Dust Detection Algorithm Research Articles

Comparison and evaluation of dust detection algorithms using MODIS Aqua/Terra Level 1B data and MODIS/OMI dust products in the Middle East

Dust storms have a major impact on air quality, economic loss, and human health over large regions of the Middle East. Because of the broad extent of dust storms and also political–security issues in this region, satellite data are an important source of dust detection and mapping. The aim of this study was to compare and evaluate the performance of five main dust detection algorithms, including Ackerman, Miller, normalized difference dust index (NDDI), Roskovensky and Liou, and thermal-infrared dust index (TDI), using MODIS Level 1B and also MODIS Deep Blue AOD and OMI AI products in two dust events originating from Iraq and Saudi Arabia. Overall, results showed that the performance of the algorithms varied from event to event and it was not possible to use the published dust/no-dust thresholds for the algorithms tested in the study area. The MODIS AOD and OMI AI products were very effective for initial dust detection and the AOD and AI images correlated highly with the dust images at provincial scale (p-value <0.001), but the application of these products was limited at local scale due to their poor spatial resolution. Results also indicated that algorithms based on MODIS thermal infrared (TIR) bands or a combination of TIR and reflectance bands were better indicators of dust than reflectance-based ones. Among the TIR- based algorithms, TDI performed the best over water surfaces and dust sources, and accounted for approximately 93% and 90% of variations in the AOD and OMI AI data.

New Asia Dust Storm Detection Method Based on the Thermal Infrared Spectral Signature

As hyperspectral instruments can provide the detailed spectral information, a new spectral similarity method for detecting and differentiating dust from non-dust scenes using the Atmospheric Infrared Sounder (AIRS) observations has been developed. The detection is based on a pre-defined Dust Spectral Similarity Index (DSSI), which was calculated from the accumulated brightness temperature differences between selected 16 AIRS observation channels, in the thermal infrared region of 800–1250 cm−1. It has been demonstrated that DSSI can effectively separate the dust from non-dust by elevating dust signals. For underlying surface covered with dust, the DSSI tends to show values close to 1.0. However, the values of DSSI for clear sky surfaces or clouds (ice and water) are basically lower than those of dust, as their spectrums have significant differences with dust. To evaluate this new simple DSSI dust detection algorithm, several Asia dust events observed in northern China were analyzed, and the results agree favorably with those from the Moderate resolution Imaging Spectro radiometer (MODIS) and Cloud Aerosol LiDAR with Orthogonal Polarization (CALIOP) observations.

Dust aerosol index (DAI) algorithm for MODIS

AbstractA dust aerosol index (DAI) algorithm based on measurements in deep blue (412 nm), blue (440 nm), and shortwave IR (2130 nm) wavelengths using Moderate Resolution Imaging Spectroradiometer (MODIS) observations has been developed. Contrary to some dust detection algorithms that use measurements at thermal IR bands, this algorithm takes advantage of the spectral dependence of Rayleigh scattering, surface reflectance, and dust absorption to detect airborne dust. The DAI images generated by this algorithm agree qualitatively with the location and extent of dust observed in MODIS true color images. Quantitatively, the dust index generated for hundreds of dust outbreaks observed between 2006 and 2013 were compared to Cloud Aerosol Lidar with Orthogonal Polarization (CALIOP) Vertical Feature Mask (VFM) product and the detections are found to be accurate at 70% over land and 82% over ocean. The Probability of Correct Detection (POCD) is 80% over land and 76% over ocean. The dust detections with DAI‐based dust identification algorithm were also compared to 5 years of Aerosol Robotic Network (AERONET) observations for 13 stations with a wide range of geographical coverage. The average detection accuracy is ~70%, whereas the POCD is ~67%. The performance of DAI‐based dust detection against AERONET is slightly weaker than that against CALIOP VFM because of the limited number of matchups for some stations. For stations close to source region or coastal and island stations, the accuracy and POCD can be as high as ~85% and ~89%, respectively.

Combined dust detection algorithm by using MODIS infrared channels over East Asia

A new dust detection algorithm is developed by combining the results of multiple dust detection methods using IR channels onboard the MODerate resolution Imaging Spectroradiometer (MODIS). Brightness Temperature Difference (BTD) between two wavelength channels has been used widely in previous dust detection methods. However, BTD methods have limitations in identifying the offset values of the BTD to discriminate clear-sky areas. The current algorithm overcomes the disadvantages of previous dust detection methods by considering the Brightness Temperature Ratio (BTR) values of the dual wavelength channels with 30-day composite, the optical properties of the dust particles, the variability of surface properties, and the cloud contamination. Therefore, the current algorithm shows improvements in detecting the dust loaded region over land during daytime. Finally, the confidence index of the current dust algorithm is shown in 10×10pixels of the MODIS observations. From January to June, 2006, the results of the current algorithm are within 64 to 81% of those found using the fine mode fraction (FMF) and aerosol index (AI) from the MODIS and Ozone Monitoring Instrument (OMI). The agreement between the results of the current algorithm and the OMI AI over the non-polluted land also ranges from 60 to 67% to avoid errors due to the anthropogenic aerosol. In addition, the developed algorithm shows statistically significant results at four AErosol RObotic NETwork (AERONET) sites in East Asia.

USING AEROSOL REFLECTANCE FOR DUST DETECTION

Abstract. In this study we propose an approach for dust detection by aerosol reflectance over arid and urban region in clear sky condition. In urban and arid areas surface reflectance in red and infrared spectral is bright and hence shorter wavelength is required for this detections. Main step of our approach can be mentioned as: cloud mask for excluding cloudy pixels from our calculation, calculate Rayleigh path radiance, construct a surface reflectance data base, estimate aerosol reflectance, detect dust aerosol, dust detection and evaluations of dust detection. Spectral with wavelength 0.66, 0.55, 0.47 μm has been used in our dust detection. Estimating surface reflectance is the most challenging step of obtaining aerosol reflectance from top of atmosphere (TOA) reflectance. Hence for surface estimation we had created a surface reflectance database of 0.05 degree latitude by 0.05 degree longitude resolution by using minimum reflectivity technique (MRT). In order to evaluate our dust detection algorithm MODIS aerosol product MOD04 and common dust detection method named Brightness Temperature Difference (BTD) had been used. We had implemented this method to Moderate Resolution Imaging Spectroradiometer (MODIS) image of part of Iran (7 degree latitude and 8 degree longitude) spring 2005 dust phenomenon from April to June. This study uses MODIS LIB calibrated reflectance high spatial resolution (500 m) MOD02Hkm on TERRA spacecraft. Hence our dust detection spatial resolution will be higher spatial resolution than MODIS aerosol product MOD04 which has 10 × 10 km2 and BTD resolution is 1 km due to the band 29 (8.7 μm), 31 (11 μm), and 32 (12 μm) spatial resolutions.

Dust source identification using MODIS: A comparison of techniques applied to the Lake Eyre Basin, Australia

The impact of mineral aerosol (dust) in the Earth's system depends on particle characteristics which are initially determined by the terrestrial sources from which the sediments are entrained. Remote sensing is an established method for the detection and mapping of dust events, and has recently been used to identify dust source locations with varying degrees of success. This paper compares and evaluates five principal methods, using MODIS Level 1B and MODIS Level 2 aerosol data, to: (a) differentiate dust (mineral aerosol) from non-dust, and (2) determine the extent to which they enable the source of the dust to be discerned. The five MODIS L1B methods used here are: (1) un-processed false colour composite (FCC), (2) brightness temperature difference, (3) Ackerman's (1997: J.Geophys. Res., 102, 17069–17080) procedure, (4) Miller's (2003:Geophys. Res. Lett. 30, 20, art.no.2071) dust enhancement algorithm and (5) Roskovensky and Liou's (2005: Geophys. Res. Lett. 32, L12809) dust differentiation algorithm; the aerosol product is MODIS Deep Blue (Hsu et al., 2004: IEEE Trans. Geosci. Rem. Sensing, 42, 557–569), which is optimised for use over bright surfaces (i.e. deserts). These are applied to four significant dust events from the Lake Eyre Basin, Australia. OMI AI was also examined for each event to provide an independent assessment of dust presence and plume location. All of the techniques were successful in detecting dust when compared to FCCs, but the most effective technique for source determination varied from event to event depending on factors such as cloud cover, dust plume mineralogy and surface reflectance. Significantly, to optimise dust detection using the MODIS L1B approaches, the recommended dust/non-dust thresholds had to be considerably adjusted on an event by event basis. MODIS L2 aerosol data retrievals were also found to vary in quality significantly between events; being affected in particular by cloud masking difficulties. In general, we find that OMI AI and MODIS AQUA L1B and L2 data are complementary; the former are ideal for initial dust detection, the latter can be used to both identify plumes and sources at high spatial resolution. Overall, approaches using brightness temperature difference (BT10–11) are the most consistently reliable technique for dust source identification in the Lake Eyre Basin. One reason for this is that this enclosed basin contains multiple dust sources with contrasting geochemical signatures. In this instance, BTD data are not affected significantly by perturbations in dust mineralogy. However, the other algorithms tested (including MODIS Deep Blue) were all influenced by ground surface reflectance or dust mineralogy; making it impossible to use one single MODIS L1B or L2 data type for all events (or even for a single multiple-plume event). There is, however, considerable potential to exploit this anomaly, and to use dust detection algorithms to obtain information about dust mineralogy.

Development of a new over‐water Advanced Very High Resolution Radiometer dust detection algorithm

A new over–water dust detection algorithm is developed and applied to the 5–channel Advanced Very High Resolution Radiometer (AVHRR) imager onboard the National Oceanic and Atmospheric Administration series of polar orbiting satellites. The algorithm has been developed to improve the distinction between dust and meteorological clouds for very optically thick dust storms that would have previously been flagged as cloud under the Clouds from AVHRR Extended cloud mask algorithm. The algorithm has been assessed by making daily comparisons with data from the Aerosol Robotic Network and by making a climatological comparison with METEOSAT and Total Ozone Mapping Spectrometer data over a portion of the North Atlantic. The new algorithm improves the separation of clouds and airborne dust. Application of the new product to the 5–channel AVHRR historic data sets can provide information on the global dust signal, especially on an interannual time scale.

Analysis of winter dust activity off the coast of West Africa using a new 24‐year over‐water advanced very high resolution radiometer satellite dust climatology

A 24‐year (1982–2005) winter daytime advanced very high resolution radiometer (AVHRR) data set has been processed utilizing a new over‐water dust detection algorithm. The dust data are for the oceanic regions surrounding West Africa and provide a long‐term remotely sensed continuous record of dustiness in the region. These AVHRR dust observations are comparable to dust records produced via the Total Ozone Mapping Spectrometer and Meteosat instruments. Strong positive correlations between the wintertime Jones North Atlantic Oscillation index and this dust record are observed across the entire oceanic region, corroborating earlier studies on the relationship between the two. Also consistent with previous investigations, we find more regional positive and negative correlations between dust and the wintertime Niño 3.4 index and summertime Sahelian precipitation, respectively. Also, unique to satellite studies of interannual dust variability over the North Atlantic, we develop a wintertime AVHRR normalized difference vegetation index time series for the Sahel region. A strong relationship is seen between tropical North Atlantic dustiness and this vegetation index, suggesting the possibility that vegetation changes in the Sahel play an important role in variability of downwind dustiness.

GOES 8 retrieval of dust aerosol optical thickness over the Atlantic Ocean during PRIDE

Using 30 days of half‐hourly, high temporal resolution GOES 8 imager data and radiative transfer calculations, dust aerosol optical thickness (AOT) was retrieved over the Atlantic Ocean (14°N ∼ 26°N, 73°W–63°W) during the Puerto Rico Dust Experiment (PRIDE). Dust aerosol size distributions and complex index of refraction inferred from ground‐based measurements (1.53–0.0015i at 0.55 μm), which were used in Mie calculations and a plane‐parallel discrete ordinate radiative transfer model (DISORT) to compute look up tables for AOT retrievals. Using a combination of spectral, spatial, and temporal tests, a dust detection algorithm was developed from the GOES 8 imager data. The degradation of the signal response relative to the prelaunched calibration of the GOES 8 visible channel was 39% in July 2000 and the GOES 8 AOT detection limit was estimated to be 0.04 in AOT (0.67 μm). The satellite‐retrieved AOT were then compared with AOT values derived from ground‐based Sun photometer (SP) sites. The comparison showed that GOES 8 retrieved AOT are in good agreement with the SP derived values, with linear correlation coefficient of 0.91 and 0.80 for the two sites. The GOES 8 monthly mean 0.67 μm AOT (0.19 ± 0.13, 0.22 ± 0.12) over the two SP sites matched the monthly mean SP AOT values (0.23 ± 0.13, 0.22 ± 0.10). The linear correlation between the GOES 8 retrieved AOT and the aircraft derived values from particle probe data and airborne Sun photometer AATS‐6 measurements were 0.88 and 0.83, respectively. Besides the uncertainties from the nonspherical effect of dust aerosols, sensitivity studies showed that the uncertainties (Δτ) of the GOES 8 retrieved AOT values were mainly from the uncertainties due to the imaginary part of refractive index (Δτ = ±0.05) and surface reflectance [Δτ = ±(0.02 ∼ 0.04)]. This paper demonstrates the application of geostationary satellites to detect and retrieve dust AOT even at low to moderate AOTs. The GOES 8 imager with high temporal resolutions also captures aerosol diurnal variation in this study that can further reduce the uncertainties in the current aerosol forcing estimations caused by the high temporal variations of AOT, thereby playing a complementary role with global AOT retrievals from polar orbiting satellites.