Satellites Remote Sensing

Earth Resources Satellites

These satellites are designed mainly for natural resource monitoring – land, water, vegetation, and environment.

1. LANDSAT (USA, since 1972)

World's first dedicated Earth observation satellite series.
Provides long-term continuous data for >50 years.
Sensors: MSS (Multispectral Scanner), TM (Thematic Mapper), ETM+ (Enhanced TM), OLI (Operational Land Imager).
Resolution: 15–30 m (optical).
Applications: Land cover change, agriculture, forests, water resources.
Fact: Landsat archive is the longest continuous Earth observation record.

2. SPOT (France, 1986 onwards)

Name: Satellite Pour l'Observation de la Terre.
Sensors: HRV (High Resolution Visible), HRVIR (with Infrared).
Resolution: 1.5–20 m.
Applications: Urban studies, vegetation monitoring, mapping.
Fact: First civilian satellite to offer stereo imaging (3D views).

3. IRS (India, 1988 onwards)

Name: Indian Remote Sensing Satellite Series.
Sensors: LISS (Linear Imaging Self Scanner), PAN (Panchromatic), WiFS (Wide Field Sensor).
Resolution: 5–180 m depending on sensor.
Applications: Agriculture, water resources, forestry, disaster management.
Fact: India's IRS program is one of the largest civilian remote sensing programs in the world.

4. IKONOS (USA, launched 1999)

One of the first commercial high-resolution satellites.
Resolution: 1 m (panchromatic), 4 m (multispectral).
Applications: Urban planning, infrastructure mapping, defense, precision agriculture.
Fact: Made high-resolution satellite imagery available for civilian use.

Meteorological Satellites

These satellites are designed for weather and climate monitoring.

1. INSAT (India)

Name: Indian National Satellite System.
Operates in Geostationary Orbit (GEO, ~36,000 km).
Functions: Weather forecasting, cyclone tracking, rainfall estimation, communication.
Fact: INSAT combines meteorology + telecommunications + broadcasting.

2. NOAA (USA)

Name: National Oceanic and Atmospheric Administration Satellites.
Operates in Polar Orbit (LEO ~850 km).
Sensor: AVHRR (Advanced Very High Resolution Radiometer).
Applications: Sea surface temperature, vegetation, snow cover, weather monitoring.
Fact: Continuous data since 1970s, key for climate change studies.

3. GOES (USA)

Name: Geostationary Operational Environmental Satellites.
Orbit: Geostationary (36,000 km).
Applications: Real-time weather monitoring, storm tracking, atmosphere studies.
Fact: Provides continuous images of the same region every few minutes → crucial for cyclone and hurricane monitoring.

Optical Mechanical Scanners

These are instruments (sensors) that scan the Earth's surface in different wavelengths of the electromagnetic spectrum.

1. MSS (Multispectral Scanner) – Landsat

Records data in 4 spectral bands (visible + near IR).
Spatial resolution: ~80 m.
Use: Early land cover studies.

2. TM (Thematic Mapper) – Landsat

Improved sensor with 7 bands (visible, NIR, SWIR, thermal).
Spatial resolution: 30 m (optical), 120 m (thermal).
Use: Agriculture, geology, vegetation, water.

3. LISS (Linear Imaging Self Scanner) – IRS

Variants: LISS-I, II, III, IV.
Resolutions: 72 m (LISS-I) → 5.8 m (LISS-IV).
Use: Agriculture, forestry, resources.

4. WiFS (Wide Field Sensor) – IRS

Wide coverage, ~180 m resolution.
Use: Vegetation and crop monitoring at regional scale.

5. PAN (Panchromatic) – IRS

Records in single broad band (black & white).
Very high spatial resolution: ~5–6 m.
Use: Urban mapping, cartography, image fusion.

In short:

Earth Resource Satellites (LANDSAT, SPOT, IRS, IKONOS) → monitor land, vegetation, water.
Meteorological Satellites (INSAT, NOAA, GOES) → monitor atmosphere, weather, climate.
Optical Scanners (MSS, TM, LISS, WiFS, PAN) → instruments onboard satellites that capture data in different resolutions and spectral ranges.

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Radar Remote Sensing SAR

1. Radar and Radar Remote Sensing RADAR stands for Radio Detection and Ranging . It is an active remote sensing system that transmits microwave energy toward the Earth's surface and records the energy that is returned to the sensor as an echo or backscatter . Unlike passive optical remote sensing, radar does not depend on sunlight. Therefore, it can operate day and night and, at suitable wavelengths, can acquire data through clouds, haze and light rain. Hence, radar is widely described as an all-weather, day-and-night remote sensing technology . Basic principle Microwave pulse → transmission → interaction with surface → backscatter/echo → antenna receives signal → signal processing → radar image 2. Microwave Energy Radar systems use microwave electromagnetic radiation , generally in wavelength ranges from approximately 1 mm to 1 m . Important radar bands include: Band Approx. wavelength Common applications ...

Lidar

LiDAR (Light Detection and Ranging) is an active remote sensing technology that measures distances by illuminating a target with laser pulses and analyzing the time it takes for the reflected light to return. Unlike passive systems (e.g., cameras, multispectral sensors), LiDAR provides its own energy source (laser), allowing it to operate both day and night and even penetrate through vegetation canopies . 🔹 How LiDAR Works (Step-by-Step Process) Laser Pulse Emission The system emits rapid, short pulses of laser light (commonly in the near-infrared wavelength, 1064 nm ). Some systems emit up to hundreds of thousands of pulses per second . Interaction with Target Surface The laser beam strikes objects such as vegetation, buildings, or bare ground. Depending on the object's structure, part of the pulse may scatter or reflect. Return Signal Detection The sensor records multiple returns : First Return → typically vegetation canopy tops. ...

Geometric Correction

When satellite or aerial images are captured, they often contain distortions (errors in shape, scale, or position) caused by many factors — like Earth's curvature, satellite motion, terrain height (relief), or the Earth's rotation . These distortions make the image not properly aligned with real-world coordinates (latitude and longitude). 👉 Geometric correction is the process of removing these distortions so that every pixel in the image correctly represents its location on the Earth's surface. After geometric correction, the image becomes geographically referenced and can be used with maps and GIS data. Types 1. Systematic Correction Systematic errors are predictable and can be modeled mathematically. They occur due to the geometry and movement of the satellite sensor or the Earth. Common systematic distortions: Scan skew – due to the motion of the sensor as it scans the Earth. Mirror velocity variation – scanning mirror moves at a va...

Image Transformation

In remote sensing, image transformation is used to create new images by manipulating existing ones, enhancing the information extracted from each pixel. Multi-image manipulation and other transformations allow analysts to highlight specific features, compare time-series data, or reduce data dimensions for better interpretation. 1. Multi-Image Manipulation This technique involves applying mathematical operations to multiple images, typically from different bands, dates, or sensors, to highlight specific features or changes. Common methods include: a. Image Addition Purpose : Highlights areas of similarity or enhances features when the same objects appear in multiple images. How it Works : By adding pixel values from two images, the output image emphasizes areas where brightness is high in both. Example : Enhancing overall brightness or detecting seasonal changes by adding images from different times. b. Image Subtraction Purpose : Identifies differences between images, useful for chan...

Spatial Feature Manipulation and Filtering

1. Spatial Feature Manipulation: Spatial Frequency Spatial frequency describes how quickly brightness changes across an image. It can reveal different levels of detail, based on how often these changes occur: High Spatial Frequency : Areas where brightness changes quickly, like edges, fine textures, or detailed patterns. These parts contain high detail but can also include noise. Low Spatial Frequency : Areas with gradual changes in brightness, like smooth surfaces or broad homogeneous areas. These regions are usually featureless, such as water bodies or large agricultural fields. Zero Spatial Frequency : Represents a completely uniform area with no brightness variation. This area is very smooth and has no texture or detail. By manipulating spatial frequencies, we can enhance or suppress certain features in an image. Spatial filtering is one of the primary methods to perform this manipulation. 2. Spatial Filtering and Spatial Domain Spatial filtering is the process of applying mathe...

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