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Exploring the Potentialities of Landsat 8 and Sentinel-2 Satellite Data for Estimating the Land Surface Albedo in Urban Areas Using GEE Platform

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Computational Science and Its Applications – ICCSA 2022 Workshops (ICCSA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13379))

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Abstract

Albedo quantifies the capacity of a certain surface to reflect incident solar radiation. Therefore, this parameter is relevant in environmental and climate studies as it drives both the land surface energy balance and the interaction between surfaces and atmosphere. It can be estimated and monitored at different scales using Remote Sensing technique. However, its assessment is pretty difficult as factors, such as resultant map accuracy, processing time, and complexity of the algorithm used to retrieve it, should be considered.

The goal of this paper is to develop a proper JavaScript code in Google Earth Engine cloud environment in order to estimate surface albedo using two different satellite data, Landsat 8 and Sentinel-2, over two different study areas: Bari (Southern Italy), and Berlin (Northeastern Germany). To achieve this purpose, Landsat 8 and Sentinel-2 images, acquired in close date, were processed in GEE environment by implementing an appropriate JavaScript code. After obtaining albedo maps over both investigated sites, the two algorithms’ performances, the Silva for Landsat 8 data and the Bonafoni for Sentinel-2 images, were statistically analyzed and compared. Furthermore, to investigate the outcomes deeply, statistics metrics were computed for different land cover classes also. UrbanAtlas provided by Copernicus was used to classify the whole case studies. Both approaches showed satisfying results albeit Landsat 8 algorithm provided higher mean values than the other one.

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References

  1. Liu, Q., Wen, J., Qu, Y., He, T., Zhang, X., Wang, L.: Broadband albedo. In: Liang, S., Li, X., Wang, J. (eds.) Advanced Remote Sensing: Terrestrial Information Extraction and Applications, 1st edn, pp. 173–231. Academic Press, Cambridge (2012)

    Google Scholar 

  2. Capolupo, A., Monterisi, C., Barletta, C., Tarantino, E.: Google earth engine for land surface albedo estimation: comparison among different algorithms. In: Proceedings of SPIE 11856, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIII, p. 118560F. International Society for Optics and Photonics (2021)

    Google Scholar 

  3. Essential Climate Variables. https://gcos.wmo.int/en/essential-climate-variables. Accessed 28 Nov 2021

  4. Voogt, J.A., Oke, T.R.: Thermal remote sensing of urban climates. Remote Sens. Environ. 86(3), 370–384 (2003)

    Article  Google Scholar 

  5. The Urban Heat Island (UHI) Effect. https://www.urbanheatislands.com/. Accessed 13 Dec 2021

  6. Sangiorgio, V., Capolupo, A., Tarantino, E., Fiorito, F., Santamouris, M.: Evaluation of absolute maximum urban heat island intensity based on a simplified remote sensing approach. Environ. Eng. Sci. 39(3), 296–307 (2022)

    Article  Google Scholar 

  7. Capolupo, A., Monterisi, C., Saponaro, M., Tarantino, E.: Multi-temporal analysis of land cover changes using Landsat data through google earth engine platform. In: Proceedings of SPIE 11524, Eighth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2020), pp. 1152419. International Society for Optics and Photonics (2020)

    Google Scholar 

  8. Capolupo, A., Boccia, L.: Innovative method for linking anthropisation process to vulnerability. World Rev. Sci. Technol. Sustain. Dev. 17(1), 4–22 (2021)

    Article  Google Scholar 

  9. Trlica, A., Hutyra, L.R., Schaaf, C.L., Erb, A., Wang, J.A.: Albedo, land cover, and daytime surface temperature variation across an urbanized landscape. Earth’s Future 5, 1084–1101 (2017)

    Article  Google Scholar 

  10. Lama, G.F.C., Sadeghifar, T., Azad, M.T., Sihag, P., Kisi, O.: On the indirect estimation of wind wave heights over the Southern Coast of Caspian sea: a comparative analysis. Water 14(6), 843 (2022)

    Article  Google Scholar 

  11. Lama, G.F.C., Crimaldi, M.: Remote sensing of ecohydrological, ecohydraulic, and ecohydrodynamic phenomena in vegetated waterways: the role of leaf area index (LAI). Biol. Life Sci. Forum 3(1), 54 (2021)

    Google Scholar 

  12. Sadeghifar, T., Lama, G.F.C., Sihag, P., Bayram, A., Kisi, O.: Wave height predictions in complex sea flows through soft-computing models: case study of Persian Gulf. Ocean Eng. 245, 110467 (2022)

    Article  Google Scholar 

  13. Esposito, M., Crimaldi, M., Cirillo, V., Sarghini, F., Maggio, A.: Drone and sensor technology for sustainable weed management: a review. Chem. Biol. Technol. Agric. 8(1), 1–11 (2021). https://doi.org/10.1186/s40538-021-00217-8

    Article  Google Scholar 

  14. Capolupo, A., Monterisi, C., Sonnessa, A., Caporusso, G., Tarantino, E.: Modeling land cover impact on albedo changes in google earth engine environment. In: Gervasi, O., et al. (eds.) ICCSA 2021. LNCS, vol. 12955, pp. 89–101. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87007-2_7

    Chapter  Google Scholar 

  15. Liang, S.: Narrowband to broadband conversions of land surface albedo I algorithms. Remote Sens. Environ. 76, 210–238 (2000)

    Google Scholar 

  16. Tarantino, E.: Monitoring spatial and temporal distribution of sea surface temperature with TIR sensor data. Italian J. Remote Sens. 44(1), 97–107 (2012)

    Article  Google Scholar 

  17. Caprioli, M., Tarantino, E.: Identification of land cover alterations in the Alta Murgia National Park (Italy) with VHR satellite imagery. Int. J. Sustain. Dev. Plan. 1(3), 261–270 (2006)

    Article  Google Scholar 

  18. Capolupo, A., Saponaro, M., Fratino, U., Tarantino, E.: Detection of spatio-temporal changes of vegetation in coastal areas subjected to soil erosion issue. Aquat. Ecosyst. Health Manag. 23(4), 491–499 (2020)

    Article  Google Scholar 

  19. Sarzana, T., Maltese, A., Capolupo, A., Tarantino, E.: Post-processing of pixel and object-based land cover classifications of very high spatial resolution images. In: Gervasi, O., et al. (eds.) ICCSA 2020. LNCS, vol. 12252, pp. 797–812. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58811-3_57

    Chapter  Google Scholar 

  20. da Silva, B.B., Braga, A.C., Braga, C.C., de Oliveira, L.M.M., Montenegro, S.M.G.L., Barbosa, J.B.: Procedures for calculation of the Albedo with OLI-Landsat 8 images: application to the Brazilian semi-arid. Revista Brasileira de Engenharia Agrícola e Ambiental 20, 3–8 (2016)

    Article  Google Scholar 

  21. Varghese, D., Radulović, M., Stojković, S., Crnojević, V.: Reviewing the potential of Sentinel-2 in assessing the drought. Remote Sens. 13(17), 3355 (2021)

    Article  Google Scholar 

  22. Vanino, S., et al.: Capability of Sentinel-2 data for estimating maximum evapotranspiration and irrigation requirements for tomato crop in Central Italy. Remote Sens. Environ. 215, 452–470 (2018)

    Article  Google Scholar 

  23. Bonafoni, S., Sekertekin, A.: Albedo retrieval from Sentinel-2 by new narrow-to-broadband conversion coefficient. IEEE Geosci. Remote Sens. Lett. 17(9), 1618–1622 (2020)

    Article  Google Scholar 

  24. Hird, J.N., DeLancey, E.R., McDermid, G.J., Kariyeva, J.: Google earth engine, open-access satellite data, and machine learning in support of large-area probabilistic wetland mapping. Remote Sensing 9(12), 1315 (2017)

    Article  Google Scholar 

  25. Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., Moore, R.: Google earth engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017)

    Article  Google Scholar 

  26. Martinelli, A., Kolokotsa, D.-D., Fiorito, F.: Urban heat island in Mediterranean coastal cities: the case of Bari (Italy). Climate 8(6), 79 (2020)

    Article  Google Scholar 

  27. Leone, A., Gobattoni, F., Pelorosso, R., Calace, F.: Nature-based climate adaptation for compact cities: green courtyards as urban cool islands. Plurimondi 18, 83–110 (2020)

    Google Scholar 

  28. Peschechera, G., Tarantino, E., Fratino, U.: Crop water requirements estimation at irrigation district scale from remote sensing: a comparison between MODIS ET product and the analytical approach. In: Proceedings of SPIE 10773, Sixth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2018), p. 1077318. International Society for Optics and Photonics (2018)

    Google Scholar 

  29. Tarantino, E., Novelli, A., Aquilino, M., Figorito, B., Fratino, U.: Comparing the MLC and JavaNNS approaches in classifying multi-temporal LANDSAT satellite imagery over an ephemeral river area. Int. J. Agric. Environ. Inf. Syst. (IJAEIS) 6(4), 83–102 (2015)

    Article  Google Scholar 

  30. Vulova, S., Meier, F., Fenner, D., Nouri, H., Kleinschmit, B.: Summer nights in Berlin, Germany: modeling air temperature spatially with remote sensing, crowdsourced weather data, and machine learning. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 13, 5074–5087 (2020)

    Article  Google Scholar 

  31. Dugord, P.-A., Lauf, S., Schuster, C., Kleinschmit, B.: Land use patterns, temperature distribution, and potential heat stress risk – the case study Berlin, Germany. Comput. Environ. Urban Syst. 48, 86–98 (2014)

    Article  Google Scholar 

  32. Kühn, M., Gailing, L.: From green belts to regional parks: history and challenges of suburban landscape planning in Berlin. In: Amati, M. (ed.) Urban Green Belts in the Twenty-first Century, Chapter 10, pp. 185–202. Ashgate (2008)

    Google Scholar 

  33. Li, H., et al.: A new method to quantify surface urban heat island intensity. Sci. Tot. Environ. 624, 262–272 (2018)

    Article  Google Scholar 

  34. Urban Atlas 2018. https://land.copernicus.eu/local/urban-atlas/urban-atlas-2018. Accessed 26 Feb 2022

  35. Allen, R.G., Tasumi, M., Trezza, R.: Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC) – model. J. Irrig. Drain. Eng. 133(4), 380–394 (2007)

    Article  Google Scholar 

  36. Taha, H.: Urban climates and heat islands: albedo, evapotranspiration, and anthropogenic heat. Energy Build. 25, 99–103 (1997)

    Article  Google Scholar 

  37. Waters, R., Allen, R., Tasumi, M., Trezza, R., Bastiaanssen, W.: SEBAL (surface energy balance algorithms for land). Idaho implementation. In: Advanced Training and Users Manual. Version 1.0 (2002)

    Google Scholar 

  38. Lai, S., Leone, F., Zoppi, C.: Spatial distribution of surface temperature and land cover: a study concerning Sardinia, Italy. In: Gervasi, O., et al. (eds.) ICCSA 2020. LNCS, vol. 12253, pp. 405–420. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58814-4_29

    Chapter  Google Scholar 

  39. Crocetto, N., Tarantino, E.: A class-oriented strategy for features extraction from multidate ASTER imagery. Remote Sens. 1(4), 1171–1189 (2009)

    Article  Google Scholar 

  40. Novelli, A., Tarantino, E., Caradonna, G., Apollonio, C., Balacco, G., Piccinni, F.: Improving the ANN classification accuracy of Landsat data through spectral indices and linear transformations (PCA and TCT) aimed at LU/LC monitoring of a River Basin. In: Gervasi, O., et al. (eds.) ICCSA 2016. LNCS, vol. 9787, pp. 420–432. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-42108-7_32

    Chapter  Google Scholar 

  41. Boccia, L., Capolupo, A., Rigillo, M., Russo, V.: Terrace abandonment hazards in a Mediterranean cultural landscape. J. Hazardous Toxic Radioactive Waste 24(1) (2020)

    Google Scholar 

  42. Capolupo, A., Monterisi, C., Caporusso, G., Tarantino, E.: Extracting land cover data using GEE: a review of the classification indices. In: Gervasi, O., et al. (eds.) ICCSA 2020. LNCS, vol. 12252, pp. 782–796. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58811-3_56

    Chapter  Google Scholar 

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Authors and Affiliations

  1. Department of Civil, Environmental, Land, Building Engineering and Chemistry (DICATECh), Politecnico di Bari, via Orabona 4, 70125, Bari, Italy

    Carlo Barletta, Alessandra Capolupo & Eufemia Tarantino

Authors
  1. Carlo Barletta
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  2. Alessandra Capolupo
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  3. Eufemia Tarantino
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Corresponding author

Correspondence to Alessandra Capolupo .

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Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Østfold University College, Halden, Norway

    Sanjay Misra

  4. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  5. University of Cagliari, Cagliari, Italy

    Chiara Garau

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Barletta, C., Capolupo, A., Tarantino, E. (2022). Exploring the Potentialities of Landsat 8 and Sentinel-2 Satellite Data for Estimating the Land Surface Albedo in Urban Areas Using GEE Platform. In: Gervasi, O., Murgante, B., Misra, S., Rocha, A.M.A.C., Garau, C. (eds) Computational Science and Its Applications – ICCSA 2022 Workshops. ICCSA 2022. Lecture Notes in Computer Science, vol 13379. Springer, Cham. https://doi.org/10.1007/978-3-031-10545-6_30

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  • DOI: https://doi.org/10.1007/978-3-031-10545-6_30

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