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Soil hydrophobicity (SH) impacts agriculture. SH as an intrinsic soil property affects water runoff,retention, and chemical transport significantly. Therefore, if one attempts to assess water use in orderto improve the efficiency, hydrophobicity mapping would provide direct evidence for water-usedecision-making. The commonly used method for quantifying soil hydrophobicity is Water DropPenetration Time (WDPT) test. This procedure or other lab methods are sample based and time-consuming, which, if used in the field in a large area for hydrophobicity investigation, becomeextremely ineffective. The lack of large-scale SH monitoring and assessment, if not resolved, mayultimately prevent realizing optimal water-use efficiency for crop and farming management. Theprimary goal of this US-Israel Collaborative Project is to develop a soil-hydrophobicity analyticssystem with rapid and cost-effective hyperspectral sensing and computing capabilities, whichultimately aids practitioners for improved and rational decision-making in using agricultural water.Two specific objectives were originally designed:(1) proof of the hypothesis that SH can be sensed and identified using hyperspectral imaging

methods;(2) development of a micro-UAV based SH sensing and analysis system with both laboratory and

field verification.As we carried out the project, we have enhanced the original objectives as we discovered morechallenges and knowledge gaps by considering (1) a mechanics-based modeling approach tounderstanding the SH effects on soil properties; (2) an aerial-ground integrative sensing approach tofully characterizing soil-water properties; and (3) a deep learning approach to hyperspectral imageunderstanding.

Introduction and Objectives

International and Interdisciplinary Team

Soil Hydrophobicity Identification through Hyperspectral Spectroscopy

Hyperspectral sensing at the ground level (above) provides point-based spectral characterization of materials. Based on the success ofproving that SH is identifiable using point-based hyperspectralsensing, the envisioned goal of performing rapid hyperspectralremote sensing and SH mapping at a geospatial scale has beenattempted. However, three technical challenges were recognized:(1) Selection of hyperspectral imaging system for small or micro-

UAV use(2) Aerial imaging is affected by in-situ weather, soil water,

temperature, and solar radiance.(3) Hyperspectral image understanding, and sensor data fusion

UAV-based Hyperspectral Imaging and Aerial-Ground Sensing

Physics-based Soil Hydrophobicity Modeling

Conclusion and Potential ImpactDue to the SEED grant nature, the US-Israel project team has been focusing on knowledge exploring and technology development. To this date, we have preliminarily proven that using soil samples in the field and through a rigorous verification-validation data mining process, SH can be detected using full-spectrum hyperspectral data. This paves the way for developing an aerial UAV-based assessment system. Towards developing the sensing infrastructure, we have developed a new concept of aerial-ground networked sensing for integrated imaging, mapping, and ground-truth data collection. A unique hyperspectral ‘snapshot’ camera system has been acquired (through Cubert Inc.) and noise modeling and deep learning-based hyperspectral image analysis are being developed. Fight campaigns will be conducted in both US and Israel in the coming year. Towards analytical understanding of SH effects on water penetration, 1D and 2D simulation methods were developed by the project team. Flight campaigns will be conducted in both US and Israel in 2017.References:[1] Thenkabail, A., Lyon, P. S., Huete, J. G., & Ben-Dor, E. (2011). Characterization of soil properties using reflectance spectroscopy. In Hyperspectral Remote Sensing of Vegetation (pp. 513-558). CRC Press.[2] Chen, J., Chen, Z. (2016). Energy-Efficient Aerial and Ground Sensing for Rapid Field Response. 2016 Defense Innovation Summit (Poster presentation). [3]. Chen, H., Chen, Z. (2016). Lattice Boltzmann modeling of two-phase flows in complex porous media considering surface hydrophobicity. The 6th International Conference on Applied Mechanics and Civil Engineering, Hong Kong, China.

Acknowledgement Funding from a University of Missouri System’s Intercampus Interdisciplinary Grant and a USDA-NIFA Grant (Award No. 2015-68007-23214) for ZhiQiang Chen, Hao Chen, Jianfei Chen, and Shimin Tang is greatly appreciated. The funding from the BARD office at Israel for supporting Eyal Ben-Dor, Rony Wallch, and their students is greatly appreciated.

ZhiQiang Chen1, Eyal Ben-Dor2, Rony Wallach3, Hao Chen4, Jianfei Chen5, Shiming Tang61Associate Professor, School of Computing and Engineering, University of Missouri, Kansas City, MO (Corresponding Email: chenzhiq@umkc.edu)

2Professor, Department of Geography, Tel Aviv University, Israel 3Professor, Department of Soil and Water Sciences, Hebrew University of Jerusalem, Israel

4Postdoctoral Research, 5Graduate Student, 6Graduate Student, School of Computing and Engineering, University of Missouri, Kansas City, MO, USA

Soil Hydrophobicity Sensing, Computing, and Modeling: An Interdisciplinary FrameworkOctober 12-13, 2016, Washington, D.C.

PD (Chen)

PD (Chen) Co-PD (Ben-Dor) Co-PD (Wallach)

Two graduate students

One graduate student and one post-doc Two graduate students

Besides knowledge discovery and technologydevelopment, one goal of this project is to buildan internationally collaborative research andeducation infrastructure focusing on soil-hydrophobicity sensing, modeling, andassessment for soil and agriculture sciences. TheUS-Israel team has been very successfullyoperating since the project’s kick-off.

Soil hydrophobicity is a measurable quantity in terms ofthe solid-water contact angle (θ) at the three-phase (i.e.,gas-liquid-solid) boundary line between a partiallywettable soil matrix and water beads. Soil with the contactangle of 90 < θ < 180 shows reduced wettability; that is,infiltration of water into the soil matrix is decreased,indicating water repellency or soil hydrophobicity. Soilorganic carbon (SOC) contents have shown to bepositively correlated with soil hydrophobicity, althoughothers have found more complex relationships or nogeneral relationship between them.

Soil hydrophobicity in general has adverse impacts onagriculture. First, it directly modifies many physicalproperties of soil, including water retention, soilsorptivity, water flow, and solute transport regimes. Thesechanges in soil properties affect the rainfall-runoffrelationship and spatial-temporal water distribution in thesoil profiles. Soil hydrophobicity also changes the flowpatterns of the subsurface water by creating preferentialflows. In some cases, more than 80% of the percolatingwater is transported through preferential flows bypassingthe soil matrix that may further enhance leaching orgroundwater contamination.

Original pictures courtesty of www. http://soilquality.org.au/

Wessolek et al., 2008

(a) t = 38 ms (d) t = 38 ms

(b) t = 153 ms (e) t = 153 ms

(c) t = 413 ms (f) t = 413 ms Figure 2. Evolution of density field of liquid phase: (a), (b) and (c) in hydrophilic condition; (d), (e) and (f) in the

hydrophobic condition.

One of the most significant adverse effects of SH to soil is introduction of preferential (or ‘finger’) flow. 1-D modeling of finger flow has been well modeled by the Richard Equation method. However, is is not able to simulate unstable flow (e.g. due to SH) in porous media.

Comparative illustration of SH treated plants and non-treated plants

Hydrophobic soil observed in the field

SH Definition

Soil Hydrophobicity and Adverse Impact

PDs Expertise• Chen: Remote sensing and image-based learning and detection; mechanics-based modeling;

and UAV and sensor network technologies• Ben-Dor: Remote sensing; hyperspectral spectroscopy; soil sciences• Wallach: soil and plan sciences; soil-water modeling

Remote sensing technology plays a major role in monitoring and studying in theagriculture arena. The last 20 years have seen the evolution of a new RStechnology—hyperspectral remote sensing (HRS) that has proven very useful inmany terrestrial, atmospheric, marine, and environmental applications. In general,reflectance spectroscopy in the VNIR-SWIR (350-2,500nm) region enableseffective characterization of soil properties due to their intrinsic reflectancefeatures of the following main soil ingredients: (1) Main chemical chromophores;(2) iron oxides; (3) organic matter (OM); (4) hygroscopic water content; (5) clayminerals; and (6) carbonates. Illustrations in the left show their spectral signatureprofiles [1].

Therefore, one may hypothesize that soil hydrophobicity as a ‘lumped’ materialproperty (due to many intrinsic materials, especially, organic matter) can bedetected and identified from hyperspectral profiles. Prior to proposing this project,this was preliminarily tested (partially) true using lab-based hyperspectralspectroscopy analysis.

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hyperspectral sensing; right: preliminary success in revealing spectral signatures of hydrophobicity for two soil samples

To test the hypothesis more rigorously, two challenges were recognized: (1) how to perform hyperspectralsensing more flexibly and at a larger scale? (2) how to create a verifiable and predictive model (hyperspectraldata as input and degree of SH as output)?Led by Dr. Ben-Dor at Tel Aviv University, a ’SoilProb’ system was developed for rapid hyperspectral sensingin the field.

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Left: the design of ’SoilPro’ and field operation

Left: verification of the effectiveness of the developed SoilPro through comparing with conventional methods: (1) bare fiber method; (2) contact probe.

These results confirm the the accuracy of the SoilPro system.

To test the hypothesis and develop a predictive model, 17 field samples were collected andWDPT tests were conducted at the Hebrew University of Jerusalem led by Dr. Wallach. In themeantime, spectral data were collected using SoilPro in the field at the same locations ofthese samples. The PARACUDA system was used to correlate spectral values with the WDPTtimes (secs). The following flow chart and results show statistical significance of the resultingmodel and the validation performance.

Above: hyperspectral and WDPT data mining workflow; bottom left to right: significance of spectral prediction, and predictive model validation.

Above: different point-based hyperspectral data, in hyperspectral images, each pixel owns a hyperspectral file. Spatial pixels in the image domain construct the geospatial distribution of the spectral files, hence enabling the possibility of soil hydrophobicity mapping at a much larger scale (e.g. 2~3 km).

Traditional spectral imaging (as used in many orbital and airborne sensors) are based on spatial-scanning (push-broom type). To accommodate small copter-UAVs and to realize real-time hyperspectral imaging, the ‘snapshot’ type hyperspectral imaging system was adopted. The Cubert’s S185 camera was selected, and it will be integrated with a DJI Matrice 600 platform. The camera is able to obtain full hyperspectral cubes (135 bands) in 1/1000 sec. Such real-time ‘snapshot’ capability enables both geospatial real-time hyperspectral imaging and the possibility of 4D hyperspectral image-based field terrain reconstruction.

The project team realized the significant complexity towards rapid-geospatial-scale SH mapping and agreed that hyperspectral data alone is not sufficient in constructing a predictive SH model that predicts WDPT values (which directly measure the degree of SH). Weather data, soil temperature, moisture and dynamic solar irradiance data should be measured in the real time along with the hyperspectral imaging data. At UMKC an aerial-ground sensing network methodology has been developed. Key issues such as route and energy planning for drones, and ground sensor network (Libelium smart sensors) have been developed [2]. A ground sensor network activation technology was developed and issued through UMKC Technology Transfer (Disclosure #16UMK017).

Left: Dr. Wallach and his students developed a 1-D moving boundary model that consider the non-stability, saturation overshoot, due to variable soil wetting patterns

Bottom: effects of contact angles (CA as SH indices) on saturation distribution

As can be seen that the increased CA causes larger moisture content at the finger tips. If moisture content at the tip is larger then the tail moisture content, the flow pattern transit from stable Richards distribution to the non-monotonic saturation overshoot .

No 2D simulation of preferential flows with explicit consideration of geometric boundary conditions (as in the soil porous media) and the soil particle’s surface hydrophobicity is found in the literature. However, such simulation provides direct evaluation of the hydrophobic effects. Multi-phase fluid-porous media modeling based on the Lattice Boltzmann Method and the Shan-Chen model are developed in this project [3].

Left: comparative studies of the effects of soil hydrophobicity and soil hydrophilicity on distributed water flow in the soil porous media (water is set upper half; and gas is lower half)Right: simulation 2D preferential flow due to hydrophobicity (water is set at the surface only)