Geomatics and Environmental Engineering, vol. 17, no. 6

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Vol. 17 No. 6 (2023): Geomatics and Environmental Engineering

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Submitted
Mar 12, 2023
Accepted
Jul 24, 2023
Published
Oct 17, 2023
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This work is licensed under a Creative Commons Attribution 4.0 International License.

Extracting Land Surface Albedo from Landsat 9 Data in GEE Platform to Support Climate Change Analysis

https://doi.org/10.7494/geom.2023.17.6.35
Carlo Barletta
Politecnico di Bari, Department of Civil, Environmental, Land, Building Engineering and Chemistry (DICATECh), Bari, Italy
https://orcid.org/0000-0003-4765-6819
Alessandra Capolupo
Politecnico di Bari, Department of Civil, Environmental, Land, Building Engineering and Chemistry (DICATECh), Bari, Italy
https://orcid.org/0000-0002-4103-1992
Eufemia Tarantino
Politecnico di Bari, Department of Civil, Environmental, Land, Building Engineering and Chemistry (DICATECh), Bari, Italy
https://orcid.org/0000-0002-2468-0771

Corresponding Author(s) : Alessandra Capolupo

alessandra.capolupo@poliba.it

Geomatics and Environmental Engineering, Vol. 17 No. 6 (2023): Geomatics and Environmental Engineering

Abstract

Land surface albedo is a relevant variable in many climatic, environmental, and hydrological studies; its monitoring allows researchers to identify changes on the Earth’s surface. The open satellite data that is provided by the USGS/NASA Landsat mission is quite suitable for estimating this parameter through the remote sensing technique. The purpose of this paper is to evaluate the potentialities of the new Landsat 9 data for retrieving Earth’s albedo by applying da Silva et al.’s algorithm (developed in 2016 for the Landsat 8 data) using the Google Earth Engine cloud platform and R software. Two urban areas in Southern Italy with similar geomorphologic and climatic characteristics were chosen as study sites. After obtaining thematic maps of the albedos here, a statistical analysis and comparison among the Landsat 8 and Landsat 9 results was performed considering the entire study areas and each land use/land cover class that is provided by the Copernicus Urban Atlas 2018. This approach was also applied to the data after being filtered through Tukey’s test (used to detect and remove outliers). The analysis showed a very good correlation between the Landsat 8 and Landsat 9 estimations (ρ > 0.94 for both sites), with some exceptions that were related to some mis-corresponding values. Furthermore, the Landsat 8 and Landsat 9 outliers were generally overlapping. In conclusion, da Silva et al.’s approach appears to also be reasonably applicable to the Landsat 9 data despite some radiometric differences.

Keywords

open satellite data cloud computing climate change Copernicus Urban Atlas environmental monitoring sustainable development urban areas
Barletta, C., Capolupo, A., & Tarantino, E. (2023). Extracting Land Surface Albedo from Landsat 9 Data in GEE Platform to Support Climate Change Analysis. Geomatics and Environmental Engineering, 17(6), 35–75. https://doi.org/10.7494/geom.2023.17.6.35
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References
  1. Qu Y., Liang S., Liu Q., He T., Liu S., Li X.: Mapping surface broadband albedo from satellite observations: a review of literatures on algorithms and products. Remote Sensing, vol. 7(1), 2015, pp. 990–1020. https://doi.org/10.3390/rs70100990.
  2. 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, Academic Press, Cambridge 2012, pp. 173–231.
  3. Zhang X., Jiao Z., Zhao C., Qu Y., Liu Q., Zhang H., Tong Y., Wang C., Li S., Guo J. et al.: Review of land surface albedo: variance characteristics, climate effect and management strategy. Remote Sensing, vol. 14(6), 2022, 1382. https://doi.org/10.3390/rs14061382.
  4. Bonafoni S., Sekertekin A.: Albedo retrieval from Sentinel-2 by new narrow-to-broadband conversion coefficients. IEEE Geoscience and Remote Sensing Letters, vol. 17(9), 2020, pp. 1618–1622. https://doi.org/10.1109/LGRS.2020.2967085.
  5. Mariotto I., Gutschick V.P.: Non-Lambertian corrected albedo and vegetation index for estimating land evapotranspiration in a heterogeneous semi-arid landscape. Remote Sensing, vol. 2(4), 2010, pp. 926–938. https://doi.org/10.3390/rs2040926.
  6. Roy D.P., Wulder M.A., Loveland T.R., Woodcock C.E., Allen R.G., Anderson M.C., Helder D. et al.: Landsat-8: Science and product vision for terrestrial global change research. Remote Sensing of Environment, vol. 145, 2014, pp. 154–172. https://doi.org/10.1016/j.rse.2014.02.001.
  7. Souidi Z., Hamimed A., Mederbal K., Donze F.: Mapping latent heat flux in the western forest covered regions of Algeria using Remote Sensing data and a spatialized model. Remote Sensing, vol. 1(4), 2009, pp. 795–817. https://doi.org/10.3390/rs1040795.
  8. Bezerra B.G., da Silva B.B., dos Santos C.A.C., Bezerra J.R.C.: Actual evapotranspiration estimation using remote sensing: Comparison of SEBAL and SSEB approaches. Advances in Remote Sensing, vol. 4(3), 2015, pp. 234–247. https://doi.org/10.4236/ars.2015.43019.
  9. García D.H.: Analysis of urban heat island and heat waves using Sentinel-3 images: A study of Andalusian cities in Spain. Earth Systems and Environment, vol. 6, 2022, pp. 199–2190. https://doi.org/10.1007/s41748-021-00268-9.
  10. Barletta C., Capolupo A., Tarantino E.: 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: Malaga, Spain, July 4–7, 2022: Proceedings, Part III, Lecture Notes in Computer Science, vol. 13379, Springer, Cham 2022, pp. 435–449. https://doi.org/10.1007/978-3-031-10545-6_30.
  11. 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. Environmental Engineering Science, vol. 39(3), 2022, pp. 296–307. https://doi.org/10.1089/ees.2021.0160.
  12. Orusa T., Borgogno Mondino E.: Landsat 8 thermal data to support urban management and planning in the climate change era: a case study in Torino area, NW Italy. [in:] Erbertseder T. et al. (ed.), Remote Sensing Technologies and Applications in Urban Environments IV, Proceedings of SPIE, vol. 11157, SPIE, 2019, pp. 1–17. https://doi.org/10.1117/12.2533110.
  13. 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., Murgante B., Misra S., Garau C., Blečić I., Taniar D., Apduhan B.O. et al. (eds.), Computational Science and Its Applications – ICCSA 2021: 21st International Conference, Cagliari, Italy, September 13–16, 2021: Proceedings, Part II, Lecture Notes in Computer Science, vol. 12955, Springer, Cham 2021, pp. 89–101. https://doi.org/10.1007/978-3-030-87007-2_7.
  14. Capolupo A., Monterisi C., Saponaro M., Tarantino E.: Multi-temporal analysis of land cover changes using Landsat data through Google Earth Engine platform. [in:] Themistocleous K., Papadavid G., Michaelides S., Ambrosia V., Hadjimitsis D.G. (eds.), Eight International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2020), Proceedings of SPIE, vol. 11524, SPIE, 2020, pp. 447–458. https://doi.org/10.1117/12.2575653.
  15. Bhaga T.D., Dube T., Shekede M.D., Shoko C.: Impacts of climate variability and drought on surface water resources in Sub-Saharan Africa using Remote Sensing: A review. Remote Sensing, vol. 12(24), 2020, 4184. https://doi.org/10.3390/rs12244184.
  16. Capolupo A., Monterisi C., Barletta C., Tarantino E.: Google Earth Engine for land surface albedo estimation: Comparison among different algorithms. [in:] Neale C.M.U., Maltese A. (ed.), Remote Sensing for Agriculture, Ecosystems and Hydrology XXIII, Proceedings of SPIE, vol. 11856, SPIE, 2021, pp. 1–13. https://doi.org/10.1117/12.2597666.
  17. United Nations: Transforming Our World: The 2030 Agenda for Sustainable Development. https://sdgs.un.org/2030agenda [access: 30.01.2023].
  18. Trlica A., Hutyra L.R., Schaaf C.L., Erb A., Wang J.A.: Albedo, land cover, and day-time surface temperature variation across an urbanized landscape. Earth’s Future, vol. 5(11), 2017, pp. 1084–1101. https://doi.org/10.1002/2017EF000569.
  19. Głowienka E., Michałowska K.: Analyzing the impact of simulated multispectral images on water classification accuracy by means of spectral characteristics. Geomatics and Environmental Engineering, vol. 14(1), 2020, pp. 47–58. https://doi.org/10.7494/geom.2020.14.1.47.
  20. Zonkouan B.R.V., Bachri I., Beda A.H.J., N’Guessan K.A.M.: Monitoring spatial and temporal scales of shoreline shanges in Lahou-Kpanda (Southern Ivory Coast) using Landsat data series (TM, ETM+ and OLI). Geomatics and Environmental Engineering, vol. 16(1), 2022, pp. 145–158. https://doi.org/10.7494/geom.2022.16.1.145.
  21. Kumar S., Singh R.: Geospatial applications in Land Use/Land Cover change detection for sustainable regional development: the case of Central Haryana, India. Geomatics and Environmental Engineering, vol. 15(3), 2021, pp. 81–98. https://doi.org/10.7494/geom.2021.15.3.81.
  22. Gross G., Helder D., Begeman C., Leigh L., Kaewmanee M., Shah R.: Initial cross-calibration of Landsat 8 and Landsat 9 using the simultaneous underfly event. Remote Sensing, vol. 14(10), 2022, 2418. https://doi.org/10.3390/rs14102418.
  23. Masek J.G., Wulder M.A., Markham B., McCorkel J., Crawford C.J., Storey J., Jenstrom D.T.: Landsat 9: Empowering open science and applications through continuity. Remote Sensing of Environment, vol. 248, 2020, 111968. https://doi.org/10.1016/j.rse.2020.111968.
  24. Wulder M.A., Roy D.P., Radeloff V.C., Loveland T.R., Anderson M.C., Johnson D.M., Healey S. et al.: Fifty years of Landsat science and impacts. Remote Sensing of Environment, vol. 280, 2022, 113195. https://doi.org/10.1016/j.rse.2022.113195.
  25. 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. International Journal of Agricultural and Environmental Information Systems, vol. 6(4), 2015, pp. 83–102. https://doi.org/10.4018/IJAEIS.2015100105.
  26. Aquilino M., Tarantino E., Fratino U.: Multi-temporal land use analysis of an ephemeral river area using an artificial neural network approach on Landsat imagery. ISPRS – International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XL-5/W3, 2013, pp. 167–173. https://doi.org/10.5194/isprsarchives-XL-5-W3-167-2013.
  27. Kolodiy P., Pidlypna M.: The improvement of the agricultural yields forecasting model using the software product “Land Viewer”. Geomatics and Environmental Engineering, vol. 14(1), 2020, pp. 59–67. https://doi.org/10.7494/geom.2020.14.1.59.
  28. Liang S.: Narrowband to broadband conversions of land surface albedo I: Algorithms. Remote Sensing of Environment, vol. 76(2), 2001, pp. 213–238. https://doi.org/10.1016/S0034-4257(00)00205-4.
  29. Tasumi M., Allen R.G., Trezza R.: At-surface reflectance and albedo from satellite for operational calculation of land surface energy balance. Journal of Hydrological Engineering, vol. 13(2), 2008, pp. 51–63. https://doi.org/10.1061/(ASCE)1084-0699(2008)13:2(51).
  30. da Silva B.B., Braga A.C., Braga C.C., de Oliveira L.M.M., Montenegro S.M.G.L., Barbosa B. Jr: Procedures for calculation of the albedo with OLI-Landsat 8 images: Application to the Brazilian semi-arid. Revista Brasileira de Engenharia Agrícola e Ambiental, vol. 20(1), 2016, pp. 3–8. https://doi.org/10.1590/1807-1929/agriambi.v20n1p3-8.
  31. Gomes V.C.F., Queiroz G.R., Ferreira K.R.: An overview of platforms for big Earth observation data management and analysis. Remote Sensing, vol. 12(8), 2020, 1253. https://doi.org/10.3390/rs12081253.
  32. Gorelick N., Hancher M., Dixon M., Ilyushchenko S., Thau D., Moore R.: Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, vol. 202, 2017, pp. 18–27. https://doi.org/10.1016/j.rse.2017.06.031.
  33. Tamiminia H., Salehi B., Mahdianpari M., Quackenbush L., Adeli S., Brisco B.: Google Earth Engine for geo-big data applications: A meta-analysis and systematic review. ISPRS Journal of Photogrammetry and Remote Sensing, vol. 164, 2020, pp. 152–170. https://doi.org/10.1016/j.isprsjprs.2020.04.001.
  34. Kumar L., Mutanga O.: Google Earth Engine applications since inception: Usage, trends and potential. Remote Sensing, vol. 10(10), 2018, 1509. https://doi.org/10.3390/rs10101509.
  35. Mutanga O., Kumar L.: Google Earth Engine applications. Remote Sensing, vol. 11(5), 2019, 591. https://doi.org/10.3390/rs11050591.
  36. Capolupo A., Monterisi C., Tarantino E.: Landsat images classification algorithm (LICA) to automatically extract land cover information in Google Earth Engine environment. Remote Sensing, vol. 12(7), 2020, 1201. https://doi.org/10.3390/rs12071201.
  37. Capolupo A., Monterisi C., Caporusso G., Tarantino E.: Extracting land cover data using GEE: a review of the classification indices. [in:] Gervasi O., Murgante B., Misra S., Garau C., Blečić I., Taniar D., Apduhan B.O. et al. (eds.), Computational Science and Its Applications – ICCSA 2020: 20th International Conference, Cagliari, Italy, July 1–4, 2020: Proceedings, Part IV, Lecture Notes in Computer Science, vol. 12252, Springer, Cham 2020, pp. 782–796. https://doi.org/10.1007/978-3-030-58811-3_56.
  38. The R Project for Statistical Computing. https://www.r-project.org [access: 3.02.2023].
  39. European Environment Agency: Urban Atlas 2018 – Copernicus Land Monitoring Service. https://land.copernicus.eu/local/urban-atlas/urban-atlas-2018 [access: 3.02.2023].
  40. De Fioravante P., Strollo A., Assennato F., Marinosci I., Congedo L., Munafò M.: High resolution land cover integrating Copernicus products: a 2012–2020 map of Italy. Land, vol. 11(1), 2022, 35. https://doi.org/10.3390/land11010035.
  41. Gomarasca M.: Basics of Geomatics. Springer, Dordrecht 2009.
  42. Piano Stralcio di Bacino per l’Assetto Idrogeologico della Regione Siciliana: Relazione Generale. 2004 http://www.sitr.regione.sicilia.it/pai/CD_PAI/RELAZIONE_GENERALE_P.A.I.pdf [access: 3.02.2023].
  43. Cristiano E., Urru S., Farris S., Ruggiu D., Deidda R., Viola F.: Analysis of potential benefits on flood mitigation of a CAM green roof in Mediterranean urban areas. Building and Environment, vol. 183, 2020, 107179. https://doi.org/10.1016/j.buildenv.2020.107179.
  44. Cappadonia C., Di Maggio C., Agate M., Agnesi V.: Geomorphology of the urban area of Palermo (Italy). Journal of Maps, vol. 16(2), 2020, pp. 274–284. https://doi.org/10.1080/17445647.2020.1739154.
  45. Porta M., Buosi C., Trogu D., Ibba A., De Muro S.: An integrated sea-land approach for analyzing forms, processes, deposits and the evolution of the urban coastal belt of Cagliari. Journal of Maps, vol. 17, 2021, pp. 65–74. https://doi.org/10.1080/17445647.2020.1719441.
  46. Tukey J.W.: Easy summaries – numerical and graphical. [in:] Tukey J.W. (ed.), Exploratory Data Analysis, Addison-Wesley, Reading 1977, pp. 27–56.
  47. Wang C., Caja J., Gómez E.: Comparison of methods for outlier identification in surface characterization. Measurement, vol. 117, 2018, pp. 312–325. https://doi.org/10.1016/j.measurement.2017.12.015.
  48. Leinenkugel P., Deck R., Huth J., Ottinger M., Mack B.: The potential of open geodata for automated large-scale land use and land cover classification. Remote Sensing, vol. 11(9), 2019, 2249. https://doi.org/10.3390/rs11192249.
  49. Zhong Q., Li Y.H.: Satellite observation of surface albedo over the Qinghai-Xizang plateau region. Advances in Atmospheric Science, vol. 5, 1988, pp. 57–65. https://doi.org/10.1007/BF02657346.
  50. Bastiaanssen W.G.M., Menenti M., Feddes R.A., Holslag A.A.M.A.: Remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation. Journal of Hydrology, vol. 212–213, 1998, pp. 198–212. https://doi.org/10.1016/S0022-1694(98)00253-4.
  51. Allen R.G., Waters R., Tasumi M., Trezza R., Bastiaanssen W.: SEBAL: Surface Energy Balance Algorithms for Land: Idaho Implementation. Advanced Training and Users Manual. Version 1.0, 2002.
  52. Bastiaanssen W.G.M.: SEBAL-based sensible and latent heat fluxes in the irrigated Gediz Basin, Turkey. Journal of Hydrology, vol. 229(1–2), 2000, pp. 87–100. https://doi.org/10.1016/S0022-1694(99)00202-4.
  53. Allen R.G., Tasumi M., Trezza R.: Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC) – model. Journal of Irrigation and Drainage Engineering, vol. 133(4), 2007, pp. 380–394. https://doi.org/10.1061/(ASCE)0733-9437(2007)133:4(380).
  54. Kalnay E., Kanamitsu M., Kistler R., Collins W., Deaven D., Gandin L., Iredell M. et al.: The NCEP/NCAR 40-Year Reanalysis Project. Bulletin of the American Meteorological Society, vol. 77(3), 1996, pp. 437–472. https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.
  55. Parastatidis D., Mitraka Z., Chrysoulakis N., Abrams M.: Online global land surface temperature estimation from Landsat. Remote Sensing, vol. 9(12), 2017, 1208. https://doi.org/10.3390/rs9121208.
  56. Taha H.: Urban climates and heat islands: albedo, evapotranspiration, and anthropogenic heat. Energy and Buildings, vol. 25(2), 1997, pp. 99–103. https://doi.org/10.1016/S0378-7788(96)00999-1.
  57. Brivio P.A., Lechi G., Zilioli E.: Principi e metodi di Telerilevamento. CittàStudi, Torino 2006.
  58. Tarantino E.: Monitoring spatial and temporal distribution of Sea Surface Temperature with TIR sensor data. Italian Journal of Remote Sensing, vol. 44(1), 2012, pp. 97–107.
  59. Bresciani M., Giardino C., Stroppiana D., Dessena M.A., Buscarinu P., Cabras L., Schenk K. et al.: Monitoring water quality in two dammed reservoirs from multispectral satellite data. European Journal of Remote Sensing, vol. 52(S4), 2019, pp. 113–122. https://doi.org/10.1080/22797254.2019.1686956.
  60. Capolupo A., Saponaro M., Fratino U., Tarantino E.: Detection of spatio-temporal changes of vegetation in coastal areas subjected to soil erosion issue. Aquatic Ecosystem Health & Management, vol. 23(4), 2020, pp. 491–499. https://doi.org/10.1080/14634988.2020.1802983.
  61. Meyer L.H., Heurich M., Beudert B., Premier J., Pflugmacher D.: Comparison of Landsat-8 and Sentinel-2 data for the estimation of Leaf Area Index in temperate forests. Remote Sensing, vol. 11(10), 2019, 1160. https://doi.org/10.3390/rs11101160.
  62. Garcia-Santos V., Niclòs R., Valor E.: Evapotranspiration retrieval using S-SEBI model with Landsat-8 split-window land surface temperature products over two European agricultural crops. Remote Sensing, vol. 14(11), 2022, 2723. https://doi.org/10.3390/rs14112723.
  63. Taoufik M., Laghlimi M., Fekri A.: Comparison of land surface temperature before, during and after the COVID-19 lockdown using Landsat imagery: A case study of Casablanca City, Morocco. Geomatics and Environmental Engineering, vol. 15(2), 2021, pp. 105–120. https://doi.org/10.7494/geom.2021.15.2.105.
  64. Koulibaly C.T., Ayoade J.O.: The application of GIS and remote sensing in a spatiotemporal analysis of coastline retreat in Rufisque, Senegal. Geomatics and Environmental Engineering, vol. 15(3), 2021, pp. 55–80. https://doi.org/10.7494/geom.2021.15.3.55.
  65. Jiménez-Muñoz J.C., Sobrino J.A., Skoković D., Mattar C., Cristóbal J.: Land surface temperature retrieval methods from Landsat-8 thermal infrared sensor data. IEEE Geoscience and Remote Sensing Letters, vol. 11(10), 2014, pp. 1840–1843. https://doi.org/10.1109/LGRS.2014.2312032.
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