###### Abstract
Hyperspectral information is a type of spectral curve that can analyze the spectral characteristics of ground objects during aerial photography. This provides an identifiable source for a variety of terrestrial objects. Additionally, it is used in the construction field to provide information for building classification, change detection of land use, and object recognition. The application of hyperspectral information in relation to roads began in the 1970s, and the chemical and physical conditions (cracks, aging, etc.) of road surface pavement were measured from large-scale aerial photographs. In the 2000s, with the development of technology and cameras, measurement became possible even on small aircraft such as UAVs (Unmanned Airborne Vehicles) [6; 7; 8; 9]. Recently, research has been mainly conducted in combination with AI, such as the extraction of terrain features using deep learning algorithms, mobile-based object recognition, and damage detection [10; 11]. In relation to roads, hyperspectral information is mainly used in terms of the maintenance of pavements after construction. However, in this study, hyperspectral information is applied to earthwork, a road construction process, and in detail, we aim to measure water content, which has a significant impact on embankment and compaction. That is, the water content, which has a great influence on embankment and compaction, is to be measured. The derivation of water content using hyperspectral information has been performed in various fields and by various researchers. Most of them performed water-related research and have developed vegetation indices based on the exploration of moisture content to improve agricultural crops [12; 13; 14] or to develop parameters of water quality [15; 16]. Recently, with the growth of fields related to AI, research on the water content of soil has been extensively conducted. However, studies using data measured in actual fields are minimal, and there is a limitation in that they were generally conducted in a precise laboratory environment where the surrounding environment was controlled [17; 18; 19; 20; 21; 22]. Therefore, in this study, hyperspectral information and water content are measured in the actual field. A CCM that can check the range-type water content of the entire ground is developed. For this purpose, an outdoor experiment was conducted to mount a hyperspectral camera on a UAV drone and acquire orthophoto and hyperspectral information of the entire ground. Hyperspectral information was extracted from the location where the actual water content was measured. Here, hyperspectral information is measured in 4 nm increments in the range of 400-1000 nm, and one piece of hyperspectral information has 150 data points (total reflectance values are 150,000). Furthermore, the surface and inside water contents at specific locations were measured 1000 times using the oven-drying method. The measured actual water content and hyperspectral information were analyzed through machine learning, and a predicted equation of water content that can derive the water content when substituting the hyperspectral information was developed. Finally, the hyperspectral information in the orthophoto was converted to water content, and this was designated as a color to create a CCM.