Ukkyo Jeong

Tropospheric NO₂ data retrievals from the satellite-borne TROPOspheric Monitoring Instrument (TROPOMI) are sensitive to the vertical profile assumed in the air mass factor (AMF) calculation, especially in polluted lower-tropospheric environments. This study investigated the use of aircraft and ground-based profile information to evaluate this sensitivity near five major airports. We combined TROPOMI tropospheric NO₂ vertical column densities (VCDs), land-based Pandora observations, and airplane-based In-service Aircraft for a Global Observing System (IAGOS) descent profiles for selected airport–Pandora pairs in Japan, South Korea, and Thailand from March to July 2025. Before applying profile-based AMF recalculations, we examined whether the TROPOMI NO₂ field around each Pandora site was representative of that around the corresponding airport. The airport- and Pandora-region time series were generally coherent, although their mean NO₂ levels differed, indicating that temporal consistency and absolute spatial representativeness need to be considered separately. The lower part of the a priori NO₂ profile obtained by the TM5-MP model was then replaced with IAGOS-derived profiles to recalculate TROPOMI AMFs and VCDs. The IAGOS-profile recalculation reduced the overall low bias relative to Pandora data and improved several validation metrics, but the response differed by site. Improvements were most evident where the IAGOS descent profiles, Pandora site, and satellite-observed NO₂ field sampled similar environments. In contrast, weaker or degraded performance occurred when the aircraft profiles represented an air mass different from that influencing the Pandora-acquired column. The Pandora-scaled-profile case produced the closest agreement with Pandora results, but the comparison is more suitable as a profile-sensitivity test rather than an independent validation. Residual diagnosis showed that the remaining differences after the Pandora-scaled-profile recalculation were not mainly controlled by horizontal NO₂ heterogeneity, pixel sampling density, mean quality assurance value, or the magnitude of the AMF correction. A slant column density (SCD)-equivalent closure analysis further suggested that profile replacement alone cannot fully reconcile TROPOMI and Pandora columns, because the recalculated VCD still depends on the TROPOMI effective tropospheric SCD and other non-profile retrieval inputs. These findings demonstrate the value of IAGOS and Pandora lower-tropospheric profile information for diagnosing TROPOMI NO₂ AMF uncertainty near airports, while underscoring the need to account for slant-column retrieval uncertainty, cloud and surface effects, and spatial representativeness.

Tropospheric NO₂ data retrievals from the satellite-borne TROPOspheric Monitoring Instrument (TROPOMI) are sensitive to the vertical profile assumed in the air mass factor (AMF) calculation, especially in polluted lower-tropospheric environments. This study investigated the use of aircraft and ground-based profile information to evaluate this sensitivity near five major airports. We combined TROPOMI tropospheric NO₂ vertical column densities (VCDs), land-based Pandora observations, and airplane-based In-service Aircraft for a Global Observing System (IAGOS) descent profiles for selected airport–Pandora pairs in Japan, South Korea, and Thailand from March to July 2025. Before applying profile-based AMF recalculations, we examined whether the TROPOMI NO₂ field around each Pandora site was representative of that around the corresponding airport. The airport- and Pandora-region time series were generally coherent, although their mean NO₂ levels differed, indicating that temporal consistency and absolute spatial representativeness need to be considered separately. The lower part of the a priori NO₂ profile obtained by the TM5-MP model was then replaced with IAGOS-derived profiles to recalculate TROPOMI AMFs and VCDs. The IAGOS-profile recalculation reduced the overall low bias relative to Pandora data and improved several validation metrics, but the response differed by site. Improvements were most evident where the IAGOS descent profiles, Pandora site, and satellite-observed NO₂ field sampled similar environments. In contrast, weaker or degraded performance occurred when the aircraft profiles represented an air mass different from that influencing the Pandora-acquired column. The Pandora-scaled-profile case produced the closest agreement with Pandora results, but the comparison is more suitable as a profile-sensitivity test rather than an independent validation. Residual diagnosis showed that the remaining differences after the Pandora-scaled-profile recalculation were not mainly controlled by horizontal NO₂ heterogeneity, pixel sampling density, mean quality assurance value, or the magnitude of the AMF correction. A slant column density (SCD)-equivalent closure analysis further suggested that profile replacement alone cannot fully reconcile TROPOMI and Pandora columns, because the recalculated VCD still depends on the TROPOMI effective tropospheric SCD and other non-profile retrieval inputs. These findings demonstrate the value of IAGOS and Pandora lower-tropospheric profile information for diagnosing TROPOMI NO₂ AMF uncertainty near airports, while underscoring the need to account for slant-column retrieval uncertainty, cloud and surface effects, and spatial representativeness.

Atmospheric aerosols from natural and anthropogenic sources are a major component of air pollution and substantially affect air quality, human health, and atmospheric chemistry. Reliable and continuous aerosol optical depth (τ_aer) measurements from ground-based instruments are therefore essential for characterizing aerosol loading and its spatiotemporal variability. This study presents a framework for retrieving τ_aer in the UV (315–375 nm) and visible (400–530 nm) spectral ranges using the Pandora spectrometer. In the ultraviolet (UV) range, reliable τ_aer retrieval from standard direct-Sun measurements is hindered by out-of-band stray light from visible wavelengths. To address this limitation, this study utilizes measurements acquired with a U340 filter, which suppresses this contamination in UV spectra and enables reliable τ_aer retrievals in the UV region. Through the proposed spectral fitting framework, trace gas absorption and aerosol extinction components are successfully separated from the measured total optical depth. Concurrently, τ_aer values at visible wavelengths are retrieved from standard Diffuser measurements. The retrieved UV and visible τ_aer values show good agreement with collocated AErosol RObotic NETwork (AERONET) data, with R² = 0.88–0.99, RMSE = 0.016–0.039, |MBE| = 0.002–0.023, slopes of 0.93–1.01, and |intercepts| < 0.01. Together with the trace gas products simultaneously retrieved by Pandora, these τ_aer datasets can serve as a valuable resource for air pollution analysis.

To improve understanding of air quality in Asia and support satellite validation, the Korean government launched the Pandora Asia Network (PAN) in 2021. The high temporal resolution and simultaneous retrievals of nitrogen dioxide (NO2) and aerosol optical depth (AOD) from the PAN allow for detailed analysis of aerosol and precursor variability. This study evaluated NO2 and AOD levels at various sites across major Asian cities with different air quality regulations, using consistent quality measurements from Pandora and satellite data. Generally, AOD from the Visible Infrared Imaging Radiometer Suite (VIIRS) Deep Blue product showed good agreement with the PAN retrievals, whereas TROPOspheric Monitoring Instrument (TROPOMI) generally underestimated NO2 levels relative to Pandora, especially in highly polluted cities such as Dhaka and Singapore. The NO2-to-AOD ratio (NAR), which assesses aerosol characteristics associated with NO2 sources, was higher in major cities such as Seoul and Singapore, whereas lower values were observed in areas with uncontrolled or limited combustion activity. Differences in NO2 measurements between TROPOMI and Pandora led to discrepancies in NAR between satellite and ground-based data, particularly in Singapore, Islamabad, and Dhaka. Using high-frequency PAN sampling, we calculated the daily correlation coefficient between AOD and NO2 (rd) at each site. High values of rd were observed in Islamabad and Dhaka, where ammonia (NH3) and NO2 levels were elevated; both are important tracers of nitrate aerosol formation. This study estimated the NOx emission contribution factor to AOD (NCF) based on a statistical relationship among rd, NO2, NH3, and water vapor (H2O), which can serve as a diagnostic tool for evaluating aerosol emission sources across Asian countries.

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), O ₂ 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 O ₂ –O ₂ 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 O ₂ –O ₂ 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 O ₂ –O ₂ absorption band were on average about 1 km lower than O ₂ 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.

Hoejun Choi, Ukkyo Jeong. Korean J. Remote Sens. 2026;42:77-90. https://doi.org/10.7780/kjrs.2026.42.1.6

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.