Multi-angle Imaging SpectroRadiometer Research Articles

Regional Biases in MODIS Marine Liquid Water Cloud Drop Effective Radius Deduced Through Fusion With MISR.

Satellite measurements from Terra's Moderate Resolution Imaging Spectroradiometer (MODIS) represent our longest, single‐platform, global record of the effective radius (Re) of the cloud drop size distribution. Quantifying its error characteristics has been challenging because systematic errors in retrieved Re covary with the structural characteristics of the cloud and the Sun‐view geometry. Recently, it has been shown that the bias in MODIS Re can be estimated by fusing MODIS data with data from Terra's Multi‐angle Imaging SpectroRadiometer (MISR). Here, we relate the bias to the observed underlying conditions to derive regional‐scale, bias‐corrected, monthly‐mean Re 1.6, Re 2.1, and Re 3.7 values retrieved from the 1.6, 2.1, and 3.7 μm MODIS spectral channels. Our results reveal that monthly‐mean bias in Re 2.1 exhibits large regional dependency, ranging from at least ~1 to 10 m (15 to 60%) varying with scene heterogeneity, optical depth, and solar zenith angle. Regional bias‐corrected monthly‐mean Re 2.1 ranges from 4 to 17 m, compared to 10 to 25 μm for uncorrected Re 2.1, with estimated uncertainties of 0.1 to 1.8 μm. The bias‐corrected monthly‐mean Re 3.7 and Re 2.1 show difference of approximately +0.6 μm in the coastal marine stratocumulus regions and down to approximately −2 m in the cumuliform cloud regions, compared to uncorrected values of about −1 to −6 μm, respectively. Bias‐corrected Re values compare favorably to other independent data sources, including field observations, global model simulations, and satellite retrievals that do not use retrieval techniques similar to MODIS. This work changes the interpretation of global Re distributions from MODIS Re products and may further impact studies, which use the original MODIS Re products to study, for example, aerosol‐cloud interactions and cloud microphysical parameterization.

Atmospheric corrosion from aerosol loading Over Yekepa

In this research, the impact of aerosol loading on the atmospheric corrosion over Yekepa is presented. Fifteen years primary (aerosol optical depth) dataset was obtained from the Multi-angle Imaging Spectro-Radiometer (MISR). Aerosol loading were generated fom the primary dataset. The univariate statistic over Yekepa shows the mean value of 0.52038392857143 0.44143303571429 0.39041071428571 in X, Y and Z directions prespectively. It was confirmed that the current trends of environmental events over Yekepa-Liberia could increase atmospheric corrosion.

Nonlinear Distribution Regression for Remote Sensing Applications

In many remote sensing applications, one wants to estimate variables or parameters of interest from observations. When the target variable is available at a resolution that matches the remote sensing observations, standard algorithms, such as neural networks, random forests, or the Gaussian processes, are readily available to relate the two. However, we often encounter situations where the target variable is only available at the group level, i.e., collectively associated with a number of remotely sensed observations. This problem setting is known in statistics and machine learning as multiple instance learning (MIL) or distribution regression (DR). This article introduces a nonlinear (kernel-based) method for DR that solves the previous problems without making any assumption on the statistics of the grouped data. The presented formulation considers distribution embeddings in reproducing kernel Hilbert spaces and performs standard least squares regression with the empirical means therein. A flexible version to deal with multisource data of different dimensionality and sample sizes is also presented and evaluated. It allows working with the native spatial resolution of each sensor, avoiding the need for matchup procedures. Noting the large computational cost of the approach, we introduce an efficient version via random Fourier features to cope with millions of points and groups. Real experiments involve the Soil Moisture Active Passive (SMAP) vegetation optical depth (VOD) data for the estimation of crop production in the U.S. Corn Belt and the Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi-angle Imaging SpectroRadiometer (MISR) reflectances for the estimation of aerosol optical depth (AOD). An exhaustive empirical evaluation of the method is done against naive (linear and nonlinear) approaches based on input-space means as well as previously presented methods for MIL. We provide source code of our methods in http://isp.uv.es/code/dr.html .

Ice Cloud Optical Thickness, Effective Radius, And Ice Water Path Inferred From Fused MISR and MODIS Measurements Based on a Pixel‐Level Optimal Ice Particle Roughness Model

AbstractThe Multi‐angle Imaging SpectroRadiometer (MISR) provides measurements over a wider scattering‐angle range for a given location than a cross‐track scanning sensor such as the MODerate resolution Imaging Spectroradiometer (MODIS). Based on a full year (2013) of fused MISR‐MODIS datasets, we develop a variable surface roughness model for ice particles with the goal of identifying the optimal degree of roughness in the ice model for a given pixel containing single‐layer ice clouds. For the MISR‐MODIS observations over oceans, severe roughness values are often selected for a pixel when optical thickness (τ) and particle effective radius (Reff) are large in conjunction with larger cloud heterogeneity index (Hσ) or a warmer cloud top temperature. Furthermore, τ, Reff, and ice water path are retrieved with the optimal model and compared to operational MODIS Collection 6 (MC6) products that assume a constant roughness. In general, the retrievals based on the present optimal model lead to greater consistency with MISR measurements, and result in larger median τ by 10.1% and smaller median Reff by 6.5% but almost identical ice water path in comparison with the MC6 counterparts. The higher average τ value is caused by a slightly larger number of large τ cases, but the smaller average Reff value is due to the shifting of the retrieved Reff value toward smaller values by approximately 2–4 μm in comparison to the MC6 distribution over all seasons. Both τ retrievals have similar regional and monthly variations, but a larger annual cycle of Reff is associated with the optimal model.

Bi-Directional Reflectance Factor Determination of the Railroad Valley Playa

Vicarious calibration is the determination of an on-orbit sensor’s radiometric response using measurements over test sites such as Railroad Valley (RRV), Nevada. It has the highest accuracy when a remote sensor’s view angle is aligned with that of the surface measurements, namely at a nadir view. For view angles greater than 10°, the dominant error is the uncertainty in the off-nadir correction factor. The factor is largest in the back-scatter principal plane and can reach 20%. The Orbiting-Carbon Observatory has access to a number of datasets to determine this deviation. These include measurements from field instruments such as the Portable Apparatus for Rapid Acquisition of Bidirectional Observation of the Land and Atmosphere (PARABOLA), as well as satellite measurements from Multi-angle Imaging SpectroRadiometer (MISR) and MODerate resolution Imaging Spectroradiometer (MODIS). The correction factor derived from PARABOLA is consistent in time and space to within 2% for view angles as large as 30°. Field spectrometer data show that the correction term is spectrally invariant. For this reason, a time-invariant model of RRV surface reflectance, along with empirically derived coefficients, is sufficient to use in the calibration of off-nadir sensors, provided there has been no recent rainfall. With this off-nadir correction, calibrations can be expected to have uncertainties within 5%.

Multiangle Imaging Spectroradiometer (MISR) and Moderate Resolution Imaging Spectrometer (MODIS) Aerosol Optical Depth (AOD) spatial variations in Peninsular Malaysia

Aerosol Optical Depth of Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multiangle Imaging Spectroradiometer (MISR) over peninsular Malaysia for the year 2015 were analysed. The AODMODIS retrieved using a retrieval algorithm which utilizes robust cloud screening and on-board calibration techniques with 10 x 10 km spatial resolution. Meanwhile, the AODMISR from AOD daily of Level-3 at spectral bands (446, 558, 672, and 866 nm) with a cross-track ground spatial resolution of 0.5° x 0.5° was used. The preliminary results showed a trend of high reading from AODMODIS which concentrated at the Eastern region and Southern West of Peninsular Malaysia. The initial pattern of AOD distributions had shown the highly concentrated was located at rapid urbanized and industrial cities. Low reading from AODMISR has been retrieved over Peninsular Malaysia which further analyzed is needed, but the initial pattern of AOD shows the distribution was concentrated at urban areas.

Estimation of CO2 Emissions from Wildfires Using OCO-2 Data

The biomass burning model (BBM) has been the most widely used method for estimation of trace gas emissions. Due to the difficulty and variability in obtaining various necessary parameters of BBM, a new method is needed to quickly and accurately calculate the trace gas emissions from wildfires. Here, we used satellite data from the Orbiting Carbon Observatory-2 (OCO-2) to calculate CO2 emissions from wildfires (the OCO-2 model). Four active wildfires in Siberia were selected in which OCO-2 points intersecting with smoke plumes identified by Aqua MODIS (MODerate-resolution Imaging Spectroradiometer) images. MODIS band 8, band 21 and MISR (Multi-angle Imaging SpectroRadiometer) data were used to identify the smoke plume area, burned area and smoke plume height, respectively. By contrast with BBM, which calculates CO2 emissions based on the bottom–top mode, the OCO-2 model estimates CO2 emissions based on the top–bottom mode. We used a linear regression model to compute CO2 concentration (XCO2) for each smoke plume pixel and then calculated CO2 emissions for each wildfire point. The CO2 mass of each smoke plume pixel was added to obtain the CO2 emissions from wildfires. After verifying our results with the BBM, we found that the biases were between 25.76% and 157.11% for the four active fires. The OCO-2 model displays the advantages of remote-sensing technology and is a useful tool for fire-emission monitoring, although we note some of its disadvantages. This study proposed a new perspective to estimate CO2 emissions from wildfire and effectively expands the applied range of OCO-2 satellite data.

Assessment of regional aerosol radiative effects under the SWAAMI campaign – Part 1: Quality-enhanced estimation of columnar aerosol extinction and absorption over the Indian subcontinent

Abstract. Improving the accuracy of regional aerosol climate impact assessment calls for improvement in the accuracy of regional aerosol radiative effect (ARE) estimation. One of the most important means of achieving this is to use spatially homogeneous and temporally continuous datasets of critical aerosol properties, such as spectral aerosol optical depth (AOD) and single scattering albedo (SSA), which are the most important parameters for estimating aerosol radiative effects. However, observations do not provide the above; the space-borne observations though provide wide spatial coverage, are temporal snapshots and suffer from possible sensor degradation over extended periods. On the other hand, the ground-based measurements provide more accurate and temporally continuous data but are spatially near-point observations. Realizing the need for spatially homogeneous and temporally continuous datasets on one hand and the near non-existence of such data over the south Asian region (which is one of the regions where aerosols show large heterogeneity in most of their properties), construction of accurate gridded aerosol products by synthesizing the long-term space-borne and ground-based data has been taken up as an important objective of the South West Asian Aerosol Monsoon Interactions (SWAAMI), a joint Indo-UK field campaign, aiming at characterizing aerosol–monsoon links and their variabilities over the Indian region. In Part 1 of this two-part paper, we present spatially homogeneous gridded datasets of AOD and absorption aerosol optical depth (AAOD), generated for the first time over this region. These data products are developed by merging the highly accurate aerosol measurements from the dense networks of 44 (for AOD) and 34 (for AAOD) ground-based observatories of Aerosol Radiative Forcing over India NETwork (ARFINET) and AErosol RObotic NETwork (AERONET) spread across the Indian region, with satellite-retrieved AOD and AAOD, following statistical assimilation schemes. The satellite data used for AOD assimilation include AODs retrieved from MODerate Imaging Spectroradiometer (MODIS) and Multiangle Imaging SpectroRadiometer (MISR) over the same domain. For AAOD, the ground-based black carbon (BC) mass concentration measurements from the network of 34 ARFINET observatories and satellite-based (Kalpana-1, INSAT-3A) infrared (IR) radiance measurements are blended with gridded AAODs (500 nm, monthly mean) derived from Ozone Monitoring Instrument (OMI)-retrieved AAODs (at 354 and 388 nm). The details of the assimilation methods and the gridded datasets generated are presented in this paper. The merged gridded AOD and AAOD products thus generated are validated against the data from independent ground-based observatories, which were not used for the assimilation process but are representative of different subregions of the complex domain. This validation exercise revealed that the independent ground-based measurements are better confirmed by merged datasets than the respective satellite products. As ensured by assimilation techniques employed, the uncertainties in merged AODs and AAODs are significantly less than those in corresponding satellite products. These merged products also all exhibit important large-scale spatial and temporal features which are already reported for this region. Nonetheless, the merged AODs and AAODs are significantly different in magnitude from the respective satellite products. On the background of above-mentioned quality enhancements demonstrated by merged products, we have employed them for deriving the columnar SSA and analysed its spatiotemporal characteristics. The columnar SSA thus derived has demonstrated distinct seasonal variation over various representative subregions of the study domain. The uncertainties in the derived SSA are observed to be substantially less than those in OMI SSA. On the backdrop of these benefits, the merged datasets are employed for the estimation of regional aerosol radiative effects (direct), the results of which would be presented in a companion paper, Part 2 of this two-part paper.

MAIAC Thermal Technique for Smoke Injection Height From MODIS

We present a new algorithm to derive smoke plume height ( $\text{H}^{\mathrm {a}}$ ) using the thermal contrast from the rising mixture of aerosol and emitted gases in the Moderate Resolution Imaging Spectroradiometer (MODIS) 11- $\mu \text{m}$ channel. Validation shows good agreement with the wind-corrected Multi-angle Imaging SpectroRadiometer (MISR)-MISR Interactive Explorer (MINX) values, with about 60% of the MODIS Terra thermal retrievals within 500 m of MISR $\text{H}^{\mathrm {a}}$ and 450 m lower on average. The bias is expected because the thermal technique represents an effective rather than a top plume height from MISR MINX. The comparison of MODIS Aqua retrievals with Cloud-Aerosol LiDAR with Orthogonal Polarization (CALIOP) Cloud-Aerosol LiDAR and Infrared Pathfinder Satellite Observations (CALIPSO) shows similar statistics, with a standard deviation of 458 m for the mean plume height and 216 m lower on average. $\text{H}^{\mathrm {a}}$ is part of the Multi-Angle Implementation of Atmospheric Correction (MAIAC) MODIS Collection 6 suite of products (MCD19), accessible via the Land Product Distributed Active Archive Center (LP DAAC). Aerosol injection height is reported in the daily MAIAC atmospheric product MCD19A2 along with the cloud mask (CM), column water vapor, aerosol optical depth (AOD), AOD uncertainty, and aerosol type, at 1-km resolution on a global sinusoidal grid. Despite some limitations, the vastly increased coverage from MODIS observations makes it a valuable data set complementing the established MISR and CALIOP products.

Joint 3D-Wind Retrievals with Stereoscopic Views from MODIS and GOES

Atmospheric motion vectors (AMVs), derived by tracking patterns, represent the winds in a layer characteristic of the pattern. AMV height (or pressure), important for applications in atmospheric research and operational meteorology, is usually assigned using observed IR brightness temperatures with a modeled atmosphere and can be inaccurate. Stereoscopic tracking provides a direct geometric height measurement of the pattern that an AMV represents. We extend our previous work with multi-angle imaging spectro–radiometer (MISR) and GOES to moderate resolution imaging spectroradiometer (MODIS) and the GOES-R series advanced baseline imager (ABI). MISR is a unique satellite instrument for stereoscopy with nine angular views along track, but its images have a narrow (380 km) swath and no thermal IR channels. MODIS provides a much wider (2330 km) swath and eight thermal IR channels that pair well with all but two ABI channels, offering a rich set of potential applications. Given the similarities between MODIS and VIIRS, our methods should also yield similar performance with VIIRS. Our methods, as enabled by advanced sensors like MODIS and ABI, require high-accuracy geographic registration in both systems but no synchronization of observations. AMVs are retrieved jointly with their heights from the disparities between triplets of ABI scenes and the paired MODIS granule. We validate our retrievals against MISR-GOES retrievals, operational GOES wind products, and by tracking clear-sky terrain. We demonstrate that the 3D-wind algorithm can produce high-quality AMV and height measurements for applications from the planetary boundary layer (PBL) to the upper troposphere, including cold-air outbreaks, wildfire smoke plumes, and hurricanes.

Oceanic Aerosol Loading Derived From MISR's 4.4 km (V23) Aerosol Product

AbstractA new version (V23) of the Multi‐angle Imaging SpectroRadiometer (MISR) Level 2 (swath‐based) aerosol product was publicly released in mid‐2018. V23 has seen many updates to the retrieval process, most notably over dark water (DW), which applies over the global oceans and other deep bodies of water. Here the quality of the new MISR DW retrievals is assessed using surface‐based aerosol optical depth (AOD) observations from the Maritime Aerosol Network (MAN) and island/coastal sites within the AErosol RObotic NETwork (AERONET). About 406 MISR/MAN and 11,015 MISR/AERONET collocated observations were identified. Comparison against MAN reveals the unparalleled accuracy of the V23 AOD retrievals: the correlation coefficient is 0.98, the root‐mean‐square error is 0.041, and the mean bias is −0.002. Aerosol spatial heterogeneity negatively affects comparison statistics against AERONET observations—applying stricter collocation criteria drastically improves agreement between MISR and AERONET. The reported AOD uncertainties realistically represent retrieval errors as they exhibit behavior similar to that of the standard deviation of a Gaussian distribution. Over the 18 years of MISR data analyzed, the global area‐weighted average AOD over oceans is 0.103; it becomes 0.151 when the averaging is restricted to locations where MAN observations are available. The AOD uncertainties are generally higher than average in offshore areas dominated by anthropogenic and biomass burning aerosols and lower than average in regions with mineral dust outflows. These results suggest future directions for improving the predefined aerosol mixtures used in the MISR aerosol retrieval look‐up tables.

Air pollution Estimation Over Ponta

The principal challenge of air pollution reportage in most communities of West Africa is the unavailability of working data. In this paper, satellite measurements were used to derive salient parameter (aerosol loading) to describe air quality over Ponta-Cape Verde was considered. Fifteen years primary (aerosol optical depth) dataset was obtained from the Multi-angle Imaging Spectro-Radiometer (MISR). The dataset is important to quantify the extent of air pollution in the research area. The statistical analysis of the dataset was also presented. It was observed that air pollution over Ponta is seasonal and comes from same source. Hence the pollution over is sustained due to a same pollution routine over the years.

Analysis of aerosol optical depth and aerosol loading Over Voinjama: A projected estimation of atmospheric corrosion

The World Health Organization (WHO) warning on air pollution is a verified in this research by corroborating it with fifteen years primary (aerosol optical depth) dataset that was obtained from the Multi-angle Imaging Spectro-Radiometer (MISR) over Voinjama-Liberia. The detailed statistical and computational analysis was carried out using computational softwares. The spatial amd surface plot the pollution over the geographical location may constantly be dispersed from same sources of pollution. The implication of the aerosol optical depth (AOD) on the atmospheric corrosion was corroborated using mathematical estimations. The highest corrosion rate is given as 2.6 and the average corrosion rate over Voinjama is 0.5.

Air pollution Estimation Over Brikama

Recent reports on the air pollution over Brikama-Gambia by United Nations Environment Programme (UNEP) and the National Environment Agency (NEA) may be shocking. In this paper, fifteen years primary (aerosol optical depth) dataset was obtained from the Multi-angle Imaging Spectro-Radiometer (MISR). The secondary datasets were generated from the primary dataset to understand the short and long term effect of aerosol loading over Brikama. The dataset is important to help researcher understand the dynamics of the aerosol loadings in the area. It was discovered that the air pollution over Brikama is not seasonal and do not come from same source. It was observed that the pollution over Brikama is determined by the massisve influx and migration of human population.

Air quality investigation over Moundou Chad

The air quality over Moundou-Chad was examined using satellite dataset. Fifteen years aerosol optical depth (AOD) was obtained from the Multi-angle Imaging Spectro-Radiometer (MISR). The AOD dataset is statisticaly stable for further prediction. The highest AOD mean was observed in 2008. The standard deviation, average deviation and coefficient of variation all agrees that the AOD dataset is reliable. It is shown that the aerosol loading over Moundou is high and the size of the aerosol is very tiny to deposit in the human lungs.

Air pollution Estimation Over Bouake

Air pollution over Bouake-Cote d’Ivoire was considered. Fifteen years primary (aerosol optical depth) dataset was obtained from the Multi-angle Imaging Spectro-Radiometer (MISR). Its aerosol loading dataset were generated from the primary dataset. The dataset depicts the air quality over the area. The statistical analysis of the dataset was also presented. The close ranges of values of the variance, average deviation and standard deviation are close indication that the correlation of each year would be naturally be >0.75. The averages of the AOD over the years i.e. is within the range of 0.4±0.05. It was observed that air pollution over Ponta is seasonal and comes from same source. Hence the pollution over is sustained due to a same pollution routine over the years. If the sources of anthropogenic emission in the region were not curbed, there would be massive health discomforts in the nearest future.

Validation, comparison, and integration of GOCI, AHI, MODIS, MISR, and VIIRS aerosol optical depth over East Asia during the 2016 KORUS-AQ campaign

Abstract. Recently launched multichannel geostationary Earth orbit (GEO) satellite sensors, such as the Geostationary Ocean Color Imager (GOCI) and the Advanced Himawari Imager (AHI), provide aerosol products over East Asia with high accuracy, which enables the monitoring of rapid diurnal variations and the transboundary transport of aerosols. Most aerosol studies to date have used low Earth orbit (LEO) satellite sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multi-angle Imaging Spectroradiometer (MISR), with a maximum of one or two overpass daylight times per day from midlatitudes to low latitudes. Thus, the demand for new GEO observations with high temporal resolution and improved accuracy has been significant. In this study the latest versions of aerosol optical depth (AOD) products from three LEO sensors – MODIS (Dark Target, Deep Blue, and MAIAC), MISR, and the Visible/Infrared Imager Radiometer Suite (VIIRS), along with two GEO sensors (GOCI and AHI), are validated, compared, and integrated for a period during the Korea–United States Air Quality Study (KORUS-AQ) field campaign from 1 May to 12 June 2016 over East Asia. The AOD products analyzed here generally have high accuracy with high R (0.84–0.93) and low RMSE (0.12–0.17), but their error characteristics differ according to the use of several different surface-reflectance estimation methods. High-accuracy near-real-time GOCI and AHI measurements facilitate the detection of rapid AOD changes, such as smoke aerosol transport from Russia to Japan on 18–21 May 2016, heavy pollution transport from China to the Korean Peninsula on 25 May 2016, and local emission transport from the Seoul Metropolitan Area to the Yellow Sea in South Korea on 5 June 2016. These high-temporal-resolution GEO measurements result in more representative daily AOD values and make a greater contribution to a combined daily AOD product assembled by median value selection with a 0.5∘×0.5∘ grid resolution. The combined AOD is spatially continuous and has a greater number of pixels with high accuracy (fraction within expected error range of 0.61) than individual products. This study characterizes aerosol measurements from LEO and GEO satellites currently in operation over East Asia, and the results presented here can be used to evaluate satellite measurement bias and air quality models.

Studying the Impact on Urban Health over the Greater Delta Region in Egypt Due to Aerosol Variability Using Optical Characteristics from Satellite Observations and Ground-Based AERONET Measurements

This research addresses the aerosol characteristics and variability over Cairo and the Greater Delta region over the last 20 years using an integrative multi-sensor approach of remotely sensed and PM10 ground data. The accuracy of these satellite aerosol products is also evaluated and compared through cross-validation against ground observations from the AErosol RObotic NETwork (AERONET) project measured at local stations. The results show the validity of using Multi-angle Imaging Spectroradiometer (MISR) and Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on the Terra and Aqua platforms for quantitative aerosol optical depth (AOD) assessment as compared to Ozone Monitoring Instrument (OMI), Sea-viewing Wide Field-of-view Sensor (SeaWiFS), and POLarization and Directionality of the Earth’s Reflectances (POLDER). In addition, extracted MISR-based aerosol products have been proven to be quite effective in investigating the characteristics of mixed aerosols. Daily AERONET AOD observations were collected and classified using K-means unsupervised machine learning algorithms, showing five typical patterns of aerosols in the region under investigation. Four seasonal aerosol emerging episodes are identified and analyzed using multiple indicators, including aerosol optical depth (AOD), size distribution, single scattering albedo (SSA), and Ångström exponent (AE). The movements and detailed aerosol composition of the aforementioned episodes are demonstrated using NASA’s Goddard Space Flight Center (GSFC) back trajectories model in collaboration with aerosol subtype products from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission. These episodes indicate that during the spring, fall, and summer, most of the severe aerosol events are caused by dust or mixed related scenarios, whereas during winter, aerosols of finer size lead to severe heavy conditions. It also demonstrates the impacts of different aerosol sources on urban human health, which are presented by the variations of multiple parameters, including solar radiation, air temperature, humidity, and UV exposure. Scarce ground PM10 data were collected and compared against satellite products, yet owed to their discrete nature of availability, our approach made use of the Random Decision Forest (RDF) model to convert satellite-based AOD and other meteorological parameters to predict PM10. The RDF model with inputs from the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) and Global Land Data Assimilation System (GLDAS) datasets improves the performance of using AOD products to estimate PM10 values. The connection between climate variability and aerosol intensity, as well as their impact on health-related PM2.5 over Egypt is also demonstrated.

Intercomparison of Multiple Satellite Aerosol Products against AERONET over the North China Plain

In this study, using Aerosol Robotic Network aerosol optical depth (AOD) products at three stations in the North China Plain (NCP)—a heavily polluted region in China—the AOD products from six satellite-borne radiometers: the Moderate Resolution Imagining Spectroradiometer (MODIS), the Multiangle Imaging Spectroradiometer (MISR), Ozone Mapping Imaging (OMI), the Visible Infrared Imaging Radiometer (VIIRS), the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), and Polarization and Directionality of the Earth’s Reflectances (POLDER), were thoroughly validated, shedding new light on their advantages and disadvantages. The MODIS Deep Blue (DB) products provide more accurate retrievals than the MODIS Dark Target (DT) and other satellite products at the Beijing site (BJ,a megacity), with higher correlations with AERONET (R > 0.93), lower mean absolute bias (MB < 0.012), and higher percentages (>68%) falling within the expected error (EE). All MODIS DT and DB products perform better than the other satellite products at the Xianghe site (XH, a suburb). The MODIS/Aqua DT products at both 3-km and 10-km resolutions performed better than the other space-borne AOD products at the Xinglong site (XL, a rural area at the top of a mountain). MISR, VIIRS, and SeaWiFS tend to underestimate high AOD values and overestimate AOD values under very low AOD conditions in the NCP. Both OMI and POLDER significantly underestimate the AOD. In terms of data volume, MISR with the limited swath width of 380 km has less data volume than the other satellite sensors. MODIS products have the highest sampling rate, especially the MODIS DT and DB merged products, and can be used for various climate study and air-quality monitoring.

A simple technique to evaluate aerosols loading in the atmosphere of Lome-Togo

The reality of aerosol loading is synonymous to climate change. According to National Aeronautics and Space Administration (NASA) imagery, aerosol deposition over West is very high compared to other parts of the world. The danger of aerosol loading is grave when in excess and little effort is made by government in the West Africa region to embark on ground measurement of aerosol optical depth (AOD). In this paper, the satellite measurement (Multi-angle Imaging Spectro-Radiometer) was used to obtain the aerosol optical over the research area. The computational analysis was carried out using the MATLAB software. The percentage of increase of aerosols loading was high in 2001, 2005 and 2011 that was 5.7%, 24.5%, 8% and 11.1%. Cumulatively increase of aerosol loading within 15 years was 11.7%. It is therefore recommended that government in the region should be more committed to ground measurement of AOD to ascertain the state of air pollution in the lower troposphere.