Fiveable
Fiveable
Fiveable
Fiveable

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
Top images from around the web for Satellite Platforms
  • 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


© 2025 Fiveable Inc. All rights reserved.
AP® and SAT® are trademarks registered by the College Board, which is not affiliated with, and does not endorse this website.

© 2025 Fiveable Inc. All rights reserved.
AP® and SAT® are trademarks registered by the College Board, which is not affiliated with, and does not endorse this website.
Glossary
Glossary