Harvesting insights : a comprehensive analysis of agricultural lands using remote sensing

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Bubshait, Roaya and Aljawder, Aysha and Almahmeed, Aysha; (2026) Harvesting insights : a comprehensive analysis of agricultural lands using remote sensing. In: 2025 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology (ICARES). IEEE, IDN, pp. 1-7. ISBN 9798331557072 (https://doi.org/10.1109/icares67579.2025.11371551)

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Abstract

Climate change has emerged as a critical global challenge, with far-reaching consequences for our planet's ecosystems, weather patterns, and human societies. With the advancement in technology today, decision-makers have begun to move away from costly and traditional monitoring methods to leveraging advanced techniques such as remote sensing for agricultural monitoring. Traditional methods of assessing agricultural lands often involve time-consuming and costly field surveys. However, with recent advancements in remote sensing technology, and with the availability of satellite imagery, it has become possible to utilize remote sensing indices to assess agricultural lands more efficiently and accurately. This study focuses on utilizing Sentinel-2 and Landsat-8 satellite images to provide spatiotemporal insights on a seasonal basis. The primary objective of this study is to assess the agricultural lands in the Northern Governorate of the Kingdom of Bahrain by analysing the potential of remote sensing indices in characterizing and quantifying key agricultural parameters such as vegetation health, water stress, temperature levels and soil conditions. To extract meaningful geospatial insights from satellite imagery, various remote sensing indices have been applied such as the Normalized Difference Chlorophyll Index (NDCI), Normalized Difference Moisture Index (NDMI), Soil Moisture Index (SMI), Soil Salinity Index (SSI) and Land Surface Temperature (LST). The comparative analysis reveals key spatial and temporal dynamics between all indices. Correlation coefficient (r) highlights relationships between NDCI, SMI, and NDMI, showing notable interdependence in agricultural parameters with r values ranging from 0.623 to 0.783 across summer and winter seasons.

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