沙尘是地球系统能量平衡、全球气候系统和生物地球化学循环的重要影响因子，并对空气质量和人类健康有着显著影响。卫星热红外波段观测可以同时获得白天和夜间的沙尘信息，热红外波段的亮温差是常用的沙尘检测指数。但是，热红外传感器所捕获的是地表及大气的混合信息，地表和大气状况对大气顶层亮温的影响非常复杂且相互叠加，特别是亮温差对于温度敏感，导致从热红外波段亮温自动提取沙尘信息十分困难。本研究提出一种独立于温度的热红外地表背景动态合成方法，去除了温度对于亮温差的影响。首先基于大气辐射传输模型，模拟在不同大气温度、水汽剖面和地表特性状况下，沙尘气溶胶光学厚度和热红外波段大气顶层亮温的关系。在此基础上，以长时间序列MODIS观测为数据源，建立晴空状况下像元级热红外波段亮温关系。由此动态计算不同温度下的亮温差参考，与实际观测亮温差比较，设计动态参考亮温差指数DRBTDI实现沙尘与地表和云等地物的分离。利用MODIS热红外波段观测驱动DRBTD算法检测了中国北方和蒙古白天及夜间的沙尘，并利用OMI吸收性气溶胶指数AI产品、MODIS气溶胶光学厚度AOD产品和CALIOP激光雷达观测对反演结果进行验证。结果表明，DRBTD算法可以有效区分沙尘、云和地表。在白天，DRBTDI与OMI AI和MODIS AOD的相关系数达到0.79和0.77。在夜间，DRBTDI与CALIOP沙尘AOD的相关系数也达到0.78。基于该算法，利用2000-2008年MODIS观测进行了覆盖中国区域1km分辨率的沙尘检测，确定了区域内的主要沙尘源区，并分析了其空间分布和季节变化规律。结果表明，沙尘主要发生在中国北方，主要源区位于中国北方的沙漠和戈壁，包括塔里木盆地、河西走廊、中国北方和蒙古的戈壁、科尔沁沙地以及柴达木盆地。强沙尘暴主要发生在中国北方和西北部的沙漠区域，特别是塔克拉玛干沙漠。每年3-4月为沙尘最频发的时段，7-9月频率最低。研究区域上午比下午的沙尘发生频率更高。

Mineral dust is an important factor in earth system energy budget, global climate, and biogeochemical cycles. It also affects air quality and human health. Satellite radiometer measurements in thermal infrared channels are advantageous in monitoring the spatial and temporal variations of dust events during both the daytime and the nighttime. The brightness temperature difference (BTD) between two thermal infrared bands is a common index for dust detection. However, the satellite measurements provide the mixed signal of both the atmosphere and land surface. Since the properties of underlying surface and atmospheric profiles have complicated effects on the observed top of atmosphere (TOA) brightness temperature (BT), and the BTD is sensitive to observed surface, it is challenging to retrieve dust optical properties from satellite measurements.In this paper, a novel method for compositing land surface references in thermal infrared channels is developed to remove the influence of observed temperature on the BTD. The atmospheric transfer model is used to simulate the relationships between aerosol optical depth and TOA BTs in thermal infrared bands in various atmospheric temperature and water vapor profiles, as well as underlying surface properties. Based on these simulations, the algorithm establishes the clear-sky linear relationships pixel by pixel between the brightness temperatures (BTs) at thermal infrared channels by using long-term MODIS observations. From these relationships, the reference BTDs are dynamically generated according to the observed brightness temperatures. Next, the DRBTDI, which is the difference of the observed BTD and the reference BTD, is created and used to separate the dust from other observed objects. This algorithm is applied to MODIS observations to detect several dust events during the daytime and the nighttime over Mongolia and northwestern and northern China. The results are compared with Ozone Monitoring Instrument aerosol index (OMI AI), MODIS Deep Blue aerosol optical depth (AOD), and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) observations. The comparisons indicate that the DRBTD algorithm can effectively distinguish dust from clouds and land surface. During the daytime, the DRBTDI is correlated with the OMI AI and MODIS AOD with a correlation coefficient of Pearson (r) of 0.79 and 0.77, respectively. At night, the DRBTDI is correlated with the CALIOP dust AOD with an r of 0.78.The climatology of dust storms in China with 1 km resolution is obtained based on our algorithm applied to MODIS satellite observations from 2000 to 2008. The main source regions of dust events are found, and their spatial and temporal patterns are analyzed. Dust events mainly occur in northern China. The major source regions are the desert and Gobi area, including the Tarim Basin, the Hexi Corridor and the Gobi in Mongolia and north China, Horqin Sandy land and Tsaidam Basin. Severe dust storms occur mainly in the deserts in north and southwest China, with the largest source region being the Taklimakan desert. Dust storm frequency is highest in March and April, and lowest from July to September. Dust events occur more frequently in the morning than in the afternoon.