E3S Web of Conferences 415, 03017 (2023)
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https://doi.org/10.1051/e3sconf/202341503017

Soil Water Content Regression Analysis of Measurement Data
from Hyperspectral Camera in Weathered Granite Soils
Hwan-Hui Lim1*, Seung-Rae Lee1, Enok Cheon1, and Yeeun Nam1
1Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, Daejeon

Abstract. Soil water content is one of the most common physical parameters that cause landslides or debris
flow. Therefore, it is of very importance to determine or predict the water content quickly and nondestructively. This study investigates the hyperspectral information in the visible near-infrared regions(VNIR) of different samples of granite soils possessing varying water content. Totally 162 granite samples
were taken from each mountain area. The samples with different water contents were examined using a
hyperspectral radiometer operating in the 400~1000nm range by obtaining the spectral curves. It was foundthat the variation of VNIR is consistent with the water content variation of weathered granite soils, and thehyperspectral camera was able to detect it. A Partial Least Squares Regression (PLSR) analysis was applied
to develop a calibration-model. The PLSR model resulted in a good correlation between the water content
and VNIR profile. The results demonstrate that the hyperspectral camera combined with the PLSR model
can be a useful and non-destructive tool for determining soil water contents in the weathered granite soils.

1 Introduction
Soil water content seriously affects soil's physical andchemical properties, and changes in soil properties canlead to landslides or debris flow. The prediction of soil
water content plays a decisive role in landslides anddebris flow monitoring. Existing water content
measurement has been instruments made based on fieldtests or field sensor-based measurements.
However, both methods have limitations in that they
can measure water content in relatively local areas.
Therefore, it is necessary to develop a technology thatcan measure a large area's water content variation.
During the last two decades, near-infrared (NIR)
spectroscopy has been widely employed as a useful tool
for analyzing soil properties. NIR spectroscopy can beused to evaluate the properties of soil that are notdisturbed by light [7, 11]. Mouazen et al. [5] used visualand near-infrared (VNIR) hyperspectral imaging todetermine the presence of chemicals (carbon andphosphorous) in soil and to measure soil parameters,such as moisture content and pH. Zhang et al. [13]developed a technique for the classification of soil using
NIR hyperspectral images. Wang et al. (2018) [12]developed a technique for analyzing the degree of heavy
metal contamination of soil using remote sensing andhyperspectral images in NIR.
In this study, we developed a variation of the soilwater content prediction model using the hyperspectral
technique to overcome the limitations of existingmeasurement techniques. The objectives of this study
are to develop a water content prediction model using_______________________________

hyperspectral images. For this purpose, the change ofreflectance according to the water content in the nearinfrared region was confirmed, and the most relevant
parameter was analyzed. Finally, the water content
prediction model developed with the corresponding
parameters is compared with the actual soil watercontent. Based on these findings, we intend to provide
reference materials that can be used for analyzing thewater content of soil using hyperspectral near-infrared
images to decide the susceptibility of landslides in a
mountainous area.

2 Materials and methods
2.1 Study area
The study areas are placed in Seoul’s southern part (Mt.
Umyeon: 37.45° N, 126.9° E; Mt. Guryong: 37.47° N,127.06° E; Mt. Daemo: 37.48° N, 127.08° E), as shownin Figure 1. The study areas are comprised of graniticgneiss.
A total of 162 granite soil samples were collected fromthe study areas within a depth of 30 cm from the surface.
First, a sieve analysis was conducted using soil thatpassed through a No. 40 sieves (0.425 mm). The soils
were also dried at 110 ℃ in an oven for 24 hours. And
the water was added step by step to measure the watercontent. Soil water content was calculated by thefollowing equation (Equation 1). Table 1 shows thematerial properties of the 162 soils.
𝑤𝑎𝑡𝑒𝑟 𝑐𝑜𝑛𝑡𝑒𝑛𝑡 (%) =𝑚𝑎𝑠𝑠 𝑜𝑓 𝑤𝑒𝑡 𝑤𝑜𝑖𝑙−𝑚𝑎𝑠𝑠 𝑜𝑓 𝑑𝑟𝑦 𝑠𝑜𝑖𝑙
𝑚𝑎𝑠𝑠 𝑜𝑓 𝑤𝑒𝑡 𝑠𝑜𝑖𝑙

(1)

* Corresponding author: hwanhui@kaist.ac.kr© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0(https://creativecommons.org/licenses/by/4.0/).

E3S Web of Conferences 415, 03017 (2023)
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2.2 Hyperspectral camera system
The hyperspectral camera system consists of a hyper
spectral camera, a complementary metal-oxidsemiconductor (CMOS) sensor, six 150W Halogen
lamps, and a 40 x 20 Lab-scanner (Spectral ImagingLtd., Oulu, Finland, Figure 2a). The software forscanning speed (CNC USB controller) and hypercube
data recorder (Lumo Recorder) provides exposure time,binning mode, wavelength range, and image acquisition[8]. Hyperspectral cameras were placed in a dark room
to minimize errors. The design of the hyperspectral
camera is shown in Figure 2b.
2.3 Image correction

Fig. 1. Location map showing the study area of 162 granite
soil samples.

Hyperspectral images were acquired using the line
scanning technique of the hyperspectral camera system.
Each sample was placed on a slider table and scannedline by line to get an initial hyperspectral image. After
capturing the hyperspectral image, a dark reference anda white reference were obtained. The normalization
process is essential. We can remove noise values fromhyperspectral images and convert them to relativevalues using 100% reflectance of the white reference
through this process. The white reference was acquired
from a Teflon whiteboard with 99% reflectivity. The
dark reference was obtained by turning off the lightsource and completely covering the camera lens with acap. The reflectance (%) of the sample acquired basedon the white reference was calculated using thefollowing equation.
𝑅𝑒𝑓𝑙𝑒𝑐𝑡𝑎𝑛𝑐𝑒 =𝑅𝑎𝑤 𝑡1 −𝐷𝑎𝑟𝑘 𝑡1
𝑊ℎ𝑖𝑡𝑒 𝑡2 −𝐷𝑎𝑟𝑘 𝑡2𝑥𝑡2𝑡1(2)where the Raw reflectance is the reference measured onan actual object, Dark is the dark reference, and Whiteis the white reference, t1 is the integration time in awhite reference, and t2 the integration time in a darkreference [10].

3 Result and discussion
3.1 Investigation of parameters related to watercontent variation
The near-infrared regions (NIR, 800~1000 nm) aresuggested as having a strong correlation with soil watercontent variation [4]. Three parameters related to watercontent variation were selected in NIR: the water index(R970/R900, Figure 3a), which is one of the spectral
vegetation indices; the depth of 800nm to 1000nm(Figure 3b); and the area of reflectance (Figure 3c).
Partial Least Square Regression (PLSR) analysis wasperformed by setting each parameter and variation ofwater content as variables. Figure 4 shows therelationship between water content variation and theselected three parameters from each of Mt. Umyeon, Mt
Guryong, and Mt. Daemo weathered granite soils.

Fig. 2. Hyperspectral camera system: (a) Hyperspectral
camera in the laboratory and (b) Design of the hyperspectral
camera.

2

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where 𝐴𝑡 is the predicted value, 𝐹𝑡 is the actual value, andn is the duration of the observed value. The 162
weathered granite soils were randomly assigned to thecalibration and the validation sets with a 7:3 ratio, which

Among the parameters, the area of reflectance in NIR
showed the highest coefficient of determination, and thewater index and depth at 1000nm showed the lower
coefficient of determination overall. In all 162
weathered granite soils, it was confirmed that the area ofreflectance in NIR is the parameter most correlated withthe water content variation in VNIR.

Fig. 4. Relationship between water content variation andparameters of granite soils (Mt. Umyeon, Mt. Guryong, Mt.
Daemo): (a) Water Index and (b) Depth at 1000nm and (c)Area of reflectance.

Fig. 3. Parameters of soils related to water content variation:(a) Water Index, (b) Depth at 1000nm, and (c) Area ofreflectance.

is equivalent to a total of 40 calibration samples and 17validation samples for Mt. Umyeon soil samples; 38calibration samples and 16 validation samples for Mt.
Guryong soil samples; and 36 calibration samples and15 validation samples for Mt. Daemo soil samples.
Figure 5 shows the MAPE analysis results of theregression analysis model. The MAPE of Mt. Umyeonis 7.3%, the MAPE of Mt. Guryong is 10.2%, and theMAPE of Mt. Daemo is 7.1%, which is a relatively
accurate prediction.

3.2 Estimation of water content variation of soil
according to reflectanceIn general, the indicators used to evaluate the goodnessof fit of predictive models are mean absolute percentage
error (MAPE), root mean square error (RMSE), mean
absolute error (MAE), and maximum absolute
percentage error (Max-APE) [9]. MAPE has become
increasingly popular as a performance measure inforecasting [1, 2, 6], as it is easy to interpret andunderstand in addition to being highly reliable [3]. Alldata points, both predicted values and results fromlaboratory tests, were used to compute the coefficient ofdetermination (R2).
1𝐴 −𝐹𝑡𝑛𝐴𝑡

𝑀𝐴𝑃𝐸 = ∑𝑛𝑡=1 | 𝑡|

4 Conclusions
In this study, we demonstrated the potential ofhyperspectral techniques to estimate the variation of soil
water content. The parameter most correlated with thewater content in the VNIR was determined from thehyperspectral reflectance data.

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using the hyperspectral technique. A total of 162 granitesoils (Mt. Umyeon, Mt. Guryong, and Mt. Daemo) wereexamined by applying the hyperspectral technique. The
results demonstrated that the developed models werecapable of variation of water content prediction.
There are not many studies on the acquisition of soil
properties for a wide area using hyperspectralreflectance; therefore, it is expected that it can be usedas primary data for a later study on obtaining propertiesof a wide area using the hyperspectral technique. The
accuracy may be somewhat inferior to the existingmeasurement method. However, it is expected that thevariation of the water content of the wide-area can bemeasured using a drone in the future, so active disasterprevention is likely possible. Finally, for modeling theacquisition of soil property values, research is needed toestimate different characteristics according to the typeof soil. It is determined that an applicability study using
drone and satellite images is necessary for future
research.
This research was funded by the Basic Research Laboratory
Program through the National Research Foundation of Korea
funded by the Ministry of Science and ICT (NRF-2018
R1A4A102576513) and Innovative Talent Education Programfor Smart City by Korea Ministry of Land, Infrastructure andTransport (MOLIT) of Korean government.

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Fig. 5. Comparison of measured water content versus
predicted water content using regression analysis: (a) Mt.
Umyeon and (b) Mt. Guryong and (c) Mt. Daemo.

We also developed a regression analysis model for
predicting the variation of the water content of eachweathered granite soils. The research can besummarized as follows:
1) A total of 162 granite weathered soil samples werecollected from Mt. Umyeon, Mt. Guryong, and Mt.
Daemo in Seoul. Hyperspectral near-infrared images
were acquired in 224 bands from 400 to 1000nm.
2) Water index (R970/R900), the depth at 1000nm, andthe area of reflectance in NIR were used to find theparameters most relevant to water content variationin VNIR. The regression analysis result of theregression analysis showed that the area ofreflectance in NIR is the best parameter correlating
to the soil water content variation.
3) The variation of soil water content regression model
was developed using the area of reflectance in NIR
parameter. As a result of comparing and verifying
with the actual soil water content variation, it wasconfirmed that the MAPE of each soil was less than
11%. Therefore, it can be concluded that theregression model can be successfully used to predictvariations in soil water content accurately.
This study aimed to classify soil types and predict soil
water content over large areas to detect landslide
hazards, which traditionally require a considerable timeand human power, by implementing a simple method4