青藏高原是卫星反演云参数的热点和难点区域。选取1982年—2015年0.1°空间分辨率的Patmos-x、0.25°空间分辨率的CLARA-A2的NOAA/AVHRR下午星数据和2003年—2015年0.05°空间分辨率的Aqua/MODIS C6等3种云量数据，针对青藏高原区域，从数据的反演算法和数据的空间属性出发，进行比对分析。Patmos-x和CLARA-A2具有相同的数据源和相近的云检测算法。与地面观测云量相比，Patmos-x云量与地面观测云量间的相关性大于0.8，MODIS次之，CLARA-A2云量与地面观测云量的相关性很弱。3种数据均表现出高原东部云量多于西部云量，北部云量多于南部的云量空间分布特征和白天云量大于夜间云量的时间分布特征。量值上CLARA-A2云量大于Patmos-x。2003年—2015 年夜间Aqua/MODIS 年均云量比CLARA-A2高8.82%。34年间Patmos-x和CLARA-A2年均云量以减少为主，夜间云量的变化趋势比白天云量变化趋势明显，CLARA-A2云量的变化趋势较Patmos-x明显。2000年是高原区域云量由偏多到偏少变化的拐点。1、4、10月多年云量以减少为主要变化趋势，7月云量以弱增多为主要变化特征。

Tibetan Plateau (TP) plays an important role in adjusting the large-scale atmospheric circulation in the northern hemisphere and the atmosphere–sea interaction from the equator to the middle latitude in the North Pacific. Obtaining complete observation data based on ground observations over TP is difficult. Satellite provides good observational data over the Tibetan Plateau. Considering the complex underlying surface types and geographical elevations in the Tibetan Plateau region, three kinds of long-term cloud fraction data that came from PATMOS-x/AVHRR, CLARA-A2/AVHRR, and MODIS / Aqua were analyzed from the perspective of data retrieval methods and data spatial attributes.The relationship among the three kinds of satellite cloud fraction and the ground observation cloud fraction was analyzed at first. Correlation analysis, linear trend, and accumulate bias were used to analyze the data. The analysis data were selected from instantaneous orbital observations and monthly and annual mean value.The annual mean cloud fraction of the three kinds of data are similar, but seasonal cloud fraction is different. CLARA-A2 has the smallest cloud fraction in summer and the highest cloud fraction in winter. Patmos-x agreed well with the ground observation. The correlation relationship between CLARA-A2 and ground was weak. Aqua/MODIS had good relationship in autumn and less correlation in spring and summer.The three kinds of long-term cloud fraction data showed similar spatial and temporal distribution. During daytime, CLARA-A2 has larger cloud fraction than MODIS and PATMOS-x. At nighttime, MODIS has the maximum cloud fraction value, and PATMOS-x and CLARA-A2 have similar values. All three kinds of cloud data committed a mistake with snow along the ridge of a mountain. The linear regression and accumulate bias analysis showed that the annual mean cloud fraction of PATMOS-x and CLARA-A2 displayed a decreasing trend from 1982 to 2015. The trend of the night time cloud fraction was more obvious than that of daytime. CLARA-A2 displayed more obvious trend than PATMOS-x, especially at night. The year of 2000 is a turning point for the change in cloud cover over the plateau area from high to low. In January, April, and October, the decrease in cloud amount is the main change trend. Meanwhile, in July, the weak increase is the main change characteristic.Three kinds of satellite cloud data have good comparability. Three kinds of data obtained different correlations when compared with the ground observation. The reasons may come from matched data with different spatial and temporal characteristics, different payloads with various observation abilities and different data set with different cloud detection algorithms.The stability of satellite orbit and high quality of instrument calibration are the baselines of long-term climate data. MODIS has stable instrument orbit and calibration. Thus, its long term cloud data have good homogeneity.