沙尘作为对流层气溶胶的主要成分，对气候系统有许多影响；同时，作为环境污染物，对人类健康危害也很大。沙尘天气一般在春季爆发，对中国北方大部分区域的生产和生活有较大影响。以往针对沙尘遥感监测人们开展了许多研究，取得了一定的效果。但对于一些云和沙尘混合的复杂状况，传统方法识别效果较差，几乎不能有效识别出沙尘。采用葵花8号（Himawari-8）卫星数据，提出一种针对性的识别方法。引入了0.46 μm和0.51 μm反射率差值RDI，统计发现该指数在一定范围内可以表现出沙尘连续性特征，并有效地将中高云和大部分地表与沙尘区分开来。碎积云的RDI值分布与沙尘的较为相似，为此进一步引入了灰度熵方法来滤除。例举了3次沙尘过程的判识结果，并结合地面观测数据进行了验证。其中对2017年5月4日沙尘的地面验证表明，位于云沙混合区的27个站中有22个站的地面观测与判识相一致。对于一些复杂条件下的沙尘，该方法是对分裂窗亮温差的有效补充。

Dust, as the main component of aerosols, has numerous effects on a climate system. Simultaneously, dust is harmful to human health as an environmental pollutant. The dust weather generally erupts in spring, thereby significantly affecting the production and life of most regions in Northern China. In the past, many studies have been conducted to identify dust by remote sensing. However, the traditional method has a poor effect and can hardly recognize dust in several complex situations, such as cloud-dust mixing. The dust process is typically accompanied by clouds, which are the main interfering factors in identifying dust. The judgment of pure dust is improved in the thermal infrared band, but the effect is poor for the dust mixed with clouds. In terms of microwave, the sensor is carried mostly by a polar orbit satellite, which time and space resolutions are low. This sensor cannot display real-time dust monitoring and warning.
In this study, a new method was proposed using the data from the Himawari-8 satellite. In fact, the dust mixed with clouds demonstrated better successive distribution characteristics in space than in medium clouds and fractocumulus. Thus, the dust mixed with clouds could be identified. A difference in the reflectivity of 0.46and 0.51 μm in a certain range could properly exhibit the continuity characteristics of dust and effectively distinguish clouds and most surfaces with dust by analyzing several visible channels of the Himawari-8. A threshold less than 10-15 could cover most dust mixed with clouds in accordance with the experimental statistics. However, an RDI value of broken cumulus was mainly distributed between 5 and 15, which was similar to the RDI value of dust. Therefore, we introduced the entropy of brightness. In this study, pure dust was identified using a BTD value that is less than 0, and the dust mixed with clouds was identified through the new method.
In the spring and summer of 2017, several types of dust accumulated in Inner Mongolia in China and its surrounding areas. We used the satellite data for April 16, May 4, and August 2 combined with visual interpretation and ground observation results to analyze and verify the proposed method. In the two dust processes of April 16 and August 2, we selected three typical regions with mixed cloud and dust. The dust storm on May 4 was large. Therefore, this dust storm was used to analyze and validate the algorithm on a large scale. The verification of dust on May 4, 2017, showed that the observations of the 22 stations were consistent with the results of 27 stations located in the cloud-sand mixing region. The algorithm proposed in this study achieved a significant result in dust recognition under various cloud-sand mixing conditions.
A new method based on the brightness entropy of RDI was proposed in this study. The method could effectively identify dust mixed with clouds in comparison with the results of traditional methods or using visual interpretation and ground observation. This method compensates for the limitations of existing algorithms and data to a large extent. However, significant complications in recognition of certain floating dust and large-thickness cloud still exist. The accuracy of recognition will also be affected by the complex condition of the surface. This study reveals that the method exerted a certain effect on pure dust, which can be further discussed in the other research. In addition, this method is still limited when identifying dust at night.