field variability index
discover how heterogeneous is your field with the variability index model
multi factorial field zoning
The Variability Index model analyzes historical satellite imagery data to measure how variable and consistent is a field's productivity patterns across seasons.
profitable precision ag
profitable precision ag field by field
The model helps to identify which fields have consistent high and low productivity zones across seasons, enabling growers to invest in profitable variable rate applications only where the spatial patterns justify the technology costs.
Dual dimension analysis
Combines both spatial and temporal variability, providing a robust and stable field assessment.
Multi-season intelligence
Leverages 5 years of Sentinel-2 satellite data to identify persistent productivity patterns across seasons and eliminating the noise from temporary field variations.
Standardized cross-field comparability
Produces standardized indexes that enables direct comparison of variability across fields of different sizes, crops, geographies, and management systems.
Dual dimension analysis
Combines both spatial and temporal variability, providing a robust and stable field assessment.
Multi-season intelligence
Leverages 5 years of Sentinel-2 satellite data to identify persistent productivity patterns across seasons and eliminating the noise from temporary field variations.
Standardized cross-field comparability
Produces standardized indexes that enables direct comparison of variability across fields of different sizes, crops, geographies, and management systems.
Model Insights
Model Insights
Understand the field variability dimensions
Variability index
Indicates how variable is the vegetation in the field across multiple seasons
Consistency index
Indicates how consistent is the heterogeneity pattern across seasons
VxC index
Overall assessment of how variable and how consistent the spatial structure of vegetation is in a field
Key Characteristics
Key Characteristics
Discover how the model can provide objective, scalable, and actionable field heterogeneity assessments.
Satellite-based remote sensing
Enable non-invasive and scalable analysis across large field portfolios with cloud-resilient processing.
Temporal consistency evaluation
Automatically detect and analyzes past growing periods to capture reliable long-term patterns.
Complementary index metrics
Three metrics (variability, consistency, and weighted average) on a standardized 0-1 scale to provide comprehensive field heterogeneity assessment.
Clustering-based spatial analysis
Uses advanced algorithms to automatically identify and compare productivity zones across multiple seasons, determining how consistently these spatial patterns occur in the field.