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<p>This research explores the downscaling of Land Surface Temperature (LST) from 30&#xa0;m to 3&#xa0;m using multispectral indices derived from Landsat-8/9, PlanetScope, and Sentinel-2 datasets. LST is a critical parameter for monitoring agricultural landscapes, particularly for assessing soil moisture, crop health, and irrigation needs. However, the spatial resolution of LST from Landsat-8/9 and MODIS often fails to capture the intricate heterogeneity of agricultural fields. By leveraging multispectral data, this study enhances LST resolution, enabling a more detailed analysis of LST variations across different crop types, specifically focusing on paddy fields during their early growing season. Comparative analysis revealed that the 3-meter resolution data provided significantly greater detail, uncovering fine-scale LST variations obscured in the 30-meter data. Furthermore, the downscaled LST data were instrumental in accurately estimating paddy field areas and analyzing temporal changes for irrigation management and resource allocation. These findings underscore the potential of high-resolution LST data in advancing precision agriculture and emphasize the importance of integrating multispectral data for enhanced agricultural monitoring.</p>

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Development of high-resolution land surface temperature and paddy area estimation technique using multi-source satellite image-based downscaling

  • 期刊论文

  • 发表日期：

    2026年3月25日

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  • Kiramat Hussain,
  • Jeongho Han,
  • Kyoung Jae Lim,
  • Jonggun Kim

摘要

This research explores the downscaling of Land Surface Temperature (LST) from 30 m to 3 m using multispectral indices derived from Landsat-8/9, PlanetScope, and Sentinel-2 datasets. LST is a critical parameter for monitoring agricultural landscapes, particularly for assessing soil moisture, crop health, and irrigation needs. However, the spatial resolution of LST from Landsat-8/9 and MODIS often fails to capture the intricate heterogeneity of agricultural fields. By leveraging multispectral data, this study enhances LST resolution, enabling a more detailed analysis of LST variations across different crop types, specifically focusing on paddy fields during their early growing season. Comparative analysis revealed that the 3-meter resolution data provided significantly greater detail, uncovering fine-scale LST variations obscured in the 30-meter data. Furthermore, the downscaled LST data were instrumental in accurately estimating paddy field areas and analyzing temporal changes for irrigation management and resource allocation. These findings underscore the potential of high-resolution LST data in advancing precision agriculture and emphasize the importance of integrating multispectral data for enhanced agricultural monitoring.