Dust Storm Detection Research Articles

Evaluating Data Product Exposure Metrics for Use in Epidemiologic Studies of Dust Storms.

Dust storms are increasing in frequency and correlate with adverse health outcomes but remain understudied in the United States (U.S.), partially due to the limited spatio-temporal coverage, resolution, and accuracy of current data sets. In this work, dust-related metrics from four public areal data products were compared to a monitor-based "gold standard" dust data set. The data products included the National Weather Service (NWS) storm event database, the Modern-Era Retrospective analysis for Research and Applications-Version 2, the EPA's Air QUAlity TimE Series (EQUATES) Project using the Community Multiscale Air Quality Modeling System (CMAQ), and the Copernicus Atmosphere Monitoring Service global reanalysis product. California, Nevada, Utah, and Arizona, which account for most dust storms reported in the U.S., were examined. Dichotomous and continuous metrics based on reported dust storms, particulate matter concentrations (PM10 and PM2.5), and aerosol-type variables were extracted or derived from the data products. Associations between these metrics and a validated dust storm detection method utilizing Interagency Monitoring of Protected Visual Environments monitors were estimated via quasi-binomial regression. In general, metrics from CAMS yielded the strongest associations with the "gold standard," followed by the NWS storm database metric. Dust aerosol (0.9-20μm) mixing ratio, vertically integrated mass of dust aerosol (9-20μm), and dust aerosol optical depth at 550nm from CAMS generated the highest standardized odds ratios among all metrics. Future work will apply machine-learning methods to the best-performing metrics to create a public dust storm database suitable for long-term epidemiologic studies.

Dynamic evolution of spring sand and dust storms and cross-border response in Mongolian plateau from 2000 to 2021

ABSTRACT According to the United Nations Sustainable Development Goals (SDG 15.3), frequent sand and dust storms (SDSs) in the spring are a long-term challenge to the prevention and control of land degradation on the Mongolian plateau. In this study, MODIS remote sensing data are used to monitor and analyse SDS events on the Mongolian Plateau. The annual distribution of spring SDSs (March to May) from 2000 to 2021 are obtained based on the dust storm detection index. The overall classification accuracy is 85.24% and the kappa coefficient is 0.7636. Results show a decrease in the overall frequency of SDS events, where storm events in 2000–2010 are significantly higher than those in the second decade. The cross-border regions between China and Mongolia appear to be SDS intensity centers, particularly those in southern Mongolia. Precipitation exhibits a strong negative correlation with the area affected by SDS, and the correlation coefficient is – 0.72. The increase in barren and sand contributes primarily to the increase in SDS, whereas wind prevention and sand control projects undertaken by the Mongolian and Chinese governments promote regional restoration. Policies pertaining to cross-board sandstorm responses on the Mongolian Plateau are recommended.

Direct Detection of Severe Biomass Burning Aerosols from Satellite Data

The boundary between high-concentration aerosols (haze) and clouds is ambiguous and the mixing of aerosols and clouds is complex in terms of composition and structure. In particular, the contribution of biomass burning aerosols (BBAs) to global warming is a source of uncertainty in the global radiation budget. In a previous study, we proposed a method to detect absorption aerosols such as BBAs and dust using a simple indicator based on the ratio of violet to near-ultraviolet wavelengths from the Global Change Observation Mission-Climate/Second-Generation Global Imager (GCOM-C/SGLI) satellite data. This study adds newly obtained SGLI data and proposes a method for the direct detection of severe biomass burning aerosols (SBBAs). Moreover, polarization data derived from polarization remote sensing was incorporated to improve the detection accuracy. This is possible because the SGLI is a multi-wavelength sensor consisting of 19 channels from 380 nm in the near-ultraviolet to thermal infrared, including red (674 nm) and near-infrared (869 nm) polarization channels. This method demonstrated fast SBBA detection directly from satellite data by using two types of wavelength ratio indices that take advantage of the characteristics of the SGLI data. The SBBA detection algorithm derived from the SGLI observation data was validated by using the polarized reflectance calculated by radiative transfer simulations and a regional numerical model—scalable computing for advanced library and environment (SCALE). Our algorithm can be applied to the detection of dust storms and high-concentration air pollution particles, and identifying the type of high-concentration aerosol facilitates the subsequent detailed characterization of the aerosol. This work demonstrates the usefulness of polarization remote sensing beyond the SGLI data.

Detecting dust loads in the atmosphere over Thar desert by using MODIS and INSAT-3D data

• Dust detection algorithm based on MODIS and INSAT-3D were developed for Thar desert. • Brightness temperature difference and dust indices were used to detect dust load. • Integrated dust detection approach using MODIS and INSAT-3D was complementary. • Dust load detection over Indian Thar desert was successfully validated and tested. Suspended dust particles in atmosphere have adverse impacts on environment, ecosystem as well as on human health. To avoid negative impacts of dust storm events, early warning system to predict it well in advance may be a suitable option. However, for this purpose, assessment on magnitude of dust load and its dynamics in atmosphere is a primary requirement. The present study aims to develop remote sensing based assessment of dust load in atmosphere specifically over the Indian Thar Desert region. The severe dust storm event occurred on 5 th June 2017 over the Indian Thar Desert has been used in this study to develop integrated dust detection algorithm using split window technique, mid-infrared technique and different dust indices derived from MODIS and INSAT-3D data. Evaluation of the developed algorithm revealed that the area classified under dust load depends on threshold value of dust indices used in the algorithm, type of dust detection techniques followed and the specifications of remote sensing sensors used to retrieve the dust image. The integrated dust detection algorithm developed in this study has the capability to eliminate the problem in variations of predicted dust loadings in atmosphere. Validation of the developed algorithm to detect dust pixels showed good agreement with independent observations on aerosol optical depth (AOD), wind speed profile data and ground visibility data. The method adopted can be helpful to implement an operational system for detection and monitoring of dust storms over the Thar Desert region.

A novel hybrid sand and dust storm detection method using MODIS data on GEE platform

ABSTRACT Accurate sand and dust storm (SDS) detection is important for assessing SDS disaster risk. Machine learning (ML) based SDS detection approaches have been widely used in recent years due to their higher accuracy and better detection results. However, this approach usually requires manual annotation of numerous training samples that are, in practice, laborious and time-consuming. To overcome this challenge, we propose a novel hybrid SDS detection method that combines the support vector machine (SVM) algorithm implemented on the Google Earth Engine (GEE) cloud computing platform with a spectral index to aid the automatic labelling of training samples. Based on 8 SDS events captured by MODIS over Arid Central Asia (ACA), the effectiveness and accuracy of this method were assessed and compared to traditional approaches. The experimental results indicate that the proposed method can distinguish between mixed pixels (thin cloud and land surface) and SDS pixels and that it minimizes misdetection more effectively. This method achieved more than 98% training accuracy and validation accuracy in SDS detection.

Dust storm detection of a convolutional neural network and a physical algorithm based on FY-4A satellite data

Quantitative identification of a dust storm weather is key to forecasting and early warning of dust storm disasters. However, traditional visibility ground-based measurements cannot be extended to regional observations. Remote sensing of dust storms is associated with large uncertainties in dust thresholds. For accurate quantification of the dust storm region, this study proposes a dust storm mask algorithm to identify dust storm in the Tarim Basin. The dust storm mask includes two algorithms to identify the dust storm outbreak and the spatial extent by using the Advanced Geostationary Radiation Imager (AGRI) on board the FY-4A satellite. A deep learning convolutional neural network (CNN) is employed for the dust storm mask, and the AGRI bands 1–3, 5–6, and 11–13 are used as model parameters. A physical algorithm (PA) is adopted to construct a dust storm mask using three physical dust indices: the Normalized Difference Dust Index (NDDI2.25μm-0.47μm/2.25μm+0.47μm), the Dust Ratio Index (DRI7.10μm/3.75μm), and the Brightness Temperature Difference (BTD3.75μm-13.50μm). The results show that the CNN algorithm has a higher classification accuracy on dust storm detection compared to the PA. This advantage suggests that the CNN can effectively monitor large-scale dust storms. The dust storm identification results were compared and analyzed with the AGRI true color, Aerosol Optical Depth products, and Ultra Violet Aerosol Index products.

Detection of Martian dust storms using mask regional convolutional neural networks

Martian dust plays a crucial role in the meteorology and climate of the Martian atmosphere. It heats the atmosphere, enhances the atmospheric general circulation, and affects spacecraft instruments and operations. Compliant with that, studying dust is also essential for future human exploration. In this work, we present a method for the deep-learning-based detection of the areal extent of dust storms in Mars satellite imagery. We use a mask regional convolutional neural network, consisting of a regional-proposal network and a mask network. We apply the detection method to Mars daily global maps of the Mars global surveyor, Mars orbiter camera. We use center coordinates of dust storms from the eight-year Mars dust activity database as ground-truth to train and validate the method. The performance of the regional network is evaluated by the average precision score with 50% overlap (mAP_{50}), which is around 62.1%.

ESTIMATION OF NITROGEN DIOXIDE (NO2) CONCENTRATION USING FENGYUN-4A DUST STORM DETECTION (FY-4A DSD) PRODUCT DURING THE COVID-19 LOCKDOWN IN METRO MANILA, PHILIPPINES

Abstract. The coronavirus disease was discovered in 2019 (COVID-19) and was eventually declared by the World Health Organization (WHO) as a global pandemic on March 11, 2020. This study aims to determine the correlation of Dust Storm Detection (DSD) index from the Fengyun-4A (FY-4A) data, NO2 ground observations, and daily positivity rate of COVID-19 cases in Metro Manila and predict the confirmed daily cases of COVID-19 using the established correlations. FY-4A DSD products were used to obtain different Dust Storm Indices (DSI) and daily COVID-19 confirmed cases were tallied during the period November 01–30, 2020, and March 01–31, 2021. Ground-observed NO2 levels from Environmental Management Bureau (EMB) monitoring stations were gathered for validation and regression analysis. Results of linear regression analysis between the DSI and NO2 exhibited a weak correlation (0.24) with the available observations at the specified period. Ground-observed NO2 levels exhibit the same trend with the daily positivity rate of COVID-19 considering only a smaller area and short period of observations. Moreover, results showed a weak correlation (0.07) between the positivity rate of COVID-19 case as a function of the DSD Index and ground-observed NO2 levels. Uncertainty of results from this study may be attributed to the fact that it has focused on a relatively small area due to limited available ground observations. Therefore, it is recommended to apply the same analysis on different periods of observations using as many NO2 level ground observations as available and determine if variables follow the same trend and correlations as reported in this study.

A new threshold free dust storm detection index based on MODIS reflectance and thermal bands

ABSTRACT Wind and soil confrontation under specific atmospheric conditions leads to the horrendous phenomenon of dust storms, particularly in arid lands. The negative impact of storms on the health and life of living beings is evident, resulting in many losses in the economic, social, and environmental sectors. Dust storm detection is the most important topic in dust studies, where many algorithms and indices have already been proposed using satellite imageries and remote sensing techniques. Despite the advantage of dust detection, these algorithms present drawbacks. This includes the need to determine different thresholds for different regional events or simply better respond to oceanic dust storms. The objective of this study is to develop a new Dust Storm Detection Index, called DSDI, without the need to determine different thresholds for each event. To establish the new index, it was necessary to distinguish the properties of dust, clouds, and land surfaces in the images of the MODIS sensors. These properties have been characterized by analyzing their spectral and thermal profiles at different wavelengths during dust storms from 2004 to 2009 in Yazd province, Central Iran, which is constantly coping with severe storms. Spectral and thermal ranges of 0.46, 0.56, 3.9, 1.4, 11, 12, and 13.6 μm were the most suitable discriminating bands of dust from the cloud and land surfaces. The brightness temperature difference of the thermal bands and the spectral ratio of reflectance bands, i.e. DSDI, has developed an appropriate relationship for separating land surfaces and clouds of dust particles. There was a significant correlation between DSDI and horizontal visibility (P-value= 0.05 & 0.01). It confirmed the success of this algorithm to detect storms in the study area, as an arid land.

Review of dust storm detection algorithms for multispectral satellite sensors

Satellite remote sensing has been extensively utilized for monitoring dust storms in space and time. Dust storm detection using satellite observations is important to analyze the dust storm trajectories and sources. This paper reviews the algorithms for dust storm detection used in multispectral satellite sensors, spanning visible to thermal wavelengths. Four categories of dust detection algorithms are summarized, namely, dust spectral index algorithms, temporal anomalous detection algorithms, spatial coherence tested algorithms (physical-based algorithms) and machine learning-based algorithms. Following discussions of dust storm detection algorithms, the dust presence validation methods are also reviewed. Future developments for dust storm detection are focused upon three aspects: detection of dust storms at nighttime; development of more efficient machine learning methods for retrieval; and integrating physical and machine learning methods for satellite images.

A New Approach for Dust Storm Detection Using MODIS Data

Dust storms which are considered as a cause of climatic and public health concerns are induced by natural and anthropogenic activities. In this paper, based on Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images, a new algorithm is developed to automatically detect dust storms over water body and land surfaces. The proposed method applies two different strategies for dust storm detection over water and land surfaces through the combination of various dust indices extracted from reflectance and thermal MODIS channels. It overcomes the disadvantages of previously developed algorithms taking into account the MODIS spectral band 7 (2105–2155 nm) to discriminate between dark and bright land surfaces. Different newly defined dust indices using proper thresholds are then employed for dust storm detection over two land surface classes as well as the water body. The proposed algorithm is applied to six dust-contaminated MODIS images covering southwestern Iran, northeastern Saudi Arabia, and southeastern Iraq. The results show that the proposed dust storm detection method performs well over areas covering features such as bright land surfaces and clouds whose spectral behavior is similar to that of the dust plumes. The results are compared to those obtained from two widely used previously developed dust storm detection algorithms. The comparison of the results with those achieved from visual inspections demonstrates that the proposed method detects the dust storm with the accuracy of ~ 95%, whereas the other two methods result in the dust detection accuracies of ~ 69% and ~ 68%. Furthermore, the proposed approach is found to be promising for dust storm detection over the study area.

An improved dust identification index (IDII) based on MODIS observation

ABSTRACT Satellite remote sensing may serve as an ideal technique to detect dust storms for high temporal and spatial scales. In this paper, we propose an improved dust identification index (IDII) based on Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. The IDII algorithm was used to monitor 129 dust storm events over the West Asia region from 2016 to 2018. Ground-based observations of synoptic stations, RGB images, and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), and Ozone Monitoring Instrument (OMI) were implemented for validating IDII algorithm. In addition, the performance of the proposed algorithm was compared with the Global Dust Detection Index (GDDI). The results show that the accuracy (Ac), Probability Of Correct positive Detection (POCD), and Probability Of False-positive Detection (POFD) for the IDII and GDDI are 82%, 85%, 33%, and 71%, 74%, 27%, respectively. Also over the water as a challenging object, it is clearly seen that the IDII performs much better than GDDI method. Our results suggest that the proposed approach can deal with the common limitations of dust detection algorithms, i.e. dust detection over different surfaces (land and water), seasonal changes, and similarity between dust and other objects like clouds.

Developing a dust storm detection method combining Support Vector Machine and satellite data in typical dust regions of Asia

Enhancing the dust storm detection is a key part for the environmental protection, human healthy and economic development. The goal of this paper is to propose a new Support Vector Machine (SVM)-based method to automatically detect dust storms using remote sensing data. Existing methods dealing with this problem are usually threshold-based that are of great complexity and uncertainty. In this paper we propose a simple and reliable method combining SVM with MODIS L1 data and explore the optimal band combinations used as the feature vectors of SVM. The developed method was evaluated by MODIS and OMI data qualitatively and quantitatively on three study sites located in the Arabian Desert, Gobi Desert and Taklimakan Desert, and it was also compared to three other traditional methods based on their accuracy, complexity, reliability and sensitivity to thresholds. The detection results demonstrated that the combination of (Band7 − Band3)/(Band7 + Band3) ((B7 − B3)/(B7 + B3)), Band20 − Band31 (B20 − B31), and Band31/Band32 (B31/B32) can detect the dust storms more precisely than other individual bands or their combination. The comparison among those cases indicated that the proposed automatic method exhibited an advantage of minimizing the uncertainty and complexity, which were the limits of defining thresholds based on the threshold-based methods. The conclusions can provide references for studies that focus on statistical-based dust storm detection.

DUST STORM DETECTION USING MODIS DATA OVER THE MIDDLE EAST

Abstract. Dust storms are one of the common phenomena in the arid and semi-arid regions which cause many economic and environmental losses also affect human health. Therefore, it is necessary to be able to detect dust storms. Several methods exist for dust monitoring, such as Ground-based measurements, satellite remote sensing, video surveillance, wireless sensors. Remote sensing technology provides wide coverage, high spectral and temporal resolutions, even near real-time data, which can offer a valuable data source for dust storm monitoring. We used an algorithm based on Moderate Resolution Imaging Spectroradiometer (MODIS) images for detecting dust storm over the Middle East. The proposed algorithm uses the brightness temperature using multi-bands. The performance of the algorithm was evaluated using the ground-based observations of synoptic stations. The results showed that by applying the algorithm, the dust area can be clearly separated, especially in the regions that cloud is mixed with dust and achieved overall accuracy was ~78%.

Quantitative Detection of Dust Storms with the Millimeter Wave Radar in the Taklimakan Desert

In order to conduct real-time quantitative monitoring of dust storms, Ka-band millimeter wave radar (MMWR) was utilized for the consecutive detection of dust storms over the Taklimakan Desert from April to June 2018. The retrievals of the reflectivity factor, dust spectrum distribution and dust mass concentration were carried out with the power spectrum data detected by MMWR for three dust storm processes. The analysis shows that: The probability density distribution of dust conforms to the lognormal distribution. During the dust storm processes, the effective detection height of the reflectivity factor was within 2000 m and the range of the reflectivity factors was between-25 dBZ and 25 dBZ. During the floating dust period, the effective height of the dust spectrum distribution was lower than 300 m and the values of dust mass concentration were less than 31.62 μg·m-3,at a height of 200 m. Furthermore, during the blowing sand stage, the effective height of the dust spectrum distribution was normally lower than 600 m and the values of dust mass concentration were mainly less than 316.23 μg·m-3,at a height of 200 m. During the dust storm period, the effective height of the dust spectrum distributionexceeded1000 m; when the height was 100 m, the values of dust mass concentration were between 1220 μg∙m-3 and 42,146 μg∙m-3 and the average mass concentration was 9287 μg·m-3; whereas, the values of dust mass concentration were between 2 μg∙m-3 and 820 μg∙m-3 when the height was 1200m and the average mass concentration was 24 μg∙m-3. The relationship between the reflectivity factor Z and the dust mass concentration M isdefined as Z=651.6M0.796. Compared with the observational data from Grimm180 particle detector, the data of the retrieved dust mass concentration are basically accurate and this retrieved method proves to be feasible. Thus, the MMWR cans be used as a new device for quantitative monitoring of dust storms.

Spatial analysis of occurrence probability of dusty days in west and southwest of Iran

In Iran, one of the environmental issues is dust storms, especially in the west and the southwest. These storms are important factors in soil erosion, economic damage to industry, agriculture, transportation sectors, and human life. Therefore, the recognition of the frequency, occurrence probability, and the return period of these storms can be instrumental in reducing damages. The purpose of this study was the spatial analysis of the occurrence probability of dusty days in the west and southwest of Iran in April, May, June, and July, using the Markov chain. The daily dust data was used in 14 synoptic stations during 24 years (1990–2013). In this study, to detect dust storms, a horizontal visibility factor of ≤ 1000 m was used for all meteorological codes. At first, the days were divided into two groups of normal days and dusty days and then the frequency of matrices, probability of transmission, and the stable matrix were calculated. Finally, the spatial distribution of the occurrence probability and the return period of the dust within 2 to 5 days were depicted. The results showed that the average occurrence probability of 2-day dusts was 15% in April and May, 22% in June, and 24% in July. Also, the occurrence probability of 3-day dusts decreased to 4%, 8%, and 9%, respectively. The return period of 1-day dust in all stations of the area and all months was 1.25 day on average; however, due to the increase in the duration of the dust period, its return period increased exponentially. Spatial distribution of stable matrix also revealed that the occurrence probability of dust in the western and southeastern parts of the studied area was more than those of the others.

Theoretical analysis of potential applications of microwave radar for sandstorm detection

Based on the Rayleigh scattering theory and the meteorological radar equation, in this research, effective detection range has been proposed theoretically to evaluate the working of microwave radar during electrified dust storms. In this regard, the effects of particles concentration and charge quantity of the dust on the performance of microwave radar were analyzed. The results show that particle charges play an integral role in significantly enhancing the radar echoes which signal dust storms, thus making it possible to use microwave radar for dust storm detection. In addition, numerical simulation results have also illustrated that 35 GHz radar is more suitable for remote detection of the vertical structure of a dust layer. In the end, an inversion analysis method used for building the particle concentration profile through radar has been discussed. This study shall prove to be useful in the development of a new monitoring method in the research of sand storms.

Dust Detection and Aerosol Properties Over Arabian Sea Using MODIS Data

The present study deals with the use of Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared bands in the dust detection. Eight dust storm cases over the Arabian Sea have been selected (four TERRA and four AQUA) during the year 2002–2008. The brightness temperature (BT) difference method has been applied on MODIS thermal bands 29 (8 µm), 31 (11 µm) and 32 (12 µm) to detect dust storms over the Arabian Sea. The performance assessment of BT differences (BT29–BT31 and BT31–BT32) has shown that BT31–BT32 has performed better to BT29–BT31. We suggest that BT31–BT32 is an effective combination of MODIS bands for dust detection over oceans and sea. The maximum (Dmax) and minimum dust (Dmin) intensity locations have also been identified in all the eight dust storm cases. The aerosol properties (aerosol optical thickness, τ; asymmetry factor g and Angstrom exponent α) over Dmax and Dmin have been studied using MODIS Level 2 data. In AQUA dust storms cases τ values (Dmax) were higher than TERRA dust cases, whereas g values were nearly same. The α was always positive in case of TERRA dust cases; however in AQUA negative α was also reported. Afternoon dust storms are more intense compared to forenoon dust storms and dust particles are also coarser.

MODIS satellite data evaluation for sand and dust storm monitoring in Saudi Arabia

ABSTRACTThe impacts of wind-blown desert sand and dust are a major concern of environmental and climate study due to their global extent. This article investigates the sand and dust storms detection in Saudi Arabia using Moderate Resolution Imaging Spectroradiometer (MODIS) data, both from Terra and Aqua satellite systems for the years 2002–2011. Normalized Difference Dust Index (NDDI) is applied for the detection of sand and dust storms whilst MODIS band 31 is applied to discriminate atmospheric sand and dust from that present on the ground. In addition, the data from Meteosat satellite, AERONET station, and meteorological stations are used to validate NDDI-based sand and dust storm events. The results of the study show that NDDI can successfully identify and differentiate sand and dust storms from clouds whilst MODIS band 31 can discriminate aerial and surface sand and dust over Saudi Arabia. The results also show that the multi-source data, that is MODIS, Meteosat, AERONET, and meteorological stations, can be very valuable for tracking sand and dust storm events. As no such attempt in the past has been made in Saudi Arabia, it is envisaged that the results of this study will be helpful in planning remote-sensing data for the climate change study in the region.

A Dynamic Enhancement With Background Reduction Algorithm: Overview and Application to Satellite‐Based Dust Storm Detection

AbstractThis paper describes a Dynamic Enhancement Background Reduction Algorithm (DEBRA) applicable to multispectral satellite imaging radiometers. DEBRA uses ancillary information about the clear‐sky background to reduce false detections of atmospheric parameters in complex scenes. Applied here to the detection of lofted dust, DEBRA enlists a surface emissivity database coupled with a climatological database of surface temperature to approximate the clear‐sky equivalent signal for selected infrared‐based multispectral dust detection tests. This background allows for suppression of false alarms caused by land surface features while retaining some ability to detect dust above those problematic surfaces. The algorithm is applicable to both day and nighttime observations and enables weighted combinations of dust detection tests. The results are provided quantitatively, as a detection confidence factor [0, 1], but are also readily visualized as enhanced imagery. Utilizing the DEBRA confidence factor as a scaling factor in false color red/green/blue imagery enables depiction of the targeted parameter in the context of the local meteorology and topography. In this way, the method holds utility to both automated clients and human analysts alike. Examples of DEBRA performance from notable dust storms and comparisons against other detection methods and independent observations are presented.