scholarly journals Himawari-8/AHI and MODIS Aerosol Optical Depths in China: Evaluation and Comparison

2019 ◽  
Vol 11 (9) ◽  
pp. 1011 ◽  
Author(s):  
Tingting Jiang ◽  
Bin Chen ◽  
Karen Kie Yan Chan ◽  
Bing Xu
Keyword(s):  
Comprehensive Evaluation ◽  
Asia Pacific ◽  
Dynamic Monitoring ◽  
Retrieval Accuracy ◽  
Deep Blue ◽  
Moderate Resolution ◽  
Level 3 ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Difference

The geostationary earth orbit satellite—Himawari-8 loaded with the Advanced Himawari Imager (AHI) has greatly enhanced our capacity of dynamic monitoring in Asia–Pacific area. The Himawari-8/AHI hourly aerosol product is a promising complementary source to the MODerate resolution Imaging Spectroradiometer (MODIS) daily aerosol product for near real-time air pollution observations. However, a comprehensive evaluation of AHI aerosol optical depth (AOD) is still limited, and the difference in performances of AHI and MODIS remains uncertain. In this study, we evaluated the Himawari-8/AHI Level 3 Version 3.0 and MODIS Collection 6.1 Deep Blue AOD products over China against AOD measurements from AErosol RObotic NETwork (AERONET) sites in a spatiotemporal comparison of the products from February 2018 to January 2019. Results showed that AHI AOD achieved a moderate agreement with AERONET with a correlation coefficient of 0.75 and a root-mean-square-error of 0.26, which was slightly inferior to MODIS. The retrieval accuracy was spatially and temporally varied in AHI AOD, with higher accuracies for XiangHe and Lulin sites as well as in the morning and during the summer. The dependency analysis further revealed that the bias in AHI AOD was strongly dependent on aerosol loading and influenced by the Ångström Exponent and NDVI while those for MODIS appeared to be independent of all variables. Fortunately, the biases in AHI AOD could be rectified using a random forest model that contained the appropriate variables to produce sufficiently accurate results with cross-validation R of 0.92 and RMSE of 0.15. With these adjustments, AHI AOD will continue to have great potential in characterizing precise dynamic aerosol variations and air quality at a fine temporal resolution.

scholarly journals Evaluation of Terra-MODIS C6 and C6.1 Aerosol Products against Beijing, XiangHe, and Xinglong AERONET Sites in China during 2004-2014

2019 ◽  
Vol 11 (5) ◽  
pp. 486 ◽  
Author(s):  
Muhammad Bilal ◽  
Majid Nazeer ◽  
Janet Nichol ◽  
Zhongfeng Qiu ◽  
Lunche Wang ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Deep Blue ◽  
Moderate Resolution ◽  
Level 3 ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Terra Modis ◽  
Level 2 ◽  
Better Than

In this study, Terra-MODIS (Moderate Resolution Imaging Spectroradiometer) Collections 6 and 6.1 (C6 & C6.1) aerosol optical depth (AOD) retrievals with the recommended high-quality flag (QF = 3) were retrieved from Dark-Target (DT), Deep-Blue (DB) and merged DT and DB (DTB) level–2 AOD products for verification against Aerosol Robotic Network (AERONET) Version 3 Level 2.0 AOD data obtained from 2004–2014 for three sites located in the Beijing-Tianjin-Hebei (BTH) region. These are: Beijing, located over mixed bright urban surfaces, XiangHe located over suburban surfaces, and Xinglong located over hilly and vegetated surfaces. The AOD retrievals were also validated over different land-cover types defined by static monthly NDVI (Normalized Difference Vegetation Index) values obtained from the Terra-MODIS level-3 product (MOD13A3). These include non-vegetated surfaces (NVS, NDVI < 0.2), partially vegetated surfaces (PVS, 0.2 ≤ NDVI ≤ 0.3), moderately vegetated surfaces (MVS, 0.3 < NDVI < 0.5) and densely vegetated surfaces (DVS, NDVI ≥ 0.5). Results show that the DT, DB, and DTB-collocated retrievals achieve a high correlation coefficient of ~ 0.90–0.97, 0.89–0.95, and 0.86–0.95, respectively, with AERONET AOD. The DT C6 and C6.1 collocated retrievals were comparable at XiangHe and Xinglong, whereas at Beijing, the percentage of collocated retrievals within the expected error (↔EE) increased from 21.4% to 35.5%, the root mean square error (RMSE) decreased from 0.37 to 0.24, and the relative percent mean error (RPME) decreased from 49% to 27%. These results suggest significant relative improvement in the DT C6.1 product. The percentage of DB-collocated AOD retrievals ↔EE was greater than 70% at Beijing and Xinglong, whereas less than 66% was observed at XiangHe. Similar to DT AOD, DTB AOD retrievals performed well at XiangHe and Xinglong compared with Beijing. Regionally, DB C6 and C6.1-collocated retrievals performed better than DT and DTB in terms of good quality retrievals and relatively small errors. For diverse vegetated surfaces, DT-collocated retrievals reported small errors and good quality retrievals only for NVS and DVS, whereas larger errors were reported for PVS. MVS. DB contains good quality AOD retrievals over PVS, MVS, and DVS compared with NVS. DTB C6.1 collocated retrievals were better than C6 over NVS, PVS, and DVS. C6.1 is substantially improved overall, compared with C6 at local and regional scales, and over diverse vegetated surfaces.

scholarly journals Comparison of the cloud top heights retrieved from MODIS and AHI satellite data with ground-based Ka-band radar

2020 ◽  
Vol 13 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Juan Huo ◽  
Daren Lu ◽  
Shu Duan ◽  
Yongheng Bi ◽  
Bo Liu
Keyword(s):  
Satellite Data ◽  
Cloud Base ◽  
Ka Band ◽  
Retrieval Accuracy ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Radar Measurements ◽  
The Mean ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Difference

Abstract. To better understand the accuracy of cloud top heights (CTHs) derived from passive satellite data, ground-based Ka-band radar measurements from 2016 and 2017 in Beijing are compared with CTH data inferred from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Himawari Imager (AHI). Relative to the radar CTHs, the MODIS CTHs are found to be underestimated by−1.10 ± 2.53 km on average and 49 % of CTH differences are within 1.0 km. The AHI CTHs are underestimated by −1.10 ± 2.27 km and 42 % are within 1.0 km. Both the MODIS and AHI CTH retrieval accuracy depends strongly on the cloud depth (CD). Large differences are mainly due to the retrieval of thin clouds of CD <1 km, especially when the cloud base height is higher than 4 km. For clouds with CD >1 km, the mean CTH difference decreases to -0.48±1.70 km for MODIS and to -0.76±1.63 km for AHI. It is found that MODIS CTHs with higher values (i.e. >6 km) show smaller discrepancy with radar CTH than those MODIS CTHs with lower values (i.e. <4 km). Statistical analysis illustrate that the CTH difference between the two satellite instruments is lower than the difference between the satellite instrument and the ground-based Ka-band radar. The monthly accuracy of both CTH retrieval algorithms is investigated and it is found that summer has the smallest retrieval difference.

scholarly journals Comparison of the cloud top heights retrieved from MODIS and AHI satellite data with ground-based Ka-band radar

2019 ◽  
Author(s):  
Juan Huo ◽  
Daren Lu ◽  
Shu Duan ◽  
Yongheng Bi ◽  
Bo Liu
Keyword(s):  
Satellite Data ◽  
Radar Data ◽  
Retrieval Algorithm ◽  
Ka Band ◽  
Retrieval Accuracy ◽  
Retrieval Algorithms ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Radar Measurements ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. To better understand the accuracy of cloud top heights (CTHs) derived from passive satellite data, ground-based Ka-band radar measurements from 2016 and 2017 in Beijing were compared with CTH data inferred from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Himawari Imager (AHI). Relative to the radar CTHs, the MODIS CTHs were found to be underestimated by −1.10 ± 2.53 km and 49 % of CTH differences were within 1.0 km. Like the MODIS results, the AHI CTHs were underestimated by −1.10 ± 2.27 km and 42 % were within 1.0 km. Both the MODIS and AHI retrieval accuracy depended strongly on the cloud depth (CD). Large differences were mainly occurring for the retrieval of thin clouds of CD  1 km, the CTH difference decreased to −0.48 ± 1.70 km for MODIS and to −0.76 ± 1.63 km for AHI. MODIS CTHs greater than 6 km showed better agreement with the radar data than those less than 4 km. Statistical analysis showed that the average AHI CTHs were lower than the average MODIS CTHs by −0.64 ± 2.36 km. The monthly accuracy of both retrieval algorithms was studied and it was found that the AHI retrieval algorithm had the largest bias in winter while the MODIS retrieval algorithm had the lowest accuracy in spring.

scholarly journals Pendugaan Kejadian Pemutihan Karang Berdasarkan Analisis Suhu Permukaan Laut (SPL) Tahun 2015-2016 di Perairan Bali

2017 ◽  
Vol 4 (2) ◽  
pp. 286
Author(s):  
Jajang Nuryana ◽  
I Gede Hendrawan ◽  
Widiastuti Karim
Keyword(s):  
Coral Bleaching ◽  
Hot Spot ◽  
Satellite Image ◽  
Distribution Patterns ◽  
Triangle Center ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Difference ◽  
Level 2

National Ocean Atmospheric Administrations (NOAA) by the program coral reef Watch (CRW) has developed a method to estimate the potential of coral bleaching using Sea Surface Temperature (SST). The products are hot spot (HS) and degree heating week (DHW). HS is the SST 1°C (SSTL?1) above normal and DHW is the length of HS inhabits a place. The CRW product do not provided detail informations because it has a lower resolution. It is need a satellite image with a higher resolution to provide better informations. One of the satellite images that can be used is Moderate Resolution Imaging Spectroradiometer (MODIS) with a spatial resolution of 1 km. The purpose of this study was to know HS and DHW distribution patterns and status of coral bleaching in Bali waters seen from the analysis of HS and DHW. MODIS data is used daily, then do mosaicing process to get a weekly SPL (8 daily) and the monthly SST. Monthly SPL normally used to get maximum montly mean (MMM). HS obtained from the difference between 8 daily weekly SST and SST normal (MMM).).Location bleaching based on data Coral Triangle Center (CTC) and coralwatch.org.  SST results revealed difference of SPL in 2015 and 2016 amounted to 1.48°C. Highest DHW in Bali Hai, Nusa Penida is 10 465° C-weeks in April 2016. Based on the value HS and DHW coral reefs in Bali waters threatened bleaching level Alert 1 and Alert level 2.

scholarly journals Optimizing CALIPSO Saharan dust retrievals

2013 ◽  
Vol 13 (23) ◽  
pp. 12089-12106 ◽  
Author(s):  
V. Amiridis ◽  
U. Wandinger ◽  
E. Marinou ◽  
E. Giannakaki ◽  
A. Tsekeri ◽  
...  
Keyword(s):  
Northern Africa ◽  
Saharan Dust ◽  
Individual Level ◽  
Dust Extinction ◽  
Absolute Bias ◽  
Moderate Resolution ◽  
Level 3 ◽  
Lidar Ratio ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. We demonstrate improvements in CALIPSO (Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations) dust extinction retrievals over northern Africa and Europe when corrections are applied regarding the Saharan dust lidar ratio assumption, the separation of the dust portion in detected dust mixtures, and the averaging scheme introduced in the Level 3 CALIPSO product. First, a universal, spatially constant lidar ratio of 58 sr instead of 40 sr is applied to individual Level 2 dust-related backscatter products. The resulting aerosol optical depths show an improvement compared with synchronous and collocated AERONET (Aerosol Robotic Network) measurements. An absolute bias of the order of −0.03 has been found, improving on the statistically significant biases of the order of −0.10 reported in the literature for the original CALIPSO product. When compared with the MODIS (Moderate-Resolution Imaging Spectroradiometer) collocated aerosol optical depth (AOD) product, the CALIPSO negative bias is even less for the lidar ratio of 58 sr. After introducing the new lidar ratio for the domain studied, we examine potential improvements to the climatological CALIPSO Level 3 extinction product: (1) by introducing a new methodology for the calculation of pure dust extinction from dust mixtures and (2) by applying an averaging scheme that includes zero extinction values for the nondust aerosol types detected. The scheme is applied at a horizontal spatial resolution of 1° × 1° for ease of comparison with the instantaneous and collocated dust extinction profiles simulated by the BSC-DREAM8b dust model. Comparisons show that the extinction profiles retrieved with the proposed methodology reproduce the well-known model biases per subregion examined. The very good agreement of the proposed CALIPSO extinction product with respect to AERONET, MODIS and the BSC-DREAM8b dust model makes this dataset an ideal candidate for the provision of an accurate and robust multiyear dust climatology over northern Africa and Europe.

scholarly journals Spatial and seasonal variations of aerosols over China from two decades of multi-satellite observations – Part 1: ATSR (1995–2011) and MODIS C6.1 (2000–2017)

2018 ◽  
Vol 18 (15) ◽  
pp. 11389-11407 ◽  
Author(s):  
Larisa Sogacheva ◽  
Gerrit de Leeuw ◽  
Edith Rodriguez ◽  
Pekka Kolmonen ◽  
Aristeidis K. Georgoulias ◽  
...  
Keyword(s):  
Time Series ◽  
Seasonal Variations ◽  
Eastern Coast ◽  
Deep Blue ◽  
Modis Aod ◽  
Yearly Average ◽  
Offset Correction ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Difference

Abstract. 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, 1995–2002), the Advanced ATSR (AATSR, 2002–2012) (together ATSR) and the MODerate resolution Imaging Spectroradiometer (MODIS) aboard the Terra satellite (2000–2017). 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 23 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 (−0.07). 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. ADV–MODIS 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 2008–2009. 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 1995–2017 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.

scholarly journals The Collection 6 MODIS aerosol products over land and ocean

2013 ◽  
Vol 6 (11) ◽  
pp. 2989-3034 ◽  
Author(s):  
R. C. Levy ◽  
S. Mattoo ◽  
L. A. Munchak ◽  
L. A. Remer ◽  
A. M. Sayer ◽  
...  
Keyword(s):  
Arid Land ◽  
Data Set ◽  
Deep Blue ◽  
New Information ◽  
Spatial Coverage ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Land Surfaces ◽  
Cloud Mask

Abstract. The twin Moderate resolution Imaging Spectroradiometer (MODIS) sensors have been flying on Terra since 2000 and Aqua since 2002, creating an extensive data set of global Earth observations. Here, we introduce the Collection 6 (C6) algorithm to retrieve aerosol optical depth (AOD) and aerosol size parameters from MODIS-observed spectral reflectance. While not a major overhaul from the previous Collection 5 (C5) version, there are enough changes that there are significant impacts to the products and their interpretation. The C6 aerosol data set will be created from three separate retrieval algorithms that operate over different surface types. These are the two "Dark Target" (DT) algorithms for retrieving (1) over ocean (dark in visible and longer wavelengths) and (2) over vegetated/dark-soiled land (dark in the visible), plus the "Deep Blue" (DB) algorithm developed originally for retrieving (3) over desert/arid land (bright in the visible). Here, we focus on DT-ocean and DT-land (#1 and #2). We have updated assumptions for central wavelengths, Rayleigh optical depths and gas (H2O, O3, CO2, etc.) absorption corrections, while relaxing the solar zenith angle limit (up to ≤ 84°) to increase poleward coverage. For DT-land, we have updated the cloud mask to allow heavy smoke retrievals, fine-tuned the assignments for aerosol type as function of season/location, corrected bugs in the Quality Assurance (QA) logic, and added diagnostic parameters such topographic altitude. For DT-ocean, improvements include a revised cloud mask for thin-cirrus detection, inclusion of wind speed dependence on the surface reflectance, updates to logic of QA Confidence flag (QAC) assignment, and additions of important diagnostic information. At the same time, we quantified how "upstream" changes to instrument calibration, land/sea masking and cloud masking will also impact the statistics of global AOD, and affect Terra and Aqua differently. For Aqua, all changes will result in reduced global AOD (by 0.02) over ocean and increased AOD (by 0.02) over land, along with changes in spatial coverage. We compared preliminary data to surface-based sun photometer data, and show that C6 should improve upon C5. C6 will include a merged DT/DB product over semi-arid land surfaces for reduced-gap coverage and better visualization, and new information about clouds in the aerosol field. Responding to the needs of the air quality community, in addition to the standard 10 km product, C6 will include a global (DT-land and DT-ocean) aerosol product at 3 km resolution.

scholarly journals Aerosol Optical Depth from MODIS satellite data above the Pierre Auger Observatory

2019 ◽  
Vol 197 ◽  
pp. 02011
Author(s):  
Nataliia Borodai
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Satellite System ◽  
Horizontal Resolution ◽  
Pierre Auger Observatory ◽  
Fluorescence Detector ◽  
Deep Blue ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

Aerosol optical depth can be retrieved from measurements performed by Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument. The MODIS satellite system includes two polar satellites, Terra and Aqua. Each of them flies over the Pierre Auger Observatory once a day, providing two measurements of aerosols per day and covering the whole area of the Observatory. MODIS aerosol data products have been generated by three dedicated algorithms over bright and dark land and over ocean surface. We choose the Deep Blue algorithm data to investigate the distribution of aerosols over the Observatory, as this algorithm is the most appropriate one for semi-arid land of the Pierre Auger Observatory. This data algorithm allows us to obtain aerosol optical depth values for the investigated region, and to build cloud-free aerosol maps with a horizontal resolution 0.1°×0.1°. Since a suffcient number of measurements was obtained only for Loma Amarilla and Coihueco fluorescence detector (FD) sites of the Pierre Auger Observatory, a more detailed analysis of aerosol distributions is provided for these sites. Aerosols over these FD sites are generally distributed in a similar way each year, but some anomalies are also observed. These anomalies in aerosol distributions appear mainly due to some transient events, such as volcanic ash clouds, fires etc. We conclude that the Deep Blue MODIS algorithm provides more realistic aerosol optical depth values than other available algorithms.

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