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Study / Remote Sensing
Remote Sensing
Theory notes on imagery, sensors and elevation data. Where a hands-on practical exists, its icon sits beside the topic.
11 theory notes
Foundations
2 guides- 01Remote Sensing Fundamentals: A Beginner's GuideHow satellites, aircraft and drones observe the Earth: passive vs active sensing, spectral bands, the four types of resolution, Landsat vs Sentinel-2, spectral indices, and how imagery becomes land-cover and change information.
- 02Earth Observation Satellites: Orbits, Sensors and Products ExplainedHow satellite orbits, swaths, revisit times, bands and processing levels decide which imagery fits which GIS or remote sensing task, with Landsat, Sentinel and Indian missions compared.
Reading the spectrum
2 guides- 03Spectral Signatures: How Surfaces Reflect Light at Different WavelengthsWhy vegetation, water, soil and buildings look different in different bands, how to read a spectral curve, and how real Sentinel-2 values from Bengaluru show it.
- 04Spectral Indices: NDVI, NDWI, MNDWI, NDBI and NBR ExplainedHow a spectral index turns bands into one number per pixel, the formulas and band numbers for Sentinel-2 and Landsat 8, how to read the values, and where each index goes wrong.
Elevation and mapping
2 guides- 05DEM, DSM and DTM: Elevation Models ExplainedWhat a Digital Elevation Model is, where DEMs come from, and how DEM, DSM and DTM differ, with a worked example and advice on checking which one a dataset really is.
- 06Drone Mapping Fundamentals: Orthorectification, Orthomosaics and GSDHow overlapping drone photographs become a map: relief displacement and orthorectification, how an orthomosaic is built and used, and why ground sampling distance is not the same as accuracy.
Classification and change
2 guides- 07Image Classification Methods: K-means, Maximum Likelihood, Random Forest and Accuracy AssessmentHow a satellite image becomes a land use and land cover map: the unsupervised and supervised methods compared, what training samples need, and how to measure accuracy with a confusion matrix.
- 08Remote Sensing Time Series: Composites, Gaps, Phenology and Trends ExplainedHow a stack of satellite images becomes a curve for every place: what a time series needs, how monthly composites are made, what to do about cloud gaps, how to read a seasonal curve, and why one year is not a trend.
Preparing imagery
1 guide- 09Cloud Masking and Scene Classification in Optical ImageryWhy clouds, shadows and haze spoil optical images, how a scene classification layer marks them, what each mask choice costs, and why a mask never gives a perfect result.
Other sensors
2 guides- 10Radar and SAR Basics: Backscatter, Polarization and SpeckleHow synthetic aperture radar images the ground through cloud and at night, what backscatter and decibels mean, why water is dark and buildings are bright, and what speckle and terrain do to the picture.
- 11Land Surface Temperature, Night Lights and Soil Moisture: What the Numbers MeanHow satellites measure ground temperature, light at night and the water in the soil, what units and overpass times to know, and why each of these numbers is easy to over-read.