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Using hyperspectral data collected by the Airborne Compact Atmospheric Mapper (ACAM) and a shipborne radiometer in Chesapeake Bay in July–August 2011, this study investigates diurnal changes of surface remote sensing reflectance (Rrs). Atmospheric correction of ACAM data is performed using the traditional “black pixel” approach through radiative transfer based look-up-tables (LUTs) with non-zero Rrs in the near-infrared (NIR) accounted for by iterations. The ACAM-derived Rrs was firstly evaluated through comparison with Rrs derived from the Moderate Resolution Imaging Spectroradiometer satellite measurements, and then validated against in situ Rrs using a time window of ±1 h or ±3 h. Results suggest that the uncertainties in ACAM-derived Rrs are generally comparable to those from MODIS satellite measurements over coastal waters, and therefore may be used to assess whether Rrs diurnal changes observed by ACAM are realistic (i.e., with changes > 2 × uncertainties). Diurnal changes observed by repeated ACAM measurements reaches up to 66.8% depending on wavelength and location and are consistent with those from the repeated in situ Rrs measurements. These findings suggest that once airborne data are processed using proper algorithms and validated using in situ data, they are suitable for assessing diurnal changes in moderately turbid estuaries such as Chesapeake Bay. The findings also support future geostationary satellite missions that are particularly useful to assess short-term changes.

The Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO) instrument is an airborne hyperspectral spectrometer measuring backscattered solar radiation in the ultraviolet (290–400 nm) and visible (415–695 nm) wavelength regions. This paper presents high-resolution sulfur dioxide (SO2) maps over the Korean Peninsula, produced by SO2 retrievals from GeoTASO measurements during the Korea–United States Air Quality Field Study (KORUS-AQ) from May to June 2016. The highly sensitive GeoTASO instrument with a spatial resolution of ~250 m × 250 m can detect point emission sources of SO2 within its fields of view, even without merging multiple overlapping observations. To retrieve SO2 vertical columns from the GeoTASO measurements, we apply an algorithm based on principal component analysis (PCA), which is effective in suppressing noise and biases in SO2 retrievals. The retrievals successfully capture SO2 plumes and various point sources such as power plants, a petrochemical complex, and a steel mill, located in South Chungcheong Province, some of which are not detected by a ground-based in situ measurement network. Spatial distributions of SO2 from GeoTASO observations in source areas are consistent with those from the Stack Tele-Monitoring System reports and airborne in situ SO2 measurements. Comparisons of SO2 retrievals from GeoTASO and existing satellite sensors demonstrate the significance of high-resolution SO2 observations, by indicating that GeoTASO detects small SO2 emission sources that are not precisely resolved by single overpasses of satellites. To assess future geostationary SO2 observations having a higher spatial resolution, we upscale the GeoTASO SO2 retrievals to a spatial resolution of the Geostationary Environment Monitoring Spectrometer (GEMS). Since the upscaled GeoTASO retrievals also detect SO2 plumes clearly, we expect from GEMS to identify even small SO2 emission sources over Asia.