• Korean Journal of Remote Sensing
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• v.33 no.5_1
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• pp.599-605
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• 2017

Accurate cloud discrimination in satellite images strongly affects accuracy of remotely sensed parameter produced using it. Especially, cloud contaminated pixel over ocean is one of the major error factors such as Sea Surface Temperature (SST), ocean color, and chlorophyll-a retrievals,so accurate cloud detection is essential process and it can lead to understand ocean circulation. However, static threshold method using real-time algorithm such as Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Himawari Imager (AHI) can't fully explained reflectance variability over ocean as a function of relative positions between the sun - sea surface - satellite. In this paper, we assembled a reflectance spectral library as a function of Solar Zenith Angle (SZA) and Viewing Zenith Angle (VZA) from ocean surface reflectance with clear sky condition of Advanced Himawari Imager (AHI) identified by NOAA's cloud products and spectral library is used for applying the Dynamic Time Warping (DTW) to detect cloud pixels. We compared qualitatively between AHI cloud property and our results and it showed that AHI cloud property had general tendency toward overestimation and wrongly detected clear as unknown at high SZA. We validated by visual inspection with coincident imagery and it is generally appropriate.

• Korean Journal of Remote Sensing
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• v.32 no.6
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• pp.589-601
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• 2016

Sea surface currents were estimated by applying the Maximum Cross Correlation (MCC), Zero-mean Sum of Absolute Distances (ZSAD), and Zero-mean Sum of Squared Distances (ZSSD) algorithms to Himawari-8/Advanced Himawari Imager (AHI) thermal infrared channel data, and the comparative analysis was performed between the results of these algorithms. The sea surface currents of the Kuroshio Current region that were retrieved using each algorithm showed similar results. The ratio of errors to the total number of estimated surface current vectors had little difference according to the algorithms, and the time required for sea surface current calculation was reduced by 24% and 18%, relative to the MCC algorithm, for the ZSAD and ZSSD algorithms, respectively. The estimated surface currents were validated against those from satellite-tracked surface drifter and altimeter data, and the accuracy evaluation of these algorithms showed results within similar ranges. In addition, the accuracy was affected by the magnitude of brightness temperature gradients and the time interval between satellite image data.

• Journal of Korean Society for Atmospheric Environment
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• v.34 no.3
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• pp.493-507
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• 2018

Due to high spatio-temporal variability of amount and optical/microphysical properties of atmospheric aerosols, satellite-based observations have been demanded for spatiotemporal monitoring the major aerosols. Observations of the heavy aerosol episodes and determination on the dominant aerosol types from a geostationary satellite can provide a chance to prepare in advance for harmful aerosol episodes as it can repeatedly monitor the temporal evolution. A new geostationary observation sensor, namely the Advanced Himawari Imager (AHI), onboard the Himawari-8 platform, has been observing high spatial and temporal images at sixteen wavelengths from 2016. Using observed spectral visible reflectance and infrared brightness temperature (BT), the algorithm to find major aerosol type such as volcanic ash (VA), desert dust (DD), polluted aerosol (PA), and clean aerosol (CA), was developed. RGB color composite image shows dusty, hazy, and cloudy area then it can be applied for comparing aerosol detection product (ADP). The CALIPSO level 2 vertical feature mask (VFM) data and MODIS level 2 aerosol product are used to be compared with the Himawari-8/AHI ADP. The VFM products can deliver nearly coincident dataset, but not many match-ups can be returned due to presence of clouds and very narrow swath. From the case study, the percent correct (PC) values acquired from this comparisons are 0.76 for DD, 0.99 for PA, 0.87 for CA, respectively. The MODIS L2 Aerosol products can deliver nearly coincident dataset with many collocated locations over ocean and land. Increased accuracy values were acquired in Asian region as POD=0.96 over land and 0.69 over ocean, which were comparable to full disc region as POD=0.93 over land and 0.48 over ocean. The Himawari-8/AHI ADP algorithm is going to be improved continuously as well as the validation efforts will be processed by comparing the larger number of collocation data with another satellite or ground based observation data.

• Korean Journal of Remote Sensing
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• v.32 no.1
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• pp.67-71
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• 2016

The surface reflectance is essential to retrieval various indicators related land properties such as vegetation index, albedo and etc. In this study, we estimated surface reflectance using Himawari-8 / Advanced Himawari Imager (AHI) channel data. In order to estimate surface reflectance from Top of Atmosphere (TOA) reflectance, the atmospheric correction is necessary because all of the TOA reflectance from optical sensor is affected by gas molecules and aerosol in the atmosphere. We used Second Simulation of a Satellite Signal in the Solar Spectrum Vector (6SV) Radiative Transfer Model (RTM) to correct atmospheric effect, and Look-Up Table (LUT) to shorten the calculation time. We verified through comparison Himawri-8 / AHI surface reflectance and Proba-V S1 products. As a result, bias and Root Mean Square Error (RMSE) are calculated about -0.02 and 0.05.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.1101-1117
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• 2018

The clear sky radiance (CSR) is one of the baseline products of the Himawari-8 which was launched on October, 2014. The CSR contributes to numerical weather prediction (NWP) accuracy through the data assimilation; especially water vapor channel CSR has good impact on the forecast in high level atmosphere. The focus of this study is the quality analysis of the CSR of the Himawari-8 geostationary satellite. We used the operational CSR (or clear sky brightness temperature) products in JMA (Japan Meteorological Agency) as observation data; for a background field, we employed the CSR simulated using the Radiative Transfer for TOVS (RTTOV) with the atmospheric state from the global model of KMA (Korea Meteorological Administration). We investigated data characteristics and analyzed observation minus background statistics of each channel with respect to regional and seasonal variability. Overall results for the analysis period showed that the water vapor channels (6.2, 6.9, and $7.3{\mu}m$) had a positive mean bias where as the window channels(10.4, 11.2, and $12.4{\mu}m$) had a negative mean bias. The magnitude of biases and Uncertainty result varied with the regional and the seasonal conditions, thus these should be taken into account when using CSR data. This study is helpful for the pre-processing of Himawari-8/Advanced Himawari Imager (AHI) CSR data assimilation. Furthermore, this study also can contribute to preparing for the utilization of products from the Geo-Kompsat-2A (GK-2A), which will be launched in 2018 by the National Meteorological Satellite Center (NMSC) of KMA.

• Korean Journal of Remote Sensing
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• v.36 no.3
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• pp.425-439
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• 2020

Through the operations of advanced geostationary meteorological satellite such as Himawari-8 and GK2A, higher resolution and frequency of AOD (Aerosol Optical Depth) data have become available. In this study, we analyzed the characteristics of Himawari-8/AHI (Advanced Himawari Imager) aerosol properties using the recent 4 years (2016~2019) of Sun photometer data observed at the five stations(Seoul National University, Yonsei University, Hankuk University of Foreign Studies, Gwangju Institute of Science and Technology, Anmyon island) which is a part of the AERONET (Aerosol Robotic Network). In addition, we analyzed the causes for the AOD differences between Himawari AOD and Sun photometer AOD. The results showed that the two AOD data are very similar regardless of geographic location, in particular, for the clear condition (cloud amount < 3). However, the quality of Himawari AOD data is heavily degraded compared to that of the clear condition, in terms of bias (0.05 : 0.21), correlation (0.74 : 0.64) and RMSE (Root Mean Square Error; 0.21 : 0.51), when cloud amount is increased. In general, the large differences between two AOD data are mainly related to the cloud amount and relative humidity. The Himawari strongly overestimates the AOD at all five stations when cloud amount and relative humidity are large. However, the wind speed, precipitable water, height of cloud base and Angstrom Exponent have been shown to have no effect on the AOD differences irrespective of geographic location and cloud amount. The results suggest that caution is required when using Himawari AOD data in cloudy conditions.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.1025-1032
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• 2018

Snow Cover is a form of precipitation that is defined by snow on the surface and is the single largest component of the cryosphere that plays an important role in maintaining the energy balance between the earth's surface and the atmosphere. It affects the regulation of the Earth's surface temperature. However, since snow cover is mainly distributed in area where human access is difficult, snow cover detection using satellites is actively performed, and snow cover detection in forest area is an important process as well as distinguishing between cloud and snow. In this study, we applied the Normalized Difference Snow Index (NDSI) and the Normalized Difference Vegetation Index (NDVI) to the geostationary satellites for the snow detection of forest area in existing polar orbit satellites. On the rest of the forest area, the snow cover detection using $R_{1.61{\mu}m}$ anomaly technique and NDSI was performed. As a result of the indirect validation using the snow cover data and the Visible Infrared Imaging Radiometer (VIIRS) snow cover data, the probability of detection (POD) was 99.95 % and the False Alarm Ratio (FAR) was 16.63 %. We also performed qualitative validation using the Himawari-8 Advanced Himawari Imager (AHI) RGB image. The result showed that the areas detected by the VIIRS Snow Cover miss pixel are mixed with the area detected by the research false pixel.

• Korean Journal of Remote Sensing
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• v.35 no.3
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• pp.483-489
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• 2019

True color images display colors similar to natural colors. This has the advantage that it is possible to monitor rapidly the complex earth atmosphere phenomenon and the change of the surface type. Currently, various organizations are producing true color images. In Korea, it is necessary to produce true color images by replacing generations with next generation weather satellites. Therefore, in this study, visual enhancement for true color image production was performed using Top of Atmosphere (TOA) data of Advanced Himawari Imager (AHI) sensor mounted on Himawari-8 satellite. In order to improve the visualization, we performed two methods of Nonlinear enhancement and Histogram equalization. As a result, Histogram equalization showed a strong bluish image in the region over $70^{\circ}$ Solar Zenith Angle (SZA) compared to the Nonlinear enhancement and nonlinear enhancement technique showed a reddish vegetation area.

• Korean Journal of Remote Sensing
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• v.32 no.6
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• pp.681-691
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• 2016

Using various satellite measurements in UV, visible and IR, diverse algorithms to retrieve aerosol information have been developed and operated to date. Advanced Himawari Imager (AHI) onboard the Himawari 8 weather satellite was launched in 2014 and has 16 channels from visible to Thermal InfRared (TIR) in high temporal and spatial resolution. Using AHI, it is very valuable to retrieve aerosol optical properties over dark surface to demonstrate its capability. To retrieve aerosol optical properties using visible and Near InfRared (NIR) region, surface signal is very important to be removed which can be estimated using minimum reflectivity method. The estimated surface reflectance is then used to retrieve the aerosol optical properties through the inversion process. In this study, we retrieve the aerosol optical properties over dark surface, but not over bright surface such as clouds, desert and so on. Therefore, the bright surface was detected and masked using various infrared channels of AHI and spatial heterogeneity, Brightness Temperature Difference (BTD), etc. The retrieval result shows the correlation coefficient of 0.7 against AERONET, and the within the Expected Error (EE) of 49%. It is accurately retrieved even for low Aerosol Optical Depth (AOD). However, AOD tends to be underestimated over the Beijing Hefei area, where the surface reflectance using the minimum reflectance method is overestimated than the actual surface reflectance.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.861-870
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• 2021

In this study, fused Aerosol Optical Depth (AOD) data were produced using AOD products from the Geostationary Ocean Color Imager (GOCI) onboard Communication, Oceanography and Meteorology Satellite (COMS)satellite and the Advanced Himawari Imager (AHI) onboard Himawari-8. Since the spatial resolution and the coordinate system between the satellite sensors are different, a preprocessing was first preceded. After that, using the level 1.5 AOD dataset of AErosol RObotic NETwork (AERONET), which is ground-based observation, correlations and trends between each satellite AOD and AERONET AOD were utilized to produce more accurate satellite AOD data than the originalsatellite AODs. The fused AOD were found to be more accurate than the originalsatellite AODs. Root Mean Square Error (RMSE) and mean bias of the fused AODs were calculated to be 0.13 and 0.05, respectively. We also compared errors of the fused AODs against those of the original GOCI AOD (RMSE: 0.15, mean bias: 0.11) and the original AHI AOD (RMSE: 0.15, mean bias: 0.05). It was confirmed that the fused AODs have betterspatial coverage than the original AODsin areas where there are no observations due to the presence of cloud from a single satellite.