Agriculture & vegetation
Fields, crops and canopies: boundaries, vegetation indices over a season, yield maps, management zones and prescription files.
A field is a polygon with a season attached. That is the difference between this topic and the rest of the imagery work: almost nothing here is answered by one date, because a vegetation index over a growing season is a curve, and a single observation is one slice through it. The same field read in April, July and October supports three different conclusions, all honest.
The other half is boundaries. Field parcels are drawn by agencies, by farmers and by algorithms, and they disagree at the edges; management zones are derived rather than given; and a prescription file has to match the machine that will read it. Yield data arrives as a point cloud from a moving combine, with all the speed and lag artefacts that implies.
Below: the concepts cover indices, phenology and zoning; the how-to guides build time series, delineate zones and export; and the fixes are the specific ways a season of imagery misleads.
🧭 Start with the concepts
The ideas the how-to guides below assume you already have.
⚙️ Then do the task
Step-by-step walkthroughs with code you can copy and adapt.
- How to delineate field boundaries from imagery in Python
- How to classify crop types from a satellite time series
- How to clean yield monitor data in Python
- How to build management zones with clustering in Python
- How to extract phenology metrics from an NDVI time series
- How to calculate growing degree days from gridded weather
🔧 When it breaks
The errors this topic produces, with the cause explained rather than just the command.
Learning paths through this topic
A curated route rather than a list — the guides in the order that builds on itself.
Related topics
Subjects that keep coming up alongside this one, most overlap first.