19 posts
Turn 25 Cloud Optimised GeoTIFFs of Eryri into a static STAC catalogue with pystac and rio-stac, validate it against the JSON schemas, query it, serve it as static files, and compare it with the Earth Search and Planetary Computer STAC APIs through pystac-client.
Create a satellite timelapse in Python from free Sentinel-2 imagery. Select clear scenes with STAC, mask cloud, build one median composite per summer, apply a consistent stretch, add date labels and a scale bar, and export an animated GIF, WebP and MP4 of eleven years of construction at Hinkley Point C.
Detect and quantify methane plumes in Python with NASA's EMIT imaging spectrometer: load open EMIT methane enhancement data from NASA VEDA without a login, separate three Dallas-Fort Worth landfill plumes from the noise, compute the integrated mass enhancement, estimate emission rates with ERA5 winds, and compare them with EPA greenhouse gas inventory figures.
Map earthquake damage from free Sentinel-1 radar in Python. Compute the log ratio and a z-score against a pre-event stack for Antakya after the February 2023 Türkiye earthquakes, aggregate to a 200 m grid, and score the map against 730 destroyed buildings mapped in OpenStreetMap.
Estimate surface soil moisture from free Sentinel-1 radar in Python with the TU Wien change-detection method. Build dry and wet references per orbit, map relative soil moisture at 100 m, and test it honestly against four COSMOS-UK stations and their rain gauges.
Mask clouds and cloud shadows in Sentinel-2 L2A imagery with Python. Compare the Scene Classification Layer (SCL), s2cloudless, OmniCloudMask and a simple threshold over the Lake District, measure how far they agree, score them against 186 reference points and build a cloud-free median composite.
Map snow cover in Python with the Normalised Difference Snow Index (NDSI) from Sentinel-2 and the daily MODIS snow product on the Microsoft Planetary Computer: choosing the NDSI threshold, separating snow from cloud, snow-covered area through the winter of 2025–26 in the Cairngorms, and the snowline by elevation and aspect with the Copernicus DEM.
Measure shoreline change on one of Europe's fastest-eroding coasts with free Sentinel-2 imagery and Python: MNDWI and Otsu thresholding, sub-pixel shorelines with find_contours, shore-normal transects, a tide correction from open tide-gauge data, end-point and linear regression rates, and an honest comparison with East Riding of Yorkshire Council's cliff monitoring.
Map nitrogen dioxide air pollution from Sentinel-5P TROPOMI Level-2 data in Python: stream a full year of orbits from the Planetary Computer, apply the qa_value > 0.75 filter, grid swaths to a regular raster, build monthly and seasonal composites, rank cities and measure the weekend drop, with an honest explanation of what a tropospheric column is.
Map wildfire burn severity in Python with free Sentinel-2 imagery: NBR calculation, cloud and water masking with SCL, dNBR with an offset, USGS severity classes and burned hectares, a burn perimeter, fire progression, RdNBR and an eight-year recovery record for the 2018 Saddleworth Moor fire.
Monitor crops through cloud with free Sentinel-1 radar. Build VV, VH and VH/VV ratio time series for 247 fields in Cambridgeshire, beat speckle by field-averaging, read the growth signatures of winter wheat and oilseed rape, and compare with the gaps that cloud leaves in Sentinel-2 NDVI.
Run Meta's Segment Anything Model on high-resolution aerial imagery with segment-geospatial (samgeo), on a CPU. Generate masks automatically, vectorise them to a GeoPackage, filter them by area, shape and NDVI to find buildings, and prompt SAM with points and boxes, checked against OpenStreetMap.
Calculate land surface temperature from Landsat 8 and 9 Collection 2 Level-2 data in Python: ST_B10 scale and offset, QA_PIXEL cloud masking, LST by land cover and NDVI, an urban-rural transect, hotspots with OpenStreetMap and an honest comparison with air temperature, for London in the July 2022 heatwave.
Supervised land cover classification in Python: build seasonal Sentinel-2 composites with odc-stac, sample labels from ESA WorldCover, train a scikit-learn random forest, test it with spatial cross-validation and map Sheffield and the Peak District.
Build a five-year Sentinel-2 NDVI time series in Python: load a lazy dask datacube with odc-stac, mask cloud with SCL, make monthly composites, extract field curves and derive crop phenology.
Detect ships in free Sentinel-1 SAR imagery with a cell-averaging CFAR detector written in NumPy and SciPy: mask the land, set a threshold from a speckle model, cluster detections into targets, reject wind turbines, sidelobes and ambiguities, compare VV with VH and export the vessels as GeoJSON.
Track chlorophyll, turbidity and peat colour proxies in four Peak District reservoirs with free Sentinel-2 imagery: cloud and water masking for small, dark upland lakes, NDCI and NDTI time series from 2019 to 2026, anomaly ranking, maps, tidy CSVs and an honest check against Environment Agency samples.
Map the 2022 Sindh floods with free Sentinel-1 radar from the Microsoft Planetary Computer: convert backscatter to decibels, remove speckle with a Lee filter, pick a water threshold with Otsu's method, and measure the flood extent and how it receded over seven months.
Search seven summers of Sentinel-2 imagery with pystac-client, stream only the bands you need from Cloud Optimised GeoTIFFs, mask clouds, compute NDWI and MNDWI and measure how much of Lake Mead dried up between 2019 and 2025.