Research ArticleEvaluation of Calibration Method for Field Application ofUAV-Based Soil Water Content Prediction Equation

Donggeun Kim ,1 Younghwan Son ,2 Jaesung Park ,3 Taejin Kim,1 and Jihun Jeon1

1Department of Rural Systems Engineering, Seoul National University, Seoul KS013, Republic of Korea2Department of Rural Systems Engineering, Research Institute for Agriculture and Life Sciences, Seoul National University,Seoul KS013, Republic of Korea3Department of Bioresource Engineering, McGill University, Ste-Anne-de-Bellevue, QC, Canada H9X 3V9

Correspondence should be addressed to Younghwan Son; syh86@snu.ac.kr

Received 5 September 2018; Revised 21 November 2018; Accepted 9 December 2018; Published 2 January 2019

Academic Editor: Venu G. M. Annamdas

Copyright © 2019 Donggeun Kim et al. .is is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work isproperly cited.

.e objective of this study is to monitor the water content of soil quickly and accurately using a UAV. Because UAVs have higherspatial and temporal resolution than satellites, they are currently becoming more useful in remote sensing areas. We developed awater content estimation equation using the color of the soil and suggested a calibration method for field application. Since theresolution of the images taken by the UAV is different according to the altitude, the water content estimation formula is developedby using the images taken at each altitude. In order to calibrate the color difference according to lighting conditions, thecalibration method using field data were proposed. .e results of the study showed an altitude-specific estimation equation usingRGB values of the UAV image through linear regression. .e appropriate number of field data needed for calibration for siteapplication of the estimation equation was found between 4 and 10. On-site application results of the proposed calibrationmethodshowed RMSE accuracy of 1.8 to 2.9%. .us, the water content estimation and calibration method proposed in this study can beused in effectively monitoring the water content of soil using UAVs.

1. Introduction

.e water content is an important factor affecting thestrength and behavior of the soil. Monitoring the watercontent is also essential in determining the appropriateamount and timing of irrigation [1, 2]. Generally, sensorssuch as time-domain reflectometry (TDR) and frequency-domain reflectometry (FDR) are used to monitor the watercontent of soils. .e TDR sensors can measure the watercontent with high accuracy, but they are expensive [3–7]..e FDR sensors, on the other hand, are inexpensive butrequire site-specific calibration [8–10]. Because of theseproblems, it is difficult to monitor the water content usingsensors in a wide area.

Since the color becomes darker as the soil gets wet, manystudies have been conducted to estimate the water content ofthe soil using the color of the soil [11–13]. As a method of

monitoring the color of the soil, there is an observation usinga satellite image..emethod using the satellite image has theadvantage of monitoring a wide area periodically. However,the cost of satellite images is high, and the reliability of themeasured values is relatively low due to the low resolution.

Aerial photography using UAVs can be a proper al-ternative to satellite. UAVs are a cost- and time-effective toolin compared to traditional UAVs because they can providehigh-resolution aerial images within a few hours and haveshort repletion times [14, 15]. UAVs are used for variousareas because they are suitable for monitoring difficultterrain [16–18]. Moon et al. [18] had been classified landcover using aerial photography by UAVs. Park and Um [19]used UAVs to make a digital surface model (DSM) forsurveying construction areas. Also, Jeong et al. [20] de-veloped a monitoring system for civil works using UAVs.Niethammer et al. [21] and Stumpf et al. [22] had produced

HindawiAdvances in Civil EngineeringVolume 2019, Article ID 2486216, 10 pageshttps://doi.org/10.1155/2019/2486216

valuable digital terrain models of the landslide area in Franceby UAVs. Also, Rau et al. [23] used the UAV to observelandslides caused by typhoons. A number of studies are alsounderway to predict the water content of soil using UAVs.Park [24] performed a soil classification and characteristicanalysis of the forest and reclaimed land soil through thedigital image processing of UAV images. Chang and Hsu[25] estimated the soil water content using thermal infraredsensor-equipped UAVs.

When photographing with a UAV, the color of thepicture changes depending on lighting conditions. When thewater content is estimated by simply using the colors on thephotograph without considering the color change due tolighting conditions, a large error may occur..erefore, thereis a need for a monitoring method capable of correcting theerror caused by the difference in lighting conditions.

.e purpose of this study is to use a UAV image tomonitor the water content of the soil quickly and accurately.As mentioned earlier, as the ratio increases, the color of thesoil becomes darker, so we want to predict the water contentusing the color of the soil shown in the picture. At this point,UAVs were used to obtain pictures of monitoring areas withmore convenience. First, a water content prediction equa-tion was developed using RGB values in soil photography.As the resolution of the UAV image varies according to theflight altitude, the accuracy of the predicted equation wascompared by the flight altitude. Second, to improve accu-racy, a method of calibration is proposed using field databecause the colors of the UAV images change depending onthe lighting conditions. .e number of field data for propercalibration is proposed. Finally, to evaluate the applicabilityof the developed water content prediction method, the watercontent predicted by the UAV image was compared to theactual measured data.

2. Materials and Methods

2.1. Materials. Aerial photography and soil sampling wereperformed in the study area. .e study area is located inHwaseong-si, Gyenggi-do, South Korea (37°12′39.4″N,126°39′31.4″E). .ere is a lake on the left side of the studyarea and a cropland on the right side. .e study area islocated in the west of Hwaseong City and faces the YellowSea. It is a flat plain with a low altitude of less than 50m and agentle slope of less than 5%..e average annual temperaturein the study area is 12.3°C, and the annual precipitation is1,226.5mm. Figure 1 shows the location of the study areaand the flight path of the UAV.

As a result of the particle size distribution test, the soil ofthe study area has 77.6% of sand (>0.05mm) content, 17.2%of silt (0.002 to 0.05mm) content, and 5.2% of clay(<0.002mm) content. According to the United States De-partment of Agriculture soil classification method [26], thesoil of the study area is classified as sandy loam.

2.2. Aerial Photography Using UAVs. We used Mavic Pro(DJI Co.), one of the commercial UAVs, to acquire aerialphotographs of the soil surface. .e Mavic Pro has a built-in

4000 × 3000 pixel camera for high-resolution images. Aerialphotographs were taken at 150m altitude, and the total flightdistance is about 7400m. .e flight path is a one round tripto the study area, with a flight time of approximately15minutes and a flight speed of 8m/s. .e flight path isshown in Figure 1. Aerial photographs were taken five timesduring the period from February 2017 to January 2018 forthe study area, and surface soil samples were taken after thephotographs were taken.

To convert the photographed images into a digital ele-vation model (DEM), we used PhotoScan (Agisoft Co.), a 3Dspatial information generation program. .e processingprocedure to build the DEM by PhotoScan includes fourstages such as camera alignment, generating dense pointcloud, mesh, and texturing. In the camera alignment stage,the program searches for common points on the photo-graphs using GPS information and finds camera positionsand calibration parameters. In generating the dense pointcloud stage, the program calculates depth information foreach camera and combines it into a single dense point cloud.Based on the point cloud information, the program gen-erates a polygonal mesh model in building the mesh stage.Finally, the program generates an orthophoto or DEM bytexturing on the surface of mesh.

Ground resolution of the image in this study is 0.09m/pixel. .e spatial resolution of a commonly used satelliteimage is from 1.5 (SPOT-6/7) to 30 (LANDSAT) m/pixel,and the temporal resolution is 16 days for LANDSAT and 1to 3 days for SPOT-6/7. UAV, on the other hand, has a veryhigh spatial resolution and high temporal resolution becauseit can be photographed whenever the weather is good. Sincethe UAV image had a lower price and a higher resolutionthan a satellite image, it was suitable for monitoring the soilsurface.

2.3. Development and Validation of a Water Content Esti-mation Formula Using the Soil Color. Five fully saturated40 cm× 30 cm sized soils were prepared with fully saturatedsoil (Figure 2). Each fan was dried for a certain time to adjustthe water content. When soil photographs are acquiredunder indoor lighting and when the water content is high,the photograph is very bright due to the reflection of thelight. .erefore, in order to minimize the effect of reflectionson the high water content area during RGB extraction,outdoor photography was performed. Images were acquiredat different altitudes to further confirm the effect of colorchange due to the altitude. .e average RGB value of 20points is extracted from each fan, the sample of the cor-responding point is sampled, and the water content ismeasured and used for analysis.

In order to use the water content estimation formula, thelighting condition and the photographing condition shouldbe the same between the image used at the time of devel-opment of the prediction formula and the image of the fieldto apply the prediction formula. It is possible to adjust thephotographing conditions to be the same, but it is verydifficult to adjust the lighting conditions to be the same..isis because the lighting condition at the time of

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photographing continuously changes according to weatherconditions, time. It is possible to calibrate the color if thelighting conditions of the �eld can be accurately measured,but this is di�cult to apply easily. �erefore, a convenientmethod is to collect soils at several sites in the �eld andmeasure the water content to perform calibration. �ecalibration method of the RGB color is summarized inFigure 3. It is possible to perform color correction of thephotograph based on the result of extracting the color of thesampling point of the soil sample from the image acquiredby the UAV. �e more the soil sampling points used forcalibration, the higher the accuracy. However, it is not ef-�cient to sample so many points of soil. �erefore, theminimum number of soil samples that can improve theaccuracy of color correction should be selected. In this study,RMSE was calculated by applying the water content esti-mation formula to the color corrected image after selecting

several arbitrary points (1, 2, 4, and 10) among the soil at 168sites acquired in the �eld. At this time, the RMSE changesdepending on which point is arbitrarily selected. �erefore,the accuracy of the correction was evaluated by the averageand standard deviation of the RMSE calculated by repeatingthe extraction 1000 times according to the number of thedesignated sampling.

�e water content estimation formula was veri�ed usingthe images taken from the SH reclaimed land. After aerialphotographing, the surface soil was sampled and the watercontent was measured. Sampling locations and methods aresummarized in Figure 4. Total 215 soil samples in 5 di�erenttimes (2017.02.08, 2017.03.14, 2017.04.20, 2017.05.26, and2018.01.18) were collected for veri�cation of the watercontent estimation formula.

3. Results and Discussion

3.1. Development of the Water Content Estimation FormulaUsing the Soil Color. �e aerial photographs of the soilsurface layer obtained by using the UAVs are shown inFigure 5.

Immediately after aerial photographing, 20 samples weretaken from the surface layer of the soil, and the water contentwas measured. �e aerial photographs taken at each altitudewere enlarged to extract the color information at the pointwhere the water content was measured. �e average watercontent of each fan was 1.1, 7.4, 18.4, 24.2, and 30.8%.

�e water content obtained from the �ve planes and theRGB values of the measurement points are shown in

✓ Measurement of water content✓ Extraction of mean RGB value

Figure 2: Soil sampling location for water content measurementand RGB value extraction.

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Figure 6. As the water content increases, the RGB value tendsto decrease exponentially. .e change of the RGB valueaccording to the water content was large when the altitude

was high..e deviation of RGB values according to the watercontent ratio was not great up to 100m, but it increasedsharply at higher altitudes. .is phenomenon is believed to

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Figure 4: Soil sampling method in this study: (a) selection of sampling areas; (b) sampling location; (c) topsoil sampling.

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be caused by the decrease in the resolution of aerial pho-tographs as altitude increases..erefore, it is desirable to usethe aerial photographs obtained in the low altitude areawhen constructing the correlation formula between thewater content and the RGB value.

.e change of the coefficient of determination betweenwater content and RGB according to altitude is shown inFigure 7. As a result of the determination of the coefficient ofdetermination, the coefficient of determination of the Bvalue was relatively low compared to the values of R and G,and the deviation was also large. .e coefficient of de-termination is high at the altitude of 100m from the wholewater content range (1.1∼30.8%), but at higher altitudes, the

coefficient of determination is greatly decreased. In themiddle water content ratio (18.4%∼24.2%) and the highwater content ratio (24.2∼30.8%), the overall coefficient ofdetermination decreased with increasing altitude.

.e estimated regression equation of the water content(ω) using the RGB value of the soil surface layer by altitude isas follows. Table 1 shows the values of a, b, c, and d foraltitude:

ω � aR + bG + cB + d. (1)

As the shooting altitude increases, the resolution of thepicture is lowered, so the distribution of the RGB values ofthe surface layer varies. For this reason, coefficients of the

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Figure 5: Aerial photographs of the soil surface using the UAV. Altitude: (a) 20m; (b) 50m; (c) 100m; (d) 150m.

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water content estimation formula using RGB values arecalculated differently according to the shooting altitude. .eaccuracy of the water content estimation formula at eachshooting altitude was evaluated by RMSE..e water contentestimation formula derived from the photographs taken at20, 50, and 100m altitude showed high accuracy with RMSEof 3%. .e RMSE was found to be somewhat higher in thewater content estimation equation derived from the pho-tograph taken at 150m altitude because the resolution of thephotograph was low. Overall, the lower the shooting altitude,the higher the accuracy of the water content estimationequation. .erefore, the water content estimation equationof 20m is used for later field application verification and fieldapplication.

3.2. Validation of the Water Content Estimation FormulaUsing the Soil Color. .ere are considerations that must beconsidered before using the estimation equation given inequation (1). .at is, the lighting conditions at the site andthe place where equation (1) was developed may not co-incide. If the lighting conditions are different, the colors of

the soil surface may differ, even if they are soil of the samewater content. To solve this problem, after taking soil fromthe site after the shooting, calibration should be performedusing the color information and the water content of thesampling point. In Park’s study [24], one point of field datawas used for calibration in estimating the water contentsusing UAV images. However, even if the calibration iscarried out, the accuracy of the calibration changes dependson the point at which the soil sample obtained in the field issampled..ere is a difference as shown in Figure 8 accordingto the data used for the correction of the RGB values of thedata set. Figures 8(a) and 8(c) show the case where thecorrection is not performed properly and Figure 8(b) thecase where the appropriate correction is made. If the soilcollected for calibration in a large area of the topsoil is wellrepresented by the whole soil as shown in Figure 8(b), theaccuracy of the estimation equation is high. However, asshown in Figures 8(a) and 8(c), the accuracy is greatly re-duced when the soil in a particular area is used for correctionin the whole soil.

Accuracy can vary depending on the choice of calibra-tion data when applying the water content estimation

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formula in the field. .erefore, the distribution of RMSEaccording to the selection of data was confirmed by using168 data points obtained in the field in order to verify howthe accuracy according to the selection of correction datavaries. .e accuracy of the correction may also depend onthe number of selected data. To take this into consideration,1, 2, 4, and 10 randomly selected from 168 data were selectedand corrected by the average value. .e proposed procedurewas repeated 10,000 times to estimate RMSE and estimatethe expected value and reliability of RMSE. .e probability

density function of RMSE according to the number of dataselections is shown in Figure 9. Table 2 summarizes averageand standard deviation of RMSE according to the number ofcorrection data.

.e mean and standard deviation of RMSE decreased asthe number of data for calibration increased. In the case ofone-point correction, the value of RMSE is large and thedeviation is large in each trial. As the number of data forcalibration increases, the frequency at which a higher rangeRMSE decreases significantly. For 10-point calibration, theprobability of RMSE less than 3% is about 55%, and theprobability of RMSE more than 4% is about 1% and veryhigh accuracy. As a result, the accuracy of the water contentestimation equation increases with the number of correctiondata. It is possible to obtain very high accuracy when ac-quiring 10 or more data, but it is time-consuming to acquirea large amount of data in the field. .erefore, it is mostreasonable to obtain 4∼10 calibration data considering bothaccuracy and economic efficiency.

Figure 10 shows the results of applying the water contentestimation formula to the aerial photographs taken using the

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Figure 7: Change of coefficient of determination between the water content and RGB values according to the altitude. Water content range:(a) 1.1∼30.8%; (b) 18.4∼24.2%; (c) 24.2∼30.8%.

Table 1: Coefficient of the water estimation formula using RGBvalues.

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Coefficient

a −0.339 −0.105 −0.709 −1.187b −0.361 −0.409 −0.027 0.909c 0.264 0.061 0.319 −0.026d 65.2 66.4 55.3 43.5

R2 0.96 0.94 0.96 0.47RMSE (%) 2.21 2.74 2.26 7.87

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Figure 9: Probability density of RMSE according to calibration: (a) 1-point calibration; (b) 2-point calibration; (c) 4-point calibration;(d) 10-point calibration.

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UAV..e RMSE of the water content estimation equation is1.8∼2.9%, which predicts the water content of the soilsurface relatively well. .erefore, if the calibration is per-formed by obtaining 4 to 10 calibration samples as shownabove, the water content of the soil surface can be well-predicted by using the UAVs.

4. Conclusions

In this study, evaluation of the calibration method of theUAV-based soil water content prediction equation wasexamined. .e following conclusions were obtained. .erelationship between the soil water content and the soil colorwas investigated in aerial photographs taken at various al-titudes. As a result, the RGB values decreased as the watercontent increased, and the RGB values varied greatly as thealtitude increased. .erefore, the coefficient of de-termination between the RGB value and the water contentdecreased with increasing altitude. .e linear regressionanalysis was used to predict the water content of the soilusing the RGB values of aerial photographs. In order toapply the water content estimation formula using RGBvalues, color correction using soils sampled in the site should

be performed. Accuracy of the water content estimationformula can vary greatly depending on which soil is selectedas calibration data in the field. .erefore, 1, 2, 4, and 10 datawere randomly selected based on the 168 calibration dataobtained from the field, and then the correction was per-formed. .en, the accuracy was analyzed by comparing therespective RMSEs. .e mean and standard deviation ofRMSE decreased with the increasing number of calibrationdata. Considering accuracy and efficiency, it is reasonable touse 4 to 10 calibration data. .e accuracy was confirmed byapplying the proposed calibration method (4-point cali-bration) to 215 points at 5 points. .e RMSE was 1.8∼2.9%,which is considered to be highly applicable in the field.

Data Availability

.e data used to support the findings of this study are in-cluded within the article.

Conflicts of Interest

.e authors declare that they have no conflicts of interest.

Acknowledgments

.is research was supported by the Basic Science ResearchProgram through the National Research Foundation ofKorea (NRF) funded by the Ministry of Science, ICT &Future Planning (NRF-2014R1A2A1A11051680), the KoreaInstitute of Planning and Evaluation for Technology in Food,Agriculture, Forestry & Fisheries, and the Ministry of Ag-riculture, Food and Rural Affairs (114147-3).

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Table 2: Accuracy of the estimation equation according to thenumber of correction data.

Number of data for calibration1-point 2-point 4-point 10-point

Average 3.692 3.336 3.163 3.042Standard deviation 0.971 0.692 0.503 0.316

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17.02.08 (RMSE = 2.9%)17.03.14 (RMSE = 2.1%)17.04.20 (RMSE = 2.1%)

17.05.26 (RMSE = 1.8%)18.01.18 (RMSE = 2.7%)

1:1 line

Figure 10: Comparison of observed/predicted water contentsusing the water content estimation formula.

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