Fundamentals of Remote Sensing and Geospatial Analysis

Explore the electromagnetic spectrum from gamma rays to radio waves, detailing visible and infrared bands, wavelengths, units, water penetration, and the wave-particle nature of light.

Explore how shortwave and longwave electromagnetic radiation travels from the sun to the surface and back through the atmosphere, undergoing absorption, reflection, scattering, and atmospheric windows that impact remote sensing.

Explore Rayleigh and Mie scattering and non-selective scattering, contrasting scattering with reflection, and explain how particle size and wavelength affect sky color, sunsets, and remote sensing image quality.

Compare passive and active remote sensors; passive sensors rely on reflected sunlight and cover optical and thermal platforms, while active sensors emit energy, enabling LiDAR and radar measurements through clouds.

Compare multispectral and hyperspectral passive sensors by their spectral bands and reflectance data; multispectral uses a few discrete bands, while hyperspectral uses hundreds of continuous bands.

Explore spectral signatures that show how water, bare ground, vegetation, and rocks reflect wavelengths, with reflectance from 0 to 100%, enabling material identification in multispectral and hyperspectral remote sensing.

Identify and compare the four main resolution types in remote sensing—spatial, spectral, temporal, and radiometric—through pixel size, the number of spectral bands, revisit frequency, and sensor sensitivity.

Examine popular satellite sensors, including Landsat 8 with nine spectral bands and two thermal infrared sensors, and Sentinel-2 with 13 bands across 10, 20, and 60 m resolutions.

Explain how digital numbers assign brightness values to pixels in satellite images, including grayscale, multispectral bands, and color composites, and outline geometric corrections and simple visualization.

Radiometric calibration converts sensor digital numbers to radiance and top of atmosphere reflectance, enabling atmospheric corrections and improving accuracy by accounting for sensor traits, sun elevation, and acquisition method.

Apply atmospheric corrections to convert top-of-atmosphere to bottom-of-atmosphere reflectance, using Flash and dark object subtraction to remove scattering and absorption in pre-processing.

Explore how to classify multispectral remote sensing images using supervised and unsupervised approaches, per pixel and object oriented classifications, training sites, ground truth, and clustering to map land cover.

Explore how remote sensing analyzes vegetation to assess health and crop yield using spectral signatures, chlorophyll, and indices like NDVI and LAI, plus red edge shifts and near-infrared bands.

Explore geological spectral signatures for remote sensing applications, including bedrock and structural mapping, minerals detection, and sedimentation mapping, emphasizing that useful mineral discrimination requires longer wavelengths above 1000 nanometers.

Create custom spectral ratios through image arithmetic to reveal spectral variations between surface covers. Compare slopes at 400–575 nm, 830–900 nm, and 675–700 nm to assess sediment concentration.

Explore software options for remote sensing, including Envy, Eros Imagine, and Isa Snap, with multispectral, hyperspectral, and radar support. Access data via Earth Explorer and the ESA open access hub.