Sensitivity of near‐infrared transmittance calculations for remote sensing applications to recent changes in spectroscopic information

Abstract

An accurate determination of atmospheric transmittance relies greatly on the quality of both absorption line parameters and continuum absorption model. A line‐by‐line radiative transfer model has been used to determine the magnitude of the changes in atmospheric transmittance due to recent updates of HITRAN (HIgh‐resolution TRANsmission) line parameters and differences in water vapour continuum formulation. The radiative transfer calculations were carried out at two narrow near‐infrared carbon dioxide bands near 4,854 cm−1 (2.06 μm) and 6,211 cm−1 (1.61 μm), which are currently used by satellite‐based instruments for retrieval of atmospheric CO2 concentrations. Transmittance calculations using line parameters from the last three HITRAN editions (2008, 2012 and 2016) show that HITRAN2016 is more similar to HITRAN2012 than HITRAN2008. However, differences of up to about 5% were obtained between transmittances computed using HITRAN2016 and HITRAN2012. Considering the fact that some groups still use HITRAN2012 in forward models for very high (sub percent) accuracy retrievals of CO2 from satellite measurements, these differences are significant and should be accounted for in the uncertainty budget. Transmittances calculated using the semi‐empirical MT_CKD 2.5 (Mlawer–Tobin–Clough–Kneizys–Davies) and the laboratory‐measured CAVIAR (Continuum Absorption at Visible and Infrared Wavelengths and its Atmospheric Relevance) water vapour continuum models differ by up to about 5–6%. The impact of the continuum formulation adopted for near‐infrared transmittance calculations needs to be quantitatively assessed, most especially as the strength of the water vapour continuum in this spectral region is still contested.

Continuity of Top-of-Atmosphere, Surface, and Nadir BRDF-Adjusted Reflectance and NDVI between Landsat-8 and Landsat-9 OLI over China Landscape

“…An interesting observation is that the OLS regression slopes for surface reflectance exhibit closer proximity to unity for the VNIR bands when compared to the TOA reflectance regressions. This phenomenon is likely attributed to the heightened susceptibility of VNIR bands to atmospheric contamination [43,44]. Conversely, there is minimal deviation between the OLS slopes for the SWIR bands and NDVI between the TOA and surface reflectance datasets.…”