Remote Sensing Index Framework · Al Lith Governorate · Makkah Province · KSA

Satellite Intelligence
Reading the Land from Space

Spectral indices derived from Sentinel-2, Landsat 8/9 and Sentinel-1 SAR satellite data reveal the current physical condition of the Al Lith study area. Each index measures a specific land property — vegetation health, soil salinity, moisture, temperature, and land cover — providing objective evidence that feeds directly into the land capability and suitability assessments.

SensorsSentinel-2 · Landsat 8/9 · Sentinel-1 SAR
Indices23 Spectral + SAR
Resolution10m – 30m
Coverage Period2015 – 2025
Data Sources Sentinel-2 MSI — ESA Copernicus · 10m · Free Access Landsat 8/9 OLI — USGS · 30m · Free Access Sentinel-1 SAR — ESA Copernicus · 10m · Free Access Google Earth Engine — Processing Platform Sample values are reference ranges for interpretation guidance. Actual values require field-validated image processing.

Vegetation Indices

Measure the density, health and stress of vegetation using the differential reflectance of plant leaves in red and near-infrared wavelengths. Healthy vegetation strongly absorbs red light for photosynthesis and reflects near-infrared; stressed or absent vegetation reverses this pattern.
Tier 1 + 2

Soil & Degradation Indices

Map the physical and chemical properties of exposed bare soils using short-wave infrared and visible band combinations. Distinguish between different soil types, identify degraded areas, map clay and carbonate content, and assess iron oxide distribution as a proxy for soil fertility zones.
Tier 1 + 2

Salinity Indices

Detect and map soil and water salinity from space using the high reflectance of salt crusts in visible and short-wave infrared bands. Critical for identifying sabkha exclusion zones, monitoring salinisation progression, and mapping coastal salt flat boundaries in the Al Lith study area.
Tier 1 + 2

Water & Moisture Indices

Quantify surface water extent, soil moisture content, and vegetation water stress. Short-wave infrared bands are uniquely sensitive to liquid water in soil and plant tissues. Essential for mapping wadi channels, flood extents, irrigation distribution, and soil moisture conditions before planting.
Tier 1 + 2

Thermal & Built-up Indices

Land Surface Temperature measures the thermal emission from the ground surface, revealing heat stress zones, cooling effect of vegetation, and urban heat islands. Built-up indices distinguish human settlement and infrastructure from bare soil — critical for land use exclusion mapping.
Tier 1 + 2

Change Detection & SAR

Multi-temporal analysis reveals how the land has changed over the 2015–2025 decade. Synthetic Aperture Radar (SAR) from Sentinel-1 penetrates clouds, dust, and haze — providing data when optical sensors are blind. Together, these techniques detect dune migration, agricultural expansion, desertification, and flood events.
Tier 3 + SAR

Select an index card to see its formula, plain language explanation, sample Al Lith values, and interpretation guide.

Remote Sensing for Agricultural Land Evaluation — Satellite-derived spectral indices provide spatially continuous, temporally repeatable, and objective measurements of land condition across the entire Al Lith study area at 10–30m resolution. This replaces or validates point-based field surveys that are expensive and spatially limited.

How indices feed into the evaluation framework: Remote sensing indices are not standalone outputs — they are inputs that help derive or validate land characteristics. Low NDVI confirms bare soil conditions consistent with the land capability assessment. High SI-3 values confirm the ECe measurements that classify a zone as CN due to salinity. SAR backscatter validates flood hazard mapping from hydrological models.

Three tiers + SAR: Tier 1 indices are the core operational set used in every agricultural remote sensing study. Tier 2 adds diagnostic depth for specific soil and land cover questions. Tier 3 provides the temporal change perspective. SAR provides cloud-penetrating capability that makes the dataset reliable year-round in the dusty Al Lith coastal environment.

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