Retrieving Land Surface Bidirectional Reflectivity From Chinese FengYun-3D MERSI-II Mid-Infrared Data Using an Improved Nonlinear Split-Window Algorithm

China’s new generation of polar-orbiting meteorological satellite, FengYun-3D (FY-3D), is equipped with the Medium Resolution Spectral Imager II (MERSI-II) sensor, which greatly enhances Chinese capacity for comprehensive space environment detection and meteorological remote sensing. MERSI-II has two mid-infrared (MIR) channels with the potential to apply split-window (SW) algorithms. We have developed an improved nonlinear SW (NSW) algorithm to retrieve the ground brightness temperature (GBT)Tgb⁰without the contribution of the solar direct beam from MERSI-II MIR data, enabling the accurate retrieval of surface bidirectional reflectivityρ bbased on the radiative transfer equation (RTE). Compared with the traditional estimation method, this article assumed a linear relationship betweenρ bof MIR channels 20 and 21, and believed that under dry-cool atmospheric conditions,Tgb⁰is not equal. Considering different solar zenith angles (SZAs), by iterating the combination of atmospheric and surface parameters under reasonable variations, the MODerate spectral resolution atmospheric TRANsmittance (MODTRAN) model version 5.2 was used to obtain the simulation datasets for determining the NSW coefficients. In the practical retrieval, the precise algorithm coefficients are only relevant to SZA. TheR²fit by the NSW algorithm is 0.99, and the root-mean-square error (RMSE) is less than 0.58 K for different SZAs. Under different SZAs, comparing the actual value ofρ bwith the proposed method, the bias is less than1.57× 10⁻³, and the RMSE is less than2.21× 10⁻³. A detailed sensitivity analysis found that the errors in atmospheric water vapor content (WVC), CO2 concentrations, O3 concentrations, horizontal visibilities (VISs), and instrumental noise were below the order of10⁻⁴, and their effects on the retrieval ofρ bwere negligible. This study employed two methods to validateTgb⁰, thereby indirectly validatingρ b. First, the moderate resolution imaging spectrometer (MODIS) land surface emissivity (LSE) product MYD11C1 and the ERA5-Land land surface temperature (LST) product were used to validateTgb⁰, yielding a bias of 0.9 K and an RMSE of 1.13 K. Second, in situ measurements from the surface radiation budget network (SURFRAD) sites were used to validateTgb⁰, resulting in a bias of 0.74 K and an RMSE of 1.83 K. Overall, results showed that the proposed algorithm has good accuracy and can improve its applications in relevant fields.