A Method for Reconstructing Surface Spectral Reflectance With Missing RadCalNet Data

Data gaps exist in the measured spectral reflectance and atmospheric data from the radiometric calibration network (RadCalNet) due to instrument malfunctions or weather-related interferences, which severely impedes the application of the data. Therefore, developing a method to fill these missing RadCalNet data is a pressing issue. This study focuses on four RadCalNet sites with distinct surface types and proposes a high-precision bottom-of-atmosphere (BOA) spectral reflectance model. With on-site atmospheric data from RadCalNet, the predicted results achieve a root mean square error (RMSE) of no more than 1.26%. In scenarios where in situ atmospheric conditions are completely missing, the ERA5 dataset is used as a substitute and validated with Landsat 8 surface reflectance products; the absolute errors for all sites did not exceed 4.58%, validating the proposed method’s effectiveness. Additionally, the importance of input parameters and the impact of their uncertainties on prediction accuracy are discussed.