Quantifying spatial distribution of soil moisture using a cosmic ray and capacitance sensor network

Auro Almeida, Craig Baillie,

Dale Worledge, Philip Smethurst CSIRO Ecosystem Sciences and

Sense-T, Private Bag 12, Hobart TAS 7001, Australia

Email auro.almeida@csiro.au

Ritaban Dutta, Andrew Terhorst Intelligent Sensing and Systems

Laboratory and Sense-T, CSIRO, Hobart, TAS 7001, Australia

Trenton Franz

Dep. of Hydrology and Water Resources, Univ. of Arizona,

Tucson, Arizona 85721.

Abstract— This study combines soil moisture capacitance probes and cosmic-ray neutron probe with a Fuzzy Inference System to estimate variability of soil moisture in a ~28 ha circle over time. The technique demonstrates that the cosmic-ray neutron probe’s average neutron count and a network of capacitance probes may be useful for estimating spatial and temporal variability in soil moisture patterns within the probe’s footprint.

I. INTRODUCTION Soil moisture has a strong influence on food and fiber production. Information about soil water content at field-scale at hourly or daily time steps is critical for managing agricultural production and irrigation scheduling. However, this information is usually unavailable. This information is also important for water management at larger scales, e.g. for assessment of flood and erosion risk in catchments. Hence, there is a need to continuously predict landscape soil moisture. The cosmic-ray neutron soil moisture method [1] [2] and probe developed by Hydroinnova, NM, USA, enable measurements of the near surface soil moisture using low-energy neutrons generated by incoming high energy cosmic rays. The probe measures ambient neutron intensity and thus area-average water content over a ~28 ha area (a circle with a radius of ~300 m at sea level in dry air [7]) and vertical depths of ~12 to 70 cm [1]. This is a new technology increasingly used in the USA [1], (data available at http://cosmos.hwr.arizona.edu/), and in several other countries including Australia which has 11 probes installed across the country covering different biophysical regions. The probe measures fast or moderated neutron counts that are inversely correlated with average soil moisture content [3], [4]. However this sensor does not predict how the soil moisture is distributed within the footprint. We present here the results of one of the cosmic-ray neutron probes and corresponding data from capacitance probes deployed at Tullochgorum farm in the northeast of Tasmania, Australia. In addition, we present an example of the ANFIS framework at the study site and the potential for subfootprint estimates of soil moisture.

II. METHODOLOGY We established a network of 25 capacitance probes measuring five depths (10, 20, 30, 40, 50 cm) distributed at 1, 25, 100 and 200 m radius from the cosmic-ray neutron probe (Fig. 1) The two main objectives of the study were: 1) Calibration of the cosmic-ray neutron probe; 2) Modeling the spatial distribution of soil moisture by combining data from capacitance probes and machine learning techniques. A Multiple Adaptive Neuro Fuzzy Inference System (m-ANFIS) framework was developed to achieve the second objective. The cosmic-ray neutron probe (Fig. 2a) is a method used to estimate area-averaged hourly soil moisture for an effective depth over its ~28 ha footprint. To estimate the subfootprint soil moisture at the study site calibrated capacitance probes were also installed (Fig. 2b). The study area is located in a gently sloping area of pasture; the altitude varies between 244 and 256 meters above sea-level.

Fig. 1: Cosmic-ray neutron probe location (centre) area of measurement and distribution of the 25-capacitance probes.

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978-1-4673-4642-9/13/$31.00 ©2013 Crown

Fig. 2: a) Cosmic-ray neutron probe and raingauge b) Clogger. We compared average soil moisture from theprobes with the soil moisture estimated fromneutron probe for the same time and respectidepth. Machine learning techniques were uselinear, data-driven approach to predict subfomoisture patterns.

III. RESULTS A. Soil moisture profiles using capacitanceThe Sentek EasyAg 50 capacitance probes wmeasuring volumetric soil water content b105oC and soil bulk density on samples fro50 cm) in close proximity to the sensor. example of data from three of the twenprobes, demonstrating high variability of soithe landscape. B. Area-average soil moisture using cosmic

probe Neutron counts from the cosmic-ray neucorrected to account water vapor [8], lattice organic carbon [1]. Soil moisture effective depth of the cosmic-rand capacitance probes where calculatedfollowing the procedure described in [5] adepth varied from 13-33 cm and soil moistu0.3 cm3 cm-3 during the study period.

Fig. 3: Variability of soil moisture over time in the topthe 25-capacitance probes.

Soil moisture for the top 30 cm

a)

Capacitance probe and

e 25 capacitance m the cosmic-ray ive effective ed to adapt a non-otprint soil

e probes were calibrated by by weight loss at m five depths (0-Fig. 3 shows an

nty five installed il water content in

c-ray neutron

utron probe were water [7] and soil

ray neutron probe d for each time and [6]. Effective ure from 0.035 to

p 30 cm of soil at 3 of

Fig 4: Comparison of measured neutron counprobe and respective average soil moisture fcorrected to effective depth. A comparison of neutron counts acorrected to effective depth from thfor each timestamp shows good agredifferent sensor technologies (Fig. 4)C. ANFIS Based Soil Moisture EstimThe Neuro Fuzzy Inference SystemSugeno fuzzy inference system thanetworks and fuzzy logic principles Integrated hourly data , including corainfall (mm/hr), atmospheric pressdepths of soil moisture (cm3 cm-3) probes were used to train and test anrepresenting one node. Soil moisturusing capacitance probes were ustraining the ANFIS blocks. Fig. framework. Independent ANFIS blocks were traseries data from each probe. ANFIStraining and part for develop theevaluate the prediction performanclinear training-testing were used whwas separated into small sets. Iobservation in the integrated data sobservations were used for trainingwas round (0.6*N), where size of te(0.6*N)}.

Fig. 5 The m-ANFIS framework for dynamicfrom the network of capacitance soil moisture

0.0000

0.0500

0.1000

0.1500

0.2000

0.2500

2200 2400 2600

Soil

mo

itu

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Neutron

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nts from the cosmic-ray neutron from the 25 capacitance probes

nd average soil moisture he 25 capacitance probes eement between two very ). mation m (ANFIS) is a Takagi–at integrates both neural [9]. osmic-ray neutron counts, sure (mBar) and five soil

measured in capacitance n individual ANFIS block e measured at five depths sed as ground truth for 5 shows the m-ANFIS

ained with individual time S uses part of the data for e predictive model, and e. For this study various ere the integrated data set If the total number of set was N, if 60% of the g then the training matrix esting set was {N - round

ally processing multi nodal data e sensors

y = 3E+09x-3.018

R² = 0.8828

2800 3000 3200

n count

The best generalization and prediction accuracy was 97% while using 90% of the data for training and 10% for testing; where as 92% prediction accuracy was achieved using 70% data for training and 30% for testing. Prediction accuracy between two time series data was estimated using cross correlation calculation. High prediction accuracy indicates the potential effectiveness of the capacitance probe network and m-ANFIS system for cosmic-ray neutron probe calibration. Multi nodal predictions from the m-ANFIS framework are used to develop dynamic, hourly, soil water surfaces using cubic interpolation (Fig. 6). The dynamic soil moisture surface created from the single cosmic-ray neutron probe using machine learning could potentially be a novel framework for solving complex soil moisture prediction problems across the landscape. Fig. 6(a) shows the surface produced for 11th of June 2013 at 12:00 AM from the 25 capacitance probes and Fig. 6(b) shows a similar surface estimated using the cosmic-ray neutron probe applying m-ANFIS, the percentage difference between the two estimations is shown in Fig. 6(c).

Fig. 6 a) Soil moisture surface estimated by interpolation of capacitance probes, b) soil moisture produced by the m-ANFIS (m=25) with the soil moisture from cosmic-ray neutron probe, c) Soil moisture error surface to demonstrate the similarity measure between capacitance probes surface and m-ANFIS surface.

IV. CONCLUSIONS Combining soil moisture measurements from the network of capacitance probes and the cosmic-ray neutron probe with the machine learning technique provided a new approach to quantify soil moisture variability over time and location in an area of ~28 ha. Cosmic-ray neutron-based predictions of soil moisture were adequate for testing points within the training period and provided a similar visualization of soil moisture surfaces. The next step is to evaluate the reliability of the algorithms for predicting soil water from cosmic-ray neutron probe data beyond the training period, which, if successful, would enhance the effectiveness of the cosmic-ray neutron probe.

ACKNOWLEDGMENT We acknowledge Rob and James Marshall for allowing us to run our experiment on their farm. We thank David McJannet from CSIRO for the technical discussions on operation of the cosmic-ray neutron probe. The received funding assistance from the Tasmanian node of the Australian Centre for Broadband Innovation, and the Tasmanian Government.

REFERENCES [1] M. Zreda, W. J. Shuttleworth, X. Xeng, C. Zweck, D. Desilets, T. E.

Franz, and R. Rosolem. The COsmic-ray Soil Moisture Observing System, Hydrology and Earth System Sciences, 16, 4079-4099. doi:10.5194/hess-16-1-2012.

[2] D. Desilets, M. Zreda, and T.P.A Ferre. Nature's neutron probe: Land surface hydrology at an elusive scale with cosmic rays. Water Resources Research, 46 (11): W11505, 2010.

[3] M. Zreda, D. Desilets, T.P.A. Ferre, and R.L. Scott. Measuring soil moisture content non-invasively at intermediate spatial scale using cosmic-ray neutrons. Geophysical Research Letters, 35 (21): L21402, 2008.

[4] T. E. Franz., M. Zreda, R. Rosolem, and P. A. Ferre. A universal calibration function for determination of soil moisture with cosmic-ray neutrons, Hydrology and Earth System Sciences, 17, 453-460. doi:10.5194/hess-17-453-2013.

[5] T. E. Franz, M. Zreda, P. A. Ferre, R. Rosolem, C. Zweck, S. Stillman, X. Zeng, and W. J. Shuttleworth. Measurement depth of the cosmic-ray soil moisture probe affected by hydrogen from various sources, Water Resources Research, 48. doi:10.1029/2012WR011871,2012.

[6] T. E. Franz, M. Zreda, R. Rosolem, and P. A. Ferre. Field validation of cosmic-ray soil moisture sensor using a distributed sensor network, Vadose Zone Journal, 11(4). doi:10.2136/vzj2012.0046, 2012.

[7] D. Desilets and M. Zreda Footprint diameter for a cosmic-ray soil moisture probe: Theory and monte carlo simulations, Water Resources Research. doi:10.1002/wrcr.20187, 2013.

[8] R. Rosolem, W. J. Shuttleworth, M. Zreda, T. E. Franz, X. Zeng, and S. A. Kurc. The Effect of Atmospheric Water Vapor on the Cosmic-ray Soil Moisture Signal, J. Hydrometeorol. doi:10.1175/JHM-D-12-0120.1, 2013.

[9] R. Dutta, A.Terhorst. Adaptive Neuro-Fuzzy Inference System Based Remote Bulk Soil Moisture Estimation: Using CosmOz Cosmic Ray Sensor. IEEE Sensors Journal, Volume:13 , Issue: 6, pp 2374-2381, 2013.