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

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

doi:10.1117/12.2597666

12 September 2021 Google Earth Engine for land surface albedo estimation: comparison among different algorithms

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

Proceedings Volume 11856, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIII; 118560F (2021) https://doi.org/10.1117/12.2597666
Event: SPIE Remote Sensing, 2021, Online Only

Abstract

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.

Citation Download Citation

A. Capolupo, C. Monterisi, C. Barletta, and E. Tarantino "Google Earth Engine for land surface albedo estimation: comparison among different algorithms", Proc. SPIE 11856, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIII, 118560F (12 September 2021); https://doi.org/10.1117/12.2597666

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
13 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Earth observing sensors

Landsat

Satellites

Algorithm development

Error analysis

Satellite imaging

Sensors

Show All Keywords

Keywords/Phrases

Search In:

Publication Years