Weld County, Colorado
Not every low-NDVI field is stressed.
Follow a Planet Tanager scene in Clarity. NDVI flags a set of fields as underperforming; residue, bare-soil, and active-crop layers then show which of them deserve a scouting visit.
Turn spectral signals into field priorities.
Clarity AI connects imagery, agronomic context, analysis, and review so the output helps decide where to look and what to investigate next.
Find signals worth investigating
Map stress, pigment, moisture, and soil-related variation that may be difficult to distinguish in standard imagery.
Prioritize limited field time
Rank fields or zones for scouting, sampling, irrigation, nutrient, or disease review.
Compare change over time
Repeat the approved workflow across new acquisitions to track whether conditions are improving, stable, or worsening.
From imagery to field action.
1Start with a useful question
Define the crop, geography, timing, known conditions, and action the result should support before choosing data or methods.
2Use more than one analytical path
Combine familiar vegetation indices and classical analysis with patented spectral unmixing and trained models where they add value.
3Ground the result in agronomy
Build and validate the workflow with your agronomists or ours, preserving assumptions, field observations, and the evidence behind each output.
Tell us about your fields.
Share the crop, the area, and the decision you need to make. We’ll look at what the imagery can show and propose a first analysis, with the stress layers and the evidence behind them ready to explore in Clarity.
Discuss your fieldsFrequently asked questions
A few practical details before you start.