Course Details

Atmospheric Correction and Image Calibration Training Course

Introduction

Remote sensing, the science of acquiring information about the Earth's surface without direct physical contact, relies heavily on data collected by sensors on satellites, aircraft, and drones. However, the raw digital numbers (DNs) recorded by these sensors are not direct measurements of the Earth's surface properties. Before the electromagnetic radiation emitted or reflected from the Earth's surface reaches the sensor, it interacts with the atmosphere. Gases and aerosols in the atmosphere can absorb, scatter, and reflect radiation, distorting the spectral signature of the target on the ground. This atmospheric interference makes it challenging to accurately compare images acquired at different times, from different sensors, or under varying atmospheric conditions. Atmospheric Correction is the crucial process of removing these atmospheric effects to derive the true surface reflectance or radiance, allowing for meaningful quantitative analysis. Similarly, Image Calibration involves converting raw sensor DNs into standardized, physically meaningful units (like radiance or reflectance) to ensure consistency and comparability across different images and sensors. Without proper atmospheric correction and image calibration, any subsequent analysis, such as land cover classification, change detection, or biophysical parameter retrieval, will be prone to significant errors and inaccuracies, leading to unreliable results and flawed decision-making. Many remote sensing practitioners understand the concept but lack the practical skills to apply the various sophisticated models and techniques available for these essential pre-processing steps.

Conversely, mastering Atmospheric Correction and Image Calibration empowers professionals to transform raw, noisy remote sensing data into accurate, scientifically reliable information. This specialized skill set is fundamental for conducting robust quantitative analysis, enabling precise monitoring, accurate mapping, and confident decision-making across a wide array of environmental, agricultural, and urban applications. Our intensive 5-day "Atmospheric Correction and Image Calibration" training course is meticulously designed to equip remote sensing analysts, GIS professionals, environmental scientists, researchers, and anyone working with quantitative remote sensing data with the essential theoretical knowledge and practical, hands-on skills required to confidently apply various calibration and atmospheric correction methods using industry-standard software.

Duration

5 Days

Target Audience

The "Atmospheric Correction and Image Calibration" training course is ideal for a wide range of professionals and researchers who need to perform accurate quantitative analysis of remote sensing data. This includes:

• Remote Sensing Analysts: Seeking to deepen their understanding and practical skills in pre-processing.
• GIS Professionals: Who perform quantitative analysis on satellite, aerial, or drone imagery.
• Environmental Scientists: For accurate land cover change detection, vegetation health assessment, and water quality monitoring.
• Agriculturalists and Agronomists: For precise crop health analysis, yield estimation, and soil property mapping.
• Climate Change Researchers: Working with long-term time series data requiring consistent radiometric values.
• Geologists and Mineral Exploration Specialists: For accurate spectral signature analysis of geological features.
• Researchers and Academics: In Earth sciences, environmental studies, and geography, who rely on quantitative remote sensing.
• Anyone whose work requires comparing remote sensing data from different dates, sensors, or locations accurately.

Course Objectives

Upon successful completion of the "Atmospheric Correction and Image Calibration" training course, participants will be able to:

• Understand the fundamental physical principles behind image calibration and atmospheric correction.
• Identify the various sources of radiometric and atmospheric distortions in remote sensing data.
• Perform basic radiometric calibration steps to convert Digital Numbers (DNs) to radiance and reflectance.
• Apply empirical atmospheric correction methods, such as Dark Object Subtraction (DOS).
• Utilize physically-based atmospheric correction models (e.g., FLAASH, ATCOR - conceptually and practically).
• Assess the quality and impact of atmospheric correction on derived products (e.g., vegetation indices).
• Implement terrain correction and topographic normalization techniques for rugged terrain.
• Formulate a robust pre-processing workflow for preparing remote sensing data for quantitative analysis.

Course Modules

Module 1: Fundamentals of Radiometry and Atmospheric Effects

• Overview of the electromagnetic spectrum and remote sensing data acquisition.
• Understanding Digital Numbers (DNs) and their relationship to radiation.
• Sources of radiometric error: Sensor noise, instrument drift, vignetting.
• Atmospheric constituents: Gases (water vapor, ozone) and aerosols (dust, smoke).
• Atmospheric phenomena: Absorption, scattering (Rayleigh, Mie), and their impact on image quality.

Module 2: Radiometric Calibration: From DNs to Radiance

• Principles of radiometric calibration: Converting raw DNs to physically meaningful units.
• Understanding radiance (spectral radiance) and its units.
• Sensor calibration parameters: Gain, bias, offset, and their application.
• Converting DNs to Top-of-Atmosphere (TOA) Radiance using sensor-specific metadata.
• Practical exercises in calculating TOA Radiance for various satellite sensors.

Module 3: Radiometric Calibration: From Radiance to Reflectance

• Understanding Top-of-Atmosphere (TOA) Reflectance and its importance for comparability.
• Solar illumination geometry: Solar zenith angle, solar azimuth angle.
• Converting TOA Radiance to TOA Reflectance using solar geometry and sensor metadata.
• Limitations of TOA Reflectance for quantitative surface characterization.
• Why surface reflectance is the target for accurate analysis.

Module 4: Empirical Atmospheric Correction Methods

• Introduction to empirical atmospheric correction: Simpler models, less atmospheric data required.
• Dark Object Subtraction (DOS): Principles, assumptions, and application.
• Histogram Minimum Adjustment: A variation of DOS.
• Empirical Line Method (ELM): Using ground reference targets for calibration.
• Advantages and limitations of empirical methods for different scenarios.

Module 5: Physically-Based Atmospheric Correction Models (Part 1)

• Introduction to physically-based models: Radiative Transfer Models (RTMs).
• Key RTMs used in remote sensing (e.g., MODTRAN, 6S - conceptual overview).
• Parameters required for RTMs: Atmospheric model (e.g., Mid-Latitude Summer), aerosol type, visibility/optical depth, water vapor.
• Introduction to popular software implementations: FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes).
• Detailed workflow for using FLAASH in processing software.

Module 6: Physically-Based Atmospheric Correction Models (Part 2)

• Introduction to ATCOR (ATmospheric CORrection) software and its capabilities.
• Differences and similarities between FLAASH and ATCOR.
• Parameterization of ATCOR for various sensor types and atmospheric conditions.
• Advanced atmospheric effects addressed by RTMs: Adjacency effect, cirrus clouds.
• Practical exercises in applying physically-based models to different datasets.

Module 7: Topographic Correction and Data Consistency

• Understanding topographic effects: Illumination variations in rugged terrain (shadows, foreshortening).
• Need for topographic correction: Normalizing surface reflectance in mountainous areas.
• Methods for topographic correction: Cosine correction, C-correction, Minnaert correction.
• Utilizing Digital Elevation Models (DEMs) in topographic correction.
• Ensuring consistency of corrected images for multi-temporal analysis.

Module 8: Quality Assessment, Validation, and Workflow Integration

• Assessing the quality of corrected images: Visual inspection, statistical comparison.
• Validation of atmospheric correction: Comparing derived surface reflectance with ground measurements (e.g., spectrometer data).
• Impact of correction on derived products: Improved vegetation indices, classification accuracy.
• Developing an efficient and repeatable pre-processing workflow.
• Best practices and considerations for operational atmospheric correction.

CERTIFICATION

• Upon successful completion of this training, participants will be issued with Macskills Training and Development Institute Certificate

TRAINING VENUE

• Training will be held at Macskills Training Centre. We also tailor make the training upon request at different locations across the world.

AIRPORT PICK UP AND ACCOMMODATION

• Airport pick up and accommodation is arranged upon request

TERMS OF PAYMENT

Payment should be made to Macskills Development Institute bank account before the start of the training and receipts sent to info@macskillsdevelopment.com