## 4 Discussion
One of the primary goals of high-resolution optical satellites is to estimate categorical variables such as image interpretation, land cover, land use, and change detection. Therefore, relative (pixel-to-pixel within uniform scenes) radiometric correction was performed to improve their uniformity and sharpness, and to eliminate pixel noise from non-uniform CCD array sensitivity. During the in-orbit-test (IOT), non-uniformity correction (NUC) based on the slide-slither image observation method and an image data restoration (de-noising) algorithm were applied to KOMPSAT-3 and -3A to reduce non-uniformity, eliminate sparkle lines, and minimize the pattern noise by selecting dark DN areas. The raw DN of the CCD sensors are unavoidably changed by this process [47]. More specifically, the NUC method resulted in the greatest DN change, a variation of about 3%. In addition, modulation transfer function compensation (MTFC) de-noising and pixel burst, respectively, resulted in less than a 1% change in the DN. Therefore, a 5% error, which is a sufficient margin due to the DN distortion by the relative radiometric correction. In this study, we did not interpret how DN variations caused by relative radiometric correction propagate into the absolute radiometric calibration of KOMPSAT-3 and -3A. Different methods of relative radiometric correction exist, such as MTFC, NUC, de-noising, and pixel burst correction; however, the distortion of DN due to relative radiometric correction is too complicated, because it depends on the conditions of the reference target, location, and atmospheric correction. In future research, we aim to interpret the effects of relative radiometric accuracy assessment. In this study, however, we focused on updating the absolute radiometric calibration results of KOMPSAT-3 and KOMPSAT-3A according to TDI adjustments and sensor degradation over time. In the case of solar irradiance, we used solar irradiance profiles recommended by the Committee on Earth Observation Satellites (CEOS) community [48] as input parameters for the radiative transfer model. In this study, a 1% radiance error was employed since there is no evidence for a change to the solar irradiance data that are used in radiative transfer model calculation [49]. Although there are differences in the wavelength resolution between the 1 nm solar irradiance data and the 2.5 nm spectral resolution of the 6S radiative model do exist, the CEOS profiles have a higher spectral resolution than the 6S model, meaning that this difference should not significantly increase the uncertainty of the input values. In this study, the last two cases of KOMPSAT-3 and -3A satellites in Table 4 were used for the official DN to radiance coefficients of the satellites because they are most similar to ASD Fieldspec(r) 3 geometry. However, although the geometry discrepancy between the satellites and the ASD Fieldspec(r) 3 is low, observation differences remain. Therefore, we assigned a radiance error of less than 1% due to the last two cases. During the field campaign, errors in the surface reflectance measurement and fundamental instrument errors of ASD Fieldspec(r) were also expected. In the case of surface reflectance error from ASD Fieldspec(r), the reflectance variation within each wavelength SRF of KOMPSAT-3 and -3A has an uncertainty of approximately 1% for each multispectral band when measuring ASD (Figure 6). The dual ASD Fieldspec(r) radiometer system was performed to reduce the inherent error of the radiometer and obtain stable and reliable reflectance values for the tarps. By using the dual radiometer system, we minimized the error due to the variation in weather conditions by simultaneously measuring the white reference and the target using the two radiometers. Thus, the errors can be reduced by comparing single ASD Fieldspec(r) 3 measurements. Also, the typical instrument error of ASD Fieldspec(r) was assigned as less than 1%.