22 research outputs found
STATISTICAL STUDY OF MODIS ALGORITHMS IN ESTIMATING AEROSOL OPTICAL DEPTH OVER THE CZECH REPUBLIC
As a result of the rapid development of remote sensing techniques and accurate satellite observations, it has become customary to use these technologies in ecological and aerosols studies on a regional and global level. In this paper, we analyse the performance of three Moderate Resolution Imaging Spectroradiometer (MODIS) algorithms in estimating Aerosol Optical Depth (AOD) in the Czech Republic to gain knowledge about their accuracy and uncertainty. The Dark Target (DT), the Deep Blue (DB), and the merged algorithm (DTB) of the MODIS latest collection 6.1 Level 2 aerosol products (MOD04_L2) were tested by comparing its results with the measurements of Aerosol Robotic Network (AERONET) Level 3 Version 2.0 ground station at Brno airport. The DT algorithm is compatible the best with AERONET observations with a correlation coefficient (R = 0.823), retrievals falling within the EE envelope (EE% = 82.67%), root mean square error (RMSE = 0.059), and mean absolute error (MAE = 0.044). The DTB algorithm provided close results of the DT algorithm but with less accuracy, on the other hand the DB algorithm has the lowest accuracy between all, but this algorithm was able to provide a bigger sample size than the other two algorithms
Evaluation and comparison of CMIP6 models and MERRA-2 reanalysis AOD against Satellite observations from 2000 to 2014 over China
Rapid industrialization and urbanization along with a growing population are contributing significantly to air pollution in China. Evaluation of long-term aerosol optical depth (AOD) data from models and reanalysis, can greatly promote understanding of spatiotemporal variations in air pollution in China. To do this, AOD (550 nm) values from 2000 to 2014 were obtained from the Coupled Model Inter-comparison Project (CIMP6), the second version of Modern-Era Retrospective analysis for Research, and Applications (MERRA-2), and the Moderate Resolution Imaging Spectroradiometer (MODIS; flying on the Terra satellite) combined Dark Target and Deep Blue (DTB) aerosol product. We used the Terra-MODIS DTB AOD (hereafter MODIS DTB AOD) as a standard to evaluate CMIP6 Ensemble AOD (hereafter CMIP6 AOD) and MERRA-2 reanalysis AOD (hereafter MERRA-2 AOD). Results show better correlations and smaller errors between MERRA-2 and MODIS DTB AOD, than between CMIP6 and MODIS DTB AOD, in most regions of China, at both annual and seasonal scales. However, significant under- and over-estimations in the MERRA-2 and CMIP6 AOD were also observed relative to MODIS DTB AOD. The long-term (2000â2014) MODIS DTB AOD distributions show the highest AOD over the North China Plain (0.71) followed by Central China (0.69), Yangtse River Delta (0.67), Sichuan Basin (0.64), and Pearl River Delta (0.54) regions. The lowest AOD values were recorded over the Tibetan Plateau (0.13 ± 0.01) followed by Qinghai (0.19 ± 0.03) and the Gobi Desert (0.21 ± 0.03). Large amounts of sand and dust particles emitted from natural sources (the Taklamakan and Gobi Deserts) may result in higher AOD in spring compared to summer, autumn, and winter. Trends were also calculated for 2000â2005, for 2006â2010 (when China introduced strict air pollution control policies during the 11th Five Year Plan or FYP), and for 2011â2014 (during the 12th FYP). An increasing trend in MODIS DTB AOD was observed throughout the country during 2000â2014. The uncontrolled industrialization, urbanization, and rapid economic development that mostly occurred from 2000 to 2005 probably contributed to the overall increase in AOD. Finally, China's air pollution control policies helped to reduce AOD in most regions of the country; this was more evident during the 12th FYP period (2011â2014) than during the 11th FYP period (2006â2010). Therefore this study strongly advises the authority to retain or extend these policies in the future for improving air quality
Spatial and seasonal variations of aerosols over China from two decades of multi-satellite observations â Part 1: ATSR (1995â2011) and MODIS C6.1 (2000â2017)
Aerosol optical
depth (AOD) patterns and interannual and seasonal variations over China are
discussed based on the AOD retrieved from the Along-Track Scanning Radiometer
(ATSR-2, 1995â2002), the Advanced ATSR (AATSR, 2002â2012) (together ATSR) and
the MODerate resolution Imaging Spectroradiometer (MODIS) aboard the Terra
satellite (2000â2017). The AOD products used were the ATSR Dual View (ADV)
v2.31 AOD and the MODIS/Terra Collection 6.1 (C6.1) merged dark target (DT) and
deep blue (DB) AOD product. Together these datasets provide an AOD time
series for 23Â years, from 1995 to 2017. The difference between the AOD values
retrieved from ATSR-2 and AATSR is small, as shown by pixel-by-pixel and
monthly aggregate comparisons as well as validation results. This allows for
the combination of the ATSR-2 and AATSR AOD time series into one dataset without offset
correction.ADV and MODIS AOD validation results show similar high correlations with the
Aerosol Robotic Network (AERONET) AOD (0.88 and 0.92, respectively), while
the corresponding bias is positive for MODIS (0.06) and negative for ADV
(â0.07). Validation of the AOD products in similar conditions, when ATSR and
MODIS/Terra overpasses are within 90 min of each other and when both
ADV and MODIS retrieve AOD around AERONET locations, show that ADV performs
better than MODIS in autumn, while MODIS performs slightly better in spring
and summer. In winter, both ADV and MODIS underestimate the AERONET AOD.Similar AOD patterns are observed by ADV and MODIS in annual and seasonal
aggregates as well as in time series. ADVâMODIS difference maps show that
MODIS AOD is generally higher than that from ADV. Both ADV and MODIS show
similar seasonal AOD behavior. The AOD maxima shift from spring in the south to summer along the eastern coast further north.The agreement between sensors regarding year-to-year AOD changes is quite good.
During the period from 1995 to 2006 AOD increased in the southeast (SE) of China. Between
2006 and 2011 AOD did not change much, showing minor minima in 2008â2009.
From 2011 onward AOD decreased in the SE of China. Similar patterns
exist in year-to-year ADV and MODIS annual AOD tendencies in the overlapping
period. However, regional differences between the ATSR and MODIS AODs are
quite large. The consistency between ATSR and MODIS with regards to the AOD
tendencies in the overlapping period is rather strong in summer, autumn and
overall for the yearly average; however, in winter and spring, when there is a
difference in coverage between the two instruments, the agreement between
ATSR and MODIS is lower.AOD tendencies in China during the 1995â2017 period will be discussed in more
detail in Part 2 (a following paper: Sogacheva et al., 2018), where a method to combine AOD time series from ADV and
MODIS is introduced, and combined AOD time series are analyzed.</p
Spatial and seasonal variations of aerosols over China from two decades of multi-satellite observations â Part 2: AOD time series for 1995â2017 combined from ATSR ADV and MODIS C6.1 and AOD tendency estimations
Understanding long-term variations in aerosol loading is
essential for evaluating the health and climate effects of airborne
particulates as well as the effectiveness of pollution control policies.
The expected satellite lifetime is about 10 to 15Â years. Therefore, to study the
variations of atmospheric constituents over longer periods information
from different satellites must be utilized.Here we introduce a method to construct a combined annual and seasonal long
time series of AOD at 550 nm using the Along-Track Scanning Radiometers
(ATSR: ATSR-2 and AATSR combined) and the MODerate resolution Imaging
Spectroradiometer on Terra (MODIS/Terra), which together cover the 1995â2017 period. The
long-term (1995â2017) combined AOD time series are presented for all of
mainland China, for southeastern (SE) China and for 10 selected regions in
China. Linear regression was applied to the combined AOD time series
constructed for individual L3 (1° âĂâ 1°) pixels to
estimate the AOD tendencies for two periods: 1995â2006 (P1) and 2011â2017
(P2), with respect to the changes in the emission reduction policies in China.During P1, the annually averaged AOD increased by 0.006 (or 2 % of the
AOD averaged over the corresponding period) per year across all of mainland
China, reflecting increasing emissions due to rapid economic development. In
SE China, the annual AOD positive tendency in 1995â2006 was 0.014 (3 %)
per year, reaching maxima (0.020, or 4 %, per year) in Shanghai and the
Pearl River Delta regions. After 2011, during P2, AOD tendencies reversed
across most of China with the annually averaged AOD decreasing by â0.015 (â6 %)
per year in response to the effective reduction of the anthropogenic emissions of
primary aerosols, SO2 and NOx. The strongest AOD decreases were observed
in the Chengdu (â0.045, or â8 %, per year) and Zhengzhou (â0.046, or
â9 %, per year) areas, while over the North China Plain and coastal areas
the AOD decrease was lower than â0.03 (approximately â6 %) per year. In
the less populated areas the AOD decrease was small.The AOD tendency varied by both season and region. The increase in the
annually averaged AOD during P1 was mainly due to an increase in summer and
autumn in SE China (0.020, or 4 %, and 0.016, or 4 %, per year,
respectively), while during winter and spring the AOD actually decreased
over most of China. The AOD negative tendencies during the
2011â2017 period were larger in summer than in other seasons over the whole of China
(ca. â0.021, or â7 %, per year) and over SE China (ca. â0.048, or â9 %, per year).The long-term AOD variations presented here show a gradual decrease in the
AOD after 2011 with an average reduction of 30 %â50 % between 2011 and
2017. The effect is more visible in the highly populated and industrialized
regions in SE China, as expected.</p
Intercomparison in spatial distributions and temporal trends derived from multi-source satellite aerosol products
Satellite-derived aerosol products provide long-term and large-scale
observations for analysing aerosol distributions and variations,
climate-scale aerosol simulations, and aerosolâclimate interactions.
Therefore, a better understanding of the consistencies and differences among
multiple aerosol products is important. The objective of this study is to
compare 11 global monthly aerosol optical depth (AOD) products,
which are the European Space Agency Climate Change Initiative (ESA-CCI)
Advanced Along-Track Scanning Radiometer (AATSR), Advanced Very High
Resolution Radiometer (AVHRR), Multi-angle Imaging SpectroRadiometer
(MISR), Moderate Resolution Imaging Spectroradiometer (MODIS), Sea-viewing
Wide Field-of-view Sensor (SeaWiFS), Visible Infrared Imaging Radiometer
(VIIRS), and POLarization and Directionality of the Earth's Reflectance
(POLDER) products. AErosol RObotic NEtwork (AERONET) Version 3 Level 2.0
monthly measurements at 308 sites around the world are selected for
comparison. Our results illustrate that the spatial distributions and
temporal variations of most aerosol products are highly consistent globally
but exhibit certain differences on regional and site scales. In general, the
AATSR Dual View (ADV) and SeaWiFS products show the lowest spatial coverage
with numerous missing values, while the MODIS products can cover most areas
(average of 87 %) of the world. The best performance is observed in
SeptemberâOctoberâNovember (SON) and the worst is in JuneâJulyâAugust (JJA).
All the products perform unsatisfactorily over northern Africa and Middle
East, southern and eastern Asia, and their coastal areas due to the influence from
surface brightness and human activities. In general, the MODIS products show
the best agreement with the AERONET-based AOD values on different spatial
scales among all the products. Furthermore, all aerosol products can capture
the correct aerosol trends at most cases, especially in areas where aerosols
change significantly. The MODIS products perform best in capturing the
global temporal variations in aerosols. These results provide a reference
for users to select appropriate aerosol products for their particular
studies.</p
Estimating PM2.5 Concentrations Using 3 KM MODIS AOD Products: A Case Study in British Columbia, Canada
PM2.5 refers to fine particles with diameters smaller than 2.5 ÎŒm. The rising level of PM2.5 reveals adverse effects on climate change, economic losses, international conflicts, and public health. Exposure to the high level of PM2.5 would increase the risk of premature death, especially for people with weak immune systems, such as children and elder people. The main sources of PM2.5 include combustion of biomass, vehicle and industrial emissions, and wildfire smoke. British Columbia (BC), Canada, with a land area of 944,735 km2 and 27 regional districts, experienced its record-breaking wildfire season in 2017. However, due to the uneven distribution of PM2.5 ground monitoring stations in BC, PM2.5 concentrations in the rural area are difficult to retrieve. Remote sensing techniques and geographical information systems (GIS) could be used as supplementary tools to estimate PM2.5 concentrations. Aerosol Optical Depth (AOD) has been proven to have a strong correlation with PM2.5. Moderate Resolution Imaging Spectroradiometer (MODIS) provides AOD products in both 3 km and 10 km resolutions. The 3 km MODIS AOD products were released in 2013, and have been widely used to estimate PM2.5 concentrations in several studies.
This study adopted the 3 km Aqua MODIS AOD products to estimate PM2.5 concentrations in BC in the year of 2017 by combining ground station measurements, meteorological and supplementary data. MODIS AOD products were validated with ground-level AErosol RObotic NETwork (AERONET) AOD data. The Multiple Linear Regression (MLR) model, Geographically Weighted Regression (GWR) model, and a novel theoretical model were then conducted to estimate PM2.5 concentrations by integrating MODIS AOD products, ground-level PM2.5 concentrations, meteorological and supplementary data. After comparing the performance of the three models, the GWR model was used to generate annual, seasonal, and monthly spatial distribution maps of PM2.5. The application feasibility of MODIS AOD products in predicting PM2.5 was also examined.
The validation results showed that there was a strong correlation between the MODIS AOD and the AERONET AOD. The GWR model had the best prediction performance, while the MLR generated the worst prediction results. After analyzing the spatial distribution maps of PM2.5 with ground-level PM2.5 distribution maps, it could be concluded that the PM2.5 concentrations estimated by the GWR model almost follow the same trend as ground station measured PM2.5. In addition, PM2.5 concentrations were the highest in summer and August based on the estimation results of seasonal and monthly GWR models. It indicated that the application feasibility of MODIS AOD products in predicting PM2.5 concentrations during BCâs wildfire season was high
Observations of the Interaction and Transport of Fine Mode Aerosols With Cloud and/or Fog in Northeast Asia From Aerosol Robotic Network and Satellite Remote Sensing
Analysis of Sun photometer measured and satellite retrieved aerosol optical depth (AOD) datahas shown that major aerosol pollution events with very highfine mode AOD (>1.0 in midvisible) in theChina/Korea/Japan region are often observed to be associated with significant cloud cover. This makesremote sensing of these events difficult even for high temporal resolution Sun photometer measurements.Possible physical mechanisms for these events that have high AOD include a combination of aerosolhumidification, cloud processing, and meteorological covariation with atmospheric stability andconvergence. The new development of Aerosol Robotic Network Version 3 Level 2 AOD with improved cloudscreening algorithms now allow for unprecedented ability to monitor these extremefine mode pollutionevents. Further, the spectral deconvolution algorithm (SDA) applied to Level 1 data (L1; no cloud screening)provides an even more comprehensive assessment offine mode AOD than L2 in current and previous dataversions. Studying the 2012 winter-summer period, comparisons of Aerosol Robotic Network L1 SDA dailyaveragefine mode AOD data showed that Moderate Resolution Imaging Spectroradiometer satellite remotesensing of AOD often did not retrieve and/or identify some of the highestfine mode AOD events in thisregion. Also, compared to models that include data assimilation of satellite retrieved AOD, the L1 SDAfinemode AOD was significantly higher in magnitude, particularly for the highest AOD events that were oftenassociated with significant cloudiness
Seasonal Aerosol Classification Over South Asia by Satellite based Atmospheric Optical Data
Aerosol optical characteristics have been investigated to explore regional and seasonal inconsistencies of aerosols and to define the dominant type throughout South Asia from 2001 to 2021. MODIS aerosol products from collection 6.1 have been used in present study, that comprise daily values of Angstrom exponent (AE) and aerosol optical depth (AOD) data. MODIS-derived AODs are validated by using nine ground-based AERONET station data. Overall, an adequate correlation is found among the two datasets. However, an overestimation of the MODIS retrievals is found in one site named Jaipur and underestimations are found at two sites named as Gandhi-college and Karachi. The seasonal evaluation shows that aerosol distribution found between 0 and 1.05, depending on the change in geographical location. The highest AOD value originates over the Indo-Gangetic plain (IGP), mostly throughout warm season. The second maximum AOD value covers a large area of South Asia during spring, summer and autumn. The lowest values of AOD are found in winter season excluding the IGP. A region with high aerosol optical depth (AOD) values support a low value of angstrom exponent (AE) indicating the coarse aerosol during warm seasons (spring and summer) over IGP. The region with high AOD and high AE values is showing fine aerosol during the mild to cold seasons (autumn and winter). The threshold values for AOD and AE have been used to classify aerosols. The results demonstrate that urban/industrial aerosols prominent in every season across the region dominate in spring and summer due to frequent occurrence of dust events. The mixed type aerosol is second largest contributor in aerosol formation in all seasons. The Biomass burning/smoke aerosol is dominant over IGP due to open forest and crop burning in autumn. Clean and maritime aerosol has small unnoticeable involvement in the studied region
AEROsol generic classification using a novel Satellite remote sensing Approach (AEROSA)
Numerous studies (hereafter GA: general approach studies) have been made to classify aerosols into desert dust (DD), biomass-burning (BB), clean continental (CC), and clean maritime (CM) types using only aerosol optical depth (AOD) and Ă
ngström exponent (AE). However, AOD represents the amount of aerosol suspended in the atmospheric column while the AE is a qualitative indicator of the size distribution of the aerosol estimated using AOD measurements at different wavelengths. Therefore, these two parameters do not provide sufficient information to unambiguously classify aerosols into these four types. Evaluation of the performance of GA classification applied to AErosol Robotic NETwork (AERONET) data, at sites for situations with known aerosol types, provides many examples where the GA method does not provide correct results. For example, a thin layer of haze was classified as BB and DD outside the crop burning and dusty seasons respectively, a thick layer of haze was classified as BB, and aerosols from known crop residue burning events were classified as DD, CC, and CM by the GA method. The results also show that the classification varies with the season, for example, the same range of AOD and AE were observed during a dust event in the spring (20th March 2012) and a smog event in the autumn (2nd November 2017). The results suggest that only AOD and AE cannot precisely classify the exact nature (i.e., DD, BB, CC, and CM) of aerosol types without incorporating more optical and physical properties. An alternative approach, AEROsol generic classification using a novel Satellite remote sensing Approach (AEROSA), is proposed to provide aerosol amount and size information using AOD and AE, respectively, from the Terra-MODIS (MODerate resolution Imaging Spectroradiometer) Collection 6.1 Level 2 combined Dark Target and Deep Blue (DTB) product and AERONET Version 3 Level 2.0 data. Although AEROSA is also based on AOD and AE, it does not claim the nature of aerosol types, instead providing information on aerosol amount and size. The purpose is to introduce AEROSA for those researchers who are interested in the generic classification of aerosols based on AOD and AE, without claiming the exact aerosol types such as DD, BB, CC, and CM. AEROSA not only provides 9 generic aerosol classes for all observations but can also accommodate variations in location and season, which GA aerosol types do not.</jats:p