Atmospheric Window

The atmospheric window in remote sensing refers to specific wavelength ranges within the electromagnetic spectrum that can pass through the Earth's atmosphere relatively unimpeded. These windows are crucial for remote sensing applications because they allow us to observe the Earth's surface and atmosphere without significant interference from the atmosphere's constituents.

Key facts and concepts about atmospheric windows:

Visible and Near-Infrared (VNIR) window: This window encompasses wavelengths from approximately 0.4 to 1.0 micrometers. It is ideal for observing vegetation, water bodies, and land cover types.
Shortwave Infrared (SWIR) window: This window covers wavelengths from approximately 1.0 to 3.0 micrometers. It is particularly useful for detecting minerals, water content, and vegetation health.
Mid-Infrared (MIR) window: This window spans wavelengths from approximately 3.0 to 8.0 micrometers. It is valuable for identifying various materials, including rocks, soil, and clouds.
Thermal Infrared (TIR) window: This window covers wavelengths from approximately 8.0 to 14.0 micrometers. It is used to measure surface temperature and detect heat sources.
Microwave window: This window encompasses wavelengths from approximately 1 millimeter to 1 meter. It is used for radar imaging and can penetrate clouds and vegetation to observe the underlying surface.

General Terms:

Atmospheric window: A specific range of wavelengths in the electromagnetic spectrum that can pass through the Earth's atmosphere relatively unimpeded.
Electromagnetic spectrum: The range of all types of electromagnetic radiation, from radio waves to gamma rays.
Absorption band: A range of wavelengths where atmospheric gases or particles absorb most of the incoming radiation.
Transmission window: A range of wavelengths where atmospheric gases or particles transmit most of the incoming radiation.

Specific Terms:

Visible and Near-Infrared (VNIR) window: Covers wavelengths from approximately 0.4 to 1.0 micrometers, used for observing vegetation, water bodies, and land cover.
Shortwave Infrared (SWIR) window: Covers wavelengths from approximately 1.0 to 3.0 micrometers, used for detecting minerals, water content, and vegetation health.
Mid-Infrared (MIR) window: Covers wavelengths from approximately 3.0 to 8.0 micrometers, used for identifying various materials like rocks, soil, and clouds.
Thermal Infrared (TIR) window: Covers wavelengths from approximately 8.0 to 14.0 micrometers, used for measuring surface temperature and detecting heat sources.
Microwave window: Covers wavelengths from approximately 1 millimeter to 1 meter, used for radar imaging and can penetrate clouds and vegetation.

Related Concepts:

Atmospheric absorption: The process by which atmospheric gases or particles absorb electromagnetic radiation.
Atmospheric scattering: The process by which atmospheric gases or particles scatter electromagnetic radiation in different directions.
Atmospheric transmittance: The fraction of electromagnetic radiation that passes through the atmosphere without being absorbed or scattered.
Radiative transfer: The transfer of energy through electromagnetic radiation.

Factors affecting atmospheric windows:

Atmospheric gases: Gases like water vapor, carbon dioxide, and ozone absorb radiation at specific wavelengths, creating atmospheric absorption bands.
Aerosols: Particles suspended in the atmosphere, such as dust, smoke, and haze, can scatter and absorb radiation, reducing the transparency of the atmosphere.
Cloud cover: Clouds can block radiation, limiting the effectiveness of remote sensing observations.

Importance of atmospheric windows:

Remote sensing applications: Atmospheric windows are essential for various remote sensing applications, including land cover mapping, environmental monitoring, disaster management, and resource assessment.
Satellite imagery: Satellites are equipped with sensors that operate within atmospheric windows to capture high-quality images of the Earth's surface.
Scientific research: Atmospheric windows are used in scientific research to study the Earth's climate, ecosystems, and natural hazards.

Comments

Accuracy Assessment

Accuracy assessment is the process of checking how correct your classified satellite image is . 👉 After supervised classification, the satellite image is divided into classes like: Water Forest Agriculture Built-up land Barren land But classification is done using computer algorithms, so some areas may be wrongly classified . 👉 Accuracy assessment helps to answer this question: ✔ "How much of my classified map is correct compared to real ground conditions?" Goal The main goal is to: Measure reliability of classified maps Identify classification errors Improve classification results Provide scientific validity to research 👉 Without accuracy assessment, a classified map is not considered scientifically reliable . Reference Data (Ground Truth Data) Reference data is real-world information used to check classification accuracy. It can be collected from: ✔ Field survey using GPS ✔ High-resolution satellite images (Google Earth etc.) ✔ Existing maps or survey reports 🧭 Exampl...

Change Detection

Change detection is the process of finding differences on the Earth's surface over time by comparing satellite images of the same area taken on different dates . After supervised classification , two classified maps (e.g., Year-1 and Year-2) are compared to identify land use / land cover changes . Goal To detect where , what , and how much change has occurred To monitor urban growth, deforestation, floods, agriculture, etc. Basic Concept Forest → Forest = No change Forest → Urban = Change detected Key Terminologies Multi-temporal images : Images of the same area at different times Post-classification comparison : Comparing two classified maps Change matrix : Table showing class-to-class change Change / No-change : Whether land cover remains same or different Main Methods Post-classification comparison – Most common and easy Image differencing – Subtract pixel values Image ratioing – Divide pixel values Deep learning methods – Advanced AI-based detection Examples Agricult...

Geographic phenomena fields objects boundaries.

In geography, geographic phenomena refer to features or processes that can be observed and studied on Earth's surface. These phenomena can be classified into three main categories: fields , objects , and boundaries . Each category has distinct characteristics, representations, and applications in Geographic Information Systems (GIS). 1. Fields A field represents continuous, spatially varying data where a value is present at every location within the study area. It describes conditions that exist across a geographic area. Characteristics : Continuity : Fields have no discrete boundaries; the data is continuous. Gradual Variability : The values of a field change gradually across space. Representation : Typically modeled using raster data in GIS, where a grid structure assigns a value (e.g., temperature or elevation) to each cell. Examples : Temperature Map : Shows temperature variation across a region. Rainfall Distribution : Displays rainfall levels over a large g...

Landsat 8 Band designation and Band Combination.

Landsat 8 Band designation and Band Combination. Landsat 8-9 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) Bands Wavelength (micrometers) Resolution (meters) Band 1 - Coastal aerosol 0.43-0.45 30 Band 2 - Blue 0.45-0.51 30 Band 3 - Green 0.53-0.59 30 Band 4 - Red 0.64-0.67 30 Band 5 - Near Infrared (NIR) 0.85-0.88 30 Band 6 - SWIR 1 1.57-1.65 30 Band 7 - SWIR 2 2.11-2.29 30 Band 8 - Panchromatic 0.50-0.68 15 Band 9 - Cirrus 1.36-1.38 30 Band 10 - Thermal Infrared (TIRS) 1 10.6-11.19 100 Band 11 - Thermal Infrared (TIRS) 2 11.50-12.51 100 Vineesh V Assistant Professor of Geography, Directorate of Education, Government of Kerala. https://www.facebook.com/Applied.Geography http://geogisgeo.blogspot.com

Landsat band composition

Short-Wave Infrared (7, 6 4) The short-wave infrared band combination uses SWIR-2 (7), SWIR-1 (6), and red (4). This composite displays vegetation in shades of green. While darker shades of green indicate denser vegetation, sparse vegetation has lighter shades. Urban areas are blue and soils have various shades of brown. Agriculture (6, 5, 2) This band combination uses SWIR-1 (6), near-infrared (5), and blue (2). It's commonly used for crop monitoring because of the use of short-wave and near-infrared. Healthy vegetation appears dark green. But bare earth has a magenta hue. Geology (7, 6, 2) The geology band combination uses SWIR-2 (7), SWIR-1 (6), and blue (2). This band combination is particularly useful for identifying geological formations, lithology features, and faults. Bathymetric (4, 3, 1) The bathymetric band combination (4,3,1) uses the red (4), green (3), and coastal bands to peak into water. The coastal band is useful in coastal, bathymetric, and aerosol studies because...

Vineesh V, Geography

Search This Blog