###### Abstract  
In road construction, the compaction of the subgrade layer, which is one of the earthwork fields, is an essential procedure to support the pavement layer and traffic load. For the quality control of subgrades, water content must be measured. Currently, the measurement of water content is performed at specific locations in a large area of subgrades and has the disadvantage of taking a long time to derive. Because this is difficult to immediately confirm, inefficiencies arise in terms of construction schedule and quality control. Therefore, in this study, a CCM (Color-Coded Map) was proposed through hyperspectral remote sensing using drones. This method is a range-type water-content measurement method that can acquire data in a short time (about 20 min) and can be easily confirmed visually. For this, a predicted equation that can convert hyperspectral information into water content information is developed. Multivariate linear regression, a machine learning technique, was applied to the database (of actual measured water content and hyperspectral information). The predicted and measured water contents showed a coefficient of determination of 0.888, and it was confirmed that CCMs can also be presented in various ways depending on user settings. Keywords:road construction; water content; hyperspectral information; UAV; machine learning; multivariate linear regression; CCM +
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cone test, the dynamic cone penetration test, and the California bearing ratio test. However, these methods are point measurements, and the actual measurement range is less than 1% of the entire construction site, so there is a possibility of serious damage during the construction process. If the range-type water content of the entire ground is measured, the quality control of the subgrade can be made easier. The subgrade layer must achieve a degree of compaction of 95% or more, and the degree of compaction can be calculated using the unit weight of the soil on site and the laboratory-based maximum dry unit weight, as shown in Equation (1). The maximum dry unit weight can be measured through laboratory tests of transported soil and is a value known in advance. The dry unit weight of soil in the field can be confirmed when the transported soil is completely dry, but it is impossible to confirm in practice because it is generally brought in in a wet state. The wet unit weight of soil in the field can be calculated because the weight of the soil can be known during the import process, and the construction area and height can be checked after compaction. In other words, if the water content is known, it is possible to calculate the degree of compaction in the entire ground. Here, \(D_{c}\) is the degree of compaction (%), \(\gamma_{d,field}\) is the dry unit weight of soil in the field, \(\gamma_{d,max}\) is the maximum dry unit weight of soil in the laboratory, \(\gamma_{field}\) is the unit weight of soil in the field, and \(w\) is water content. \[D_{c}=(\gamma_{d,field}/\gamma_{d,max})\times 100\%=[\gamma_{field}/(1+w)]/ \gamma_{d,max}\times 100\% \tag{1}\]  
In general, the traditional method of measuring the water content of the ground is used, comparing the weight of the soil sample in the field and the weight of the dried sample. However, there are also methods called TDR (Time-Domain Reflectometry) and GPR (Ground-Penetrating Radar) [3; 4]. However, these methods cannot measure the water content of the entire construction site and have several limitations: they require long periods of time and the manual input of various parameters during the data measurement process. Hyperspectral remote sensing can be an alternative to existing water-content measurement methods in that it can perform range-type measurements, not existing point measurements. This field acquires correlation information about objects, such as spatial, radiation, and spectral information, and allows the entire observation data, including high-level spectral details, to be easily and quickly obtained [5].