📊 Types of Resolution in Remote Sensing
| Category | Spatial Resolution | Spectral Resolution | Temporal Resolution | Radiometric Resolution |
|---|---|---|---|---|
| 📌 Definition | Size of the ground area represented by a pixel | Ability to resolve and separate wavelengths in the EM spectrum | Frequency of image acquisition for the same location | Sensitivity to detect subtle differences in reflected/emitted energy |
| 📏 Measured As | Pixel size (e.g., 10 m, 30 m, 0.5 m) | Number, width, and location of spectral bands | Time interval between observations (e.g., 1 day, 5 days) | Number of bits per pixel (e.g., 8-bit = 256 levels) |
| 📈 High Resolution Means | Smaller pixels; more spatial detail | More, narrower bands across the spectrum | More frequent data capture (shorter revisit) | Greater ability to detect subtle radiance differences |
| 🎯 Typical Use Cases | Urban mapping, precision agriculture, habitat delineation | Crop species classification, mineral detection, vegetation indices | Change detection, phenological studies, emergency response | Monitoring crop stress, water turbidity, thermal variation |
| 📷 Examples | WorldView-3 (0.31 m), Sentinel-2 (10 m), Landsat 8 (30 m) | AVIRIS (224 bands), Hyperion (220), Sentinel-2 (13 bands) | MODIS (1–2 days), Sentinel-2 (5 days), Landsat 8 (16 days) | Landsat 8 (12-bit), Sentinel-2, MODIS (12–16 bit) |
| 💾 Impact on File Size | Higher resolution = larger files due to more pixels | More bands = larger multispectral/hyperspectral files | Frequent captures = higher data volumes over time | Higher bit depth = more data per pixel, increasing file size |
| ⚖️ Trade-offs | Higher detail reduces swath width and increases data size | Narrow bands increase noise and may reduce SNR | High revisit often comes with lower spatial resolution | Higher radiometric resolution increases data volume and processing load |
| ⚙️ Affected By | Sensor design and altitude; optics; orbit geometry | Sensor filter technology; wavelength detection limits | Satellite orbit (sun-synchronous, geostationary), swath width | Sensor electronics and analog-to-digital converter precision |
| 🔗 Related Concepts | Ground Sampling Distance (GSD), pixel footprint | Bandwidth, hyperspectral vs. multispectral | Temporal frequency, repeat cycle, temporal aliasing | Digital Number (DN), signal-to-noise ratio (SNR) |
📝 Note: Understanding these features helps professionals choose the right data for tasks like land cover change, disaster mapping, environmental monitoring, and precision agriculture.