Updating Absolute Radiometric Characteristics for KOMPSAT-3 and KOMPSAT-3A Multispectral Imaging Sensors Using Well-Characterized Pseudo-Invariant Tarps and Microtops II

Asia-Pacific J Atmos Sci

Seo

et al. 2020

Self Cite

We propose a methodology employing an interpolation technique on the Second Simulation of a Satellite Signal (6S) look-up table (LUT) to improve surface reflectance retrieval using Himawari-8/Advanced Himawari Imager (AHI). A minimum curvature surface (MCS) technique was used to refine the 6S LUT, and the solar zenith angle (SZA) and viewing zenith angle (VZA) increments were narrowed by 0.5°. The interpolation processing time was relatively short, about 3172 s per channel, and the interpolated xa and xb were well represented by the changes in SZA and VZA. An evaluation of the interpolated xa and xb for six cases revealed a relative mean absolute error of less than 5% for all channels and cases; however, a slight difference was evident for higher values of SZA and VZA. To evaluate the surface reflectance, we compared the surface reflectance derived using 6S LUT with that calculated using 6S only. Application of the interpolated 6S LUT showed a lower relative root mean square error (RRMSE) of 0.65% to 9.29% for all channels, than before interpolation. The improvement in surface reflectance measurements increased with the SZA. For a SZA above 75°, the RRMSE improved significantly for all channels (by 11.33-45.1%). In addition, when the MCS method was applied, the surface reflectance measurements improved without spatial discontinuity and showed good agreement with 6S results in a linear profile analyses. Thus, the method proposed can improve LUT based surface reflectance measurements in less time and increase the availability of surface reflectance data based on geostationary satellites.

Section: S Rtmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improvements of 6S Look-Up-Table Based Surface Reflectance Employing Minimum Curvature Surface Method

Asia-Pacific J Atmos Sci

Seo

et al. 2020

Self Cite

“…The experimental area is Seoul, which mainly contains built-up areas and forests. KOMPSAT-3 and KOMPSAT-3A have the same spectral response functions and practical spectral ranges as the bands that are affected by atmospheric constituents and the surface, where the main difference between them is only the spatial resolution [53]. Therefore, the experiment is performed by resampling KOMPSAT-3 at 2.2 m, which is the same resolution as KOMPSAT-3A, and the size of the images is set to 2000 × 1500 pixels.…”

Section: Additional Datasetmentioning

confidence: 99%

Multilayer Perceptron-Based Phenological and Radiometric Normalization for High-Resolution Satellite Imagery

Seo

2019

Applied Sciences

Radiometric normalization is an essential preprocessing step that must be performed to detect changes in multi-temporal satellite images and, in general, relative radiometric normalization is utilized. However, most relative radiometric normalization methods assume a linear relationship and they cannot take into account nonlinear properties, such as the distribution of the earth's surface or phenological differences that are caused by the growth of vegetation. Thus, this paper proposes a novel method that assumes a nonlinear relationship and it uses a representative nonlinear regression model-multilayer perceptron (MLP). The proposed method performs radiometric resolution compression while considering both the complexity and time cost, and radiometric control set samples are extracted based on a no-change set method. Subsequently, the spectral index is selected for each band to compensate for the phenological properties, phenological normalization is performed based on MLP, and the global radiometric properties are adjusted through postprocessing. Finally, a performance evaluation is conducted by comparing the results herein with those from conventional relative radiometric normalization algorithms. The experimental results show that the proposed method outperforms conventional methods in terms of both visual inspection and quantitative evaluation. In other words, the applicability of the proposed method to the normalization of multi-temporal images with nonlinear properties is confirmed. Relative radiometric normalization, on the other hand, is an image-based normalization method that selects one image as a reference and matches the radiometric characteristics of a subject image [13,14]. That is, relative radiometric normalization is generally utilized since the multi-temporal images are normalized into common scales without extra parameters [12,15].Relative radiometric normalization can be classified into two categories: a global statistics-based method and a radiometric control set sample (RCSS)-based method. The former is performed by using statistical values of all pixels in the image, which includes histogram matching, minimum-maximum (MM), mean-standard deviation (MS), and simple regression (SR) [15,16]. In contrast, the latter is based on invariant pixels, known as RCSSs, which include dark set-bright set (DB), pseudo invariant features (PIF), and no-change sets (NC) [17][18][19][20]. At this time, except for histogram matching, normalization is performed through regression equations, while assuming a linear relationship between the pixels at the same position in each band [21]. However, most remote sensing data are nonlinearly distributed, and the surface of the earth is composed of a complex mixture of natural and man-made features that exhibit nonlinear characteristics [22][23][24]. Changes that are caused by vegetation in particular have the most typical characteristics, including nonlinearity, which induces serious disturbances when change detection is performed [25,26]. In addition, since optical sa...

“…Shin et al [17] conducted a comparative study with both results of the TOA reflectance calculation using KOMPSAT-3A and Landsat 8 OLI images in the test area and reported that the quality of the KOMPSAT-3A images was consistently maintained. In a pilot study on the calibration of KOMPSAT-3/3A images, Yeom et al [18] performed calculations of TOA reflectance using the second simulation of the satellite signal in the solar spectrum (6S) algorithm, an important radiated transport model, and analyzed the absolute radiometric characteristics of KOMPSAT-3/3A. Ahn et al [19] produced atmospherically corrected images of Landsat or Sentinel-2 images and used them to propose an adjustment factor for KOMPSAT-3 for TOA reflectance.…”

Section: Introductionmentioning

confidence: 99%

Determination of the Normalized Difference Vegetation Index (NDVI) with Top-of-Canopy (TOC) Reflectance from a KOMPSAT-3A Image Using Orfeo ToolBox (OTB) Extension

Kim

et al. 2020

IJGI

Surface reflectance data obtained by the absolute atmospheric correction of satellite images are useful for land use applications. For Landsat and Sentinel-2 images, many radiometric processing methods exist, and the images are supported by most types of commercial and open-source software. However, multispectral KOMPSAT-3A images with a resolution of 2.2 m are currently lacking tools or open-source resources for obtaining top-of-canopy (TOC) reflectance data. In this study, an atmospheric correction module for KOMPSAT-3A images was newly implemented into the optical calibration algorithm in the Orfeo Toolbox (OTB), with a sensor model and spectral response data for KOMPSAT-3A. Using this module, named OTB extension for KOMPSAT-3A, experiments on the normalized difference vegetation index (NDVI) were conducted based on TOC reflectance data with or without aerosol properties from AERONET. The NDVI results for these atmospherically corrected data were compared with those from the dark object subtraction (DOS) scheme, a relative atmospheric correction method. The NDVI results obtained using TOC reflectance with or without the AERONET data were considerably different from the results obtained from the DOS scheme and the Landsat-8 surface reflectance of the Google Earth Engine (GEE). It was found that the utilization of the aerosol parameter of the AERONET data affects the NDVI results for KOMPSAT-3A images. The TOC reflectance of high-resolution satellite imagery ensures further precise analysis and the detailed interpretation of urban forestry or complex vegetation features.