Satellite Imagery
From a scene on a catalogue to a number you can defend.
Satellite imagery is free, enormous and full of quiet traps: bands stored at three resolutions on two different origins, integers that are only reflectance after a scale and an offset, and a cloud mask that agrees with the sky about two thirds of the time. These guides go from what the bands actually measure through loading them into an aligned array, computing indices, masking cloud, compositing a clear scene out of many dirty ones, and on to time series, change detection and land cover — with the arithmetic measured on real Sentinel-2 tiles rather than assumed.
The two guides that save the most time are the radiometric levels one at stage two and the cloud masking one at the end. Between them they cover the two ways a perfectly executed index turns out to have measured something else: the wrong processing level, and a cloud shadow nobody masked.
Start here
The cornerstone guide — the whole workflow end to end. Everything after it goes deeper into one part of it.
Step 1 Spectral Bands Explained: What Satellite Imagery Actually Measures spectral bands explained Begin the path →The full path, in order
The concepts underneath the work, then each task, then the errors that task produces. Work down, or jump to the step you need.
- Radiometric Levels Explained: DN, TOA and Surface Reflectance radiometric levels explained 🧭 Concept
- Sentinel-2 or Landsat? Choosing a Satellite Imagery Source sentinel 2 vs landsat explained 🧭 Concept
- How to Load Sentinel-2 Bands into Python as an Analysis-Ready Array load sentinel2 bands python ⚙️ How-To
- Satellite Bands Have Different Shapes or Do Not Align satellite bands do not align 🔧 Fix
- How to Resample Satellite Bands to a Common Grid resample satellite bands python ⚙️ How-To
- Spectral Indices Explained: NDVI, NDWI and What They Assume spectral indices explained 🧭 Concept
- How to Calculate NDVI from Sentinel-2 in Python calculate ndvi python ⚙️ How-To
- NDVI Values Are Above 1 or Below −1: How to Fix It ndvi values out of range 🔧 Fix
- Cloud Masking Explained: QA Bands, Scene Classification and What They Miss cloud masking explained 🧭 Concept
- How to Mask Clouds in Sentinel-2 Imagery with Python mask clouds sentinel2 python ⚙️ How-To
- How to Build a Cloud-Free Composite from Many Satellite Scenes cloud free composite python ⚙️ How-To
- My Composite Has Holes, Stripes or Grey Patches composite has holes or stripes 🔧 Fix
- How to Extract a Vegetation Index Time Series for a Polygon ndvi time series polygon python ⚙️ How-To
- How to Detect Change Between Two Satellite Images in Python detect change satellite images python ⚙️ How-To
- How to Classify Land Cover from Satellite Imagery in Python classify land cover python ⚙️ How-To
Topics this path covers
A path is a route; a topic is everything the site has on a subject. Open a topic to see the guides this path skips.