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Comparative analysis of surface albedo estimation methods using Landsat 8 and 9 satellite imagery over multiple ground observation sites
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DOI：10.1016/j.asr.2026.02.048.png)
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Abstract
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Global population growth and climate change increasingly complicate the management of energy balance and water resources, exerting significant pressure on environmental systems. In this context, surface albedo, defined as the proportion of solar energy reflected by the Earth's surface, plays a crucial role in understanding the planet's energy dynamics. However, accurately determining albedo values through ground-based measurements remains challenging due to technical and financial constraints. This study aims to compare the performance of three surface albedo estimation models-Silva et al. (2016), Naegeli et al. (2017), and Andres-Anaya et al. (2023)-and evaluates their applicability using Landsat 8 and Landsat 9 satellite data. The analysis further assesses the contribution of these datasets and algorithms to environmental monitoring applications. Satellite data were processed using the Google Earth Engine platform, while validation was performed with in situ measurements from eight stations belonging to the SURFRAD and URBANFLUXES networks. Results indicate that Landsat 8 generally outperforms Landsat 9, exhibiting lower root mean square error (RMSE) values across most stations. Specifically, RMSE values for Landsat 8 were 0.086, 0.044, and 0.045 for the Silva et al. (2016), Naegeli et al. (2017), and Andres-Anaya et al. (2023) algorithms, respectively, compared to 0.098, 0.063, and 0.054 for Landsat 9. These findings demonstrate that Landsat 8 provides more consistent and reliable surface albedo estimates. Overall, the study highlights the potential of satellite-derived albedo as a reliable tool for environmental monitoring and climate modeling. The results emphasize the importance of selecting appropriate algorithms and satellite datasets according to surface and regional characteristics, thereby supporting the development of more effective monitoring and modeling strategies in the context of climate change mitigation. (c) 2026 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Keywords:
Remote sensing
Surface albedo
Landsat 8
Landsat 9
SURFRAD
URBANFLUXES
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