目的 云覆盖着地球上空大部分区域，在地球水循环、地气系统能量平衡和辐射传输过程中有着重要的作用，同时云也是天气气候中最重要、最活跃的因子之一；此外，云覆盖地表信息，导致影像配准、融合等处理过程的很多问题，所以云检测十分重要。方法 基于2015年发射的深空气候观测台（DSCOVR）卫星搭载的地球彩色成像相机（EPIC）数据，针对EPIC数据波段范围较广和影像数据是半球尺度的特点，以云指数法作为基础，提出一种新的面向半球尺度数据的云检测方法。首先，分析EPIC数据各个波段的波段特征，尤其是紫光波段，然后根据云在不同波段的反射特性，以指数的形式完成波段组合进行云检测，再与SVM（support vector machine）云检测法和可见光云检测法进行比较，最后利用EPIC L2产品对所获得的云分布图和统计云量值进行结果验证，以正确率、漏检率、误检率和Kappa系数作为参考标准完成精度评定。结果 实际EPIC夏季（2017年7月）和冬季（2017年1月）数据的实验结果表明，本文方法的正确率均高于91%，Kappa系数大于0.9；其他方法的正确率均低于89%，且Kappa系数在0.8左右，均小于0.9。所以本文能够有效地检测到薄云（即使在冬季），且云量和云的分布都最为接近实际。结论 在EPIC影像的云检测过程中，本文方法从云分布图和云量结果两个方面都优于可见光云检测法和SVM云检测法，经EPIC L2产品验证，本文方法有效、可靠，且能够快速获得半球范围内云的分布情况，有助于对全球云的动态研究和自然天气预测。

Objective Clouds cover most of the Earth's space and play an important role in the Earth's water cycle, its energy balance, and radiation transmission. Concurrently, clouds are one of the most vital and active factors in weather and climate. They usually cover ground information, thereby causing many problems and difficulties in the processing of image registration and fusion. Thus, cloud detection is highly significant and necessary. Method On the basis of the Earth polychromatic image camera (EPIC) data from the deep space climate observatory (DSCOVR) satellite launched in 2015, we study the characteristics of EPIC data, including the hemisphere scale and the wide range of band spectra, including ultraviolet, visible, and infrared bands. Then, we propose a new cloud detection method for EPIC data with hemispherical scale on the basis of the normalized difference cloud index (NDCI). First, we analyze the different reflection characteristics of different bands, which are determined by the physical properties of objects. In particular, the ultraviolet bands of EPIC data are new. Combined with the applications of EPIC data bands, 340, 388, 680, and 780 nm are identified as the main research bands. Second, we analyze the reflection characteristics of clouds, including thin clouds and residual clouds. According to the above two aspects, we define the cloud index (CI) to detect clouds, thereby effectively reducing the influence of the underlying surface on cloud detection results. On the basis of the research bands, we design two CI indexes. CI (340) is the difference between the reflectivity of the 680 and 340 nm bands divided by the reflectivity of the 780 nm band. CI (388) is the difference between the reflectivity of the 680 nm band and the 388 nm band divided by the reflectivity of the 780 nm band. The method is analyzed in terms of the cloud amount and cloud distribution. Result To verify the effectiveness of the proposed cloud detection method, we compare three other cloud detection methods, namely, the visible light cloud detection method, support vector machine (SVM) cloud detection method, and traditional NDCI cloud detection method. The EPIC data that correspond to summer (July 3, 2017) and winter (January 3, 2017) are used to conduct the experiments. The comparison results consider cloud distribution and cloud amount. In the experimental cloud distribution results, the cloud distribution obtained by the proposed method is most consistent with the cloud distribution in the original EPIC image combined with RGB true color. The results of cloud distribution also show that the proposed method effectively detects thin clouds and residual clouds that are not detected by other methods, even in winter and in summer. The traditional NDCI cloud detection method misjudges a large amount of land as the cloud. Thus, the cloud amount of the traditional NDCI method is not included in the comparison. CI (388) is the optimal band combination for cloud detection in winter and summer. In July, the cloud amounts of the visible light cloud detection method, CI (340), CI (388), and SVM method are 21.07%, 26.90%, 31.40%, and 32.49%, respectively. Except for the visible light method, the maximum difference between the other methods is 5.59%. In January, the cloud amounts of four methods are 30.60%, 35.34%, 38.50%, and 31.34%, respectively. To validate the results of the cloud and cloud distribution, the results are verified using the EPIC L2 data, including the reflectivity product, CF340 product, and CF388 product. The mean cloud amount of the three products in July is 32.33%. In summer, the cloud distributions of various methods are consistent with the cloud distributions of products. The differences between the visible light method, CI (340), CI (388), and SVM method with the product mean are 11.26%, 5.43%, 0.93%, and 0.16%, respectively. The difference in the cloud amount between the SVM method and the product is the smallest, followed by that between CI (388) and the product. The mean cloud amount of products in winter is 37.34%. The difference in the cloud amount between the product and CI (388) is the smallest at 1.16%. Finally, the accuracy evaluation, including the correct detection ratio, missed detection ratio, false detection ratio, and kappa coefficient, is completed. Regardless of the season, the correct detection ratios of the four methods are more than 80%. In winter, the detection accuracy of the four methods is lower than that in summer. For the visible light method, the kappa coefficients in summer and winter are 0.84 and 0.79, respectively, which are the lowest of the four methods; the correct ratios are also the lowest at 84.40% and 80.07%, respectively. For the SVM method, the overall accuracy is up to 88.26%, and the lowest is 86.01%. The kappa coefficients for summer and winter are 0.88 and 0.86, respectively. The cloud distribution of the SVM method is closer to that of the EPIC product than to that of the visible light method. In the band combination of the CI method for summer, the correct ratios of CI (388) and CI (340) are 94.34% and 93.24%, respectively. The correct ratio of CI (388) in winter is as high as 92.96%. CI (388) has the largest Kappa coefficient with 0.94 in summer and 0.92 in winter. The correct ratio of our method is greater than 91%, and the Kappa coefficient is greater than 0.9. However, the correct ratios of the other methods are less than 89%, and the Kappa coefficients are at approximately 0.8. Therefore, in winter and summer, the CI (388) band combination obtains the best cloud distribution and cloud amount as the EPIC L2 product. Conclusion In the cloud detection process for an EPIC image, our proposed cloud detection method is superior to the visible light cloud detection method and SVM method in terms of cloud distribution and cloud amount. The findings are valid and reliable according to the EPIC L2 product verification. Moreover, the proposed method can quickly obtain cloud distribution and cloud amount within the hemisphere, which is helpful for dynamic research and natural weather prediction of global clouds.