- Remote Sensing Technologies in Turf Management: A Practical Comparison

Remote sensing technologies are increasingly used in turf management to assess turf conditions, variability, and moisture-related stress. However, not all remote sensing tools measure the same physical properties — and not all of them measure soil moisture at all.

1. How Remote Sensing Technologies Differ (Big Picture)

Remote sensing tools used in turf management differ in what physical signal they measure and whether they measure moisture directly or indirectly. The commonly used technologies fall into two main signal families:

1. Light-based sensing (optical)
2. Microwave-based sensing

Each family answers a different type of question.

2. Optical Remote Sensing Technologies

(Measure turf appearance, not soil moisture)

Light-based sensors detect reflected or emitted light from the turf surface. They measure turf appearance, not soil moisture, and are widely used for turf health and stress monitoring.

2.1 Visible & Multispectral Imaging (e.g. NDVI)

• Signal measured: Reflected sunlight (visible + near-infrared)

• What it indicates: Turf color and vigor, Relative stress, Turf density and uniformity

• Key limitation: These systems do not measure soil moisture. They infer stress from appearance, which can be influenced by turf species, mowing height, shade, topdressing, dew, and lighting conditions.

2.2 Thermal Infrared Imaging

• Signal measured: Re-radiated heat from the canopy

• What it indicates: Canopy temperature, Relative heat or water stress

• Key limitation: Thermal signals are strongly influenced by wind, sun angle, slope, shade, and weather. They measure surface temperature, not subsurface moisture.

2.3 AI / Sensor Fusion Approaches

• How they work: Combine visible, multispectral, thermal (and sometimes radar) inputs using models or machine learning to estimate moisture or stress.

• Key limitation: If none of the input signals directly measure subsurface moisture, the output remains a modeled estimate, not a physical measurement. AI models can be trained to output any number of physical parameters; micro-nutrient concentrations, turf stress, moisture, salinity, etc., but research has shown that the various insights generated by multispectral data are all highly correlated and do not contain significant independent information. The best algorithm can’t see beneath the surface if none of the sensors can.

3. Microwave-Based Remote Sensing Technologies

Microwave signals interact strongly with liquid water, making them particularly relevant for moisture-related applications. Microwave systems fall into two distinct categories:

1. Active microwave sensing
2. Passive microwave sensing

4. Active Microwave & Electromagnetic Sensors

4.1 Synthetic Aperture Radar (SAR)

• Signal measured: Reflected microwave energy

• What it measures: Surface roughness, Turf density, Layer structure

• Typical frequency: 6–10 GHz
  Sensitive depth: ~1 cm (≈ 0.5 in)

• Limitation for turf: Primarily surface-sensitive and strongly influenced by leaf orientation and mowing direction. Not well-suited for root-zone moisture measurement.

4.2 Electromagnetic Inductance (EMI)

• Signal measured: Electrical conductivity

• What it measures: Soil texture, Salinity, Bulk conductivity patterns

• Typical depth: 20–50 cm (8–20 in)

• Pros: High spatial resolution, Useful for soil zoning and mapping
  Cons: Does not directly measure moisture, strongly influenced by salinity, minerals, and metal interference, measurement depth is often too deep for turf irrigation decisions, doesn't work on mowers

5. Passive Microwave Remote Sensing (L-Band Radiometry)

(Direct soil moisture measurement)

5.1 What Passive Microwave Radiometry Measures

Passive microwave radiometers do not transmit energy. They measure naturally emitted microwave radiation from the soil. This emission is directly related to the dielectric constant, which is strongly controlled by liquid water content — the same physical property measured by TDR probes.

5.2 L-Band Microwave Radiometry

Compared to other remote sensing approaches used in turf, passive L-band microwave radiometry offers a distinct set of strengths and tradeoffs:

• Measures: dielectric constant of soil (directly related to volumetric water content, the same physical property measured by TDR) 

• Typical frequency: ~1.4 GHz (L-band)
  Sensitive depth: ~10 cm (≈ 3–4 in)

• Why L-band matters: Longer wavelengths penetrate deeper into the soil, which means measurement aligns with the active turf root zone. Minimally affected by surface appearance or vegetation.

• Pros: Direct subsurface moisture measurement, Large-area coverage aligned with irrigation infrastructure, Passive sensing (no transmission, no radiation), Unaffected by light, cloud cover, turf color, or canopy appearance, Minimally affected by surface appearance, vegetation (grass type), or dew.
  Cons: Requires mounting on equipment or dedicated passes, Measures averaged moisture across a footprint rather than single-point variability, Rare susceptibility to interference from strong L-band broadcast sources

5.3 From Space to Turf

L-band radiometry has been used for decades in satellite missions (e.g. ESA’s SMOS) to measure soil moisture globally.

Limitation from space: Spatial resolution is coarse (>200 sq. miles per pixel).
Field-scale adaptation: Portable L-band radiometers apply the same physics at turf scale, enabling:

• Direct root-zone moisture measurement

• Repeatable coverage across entire playing surfaces

• Integration into daily maintenance workflows

6. Why This Matters for Turf Management

Different sensing technologies answer different questions:

• Light-based sensors: How does the turf look?

• Active microwave & EMI: How does the surface or soil structure vary?

• Passive microwave (L-band): How much water is actually in the root zone?

Understanding these distinctions helps superintendents choose the right tool for irrigation decisions, stress prevention, and long-term performance management.