Introduction to Remote Sensing
Remote sensing enables observation of Earth from space and air, providing repetitive, consistent data across vast areas.
Theory & Foundations
Remote sensing exploits the electromagnetic spectrum. Different materials interact uniquely with EM energy, creating spectral signatures sensors detect from orbit.
The EM spectrum ranges from gamma rays to radio waves. Remote sensing uses visible (0.4–0.7μm), NIR (0.7–1.3μm), SWIR (1.3–3.0μm), thermal IR (3–14μm), and microwave (1mm–1m) regions.
Passive sensors detect reflected sunlight or emitted thermal energy — they need illumination and clear skies. Active sensors (radar, LiDAR) generate their own energy and work through clouds, day or night.
Four resolution types define sensor capability: Spatial (pixel size), Spectral (number of bands), Temporal (revisit frequency), Radiometric (sensitivity in bits). Higher spatial resolution usually means lower temporal coverage.
When EM energy hits a surface: absorption + reflection + transmission = 100%. Water absorbs NIR (appears dark), healthy vegetation reflects NIR strongly (appears bright). These patterns create unique spectral signatures.
In-Depth Coverage
The Electromagnetic Spectrum
Remote sensing exploits different wavelength regions for specific information.
- Visible (0.4–0.7μm): Blue, green, red
- Near-IR (0.7–1.3μm): Vegetation health indicator
- SWIR (1.3–3.0μm): Moisture, mineral mapping
- Thermal IR (3–14μm): Surface temperature
- Microwave (1mm–1m): Penetrates clouds, soil moisture
Resolution Types
Four dimensions define sensor capability.
- Spatial: Sentinel-2=10m, Landsat=30m, MODIS=250m
- Spectral: Landsat=11 bands, Sentinel-2=13, Hyperion=242
- Temporal: MODIS=daily, Sentinel-2=5 days, Landsat=16 days
- Radiometric: 8-bit (256 values) to 16-bit (65,536)
Active vs Passive Sensors
The fundamental distinction in remote sensing technology.
- Passive optical: Needs sunlight + clear sky (Landsat, Sentinel-2)
- Passive thermal: Day/night but needs clear sky (MODIS LST)
- Active SAR: Day/night, through clouds (Sentinel-1, ALOS-2)
- Active LiDAR: Day/night, highly accurate 3D (ICESat-2)
Key Techniques
Data Visualization
- 📡AcquireDownload from USGS, Copernicus, GEE
- ⚙️Pre-ProcessAtmospheric correction, georeferencing
- 🔍EnhanceBand composites, pan-sharpening
- 📊AnalyzeClassification, indices, change detection
- ✅ValidateGround truth, confusion matrix
Practical Workflow
- Define monitoring objective
- Select sensor based on resolution needs
- Search data archives
- Pre-process: calibration, atmospheric correction
- Geometric correction if needed
- Enhancement: band composites, filtering
- Analysis: classification, indices, change detection
- Validate with ground truth data
Software & Tools
Video Tutorials
Real-World Application
Real-World Projects
Satellite NDVI time series tracks mangrove health in world's largest mangrove forest.
Case Study
Problem-Based Learning
Amazon 80%+ cloud cover during wet season. Optical satellites cannot see ground.
Solution: Use Sentinel-1 SAR radar which penetrates clouds. Process backscatter time series — deforested areas show change.