This study investigates spatiotemporal variations in column-average dry-air mole fractions of methane (XCH4) over the Korean Peninsula from 2019 to 2024 using TROPOspheric Monitoring Instrument (TROPOMI) satellite observations. To assess data accuracy, satellite retrievals were compared with ground-based Total Carbon Column Observing Network (TCCON) measurements at Anmyeondo, Korea, and Saga, Japan. Despite limited data availability at Anmyeondo (N = 7) due to harsh coastal conditions, the validation showed a high correlation (R > 0.88) and consistent seasonal variations at both sites, confirming the accuracy of TROPOMI retrievals. The results revealed a distinct "west-high, east-low" spatial pattern of XCH4 over the Korean Peninsula. This distribution reflects the concentration of major anthropogenic sources in western regions, including the densely populated Seoul Metropolitan Area, large-scale industrial complexes, and coal-fired power plants, showing strong qualitative and quantitative agreement with the Emissions Database for Global Atmospheric Research (EDGAR) 2025 emission inventory. In general, the mean XCH4 concentration increased continuously across the Korean Peninsula, except for a temporary stagnation in 2022. Notably, a synchronized rapid surge was observed across all major regions in 2024, peaking at 1912.58 ppb. Regional analysis indicated that, while local industrial emissions (e.g., in Gyeongnam) drove regional heterogeneity until 2023, the sharp increase in 2024 was likely driven by strong external factors, such as the global rise in background methane levels or large-scale meteorological influences. These findings provide important insights into regional variations in methane levels and emissions across the Korean Peninsula. These results demonstrate that TROPOMI not only effectively captures anthropogenic emission patterns aligned with EDGAR but also identifies potential natural sources unique to the Korean Peninsula's environmental characteristics. By bridging the gap between existing emission inventories and real-world observations, our findings highlight the critical role of satellite-based monitoring in establishing comprehensive, data-driven strategies for regional methane management and achieving national climate goals.

Abstract Accurate retrieval of aerosol and cloud properties is essential for improving both climate data records and trace gas observations by satellites. The Global Observing Satellite for Greenhouse gases and Water cycle (GOSAT-GW), launched on 29 June 2025, carries the Total Anthropogenic and Natural emissions mapping SpectrOmeter-3 (TANSO-3), which is designed to provide high-spectral-resolution measurements in the visible (~ 450 nm), O2 A band, and near-infrared (~ 1600 nm) regions. To prepare for operation, an aerosol and cloud retrieval algorithm was developed using Tropospheric Monitoring Instrument (TROPOMI) Level 1B data as a proxy. The algorithm retrieves the aerosol optical depth (AOD) and cloud optical depth (COD) from band 1 only from TANSO-3, and estimates the aerosol layer height (ALH) and cloud layer height (CLH) using O2–O2 absorption at 477 nm. Lookup tables were generated with the linearized pseudo-spherical vector Discrete Ordinate Radiative Transfer, and additional corrections were applied to account for relative humidity, temperature dependence, and systematic offsets in the O2–O2 absorption band. During the airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) 2024 campaign, the AODs showed good agreement with AERONET (correlation coefficient (R) = 0.781, root mean square error (RMSE) = 0.265), while comparison with Pandora SMART-s retrievals showed more moderate agreement (R = 0.444, RMSE = 0.378). ALH retrievals agreed with High Spectral Resolution Lidar-2 (HSRL-2) data within ± 1 km for AODs greater than 0.5, while under low-aerosol loading conditions the retrieved ALH became less sensitive and tended to converge toward near-surface values. COD retrievals were consistent with TROPOMI operational products, while CLH retrievals based on the O2–O2 absorption band were on average about 1 km lower than O2 A band-based results, likely due to differences in the vertical sensitivity of the two absorption bands. With further refinement in surface reflectance treatment, aerosol classification, and cloud phase detection, the algorithm will contribute to the effective use of GOSAT-GW observations for atmospheric research and air quality applications.

Surface reflectance plays a critical role in nitrogen dioxide (NO2) air mass factor (AMF) calculations, yet most operational surface reflectance retrievals are performed without explicitly accounting for NO2 absorption, potentially introducing radiative inconsistencies in the retrieval chain. Using observations from the Geostationary Environmental Monitoring Spectrometer (GEMS) over East Asia during November–December 2024, this study quantifies the radiative impact of NO2-consistent surface reflectance treatment on AMF calculations. A vector linearized discrete ordinate radiative transfer model (VLIDORT)–based NO2 transmittance look-up table is applied within the atmospheric correction framework to correct top-of-atmosphere reflectance, producing NO2-consistent and non-consistent surface reflectances that are independently propagated through identical radiative transfer simulations. Results show that neglecting NO2 absorption leads to systematic underestimation of AMFs, with median relative differences (ΔAMF) increasing from near zero under low-NO2 conditions to approximately 1–2% (95th percentile >3%) under high-NO2 (≥ 2.0 DU) and bright-surface (ρ ≥ 0.08) conditions. These findings demonstrate that radiative consistency between surface reflectance treatment and AMF calculation plays a critical role in interpreting AMF sensitivity in geostationary observations.

The SMART-P (Spectral Measurements for Atmospheric Radiative Transfer–Polarimeter) algorithm was developed to retrieve aerosol and ocean parameters from PolCube measurements. The PolCube is a multi-angular polarimeter (MAP) aboard the BusanSat-B CubeSat scheduled for launch in 2026, which measures polarized radiances at 410, 555, 670, and 865 nm from four viewing angles. This study presents the theoretical basis of the algorithm and conducts a sensitivity analysis of aerosol inversions over the ocean processed by SMART-P under the expected measurement conditions for PolCube observations. The results indicate that the degree of linear polarization (DoLP) significantly increases the information content of the real part of the refractive index and of the fine-mode particle-size parameters relative to radiance-only measurements. Enhanced measurement sensitivity enables more accurate retrieval of fine-dominated aerosol properties, such as smoke and sulfate. The sensitivity analysis also shows that the ocean surface reflectivity is the most critical forward-model parameter affecting aerosol-property retrievals. The SMART-P algorithm will support the BusanSat-B mission to understand the role of aerosol particles in the climate system and air quality.

Identifying the spatial distribution of nitrogen dioxide (NO2) without gaps is critical in Asia, where anthropogenic emissions are intense. Since its launch in 2020, the Geostationary Environment Monitoring Spectrometer (GEMS) has enabled unprecedentedly high spatiotemporal resolution monitoring of air pollutants on the Asian continent. Using data from AirKorea in-situ monitors and six Pandora instruments during the Airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) campaign in Gyeonggi and Chungcheong Provinces, this study evaluated the accuracy of GEMS NO2 products against Pandora data and examined the relationship between NO2 vertical column density (VCD) and in-situ NO2 volume mixing ratio (VMR). GEMS showed considerable agreement with Pandora (correlation coefficient [R] = 0.86, root mean squared error [RMSE] = 0.114 DU, and mean bias error [MBE] = -0.013 DU). Despite the inhomogeneity of vertical NO2 distribution between column and in-situ measurements, they showed a meaningful correlation (Pandora-AirKorea: R = 0.50, and GEMS-AirKorea: R = 0.58). Notably, there were significant relationships between collocated NO2 VCD-VMR at around 14:00 Korea Standard Time (Pandora-AirKorea: R = 0.88, and GEMS-AirKorea: R = 0.82). Overall, this study concludes that GEMS monitoring of NO2 VCD is effective for detecting surface-level NO2 pollution with broad spatial coverage in Asia during winter.

The algorithm developed for retrieving Nitrogen Dioxide (NO2) profiles utilizes optimal estimation and is based on sky measurement data obtained from the Pandora instrument. In this study, the Aerosol Optical Thickness (AOT) was calculated and employed as an input parameter through the SMART-s algorithm (Jeong et al., 2022). The NO2 profile was retrieved by least-square fitting utilizing the VLIDORT radiative transfer model with a priori information derived from Community Earth System Model (CESM) data. Pandora measurements were taken in Yongin, South Korea from December 2021 to January 2022. The retrieved NO2 profile was compared with surface NO2 concentrations near two in situ sites. The correlation and Root Mean Square Error (RMSE) between the surface concentration measured by Pandora and the two in situ sites were approximately 0.56 and 12.24 ppb, respectively. A higher correlation was observed with in situ locations positioned along the line of sight compared to nearer sites. This correlation was further enhanced when incorporating aerosol optical thickness directly obtained from Pandora measurements. The findings of this study suggest that considering aerosol information in the retrieval of NO2 profiles, as measured values, can contribute to improvements.

The Geostationary Environment Monitoring Spectrometer (GEMS), launched in 2020, provides both temporally and spatially continuous air quality data from geostationary Earth orbit (GEO). This study first investigates the seasonal variations and diurnal behavior of nitrogen dioxide (NO2) tropospheric vertical column densities (TropVCDs) over the Seoul metropolitan area (SMA) using GEMS data, retrieved by the IUP-UB algorithm. We find that the magnitude of the NO2 TropVCDs and their diurnal behavior have significant seasonal dependences. In January, the highest NO2 TropVCD values in the range 27.5–28.9×1015 molec.cm-2 during the four seasons were observed at 15:00 local time (LT) and NO2 TropVCD increases from the first retrieved values at 10:00 LT. On the other hand, we find the lowest values (7.4–8.8×1015 molec.cm-2) are at ∼14:00 LT in July. The VCD values in July increased up to 10:00 LT and then decreased until 14:00 LT but then began to increase again. These different diurnal behaviors of the TropVCDs in the different seasons reflect the differences in photochemical and meteorological conditions as well as the emissions of NOx. Photochemical transformations are typically more rapid in July and slower in January. The absolute values and diurnal behavior of NO2 TropVCDs are significantly influenced by the wind speed, except in July. Moderate (wind speed ≥3 m s−1) or strong wind (wind speed >5 m s−1) reduced the magnitude of the diurnal behavior in January, implying that the NO2 plumes were transported downwind. Finally, we compared the retrieved NO2 TropVCDs by using different a priori NO2 data simulated by TM5 and WRF-Chem, calculated using the most recent emission inventories. Although simulated VCDs from WRF-Chem and TM5 show differences of up to a factor 2.75, retrieved NO2 TropVCDs using each a priori data have almost identical values and diurnal behaviors, except in July. Notably, the diurnal behavior of the retrieved NO2 TropVCDs is independent of that from the two chemical transport models, indicating that observations of slant column densities are the dominant factor in determining the diurnal behavior of NO2 TropVCDs. Changes in the model horizontal resolution and volatile organic compound (VOC) emission inventory do not significantly affect the retrieved NO2 TropVCDs in this study. However, when the a priori NO2 vertical profile was fixed as the values at 13:45 LT, the diurnal patterns of NO2 TropVCDs showed significant changes, with differences of up to −18.3 %.

The impact of stratospheric aerosols on Earth’s climate, particularly through atmospheric heating and ozone depletion, remains a critical area of atmospheric research. While satellite data provide valuable insights, independent validation methods are necessary for ensuring accuracy. Twilight near-infrared (NIR) radiometry offers a promising approach for investigating aerosol properties, such as optical depth and layer height, at high altitudes. This study aims to evaluate the effectiveness of twilight radiometry in corroborating satellite data and assessing aerosol characteristics. Two methods based on twilight radiometry—the color ratio and the derivative method—are employed to derive the aerosol layer height and optical depth. Radiances at 450, 550, 762, 775, and 1050 nm wavelengths are analyzed at varying solar zenith angles, using zenith viewing geometry for consistency. Comparisons of aerosol optical depths (AODs) between Research Pandora (ResPan) and AErosol RObotic NETwork (AERONET) data (R = 0.99) and between ResPan and Modern-Era Retrospective analysis for Research and Applications (MERRA-2) data (R = 0.86) demonstrate a strong correlation. Twilight ResPan data are also used to estimate the aerosol layer height, with results in good agreement with SAGE and lidar measurements, particularly following the Hunga Tonga eruption in Lauder, New Zealand. The simulation database, created using the libRadtran DISORT and Monte Carlo packages for daylight and twilight calculations, is capable of detecting AODs as low as 10−3 using the derivative method. This work highlights the potential of twilight radiometry as a simple, cost-effective tool for atmospheric research and satellite data validation, offering valuable insights into aerosol dynamics at stratospheric altitudes.

To understand the dominant chemical mechanisms driving wintertime secondary PM2.5 formation and to validate GEMS L2 data, the National Institute of Environmental Research and the National Aeronautics and Space Administration (NASA) conducted the Airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) campaign across four Asian countries (Korea, the Philippines, Malaysia, and Thailand) from February to March 2024. During this campaign, we deployed six Pandora instruments, two AQProfilers, and five AERONET systems around the Seoul metropolitan region. Using these ground-based instruments, we retrieved nitrogen dioxide, ozone and formaldehyde vertical column densities, as well as aerosol properties, and compared the results with GEMS L2 products. A comparison of NO₂ observed by GEMS with that from ground-based remote sensing instruments revealed a correlation coefficient of over 0.6 across all regions. Additionally, a performance comparison of GEMS NO₂ across different versions showed that the overestimation observed in GEMS v2 results was improved in the v4 results. Furthermore, we also compared results from NASA GeoTASO with those from GEMS during this period.

Ground-based remote sensing is widely used in various research fields related to the atmospheric environment study. With the increased operations of satellites and airborne observation in recent years, ground-based observation instruments have become more frequent, and their operation and applications have become more diverse. This paper provides an introduction to ground-based remote sensing instruments used in atmospheric environment research, especial Lidar instrument. Also, this paper summarizes the key research areas adopted by the ground-based remote sensing dataset. Since the classical ground-based instruments such as the Brewer and Dobson spectrophotometers, a variety of instruments for observing aerosols and trace gases are now being operated both domestically and internationally. Through these instruments, it has been confirmed that ground-based remote sensing has been actively utilized for satellite/airborne data validation, atmospheric quality characteristics analysis, and long-term changes in air quality.

Busan’s major port is among the largest trading ports worldwide; however, it is also one of the ten most polluted ports globally. This study aims to assess the effectiveness of satellite-derived aerosol data for monitoring particulate matter levels in Busan. Aerosol optical depth (AOD) from the Visible Infrared Imaging Radiometer (VIIRS) Deep Blue product tends to be sparse near coastlines due to higher retrieval uncertainties. To increase the number of samples along the coastal area, we established optimized quality control criteria, resulting in more than three times the number of samples. The VIIRS AOD showed a positive correlation with surface particulate matter (PM2.5) measurements (r = 0.42). The ratios of VIIRS AOD to surface PM2.5 and PM10 were higher in coastal areas, probably due to greater hygroscopic growth of particles. This connection can assist in estimating surface PM concentrations using satellite data. Both VIIRS AOD and surface PM concentrations exhibit a negative correlation with terrain elevation, primarily due to the locations of emission sources and altitude-dependent weather factors such as temperature and humidity. We expect that combining higher-resolution ancillary databases, including digital elevation maps and meteorology, with satellite-based AOD will enhance the detail of air quality evaluations in port cities.

Accurate retrieval of satellite-derived aerosol optical depth (AOD) is critical for air quality forecasting, especially when AOD is assimilated into models. However, if retrieval errors in the satellite-derived AOD are not corrected or characterized, they can lead to false analysis increments and ultimately degrade the quality of data assimilation (DA). The issue also arises in the Geostationary Ocean Color Imager (GOCI)-II AOD product, which remains susceptible to cloud contamination. To address the issue, this study developed a cloud screening framework for GOCI-II AOD that combines two processes: (a) cloud removal using cloud type information from Himawari-8/9 Advanced Himawari Imager (AHI) and (b) a statistical cloud filtering based on AOD retrieval characteristics. Comparisons with Aerosol Robotic Network (AERONET) AOD demonstrated that the cloud screening framework effectively mitigated overestimation of GOCI-II AODs compared to AERONET AODs. Consequently, the cloud-screened GOCI-II AOD showed a better agreement with AERONET AOD, improving Pearson's correlation coefficient from 0.77 to 0.82 and decreasing root-mean-square error from 0.175 to 0.110. Despite these improvements, the normalized biases of the cloud-screened GOCI-II AOD against AERONET AOD still exhibited diurnal variations, with GOCI-II AODs being overestimated in coastal regions and underestimated in inland sites, under low-AOD conditions. Nonetheless, the enhanced consistency of the cloud-screened GOCI-II AOD with AERONET AOD and its more Gaussian-like error distribution supports the utility of the cloud screening framework in improving the quality of GOCI-II AOD for DA systems.

The Geostationary Environment Monitoring Spectrometer (GEMS), onboard the Geostationary Korea Multi-Purpose Satellite-2B (GEO-KOMPSAT-2B) satellite, provides hourly measurements of nitrogen dioxide (NO2) over East Asia, enabling continuous monitoring of air quality and pollution transport. To ensure data reliability, the GEMS NO2 retrieval algorithm has undergone iterative improvements. This study presents a comprehensive comparison and evaluation of the GEMS NO2 Version 2.0 (V2.0) and Version 3.0 (V3.0) data products using independent satellite (TROPOspheric Monitoring Instrument, TROPOMI) and ground-based Pandora observations. V3.0 introduces key updates, including revised a priori NO2 vertical profiles, revised tropopause pressure, recalculated air mass factors (AMFs), and updated auxiliary inputs such as aerosol optical depth, cloud, ozone, and surface reflectance. Pixel-by-pixel intercomparison between V2.0 and V3.0 shows strong overall correlation, while regression slopes below unity indicate reduced total NO2 columns in V3.0 due to the removal of the overestimated tropospheric signal. Stratospheric columns increased substantially, correcting the unrealistic underestimation observed in V2.0 and yielding more physically consistent vertical distributions. Comparison with TROPOMI demonstrates that V3.0 reduces systematic overestimation over industrial regions such as northeastern China and the Korean Peninsula, achieving improved spatial agreement and higher correlations. Validation with Pandora measurements across six East Asian sites (Beijing, Seoul, Busan, Bangkok, Tsukuba, and Tokyo) confirms that V3.0 decreases regional biases and root-mean-square errors (RMSE) by approximately 20%, with enhanced correlation coefficients. Overall, the algorithmic refinements in GEMS NO2 V3.0 lead to improved accuracy, stability, and physical realism in both tropospheric and stratospheric retrievals. The results demonstrate the enhanced capability of GEMS for diurnal and seasonal monitoring of air quality across East Asia, supporting its use for emission estimation, pollution transport analysis, and long-term trend assessment.

To validate L2 data of the Geostationary Environment Monitoring Spectrometer (GEMS) and to understand the causes of particulate matter generation in winter season, the National Institute of Environmental Research (NIER) in Korea and the National Aeronautics and Space Administration (NASA) in the U.S. will carry out a field campaign over four Asian countries (South Korea, the Philippines, Malaysia, and Thailand) in February through March 2024. In this campaign, NIER will install 12 ground-based remote sensing instruments such as Pandora, Max-DOAS, and AQ-Profiler in and around the Seoul metropolitan area and retrieve nitrogen dioxide (NO2), formaldehyde, ozone vertical column density, NO2 vertical profile as well as aerosol properties (aerosol optical depth, single scattering albedo, size distribution, etc.). Especially, to investigate pixel inhomogeneity, we installed four instruments around an industrial complex within one GEMS pixel. During this campaign, NASA will observe NO2 and HCHO vertical column density using air-borne remote sensing instruments (e.g., GCAS: GeoCAPE airborne Simulator). Through this campaign, we plan to validate the performance of GEMS L2 data using ground-based and airborne measurements.

In this study, we present the PolCube, a multi-angle, multi-spectral push-broom imaging polarimeter, onboard a BusanSat-B CubeSat, scheduled for launch in 2026. The instrument operates at four wavelengths-410, 555, 670, and 865 nm-across four angles. Before its launch, an airborne observation campaign was conducted using the engineering qualification model (EQM) of the PolCube (Air-PolCube).The aerosol optical depth (AOD) is retrieved from the flight measurements for the East Sea on May 8 similar to 9, 2024. For aerosol optical depth (AOD) retrieval, a linear conversion methodology was implemented to transform Air-PolCube digital number (DN) values to VIIRS top-of-atmosphere (TOA) radiances. This approximate approach utilized reference data from May 8, 2024. The derived scaling factors and offsets were applied to convert DN values to radiances, allowing AOD to be retrieved from the scaled radiance. During the airborne campaign, VIIRS 550 nm AOD products showed higher AOD on May 9 (similar to 0.4) compared to May 8 (similar to 0.15). The AOD retrievals, based on the look-up table (LUT) methodology applied to the scaled Air-PolCube radiance, showed high correlation (R= 0.91) and low bias (MBE= 0.021) in comparison with the VIIRS 550 nm AOD. Following the successful launch of the CubeSat, Level 1B products with absolute calibration coefficients and precise geometric corrections are anticipated to enhance the capability for aerosol property retrievals. The airborne campaign demonstrated the feasibility of AOD retrieval using Air-PolCube measurements and provided an initial evaluation of PolCube's performance.

The Pandora spectrometer is a valuable tool for air quality monitoring and satellite validation. From 2020 to 2024, the National Institute of Environmental Research (NIER), in collaboration with the Korea International Cooperation Agency (KOICA), the Korea Environment Corporation (KECO), and the United Nations Economic and Social Commission for Asia and the Pacific (UNESCAP), successfully established the Pandora Asia Network (PAN) by installing 20 Pandora instruments within the field of view of the Geostationary Monitoring Spectrometer (GEMS). These units were installed four in Thailand, three in Indonesia, three in Mongolia, two in Laos, four in the Philippines, three in Vietnam, and one in Cambodia. PAN could provide long-term data to validate GEMS data for Southeast Asia, along with the Pandora instruments installed in Korea, Japan, Singapore, and Malaysia. The comparison results showed a high correlation between GEMS and Pandora. NIER plans to process PAN data in near-real time and provide comparison figures with GEMS, offering greater convenience to GEMS data users. This is expected to contribute not only to GEMS validation but also to monitoring air pollution in the Asian region.

Busan is the 6th largest port city in the world, where nitrogen dioxide (NO2) emissions from transportation and port industries are significant. This study aims to assess the NO2 products of the Geostationary Environment Monitoring Spectrometer (GEMS) over Busan using ground-based instruments (i.e., surface in-situ network and Pandora). The GEMS vertical column densities of NO2 showed reasonable consistency in the spatiotemporal variations, comparable to the previous studies. The GEMS data showed a consistent seasonal trend of NO2 with the Korea Ministry of Environment network and Pandora in 2022, which is higher in winter and lower in summer. These agreements prove the capability of the GEMS data to monitor the air quality in Busan. The correlation coefficient and the mean bias error between the GEMS and Pandora NO2 over Busan in 2022 were 0.53 and 0.023 DU, respectively. The GEMS NO2 data were also positively correlated with the ground-based in-situ network with a correlation coefficient of 0.42. However, due to the significant spatiotemporal variabilities of the NO2, the GEMS footprint size can hardly resolve small-scale variabilities such as the emissions from the road and point sources. In addition, relative biases of the GEMS NO2 retrievals to the Pandora data showed seasonal variabilities, which is attributable to the air mass factor estimation of the GEMS. Further studies with more measurement locations for longer periods of data can better contribute to assessing the GEMS NO2 data. Reliable GEMS data can further help us understand the Asian air quality with the diurnal variabilities.

Water vapor is one of the most important species in the hydrological cycle, mainly emitted from ocean evaporation. In this study, we deployed the Spectral Measurements for Atmospheric Radiative Transfer-spectroradiometer (SMART-s) and AErosol RObotic NETwork (AERONET) instruments at Suwon, South Korea, during the Airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) campaign from December 2023 to February 2024. The newly developed SMART-s total precipitable water (TPW) retrieval algorithm utilizes direct-Sun measurements based on the spectral Langley calibration method. The SMART-s retrievals of the TPW showed excellent agreement with the collocated AERONET products (correlation coefficient of 0.99, slope of 1.03, and offset of-0.04). The TPW measurements from globally networked SMART-s or Pandora instruments can provide valuable information for analyzing interactions between trace gases (e.g., H2O, NO2, HCHO, SO2,O3), aerosols, and clouds, thereby contributing to understanding its role in the climate and hydrological systems.

In satellite remote sensing applications, enhancing the precision of level 2 (L2) algorithms relies heavily on the accurate estimation of the surface reflectance across the ultraviolet (UV) to visible (VIS) spectrum. However, the mutual dependence between the L2 algorithms and the surface reflectance retrieval poses challenges, necessitating an alternative approach. To address this issue, many satellite algorithms generate Lambertian-equivalent reflectivity (LER) products as a priori surface reflectance data; however, this often results in an underestimation of these data. This study is the first to assess the applicability of background surface reflectance (BSR), derived using a semi-empirical bidirectional reflectance distribution function (BRDF) model, in an operational environmental satellite algorithm. This study pioneered the application of the BRDF model to hyperspectral satellite data at 440 nm, aiming to provide more realistic preliminary surface reflectance data. In this study, the Geostationary Environment Monitoring Spectrometer (GEMS) data were used, and a comparative analysis of the GEMS BSR and GEMS LER retrieved in this study revealed an improvement in the relative root mean squared error (rRMSE) accuracy of 3 %. Additionally, a time series analysis across diverse land types indicated a greater stability exhibited by the BSR than by the LER. For further validation, the BSR was compared with other LER databases using ground-truth data, yielding superior simulation performance. These findings present a promising avenue for enhancing the accuracy of surface reflectance retrieval from hyperspectral satellite data, thereby advancing the practical applications of satellite remote sensing algorithms.