PROCEEDINGS of the23rd International Congress on Acoustics
9 to 13 September 2019 in Aachen, Germany

Acoustic Remote Sensing for Irrigation Systems Control inAgriculture
Anna RADIONOVA1,2; Chandra Prasad GHIMIRE3; Laura GRUNDY3; Seth LAURENSON3; Stuart
BRADLEY2,4; Val SNOW3
1

Department of Physics, The University of Auckland, New Zealand
2

Auckland UniServices Ltd, New Zealand
3

AgResearch Ltd, New Zealand

4

Inverse Acoustics Ltd, New Zealand

ABSTRACTThe paper addresses the problem of measuring free water on the surface of agricultural soils by an accurate
real-time acoustic method. The generation of free water is the result of a fine balance between the irrigation
rate and the rate at which the soil can transport water away from the surface and is the primary cause of
inefficient and environmentally-harmful losses during an irrigation event. The innovative component of theproject is vested in the development of directional acoustic arrays and sophisticated signal processing which
can remotely detect free water on the soil surface via changes in reflectivity. The proposed method estimates
the amount of free water on the soil surface based on the changes in the amplitude of the reflected soundwaves. Our preliminary results show that sound wave reflectivity depends on the proportion of the soil surface
covered by water. The presented results are based on both laboratory and field measurements.
Keywords: Acoustic sensor array, Water measurement, Reflected sound wave

1. INTRODUCTION
Irrigation provides significant economic benefit to New Zealand and to farmers (1, 2). However,when irrigation transports pollutants (nitrogen, phosphorus, sediment, faecal bacteria and othermicroorganisms) to water bodies it degrades water quality and so causes significant environmental
harm (3-5). A smart technology to improve irrigation by avoiding the transport of pollutants would betransformative for both the industry and the environment. Most of the transport of pollutants occurs
when free water is generated on the soil surface (3). Free water is not bound to the soil surfaces but
is instead free to flow directly into water bodies, carrying with it pollutants. The conditions on thesoil surface that lead to free water vary strongly and unpredictably in space and time and current smart
irrigation technology cannot respond to this variation.
Soil is a porous medium with a range of pore sizes and a complex geometry of pores (6-8). The
combination of the size range and geometry of the pores opening to the soil surface, slope and microtopography of the soil surface with the rate at which irrigation is applied determines the developmentof small isolated pockets of water and then onto the generation of surface water flow (9, 10). The soil
surface is naturally rough (9, 11, 12) while the small pockets of water that develop in the surface
depressions is smooth. As the proportion of the soil that is covered in water increases, and therefore
the risk of runoff also increases, we expect that the acoustic reflectivity of the soil surface will increase.
There is a considerable literature around ground surface acoustic reflectance and absorption
properties (13), as a sound wave travels both directly and via reflections from the surface. The semi empirical models of sound propagation depend on acoustic flow resistivity, and other parameters suchas soil porosity. The acoustic flow resistivity is a measure of the air space volume in the soil near the12870

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surface with sound penetration being limited to around 10 mm (14). The near-surface nature of theacoustic interaction is ideal for the purposes of sensing the presence of free water on the soil surface,as water is a more acoustically reflective surface than soil. According to the previous studies, porosityand flow resistivity are considered to be the most important soil characteristics that affect acousticimpedance. The amplitude of the reflected sound wave is expected to increase as the soil becomesmore saturated. Rather limited studies can be found in the literature regarding acoustic reflectivity
and saturated soil conditions (15-19).
The objectives of this study were to investigate the onset of free water in the soil via changes inreflectivity of the sound waves and to develop an inexpensive and accurate free water sensor for
irrigation systems. The preliminary results presented in this paper are based on both laboratory andfield measurements.

2. THEORETICAL ASPECTS OF THE ACOUSTIC SYSTEM
As described below, we used an acoustic array antenna of length 0.8 m. The ‘far field’ region(Fresnel parameter > 1) for this antenna is beyond a range of about 1 m at a frequency of 4 kHz. In
our laboratory setup, the distance between the array center to the center of the tray holding the target
soil surface is 1.56 m (2.6 array diameters). Thus, the “far field” approximations can be used. The tray
subtends an angle of about ±4° at the array.
2.1 Phased Array Theory
A linear array of regularly and closely spaced seven KSN1005A super-horn speakers wasconstructed. This geometry can be described by M omni-directional speakers having spacing (20):(1)d D/ M
where D is the length of the array, giving a normalised intensity patternI TI0§1·sin 2 ¨ kDsinT ¸
1
©2¹2M
§ 1·
sin 2 ¨kDsinT ¸© 2M¹(2)where k is the wavenumber. The KSN1005A speakers are not omnidirectional, but have an angularresponse, which changes little over the beam-widths. Figure 1 shows the intensity pattern for M = 7,
d = 0.08 m, f = 1 kHz and 3 kHz, and sound speed c = 340 m s -1. The half-power half beam-width isc/(2Mfd), or 17° at 1 kHz.

Figure 1 – The theoretical beam pattern for the M = 7 speaker array at frequencies of 1 kHz and 3 kHz
We have evaluated the design in the laboratory environment over the frequency range of 0.5 kHz
to 4 kHz in 100 Hz steps, and with time delays between array elements giving peaks shifts of 0°, ±30°,±45°, and ±60°. The angle of sound wave propagation for the phased array can be altered directlyfrom the code. Thirty-six frequencies, seven delays, and measurements at sixteen microphone
positions were conducted. One hundred measurements were taken on average for each of these
configurations. Figure 2 shows an example of the results for these tests.

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Figure 2 – Measured beam pattern compared with theoretical at 3.4 kHz and a zero progressive time delay
between speakers
There are some variations between theory and measurement around the central beam lobe, andlarger variations at larger angles. These are most likely due to small variations in speaker gain acrossthe seven speakers. This was optimized by the equalization of the gains for all speakers in the final
configuration.
2.2 Reflection from Soil and Water
Reflectivity from soil can be estimated by Attenborough’s 4-parameter model for the acoustic
impedance of a ground surface (21). Typical values of the model’s parameters are soil porosity =0.3, soil tortuosity = 1.35, flow resistivity = 10 – 103 kPa s m-2 , and pore shape factor ratio = 0.75.
Some results are shown in Figs. 3 and 4, using these values.

Figure 3 – Normalised acoustic ground impedance as a function of frequency

Figure 4 – Reflectivity as a function of incident angle
The greatest frequency variation between water and soil in impedance occurs at low frequencies

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(Figure 3). However, the highest contrast between a water surface (which is generally considered tohave a reflectivity of 1) and a soil surface is at higher frequencies (Figure 4). Given the generally
sharper beam at higher frequencies, and the better soil-water contrast at higher frequencies, theoptimum operating frequency for future experiments was selected in a range from to 2 to 4 kHz.

3. LABORATORY BASED MEASUREMENTS
3.1 Experimental Set Up
The preliminary experiments were designed to determine how different soil water contents affected
the amplitude of the acoustic signal. The laboratory set up for these e xperiments is shown in Figure
5.

Figure 5 – Layout of the phased array of speakers (at left), the sample tray (center), and the single
microphone (right)
The acoustic sensor consists of the phased acoustic transmitting array and an omnidirectional
microphone. The sample trays measured 0.385 m by 0.285 m with the height of dry soil being 0.75 m
above the laboratory floor. The dry soil mass was 8.4 kg and estimated soil dry bulk density used forthese tests was 1.02 kg m-3 . The signal amplitudes were recorded for a dry soil, and wet soil with 1 L,
2 L of water added to the sample tray and an over saturated soil (3 L of water added) conditions. The
latter sample showed free water covering the soil surface. Soil with added water samples were allowed
to equilibrate for at least 24 hours under a plastic cover to prevent slow evaporation.
The laboratory experiments were carried out in an anechoic chamber and experimental code tocontrol the acoustic system was created in MATLAB R2015b.
3.2 Results
Initial tests were conducted in order to determine whether there is a change in signal strength when
the soil becomes saturated and pooling of surface water begins and to what extent. The definition of‘saturated’ and ‘pooling’ here is very crude at this stage, and simply referrers to the visually observed
surface water in a tray. Measurements were conducted on a dry soil, and with 1, 2, and 3 L of water
added to the sample tray while the acoustic array angle was locked at 45 ο. The results of thesemeasurements are shown in Figure 6.

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Figure 6 – Normalised (against the relevant dry soil value) microphone output at selected frequencies in therange 0.5 kHz to 3.9 kHz versus water added to the soil sample
Multiple runs were performed over each sample and at each frequency and an averaged result
plotted on Figure 6. The errors of these measurements were plotted within the size of data points foreach measurement. The 1 and 2 L cases give very similar amplitude reflectivity values. All
measurements on wetted soil shows amplitudes around 50% higher than for dry soil measurements
and the amplitudes for 3 L added water dramatically increased compared to th e 1 and 2 L water added
to the dry soil. In addition, these measurements showed that variability increased a bove a frequency
of about 3 kHz. The variability above 3 kHz is most likely due to some direct sound combining
destructive and constructively with the reflected sound.

4. PRELIMINARY FIELD TESTING OF THE ACOUSTIC SYSTEM
4.1 Experimental Set UpThe geometry comprised the speaker array mounted on a tripod and angled at 45° and at the heightof the single microphone, which was also on a tripod. Between the two , near the ground, a soil-filled
pan with dimensions 0.6 x 0.44 m was placed. This pan was set up a manometer so that water level
could be continuously varied. On a frame above these items a downward facing 3D structured lightcamera (Intel RealSenseTM Depth Camera D415) viewed the earth-filled tray, so that its topology
could be recorded, including any pooled water. To one side of the array-microphone line a GoPro
camera (GoPro Hero 2018) also recorded the surface conditions in the tray (Fig. 7).

Figure 7 –A schematic diagram of the first field experimental set up with the dimensions.
Sinusoidal pulses of various frequencies and of 10 ms duration were generated and directed toward
the soil-filled pan. There are multiple paths to the microphone from each of the seven speakers:
directly across the space between speaker and microphone; and bouncing off the soil and surrounding
platform and then back upward to the microphone. Acoustic foam placed around the edges of the soilfilled pan helped to reduce the unwanted reflections from the platform.

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4.2 First Field Experiment Results
In these experiments, all soil samples were initially wet. The “most saturated” example had water
ponded on the surface. Figure 8 shows that, over a range of frequencies centred on ~ 3 kHz, there wasa clear and consistent demarcation between the ponded water condition and the soil surface conditionof wet but not ponded.

Figure 8 – The relative amplitude of the correlated outputs for a range of frequencies and for a ponded soil
surface (blue) and a wet but non-ponded soil surface (red).
The field experiment showed that the basic setup worked outside of the acoustically-controlledlaboratory environment. There was a good separation between a soil surface that was merely wet anda surface that was ponded.
4.3 Second Field Experiment Results
The acoustic reflectivity experimental procedures during the second field test were carried out ina similar way to the first test. The dimensions and respective distances are indicated in Figure 9. The
main difference was in the acoustic signal codes used during this experiment. This time a 13-step
Barker code (22) and a two-tone signal were tested on a range of soil conditions in order to increase
acoustic sensor performance and tolerance towards environmental sources of noise. Acoustic
reflectivity measurements were done for a range of surface wetting conditions (controlled using themanometer), and for frequencies 2.5 kHz – 4.5 kHz with a 0.4 kHz step. Both a tonal pulse, and a 13step Barker phase-encoded pulse were evaluated. The Barker code was found to give clearer signals.

Figure 9 – A schematic diagram of the second field experimental set up with the dimensions.
The 3.3 kHz frequency consistently gave the greatest amplitudes, most likely due to the efficiencyof the speakers being high at this frequency (note that the microphone response is flat with frequency).
The set up was also tested on a soil sample when it was covered with short grass and it was foundgrass and its roots in the soil had no clear effect on measured signals during these experiments.
Figures 10 and 11 are characteristic of these results and the errors of these measurements wereplotted within the size of data points for each measurement. Figure 10 demonstrates changes of theamplitude of the reflected acoustic signal when soil tray was slowly filled in with water, and showschanges as more and more of the soil surface depressions became filled with water over time. During2875

this experiment, the measurements were progressively done from dry soil to the state where all thesoil surface depressions were connected and filled with water.

Figure 10 – Normalised amplitude response using a 13-step Barker code for 3.3 kHz (Filling)
Figure 11 shows the reverse process, where initially all the soil surface depressions were connected
and filled with water, and at the final stage water was slowly drained from the soil tray over timeleaving a wet soil surface.

Figure 11 – Normalised amplitude response using a 13-step Barker code for 3.3 kHz (Emptying)
There is a similar variation in measured amplitude at all frequencies. Here the percentage of thetray covered in free water is compared with the normalised acoustic amplitude response. There is avery clear increase in acoustic output up to a free water coverage of 40%, and some indication thatthe amplitude falls off at very high water coverage. A 13-step Barker code gave best results withrespect to signal-to-noise ratios. The trends shown in Figures 10 and 11 are reproducible and similar
for both experiments. Figure 11 is lacking the data points for the conditions from 0 to ~ 10% and ~90% of soil area covered with water, as during the second experiment, the soil absorbed some water
and was wet, while in the first experiment it was initially dry.

5. CONCLUSIONSPreliminary work to develop an acoustic sensor to measure free water on the soil surface wascarried out and the sensor system has undergone testing in both laboratory and field environments.
Angles from 30 ο to 60ο were tested during the laboratory stage of the sensor development. Based on
those results, an angle of 45° was chosen for further testing in the field. The optimal range of
frequencies was found to be between 2.5 – 4.5 kHz. This was established over different soil conditions
including presence of short grass. Two different acoustic signal codes were tested. It was found that a13-step Barker code gave better signal-to-noise tolerance compared to more simple tonal signals. The
amplitude of the reflected acoustic signal increased with both soil moisture and the proportion of thesoil surface covered in water. Our preliminary field results show that the acoustic response is reliable
for measurements of the soil surface area covered with water in a range from ~ 10 to ~ 80%, while
there are some inconsistencies in acoustic data outside of this range. These inconsistencies might beassociated with the prevalence of diffuse components over the specular components in the sound2876

waves reflected from the soil surface, as these measurements are close to the lower or higher end ofdifferent soil conditions. Although the registered signal appeared to be a bit more scattered, it was
found that short grass did not significantly adversely affect the measured acoustic signal amplitude.

ACKNOWLEDGEMENTS
The work reported here was conducted by AgResearch, the University of Auckland and Inverse
Acoustics and was funded by New Zealand’s Ministry of Business, Innovation and Employment via
its Endeavour Fund Smart Ideas contract C10X1708.

REFERENCES
1. Morgan M, Bidwell V, Bright J, McIndoe I & Robb C. Canterbury strategic water study. Lincoln Environ.
Rep. No 4557/1, 2002.
2. Irrigation
NewZealand.
Socio-economic
value
ofirrigation.
Availableat:
http://irrigationnz.co.nz/knowledge-resources/irrigation-new-zealand/socio-economic-value-ofirrigation/. (Accessed: 4th June 2017).
3. McLeod M, Aislabie J, Ryburn J & McGill A. Regionalizing potential for microbial bypass flow through
New Zealand soils. J. Environ. Qual. 2008; 37.
4. Carter DL. in Irrigation of Agricultural Crops. 1990; Agronomy Monograph No. 30: 1143–1171.
5. Blaustein RA, Pachepsky YA, Shelton DR & Hill RL. Release and Removal of Microorganisms fromLand-Deposited Animal Waste and Animal Manures: A Review of Data and Models. J. Environ. Qual.
2015; 44: 1338.
6. Jure WA, Gardner WR, Gardner WH Soil Physics 5th Edition. Wiley and Sons, New York, 1991: pp. 328.
7. Vereecken H. et al. Modeling Soil Processes: Review, Key challenges and New Perspectives. Vadose Zo.
J. 2016; 15: 1–57.
8. Papanicolaou ATN. et al. Spatial variability of saturated hydraulic conductivity at the hillslope scale:
Understanding the role of land management and erosional effect. Geoderma. 2015; 243–244: 58–68.
9. Peñuela A, Javaux M, Bielders CL. How do slope and surface roughness affect plot-scale overland flow
connectivity? J. of Hydr. 2015; 528:192-205.
10. Appels WM, Bogaart PW, van der Zee SEATM. Surface runoff in flat terrain: How field topography andrunoff generating processes control hydrological connectivity. J. of Hydr.2016; 534: 493-504.
11. Garcia Moreno R, Diaz Alvarez MC, Saa Requejo A, Valencia Delfa JL & Tarquis AM. Multiscalinganalysis of soil roughness variability. Geoderma. 2010; 160: 22–30.
12. Xingming Z, Tao J, Xiaofeng L, Yanling D & Kai Z. The temporal variation of farmland soil surface
roughness with various initial surface states under natural rainfall conditions. Soil Tillage Res. 2017; 170:
147–156.
13. Salomons EM. Computational atmospheric acoustics. Springer Science & Business Media. 2001.
14. Taraldsen G & Jonasson H. Aspects of ground effect modeling. J. Acoust. Soc. Am. 2011; 129: 47–53.
15. Brandão E, Lenzi A & Paul S. A review of the in situ impedance and sound absorption measurementtechniques. Acta Acust. united with Acust. 2015; 101: 443–463.
16. Mohamed MHA & Horoshenkov KV. Airborne acoustic method to determine the volumetric watercontent of unsaturated sands. J. Geotech. geoenvironmental Eng. 2009; 135: 1872–1882.
17. Horoshenkov KV & Mohamed MHA. Experimental investigation of the effects of water saturation on theacoustic admittance of sandy soils. J. Acoust. Soc. Am. 2006; 120: 1910– 1921.
18. Cramond AJ & Don CG. Effects of moisture content on soil impedance. J. Acoust. Soc. Am. 1987; 82:293–301.
19. Embleton TFW. Tutorial on sound propagation outdoors. J Acoust Soc Am. 1996; 100 (1): 31-48.
20. Salomons EM. Computational atmospheric acoustics.2001. Kluwer Academic Publishers. Netherlands.
21. Sabatier JM., Raspet R & Frederickson CK. An improved procedure for the determination of groundparameters using level difference measurements. J Acoust Soc Am. 1993; 94 (1): 396-399
22. Adams ER. Detection and Characterization of Phase-Coded Radar Signals. Cranfield University(RMCS). UK. 2004. SPC 03-3030.

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