Skip to main content

Satellite Remote Sensing: Concepts and Imaging Systems

 

Satellite remote sensing relies on detectors (sensors) that measure reflected/emitted electromagnetic radiation from the Earth.
How sensors collect data depends on:

  • Spectral coverage → Multispectral (few bands), Hyperspectral (hundreds), Thermal, Microwave.

  • Detector type → Discrete detectors, linear arrays, or area arrays.

  • Scanning mechanism → Scanning mirrors (whiskbroom) vs. linear arrays (pushbroom).

Multispectral Imaging Using Discrete Detectors and Scanning Mirrors

(Whiskbroom scanners)

  • Principle:

    • A single detector (or a few detectors) measures radiation one pixel at a time.

    • A scanning mirror sweeps across-track (perpendicular to the satellite path) to build up the image line by line.

    • The forward motion of the satellite provides the along-track dimension.

    • Known as a whiskbroom scanner.

  • Characteristics:

    • Good calibration stability.

    • Narrow instantaneous field of view (IFOV).

    • Susceptible to mechanical wear (moving parts).

  • Examples:

    • Landsat Series

      • MSS (Multispectral Scanner System) – 4 bands (green, red, NIR), 80 m resolution.

      • TM (Thematic Mapper, Landsat 4/5) – 7 bands (VIS–SWIR–TIR), 30 m resolution.

      • ETM+ (Enhanced Thematic Mapper, Landsat 7) – Added panchromatic 15 m band.

    • NOAA (National Oceanic and Atmospheric Administration)

      • GOES (Geostationary Operational Environmental Satellites) – meteorology, cloud monitoring.

      • AVHRR (Advanced Very High Resolution Radiometer) – 1.1 km resolution, 5–6 bands (global vegetation, SST, clouds).

    • SeaWiFS (Sea-viewing Wide Field-of-view Sensor) – Ocean color, phytoplankton, climate studies.

Multispectral Imaging Using Linear Arrays

(Pushbroom scanners)

  • Principle:

    • A linear array of detectors (CCD line) records an entire line (row) of pixels simultaneously, without moving mirrors.

    • The satellite's motion provides subsequent lines → "pushbroom" method.

  • Characteristics:

    • Higher signal-to-noise ratio (SNR).

    • No moving scanning mirrors → more reliable.

    • Allows finer spatial resolution.

  • Examples:

    • SPOT Satellites

      • SPOT 1, 2, 3: HRV (High Resolution Visible) sensors – 10–20 m resolution.

      • SPOT 4, 5: HRVIR (High Resolution Visible and Infrared) – added SWIR bands.

    • IRS (Indian Remote Sensing Satellites)

      • LISS-III – 23.5 m, 4 bands.

      • LISS-IV – 5.8 m, 3 bands (high resolution).

    • ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer, Terra) – 14 bands (VNIR, SWIR, TIR).

    • QuickBird – High-resolution commercial sensor (0.61 m pan, 2.44 m multispectral).

Imaging Spectrometry Using Linear and Area Arrays

(Hyperspectral / advanced multispectral sensors)

  • Principle:

    • Linear array detectors → pushbroom hyperspectral (hundreds of narrow bands).

    • Area array detectors → capture 2D scenes directly (frame imagers).

    • Provide very fine spectral resolution (10–20 nm) across visible, NIR, SWIR, sometimes TIR.

  • Characteristics:

    • Enables detailed spectral analysis → vegetation stress, mineral composition, atmospheric studies.

    • Large data volumes.

    • Requires high radiometric calibration.

  • Examples:

    • AVIRIS (Airborne Visible/Infrared Imaging Spectrometer, NASA) – Hyperspectral (224 bands, 10 nm width, 400–2500 nm). Used for mineral mapping, vegetation studies.

    • MODIS (Moderate Resolution Imaging Spectroradiometer, Terra & Aqua) – 36 bands, global daily coverage, climate & vegetation indices.


Imaging TypeDetector/MethodSatellites & SensorsResolutionKey Use
Multispectral (discrete detectors + scanning mirrors)Whiskbroom (moving mirror + single detectors)Landsat (MSS, TM, ETM+), NOAA-AVHRR, GOES, SeaWiFS15–1000 mLand cover, climate, ocean color
Multispectral (linear arrays)Pushbroom (CCD line arrays)SPOT (HRV/HRVIR), IRS (LISS-III, LISS-IV), ASTER, QuickBird0.6–30 mAgriculture, urban, geology
Imaging spectrometry (linear/area arrays)Hyperspectral / frame sensorsAVIRIS, MODIS10–1000 mMineral mapping, vegetation, climate


  • Whiskbroom scanners (Landsat MSS/TM/ETM+, NOAA AVHRR) = older tech, discrete detectors + scanning mirrors.

  • Pushbroom scanners (SPOT, IRS, ASTER, QuickBird) = linear CCD arrays, better resolution.

  • Hyperspectral imagers (AVIRIS, MODIS) = linear/area arrays, hundreds of bands for advanced applications.

.. 

Landsat Series

  • MSS (Multispectral Scanner System)

    • Type: Multispectral, whiskbroom scanner (discrete detectors + scanning mirror).

    • Bands: 4 bands (Green, Red, NIR).

    • Resolution: 80 m.

    • Application: Early land cover, vegetation, water bodies.

  • TM (Thematic Mapper, Landsat 4/5)

    • Type: Multispectral, whiskbroom scanner.

    • Bands: 7 bands (VIS, NIR, SWIR, TIR).

    • Resolution: 30 m (VIS–SWIR), 120 m (TIR).

    • Application: Agriculture, forestry, geology.

  • ETM+ (Enhanced Thematic Mapper, Landsat 7)

    • Type: Multispectral with panchromatic band.

    • Bands: 8 (includes 15 m pan).

    • Resolution: 15–30 m, 60 m TIR.

    • Application: Urban mapping, detailed land cover.

NOAA Satellites

  • GOES (Geostationary Operational Environmental Satellite)

    • Type: Geostationary meteorological satellite.

    • Bands: Visible, IR, Water vapor.

    • Resolution: 0.5–4 km.

    • Application: Real-time weather monitoring, cloud movement, storms.

  • AVHRR (Advanced Very High Resolution Radiometer)

    • Type: Multispectral radiometer, whiskbroom scanner.

    • Bands: 5–6 broad spectral bands (VIS, NIR, TIR).

    • Resolution: 1.1 km.

    • Application: Global vegetation (NDVI), SST, snow/ice monitoring.

SeaWiFS (Sea-viewing Wide Field-of-View Sensor)

  • Type: Ocean color radiometer.

  • Bands: 8 bands (400–900 nm).

  • Resolution: 1.1 km (global), 4 km (reduced).

  • Application: Chlorophyll mapping, phytoplankton productivity, climate studies.

SPOT (Satellite Pour l'Observation de la Terre)

  • SPOT 1–3: HRV (High Resolution Visible)

    • Type: Pushbroom scanner (linear CCD arrays).

    • Bands: 4 (Green, Red, NIR, Panchromatic).

    • Resolution: 10–20 m multispectral, 10 m pan.

  • SPOT 4–5: HRVIR (High Resolution Visible and Infrared)

    • Type: Pushbroom with SWIR.

    • Bands: 4–5 bands (includes SWIR).

    • Resolution: 10–20 m multispectral, 2.5–5 m pan.

    • Application: Agriculture, forestry, urban monitoring.

IRS (Indian Remote Sensing Satellites)

  • LISS-III (Linear Imaging Self-Scanning Sensor-III)

    • Type: Pushbroom scanner.

    • Bands: 4 (Green, Red, NIR, SWIR).

    • Resolution: 23.5 m.

  • LISS-IV

    • Type: Pushbroom scanner.

    • Bands: 3 (Green, Red, NIR).

    • Resolution: 5.8 m.

    • Application: High-resolution mapping, agriculture, land use.

ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer)

  • Type: Multispectral (VNIR, SWIR, TIR).

  • Bands: 14 bands.

  • Resolution: 15 m (VNIR), 30 m (SWIR), 90 m (TIR).

  • Special Feature: Along-track stereo imaging for DEM generation.

  • Application: Geology, volcano monitoring, mineral exploration.

QuickBird (DigitalGlobe)

  • Type: High-resolution commercial satellite.

  • Bands: 4 multispectral + Panchromatic.

  • Resolution: 0.61 m (pan), 2.44 m (multispectral).

  • Application: Urban mapping, infrastructure, defense.

AVIRIS (Airborne Visible/Infrared Imaging Spectrometer)

  • Type: Hyperspectral imaging spectrometer.

  • Bands: 224 bands (10 nm bandwidth).

  • Range: 400–2500 nm (VNIR–SWIR).

  • Resolution: Airborne (spatial resolution depends on altitude, typically 20 m).

  • Application: Mineral mapping, vegetation stress, environmental studies.

MODIS (Moderate Resolution Imaging Spectroradiometer)

  • Type: Multispectral radiometer.

  • Bands: 36 spectral bands (VIS–NIR–SWIR–TIR).

  • Resolution: 250 m (red, NIR), 500 m, 1000 m (other bands).

  • Revisit: 1–2 days (Terra & Aqua satellites).

  • Application: Climate, vegetation indices (NDVI, EVI), fire monitoring, SST.


CategorySensorsDetector typeKey Concept
Whiskbroom (scanning mirrors)Landsat MSS/TM/ETM+, NOAA AVHRR, GOES, SeaWiFSDiscrete detectors + scanning mirrorOne pixel at a time
Pushbroom (linear CCD arrays)SPOT HRV/HRVIR, IRS LISS-III/LISS-IV, ASTER, QuickBirdLinear detector arrayLine-by-line imaging
Hyperspectral / Imaging SpectrometersAVIRIS, MODISLinear or area arraysMany narrow spectral bands


  • Landsat, NOAA, SeaWiFS → older whiskbroom scanners.

  • SPOT, IRS, ASTER, QuickBird → modern pushbroom scanners (CCD linear arrays).

  • AVIRIS, MODIS → imaging spectrometers (linear/area arrays, hyperspectral or multispectral).


Comments

Post a Comment

Popular posts from this blog

Supervised Classification

Image Classification in Remote Sensing Image classification in remote sensing involves categorizing pixels in an image into thematic classes to produce a map. This process is essential for land use and land cover mapping, environmental studies, and resource management. The two primary methods for classification are Supervised and Unsupervised Classification . Here's a breakdown of these methods and the key stages of image classification. 1. Types of Classification Supervised Classification In supervised classification, the analyst manually defines classes of interest (known as information classes ), such as "water," "urban," or "vegetation," and identifies training areas —sections of the image that are representative of these classes. Using these training areas, the algorithm learns the spectral characteristics of each class and applies them to classify the entire image. When to Use Supervised Classification:   - You have prior knowledge about the c...
Post a Comment
Read more

Supervised Classification

In the context of Remote Sensing (RS) and Digital Image Processing (DIP) , supervised classification is the process where an analyst defines "training sites" (Areas of Interest or ROIs) representing known land cover classes (e.g., Water, Forest, Urban). The computer then uses these training samples to teach an algorithm how to classify the rest of the image pixels. The algorithms used to classify these pixels are generally divided into two broad categories: Parametric and Nonparametric decision rules. Parametric Decision Rules These algorithms assume that the pixel values in the training data follow a specific statistical distribution—almost always the Gaussian (Normal) distribution (the "Bell Curve"). Key Concept: They model the data using statistical parameters: the Mean vector ( $\mu$ ) and the Covariance matrix ( $\Sigma$ ) . Analogy: Imagine trying to fit a smooth hill over your data points. If a new point lands high up on the hill, it belongs to that cl...
Post a Comment
Read more

Pre During and Post Disaster

Disaster management is a structured approach aimed at reducing risks, responding effectively, and ensuring a swift recovery from disasters. It consists of three main phases: Pre-Disaster (Mitigation & Preparedness), During Disaster (Response), and Post-Disaster (Recovery). These phases involve various strategies, policies, and actions to protect lives, property, and the environment. Below is a breakdown of each phase with key concepts, terminologies, and examples. 1. Pre-Disaster Phase (Mitigation and Preparedness) Mitigation: This phase focuses on reducing the severity of a disaster by minimizing risks and vulnerabilities. It involves structural and non-structural measures. Hazard Identification: Recognizing potential natural and human-made hazards (e.g., earthquakes, floods, industrial accidents). Risk Assessment: Evaluating the probability and consequences of disasters using GIS, remote sensing, and historical data. Vulnerability Analysis: Identifying areas and p...
Post a Comment
Read more

Hazard Mapping Spatial Planning Evacuation Planning GIS

Geographic Information Systems (GIS) play a pivotal role in disaster management by providing the tools and frameworks necessary for effective hazard mapping, spatial planning, and evacuation planning. These concepts are integral for understanding disaster risks, preparing for potential hazards, and ensuring that resources are efficiently allocated during and after a disaster. 1. Hazard Mapping: Concept: Hazard mapping involves the process of identifying, assessing, and visually representing the geographical areas that are at risk of certain natural or human-made hazards. Hazard maps display the probability, intensity, and potential impact of specific hazards (e.g., floods, earthquakes, hurricanes, landslides) within a given area. Terminologies: Hazard Zone: An area identified as being vulnerable to a particular hazard (e.g., flood zones, seismic zones). Hazard Risk: The likelihood of a disaster occurring in a specific location, influenced by factors like geography, climate, an...
Post a Comment
Read more

Atmospheric Correction

It is the process of removing the influence of the atmosphere from remotely sensed images so that the data accurately represent the true reflectance of Earth's surface . When a satellite sensor captures an image, the radiation reaching the sensor is affected by gases, water vapor, aerosols, and dust in the atmosphere. These factors scatter and absorb light, changing the brightness and color of the features seen in the image. Although these atmospheric effects are part of the recorded signal, they can distort surface reflectance values , especially when images are compared across different dates or sensors . Therefore, corrections are necessary to make data consistent and physically meaningful. 🔹 Why Do We Need Atmospheric Correction? To retrieve true surface reflectance – It separates the surface signal from atmospheric influence. To ensure comparability – Enables comparing images from different times, seasons, or sensors. To improve visual quality – Remo...
Post a Comment
Read more