8.2 Satellite and airborne remote sensing techniques
6 min read•Last Updated on August 14, 2024
Remote sensing techniques revolutionize how we study Earth. Satellites and aircraft capture data from above, giving us a bird's-eye view of our planet. These tools help scientists track changes in land, water, and atmosphere over time.
Multispectral and hyperspectral imaging reveal hidden details about Earth's surface. By analyzing different wavelengths of light, we can map vegetation, identify minerals, and monitor environmental changes. These methods unlock valuable insights for geophysics and environmental science.
Remote Sensing Platforms and Capabilities
Satellite Platforms
Top images from around the web for Satellite Platforms
GPS - How it Works — Copter documentation View original
Is this image relevant?
landsat data continuity mission Archives - Universe Today View original
Is this image relevant?
Familia Sentinel | OBSERVA LA TIERRA DESDE EL ESPACIO CON LA ESA (ALUMNADO DE 14 A 18 AÑOS) View original
Is this image relevant?
GPS - How it Works — Copter documentation View original
Is this image relevant?
landsat data continuity mission Archives - Universe Today View original
Is this image relevant?
1 of 3
Top images from around the web for Satellite Platforms
GPS - How it Works — Copter documentation View original
Is this image relevant?
landsat data continuity mission Archives - Universe Today View original
Is this image relevant?
Familia Sentinel | OBSERVA LA TIERRA DESDE EL ESPACIO CON LA ESA (ALUMNADO DE 14 A 18 AÑOS) View original
Is this image relevant?
GPS - How it Works — Copter documentation View original
Is this image relevant?
landsat data continuity mission Archives - Universe Today View original
Is this image relevant?
1 of 3
Satellite remote sensing platforms orbit the Earth and collect data at regular intervals, providing global coverage and repeated observations over time (Landsat, MODIS, Sentinel)
Satellite platforms are classified based on their orbit types, each with specific characteristics and applications
Geostationary satellites (GOES) remain stationary relative to the Earth's surface, allowing for continuous monitoring of a specific region
Polar-orbiting satellites (NOAA) orbit the Earth from pole to pole, providing global coverage at regular intervals
The choice of satellite platform depends on factors such as the desired spatial and temporal resolution, area of coverage, and the specific geophysical parameters of interest
Satellite data is used for a wide range of geophysical applications, including weather forecasting, climate monitoring, land cover mapping, and natural resource management
Airborne Platforms
Airborne remote sensing platforms, such as aircraft and drones, operate at lower altitudes and offer higher spatial resolution and flexibility in data acquisition timing and location compared to satellites
Airborne platforms can carry a wide range of sensors, including optical, thermal, and radar systems, allowing for customized data collection based on specific project requirements
Aircraft-based remote sensing enables targeted data acquisition over specific areas of interest, such as mineral exploration sites, agricultural fields, or urban environments
Drones (UAVs) provide even higher spatial resolution and can be deployed rapidly for small-scale surveys or emergency response situations
Airborne remote sensing is particularly useful for detailed mapping, infrastructure monitoring, and environmental assessment applications
Multispectral vs Hyperspectral Imaging
Multispectral Imaging
Multispectral imaging involves capturing data in multiple discrete spectral bands, typically ranging from 3 to 10 bands, across the electromagnetic spectrum (visible, near-infrared, shortwave infrared)
Multispectral data is commonly used for land cover classification, vegetation monitoring, and geological mapping
The limited number of spectral bands in multispectral imaging allows for faster data processing and analysis compared to hyperspectral data
Multispectral sensors, such as Landsat and Sentinel-2, provide moderate spatial resolution (10-30 meters) and are widely used for regional to global-scale studies
Hyperspectral Imaging
Hyperspectral imaging collects data in numerous narrow, contiguous spectral bands (often more than 100) across a wide range of the electromagnetic spectrum, providing a near-continuous spectral signature of the target
The high spectral resolution of hyperspectral data enables the detection of subtle variations in geophysical parameters, such as mineral composition, soil properties, and water quality
Spectral signatures of different materials, such as rocks, soils, and vegetation, exhibit unique reflectance or absorption features that can be used for identification and characterization
Hyperspectral data allows for more detailed material identification and analysis compared to multispectral data
The processing and analysis of hyperspectral data involve techniques such as principal component analysis (PCA) and spectral unmixing to extract meaningful geophysical information
Deriving Geophysical Parameters
Pre-processing and Corrections
Pre-processing steps, such as radiometric and atmospheric corrections, are necessary to convert raw remote sensing data into reliable surface reflectance or radiance values
Radiometric corrections account for sensor-specific calibration and noise removal to ensure consistent measurements across different sensors and time periods
Atmospheric corrections remove the effects of atmospheric scattering and absorption to obtain accurate surface reflectance values
Geometric corrections are applied to remote sensing data to ensure accurate spatial positioning and alignment with ground-based measurements or other geospatial datasets
Parameter Retrieval Algorithms
Various algorithms and models are used to derive geophysical parameters from remote sensing data
Vegetation indices, such as the Normalized Difference Vegetation Index (NDVI), are calculated from red and near-infrared bands to estimate vegetation health and productivity
Surface temperature can be derived from thermal infrared bands using algorithms that account for emissivity and atmospheric effects
Soil moisture estimation relies on microwave remote sensing data and models that relate backscatter or emission to soil dielectric properties
Digital elevation models (DEMs) are generated from stereo imagery or radar interferometry to represent surface topography and elevation
Spectral unmixing techniques, such as linear spectral unmixing, are employed to estimate the fractional abundances of different materials within a pixel based on their spectral signatures
Time-series Analysis and Validation
Time-series analysis of remote sensing data allows for the monitoring of dynamic geophysical processes, such as land cover changes, vegetation phenology, and surface deformation
Multitemporal data can be used to detect trends, anomalies, and seasonal patterns in geophysical parameters
The accuracy and uncertainty of derived geophysical parameters should be assessed through validation with ground-based measurements or other independent datasets
Field surveys, in-situ sensors, and ground truthing campaigns provide reference data for validating remote sensing-derived products
Statistical measures, such as root mean square error (RMSE) and correlation coefficients, are used to quantify the agreement between remote sensing estimates and ground observations
Remote Sensing Techniques for Applications
Optical Remote Sensing
Optical remote sensing is suitable for applications such as land cover mapping, vegetation monitoring, and geological surveys, as it captures information in the visible and infrared portions of the electromagnetic spectrum
Multispectral and hyperspectral sensors are commonly used for optical remote sensing
Land cover classification algorithms, such as maximum likelihood or random forests, are applied to optical data to map different land cover types (forests, urban areas, water bodies)
Vegetation indices derived from optical data are used to assess plant health, productivity, and phenology
Geological applications of optical remote sensing include mineral mapping, lithological discrimination, and structural analysis
Thermal Infrared Remote Sensing
Thermal infrared remote sensing is useful for studying surface temperature variations, heat fluxes, and geothermal activity, as it detects emitted thermal radiation from the Earth's surface
Thermal data is used to map urban heat islands, monitor volcanic activity, and assess evapotranspiration in agricultural areas
Land surface temperature (LST) is a key parameter derived from thermal infrared data and is used in various environmental and climate studies
Thermal inertia mapping can provide insights into soil moisture and rock properties based on the response of materials to temperature changes
Radar and Lidar Remote Sensing
Radar remote sensing, such as Synthetic Aperture Radar (SAR), is effective for mapping surface deformation, soil moisture, and vegetation structure, as it can penetrate clouds and operate day and night
SAR interferometry (InSAR) is used to measure surface deformation caused by earthquakes, volcanic activity, or groundwater extraction
Polarimetric SAR data can provide information on surface roughness, soil moisture, and vegetation biomass
Lidar (Light Detection and Ranging) is valuable for generating high-resolution digital elevation models, mapping vegetation canopy structure, and detecting geological features
Airborne lidar systems, such as Airborne Laser Scanning (ALS), provide detailed 3D point clouds of the Earth's surface and are used for topographic mapping, forest inventory, and urban planning
Multi-sensor Integration
The integration of multiple remote sensing techniques, such as combining optical and radar data, can provide complementary information and improve the accuracy of geophysical parameter retrieval
Data fusion techniques, such as pan-sharpening or decision-level fusion, are used to combine data from different sensors with varying spatial, spectral, and temporal resolutions
The synergistic use of optical and radar data can enhance land cover classification, soil moisture estimation, and crop monitoring
The combination of lidar and hyperspectral data enables detailed analysis of vegetation structure and composition
Multi-sensor approaches leverage the strengths of each remote sensing technique to provide a more comprehensive understanding of geophysical processes and phenomena