14.2 Passive vs Active Sensors

Key takeaways

• Passive sensors measure natural radiation; active sensors emit and measure the return.
• Radar (SAR) and lidar are the dominant active sensors.
• Active sensors work in conditions passive ones can't — night, cloud, smoke.

Introduction

All remote sensing is one of two kinds: passive (measure what's there) or active (emit a signal and measure the return). Each has its physics, its sensors, and its ideal applications. This short lesson covers the essentials.

Passive sensors

Passive optical and thermal sensors measure:

• Reflected sunlight (visible, NIR, SWIR).
• Emitted Earth radiation (thermal IR).

Examples

• Landsat 8/9 OLI-TIRS.
• Sentinel-2 MSI.
• MODIS (Terra, Aqua).
• Planet Dove constellation.
• GOES / Himawari weather satellites.

Strengths

• Large coverage per pass.
• Natural-colour imagery familiar to viewers.
• Rich spectral information.
• Mature methods for classification, change detection.

Limits

• Require sunlight (optical sensors).
• Blocked by clouds (optical).
• Limited in twilight and polar winter.
• Atmospheric correction needed for quantitative use.

Active sensors

Active sensors emit radiation and measure what returns:

• Radar (SAR) — microwaves.
• Lidar — laser pulses.
• Altimeters — measure return time to surface.

Examples

• Sentinel-1 (SAR).
• Capella / ICEYE / Umbra (SAR constellations).
• ICESat-2 (satellite lidar).
• Airborne / terrestrial lidar.

Strengths

• Work day and night (no sunlight needed).
• Cloud-penetrating (radar).
• Measure 3D structure directly (lidar).
• Quantitative depth, heights, velocities.

Limits

• Narrower swath than optical.
• Different "image" semantics — not intuitive to read (SAR speckle, lidar point clouds).
• Smaller archives historically.
• More complex preprocessing.

SAR (Synthetic Aperture Radar)

The dominant active Earth-observation mode.

Products:

• Amplitude — strength of the return (brighter = more scattering).
• Phase — timing of the return (enables interferometry, InSAR).
• Polarisation — orientation of emitted/received waves (VV, VH, HH, HV).

Applications:

• Flood mapping (water = dark; flooded areas visible as water appearing where land was).
• Deforestation (cloud-piercing time series).
• Ship detection (strong scatterers on ocean).
• Ground subsidence (InSAR millimetre-level).
• Glacier motion.

Lidar

Pulses millions of laser photons per second; measures return times to build 3D point clouds.

Products:

• Digital Surface Model (DSM) — first returns, top of canopy / buildings.
• Digital Terrain Model (DTM) — ground returns only.
• Canopy Height Model (CHM) — DSM − DTM.
• Building footprints — segmentation of non-ground points.
• Biomass / forestry — vertical structure.

Sources: airborne (ICESat-2, drone-mounted), terrestrial (mobile mapping cars, tripod scanners).

When to combine passive + active

Many real workflows use both:

• Sentinel-2 (passive) for regular monitoring; Sentinel-1 (SAR) for cloud-free fallback.
• Optical imagery + lidar DSM for building extraction.
• Thermal imagery + SAR flood maps.

Earth Engine and similar platforms make fusion straightforward.

Cost

• Free passive: Landsat, Sentinel-2, MODIS.
• Free SAR: Sentinel-1.
• Free lidar: many national programmes (USGS 3DEP, AHN in Netherlands).
• Commercial: Planet, Maxar (passive); Capella, ICEYE (active); drone providers (lidar).

Self-check exercises

1. Why is SAR useful for flood mapping in the tropics?

The tropics have persistent cloud cover, so optical sensors rarely capture cloud-free imagery during or immediately after flood events. SAR penetrates clouds, and water surfaces appear distinctively dark in amplitude imagery. Time series of SAR reveal extent changes between pre-flood and flood acquisitions.

2. What advantage does lidar offer over photogrammetry for canopy height estimation?

Lidar directly measures distance from the sensor to the target — it sees through the canopy gaps to the ground, giving separate surface (treetop) and terrain (ground) measurements. Photogrammetry reconstructs surfaces from images, so it only sees the canopy top; it cannot directly measure under-canopy ground in forests.

3. Can SAR imagery replace optical imagery for most analyses?

No — they complement each other. SAR measures backscatter (physical roughness and dielectric properties); optical measures reflectance (spectral surface composition). Land-cover classification with SAR alone is harder than with multispectral optical because many surfaces have similar backscatter. The sweet spot is fusion — use SAR where clouds or night are a problem; use optical for rich spectral information.

Summary

• Passive = measure natural radiation (optical, thermal).
• Active = emit and measure return (SAR, lidar).
• Active works through cloud / dark / smoke; passive is spectrally richer.
• Fusion of both is the norm in modern workflows.

Further reading

• Remote Sensing and Image Interpretation (Lillesand).
• Sentinel-1 SAR product guide.
• NASA ICESat-2 lidar documentation.
• Fundamentals of SAR Interferometry (Ferretti).