空间辐射测量基准作为未来卫星定标的重要标准，为卫星数据的定量化应用提供了重要保障。通过传递定标方法实现辐射基准的传递可以提高卫星遥感器观测数据的整体精度。基于稳定场地的交叉定标作为太阳反射波段传递定标的一种主要方法，因其选定了目标均匀且反射特性较稳定的场地作为辐射基准传递对象，具有可靠性高、稳定性好、传递链条可追溯等优点。以利比亚场地为传递目标，提出一套适合于太阳反射波段的传递定标方法及其不确定性分析方案，系统分析传递定标方法的不确定性，并通过不确定性的敏感性分析给出最优的场地交叉定标的匹配方案。以气象卫星中分辨率光谱成像仪为例，以FY-3D MERSI-II和AQUA MODIS为代理数据，针对引入不确定性的主要来源：几何、时间、空间、光谱，利用大气辐射传输模型与双向反射分布函数构建不确定性分析模型，并通过蒙特卡罗方法多次模拟分析出各匹配条件对不确定性影响的敏感性，以利比亚场地为例，以各项不确定性小于1%作为约束，确定场地交叉定标匹配阈值：两遥感器观测天顶角之差应小于±7°，太阳天顶角之差小于±6°，相对方位角之差小于±15°，气溶胶厚度小于0.39，观测场地均匀性小于0.02，此条件下各通道总传递定标不确定性控制在1.5%，定标频次平均一月一次。

Owing to the stability of Pseudo-Invariation Calibration Sites(PICS), it contributes significantly to the improvement of calibration accuracy. The number of PICS is increasing as the work to continues to advance. Therefore, the frequency of cross calibrations based on dessert sites has been significantly increased. It is necessary to establish a generic site-based cross calibration as well as uncertainty analysis method to confirm calibration uncertainties for different sites. The aim of our study is to improve the overall accuracy of satellite remote sensor observations, by developing cross calibration method over desert site. In this paper, a cross calibration and uncertainty assessment scheme aims at solar bands is described, and the optimal matching scheme of the cross calibration is given by sensitivity analysis of the uncertainty. With image data of Libya site from MODIS and MERSI-II, the main uncertainty contributors are found in the geometric, temporal, spatial, and spectral domain. For these four aspects, the uncertainty analysis model is independently constructed using the atmospheric radiative transfer model and the bi-directional reflectance distribution function. The sensitivity of each matching condition to the effect of uncertainty is multiply simulated by Monte Carlo method. The geometric and atmospheric distribution patterns of satellite matching data were summarized, through statistically analyzing the matching data of MODIS and MERSI-II over Libya sites in 2020. The probability distribution density(PDF) of the matching condition is used as the input condition, and the discrete distribution of the relative deviation of Top-Of-Atmosphere (TOA) reflectance is obtained by the uncertainty analysis model. The standard deviation of the distribution of relative deviations of TOA reflectance is statistically considered as the standard uncertainty. After independent analysis of each factor of uncertainty, the total uncertainty is obtained by Root-Sum-Squares method. The total uncertainty of each channel could be controlled under 1.5%(at k = 1), when the difference of sensor zenith angles between the two remote sensors should be less than ±7°, the difference between the solar zenith angles less than ±6°, the aerosol thickness less than 0.39, and the uniformity of the observation site less than 0.02. The results between the MODIS reflectance and the digital number (DN) recorded by MERSI reveal a good linear relationship. This cross-calibration result is also in the range of 0.5%~1.5% accuracy for each band compared to operational calibrations. Even though we only applied the algorithm to MERSI-II as a demonstration, our algorithm should be applicable to other sensors with few modifications.