Dust detection over desert surfaces with thermal infrared bands using dynamic reference brightness temperature differences

Authors

  • Yang Liu,

    1. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
    2. State Key Lab for Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
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  • Ronggao Liu,

    Corresponding author
    1. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
    • Corresponding author: R. Liu, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, No11A, Datun Road, Beijing 100101, China. (liurg@igsnrr.ac.cn)

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  • Xiao Cheng

    1. State Key Lab for Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
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Abstract

[1] The brightness temperature difference (BTD) between two thermal infrared bands is a common index for dust detection. However, the BTD is sensitive to the observed temperature, which hinders its use in automatic dust detection, especially over desert land surfaces. In this paper, a dynamic reference brightness temperature differences (DRBTD) algorithm was developed to detect dust by removing the influence of the observed temperature on the BTD. Using long-term MODIS observations, the algorithm establishes the clear-sky linear relationships pixel by pixel between the brightness temperatures (BTs) at 12 and 11 µm channels and the relationships between the BTs at 8.6 and 11 µm channels. 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.

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