to align them with a standard map projection.

Convert the geocoded unwrapped phase raster into a georeferenced line-of-sight (LOS) displacement map (e.g., GeoTIFF).

Initialize the Google Earth Engine (GEE) Python API and load a DEM for an area of interest.

Upload the custom-generated LOS displacement GeoTIFF as a private GEE Image asset.

Compute key topographic variables (slope, aspect) from the DEM using server-side GEE functions.

Resample and align the InSAR displacement and topographic data layers to a common grid for analysis.

Define the meteorological criteria that classify a precipitation event as a cloudburst.

Query and process a time-series from a precipitation dataset (e.g., CHIRPS) for a region using the GEE Python API.

Apply Extreme Value Theory (EVT) to model the recurrence interval of extreme precipitation events using Python's statistical libraries.

Construct a spatial map representing the probability of a cloudburst-level precipitation event occurring.

Formulate a static landslide susceptibility index using topographic factors (e.g., slope, aspect) in GEE.

Integrate the InSAR-derived ground deformation map to create a dynamic susceptibility index.

Develop a final risk model function that combines the dynamic susceptibility with the cloudburst probability.

Implement the risk model as a GEE server-side function that generates a final risk map Image.

Instantiate an interactive map using the geemap library within a Jupyter Notebook environment.

Render the static data layers (topography, deformation) as distinct, toggleable map layers for context.

Implement an ipywidgets slider to control the cloudburst probability parameter in the risk model.

Connect the slider to a callback function that dynamically re-computes and updates the final risk layer on the map.