Agriculture
Remote sensing for agriculture: from satellite imagery to field decisions
Originally published by OLBGIS
Free, frequent satellite imagery has made field monitoring technically easy. Making it useful is harder, because a vegetation map is not a recommendation.
This piece follows the path from imagery to a decision: preparing the data, reading the signal against the crop calendar, and packaging the result so it fits how farms actually work.
Video walkthrough: [confirm: video URL]
From pixels to fields
Start with field boundaries, because everything else is summarised per field. Then build a clean time series: mask clouds, choose a consistent sensor and compute an index such as NDVI for each date.
Indices are inputs, not answers
A single NDVI value says little. The same value can be healthy early in the season and a problem later. Compare each field against its own history and against neighbouring fields on the same crop calendar, and use radar imagery where cloud blocks optical data.
Making it usable
Agronomists need zones, thresholds and alerts, not rasters. Agree with them what a flag means and what action follows, then deliver it where they already work. That is the step most imagery projects skip, and the one that decides whether anyone uses the result.