Skip to main content

Resolution of Sensors in Remote Sensing


Spatial Resolution 🗺️

  • Definition: The smallest size of an object on the ground that a sensor can detect.

  • Measured as: The size of a pixel on the ground (in meters).

  • Example:

    • Landsat → 30 m (each pixel = 30 × 30 m on Earth).

    • WorldView-3 → 0.31 m (very detailed, you can see cars).

  • Fact: Higher spatial resolution = finer details, but smaller coverage.

Spectral Resolution 🌈

  • Definition: The ability of a sensor to capture information in different parts (bands) of the electromagnetic spectrum.

  • Measured as: The number and width of spectral bands.

  • Types:

    • Panchromatic (1 broad band, e.g., black & white image).

    • Multispectral (several broad bands, e.g., Landsat with 7–13 bands).

    • Hyperspectral (hundreds of very narrow bands, e.g., AVIRIS).

  • Fact: Higher spectral resolution = better identification of materials (e.g., minerals, vegetation types).

Radiometric Resolution 📊

  • Definition: The ability of a sensor to record subtle differences in energy (brightness levels).

  • Measured as: The number of digital bits.

    • 8-bit → 2⁸ = 256 brightness levels

    • 10-bit → 1024 levels

    • 12-bit → 4096 levels

  • Example:

    • Landsat TM → 8-bit (256 levels)

    • Sentinel-2 → 12-bit (4096 levels, more sensitivity)

  • Fact: Higher radiometric resolution = better detection of slight differences (e.g., healthy vs. stressed vegetation).

Temporal Resolution ⏳

  • Definition: The frequency at which a sensor revisits and captures data for the same location.

  • Measured as: Revisit time (in days or hours).

  • Examples:

    • Landsat → 16 days

    • Sentinel-2 → 5 days

    • MODIS → 1–2 days

  • Fact: Higher temporal resolution = better for monitoring fast-changing phenomena (floods, crops, weather).


Multi-Concept (Integration of Resolutions)

In reality, no single sensor excels in all resolutions. So, remote sensing often combines them:

  • High Spatial + Low Temporal: WorldView → very detailed but not frequent.

  • Moderate Spatial + High Temporal: MODIS → frequent but coarse (global monitoring).

  • Balanced: Sentinel-2 → medium detail (10 m) and good revisit (5 days).

👉 This combination is called the Multi-Resolution Concept: using data from different sensors together to get the best results.


  • Spatial = detail of objects

  • Spectral = colors/wavelengths captured

  • Radiometric = sensitivity to brightness levels

  • Temporal = time gap between observations

  • Multi-concept = integrating different resolutions for better monitoring


Comments

  1. AnonymousJuly 16, 2026 at 7:49 AM

    What is the purpose of doing this 🤣 I am really being so funny this notes can be found in the chatgpt.. then what this......

    ReplyDelete

Post a Comment

Popular posts from this blog

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 ...
Post a Comment
Read more

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. ...
Post a Comment
Read more

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...
Post a Comment
Read more

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...
Post a Comment
Read more

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...
Post a Comment
Read more