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Spatiotemporal retrieval of the aerosol optical thickness using Landsat 8 OLI imagery for Indian urban area

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Abstract

The surge in urbanization and industrialization is majorly contributing to ambient air pollution, predominantly in terms of particulate emissions. Human health is highly susceptible to the particles suspended in the air due to their lightweight and small size (≤ 2.5 μm), called atmospheric aerosols. In India, insufficient ground-based instruments hinder continuous aerosol monitoring. However, remote sensing offers earth imagery for in-depth analysis of air quality and weather parameters. In the present study, an attempt is made to retrieve the high-resolution (30 m) AOT using Landsat 8 Operational Land Imager (L8-OLI) imagery for Pune, Maharashtra, from the years 2014 to 2021. For the atmospheric corrections and better spatiotemporal resolution, the dark target spectrum-based Image Corrections for Atmospheric Effects (iCOR) algorithm was executed. The year 2021 showed the highest mean AOT value at the Pashan location (18.537° N, 73.805° E) in Pune, India. Also, seasonal analysis (winter and summer) indicates that the mean AOT in the winter gradually increases every year. The AOT retrieved using L8-OLI with iCOR and AOT retrieved from Aerosol Robotic Network (AERONET) in situ monitoring station (± 30 min) at 440 nm showed R2 = 0.76, r = 0.83, and RMSE = 0.1012. From this, it is summarized that for L8-OLI images, the iCOR algorithm performs well for the atmospheric correction by retrieving AOT at high spatial resolution with minimum cloud cover.

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All data used are included in the published article.

Abbreviations

ACIX:

Atmospheric Correction Inter-comparison eXercise

AERONET:

Aerosol Robotic Network

AOT:

Aerosol Optical Thickness

BoA:

Bottom of the Atmosphere

CC:

Cloud Cover

CFMask:

C Function of Mask

DB:

Deep Blue

DDV:

Dense Dark Vegetation

DEM:

Digital Elevation Model

DSF:

Dark Spectrum Fitting

DT:

Dark Target

ESA:

European Space Agency

i.e.:

That is

iCOR :

Image Corrections for Atmospheric Effects

IDW:

Inverse Distance Weighted

L8-OLI:

Landsat 8 Operational Land Imager

LaSRC:

Land Surface Reflectance Code

LibRadtran:

Library for Radiative Transfer

LUT:

Look-Up-Table

MACCS:

Multi-Sensor Atmospheric Correction and Cloud Screening

MAJA:

MACCS-ATCOR Joint Algorithm

MEIT:

Multiparameter Endmember Inversion Technique

MODTRAN5:

Moderate-Resolution Atmospheric Radiance and Transmittance Model–Version 5

NASA:

National Aeronautics and Space Administration

NOAA:

National Oceanic and Atmospheric Administration

RMSE:

Root Mean Squared Error

SIMEC:

SIMilarity Environmental Correction

TIRS:

Thermal Infrared Sensor

ToA:

Top of the Atmosphere

USGS:

United States Geological Survey

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Authors and Affiliations

  1. Department of Civil Engineering, S. V. National Institute of Technology, Surat, 395007, Gujarat, India

    Akshay Chauhan, Namrata Jariwala & Robin Christian

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  1. Akshay Chauhan
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  2. Namrata Jariwala
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  3. Robin Christian
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Contributions

AC: Conceptualization and literature review, methodology, formal analysis and data retrieval, investigations, writing original draft and editing; NJ: investigation,resources, supervision, review and editing; and RC: Final review and editing.

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Correspondence to Namrata Jariwala.

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Chauhan, A., Jariwala, N. & Christian, R. Spatiotemporal retrieval of the aerosol optical thickness using Landsat 8 OLI imagery for Indian urban area. Air Qual Atmos Health (2024). https://doi.org/10.1007/s11869-024-01520-7

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