Prediction of Ground Water Content Using Hyperspectral Information through Laboratory Test
Abstract
:1. Introduction
2. Methodology
2.1. Material Properties of Soil
2.2. Laboratory Test to Obtain Hyperspectral Information
2.2.1. Analysis System to Obtain the Hyperspectral Information of the Soil
2.2.2. Procedure to Obtain Hyperspectral Information about Water Content
2.3. Extraction Method of Hyperspectral Information
3. Hyperspectral Information through Laboratory Tests
3.1. Relationshop between the Wavelength and the Reflection
3.2. Spectrum Index for Normalization from the Literature
3.3. Calculation of the Spectrum Index
4. Development of a New Spectrum Index for Water Content Prediction
4.1. Export of Specific Reflection for the Development of a New Spectrum Index
4.2. Fitting of Data for a Suitable Equation of the Spectrum Index
4.3. Equation Selection for Water Content Prediction
5. Conclusions
- (1)
- In the existing literature, various spectrum indices for prediction of water content have been proposed. However, when the spectrum information obtained in this study was substituted into the equations for the spectrum index, it was confirmed that direct use is impossible, such as overlapping values and parabola curve. This is because the equation in the literature itself has great uncertainty and various conditions (indoor and outdoor, focal length, pixel width, etc.) exist in the process of acquiring hyperspectral information. Therefore, the reliability of the existing equation should be viewed as low. An optimized equation for prediction of water content should be selected by comparing it with the experimental results.
- (2)
- The prediction equation of water content may change, depending on the wavelength. However, the relationship between the wavelength and the curve used in the existing theory could not be confirmed. It was judged that it would be appropriate to extract a significant point from the wavelength−reflectance curve. Therefore, significant points were derived at the first inflection point (wavelength at 750 nm), the point of the maximum reflectance measurement (800 nm), and the second inflection point (wavelength at 920 nm). The first inflection point was a visible region in the wavelength band and corresponded to a deep red color. The maximum and second inflection points were near-infrared rays (NIRs), which cannot be seen by the human eye. The wavelength used for the final prediction equation of water content was the first inflection point, and it was judged that the wavelength selection in the visible region would be appropriate.
- (3)
- Through the comparison of various function models, the exponential equation was selected as a suitable model, and each extraction point was compared. In addition to R-square indicating linear regression, the COV of the bias factor indicating the occurrence of error also needs to be considered. This is because the reflectance for each wavelength range has a dense range of distribution according to water content, so that the occurrence of error greatly affects the output value (water content).
- (4)
- The hyperspectral information was obtained through precise laboratory tests on standard sand in this study. The equation for converting hyperspectral information into water content was proposed. This has the disadvantage that it was derived without performing various samples and field tests. It is meaningful in that it basically normalizes the water content prediction and proposes a new prediction equation of water content using hyperspectral information.
- (5)
- The subgrade soil of a real road construction site is not simply sand but a composite of various particle sizes, and errors may occur when the water content prediction formula developed in this study is applied. Thus, the accuracy and reliability of the derived equation should be tested and confirmed. If an error occurs in the application to real ground, the developed equation for prediction should be modified with a more comprehensive selection of soil types. Therefore, in future research, we aim to derive a specific wavelength range and reflectance in which a difference occurs ac-cording to water content, rather than being affected by the type of soil. Consequently, the equation derived above will be further modified and supplemented.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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D10 1 (mm) | D30 2 (mm) | D60 3 (mm) | Coefficient of Uniformity, Cu | Coefficient of Curvature, Cc | Percent Passing No. 200 Sieve (%) | Soil Classification Based on USCS [30] |
---|---|---|---|---|---|---|
0.270 | 0.358 | 0.527 | 1.952 | 0.901 | 0.06 | SP |
Research | Spectrum Index | |
---|---|---|
Ge et al. [27] | DI = R866 − R655 | (1) |
Ge et al. [27] | RI = R866/R655 | (2) |
Ge et al. [27] | NDI = (R866 − R655)/(R866 + R655) | (3) |
Ge et al. [27] | PI = (R866 − 0.4404R655 − 0.3308)/(1 + 0.44012)1/2 | (4) |
Haboudane et al. [25] | NDVI = (R800 − R680)/(R800 + R680) | (5) |
Tian et al. [26] | DVI = R800 − R680 | (6) |
Point | Fitting Model | Equation | R2 |
---|---|---|---|
1st inflection | Linear | 0.837 | |
Polynomial | 0.994 | ||
Logarithm | 0.858 | ||
Exponential | 0.991 | ||
Slogistic | 0.968 | ||
Maximum inflection | Linear | 0.846 | |
Polynomial | 0.989 | ||
Logarithm | 0.833 | ||
Exponential | 0.986 | ||
Slogistic | 0.977 | ||
2nd inflection | Linear | 0.880 | |
Polynomial | 0.995 | ||
Logarithm | 0.871 | ||
Exponential | 0.993 | ||
Slogistic | 0.970 |
Model of Curve | Inflection Point | Bias Factor | |||
---|---|---|---|---|---|
R-Square | Average | St. Dev. | COV (%) | ||
Exponential | 1st | 0.9924 | 1.010 | 0.066 | 6.554 |
Maximum | 0.9872 | 0.992 | 0.158 | 15.956 | |
2nd | 0.9939 | 1.029 | 0.094 | 9.100 |
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Lee, K.; Park, J.J.; Hong, G. Prediction of Ground Water Content Using Hyperspectral Information through Laboratory Test. Sustainability 2022, 14, 10999. https://doi.org/10.3390/su141710999
Lee K, Park JJ, Hong G. Prediction of Ground Water Content Using Hyperspectral Information through Laboratory Test. Sustainability. 2022; 14(17):10999. https://doi.org/10.3390/su141710999
Chicago/Turabian StyleLee, Kicheol, Jeong Jun Park, and Gigwon Hong. 2022. "Prediction of Ground Water Content Using Hyperspectral Information through Laboratory Test" Sustainability 14, no. 17: 10999. https://doi.org/10.3390/su141710999