Kimchi cabbage grows in South Korea and is an essential ingredient for making kimchi with the kimjang method. The technique of accurately managing and monitoring crops such as kimchi cabbage plays an important role in stabilizing consumer prices. Unmanned aerial vehicles (UAVs) are expected to be used more widely in global and local agriculture. The agricultural sites at which kimchi cabbages are cultivated are affected by various climatic, terrain, and soil conditions, requiring technologies that can accurately and quickly acquire such information. UAVs and sensors are able to provide some of these data. In this study, we set up a cultivation environment for kimchi cabbage and investigated the correlation between a UAV-attached multispectral sensor and a field-portable spectroradiometer. Reflectance measurement using a spectroradiometer was performed on 99 kimchi cabbages in a Mt. Maebong testbed. We aimed to find a method for obtaining accurate vegetation information by combining the high spatial and temporal resolution information of the UAV observation with the spectral resolution of the spectroradiometer. Spectral analysis was used to identify the difference between healthy and poorly growing cabbage and to find the wavelength that most affected the growth. The hyperspectrum of the spectroradiometer reflected the accurate vegetation characteristics and contributed greatly to the identification of vegetation indices. A method for correcting the errors that occurred in the ground and UAV monitoring and the difference arising from the application of the broadband wavelength of the UAV and the single wavelength of the spectroradiometer through correlation analysis is presented. The calibration equation method was applied to UAV spatial information and was used to create a precise normalized distribution vegetation index (p-NDVI) map. The p-NDVI map was organized into four categories for the selection of cabbages with healthy (good) growth. Our results show that (1) the merged spectral analysis method was found to be more accurate and distinct than conventional methods, and (2) methods for estimating cabbage growth status showed a higher significant correlation than the UAV-based NDVI. At the maturity stage, high accuracy (R2 = 0.7816, RMSE = 0.06) was achieved for NDVI. Although this map is the result of the limited vegetation monitoring of UAV images taken during the maturity stage, it could be of great help for managing the quality and production of cabbage. However, the efficient management of highland kimchi cabbage requires continuous research under various conditions to enable periodic and systematic monitoring using UAVs and sensors.

The objective of this study was to investigate the basic relationships between spectral reflectance and varying concentrations of sediment in surface waters. An experimental method for determining suspended sediment concentration (SSC) in the water by use of a spectroradiometer above the water surface, in visible and near-infrared (NIR) wavelengths, is applied. The main advantage of the method is the direct comparison of spectral reflectance and the SSC, but it requires an accurate knowledge of the water body and sediment. Therefore numerous spectroradiometric measurements are carried out in situ measurements, for SSC, ranging from zero to 100 percentage and two types of sediment applied in the water tank. The results indicate that the suspended sediment causes increasing spectral reflectance response in waters. We observed that spectral reflectance increases with SSC, first at the lower wavelengths (430-480 nm), then in the middle wavelengths (570-700 nm), and finally, in the NIR domain (800-820 nm); a characteristic maximum reflectance appears at 400-670 nm. Relationships between the wavelength, integral value, and the SSC were evaluated on the basis of the regression analysis. The regression curve for the relation between the wavelength, integral value, and the SSC were determined (R 2 >0.98). Finally, the specular wavelength can be estimated to recognize the sediment and to improve SC estimation accuracy in the water.

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