A Guide to Remote Sensing Sensors

Remote sensing technology has transformed how we observe and understand our planet. At the heart of this technology are sensors-specialized instruments that capture data about Earth’s surface, atmosphere, and oceans without physical contact. Whether monitoring deforestation, tracking urban expansion, or studying climate patterns, different types of sensors serve different purposes. Understanding these sensor categories helps you appreciate why certain satellites work better for specific applications.

Passive vs. active sensors: the fundamental divide
Passive sensors
Active sensors
Imaging vs. non-imaging sensors
Imaging sensors
Non-imaging sensors
Multispectral sensors
Thermal sensors
Microwave sensors
Active microwave sensing
Passive microwave sensing
Scanning systems: how sensors build images
Across-track (whisk broom) scanners
Along-track (push broom) scanners
Choosing the right sensor for the job

Passive vs. active sensors: the fundamental divide

The most important distinction in remote sensing is between passive and active sensors. This classification is based on how each sensor type acquires the energy it measures.

Passive sensors

Passive sensors measure energy that occurs naturally-they do not generate their own illumination. The sun serves as the primary energy source for most passive remote sensing applications. When sunlight strikes Earth’s surface, it reflects back toward space, and passive sensors detect this reflected radiation.

Because passive sensors depend on external energy sources, they have certain limitations. For detecting reflected sunlight, passive sensors can only operate during daytime when the sun illuminates the Earth. Cloud cover also creates problems, blocking the sensor’s view of the ground below.

However, not all passive sensing requires daylight. Thermal infrared energy, which is naturally emitted by objects based on their temperature, can be detected both day and night-as long as enough energy is present for the sensor to record. Common examples of passive sensors include the instruments aboard Landsat satellites, MODIS, and commercial systems like those from Maxar and Planet Labs.

Active sensors

Active sensors generate their own energy source to illuminate targets. The sensor emits radiation toward the area being studied, then detects and measures the energy that reflects back. This approach offers significant advantages over passive sensing.

Active sensors can collect data at any time-day or night-and operate effectively through clouds and poor weather conditions. They can also work with wavelengths not sufficiently provided by the sun, such as microwaves. The main trade-off is that active systems require substantial power to generate enough energy to illuminate their targets adequately.

Prominent examples of active sensors include radar systems used for measuring precipitation and creating cloud profiles, Synthetic Aperture Radar (SAR) systems like those on Canada’s RADARSAT satellites, and LiDAR instruments that use laser pulses for precise elevation measurements.

A simple way to understand this distinction: consider a camera. On a bright sunny day, the camera records naturally available light (passive mode). In a dark room, the camera uses a flash to illuminate the scene (active mode).

Imaging vs. non-imaging sensors

Another way to categorize remote sensors is by their output format: whether they produce visual images or numerical data.

Imaging sensors

Imaging sensors create two-dimensional representations of areas or objects. These sensors capture spatial information that can be displayed as photographs or digital images. Optical cameras, thermal imagers, and SAR systems all fall into this category.

The images produced by these sensors serve countless applications-from monitoring land use changes and assessing crop health to detecting wildfires and mapping flood extents. The visual nature of imaging sensor output makes interpretation relatively intuitive, though sophisticated analysis often requires specialized software and expertise.

Non-imaging sensors

Non-imaging sensors generate numerical data rather than pictures. These instruments measure specific physical properties without creating a visual representation. Examples include microwave radiometers, altimeters, gravimeters, and spectrometers.

While the data from non-imaging sensors may seem less accessible than photographs, this information is essential for applications like measuring ocean surface height, determining atmospheric composition, and calculating precise elevations. Scientists process this numerical data to derive valuable insights about environmental conditions and changes.

Multispectral sensors

Multispectral imaging captures data within specific wavelength ranges across the electromagnetic spectrum. Unlike standard cameras that record only visible light, multispectral sensors detect radiation beyond what human eyes can perceive.

A typical multispectral system combines between 3 and 15 spectral bands, which may include visible light, near-infrared (NIR), short-wave infrared (SWIR), mid-wave infrared (MWIR), and long-wave infrared (LWIR) regions. This technology was originally developed for military target identification but now supports environmental monitoring, vegetation mapping, and numerous other civilian applications.

The power of multispectral sensing lies in its ability to reveal information invisible to the naked eye. Different materials reflect and absorb electromagnetic radiation differently across various wavelengths. By analyzing these spectral signatures, scientists can identify vegetation types, assess plant health, detect water quality issues, and distinguish between different soil compositions.

Modern weather satellites produce imagery in multiple spectral bands, while Earth observation satellites like Sentinel-2 and Landsat provide multispectral data used extensively for environmental research and resource management.

Thermal sensors

Thermal sensors operate in the electromagnetic spectrum between the mid-to-far-infrared and microwave ranges, typically between 9 and 14 micrometers. These instruments detect heat energy emitted by objects based on their temperature rather than reflected sunlight.

The key advantage of thermal sensing is its ability to function both day and night. Above 5 micrometers, self-emitted thermal radiation from Earth dominates, and since this phenomenon doesn’t depend directly on the sun, thermal images can be acquired around the clock.

Thermal sensors have diverse applications. Instruments like the Thermal Infrared Multispectral Scanner (TIMS) help geologists discriminate between different rock types and have proven valuable in volcanology research. Urban planners use thermal data to study heat islands-areas where cities experience higher temperatures than surrounding rural regions.

The ASTER sensor aboard NASA’s Terra satellite collects thermal data with 90-meter spatial resolution, while MODIS provides thermal measurements at 1000-meter resolution with excellent temporal coverage-revisiting the same area every one to two days. This frequent revisit capability makes MODIS particularly useful for detecting and monitoring wildfires.

Microwave sensors

Microwave remote sensing operates at longer wavelengths than optical or thermal sensing-typically between 1 centimeter and 1 meter. This wavelength range gives microwave sensors unique capabilities that complement other sensing technologies.

Microwaves can penetrate clouds and operate in all weather conditions, unlike visible and infrared sensors that struggle with cloud cover. However, the sun and Earth emit relatively little microwave radiation naturally, making passive microwave sensing challenging. This limitation led to the development of active microwave systems-primarily radar.

Active microwave sensing

Active microwave sensors like radar and SAR can emit signals and measure the returning energy. These systems find extensive use in marine studies, meteorology, and terrain mapping. SAR systems are particularly valuable because they can produce high-resolution imagery regardless of weather conditions or time of day.

The primary mission of instruments like the Advanced SAR includes measuring sea-state conditions, mapping ice-sheet characteristics, detecting vegetation changes, and monitoring pollution over oceans.

Passive microwave sensing

Microwave radiometers measure the polarity, wavelength, and intensity of naturally emitted microwave radiation to provide information about an object’s structure and composition. Applications include soil moisture measurement and atmospheric and oceanographic research.

While passive microwave sensors generally have coarser spatial resolution than active systems, they provide valuable data about surface properties that other sensors cannot easily measure.

Scanning systems: how sensors build images

Understanding how sensors physically collect data helps explain some of their characteristics and limitations. Two primary scanning approaches dominate passive optical remote sensing: across-track (whisk broom) and along-track (push broom) scanners.

Across-track (whisk broom) scanners

In a whisk broom sensor, a rotating mirror scans across the satellite’s path, reflecting light into a single detector that collects data one pixel at a time. The mirror sweeps back and forth perpendicular to the direction of travel, building up an image line by line.

Previous Landsat sensors used this whisk broom design, looking at a calibration source at the end of every row to ensure consistent measurements from orbit to orbit. However, this approach has drawbacks. The moving parts make whisk broom sensors more expensive and prone to mechanical failure-as happened with Landsat 7 when its scan line corrector failed.

On the positive side, whisk broom scanners can achieve higher resolution than push broom designs for the same swath width because the detector focuses on only a small portion of the scene at any moment.

Along-track (push broom) scanners

Push broom scanners use a line of detectors arranged perpendicular to the direction of flight. As the platform moves forward, these sensors capture an entire line of the image simultaneously, building up the complete scene strip by strip.

Landsat 8 adopted push broom technology, viewing across the entire swath at once with no moving parts. This design offers several advantages: push broom scanners are lighter, smaller, and less complex than whisk broom systems. They also provide better radiometric performance because each detector can gather light from a given area for a longer time-like a long exposure on a camera.

The main challenge with push broom sensors involves calibration. With thousands of individual detectors in the array, ensuring consistent sensitivity across all of them is considerably more complex than calibrating a single detector system.

Choosing the right sensor for the job

Each sensor type has strengths suited to particular applications. Passive optical sensors with their high spatial resolution excel at detailed land cover mapping but require clear skies and daylight. Active radar systems operate in any weather and at night, making them ideal for emergency response and polar region monitoring. Thermal sensors reveal temperature patterns that inform urban planning and fire detection. Microwave instruments measure soil moisture and ocean properties that optical sensors cannot detect.

Modern remote sensing increasingly combines data from multiple sensor types. Fusing radar imagery with optical data, for example, can overcome the limitations of each individual approach while leveraging their respective strengths. This multi-sensor integration drives more comprehensive environmental monitoring and improves our understanding of complex Earth systems.

What do you think? How might the continued miniaturization and cost reduction of satellite sensors change environmental monitoring in your region? Which combination of sensor types would be most valuable for addressing climate change challenges in your area?

References

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Table of Contents

Passive vs. active sensors: the fundamental divide 🔗

Passive sensors 🔗

Active sensors 🔗

Imaging vs. non-imaging sensors 🔗

Imaging sensors 🔗

Non-imaging sensors 🔗

Multispectral sensors 🔗

Thermal sensors 🔗

Microwave sensors 🔗

Active microwave sensing 🔗

Passive microwave sensing 🔗

Scanning systems: how sensors build images 🔗

Across-track (whisk broom) scanners 🔗

Along-track (push broom) scanners 🔗

Choosing the right sensor for the job 🔗