A Novel Interband Calibration Method for the FY3D MERSI-II Sensor Based on a Combination of Physical Mechanisms and a DNN Regression Model

Interband radiometric calibration from the mid-infrared to visible bands in the ocean specular region is an effective way to calibrate on-orbit remote sensing sensors. It assumes that the referenced band has highly accurate radiance and that the interband radiometric relationship can be obtained in the ocean specular region. Most current research employs only the radiative transfer (RT) equation to derive interband radiometric relationships. However, two variables—water-leaving radiance and whitecaps—are challenging to obtain yet crucial for radiative transfer calculations. Typically, water-leaving radiance is assigned a fixed value since empirical data, whereas whitecaps are estimated via the wind speed alone. These assumptions make the uncertainties of the calibrated bands large and different from those of real satellite-measured data, reducing the reliability of the interband relationship between the reference and calibrated bands and limiting the application of the interband radiometric calibration method. To address this issue, this study proposed a novel interband radiometric calibration method called coupled deep neural networks and radiative transfer (CDR), which integrates radiative transfer and a deep neural network (DNN) to provide a reliable relationship between referenced and to be calibrated bands without accurate water-leaving radiance and whitecaps. For the four visible bands of FY-3D/MERSI-II, the relative errors were found to be 2.12%, 4.62%, 1.89%, and 4.02%, respectively. Uncertainty analysis identified the referenced band as the largest uncertainty source, followed by chlorophyll concentration, polarization effects, and aerosol loading. The CDR algorithm can be used to calibrate historical long-term satellite data without additional measurements.