Satellite crop stress scouting plan

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# Satellite crop stress scouting plan

For one field polygon and the last 30 days. Scene ids from step 1 are passed to every imagery call.

## 1. Find usable imagery

1. Check the archive density with @[Earth Search · Scene availability](alexandria:earth-search-aws-element84-com/satellite-imagery/scene_availability), then list scenes with @[Earth Search · Search scenes](alexandria:earth-search-aws-element84-com/satellite-imagery/search_scenes) (max_cloud_cover_pct 30). Keep the three newest with low cloud.

## 2. Which zones lag

2. Read @[Earth Search · Vegetation indices](alexandria:earth-search-aws-element84-com/satellite-imagery/vegetation_indices) with those scene_ids (zone_grid 3 or 4, NDVI and a moisture index). Rank zones by how far below the field median they sit, and whether the gap widened between scenes.

## 3. Why they might lag

3. Read rainfall and GDD for the same 30 days with @[ACIS · Daily weather history](alexandria:rcc-acis-org/weather-history/daily_weather_history). Uniform dryness hits the whole field; a single lagging zone points to drainage, compaction, pests or nutrients.
4. Read @[NASA POWER · Soil moisture](alexandria:power-larc-nasa-gov/weather-history/soil_moisture) for the window. If soil moisture is fine but one zone lags, it is unlikely to be drought.

## 4. Send the scout

5. Render the newest scene over the field with @[Earth Search · Render clip](alexandria:earth-search-aws-element84-com/satellite-imagery/render_clip) (keep max_px small) and mark the worst zones.

## Write the scouting plan

- **Zone table:** zone, index value per scene, gap to median, trend.
- **Likely cause:** weather-driven or local, with the evidence.
- **Walk order:** the zones to visit first and what to look for in each.
- **Sources:** cite every figure to the capability and record it came from. Say what could not be found rather than guessing.

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