Uncertainty Evaluation of an In-Flight Absolute Radiometric Calibration Using a Statistical Monte Carlo Method

The absolute radiometric calibration of remote sensing sensors is crucial to the accurate retrieval of biogeophysical parameters through remote sensing. The radiometric calibration uncertainty is the index that describes the reliability of a calibration result and is usually empirically determined by assuming that all of the factors involved are independent of each other. Through a field campaign carried out in Inner Mongolia, China, which aimed to accurately calibrate remote sensing sensors, we developed a Monte Carlo method that statistically evaluates the radiometric calibration uncertainty. From Monte Carlo simulations, it was revealed that the overall uncertainty is much smaller than the root sum of squares of each factor, suggesting that there is some negative correlation among some of the factors. For a surface with a low reflectance (~5%), the radiometric calibration uncertainty was ~7.0%, whereas for a surface with a reflectance larger than 20%, the uncertainty was stable at ~3.0%. This result suggests that the quality of remote sensing data should be carefully examined for surfaces with a low reflectance.