CASE STUDY

High resolution turf intelligence with Q.FLY Water

How golf courses and sports pitches use centimetre-scale moisture data to manage irrigation, reduce water use, and protect surface quality.

Why moisture matters for turf

Turf is a high-value asset sustained by one of our most precious resources. Whether it's a championship golf course or a professional football pitch, surface quality directly affects playability, reputation, and revenue - and maintaining it requires precise, often intensive irrigation. In many regions, this level of water use is now under growing restriction and scrutiny.

Managing that precision is difficult. Most turf teams rely on a mix of 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.

To understand variation at scale, some operators have turned to satellite data. NDMI (Normalised Difference Moisture Index) provides a measure of moisture across vegetation and soil, but at ~20 metre resolution it cannot resolve the patterns that matter in turf. Across the two deployments below, we use Q.FLY Water to capture NDMI at centimetre-scale resolution and compare the results with satellite data.

DEPLOYMENT 1

Safaa Golf Course

Thuwal, 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 20 metre resolution. At this scale, it is useful for identifying broad trends, but it is less effective in managed turf environments where variation occurs over much smaller distances. This is clear in the survey-area view: when zoomed in, the data is coarse, and local variation is averaged within each pixel. As a result, differences within fairways, irrigation coverage, and early-stage stress are not clearly visible.

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)

Satellite RGB of survey area

Sentinel 2 NDMI of survey area

Q.FLY Water NDMI of survey area

Red = low moisture content | Blue = high moisture content

Irrigation patterns

Clear circular and patterned wet zones aligned with sprinkler systems.

Traffic & play impact

Stress zones linked to bunker play, lay-up areas, and movement corridors.

Micro-variation

Fine-scale moisture gradients across a single fairway, invisible in satellite data.

DEPLOYMENT 2

Football pitch

Cambridge, UK

Low-tier football club experiencing poor pitch conditions. Our objective was to evaluate whether high-resolution NDMI can detect stress and wear patterns to guide maintenance.

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, which limits the usefulness of the technology for informing maintenance decisions.

In this case, the available satellite NDMI data for the pitch appears as a single orange cell, with no visible internal variation. As a result, differences in surface condition across the pitch cannot be identified. 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

Visible wear patterns

Surface thinning in high-traffic areas

Moisture-driven stress

reduced moisture uptake in worn zones

Subsurface insights

Underlying drainage structure

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 is likely 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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