Solar Radiation and Remote Sensing

Satellite Remote Sensing

Satellite remote sensing is the science of acquiring information about Earth's surface and atmosphere without physical contact, using sensors mounted on satellites. These sensors detect and record electromagnetic radiation (EMR) that is either emitted or reflected from the Earth's surface.

Solar Radiation & Earth's Energy Balance

Solar Radiation is the primary source of energy for Earth's climate system. It originates from the Sun and travels through space as electromagnetic waves.
Incoming Shortwave Solar Radiation (insolation) consists mostly of ultraviolet, visible, and near-infrared wavelengths. When it reaches Earth, it can be:
- Absorbed by the atmosphere, clouds, or surface
- Reflected back to space
- Scattered by atmospheric particles
Outgoing Longwave Radiation is the infrared energy emitted by Earth back into space after absorbing solar energy. This process helps maintain Earth's thermal balance.

Electromagnetic Radiation (EMR) Spectrum

The EMR spectrum encompasses all types of electromagnetic radiation, ranging from gamma rays to radio waves. Remote sensing typically uses the visible, infrared, and microwave portions of the spectrum.

Different materials on Earth interact with different wavelengths in unique ways, which allows satellites to differentiate between water, vegetation, soil, urban areas, etc.

Interaction of EMR with Atmosphere and Surface

When solar radiation enters Earth's atmosphere and reaches the surface, it undergoes several interactions:

Absorption: Certain gases and materials absorb specific wavelengths of EMR, converting it into heat. For example, ozone absorbs UV, and water vapor absorbs infrared.
Scattering: Small particles and gases deflect radiation in multiple directions. Rayleigh scattering causes the blue sky, while Mie scattering is associated with dust and smoke.
Reflection: Some surfaces reflect incoming solar radiation back into the atmosphere. This reflection depends on the surface properties and is central to remote sensing.
Refraction: The bending of light as it passes through different media, affecting how radiation travels through the atmosphere.

Blackbody Concept & Earth

A Blackbody is an ideal object that absorbs all incoming radiation and re-emits it perfectly. Though Earth is not a perfect blackbody, the blackbody radiation laws (e.g., Planck's Law, Stefan–Boltzmann Law) help us understand Earth's emission of longwave radiation.

Albedo

Albedo is the fraction of incoming solar radiation that is reflected by a surface. Surfaces like snow have high albedo (high reflectivity), while forests or oceans have low albedo (high absorption).

This directly influences Earth's energy budget and is monitored using satellite remote sensing to assess climate change, land cover changes, etc.

Conceptual Link Summary

Solar Radiation from the Sun (mainly shortwave) enters Earth's atmosphere.
It interacts with the atmosphere and surface via absorption, scattering, reflection, and refraction.
Earth's surface emits longwave radiation, part of which escapes to space or is absorbed by greenhouse gases.
These interactions are governed by principles of the EMR spectrum and concepts like blackbody radiation.
Albedo quantifies the reflected portion of incoming solar energy.
All these energy exchanges and surface properties are measured and monitored by satellite remote sensing.

Comments

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...

RADIOMETRIC CORRECTION

Radiometric correction is the process of removing sensor and environmental errors from satellite images so that the measured brightness values (Digital Numbers or DNs) truly represent the Earth's surface reflectance or radiance. In other words, it corrects for sensor defects, illumination differences, and atmospheric effects. 1. Detector Response Calibration Satellite sensors use multiple detectors to scan the Earth's surface. Sometimes, each detector responds slightly differently, causing distortions in the image. Calibration adjusts all detectors to respond uniformly. This includes: (a) De-Striping Problem: Sometimes images show light and dark vertical or horizontal stripes (banding). Caused by one or more detectors drifting away from their normal calibration — they record higher or lower values than others. Common in early Landsat MSS data. Effect: Every few lines (e.g., every 6th line) appear consistently brighter or darker. Soluti...

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...

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...

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...

Vineesh V, Geography

Search This Blog