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\title{Exploring the Impact of Rainfall on Vegetation Dynamics in Agricultural
Land Using Sentinel-2 Data}



\author[1]{Suyog Khose}%
\author[1]{Shashi Kumar}%
\author[2]{Abhijit Behera}%
\author[3]{Ajay Satpute}%
\author[4]{Anilkumar Kamble}%
\affil[1]{Indian Institute of Technology Kharagpur}%
\affil[2]{International Water Management Institute (IWMI)}%
\affil[3]{ICAR-Indian Grassland and Fodder Research Institute}%
\affil[4]{College of Agriculture Latur, Vasantrao Naik Marathwada Krishi Vidyapeeth}%


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  \date{April 27, 2026}


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\begin{abstract}
Vegetation serves as a critical indicator of environmental health,
reflecting soil, water and air quality. Monitoring changes in vegetation
distribution and extent is essential, especially in regions prone to
climatic extremes. Traditional vegetation monitoring methods are
labor-intensive, costly, and time-consuming. Remote sensing offers a
powerful alternative, providing extensive spatial coverage and enabling
efficient analysis. This study examines the impact of rainfall on
vegetation growth in Latur, a drought-prone district in Maharashtra,
India, utilizing Sentinel-2 satellite data and the Vegetation Condition
Index (VCI) from 2017 to 2023. The 10-meter spatial resolution annual
maps from ESRI, covering the years 2017 to 2023, were utilized to assess
changes in land use and land cover (LULC). The LULC data were classified
into seven classes: Water, Trees, Flooded Vegetation, Agricultural Land,
Built Area, Bare Ground, and Rangeland. In 2017, Agricultural Land
constituted 92\% of the total area, decreasing to 90.28\% by 2023, while
the built-up area rose from 2.81\% to 3.41\%. Additionally, the annual
mean VCI decreased from 36.28 in 2017 to 15.29 in 2023. The study
assessed land use changes from 2017 to 2023, revealing a strong
correlation between rainfall and vegetative growth. Spatial maps show
significant seasonal variations in vegetation, emphasizing the need for
continuous monitoring. This research highlights the effectiveness of
Sentinel-2 data and Google Earth Engine for large-scale vegetation
monitoring in drought-prone areas.

\textbf{Keywords}: Vegetation dynamics, Sentinel-2, VCI, Google Earth
Engine, rainfall impact, drought-prone regions.%
\end{abstract}%



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