In recent years, increasing wildfire activity in the western United States has led to significant emissions of smoke aerosols, impacting the atmospheric energy balance through their absorption and scattering properties. Single scattering albedo (SSA) is a key parameter that governs these radiative effects, but accurately retrieving SSA from satellites remains challenging due to limitations in sensor resolution, low sensitivity of traditional remote sensing methods, and uncertainties in radiative transfer modeling, particularly from surface reflectance and aerosol characterization. Smoke optical properties evolve rapidly after emission, influenced by fuel type, combustion conditions, and chemical aging. Accurate SSA retrieval near the source thus requires high-temporal-resolution satellite observations. Critical Reflectance (CR) method provides this capability by identifying a unique reflectance value at which top-of-atmosphere (TOA) reflectance becomes insensitive to aerosol loading and primarily reflects aerosol absorption. SSA can be retrieved from this critical reflectance. This study presents a geostationary-based CR method using the Advanced Baseline Imager (ABI) on GOES-R satellites. The approach leverages ABI's high temporal (5-10 min) and spatial (3 km) resolution, consistent viewing geometry, and wide coverage. A tailored look-up table, based on an AOD-dependent smoke model for North America, links CR to SSA. Case studies show strong agreement with AERONET measurements, with retrieval differences mostly within 0.01-well below AERONET's +/- 0.03 uncertainty. The method captures temporal and spatial variations in smoke absorption and demonstrates robustness across daylight hours. This GEO-based CR approach offers an effective tool for high-resolution SSA retrieval, contributing to improved aerosol radiative forcing estimates and climate modeling.

The Imaging InfraRed Spectrometer (IIRS) on board Chandrayaan-2 has been providing high spatial and spectral resolution observations of the lunar surface in 256 spectral bands (0.7-5 mu m) since September 2019. It is primarily intended for mineral mapping and identifying hydration features on the lunar surface using reflectance spectra in the range of 0.7-3.2 mu m. Here, we have used the IIRS observations in the 3-5 mu m range to retrieve daytime lunar surface temperature and spectral emissivity using an optimal estimation theory -based retrieval algorithm. The surface temperature is retrieved at every pixel, while spectral emissivity is retrieved at every third pixel of the hyperspectral image. The mean uncertainty of the retrieved spectral emissivity varies from 0.04 to 0.08, while for surface temperature, it is about 3.5 K. The retrieved spectral emissivity is found to be in close agreement with the emissivity of the Apollo -16 return soil samples.