Abstract
Cross-calibration methods are widely used in high-precision remote sensor calibrations and ensure observational consistency between sensors. Because two sensors must be observed under the same or similar conditions, the cross-calibration frequency is greatly reduced; performing cross-calibrations on Aqua/Terra MODIS, Sentinel-2A/Sentinel-2B MSI and other similar sensors is difficult due to synchronous-observation limitations. Additionally, few studies have cross-calibrated water-vapor-observation bands sensitive to atmospheric changes. In recent years, standard automated observation sites and unified processing technology networks, such as an Automated Radiative Calibration Network (RadCalNet) and an automated vicarious calibration system (AVCS), have provided automatic observation data and means for independently, continuously monitoring sensors, thus offering new cross-calibration references and bridges. We propose an AVCS-based cross-calibration method. By limiting the observational-condition differences when two remote sensors transit over wide temporal ranges through AVCS observation data, we improve the cross-calibration opportunity. Thereby, cross-calibrations and observation consistency evaluations between the abovementioned instruments are realized. The influence of AVCS-measurement uncertainties on the cross-calibration is analyzed. The consistency between the MODIS cross-calibration and sensor observation is within 3% (5% in SWIR bands); that for the MSI is within 1% (2.2% in the water-vapor-observation band); and for the cross-calibration of Aqua MODIS and the two MSI, the consistency between the cross-calibration-predicted TOA reflectance and the sensor-measured TOA reflectance was within 3.8%. Thus, the absolute AVCS-measurement uncertainty is also reduced, especially in the water-vapor-observation band. This method can be applied to cross-calibrations and measurement consistency evaluations of other remote sensors. Later, the spectral-difference influences on cross-calibrations will be further studied.
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