Core Concepts
The physical and geometric ideas behind remote sensing imagery, from pixels and spectral bands to radar, thermal, and change detection.
These pages describe the measurements and the reasoning about them rather than any particular tool, so they hold whatever software you later use to open, process, or display the data. How the site places platforms alongside these ideas is set out in What is Remote Sensing Labs?.
Digital Imagery
What a remotely sensed image contains: a georeferenced grid of measurements, the physical values behind its numbers, and where its uncertainty comes from.
Spectral Bands
What a spectral band measures, how surfaces differ across the visible, infrared, and thermal regions, and how band indices are built.
Visualization
Turning sensor measurements into an honest, readable image: contrast stretches, composites, color ramps, and fixed scales for comparison.
Resolution
Spatial, spectral, temporal, and radiometric resolution: what each axis measures, how they trade against one another, and how to match them to a question.
Raster vs Vector
The raster and vector data models: what each can say about the world, how they combine, and what converting between them costs.
Coordinate Systems
Coordinate reference systems, datums, EPSG codes, projection distortion, reprojection, and the grid alignment that pixel-by-pixel comparison needs.
Time Series
Reading the same place across many dates: cadence and gaps, seasonal cycles and trends, compositing, and keeping a record consistent.
Cloud Masking
Finding and setting aside cloud, shadow, and haze in optical imagery: detection strategies, masking versus down-weighting, and checking a mask.
Classification Basics
Turning measurements into a thematic map: class schemes, features, supervised and unsupervised methods, training data, and accuracy assessment.
Change Detection
Detecting and reasoning about landscape change: kinds of change, before-and-after and continuous designs, comparability, and validation.
SAR Basics
How radar backscatter, wavelength, polarization, side-looking geometry, and speckle shape what a synthetic aperture radar image shows.
Thermal and Land Surface Temperature
What thermal-infrared imagery measures, why land surface temperature is a retrieved quantity, and how emissivity, atmosphere, scale, and timing limit it.
Snow and Ice
Observing the cryosphere: why snow and ice are comparatively easy to find and hard to characterize, and where the optical view fails.
Optical Water Quality
Reading the color of water: what pigment, sediment, and turbidity each do to the outgoing signal, and what else can imitate them.