Abstract
Tropical deep convective clouds (DCC) are proven to be an excellent invariant target for post-launch radiometric calibration of satellite visible (VIS) and near-infrared (NIR) spectral channels. The DCC technique (DCCT) is a statistical approach that collectively analyzes DCC pixels and provides a near lambertian reflectance suitable for inter-calibration. At shortwave infrared (SWIR) wavelengths, the DCC reflectance is impacted by the ice microphysical properties of particle size and optical depth, thereby increasing the temporal noise in the DCC response. The key to improving the DCCT for satellite SWIR bands calibration is proper characterization of the DCC reflectance at SWIR wavelengths.
This paper will present empirical bidirectional reflectance distribution function (BRDF) models based on multiple years of NPP-VIIRS DCC measurements to mitigate the seasonal variation in the SWIR band DCC reflectance. The DCC BRDF models are wavelength-specific and are effective in reducing the temporal noise in the DCC response by up to 50%. Application of these BRDF models to the Aqua-MODIS and JPSS-1 VIIRS imagers for radiometric inter-comparison will be discussed. Another factor that impacts the stability of the SWIR band DCC reflectance is the infrared brightness temperature (IR-BT) threshold used for identifying the DCC pixels. The optimal BT threshold for achieving the most predictable DCC response for individual SWIR bands will also be addressed. The channel-specific BRDFs and BT thresholds will help to extend the use of DCCT to SWIR bands inter-calibration.
Characterization of Deep Convective Clouds as an Invariant Target for Satellite SWIR Bands Inter-calibration
Tropical deep convective clouds (DCC) are proven to be an excellent invariant target for post-launch radiometric calibration of satellite visible (VIS) and near-infrared (NIR) spectral channels. The DCC technique (DCCT) is a statistical approach that collectively analyzes DCC pixels and provides a near lambertian reflectance suitable for inter-calibration. At shortwave infrared (SWIR) wavelengths, the DCC reflectance is impacted by the ice microphysical properties of particle size and optical depth, thereby increasing the temporal noise in the DCC response. The key to improving the DCCT for satellite SWIR bands calibration is proper characterization of the DCC reflectance at SWIR wavelengths.
This paper will present empirical bidirectional reflectance distribution function (BRDF) models based on multiple years of NPP-VIIRS DCC measurements to mitigate the seasonal variation in the SWIR band DCC reflectance. The DCC BRDF models are wavelength-specific and are effective in reducing the temporal noise in the DCC response by up to 50%. Application of these BRDF models to the Aqua-MODIS and JPSS-1 VIIRS imagers for radiometric inter-comparison will be discussed. Another factor that impacts the stability of the SWIR band DCC reflectance is the infrared brightness temperature (IR-BT) threshold used for identifying the DCC pixels. The optimal BT threshold for achieving the most predictable DCC response for individual SWIR bands will also be addressed. The channel-specific BRDFs and BT thresholds will help to extend the use of DCCT to SWIR bands inter-calibration.