Application

High resolution turf intelligence

Removing the resolution barrier for water index mapping

Why moisture matters for turf

Water is the foundation of turf quality - and it's under pressure. From golf courses to professional pitches, surface quality directly affects playability, reputation, and revenue. Maintaining it requires precise, often intensive irrigation. In many regions, that level of water use is now under increasing restriction and scrutiny.

The challenge is knowing where water is needed, and where it isn't. Most turf teams rely on visual inspection, handheld probes, and fixed sensors. These methods are trusted, but inherently local - they show what's happening at specific points, not how moisture varies across the full surface.

NDMI (Normalised Difference Moisture Index) is an established spectral index for measuring moisture variation across an entire area. Delivered via satellite, the resolution floor of 20m has kept it largely outside practical turf management. Across the two deployments below, we use Q.FLY Water to capture the same index at centimetre-scale resolution and show what becomes visible when that barrier is removed.

DEPLOYMENT 1

Golf Course

Location: Saudi Arabia

A championship-quality golf course operating in one of the world's most water-scarce environments. Optimal irrigation is essential to maintain turf quality.

Our objective was to map moisture variation across a sample of the course and compare against available satellite NDMI data​​

Comparison data

Comparison data referenced from ​Copernicus Data Space Ecosystem​​, Sentinel-2 L1C

NDMI from Sentinel-2 provides a consistent measure of moisture across vegetation and soil. At this scale it is useful for identifying broad trends, but local variation is averaged within each pixel.

Centimetre-scale NDMI captured using Q.FLY Water provides a direct comparison of the same index at much higher resolution. At this level of detail, variation within individual turf features becomes visible, including irrigation patterns, localised dry zones, and fine-scale changes across the surface.

Sentinel 2 NDMI (full course)

Q.FLY Water NDMI (Full course)

Sentinel 2 NDMI of sample area

Q.FLY Water NDMI of sample area

Red = low moisture content | Blue = high moisture content

Irrigation patterns

Identify over and under-irrigated zones for targeted adjustment

Stress monitoring

Track moisture change over time to prioritise maintenance early

High-traffic wear zones

Pinpoint reduced moisture uptake consistent with surface wear

Isolating stress for healthy greens

Compressing the NDMI value range isolates moisture variation within a single green, revealing fine-scale differences in surface condition that are invisible at standard display settings. Dry edges, uneven uptake, and early stress become distinguishable before they affect playing quality or require intervention.

DEPLOYMENT 2

Football pitch

Location: UK

A non-professional football club managing a heavily used pitch with visible surface deterioration. Our objective was to evaluate whether centimetre-scale NDMI could detect stress and wear patterns to guide maintenance decisions.

Comparison data

Comparison data referenced from ​Copernicus Data Space Ecosystem​​, Sentinel-2 L1C

For smaller, highly managed turf environments such as football pitches, the application of satellite-derived NDMI is severely constrained by spatial resolution, keeping it outside of the maintenance toolkit.

By comparison, centimetre-scale NDMI captured using Q.FLY Water reveals variation across the full surface, including patterns of stress, wear, and uneven moisture distribution.

Sentinel 2 NDMI (Yellow square)

Q.FLY Water NDMI

Red = low moisture content | Blue = high moisture content

Wear & stress zones

Identify areas of high traffic wear and early-stage stress

Drainage structure

Linear drainage tiles visible

Drainage performance

Variation between drainage lines indicates section performance

Early-stage stress detection

To isolate moisture stress across the pitch, the NDMI display range has been compressed further to show values from 0 to 1.000 only. At this scale, all visible colour represents a moisture deficit -white marks the threshold, with red indicating progressively acute stress. Areas that appear uniform in the standard view begin to show meaningful variation, revealing where surface condition is already diverging and where deterioration may follow without intervention.

Revealing drainage patterns

The linear features visible in the NDMI data correspond to the pitch’s drainage system. These appear due to consistent differences in moisture behaviour along drainage lines, allowing subsurface structure to be inferred from surface data.

This provides additional context for interpreting surface condition, helping to distinguish between stress caused by drainage, irrigation, or usage patterns.

Summary

Insight to action​

Moisture data that drives smarter decisions

Targeted intervention, reduced waste, and clearer insights.

Irrigation optimisation

Redirect water to dry zones and reduce over-irrigation.

Targeted maintenance

Focus on specific areas of failure or stress.

Effective monitoring

Track consistency and change over time.

Early intervention

Identify issues before visible deterioration.

Data-led decisions

Replace guesswork with measurable moisture data.

Cost reduction

Lower water use and operational spend.

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