Mr. Carlo Barletta Profile

Carlo Barletta

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Proceedings Article | 12 September 2021 Paper

Google Earth Engine for land surface albedo estimation: comparison among different algorithms

A. Capolupo, C. Monterisi, C. Barletta, E. Tarantino

Proceedings Volume 11856, 118560F (2021) https://doi.org/10.1117/12.2597666

KEYWORDS: Earth observing sensors, Landsat, Satellites, Algorithm development, Sensors, Satellite imaging, Error analysis, Data acquisition, Vegetation

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Albedo has long been recognized as a relevant bio-geophysical variable to model Earth surface and it was involved in all the climate simulation models. Therefore, the correct modelling of albedo is essential to reduce the error propagation in the prediction algorithms. To meet such a purpose, different methods have been developed over the past years. Among them, the simplified approach proposed by Liang in 2000 and the corrected algorithm introduced by Silva et al. (2016) are commonly used. To the best of our knowledge, the outcomes produced by applying such techniques have not been investigated yet. The present paper is intended to explore the potentialities of Google Earth Engine (GEE) platform in estimating land surface albedo from three medium-resolution geospatial data gathered by different Landsat sensors in diverse acquisition periods. Java-script code was developed to numerically implement the above-mentioned algorithms in GEE environment. Their performances were compared and the error committed adopting the simplified method was quantified. As a result, the corrected algorithm reported more accurate values. Nevertheless, its complexity implies a high implementation difficulty and, consequently, a higher processing time is required to handle the data. Conversely, the simplified approach allowed to estimate land surface albedo in a short time. Quantifying the error committed using the simplified approach allows us to correct its results, improving their accuracy. Although obtained results are preliminary, this research enhanced the possibility to model the albedo by adopting the simplified algorithm after correcting it. This implies to reduce error propagation and, simultaneously, to speed up the data handling.

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