9 posts
Process large satellite rasters in Python with dask and rioxarray. Open eight full Sentinel-2 scenes lazily, choose a chunk size, inspect the task graph, watch CPU and memory with dask diagnostics, compare peak memory with eager NumPy, time chunk sizes and thread counts, and write a median NDVI GeoTIFF to disk chunk by chunk.
A free, phone-friendly check for any farm: whether the rains have started, when they usually start, rain and heat for the next 10 days and how green the crops are. Examples from Lilongwe, Malawi, and Gazipur, Bangladesh.
Two free tools for any district: is this season's rain and crop growth better or worse than usual, and where did it flood and how many people live there? Worked examples from Marsabit, Kenya, in 2022 and Feni, Bangladesh, in 2024.
An introduction to the TechnicalGIS climate time-series tools: five open datasets, how to draw an area on the map, worked examples from London, north Evia, Nairobi and Las Vegas, the processing limits and the terms for reusing every output.
Run a pixel-wise Mann-Kendall trend test and Theil-Sen slope on 24 years of MODIS NDVI in Python. Read MOD13A1 from the Microsoft Planetary Computer, mask it with the QA layer, aggregate to growing-season means, vectorise the tests with xarray.apply_ufunc (checked against pymannkendall), compare the seasonal and modified Mann-Kendall tests, control the false discovery rate across 229,229 pixels, and map greening and browning on China's Loess Plateau.
Map blanket bog, heath, bracken, grassland, woodland, water and built-up land in the Peak District from 2023 Sentinel-2 seasonal composites with an eight-rule spectral index classifier, then assess it properly: a confusion matrix, user's and producer's accuracy, kappa and its caveats, block-bootstrap confidence intervals, and why a random train/test split overstates accuracy.
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.
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.
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.