How NDVI is Calculated
NDVI is calculated from multispectral drone imagery using a simple formula: NDVI = (NIR - Red) / (NIR + Red), where NIR is near-infrared reflectance and Red is red-wavelength reflectance. The formula compares the difference between near-infrared and red reflectance to their sum, creating a normalized index independent of illumination intensity. This normalization is critical—it allows NDVI values to be compared across different lighting conditions, flight times, and seasons without recalibration.
Multispectral drones capture light in discrete spectral bands—typically red, green, blue, and near-infrared—using calibrated sensors. DroneField applies radiometric calibration to convert sensor values to standardized reflectance measurements, ensuring consistent NDVI across flights. Once reflectance values are standardized, NDVI computation is straightforward: divide the orthomosaic into pixels, apply the formula to each pixel, and output an NDVI map where color intensity represents vegetation vigor. The result is a quantitative vegetation map showing spatial patterns of crop health.
Interpreting NDVI Values
NDVI values provide a quantitative plant vigor scale. Water has NDVI near -0.4 because it absorbs near-infrared light. Bare soil ranges from -0.1 to 0.2. Living vegetation typically ranges from 0.3 (sparse or stressed) to 0.8+ (dense, healthy biomass). Within a field, higher NDVI indicates greater biomass and vigor; lower NDVI suggests stress, sparse vegetation, or bare areas.
During crop growth, NDVI follows predictable temporal patterns. Early in the season, NDVI is low as seedlings establish. NDVI rises through vegetative growth, peaks at canopy closure, and declines as crops mature and senesce. Deviations from this expected pattern indicate problems: NDVI that plateaus or declines during active growth suggests stress or disease; NDVI that remains low suggests poor establishment or nutrient deficiency. DroneField's classification tools segment field NDVI into zones—high vigor, moderate, low—enabling targeted management responses.
NDVI for Management Decisions
NDVI maps directly support several precision agriculture decisions. Variability mapping uses NDVI to identify spatial zones of differing vigor within a field, highlighting areas requiring investigation or different management. These NDVI-defined zones can be converted to variable-rate prescriptions: apply higher nitrogen rates to low-NDVI areas, fungicides to moderate-NDVI zones, and reduce inputs in high-vigor areas to save costs.
Trend analysis uses time-series NDVI (flights repeated at intervals) to track crop response to management and identify emerging problems. Post-application monitoring compares NDVI before and after treatment to quantify efficacy. Pest and disease detection uses NDVI to identify stress zones that may harbor problems, directing agronomist scouting to high-risk areas. Irrigation scheduling uses NDVI to assess water stress and optimize application timing. In each case, NDVI provides quantitative, spatial data to support agronomic decisions.
NDVI Limitations and Considerations
NDVI is a powerful tool but has limitations. NDVI measures canopy reflectance, not root health or soil conditions directly. Dense canopies saturate NDVI, reaching maximum values even as underlying biomass increases—NDVI cannot distinguish between a thick-leaved crop and an extremely dense crop. NDVI also cannot directly measure nutrient status; rather, nutrient deficiencies cause reflectance changes that NDVI detects. Environmental factors (lighting angle, atmospheric conditions, leaf surface wetness) influence reflectance and thus NDVI, making standardization through radiometric calibration essential.
For these reasons, NDVI is best used as one input among many agricultural observations. Combine NDVI maps with ground scouting, soil tests, weather data, and agronomic knowledge to develop management strategies. NDVI identifies where to scout and highlights patterns worth investigating, but ground truth verification is essential for confident decision-making.