• Korean Journal of Remote Sensing
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• v.33 no.6_1
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• pp.981-992
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• 2017

We, for the first time, retrieved tropospheric nitrogen dioxide ($Trop.NO_2$) vertical column density (VCD) from ground-based instrument, Pandora, using the optical density fitting based on Differential Optical Absorption Spectroscopy (DOAS)in Seoul for the period from May 2014 to December 2014. The $Trop.NO_2$ VCDs retrieved from Pandora were compared with those obtained from Ozone Monitoring Instrument (OMI). A correlation coefficient (R) between those retrieved from Pandora and those obtained from OMI is 0.55. To compare with surface $NO_2$ VMRs obtained from in-situ, Trop. $NO_2$ VCDs retrieved from Pandora and those obtained from OMI are converted into $NO_2$ VMRs in boundary layer (BLH $NO_2$ VMRs) using data measured from Atmospheric Infrared Sounder (AIRS). Surface $NO_2$ VMRs obtained from in-situ range from 5.5 ppbv to 61.5 ppbv. BLH $NO_2$ VMRs retrieved from Pandora and OMI range from 2.1 ppbv to 44.2 ppbv and from 0.9 ppbv to 11.6 ppbv, respectively. The range of BLH $NO_2$ VMRs retrieved from OMI is narrower than that of BLH $NO_2$ VMRs retrieved from Pandora and surface $NO_2$ VMRs obtained from in-situ. There is a batter correlation between surface $NO_2$ VMRs obtained from in-situ and BLH $NO_2$ VMRs retrieved from Pandora (R= 0.50)than the correlation between surface $NO_2$ VMRs obtained from in-situ and BLH $NO_2$ VMRs retrieved from OMI (R = 0.36). This poor correlation is thought to be due to the lower near-surface sensitivity of the satellite-based instrument (OMI) than Pandora, the ground-based instrument.

• Korean Journal of Remote Sensing
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• v.30 no.1
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• pp.109-126
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• 2014

We improved the Land Surface Emissivity (LSE) data (Kongju National University LSE v.2: KNULSE_v2) over the Communication, Ocean and Meteorological Satellite (COMS) observation region using recent(2009-2012) Moderate Resolution Imaging Spectroradiometer (MODIS) data. The surface emissivity was derived using the Vegetation Cover Method (VCM) based on the assumption that the pixel is only composed of ground and vegetation. The main issues addressed in this study are as follows: 1) the impacts of snow cover are included using Normalized Difference Snow Index (NDSI) data, 2) the number of channels is extended from two (11, 12 ${\mu}m$) to four channels (3.7, 8.7, 11, 12 ${\mu}m$), 3) the land cover map data is also updated using the optimized remapping of the five state-of-the-art land cover maps, and 4) the latest look-up table for the emissivity of land surface according to the land cover is used. The updated emissivity data showed a strong seasonal variation with high and low values for the summer and winter, respectively. However, the surface emissivity over the desert or evergreen tree areas showed a relatively weak seasonal variation irrespective of the channels. The snow cover generally increases the emissivity of 3.7, 8.7, and 11 ${\mu}m$ but decreases that of 12 ${\mu}m$. As the results show, the pattern correlation between the updated emissivity data and the MODIS LSE data is clearly increased for the winter season, in particular, the 11 ${\mu}m$. However, the differences between the two emissivity data are slightly increased with a maximum increase in the 3.7 ${\mu}m$. The emissivity data updated in this study can be used for the improvement of accuracy of land surface temperature derived from the infrared channel data of COMS.

• Korean Journal of Remote Sensing
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• v.33 no.2
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• pp.135-147
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• 2017

We, for the first time, estimated daily and monthly surface nitrogen dioxide ($NO_2$) volume mixing ratio (VMR) using three regression models with $NO_2$ tropospheric vertical column density (OMIT-rop $NO_2$ VCD) data obtained from Ozone Monitoring Instrument (OMI) in Seoul in South Korea at OMI overpass time (13:45 local time). First linear regression model (M1) is a linear regression equation between OMI-Trop $NO_2$ VCD and in situ $NO_2$ VMR, whereas second linear regression model (M2) incorporates boundary layer height (BLH), temperature, and pressure obtained from Atmospheric Infrared Sounder (AIRS) and OMI-Trop $NO_2$ VCD. Last models (M3M & M3D) are a multiple linear regression equations which include OMI-Trop $NO_2$ VCD, BLH and various meteorological data. In this study, we determined three types of regression models for the training period between 2009 and 2011, and the performance of those regression models was evaluated via comparison with the surface $NO_2$ VMR data obtained from in situ measurements (in situ $NO_2$ VMR) in 2012. The monthly mean surface $NO_2$ VMRs estimated by M3M showed good agreements with those of in situ measurements(avg. R = 0.77). In terms of the daily (13:45LT) $NO_2$ estimation, the highest correlations were found between the daily surface $NO_2$ VMRs estimated by M3D and in-situ $NO_2$ VMRs (avg. R = 0.55). The estimated surface $NO_2$ VMRs by three modelstend to be underestimated. We also discussed the performance of these empirical modelsfor surface $NO_2$ VMR estimation with respect to otherstatistical data such asroot mean square error (RMSE), mean bias, mean absolute error (MAE), and percent difference. This present study shows a possibility of estimating surface $NO_2$ VMR using the satellite measurement.

• Korean Journal of Remote Sensing
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• v.37 no.5_2
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• pp.1259-1268
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• 2021

The 2nd Geostationary Ocean Color Imager (GOCI-II) is the successor to the Geostationary Ocean Color Imager (GOCI), which employs one near-ultraviolet wavelength (380 nm) and eight visible wavelengths(412, 443, 490, 510, 555, 620, 660, 680 nm) and three near-infrared wavelengths(709, 745, 865 nm) to observe the marine environment in Northeast Asia, including the Korean Peninsula. However, the multispectral radiance image observed at satellite altitude includes both the water-leaving radiance and the atmospheric path radiance. Therefore, the atmospheric correction process to estimate the water-leaving radiance without the path radiance is essential for analyzing the ocean environment. This manuscript describes the GOCI-II standard atmospheric correction algorithm and its initial phase validation. The GOCI-II atmospheric correction method is theoretically based on the previous GOCI atmospheric correction, then partially improved for turbid water with the GOCI-II's two additional bands, i.e., 620 and 709 nm. The match-up showed an acceptable result, with the mean absolute percentage errors are fall within 5% in blue bands. It is supposed that part of the deviation over case-II waters arose from a lack of near-infrared vicarious calibration. We expect the GOCI-II atmospheric correction algorithm to be improved and updated regularly to the GOCI-II data processing system through continuous calibration and validation activities.

• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.251-263
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• 2019

Sentinel-2 Multi Spectral Instrument(MSI) launched by the European Space Agency (ESA) offered high spatial resolution optical products, enhanced temporal revisit of five days, and 13 spectral bands in the visible, near infrared and shortwave infrared wavelengths similar to Landsat mission. Landsat satellite imagery has been applied to various previous studies, but Sentinel-2 optical satellite imagery has not been widely used. Currently, for global coverage, Sentinel-2 products are systematically processed and distributed to Level-1C (L1C) products which contain the Top-of-Atmosphere (TOA) reflectance. Furthermore, ESA plans a systematic global production of Level-2A(L2A) product including the atmospheric corrected Bottom-of-Atmosphere (BOA) reflectance considered the aerosol optical thickness and the water vapor content. Therefore, the Sentinel-2 L2A products are expected to enhance the reliability of image quality for overall coverage in the Sentinel-2 mission with enhanced spatial,spectral, and temporal resolution. The purpose of this work is a quantitative comparison Sentinel-2 L2A products and fully simulated image to evaluate the applicability of the Sentinel-2 dataset in cultivated land growing various kinds of crops in Korea. Reference image of Sentinel-2 L2A data was simulated by airborne hyperspectral data acquired from AISA Fenix sensor. The simulation imagery was compared with the reflectance of L1C TOA and that of L2A BOA data. The result of quantitative comparison shows that, for the atmospherically corrected L2A reflectance, the decrease in RMSE and the increase in correlation coefficient were found at the visible band and vegetation indices to be significant.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.543-557
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• 2021

Because aerosols have different spectral characteristics according to the size and composition of the particle and to the satellite sensors, a comparative analysis of aerosol products from various satellite sensors is required. In South Korea, however, a comprehensive study for the comparison of various official satellite AOD (Aerosol Optical Depth) products for a long period is not easily found. In this paper, we aimed to assess the performance of the AOD products from MODIS (Moderate Resolution Imaging Spectroradiometer), VIIRS (Visible Infrared Imaging Radiometer Suite), Himawari-8, and Sentinel-3 by referring to the AERONET (Aerosol Robotic Network) sun photometer observations for the period between January 2015 and December 2019. Seasonal and geographical characteristics of the accuracy of satellite AOD were also analyzed. The MODIS products, which were accumulated for a long time and optimized by the new MAIAC (Multiangle Implementation of Atmospheric Correction) algorithm, showed the best accuracy (CC=0.836) and were followed by the products from VIIRS and Himawari-8. On the other hand, Sentinel-3 AOD did not appear to have a good quality because it was recently launched and not sufficiently optimized yet, according to ESA (European Space Agency). The AOD of MODIS, VIIRS, and Himawari-8 did not show a significant difference in accuracy according to season and to urban vs. non-urban regions, but the mixed pixel problem was partly found in a few coastal regions. Because AOD is an essential component for atmospheric correction, the result of this study can be a reference to the future work for the atmospheric correction for the Korean CAS (Compact Advanced Satellite) series.

• Korean Journal of Remote Sensing
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• v.38 no.2
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• pp.179-188
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• 2022

Global warming causes climate change and increases extreme weather events worldwide, and the occurrence of heatwaves and droughts is also increasing in Korea. For the monitoring of extreme weather, various satellite data such as LST (Land Surface Temperature), TCI (Temperature Condition Index), NDVI (Normalized Difference Vegetation Index), VCI (Vegetation Condition Index), and VHI (Vegetation Health Index) have been used. VHI, the combination of TCI and VCI, represents the vegetation stress affected by meteorological factors like precipitation and temperature and is frequently used to assess droughts under climate change. TCI and VCI require historical reference values for the LST and NDVI for each date and location. So, it is complicated to produce the VHI from the recent satellite GK2A (Geostationary Korea Multi-Purpose Satellite-2A). This study examined the retrieval of VHI using GK2A AMI (Advanced Meteorological Imager) by referencing the historical data from VIIRS (Visible Infrared Imaging Radiometer Suite) NDVI and LST as a proxy data. We found a close relationship between GK2A and VIIRS data needed for the retrieval of VHI. We produced the TCI, VCI, and VHI for GK2A during 2020-2021 at intervals of 8 days and carried out the interpretations of recent extreme weather events in Korea. GK2A VHI could express the changes in vegetation stress in 2020 due to various extreme weather events such as heatwaves (in March and June) and low temperatures (in April and July), and heavy rainfall (in August), while NOAA (National Oceanic and Atmospheric Administration) VHI could not well represent such characteristics. The GK2A VHI presented in this study can be utilized to monitor the vegetation stress due to heatwaves and droughts if the historical reference values of LST and NDVI can be adjusted in a more statistically significant way in the future work.

• Korean Journal of Remote Sensing
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• v.39 no.3
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• pp.283-295
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• 2023

With the development of sensor and satellite technology, numerous high-resolution and multi-spectral satellite images have been available. Due to their wavelength-dependent reflection, transmission, and scattering characteristics, multi-spectral satellite images can provide complementary information for earth observation. In particular, the short-wave infrared (SWIR) band can penetrate certain types of atmospheric aerosols from the benefit of the reduced Rayleigh scattering effect, which allows for a clearer view and more detailed information to be captured from hazed surfaces compared to the visible band. In this study, we proposed a multi-resolution transform-based image fusion method to combine visible and SWIR satellite images. The purpose of the fusion method is to generate a single integrated image that incorporates complementary information such as detailed background information from the visible band and land cover information in the haze region from the SWIR band. For this purpose, this study applied the Laplacian pyramid-based multi-resolution transform method, which is a representative image decomposition approach for image fusion. Additionally, we modified the multiresolution fusion method by combining a haze-guided weight map based on the prior knowledge that SWIR bands contain more information in pixels from the haze region. The proposed method was validated using very high-resolution satellite images from Worldview-3, containing multi-spectral visible and SWIR bands. The experimental data including hazed areas with limited visibility caused by smoke from wildfires was utilized to validate the penetration properties of the proposed fusion method. Both quantitative and visual evaluations were conducted using image quality assessment indices. The results showed that the bright features from the SWIR bands in the hazed areas were successfully fused into the integrated feature maps without any loss of detailed information from the visible bands.

• Korean Journal of Remote Sensing
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• v.33 no.6_1
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• pp.931-946
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• 2017

The purpose of this study is to observe and analyze soil moisture conditions with high resolution and to evaluate its application feasibility to agriculture. For this purpose, we used three Landsat-8 OLI (Operational Land Imager)/TIRS (Thermal Infrared Sensor) optical and thermal infrared satellite images taken from May to June 2015, 2016, and 2017, including the rural areas of Jeollabuk-do, where 46% of agricultural areas are located. The soil moisture conditions at each date in the study area can be effectively obtained through the SPI (Standardized Precipitation Index)3 drought index, and each image has near normal, moderately wet, and moderately dry soil moisture conditions. The temperature vegetation dryness index (TVDI) was calculated to observe the soil moisture status from the Landsat-8 OLI/TIRS images with different soil moisture conditions and to compare and analyze the soil moisture conditions obtained from the SPI3 drought index. TVDI is estimated from the relationship between LST (Land Surface Temperature) and NDVI (Normalized Difference Vegetation Index) calculated from Landsat-8 OLI/TIRS satellite images. The maximum/minimum values of LST according to NDVI are extracted from the distribution of pixels in the feature space of LST-NDVI, and the Dry/Wet edges of LST according to NDVI can be determined by linear regression analysis. The TVDI value is obtained by calculating the ratio of the LST value between the two edges. We classified the relative soil moisture conditions from the TVDI values into five stages: very wet, wet, normal, dry, and very dry and compared to the soil moisture conditions obtained from SPI3. Due to the rice-planing season from May to June, 62% of the whole images were classified as wet and very wet due to paddy field areas which are the largest proportions in the image. Also, the pixels classified as normal were analyzed because of the influence of the field area in the image. The TVDI classification results for the whole image roughly corresponded to the SPI3 soil moisture condition, but they did not correspond to the subdivision results which are very dry, wet, and very wet. In addition, after extracting and classifying agricultural areas of paddy field and field, the paddy field area did not correspond to the SPI3 drought index in the very dry, normal and very wet classification results, and the field area did not correspond to the SPI3 drought index in the normal classification. This is considered to be a problem in Dry/Wet edge estimation due to outlier such as extremely dry bare soil and very wet paddy field area, water, cloud and mountain topography effects (shadow). However, in the agricultural area, especially the field area, in May to June, it was possible to effectively observe the soil moisture conditions as a subdivision. It is expected that the application of this method will be possible by observing the temporal and spatial changes of the soil moisture status in the agricultural area using the optical satellite with high spatial resolution and forecasting the agricultural production.

• Korean Journal of Remote Sensing
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• v.28 no.4
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• pp.393-407
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• 2012

This study focuses on the correction of topographic effects caused by a combination of solar elevation and azimuth, and topographic relief in single optical remote sensing imagery, and by a combination of changes in position of the sun and topographic relief in comparative analysis of multi-temporal imageries. For the Jeju Island, Republic of Korea, where Mt. Halla and various cinder cones are located, a Landsat 7 ETM+ imagery and ASTER GDEM data were used to normalize the topographic effects on the imagery, using two topographic normalization methods: cosine correction assuming a Lambertian condition and assuming a non-Lambertian c-correction, with kernel sizes of $3{\times}3$, $5{\times}5$, $7{\times}7$, and $9{\times}9$ pixels. The effects of each correction method and kernel size were then evaluated. The c-correction with a kernel size of $7{\times}7$ produced the best result in the case of a land area with various land-cover types. For a land-cover type of forest extracted from an unsupervised classification result using the ISODATA method, the c-correction with a kernel size of $9{\times}9$ produced the best result, and this topographic normalization for a single land cover type yielded better compensation for topographic effects than in the case of an area with various land-cover types. In applying the relative radiometric normalization to topographically normalized three multi-temporal imageries, more invariant spectral reflectance was obtained for infrared bands and the spectral reflectance patterns were preserved in visible bands, compared with un-normalized imageries. The results show that c-correction considering the remaining reflectance energy from adjacent topography or imperfect atmospheric correction yielded superior normalization results than cosine correction. The normalization results were also improved by increasing the kernel size to compensate for vertical and horizontal errors, and for displacement between satellite imagery and ASTER GDEM.