SAR GRD Applications

Application-oriented analysis for calibrated/terrain-corrected SAR GRD imagery using backscatter intensity, polarization combinations and optional before/after comparison.

SAR / InSAREnd-User DocumentationTheory + Formula + Parameters

1. What This Feature Does

Application-oriented analysis for calibrated/terrain-corrected SAR GRD imagery using backscatter intensity, polarization combinations and optional before/after comparison.

Cloud workflow: choose data → configure the scientific method → run → review the resulting layer/report. No programming is required.

2. Recommended Workflow

Choose SAR GRD raster(s) and polarization bands. ↓ Select the application. ↓ Choose crop type/monitoring mode where relevant. ↓ Run the analysis. ↓ Interpret backscatter changes with knowledge of incidence angle, moisture, roughness and geometry.

3. Theory, Methods & Equations

Flood Detection / Water Detection

Open water commonly has low co-polarized backscatter because smooth surfaces reflect energy away from the sensor, while flooded vegetation can behave differently.

σ⁰_dB = 10log₁₀(σ⁰)

Best used when: mapping water/flood signatures in SAR.

Oil Spill Detection

Looks for dark damping features on the sea surface; wind shadows and natural slicks can be confusers.

Best used when: screening marine slick candidates that will be reviewed with context.

Burned Area / Forest Disturbance / Landslide Change

Uses before/after changes in backscatter and/or polarization response.

ΔdB = dB_after − dB_before

Ratio = σ⁰_after/σ⁰_before

Best used when: surface structure/moisture changed between dates.

Agriculture / Crop Monitoring

Tracks crop backscatter or dual-polarization response through growth, planting, harvest, waterlogging, anomaly or biomass-proxy modes.

PolRatio = σ⁰_cross/σ⁰_co

Best used when: time/phenology context is available.

Soil Wetness / Moisture Proxy

Uses moisture-sensitive backscatter response as a proxy rather than a universally calibrated soil-moisture retrieval.

Best used when: relative wetness patterns are needed and surface roughness/vegetation effects are understood.

4. Input Data

Input	Type	Requirement	Description
Primary / After GRD Raster	raster	Required for selected method
Before / Comparison GRD Raster	raster	Required for selected method
GRD Time Series	raster	Required for selected method	Multi-date Sentinel-1 GRD layers. Each scene is validated against the selected real polarization bands.

5. Parameters Available in the Application

Parameter	Default	Choices / Range	How to Use It
Application	flood_detection	Flood Detection, Water Detection, Oil Spill Detection, Burned Area Detection, Agriculture / Crop Monitoring, Forest Disturbance, Landslide Change Detection, SAR Change Detection, Soil Wetness / Moisture Proxy
Primary Polarization / Band	—	—	Read from the selected raster band metadata. Shown for: flood_detection, water_detection, oil_spill_detection, burned_area_detection, forest_disturbance, landslide_change_detection, sar_change_detection, soil_wetness_proxy
Comparison Polarization / Band	—	—	Shown for: flood_detection, burned_area_detection, forest_disturbance, landslide_change_detection, sar_change_detection
Primary Polarization	—	—	First real polarization selected from the GRD time-series band metadata. Shown for: agriculture_crop_monitoring
Second Polarization	—	—	Second real polarization from the same multi-band GRD products; no manual index entry. Shown for: agriculture_crop_monitoring
Crop Type	generic	Auto / Generic, Rice / Paddy, Maize / Corn, Wheat, Sugarcane, Soybean, Oil Palm, Other	Shown for: agriculture_crop_monitoring
Monitoring Mode	condition_proxy	Crop Condition, Growth Monitoring, Growth Stage, Planting Detection, Harvest Detection, Crop Anomaly, Waterlogging, Biomass Proxy	Shown for: agriculture_crop_monitoring
Processing Quality	balanced	Fast, Balanced, High Quality

6. Output & Interpretation

The result should be interpreted according to the selected method and the physical meaning of the input data. Preserve source units, coordinate reference information, NoData meaning, acquisition date, and preprocessing level when comparing results.

Scientific interpretation: an algorithm can produce a numerically valid result even when the input data are unsuitable. Always check masks, units, sensor characteristics, spatial resolution and reference data.

Important: SAR backscatter is controlled by dielectric properties, roughness, geometry, polarization and incidence angle. Application outputs should be interpreted as remote-sensing indicators unless locally calibrated.

7. Best Practices

Use analysis-ready inputs and remove invalid/cloud/noise artifacts that are not part of the target phenomenon.
Choose parameters from the physical scale of the data, not only from visual appearance.
Keep categorical and continuous rasters conceptually separate when selecting interpolation/resampling methods.
Compare the result with the source image and independent reference information.
Document the settings used when results will be compared across dates, sensors or study areas.

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