The on-orbit radiometric calibration of spaceborne sensors is the foundation of quantitative remote sensing, and the calibration accuracy directly affects the quality of quantitative remote sensing products. The wide swath of the Chinese ocean color and temperature scanner (COCTS) onboard the HY-1C satellite makes it difficult to ignore errors caused by viewing angles during calibration. Thus, this paper proposes a cross-calibration method based on the bottom-of-atmosphere (BOA) and top-of-atmosphere (TOA) spectral band adjustment factors (SBAFs). This method cross-calibrates the COCTS sensor after correcting viewing geometry and radiometric differences. It is based on the Sentinel-3/ocean and land color instrument (OLCI) sensor. First, time-series data from the OLCI sensor were used to select calibration points in the Dunhuang area. Subsequently, the bidirectional reflectance distribution function (BRDF) for each corresponding spectral band of the OLCI sensor was constructed point by point. Next, the BOA and TOA SBAFs were calculated using the interpolated continuous spectra obtained from MOD09GA products. After correcting for the BRDF and SBAF, cross-calibration between COCTS and OLCI was achieved. The results showed that the BOA SBAF model achieved higher precision calibration coefficients than the TOA SBAF model, with errors in each band below 5.76%. The proposed method can provide technical support for the cross-calibration of wide-swath sensors.

In the process of radiometric calibration, the corrections for bidirectional reflectance distribution functions (BRDFs) and spectral band adjustment factors (SBAFs) are crucial. Time-series MODIS images are commonly used to construct BRDFs by using the Ross–Li model in current research. However, the Ross–Li BRDF model is based on the linear relationship between the kernel models and is unable to take into account the nonlinear relationship between them. Furthermore, when using SBAF to account for spectral difference, a radiative transfer model is often used, but it requires many parameters to be set, which may introduce more errors and reduce the calibration accuracy. To address these issues, the random forest algorithm and a spectral interpolation convolution method using the Sentinel-2/multispectral instrument (MSI) are proposed in this study, in which the HuanJing-2A (HJ-2A)/charge-coupled device (CCD3) sensor is taken as an example, and the Dunhuang radiometric calibration site (DRCS) is used as a radiometric delivery platform. Firstly, a BRDF model by using the random forest algorithm of the DRCS is constructed using time-series MODIS images, which corrects the viewing geometry difference. Secondly, the BRDF correction coefficients, MSI reflectance, and relative spectral responses (RSRs) of CCD3 are used to correct the spectral differences. Finally, with the validation results, the maximum relative error between the calibration results of the proposed method and the official calibration coefficients (OCCs) published by the China Centre for Resources Satellite Data and Application (CRESDA) is 3.38%. When tested using the Baotou sandy site, the proposed method is better than the OCCs of the average relative errors calculated for all the bands except for the near-infrared (NIR) band, which has a larger error. Additionally, the effects of the light-matching method and the radiative transfer method, different approaches to constructing the BRDF model, using SBAF to account for spectral differences, different BRDF sources, as well as the imprecise viewing geometrical parameters, spectral interpolation method, and geometric positioning error, on the calibration results are analyzed. Results indicate that the cross-calibration coefficients obtained using the random forest algorithm and the proposed spectral interpolation method are more applicable to the CCD3; thus, they also account for the nonlinear relationships between the kernel models and reduce the error due to the radiative transfer model. The total uncertainty of the proposed method in all bands is less than 5.16%.

High resolution and wide coverage are important development directions for optical sensors on satellites. Multi-camera mosaic imaging technology is often used to combine high resolution with wide coverage. When using data from these sensors for quantitative remote sensing studies, radiometric calibration is important. Based on this, this study employs a cross-calibration method incorporating the random forest algorithm and spectral interpolation to derive the time-series calibration coefficients for the charge-coupled device (CCD) sensors on the HuanJing-2A/B (HJ-2A/B) satellites, and examines the applicability of this method. The results show that, compared with the official calibration coefficients (OCCs), the relative errors of the cross-calibration results are predominantly within 10%, with the majority falling below 5%. When compared with the measured reflectance from the Baotou sandy site, the results of this study show better consistency with the 1:1 line and have average relative errors less than 8.07%, while the OCCs have average relative errors less than 34.65%. Besides, for the bands exhibiting larger individual errors relative to the OCCs, the validation results indicate that the average relative errors of these bands are smaller than those of the OCCs when referenced against the Baotou sandy site. Furthermore, the influence of different input parameters on the bidirectional reflectance distribution function (BRDF) model, not considering BRDF correction of CCD2 and CCD3 sensors on the calibration results, using the single-day coefficients, which are close to the date of the Baotou sandy site on validation results, are discussed. Finally, the study also evaluates the radiometric consistency between different cameras. The result indicates that the method proposed is applicable to other CCD sensors on HJ-2A/B, and the cross-calibration coefficients obtained can effectively eliminate radiometric inconsistencies among different cameras.