scholarly journals Role of coarse and fine mode aerosols in MODIS AOD retrieval: a case study

2013 ◽  
Vol 6 (5) ◽  
pp. 9109-9132 ◽  
Author(s):  
M. N. Sai Suman ◽  
H. Gadhavi ◽  
V. Ravi Kiran ◽  
A. Jayaraman ◽  
S. V. B. Rao
Keyword(s):  
South India ◽  
Retrieval Algorithm ◽  
Coarse Mode ◽  
Modis Aod ◽  
Moderate Resolution ◽  
Fine Mode ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. In the present study we have compared the MODIS (Moderate Resolution Imaging Spectroradiometer) derived aerosol optical depth (AOD) data with that obtained from operating sky-radiometer at a remote rural location in South India (Gadanki, 13.45° N, 79.18° E). While the comparison between total (coarse mode + fine mode) AOD shows R2 value of about 0.71 with a negligible bias of 0.01, if one separates the AOD into fine and coarse mode, the comparison becomes very poor, particularly for fine mode with an R2 value of 0.44. The coarse mode AOD derived from MODIS and sky-radiometer compare better with an R2 value of 0.74 and also the seasonal variation is well captured by both measurements. It is shown that the fine mode fraction derived from MODIS data is more than a factor of two smaller than that derived from the sky-radiometer data. Based on these observations we argue that the selection of aerosol types used in the MODIS retrieval algorithm are not appropriate particularly in the case of South India. Instead of selecting a moderately absorbing aerosol type (as being done currently in the MODIS retrieval) a more absorbing type aerosol is better suited for fine mode aerosols, while reverse is true for the coarse mode aerosols, where instead of using "dust aerosols" which is relatively more absorbing, usage of coarse sea-salt particles which is less absorbing is more appropriate.

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 Retrieval of aerosol fine-mode fraction over China from satellite multiangle polarized observations: validation and comparison

2021 ◽  
Vol 14 (2) ◽  
pp. 1655-1672
Author(s):  
Yang Zhang ◽  
Zhengqiang Li ◽  
Zhihong Liu ◽  
Yongqian Wang ◽  
Lili Qie ◽  
...  
Keyword(s):  
Optical Parameter ◽  
Absolute Error ◽  
Retrieval Algorithm ◽  
Polarization Data ◽  
Moderate Resolution ◽  
Fine Mode ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Remote Sensing Methods ◽  
Observation Results

Abstract. The aerosol fine-mode fraction (FMF) is an important optical parameter of aerosols, and the FMF is difficult to accurately retrieve by traditional satellite remote sensing methods. In this study, FMF retrieval was carried out based on the multiangle polarization data of Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from Lidar (PARASOL), which overcame the shortcomings of the FMF retrieval algorithm in our previous research. In this research, FMF retrieval was carried out in China and compared with the AErosol RObotic NETwork (AERONET) ground-based observation results, Moderate Resolution Imaging Spectroradiometer (MODIS) FMF products, and Generalized Retrieval of Aerosol and Surface Properties (GRASP) FMF results. In addition, the FMF retrieval algorithm was applied, a new FMF dataset was produced, and the annual and quarterly average FMF results from 2006 to 2013 were obtained for all of China. The research results show that the FMF retrieval results of this study are comparable with the AERONET ground-based observation results in China and the correlation coefficient (r), mean absolute error (MAE), root mean square error (RMSE), and the proportion of results that fall within the expected error (Within EE) are 0.770, 0.143, 0.170, and 65.01 %, respectively. Compared with the MODIS FMF products, the FMF results of this study are closer to the AERONET ground-based observations. Compared with the FMF results of GRASP, the FMF results of this study are closer to the spatial variation in the ratio of PM2.5 to PM10 near the ground.

scholarly journals Role of Coarse and Fine Mode Aerosols in MODIS AOD Retrieval: a case study over southern India

2014 ◽  
Vol 7 (4) ◽  
pp. 907-917 ◽  
Author(s):  
M. N. Sai Suman ◽  
H. Gadhavi ◽  
V. Ravi Kiran ◽  
A. Jayaraman ◽  
S. V. B. Rao
Keyword(s):  
Regression Line ◽  
Single Scattering ◽  
Southern India ◽  
Retrieval Algorithm ◽  
Aerosol Model ◽  
Coarse Mode ◽  
Modis Aod ◽  
Fine Mode ◽  
R Value ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. In the present study we compare the MODIS (Moderate Resolution Imaging Spectroradiometer) derived aerosol optical depth (AOD) data with that obtained from operating sky-radiometer at a remote rural location in southern India (Gadanki, 13.45° N, 79.18° E) from April 2008 to March 2011. While the comparison between total (coarse mode + fine mode) AODs shows correlation coefficient (R) value of about 0.71 for Terra and 0.77 for Aqua, if one separates the AOD into fine and coarse mode, the comparison becomes very poor, particularly for fine mode with an R value of 0.44 for both Terra and Aqua. The coarse mode AOD derived from MODIS and sky-radiometer compare better with an R value of 0.74 for Terra and 0.66 for Aqua. The seasonal variation is also well captured by both ground-based and satellite measurements. It is shown that both the total AOD and fine mode AOD are significantly underestimated with slope of regression line 0.75 and 0.35 respectively, whereas the coarse mode AOD is overestimated with a slope value of 1.28 for Terra. Similar results are found for Aqua where the slope of the regression line for total AOD and fine mode AOD are 0.72 and 0.27 whereas 0.95 for coarse mode. The fine mode fraction derived from MODIS data is less than one-half of that derived from the sky-radiometer data. Based on these observations and comparison of single scattering albedo observed using sky-radiometer with that of MODIS aerosol models, we argue that the selection of aerosol types used in the MODIS retrieval algorithm may not be appropriate particularly in the case of southern India. Instead of selecting a moderately absorbing aerosol model (as being done currently in the MODIS retrieval) a more absorbing aerosol model could be a better fit for the fine mode aerosols, while reverse is true for the coarse mode aerosols, where instead of using "dust aerosols" which is relatively absorbing type, usage of coarse sea-salt particles which is less absorbing is more appropriate. However, not all the differences could be accounted based on aerosol model, other factors like errors in retrieval of surface reflectance may also be significant in causing underestimation of AOD by MODIS.

scholarly journals Retrieval of Aerosol Fine-mode Fraction over China from Satellite Multiangle Polarized Observations: Validation and Application

2020 ◽  
Author(s):  
Yang Zhang ◽  
Zhengqiang Li ◽  
Zhihong Liu ◽  
Yongqian Wang ◽  
Lili Qie ◽  
...  
Keyword(s):  
Optical Parameter ◽  
Absolute Error ◽  
Retrieval Algorithm ◽  
Moderate Resolution ◽  
Fine Mode ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Remote Sensing Methods ◽  
Hu Line ◽  
Observation Results

Abstract. The aerosol fine-mode fraction (FMF) is an important optical parameter of aerosols, and the FMF is difficult to accurately retrieve by traditional satellite remote sensing methods. In this study, FMF retrieval was carried out based on the multiangle polarization data of Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from Lidar (PARASOL), which overcame the shortcomings of the FMF retrieval algorithm in our previous research. In this research, FMF retrieval was carried out in China and compared with the AErosol RObotic NETwork (AERONET) ground-based observation results, Moderate Resolution Imaging Spectroradiometer (MODIS) FMF products, and Generalized Retrieval of Aerosol and Surface Properties (GRASP) FMF results. In addition, application of the FMF retrieval algorithm was carried out, a new FMF dataset was produced, and the annual and quarterly average results of FMF from 2006 to 2013 were obtained in all of China. The research results show that the FMF retrieval results of this study are comparable with the AERONET ground-based observation results in China, with correlation coefficient (r), mean absolute error (MAE), root mean square error (RMSE), and the proportion of results that fall with the expected error (Within EE) are 0.770, 0.143, 0.170, and 60.96 %, respectively. Compared with the MODIS FMF products, the FMF results of this study are closer to the AERONET ground-based observations. Compared with the FMF results of GRASP, the FMF results of this study are closer to the spatial variation in the ratio of PM2.5 to PM10 near the ground. The analysis of the annual and seasonal average FMF of China from 2006 to 2013 shows that the FMF high value area in China is mainly maintained in the area east of the Hu Line, with the highest FMF year being 2013, and the highest FMF season is winter.

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 Comparison of MODIS 250 m products for early corn yield predictions: a case study in Vojvodina, Serbia

2016 ◽  
Vol 8 (1) ◽  
Author(s):  
Miro Govedarica ◽  
Dušan Jovanović ◽  
Filip Sabo ◽  
Mirko Borisov ◽  
Milan Vrtunski ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Corn Yield ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

AbstractThe aim of the paper is to compare Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (

scholarly journals A note on meteorological variables related trends in the MODIS NPP product for Estonia / Meteoroloogilised andmed põhjustavad vigu MODISe primaarproduktsiooni hinnangutes Eesti alal

2011 ◽  
Vol 55 (1) ◽  
pp. 60-63 ◽  
Author(s):  
Alo Eenmäe ◽  
Tiit Nilson ◽  
Mait Lang
Keyword(s):  
Soil Fertility ◽  
Predictive Power ◽  
Net Primary Production ◽  
Meteorological Data ◽  
Meteorological Variables ◽  
Opposite Trend ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract The MODIS (The Moderate Resolution Imaging Spectroradiometer) yearly NPP (Net Primary Production) 1 km resolution products were collected over Estonia for years 2000-2010. The MODIS NPP product for forest pixels showed a clear West-East decreasing trend over the Estonian territory. At the same time the trunk volume increment estimates extracted from the Estonian national statistics averaged over the same period showed the opposite trend. The MODIS NPP algorithm seems to overestimate the contribution of meteorological variables and to ignore the role of soil fertility differences. To improve the predictive power of MODIS algorithm to describe local NPP differences, the local meteorological data with higher spatial resolution should be used as an input in the NPP calculations, whereas the algorithm should be modified by optimizing the input parameters and including parameters of soil fertility into the calculation scheme.

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