To obtain more abundant target information for vegetation remote sensing research, the wide field of view (WFV) of the GaoFen-6 (GF-6) satellite utilizes eight bands, including four new bands. Unfortunately, it finds the unavailable reference bands from well-calibrated sensors to implement radiometric cross-calibration of the WFV sensor for these new bands, which severely affects the radiometric quality estimation and the radiometric degradation detection of WFV. Therefore, taking the Dunhuang radiometric calibration site (DRCS) in China as the radiometric transfer platform and treating the moderate-resolution imaging spectroradiometer as a reference sensor, a novel radiometric cross-calibration method for WFV is proposed. The validation results show that compared with the official calibration coefficients (OCCs) published by the China centre for resources satellite data and application (CRESDA), the proposed method can obtain reliable radiometric cross-calibration results for each band even without available corresponding reference bands. Moreover, the influences of the interpolation method and the spectral band adjustment factor (SBAF) correction on the calibration results are discussed. The total radiometric cross-calibration uncertainty is less than 4.33%.

To obtain more abundant target information, the panchromatic and multispectral sensor (PMS) of the GaoFen-4 (GF-4) satellite utilizes five different integration times. The optimal approach for carrying out the integrated processing of absolute radiometric calibration and radiometric difference minimization of PMS images over these five integration times remains an important issue. Therefore, taking the Dunhuang radiometric calibration site (DRCS) in China as the radiometric-transfer platform and treating the Moderate-resolution Imaging Spectroradiometer (MODIS) and Landsat-8 Operational Land Imager (OLI) as the reference sensors, a new radiometric cross-calibration method of the PMS based on radiometric block adjustment (RBA) is proposed. The validation results show that the proposed method can simultaneously maintain high absolute radiometric calibration consistency with the official calibration coefficients published by the China Centre for Resources Satellite Data and Application (CRESDA) and can reduce the radiometric difference between PMS images over different integration times. In addition, the influences of the distribution of the radiometric control points (RCPs), radiometric tie points (RTPs) filter, spectral band adjustment factor (SBAF) correction, and correction of the bidirectional reflectance distribution function (BRDF) on the calibration results are discussed. The total cross-calibration uncertainty is less than 5.21%.

The wide field of view (WFV) imaging system of the GaoFen-6 (GF-6) satellite processes four popular spectral bands [blue, green, red, and near-infrared (NIR)] and four new spectral bands (costal, yellow, and two red edges). However, the corresponding reference spectral bands for these eight spectral bands from one reference sensor are insufficient in the cross-calibration process of the WFV; therefore, current cross-calibration methods should be improved. To address this problem, the Moderate-Resolution Imaging Spectroradiometer (MODIS), which has high radiometric performance with the aid of an onboard calibration system, is used as a reference sensor, and cross-calibration methods using the top of atmosphere (TOA) and the bottom of atmosphere (BOA) bidirectional reflectance distribution function (BRDF) models are developed and compared. The results reveal that the cross-calibration results in eight spectral bands with the BOA BRDF model that can obtain higher consistency with the official calibration coefficients (OCCs) compared with the TOA BRDF model. In addition, after various influencing factors are comprehensively analyzed, the spectral band adjustment factor (SBAF) correction in the shortwave infrared (SWIR) spectral bands can be neglected; moreover, by using five spectral bands (blue, green, red, NIR, and SWIR) of the MODIS sensor, the BOA BRDF model and the cubic polynomial interpolation method provide optimal cross-calibration schemes for WFV sensor. The total radiometric cross-calibration uncertainties of the proposed cross-calibration methods with the BOA BRDF model and the TOA BRDF model are less than 5.73% and 6.32%, respectively.

The Baotou sandy site in China is an important field site in the Committee on Earth Observation Satellites radiometric calibration network (RadCalNet), which freely releases the bottom-of-atmosphere (BOA) and top-of-atmosphere (TOA) spectral reflectance products in the spectral range from 400 nm to 1000 nm at 10 nm intervals with the aid of a set of automatic spectral and atmospheric parameter measurement devices. However, the released TOA and BOA spectral reflectance products are nadir-view spectral reflectance, which could produce errors if the Baotou sandy site is offset from the nadir point when they are utilized to calibrate one satellite sensor with the vicarious calibration method or to detect radiometric performance changes in the target sensor. Therefore, to address this problem and further broaden the applicable scopes of TOA spectral reflectance products, this article develops TOA and BOA bidirectional reflectance distribution function (BRDF) models using valid time-series TOA and BOA spectral reflectance products to fulfil BRDF correction. Then, the corrected TOA spectral reflectance is validated with the on-orbit measured TOA spectral reflectance of the Landsat8 Operational Land Imager (OLI) sensor with high and stable radiometric performance. After analysing the fitting accuracies of the BOA and TOA BRDF models, the influences of the filter criteria of valid time-series nadir-view BOA and TOA spectral reflectance, geo-positioning error of Landsat8/OLI, and aerosol type in the BOA BRDF correction process on the validation results are discussed. The results show that the BOA BRDF model obtains a better correction effect and is more stable than the TOA BRDF model.

As the core and foundational technology, on-orbit radiometric calibration of a space-borne sensor is of great importance for quantitative remote sensing applications. As for the space-borne multi-camera mosaic imaging sensor, however, the currently available on-orbit radiometric calibration method cannot carry out the integrated processing of on-orbit absolute radiometric calibration and relative radiometric correction simultaneously between cameras, influencing the accuracy of quantitative applications. Therefore, taking the GaoFen-1 (GF-1) wide-field-of-view (WFV) sensor as an example in this research, an innovative on-orbit radiometric calibration method is proposed to overcome this bottleneck. Firstly, according to the principle of the cross-calibration approach, we retrieve valid MODIS and GF-1 WFV image pairs over the Dunhuang radiometric calibration sites (DRCS) in China by using a set of criteria and extract the radiometric control points (RCPs) connecting in both images. Secondly, the DEM-aided block adjustment of the rational function model is applied to eliminate the geometrical misalignment of GF-1 WFV images at the same orbit. Then, the average digital numbers of spectral and spatial homogeneous surfaces are calculated and chosen as the radiometric tie points (RTPs) extracted from the overlapping region of the adjacent WFV cameras. Thirdly, the radiometric block adjustment (RBA) algorithm is introduced into on-orbit radiometric calibration of the space-borne multi-camera mosaic imaging sensor. Finally, the radiometric calibration coefficients are solved by the least square method. The validation results indicate that our proposed method can acquire high absolute radiometric calibration accuracy and achieve relative radiometric correction between cameras. Compared with the results using the cross-calibration method to calibrate each WFV camera independently, the advantages of RBA are presented. In addition, the uncertainties caused by RCPs’ distribution are discussed, which is beneficial to further optimize the calibration program.

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.