Earth Observation & Satellite Monitoring
A comprehensive course covering satellite remote sensing from fundamental physics to operational applications. Learn to acquire, process, and analyze satellite imagery for environmental monitoring, disaster response, and climate science.
What You'll Learn
Understand the physics of electromagnetic radiation and how satellites measure it
Navigate the landscape of EO satellite systems (Sentinel, Landsat, MODIS, commercial)
Access satellite data through Copernicus CDSE, USGS, Google Earth Engine, and STAC APIs
Process SAR imagery and apply InSAR/DInSAR for ground deformation measurement
Monitor earthquakes, volcanic activity, and tectonic motion using satellite geodesy
Map droughts and vegetation health using multispectral indices (NDVI, EVI, LST)
Detect and map floods using SAR backscatter thresholding techniques
Track climate indicators: sea ice extent, greenhouse gases, sea surface temperature
Classify land use and land cover with machine learning on satellite imagery
Build a DIY NOAA satellite ground station for direct APT image reception
Prerequisites
Required
- • Basic Python programming (NumPy, Matplotlib)
- • Familiarity with coordinate systems and map projections
- • High-school physics (electromagnetic waves, optics)
Recommended
- • Linear algebra (matrix operations, eigenvalues)
- • Basic statistics (distributions, hypothesis testing)
- • Familiarity with GIS concepts (rasters, vectors, CRS)
- • Experience with Jupyter notebooks or Google Colab
Tools & Software Used
Python 3.10+
Primary language
SNAP / snappy
SAR processing
Google Earth Engine
Cloud analysis
Rasterio / GDAL
Raster I/O
GeoPandas
Vector data
scikit-learn
ML classification
pystac-client
STAC data access
sentinelsat
Copernicus API
Matplotlib
Visualization
Course Modules
M1: Satellite Systems
Sentinel, Landsat, MODIS, GOES, ICESat-2, commercial constellations
M2: Physical Principles
EM spectrum, radiometric equations, SAR/InSAR theory, Planck curves
M3: Data Access & APIs
Copernicus CDSE, USGS EarthExplorer, Google Earth Engine, STAC
M4: Earthquake Monitoring
InSAR deformation, Okada model, DInSAR/PSInSAR pipelines
M5: Drought & Vegetation
NDVI, NDWI, EVI, land surface temperature, GEE drought pipelines
M6: Flood Mapping
SAR flood physics, Otsu thresholding, operational detection pipelines
M7: Climate Monitoring
Sea ice, CO₂/CH₄ from TROPOMI, SST anomalies, heatwave detection
M8: Land Use & Cover
Random forest classification, change detection, Dynamic World (GEE)
Spectral Bands Explorer
Interactive EM spectrum, Sentinel-2 & Landsat bands, band combinations
NOAA Satellite Reception
DIY hardware setup, pass prediction, APT decoding, georeferencing
Course Structure
Each module combines theoretical foundations with hands-on Python code that you can run directly in the browser. The first three modules (Satellite Systems, Physical Principles, Data Access) build the foundational knowledge. Modules 4 through 8 are application-focused and can be studied in any order based on your interest.
The Spectral Bands Explorer and NOAA Satellite Reception modules are standalone enrichment content that complement the main curriculum.
Estimated time: 40-60 hours total. Each application module (M4-M8) takes approximately 4-6 hours including the coding exercises.
Key Equations You'll Master
This course covers the mathematical foundations of remote sensing, from radiative transfer to interferometric phase analysis:
Planck's Blackbody Radiation Law
TOA Reflectance
InSAR Phase Equation
Recommended Learning Path
Foundations (Weeks 1-3)
M1: Satellite Systems, M2: Physical Principles, M3: Data Access
SAR Applications (Weeks 4-5)
M4: Earthquake Monitoring, M6: Flood Mapping
Optical Applications (Weeks 6-7)
M5: Drought & Vegetation, M8: Land Use & Cover
Climate & Integration (Weeks 8-9)
M7: Climate Monitoring, Spectral Bands Explorer, NOAA Reception