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

Transcription

1 JOURNAL OF GEOPHYSICAL RESEARCH: ATMOSPHERES, VOL. 118, , doi: /jgrd.50647, 2013 Dust detection over desert surfaces with thermal infrared bands using dynamic reference brightness temperature differences Yang Liu, 1,2 Ronggao Liu, 1 and Xiao Cheng 2 Received 3 October 2012; revised 3 July 2013; accepted 11 July 2013; published 5 August [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 Citation: Liu, Y., R. Liu, and X. Cheng (2013), Dust detection over desert surfaces with thermal infrared bands using dynamic reference brightness temperature differences, J. Geophys. Res. Atmos., 118, , doi: /jgrd Introduction [2] Mineral dust is an important factor in global climate, biogeochemical cycles, and air pollution [e.g., Mahowald et al., 2011]. It impacts climate directly by altering the radiation balance in the atmosphere through scattering and absorbing radiation [Haywood and Boucher, 2000] and indirectly by altering cloud properties [Wurzler et al., 2000]. Dust also increases ocean photosynthesis rates by carrying iron to the ocean [Krishnamurthy et al., 2010]. The characterization of dust properties and distributions at regional and global scales would help us better understand the role of dust in Earth s radiative budget and the global biogeochemical cycle [Nobileau and Antoine, 2005]. [3] Satellite remote sensing is advantageous in monitoring the spatial and temporal variations of dust events [Chiapello 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. Corresponding author: R. Liu, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, No11A, Datun Road, Beijing , China. (liurg@igsnrr.ac.cn) American Geophysical Union. All Rights Reserved X/13/ /jgrd et al., 1999]. The satellite radiometer measurements in visible and ultraviolet channels have been used effectively to detect dust. For example, the ultraviolet measurements of the Nimbus 7 Total Ozone Mapping Spectrometer (TOMS) and the Ozone Monitoring Instrument (OMI) have been used to detect mineral dust [Chiapello et al., 1999; Torres et al., 2007], and the observations in the visible bands of the SeaWiFS and MODIS have been used to characterize the properties of dust aerosols [Hsu et al., 2006]. However, these algorithms, which are based on visible and ultraviolet channels, are applicable only during the daytime. [4] Satellite radiometer measurements in thermal infrared channels provide another approach for dust detection during both the daytime and the nighttime [Shenk and Curran, 1974; Ackerman, 1989, 1997; Wald et al., 1998]. Although the brightness temperature (BT) of the dust layer is related to the physical properties of aerosol particles [Ackerman, 1997], the BT is not used as a direct indicator for automatic dust detection because it is sensitive to the temperature of the observed objects. Instead, the brightness temperature difference (BTD) between two thermal infrared spectral bands, which depends to a great extent on the mineral composition [e.g., Hudson et al., 2008], emissivity, and the particle sizes of the observed objects [Wald et al., 1998] and which usually shows strong contrasts among earth surfaces, dust, and clouds, is a more reliable index for detecting dust 8566

2 [Schepanski et al., 2007; Zhang et al., 2006]. For example, negative differences in the brightness temperature at 11 and 12 μm channels were observed for a dust-laden atmosphere in comparison to clear-sky conditions [Ackerman, 1997; Sokolik, 2002]. Airborne dust would also increase the BTD between the 8.6 and 11 μm wavelength bands [Wald et al., 1998]. [5] In addition to the atmospheric conditions, such as water vapor, aerosols, clouds, and temperature profiles, the underlying surface also makes considerable contributions to the BTDs. To eliminate the contributions of the underlying surface, several scientists have attempted to compose a reference BTD map representing the clear-sky conditions and extract the dust signals from the differences between the observed BTD and the reference BTD (hereafter referred to as the BTDanom method) [Ashpole and Washington, 2012]. Unfortunately, the BTD is slightly dependent on the observed temperature (section 5.1). Over water and dense vegetated surfaces, the temperature effect on the BTD is not obvious [Ackerman, 1997; Darmenov and Sokolik, 2005] because of the high contrast of the BTDs between the dust and the underlying surface. However, over desert and semidesert regions, the temperature effect is an impediment for dust detection due to the similarities of the emissivities of surface and airborne dust. Because the temperature of the clear-sky surface is usually much higher than that of airborne dust, the temperature effect still exists in the BTDanom method. [6] In this paper, the dynamic reference brightness temperature differences (DRBTD) algorithm is proposed to remove the temperature effect on the surface reference BTD maximally to detect airborne dust during both daytime and nighttime. A highly linear relationship was found between the brightness temperature of two thermal bands under the same emissivity and atmospheric conditions (see section 3), and this relationship is independent of temperature. The linear relationship between the brightness temperatures of 12 (BT12) and 11 μm (BT11), as well as between the brightness temperatures of 8.6 (BT8.6) and 11 μm (BT11) wavelength channels under clear-sky conditions were established pixel by pixel from the time series MODIS observations from From these relationships, the reference BTDs were dynamically generated at the pixel level to represent the clear-sky status according to the observed temperatures. The observed BTDs were later compared with the reference BTD to separate the dust from the clouds and the clear-sky surface. The desert regions in Mongolia and northwestern and northern China (33 N 54 N, 73 E 136 E), which are the main source of airborne dust in Asia [Prospero et al., 2002], were selected as the study area for the application of this algorithm. [7] The paper is organized as follows. Section 2 presents a brief introduction to the data. The linear relationships between the brightness temperatures in the 8.6, 12, and 11 μm bands are demonstrated in section 3. The algorithm is described in detail in section 4. Section 5 describes the performance of the DRBTD algorithm on minimizing underlying surface effects, the results of dust detection efforts during several dust events, and the comparisons with OMI AI (aerosol index), MODIS Deep Blue AOD (aerosol optical depth), and CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) observations during the daytime and nighttime. The influence of clouds, atmospheric water vapor, and surface changes are discussed in section 6. The final summary is presented in section Data 2.1. MODIS Data [8] The MODIS, aboard the Terra and Aqua satellites, observes the Earth s entire surface every 1 to 2 days, crossing over the equator at 10:30 and 13:30 local time (LT) during the day and at 22:30 and 01:30 LT at night. The MODIS acquires data in 20 visible and shortwave infrared spectral channels and 16 thermal infrared channels. Among these channels, the 8.6 (Band 29), 11 (Band 31), and 12 μm (Band 32) channels are widely used in dust detection. The MODIS Level 1B Calibrated and Geolocated Data Set (MOD02) contains global-calibrated and geolocated ataperture radiance values for all 36 bands. In this paper, the MOD02 radiances at wavelengths λ = 8.6, 11, and 12 μm at 1 1 km resolution at nadir for the period from February 2000 to December 2008 were gridded and converted into brightness temperature values and used in the DRBTD algorithm for dust detection. The MODIS Global Daily Level 2 Aerosol Product (MYD04_L2) provides global aerosol AOD datasets based on the Deep Blue algorithm with a spatial resolution of 10 km [Hsu et al., 2004]. These AOD products were compared with the DRBTD algorithm results. Additionally, the MODIS Precipitable Water product (MOD05_L2), which includes the column water vapor datasets with a 1 km resolution retrieved from the nearinfrared algorithm, was used to illustrate the column water vapor distributions of the study area OMI AI [9] The OMI aboard the Aura satellite flies in formation with five other satellites (including Aqua) in the international A-Train constellation for coincident Earth observations approximately 15 min behind the Aqua satellite. The OMI has collected radiative measurements of the Earth every day in near-uv and visible spectral channels since August The daily global-absorbing aerosol index (AI) was mapped from radiance measurements of the UV wavelengths and used to characterize the dust aerosols over deserts [Torres et al., 2007]. AI represents the signals of absorbing aerosols, including desert dust, carbonaceous aerosols from biomass burning, and weakly absorbing sulfate-based aerosols. AI is sensitive to many factors, such as the aerosol type and composition, aerosol layer height, and the singlescattering albedo. Generally, AI is positive for absorbing aerosols and negative for nonabsorbing aerosols. In this paper, the global daily 1.0 gridded UV AI from the Collection 3 OMI-Aura-OMTO3d product were compared with the algorithm results CALIPSO [10] The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite is also part of the NASA A-Train constellation, lagging Aqua by 1 to 2 min. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard the CALIPSO satellite has provided high resolution information on the vertical structures and properties of aerosols and clouds across the globe since June CALIOP is a polarization-sensitive lidar with two 8567

3 Figure 1. Relationships between BT8.6 and BT11, as well as between BT12 and BT11, derived from Planck s law. wavelengths (532 and 1064 nm) from a near-nadir-viewing geometry during both day and night phases of the orbit. The level 2 aerosol layer products of CALIOP include the optical depth of all of the aerosols detected within a 5 km averaged profile at 532 and 1064 nm. The DRBTD algorithm results were compared with the nighttime AOD at 532 nm to evaluate the performance of the DRBTD algorithm for nighttime dust detection. 3. Simulation of the Linear Relationships Between TOA BTs in Channel Pairs of 8.6/11 and 12/11 μm Using an Atmospheric Radiative Transfer Model [11] Based on Planck s law, it can be inferred that the BTs of the land surface at two thermal infrared bands have a linear relationship, with the slope and the offset being determined by the emissivity (ε) of these two channels. The linear relationships between BT8.6 and BT11, as well as between BT12 and BT11, are demonstrated in Figure 1. The BTs at the 8.6, 11 and 12 μm channels were calculated for land surface temperatures ranging from 260 K to 320 K and with the same surface emissivity set as that of bare soil from the SeeBor database [Borbas et al., 2005]. Similar linear relationships were also found for other surface types with different slopes and offsets. [12] For the observed top of atmosphere (TOA) BTs at multithermal infrared bands, the relationships between the different channels would be modified by the atmospheric conditions. The effects of the atmospheric conditions on the relationships of the TOA BTs at thermal infrared bands (in this study, the 8.6, 11, and 12 μm channels) were simulated by the Santa Barbara DISORT (DIScreet Ordinate Radiative Transfer) Atmospheric Radiative Transfer (SBDART) model for bare soil with the standard atmospheric profiles of midlatitude winter (MLW) (Table 1) [Ricchiazzi et al., 1998]. The dust optical properties, including the single-scattering albedo and asymmetery parameter, were defined using tabulated values for mineral nucleation mode dust from OPAC (Optical Properties of Aerosol and Cloud) [Hess et al., 1998]. The surface emissivities of bare soil at the three channels were set based on the SeeBor database [Borbas et al., 2005]. [13] Figures 2a and 2b present an example of the simulated relationships between BT8.6 and BT11 and between BT12 and BT11. The results demonstrate the linear relationships between BT12 and BT11 as well as between BT8.6 and BT11 with a slope of approximately 1 in the standard atmospheric profiles without clouds, aerosol, or water vapor. This line is referred to as the ideal pristine line. When there is dust, clouds or water vapor in the atmosphere, the relationship departs from the ideal pristine line, but it is still linear under the same atmospheric conditions. For the relationships between BT8.6 and BT11, when a dust layer is present in the atmosphere, the simulated points are above the ideal pristine line of BT8.6/BT11. With an increase in the optical depth of the dust layer, the dust points are distant from the ideal pristine line. The linear relationship between BT12 and BT11 is less sensitive to dust aerosols with dust points slightly above the ideal pristine line of BT12/BT11. The presence of clouds has complex effects on the relationships among the BTs. In Figure 2, only the results of clouds with a cloud optical depth of 1 are shown to make the figure more readable. For thin clouds with a cloud optical depth at 0.55 μm of less than 10 μm, the simulated points are far below the ideal pristine line for BT12 and BT11, while they are above the ideal pristine line for BT8.6 and BT11. For thick clouds with a cloud optical depth greater than 10 μm, the BTs significantly decrease to approximately 220 K and are little related with the underlying surface temperature. These simulated points are below the ideal pristine line for BT12 and BT11; however, they are either below or above the pristine line for BT8.6 and BT11. When atmospheric water vapor is taken into account, the slopes of the BT12/BT11 and BT8.6/BT11 relationships decrease slightly with points below the ideal pristine line. [14] The dust layer attitude and atmospheric water vapor profiles would affect the satellite-observed BTs [e.g., Brindley and Russell, 2009]. The relationships between BT8.6 (BT12) and BT11 were simulated with various dust layer heights with atmospheric profiles of MLW. Figure 2 presents the relationships between BT8.6 and BT11 (Figures 2a, 2c, and 2e), as well as that between BT12 and BT11 (Figures 2b, 2d, and 2f), with a dust layer height of 2 km, 0.5 km, and 5 km, respectively. As the dust layer altitude increases, the BT8.6, BT11, and BT12 of dust observations decrease, especially for the heavy dust layer with an AOD greater than 2.0. However, the relationships between BT8.6 (BT12) and BT11 are similar for various dust layer heights, with dust points above the ideal pristine line and the distance between them increasing with AOD for BT8.6/BT11, and dust plots around the ideal pristine line for BT12/BT11. Table 1. Parameters for BTs Relationships Simulation With SBDART Atmospheric Model Midlatitude Winter (MLW) Integrated water vapor amount (g/cm 2 ) 0, 0.214, 0.427, 0.641, 0.854, 1.068, View zenith angle 25 surface temperature 260 K 295 K, step = 1.0 K Emissivity of bare soils ε 8.6 = , ε 11 = , ε 12 = Aerosol type Mineral dust Altitude of aerosol layer 2 km Aerosol optical depth (at 0.55 μm) 0.2, 0.5, 1.0, 2.0, 3.0, 5.0 Altitude of cloud layer 4 km Cloud optical depth (at 0.55 μm) 1, 10, 30, 50, 100 Cloud drop effective radius (μm)

4 Figure 2. Simulation of relationships between BT8.6 and BT11, as well as between BT12 and BT11, for bare soil in various dust layer heights with an atmospheric profile of MLW: (a) BT8.6 versus BT11 with a dust layer height of 2 km, (b) BT12 versus BT11 with a dust layer height of 2 km, (c) BT8.6 versus BT11 with a dust layer height of 0.5 km, (d) BT12 versus BT11 with a dust layer height of 0.5 km, (e) BT8.6 versus BT11 with a dust layer height of 5 km, and (f) BT12 versus BT11 with a dust layer height of 5 km. UW stands for the integrated water vapor amount in the atmosphere (g/cm 2 ). [15] The atmospheric radiative transfer simulations were also performed with different standard atmospheric profiles to evaluate the effects of water vapor on the relationships between BT8.6 (BT12) and BT11, including MLW and MLS (Midlatitude Summer). The dust layer altitude is set to 2 km, and the surface temperature ranges from K with a 1 K step for MLS simulations. The column water vapor content of MLW is approximately g/cm 2, which is much less than that of MLS (approximately g/cm 2 ). Figures 3a and 3b show the results for MLW, and the relationships for MLS are presented in Figures 3c and 3d. For the relationships between BT8.6 and BT11, the results are similar for MLW and MLS, with dust points being observed above the ideal pristine line, and the distance between them increasing with AOD. On contrast, differences are presented for that of BT12 and BT11 in two atmospheric profiles. For MLW, the dust points are close to the ideal pristine line, while they are below the ideal pristine line for MLS, indicating that uncertainties may be introduced by DRBTDI12 when applying the algorithm in humid regions and seasons. Therefore, the current approach is more appropriate to dry regions with low water vapor content in the atmosphere. 4. Description of the DRBTD Dust Detection Algorithm 4.1. Generation of Reference BT8.6/11 and BT12/11 Relationships From Observations [16] From the simulation of Figure 2, the relationship between the BTs of thermal infrared bands is linear for the same atmospheric conditions. If BT11 is assumed to be the observed temperature, the clear-sky TOA BT8.6 and BT

5 Figure 3. Simulation of relationships between BT8.6 and BT11 as well as between BT12 and BT11 in various atmospheric water vapor profiles: (a) BT8.6 versus BT11 in MLW, (b) BT12 versus BT11 in MLW, (c) BT8.6 versus BT11 in MLS, and (d) BT12 versus BT11 in MLS. could be expressed as functions of BT11 with the slopes a 8.6 and a 12 and enhancements b 8.6 and b 12, respectively, which are related to the emissivity of the underlying surface, and the atmospheric conditions: BT8:6 ¼ a 8:6 þ b 8:6 BT11 (1) BT12 ¼ a 12 þ b 12 BT11 (2) [17] The a 8.6, b 8.6, a 12, and b 12 for the ideal pristine line can be derived from the simulation of the atmospheric transfer model if the emissivity of the underlying surface is known. Unfortunately, retrieval of the emissivity of the underlying surface is difficult. Here, a line approximate to the ideal pristine line, which is defined as the most clear-sky line, was regressed from the time series MODIS observations pixel by pixel. Because the observations for the cloud or vapor conditions are below the most clear-sky line of BT12/ BT11, a scatterplot between BT12 and BT11 for an ensemble of MODIS observations from the same position would have an upper envelope line with the slope b 12 and enhancement a 12 which represents the most clear-sky conditions of the observed site (hereafter referred to as the most clear-sky line of BT12/BT11). The observational data points for cloud conditions or humid atmospheric conditions fall below this most clear-sky line, while the points of dust conditions may lie above or below the most clear-sky line of BT12/BT11. In contrast, for scatterplots of the relationship between BT8.6 and BT11, the lower envelope line represents the clear-sky conditions of the slope a 8.6 and enhancements b 8.6 (hereafter referred to as the most clear-sky line for BT8.6/BT11). The BT8.6/BT11 data points for dust and cloud conditions are above the most clearsky line, while those for humid atmospheric conditions are below this line. Because the columnar vapor amounts are usually low in these regions, the most clear-sky line in the desert regions should be very close to the ideal pristine line. [18] The upper envelopes for BT12/BT11 and the lower envelopes for BT8.6/B11 were regressed with the MODIS observations for the 9 year period from The following steps were performed to quantify the upper and lower envelopes, taking the upper envelopes for BT12/BT11 as the example. First, for a specific site, the MODIS observations from were binned sequentially using BT11 values with a 5 K bin size. Second, the five highest BT12 values were kept in each bin, and a line was regressed through these points. Then, among the observations below the regression line, the one with the largest distance with respect to the regression line was discarded. The line was regressed using the left observations iteratively. If all the values in one bin had been removed, this bin was excluded in the next regression procedure. This regression procedure was iteratively performed until the valid bin was less than 10. The final regression line is the upper envelope line and was considered as the most clear-sky line of BT12/BT11. Because the BT values for clouds were much lower than that of land surface and dust layers and far away from the most clear-sky line, these points were discarded automatically during the regression procedure. The lower envelope line for BT8.6/BT11 was acquired by linear regression of points with the five lowest BT8.6 values in each BT11 bin, using a similar procedure for BT12/BT11. Finally, these regression lines were taken as the most clear-sky line for BT12/BT11 and BT8.6/11 and the values of a 8.6, b 8.6, a 12, and b 12 were mapped for each pixel at 1 1 km resolution over the study 8570

6 Figure 4. Scatterplots of BT8.6 versus BT11 and BT12 versus BT11 for five illustrative sites in China during , including the Taklamakan desert site, the Ulanqab semiarid site, the Qinlin forest site, the Beijing urban site, and the Qinghai Lake site. The red lines represent the clear-sky lines, which are the upper envelopes for BT12/BT11 or lower linear envelopes for BT8.6/BT

7 area. Although the dust observations lying above the clearsky line would jeopardize the regression of the upper envelope for BT12/BT11, this effect is minor because these dust observations are mainly concentrated on a few bins and its number is small with respect to the number of clear-sky observations. Figure 4 shows several samples of scatterplots for several sites in China: the Taklamakan desert site, the Ulanqab semiarid site, the Qinlin forest site, the Beijing urban site, and the Qinhai Lake site. Both the upper and lower envelopes are well defined by the regression line with an R 2 of approximately 1, which demonstrates that the linear relationship is appropriate for the given locations The Dynamic Reference Brightness Temperature Differences Index (DRBTDI) [19] Based on the relationships of clear-sky BT8.6/BT11 and BT12/BT11, for a pixel with an observed BT11 (BT11 obs ), its corresponding reference BT8.6 (RBT8.6) and reference BT12 (RBT12) can be inferred as follows: RBT8:6 ¼ a 8:6 þ b 8:6 BT11 obs (3) RBT12 ¼ a 12 þ b 12 BT11 obs (4) [20] The reference BTD for 8.6 and 11 μm (RBTD8.6)and for 12 and 11 μm (RBTD12) for each pixel, which represents the BTD at the most clear-sky condition without airborne dust, was dynamically calculated from the observed BT11 as follows: RBTD8:6 ¼ RBT8:6 BT11 obs ¼ a 8:6 þ b 8:6 BT11 obs BT11 obs (5) RBTD12 ¼ RBT12 BT11 obs ¼ a 12 þ b 12 BT11 obs BT11 obs (6) [21] The difference between the observed BTD and the reference BTD for 8.6 and 11 μm (DRBTDI8.6) and for 12 and 11 μm (DRBTDI12) can be expressed as follows: DRBTDI8:6 ¼ ðbt8:6 obs BT11 obs Þ ða 8:6 þ b 8:6 BT11 obs BT11 obs Þ ¼ BT8:6 obs ða 8:6 þ b 8:6 BT11 obs Þ (7) DRBTDI12 ¼ ðbt12 obs BT11 obs Þ ða 12 þ b 12 BT11 obs BT11 obs Þ ¼ BT12 obs ða 12 þ b 12 BT11 obs Þ (8) where BT8.6 obs, BT11 obs, and BT12 obs are the observed BT in 8.6, 11 and 12 μm bands, respectively. [22] Because DRBTDI8.6 can distinguish dust and clouds from underlying surfaces and DRBTDI12 can distinguish dust from clouds, the combination of these two indices can distinguish dust from both clouds and land surfaces. The DRBTDI is definedasthesummationofdrbtdi8.6anddrbtdi12: DRBTDI ¼ DRBTDI8:6 þ DRBTDI12 (9) [23] When the observed BTD is compared with the reference BTD dynamically derived from the same temperature, the effects of temperature on the underlying BTD can be mitigated. Because the contributions of land surface have been removed by the linear relationships between BT8.6 and BT11 and between BT12 and BT11, the DRBTDI8.6 and DRBTDI12 are signals of the atmospheric conditions, especially the dust aerosols and clouds. For clear-sky surfaces, the reference BTD is close to the observed BTD such that the DRBTDI8.6 and DRBTDI12 are approximately 0, and the DRBTDI is also approximately 0. The DRBTDI8.6 would increase rapidly and the DRBTDI12 would somewhat increase with the increase of the dust AOD, resulting in positive values of the DRBTDI. For cloud conditions, the DRBTDI12 is usually negative, while the DRBTDI8.6 is generally greater than zero. Generally, the DRBTDI12 is a large negative value for clouds. When the DRBTDI8.6 is a large positive value, the DRBTDI would be a positive value. In this case, the negative DRBTDI12 is used to identify the cloud. For a thin cloud, the DRBTDI12 is usually a small negative value and the DRBTDI would be a small positive value, resulting in a nearly zero value of DRBTDI. Therefore, the dust pixels were identified by positive DRBTDI values and DRBTDI12 and DRBTDI8.6 values greater than 0.5. The pixels with DRBTDI12 or DRBTDI8.6 values of less than 0.5 were classified as clouds based on the analyses of the observations over the study area. Although the DRBTDI8.6, DRBTDI12, and DRBTDI values are sensitive to many factors, such as atmospheric water vapor, dust single-scattering albedo, dust layer height, and viewing geometry, they are strongly positively correlated with the AOD, which means that this index can be used to semiquantitatively indicate the dust optical thickness. 5. Results 5.1. Evaluation of the Effects of Temperature on the Reference BTD [24] The reference BTD, which represents the BTD at the most clear-sky condition without airborne dust and clouds, relates to surface temperature. The effects of temperature on the reference BTD were evaluated based on a 8.6, b 8.6, a 12, and b 12 using equations (5) and (6). The reference BT8.6 and BT12 were inferred from the BT11, and the RBTD8.6 and RBTD12 were calculated from the reference BT8.6 and the reference BT12. [25] Figure 5 shows the RBTD8.6 and RBTD12 over the study area with BT11 = 260 K (Figures 5a and 5c) and BT11 = 280 K (Figures 5b and 5d). The RBTD12 and RBTD8.6 show significant variation from site to site. Generally, the RBTD12 is positive in clear-sky conditions in desert regions. For a given BT11, the RBTD12 for desert surfaces in the northeast part of the study area is greater than that for vegetated regions, which is approximately zero. Negative values of RBTD8.6 are observed for desert surfaces, which have also been demonstrated by Hansell et al. [2007]. The RBTD8.6 is greater for vegetative pixels than for desert surfaces. These differences are attributed mainly to the different emissivities of various land cover types. Noticeable variations also exist within the same land cover type, which is attributed to the differences in emissivity due to different surface components and water content. With the larger variability in the emissive nature of the 8.6 μm band, the RBTD8.6 shows a more significant spatial variability than the RBTD12. [26] The reference BTDs are highly affected by the temperature. With an increase of BT11 from 260 K to 280 K, the RBTD12 generally decreases by approximately 0.5 K, 8572

8 Figure 5. The reference BTDs for different BT11s: (a) RBTD8.6 with BT11 = 260 K, (b) RBTD8.6 with BT11 = 280 K, (c) RBTD12 with BT11 = 260 K, and (d) RBTD12 with BT11 = 280 K. especially in sparsely vegetated regions in the northwest part of the study area where the RBTD12 decreases from 1.5 K to 1.0 K. The RBTD8.6 shows more significant decreases, decreasing from approximately 0 K to 2 K when BT11 increases from 260 K to 280 K in the vegetated regions in the northern and eastern parts of the study area, whereas RBTD8.6 decreases from 3.5 K to 5.5 K in the desert surface regions. These results are consistent with those of Ashpole and Washington [2012], who suggested that the reference BTD is higher and the reference BTD is lower at nighttime than during the daytime. Thus, the bias due to temperature for the RBTD12 and the RBTD8.6 should not be neglected. The dynamic generation of clear-sky reference BTDs as proposed here should help improve the identification of the dust signals Evaluation of the Ability of the DRBTD Method to Remove Variability of the Underlying Surface [27] For those most clear observations with low water vapor and low aerosol in the atmosphere, the atmospheric effect is small. In these conditions, if a dust detection method works well, the variations from the underlying surface should be small. That is, an ideal method to remove the underlying surface variations should cause a clear-sky observed scene to appear homogeneous, and a method to remove the temperature effect should cause two clear-sky scenes with different temperatures in the same region to be identical. Otherwise, the spatial and surface temperature variations may result in great variations of the dust index even in clear-sky conditions without airborne dust loading, making it hard to separate dust from clear-sky surfaces. Two clear-sky cases were selected to Figure 6. (a) Terra MODIS RGB daytime composite image for 10 October 2008, with reflectance on bands 1, 4, and 3 for R, G, and B, respectively. (b) Column water vapor amount (cm) for 10 October (c) Surface emissivity map in the 8.3 μm channel. (d) Surface emissivity map in the 10.8 μm channel. (e) Surface emissivity map in the 12.1 μm channel. 8573

9 Figure 7. BTD, BTDanom, and DRBTDI for 10 October 2008: (a) BTD8.6-11; (b) BTD12-11; (c) BTDanom8.6,11; (d) BTDanom12,11; (e) DRBTDI8.6; and (f) DRBTDI12. evaluate the ability of the DRBTD algorithm to remove the spatial variations and temperature effects of underlying surfaces. One case is of an inhomogeneous region over northwest China and Mongolia, evaluating the removal of spatial variations of the underlying surface, and the other case is of a small homogeneous region over the Taklimakan Desert during different seasons, evaluating its performance under different surface temperature conditions. We also seek to test the performance of our new DRBTD method to remove variability in observations against traditional BTD and BTDanom methods, which also focus on elimination of the contributions of the underlying surface by composing a reference BTD map representing the clear-sky conditions and extracting the dust signals from the differences between the observed BTD and the reference BTD [Ashpole and Washington, 2012]. The BTDanom value was calculated from the difference between the observed BTD and the composite reference BTD map, which is the mean BTD from 15 days of rolling clear-sky observations [Ashpole and Washington, 2012]. The standard deviation (SD) and mean values were calculated to evaluate the homogeneity and the distribution of the results Performance for Removal of Spatial Variation of the Underlying Surface [28] A region in northwestern China and Mongolia with an area of 624,900 km 2 (represented by the pink rectangle Figure 8. Frequency of BTD, BTDanom, and DRBTDI for 10 October 2008: (a) 8.6 μm channels and (b) 12 μm channels. 8574

10 Table 2. Statistic of the BTD, BTDanom, and DRBTDI in North China and Mongolia on 10 October 2008 Mean SD BTD BTDanom8.6,1 1 DRBTDI8. 6 BTD BTDanom12,1 1 DRBTDI in Figure 6) was selected to evaluate the ability of the DRBTD algorithm to remove the spatial variations from the underlying surface. The region includes diverse surface types with different physical components, including the Tenggeli Desert, the Gobi Desert, and the semiarid region in the Gansu and Inner Mongolia provinces in China. The clearsky case that was selected was from 10 October Figures 6c, 6d, and 6e show typical surface emissivities for October 2008 in this region in 8.3, 10.8 and 12.1 μm channels, as derived from a combination of MODIS observations and laboratory-measured emissivity spectra [Seemann et al., 2008]. Though the wavelength regions do not overlap exactly with those of MODIS, the patterns should be comparable. It can be observed that the surface emissivity varies spatially, especially for 8.3 μm channels, which affects the satellite-observed BTs and make it difficult to deduce the presence of dust using thermal infrared satellite observations. The DRBTD, BTD, and BTDanom methods were applied to the thermal infrared bands in the 8.6 and 12 μm channels to the 11 μm channel from the Terra/MODIS observations. [29] The RGB (red, green, and blue) color composite image from the Terra/MODIS TOA reflectance and column water vapor amount are shown in Figures 6a and 6b, respectively. Figures 7a 7f show the maps of BTD8.6-11, BTD12-11, BTDanom8.6,11, BTDanom12,11, DRBTDI8.6, and DRBTDI12, and their corresponding distribution frequencies are shown in Figure 8. Table 2 presents the mean and SD of these indices. Generally, the DRBTDI values show much stronger homogeneity than those of the BTD and they are also slightly more homogenous than those of BTDanom. This comparison suggests that the DRBTD algorithm is more effective in the removal of the underlying surface variations. The BTD values range from 11.7 K to 3.5 K with a mean of 5.71 K and a SD of 1.73 K. The BTDanom8.6,11 has a lower SD of 0.70 K with a mean value of 0.61 K. In contrast, using our DRBTD algorithm, the values of DRBTDI8.6 are more concentrated around zero, with a lower SD of 0.41 K and a mean value of 0.32 K. Similar results were observed for the 12 μm band. The SD of DRBTDI12 (0.16 K) is smaller than Figure 9. (a) Terra MODIS RGB image of the Taklamakan Desert on 28 September 2008 (DOY272) at daytime (similar channels are used as that of Figure 6). (b) Terra MODIS RGB image of the Taklamakan Desert on 4 January 2007 (DOY4) at daytime (similar channels are used as that of Figure 6). (c) BT11 image on 28 September 2008; (d) BT11 image on 4 January (e) Column water vapor amount (cm) on 28 September (f) Column water vapor amount (cm) on 4 January

11 Figure 10. (a) Frequency of BTD8.6-11, BTDanom8.6,11, and DRBTDI8.6 on 28 September 2008 (DOY272). (b) Frequency of BTD12-11, BTDanom12,11, and DRBTDI12 on 28 September 2008 (DOY272). (c) Frequency of BTD8.6-11, BTDanom8.6,11, and DRBTDI8.6 on 4 January 2007 (DOY4). (d) Frequency of BTD12-11, BTDanom12,11, and DRBTDI12 on 4 January 2007 (DOY4). those of BTD12-11 (0.32 K) and BTDanom12,11 (0.22 K). The mean of DRBTDI12 (0.28 K) is similar to that of BTDanom12,11 (0.24 K). The spatial variabilities of BTD12-11, BTDanom12,11, and DRBTDI12 are much smaller than those of BTD8.6-11, BTDanom8.6,11, and DRBTDI8.6, respectively, which may be due to the more spatially variable emissivities of the different surface materials around the 8.6 μm band compared to those around the 12 μm band (Figures 6c and 6e) Performance for Removal of the Temperature Effect on the BTD [30] The performances for the removal of the temperature effect on the BTD by the DRBTD, BTD, and BTDanom algorithms were evaluated with two clear-sky Terra/MODIS scenes over the Taklimakan Desert, a relatively homogeneous region with an area of 40,350 km 2 (represented by the pink rectangle in Figure 9), taken in autumn (28 September 2008, DOY272, Figure 9a) and winter (4 January 2007, DOY4, Figure 9b). The BT11 of this region is approximately 310 K for 28 September 2008 (Figure 9c) and 275 K for 4 January 2007 (Figure 9d), which represents the effects of variable surface temperatures on the algorithms. The clear-sky reference maps for the BTDanom algorithm were comprised of the observations around those 2 days. The column water vapor was shown with the MOD05_L2 of the same area on the two dates (Figures 9e and 9f). [31] Figures 10a 10d show the frequency of the six indices on these 2 days. Table 3 presents their mean and SD for the September images: DRBTDI8.6 has a smaller SD (0.18 K) when compared to BTD (0.32 K) and BTDanom8.6,11 (0.30 K); likewise, the SD of DRBTDI12 (0.07 K) is far smaller than those of BTD12-11 (0.18 K) and BTDanom12,11 (0.19 K). These results are similar to the results described in section The values of both Table 3. Statistic of the BTD, BTDanom, and DRBTDI in Taklamakan Desert on 28 September 2008 (DOY272) and 4 January 2007 (DOY4) a Mean SD Mean SD BTD BTDanom8.6,11_Ref BTDanom8.6,11_Ref DRBTDI BTD BTDanom12,11_Ref BTDanom12,11_Ref DRBTDI a BTDanom8.6,11_Ref and BTDanom8.6,11_Ref refer to the BTDanom8.6,11 with reference BTD of DOY4 in 2007 and DOY272 in 2008, respectively. Similarly, BTDanom12,11_Ref and BTDanom12,11_Ref are the BTDanom12,11 with reference BTD of DOY4 in 2007 and DOY272 in

12 Figure 11. Airborne dust over Taklamakan desert on 22 April 2007 at daytime: (a) Aqua MODIS RGB color image; (b) DRBTDI image; (c) OMI AI image; and (d) MODIS Deep Blue AOD image. DRBTDI8.6 and DRBTDI12 are concentrated approximately 0, with means of 0.10 and 0.02, respectively. Although the mean value of BTDanom8.6,11 (0.05 K) is also close to 0, the BTDanom12,11 values diverge more from 0, with a mean value of 0.20 K. The BTD and BTD12-11 values show significant differences in January compared to September. With the decrease in surface temperature, the mean value of BTD increases by approximately 3.10 K (from 7.83 K to 4.73 K), while BTD12-11 increases by 0.64 K (from 0.57 K to 1.21 K). In the evaluation of the application of the BTDanom method to the image from 4 January 2007, the BTDanom8.6,11 and the BTDanom12,11 were calculated from the clear-sky reference maps on 28 September 2008 and 4 January 2007, respectively. When the clear-sky reference map from September is employed, the mean value of BTDanom8.6,11 shifts by 3.1 K from January (3.15 K) to September (0.05 K), while for BTDanom12,11, the value shifts 0.64 K from the mean value of 0.20 K in January to 0.84 K in September. When the clear-sky reference map on 4 January 2007 is used, the BTDanom8.6,11 and BTD12,11 show better consistency with the results in September, with differences of a mean value of 0.67 K and 0.33 K, respectively. This result suggests that the clear-sky reference BTD map produced by the BTDanom method in one period should not be applied to another period due to the change in the observed temperature. However, it may be difficult to generate the clear-sky reference Figure 12. Aqua MODIS BTD, BTDanom, and DRBTDI daytime images from 22 April 2007: (a) BTD8.6-11; (b) BTD12-11; (c) BTDanom8.6,11; (d) BTDanom12,11; (e) DRBTDI8.6; and (f) DRBTDI

13 Figure 13. Scatterplots with OMI AI over the Taklamakan desert on 22 April 2007: (a) BTD versus AI; (b) BTDanom8.6,11 versus AI; (c) DRBTDI8.6 versus AI; (d) BTD12-11 versus AI; (e) BTDanom12,11 versus AI; (f) DRBTDI12 versus AI; and (g) DRBTDI versus AI. BTD for some regions or seasons with abundant presentation of clouds in a short period, which will introduce uncertainties in dust detection for this method. For the DRBTD algorithm, the results in January are consistent with those in September, with differences in mean values of 0.25 K for DRBTDI8.6 and 0.03 K for DRBTDI12. Likewise, the SD values are also the smallest (0.17 K and 0.06 K) when compared to those of the BTD and BTDanom. Additionally, the atmospheric water vapor in the study area on 28 September 2008 (approximately 1.2 cm) is higher than that on 4 January 2007 (approximately 0.3 cm). With this differences in atmospheric water vapor amount and surface temperature, the results of the DRBTD algorithm show less variation on the two dates, indicating that the DRBTD algorithm is more effective in eliminating the effects of temperature and low atmospheric water vapor on the BTD Dust Detection and Comparison With OMI/ MODIS/CALIPSO Observations [32] The DRBTD algorithm was applied to two dust events that occurred during the daytime (case 1, 22 April 2007) and the nighttime (case 2, 12 April 2007) over the Taklimakan Desert, respectively. The nighttime results were compared with the CALIOP AOD products, and the daytime results were compared with the OMI AI and MODIS Deep Blue AOD products Daytime Case: 22 April 2007 [33] A heavy dust storm swept over the Taklimakan Desert on 22 April The MODIS aboard NASA s Aqua satellite captured an image of the thick dust plume between 37 N and 42 N on that day (Figure 11a). Dust was being generated around the periphery of the Taklimakan Desert basin. The dust plume was also observed in central Gansu Province (37 N 40 N, 100 E 105 E). The DRBTD method was applied to the Aqua/MODIS data and those pixels with DRBTDI8.6 or DRBTDI12 less than 0.5 K were labeled as clouds. The DRBTDI, OMI AI, and MODIS AOD images are shown in Figures 11b 11d. All three of the images showed the two thick dust plumes, with values larger than 3 for DRBTDI, 2.5 for OMI AI, and 2 for MODIS AOD, while the DRBTDI image was able to detect the detailed spatial pattern due to its fine resolution of 1 km (OMI AI and MODIS AOD have resolutions of 1.0º and 10 km, respectively). Most cloud pixels could be identified by negative DRBTDI values, whereas DRBTDI values of slightly greater than zero denoted the clear-sky nondusty pixels. [34] Figure 12 shows the corresponding BTD8.6-11, BTD12-11, BTDanom8.6,11, BTDanom12,11, DRBTDI8.6, and DRBTDI12 images. In heavy airborne dust conditions with AOD greater than 2 according to the MODIS deep blue AOD map (Figure 11d), the values of BTD12-11, BTDanom12,11, and DRBTDI12 are larger than 1.5 K. For those clear-sky pixels with AOD less than 0.5 and cloud conditions, their values are approximately equal to or less than zero. The BTD8.6-11, BTDanom8.6,11, and DRBTDI8.6 showed ambiguous relationships for dust and clouds, with positive values for most cloud and dust pixels; however, the desert surfaces in eastern Taklimakan Desert are found to have smaller values than those of dust. Therefore, the combination of observations of the 8.6, 11, and 12 μm bands is helpful in 8578

14 Figure 14. Scatterplots with MODIS Deep Blue AOD over the Taklamakan desert on 22 April 2007: (a) BTD versus AOD; (b) BTDanom8.6,11 versus AOD; (c) DRBTDI8.6 versus AOD; (d) BTD12-11 versus AOD; (e) BTDanom12,11 versus AOD; (f) DRBTDI12 versus AI; and (g) DRBTDI versus AOD. the separation of dust signals from cloud and clear-sky pixels over desert surfaces. [35] The images produced by the BTD, BTDanom, and DRBTD methods were resampled to 1.0º and 10 km by averaging the valid values in each grid and then comparing them with OMI AI and MODIS AOD, respectively. The pixels with AI less than 2 or DRBTDI8.6 or DRBTDI12 less than 0.5 K were excluded to eliminate the effects of clouds. Figure 13 presents the scatterplots of all seven indices compared to OMI AI, and Figure 14 presents the comparison to MODIS AOD. All seven indices show positive values of correlation coefficients r with OMI AI and MODIS AOD, indicating that these indices could capture the signal of airborne dust with various magnitudes of correlation. For the 8.6 μm band, the plots are dispersed for BTD and OMI AI with an r of 0.52, while it shows better correlation with MODIS AOD with an r of For BTDanom8.6,11, the values of the index increase with OMI AI and MODIS AOD, with the r approximately The DRBTDI8.6 shows good correlation with OMI AI (MODIS AOD) with an r of 0.70 (0.75). For the 12 μm band, the plots are dispersed and no significant relationships are observed for BTD12-11, BTDanom12,11, and DRBTDI12, with an r of approximately 0.65 (0.55) for OMI AI (MODIS AOD). Generally, the indices based on the 8.6 μm band show better correlations than that of the 12 μm band. It may be because the 8.6 μm channel helps to discriminate airborne dust over such a bright surface. Once DRBTDI8.6 and DRBTDI12 are combined, the plots of DRBTDI show good correlations with OMI AI and MODIS AOD, with the values of r being 0.79 and 0.77, respectively Nighttime Case: 12 April 2007 [36] Another heavy dust storm swept over the Taklimakan Desert at night on 12 April The CALIOP observed the dust plume. The blue line in Figure 15 shows the CALIPSO track at UTC, approximately 45 s before Aqua/MODIS passed overhead. Figures 16a 16d present the CALIOP observations and the BTD, BTDanom, Figure 15. Airborne dust over the Taklamakan desert on 12 April 2007 at nighttime. The RGB image is the multiday composite from clear-sky MODIS observations; the blue line shows the CALIPSO track. 8579

15 Figure 16. CALIPSO, BTD, BTDanom, and DRBTDI along the CALIPSO track from 12 April 2007 at nighttime: (a) CALIOP Version 3.01 column optical depth in the 532 nm along the track: aerosols (brown dots) and clouds (purple dots); (b) BTD12-11 (blue dots) and BTD (green dots); (c) BTDanom12,11 (blue dots), BTDanom8.6,11 (green dots); and (d) DRBTDI12 (blue dots), DRBTDI8.6 (green dots), and DRBTDI (red dots). and DRBTDI results along the track. The column optical depth for aerosol and clouds at 532 nm shows the thick dust layer located between 36.2 N and 42 N with an AOD of over 0.5 (Figure. 16a). Several dust plumes were also present between 42 N and 50 N. Clouds were observed at approximately 36 N, 40.2 N, 45 N to 48 N, and 50 N to 52 N with a cloud column optical depth of over 0.5. Generally, the contrast between BT8.6 and BT11 shows the opposite response of that of BT12 and BT11. For the BTD method, BTD is positive for clouds and negative for dust, while BTD12-11 shows opposite signals (Figure 16b). BTDanom8.6,11 is positive and BTDanom12,11 is negative for clouds (Figure 16c). However, it appears difficult to use the BTDanom method to detect airborne dust against the desert background during night hours, as BTDanom8.6,11 and BTDanom12,11 are approximately zero; this result was Figure 17. Scatterplots with CALIOP AOD over the Taklamakan desert on 12 April 2007 at nighttime: (a) BTD versus AOD; (b) BTDanom8.6,11 versus AOD; (c) DRBTDI8.6 versus AOD; (d) BTD12-11 versus AOD; (e) BTDanom12,11 versus AOD; (f) DRBTDI12 versus AOD; and (g) DRBTDI versus AOD. 8580

16 Figure 18. Dust events over northeastern China on 6 7 April 2001: (a) Terra MODIS RGB image on 6 April 2001 (DOY096), (b) Terra MODIS RGB image on 7 April 2001 (DOY097), (c) DRBTDI image at daytime on 6 April 2001, (d) DRBTDI image at nighttime on 6 April 2001, (e) DRBTDI image at daytime on 7 April 2001, and (f) DRBTDI image at nighttime on 7 April also demonstrated by Ashpole and Washington [2012], who suggested that nighttime pristine sky spectral characteristics are similar to that of dust, and dust effects on brightness temperatures decrease at nighttime. The DRBTD method could track the dust plume along the CALIPSO track, especially over the Taklimakan Desert, with a DRBTDI of approximately 2 K (Figure 16d). The presence of clouds was inferred where DRBTDI12 is negative, at approximately 40.2 N, 45 N to 48 N and 50 N to 52 N. [37] The results from the BTD, BTDanom, and DRBTD algorithms were compared with the CALIOP AOD observations along the track in scatterplots in Figure 17. The pixels covering the center of each CALIOP measurement were selected for this comparison. Pixels with CALIOP column optical depth for clouds at 532 nm larger than 0.15 or AOD = 0 or DRBTDI8.6 < 0.5 K or DRBTDI12 < 0.5 K were excluded. For the 8.6 μm band, BTD and BTDanom8.6,11 exhibit worse correlation with negative relationships to CALIOP AOD, with the dots being greatly dispersive. The spectral characteristics of land surface and airborne dust at nighttime differ from those during daytime due to many factors, such as the different surface temperature and atmosphere temperature profiles. Thus, the relationships between the indices and AOD may differ from that in daytime. The DRBTDI8.6 shows better correlation with CALIOP AOD with an r of For the 12 μm band, none of the three indices are well correlated to CALIOP AOD. No significant correlation is found for BTD12-11, which has an r of 0.60, as observed in Figure 17d. The absolute values of r for BTDanom12,11 and DRBTDI12 are less than 0.5. This poor correlation may be because that the 12 μm band is not very efficient in the discrimination of airborne dust over such a bright surface. With the combination of DRBTDI8.6 and DRBTDI12, the DRBTDI results are better correlated with CALIOP dust AOD observations than the BTD and BTDanom results are, with an r of Overall, although the absolute values of r are above 0.5 for most indices, the dots for BTD, BTDanom, and DRBTD are greatly dispersive in comparison with daytime results, indicating a worse correlation with CALIOP AOD at nighttime, especially for the 12 μm band. This result may be attributed to the reduced thermal contrast of the dust with the surface during nighttime due to such factors as the subsidence of the dust layer induced by surface cooling and a lower lapse rate [e.g., King et al., 1999; Kluser and Schepanski, 2009]. Additionally, the provisional quality stage of Version 3.01 CALIOP AOD products may also introduce uncertainties. It is still challenging to discriminate airborne dust during nighttime Tracking Airborne Dust Movement [38] One example was presented here to show the ability of the DRBTD algorithm to track airborne dust movement. The Taklimakan Desert and the Gobi Desert are the main source regions for Asian dust [Shao and Dong, 2006]. Dust aerosols that originate near these two regions are generally transported by westerly jets over eastern Asia and the Pacific Ocean and may even reach North America [Huang et al., 2008]. The transportation of dust from the Gobi and Taklimakan Deserts across Northeastern Asia on 6 7 April 2001 was tracked using our algorithm and presented in this section. [39] Two large dust events occurred in northern China and southern Mongolia on 6 April 2001 (DOY096), including the dust storms generated in the Gobi Desert and the Taklimakan Desert. Figure 18a depicts these dust events using a color composite image from the Terra/MODIS on that day. In this 8581