scholarly journals High spatial resolution aerosol retrievals used for daily particulate matter monitoring over Po valley, northern Italy

2015 ◽  
Vol 15 (1) ◽  
pp. 123-155 ◽  
Author(s):  
B. Arvani ◽  
R. B. Pierce ◽  
A. I. Lyapustin ◽  
Y. Wang ◽  
G. Ghermandi ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Urban Areas ◽  
Atmospheric Correction ◽  
Urban Air Pollution ◽  
Northern Italy ◽  
Po Valley ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Statistical Sense ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. The Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 5.1 Aerosol Optical Depth (AOD) data retrieved at 0.55 μm with spatial resolution of 10 km (MYD04) and the new 1 km Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm from MODIS is investigated in this work. We focus on evaluating the ability of these two products to characterize the spatial distribution of aerosols within urban areas. This is done through the comparison with PM10 measurements from 126 of the Italian Regional Agency for Environmental Protection (ARPA) ground monitoring stations during 2012. The Po Valley area (northern Italy) was chosen as the study domain since urban air pollution is one of the most important concerns in this region. Population and industrial activities are located within a large number of urban areas within the valley. We find that the annual correlations between PM10 and AOD are R2 = 0.90 and R2 = 0.62 for MYD04 and for MAIAC respectively. When the depth of the planetary boundary layer (PBL) is used to normalize the AOD, we find a significant improvement in the PM–AOD correlation. The introduction of the PBL information is needed for AOD to capture the seasonal cycle of the observed PM10 over the Po valley and significantly improves the PM vs. AOD relationship, leading to a correlation of R2 = 0.98 for both retrievals when they are normalized by the PBL depth. The results show that the normalized MAIAC retrieval provides a higher resolution depiction of the AOD within the Po Valley and performs as well in a statistical sense as the normalized standard MODIS retrieval for the same days and locations.

scholarly journals MODIS 3 km aerosol product: applications over land in an urban/suburban region

2013 ◽  
Vol 6 (1) ◽  
pp. 1683-1716 ◽  
Author(s):  
L. A. Munchak ◽  
R. C. Levy ◽  
S. Mattoo ◽  
L. A. Remer ◽  
B. N. Holben ◽  
...  
Keyword(s):  
Urban Areas ◽  
High Spectral Resolution ◽  
New Information ◽  
Land Cover Data ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Langley Research Center ◽  
High Spectral Resolution Lidar ◽  
Error Envelope

Abstract. MODerate resolution Imaging Spectroradiometer (MODIS) instruments aboard the Terra and Aqua satellites have provided a rich dataset of aerosol information at a 10 km spatial scale. Although originally intended for climate applications, the air quality community quickly became interested in using the MODIS aerosol data. However, 10 km resolution is not sufficient to resolve local scale aerosol features. With this in mind, MODIS Collection 6 is including a global aerosol product with a 3 km resolution. Here, we evaluate the 3 km product over the Baltimore/Washington D.C., USA, corridor during the summer of 2011, by comparing with spatially dense data collected as part of the DISCOVER-AQ campaign; these data were measured by the NASA Langley Research Center airborne High Spectral Resolution Lidar (HSRL) and a network of 44 sun photometers (SP) spaced approximately 10 km apart. The HSRL instrument shows that AOD can vary by up to 0.2 within a single 10 km MODIS pixel, meaning that higher resolution satellite retrievals may help to characterize aerosol spatial distributions in this region. Different techniques for validating a high-resolution aerosol product against SP measurements are considered. Although the 10 km product is more statistically reliable than the 3 km product, the 3 km product still performs acceptably, with more than two-thirds of MODIS/SP collocations falling within the expected error envelope with high correlation (R > 0.90). The 3 km product can better resolve aerosol gradients and retrieve closer to clouds and shorelines than the 10 km product, but tends to show more significant noise especially in urban areas. This urban degradation is quantified using ancillary land cover data. Overall, we show that the MODIS 3 km product adds new information to the existing set of satellite derived aerosol products and validates well over the region, but due to noise and problems in urban areas, should be treated with some degree of caution.

scholarly journals Evaluation of the Multi-Angle Implementation of Atmospheric Correction (MAIAC) Aerosol Algorithm for Himawari-8 Data

2019 ◽  
Vol 11 (23) ◽  
pp. 2771 ◽  
Author(s):  
Lu She ◽  
Hankui Zhang ◽  
Weile Wang ◽  
Yujie Wang ◽  
Yun Shi
Keyword(s):  
Mean Squared Error ◽  
Atmospheric Correction ◽  
Remote Sensing Data ◽  
Japan Meteorological Agency ◽  
Absolute Bias ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Highly Correlated ◽  
New Generation

Himawari-8, operated by the Japan Meteorological Agency (JMA), is a new generation geostationary satellite that provides remote sensing data to retrieve atmospheric aerosol optical depth (AOD) at high spatial (1 km) and high temporal (10 min) resolutions. The Geostationary- National Aeronautics and Space Administration (NASA) Earth exchange (GeoNEX) project recently adapted the multiangle implementation of atmospheric correction (MAIAC) algorithm, originally developed for joint retrieval of AOD and surface anisotropic reflectance with the moderate resolution imaging spectroradiometer (MODIS) data, to generate Earth monitoring products from the latest geostationary satellites including Himawari-8. This study evaluated the GeoNEX Himawari-8 ~1 km MAIAC AOD retrieved over all the aerosol robotic network (AERONET) sites between 6°N–30°N and 91°E–127°E. The corresponding JMA Himawari-8 AOD products were also evaluated for comparison. We only used cloud-free and the best quality satellite AOD retrievals and compiled a total of 16,532 MAIAC-AERONET and 21,737 JMA-AERONET contemporaneous pairs of AOD values for 2017. Statistical analyses showed that both MAIAC and JMA data are highly correlated with AERONET AOD, with the correlation coefficient (R) of ~0.77, and the root mean squared error (RMSE) of ~0.16. The absolute bias of MAIAC AOD (0.02 overestimation) appears smaller than that of the JMA AOD (0.05 underestimation). In comparison with the JMA data, the time series of MAIAC AOD were more consistent with AERONET AOD values and better capture the diurnal variations of the latter. The dependence of MAIAC AOD bias on scattering angles is also discussed.

scholarly journals Aerosols and Clouds Interactions in an Urban Atmosphere

2020 ◽  
Vol 237 ◽  
pp. 02004
Author(s):  
Indira Gunaseelan ◽  
Vijay Bhaskar
Keyword(s):  
Urban Air Pollution ◽  
Urban Atmosphere ◽  
Annual Variations ◽  
Cloud Properties ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Extensive Collection ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Aerosol Emissions ◽  
Precipitation Reduction

Aerosols create great uncertainties in studying climate change under global warming and atmospheric dynamics. To understand the impacts of aerosols on cloud properties in Madurai, we have analyzed an extensive collection of aerosol and cloud properties, obtained from the Moderate resolution Imaging Spectroradiometer (MODIS) data, over the study site during 2012-2013. Monthly, seasonal and annual variations of aerosols and clouds studied along their interactions and impacts on climate. Considering annual averages for all these parameters, most often the year 2012 was dominated with a higher presence of AOD, COD, CER, CTT, CTP whereas rainfall and CF were found to be dominated in 2013. The presence of higher CF in 2013 may be a cause for the higher rainfall and the lower level of CF in 2012 may be a cause for less rainfall. High aerosol loading in this area is due to biomass burning and urban air pollution which may significantly suppress precipitation. Increased aerosols and the local aerosol emissions may reduce the precipitation efficiency, which is responsible for the precipitation reduction and vice-versa.

scholarly journals Adaption of the MODIS aerosol retrieval algorithm using airborne spectral surface reflectance measurements over urban areas: a case study

2015 ◽  
Vol 8 (12) ◽  
pp. 5237-5249 ◽  
Author(s):  
E. Jäkel ◽  
B. Mey ◽  
R. Levy ◽  
X. Gu ◽  
T. Yu ◽  
...  
Keyword(s):  
Urban Areas ◽  
Retrieval Algorithm ◽  
Surface Reflectance ◽  
Airborne Measurements ◽  
Modis Aod ◽  
Urban Sample ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. MODIS (MOderate-resolution Imaging Spectroradiometer) retrievals of aerosol optical depth (AOD) are biased over urban areas, primarily because the reflectance characteristics of urban surfaces are different than that assumed by the retrieval algorithm. Specifically, the operational "dark-target" retrieval is tuned towards vegetated (dark) surfaces and assumes a spectral relationship to estimate the surface reflectance in blue and red wavelengths. From airborne measurements of surface reflectance over the city of Zhongshan, China, were collected that could replace the assumptions within the MODIS retrieval algorithm. The subsequent impact was tested upon two versions of the operational algorithm, Collections 5 and 6 (C5 and C6). AOD retrieval results of the operational and modified algorithms were compared for a specific case study over Zhongshan to show minor differences between them all. However, the Zhongshan-based spectral surface relationship was applied to a much larger urban sample, specifically to the MODIS data taken over Beijing between 2010 and 2014. These results were compared directly to ground-based AERONET (AErosol RObotic NETwork) measurements of AOD. A significant reduction of the differences between the AOD retrieved by the modified algorithms and AERONET was found, whereby the mean difference decreased from 0.27±0.14 for the operational C5 and 0.19±0.12 for the operational C6 to 0.10±0.15 and -0.02±0.17 by using the modified C5 and C6 retrievals. Since the modified algorithms assume a higher contribution by the surface to the total measured reflectance from MODIS, consequently the overestimation of AOD by the operational methods is reduced. Furthermore, the sensitivity of the MODIS AOD retrieval with respect to different surface types was investigated. Radiative transfer simulations were performed to model reflectances at top of atmosphere for predefined aerosol properties. The reflectance data were used as input for the retrieval methods. It was shown that the operational MODIS AOD retrieval over land reproduces the AOD reference input of 0.85 for dark surface types (retrieved AOD = 0.87 (C5)). An overestimation of AOD = 0.99 is found for urban surfaces, whereas the modified C5 algorithm shows a good performance with a retrieved value of AOD = 0.86.

scholarly journals A Global MODIS Water Vapor Database for the Operational Atmospheric Correction of Historic and Recent Landsat Imagery

2019 ◽  
Vol 11 (3) ◽  
pp. 257 ◽  
Author(s):  
David Frantz ◽  
Marion Stellmes ◽  
Patrick Hostert
Keyword(s):  
Water Vapor ◽  
Landsat Imagery ◽  
Atmospheric Correction ◽  
Atmospheric Conditions ◽  
Wet Season ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Daily Water ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Impact

Analysis Ready Data (ARD) have undergone the most relevant pre-processing steps to satisfy most user demands. The freely available software FORCE (Framework for Operational Radiometric Correction for Environmental monitoring) is capable of generating Landsat ARD. An essential step of generating ARD is atmospheric correction, which requires water vapor data. FORCE relies on a water vapor database obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). However, two major drawbacks arise from this strategy: (1) The database has to be compiled for each study area prior to generating ARD; and (2) MODIS and Landsat commissioning dates are not well aligned. We have therefore compiled an application-ready global water vapor database to significantly increase the operational readiness of ARD production. The free dataset comprises daily water vapor data for February 2000 to July 2018 as well as a monthly climatology that is used if no daily value is available. We systematically assessed the impact of using this climatology on surface reflectance outputs. A global random sample of Landsat 5/7/8 imagery was processed twice (i) using daily water vapor (reference) and (ii) using the climatology (estimate), followed by computing accuracy, precision, and uncertainty (APU) metrics. All APU measures were well below specification, thus the fallback usage of the climatology is generally a sound strategy. Still, the tests revealed that some considerations need to be taken into account to help quantify which sensor, band, climate, and season are most or least affected by using a fallback climatology. The highest uncertainty and bias is found for Landsat 5, with progressive improvements towards newer sensors. The bias increases from dry to humid climates, whereas uncertainty increases from dry and tropic to temperate climates. Uncertainty is smallest during seasons with low variability, and is highest when atmospheric conditions progress from a dry to a wet season (and vice versa).

scholarly journals Aerosol direct radiative effect over clouds from a synergy of Ozone Monitoring Instrument (OMI) and Moderate Resolution Imaging Spectroradiometer (MODIS) reflectances

2019 ◽  
Vol 12 (9) ◽  
pp. 5119-5135 ◽  
Author(s):  
Martin de Graaf ◽  
L. Gijsbert Tilstra ◽  
Piet Stammes
Keyword(s):  
Spatial Resolution ◽  
Spectral Resolution ◽  
Data Retrieval ◽  
Radiative Effect ◽  
Ozone Monitoring Instrument ◽  
Monitoring Instrument ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Ozone Monitoring ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. The retrieval of geophysical parameters is increasingly dependent on synergistic use of satellite instruments. More sophisticated parameters can be retrieved and the accuracy of retrievals can be increased when more information is combined. In this paper, a synergistic application of Ozone Monitoring Instrument (OMI), on the Aura platform, and Moderate Resolution Imaging Spectroradiometer (MODIS), on the Aqua platform, Level 1B reflectances is described, enabling the retrieval of the aerosol direct radiative effect (DRE) over clouds using the differential aerosol absorption (DAA) technique. This technique was first developed for reflectances from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on the Environmental Satellite (Envisat), which had the unique capability of measuring contiguous radiances from the ultraviolet (UV) at 240 to 1750 nm in the shortwave-infrared (SWIR), at a moderate spectral resolution of 0.2 to 1.5 nm. However, the spatial resolution and global coverage of SCIAMACHY was limited, and Envisat stopped delivering data in 2012. In order to continue the DRE data retrieval, reflectances from OMI and MODIS, flying in formation, were combined from the UV to the SWIR. This resulted in reflectances at a limited but sufficient spectral resolution, available at the OMI pixel grid, which have a much higher spatial resolution and coverage than SCIAMACHY. The combined reflectance spectra allow the retrieval of cloud microphysical parameters in the SWIR, and the subsequent retrieval of aerosol DRE over cloud scenes using the DAA technique. For liquid cloud scenes in the south-east Atlantic region with cloud fraction (CF) >0.3, the area-averaged instantaneous aerosol DRE over clouds in June to August 2006 was 25 Wm−2 with a standard deviation of 30 Wm−2. The maximum area-averaged instantaneous DRE from OMI–MODIS in August 2006 was 75.6±13 Wm−2. The new aerosol DRE over-cloud dataset from OMI–MODIS is compared to the SCIAMACHY dataset for the period 2006 to 2009, showing a very high correlation. The OMI–MODIS DRE dataset over the Atlantic Ocean is highly correlated to above-cloud AOT measurements from OMI and MODIS. It is related to AOT measurements over Ascension Island in 2016, showing the transport of smoke all the way from its source region in Africa over the Atlantic to Ascension and beyond.

scholarly journals The Impact of Seasonality and Land Cover on the Consistency of Relationship between Air Temperature and LST Derived from Landsat 7 and MODIS at a Local Scale: A Case Study in Southern Ontario

Land ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 672
Author(s):  
Michael Burnett ◽  
Dongmei Chen
Keyword(s):  
Land Cover ◽  
Spatial Resolution ◽  
Air Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Southern Ontario ◽  
Landsat 7 ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Impact

Land surface temperature (LST) and air temperature (Tair) have been commonly used to analyze urban heat island (UHI) effects throughout the world, with noted variations based on vegetation distribution. This research has compared time series LST data acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) platforms, Landsat 7 Enhanced Thematic Mapper (ETM+) and Tair from weather stations in the Southern Ontario area. The influence of the spatial resolution, land cover, vegetated surfaces, and seasonality on the relationship between LST and in situ Tair were examined. The objective is to identify spatial and seasonal differences amongst these different spatial resolution LST products and Tair, along with the causes for variations at a localized scale. Results show that MODIS LST from Terra had stronger relationships with Landsat 7 LST than those from Aqua. Tair demonstrated weaker correlations with Landsat LST than with MODIS LST in sparsely vegetated and urban areas during the summer. Due to the winter’s ability to smooth heterogenous surfaces, both LST and Tair showed stronger relationships in winter than summer over every land cover, except with coarse spatial resolutions on forested surfaces.

scholarly journals A critical assessment of high-resolution aerosol optical depth retrievals for fine particulate matter predictions

2013 ◽  
Vol 13 (21) ◽  
pp. 10907-10917 ◽  
Author(s):  
A. Chudnovsky ◽  
C. Tang ◽  
A. Lyapustin ◽  
Y. Wang ◽  
J. Schwartz ◽  
...  
Keyword(s):  
New England ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Fine Particulate Matter ◽  
Atmospheric Correction ◽  
Site Location ◽  
Lack Of Information ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. Recently, a new Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was developed for the MODerate Resolution Imaging Spectroradiometer (MODIS), which provides aerosol optical depth (AOD) at 1 km resolution. The relationship between MAIAC AOD and PM2.5 as measured by 84 EPA ground monitoring stations in the entire New England and the Harvard super site during 2002–2008 was investigated and also compared to the AOD–PM2.5 relationship using conventional MODIS 10 km AOD retrieval from Aqua platform (MYD04) for the same days and locations. The correlations for MYD04 and for MAIAC are r = 0.62 and 0.65, respectively, suggesting that AOD is a reasonable proxy for PM2.5 ground concentrations. The slightly higher correlation coefficient (r) for MAIAC can be related to its finer resolution resulting in better correspondence between AOD and EPA monitoring sites. Regardless of resolution, AOD–PM2.5 relationship varies daily, and under certain conditions it can be negative (due to several factors such as an EPA site location (proximity to road) and the lack of information about the aerosol vertical profile). By investigating MAIAC AOD data, we found a substantial increase, by 50–70% in the number of collocated AOD–PM2.5 pairs, as compared to MYD04, suggesting that MAIAC AOD data are more capable in capturing spatial patterns of PM2.5. Importantly, the performance of MAIAC AOD retrievals is slightly degraded but remains reliable under partly cloudy conditions when MYD04 data are not available, and it can be used to increase significantly the number of days for PM2.5 spatial pattern prediction based on satellite observations.

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