Retrieval of Aerosol Optical Depth Over Land at 0.490 μm from Oceansat-2 Data

Mishra, Anoop Kumar; Banerjee, Tirthankar; Kant, Yogesh; Shaik, Darga Saheb; Singh, Abhay Kumar

doi:10.1007/s12524-017-0715-5

Cited by 5 publications

(2 citation statements)

References 28 publications

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“…Moreover, the effect of vegetation on AOD showed large spatial and temporal heterogeneity, the distribution and density of vegetation in various regions are different from each other. Thus, the AVHRR NDVI results need to be corrected (Mishra et al 2018).…”

Section: Retrieval Modelmentioning

confidence: 99%

An Improved DDV Algorithm for the Retrieval of Aerosol Optical Depth From NOAA/AVHRR Data

Sun

et al. 2021

J Indian Soc Remote Sens

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Aerosol Optical Depth (AOD) is one of the important parameters to characterize the physical properties of the atmospheric aerosol, which is used to describe the extinction characteristics of the aerosol, and also to calculate the aerosol content, to assess the degree of air pollution and to study aerosol climate effect. To study the historical change of aerosol in long-time series, the advanced very high resolution radiometer (AVHRR) data earliest used for aerosol research was used in this study. Due to the lack of shortwave infrared (SWIR) (center at 2.13 lm) of the sensor, the relationship between the blue and red bands with SWIR cannot be provided, and the visible band used to calculate the normalized difference vegetation index (NDVI) contains the wavelength range of red and green, it is very difficult to calculate the accurate land surface reflectance (LSR). Therefore, based on the Dense Dark Vegetation algorithm (DDV), we propose to introduce mature MODIS vegetation index products (MYD13) to correct AVHRR NDVI, to support the estimation of AVHRR LSR, determine the relationship between corrected AVHRR NDVI and visible band LSR, and to carry out aerosol retrieval. The results show that about 63% of the data are within the error line, and there is a consistent distribution trend in the intercomparison validation with MODIS aerosol products (MYD04).

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Section: Retrieval Modelmentioning

confidence: 99%

An Improved DDV Algorithm for the Retrieval of Aerosol Optical Depth From NOAA/AVHRR Data

Sun

et al. 2021

J Indian Soc Remote Sens

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“…In this context, satellite remote sensing has the advantage of generating spatial and temporal distribution of aerosol optical properties. Aerosol retrieval satellite sensors like Moderate-Resolution Imaging Spectroradiometer (MODIS), Multiangle Imagining Spectroradiometer (MISR), Ozone Monitoring Instrument (OMI), Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), ocean colour monitor (OCM) and Medium Resolution Imagining Spectrometer (MERIS) are widely used (Shaik et al 2017;Torres and Jethva 2014;Dumka et al 2016;Mishra et al 2018). Among various sensors that routinely provide AOD throughout the globe, MODIS on board Terra and Aqua is recognized as the most extensively used and validated .…”

Section: Intruductionmentioning

confidence: 99%

Long-Term Variability of Modis 3 km Aerosol Optical Depth Over Indian Region

Shaik

Kant

Mitra

et al. 2018

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The study examined the spatio-temporal variability, validation and climatological trend of MODIS Terra and Aqua level-2 Dark Target retrieval algorithms 3-km AOD over India region during 2001&ndash;2017. Analysis reveal the MODIS AOD shows significant variability in spatial extent; Indo-Gangetic plane having high aerosol loading and Southern peninsula shows relatively low aerosol loading in all seasons. The climatological zonal averaged (throughout India) AOD500 are 0.401&plusmn;0.23 and 0.324&plusmn;0.64 for Terra and Aqua respectively whereas the ground measured all stations averaged AOD500 is 0.43&plusmn;1.2 during study period. For validation we obtained daily AOD data from nine ground network stations across India. MODIS 3-km AOD shows good agreement with ground measurements. A total of 5646 and 4920 data points are utilized for validation of MODIS Terra and Aqua AODs respectively and the overall correlation coefficients and RMSE values are 0.94 &amp; 0.08 and 0.72 &amp; 0.17 for Terra and Aqua over the Indian region. However, during monsoon season, both the MODIS AODs are highly overestimated because of atmospheric noise and turbidity. Trend slopes and significances were calculated using Mann-Kendell test at nine selected stations and whole region. AOD shows a significant positive trend (99.9% confidence level) over India with an increasing rate of 2.35yr&minus;1 and 2.03yr&minus;1 for Terra and Aqua respectively. All stations annual AODs shows the increasing trend with different significance levels except Patiala. This study reveals the potential applications of the 3-km AOD product in aerosol climatology over Indian region.

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