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DETERMINATION OF SOLAR REFLECTION COEFFICIENTS (ALBEDO) FROM SATELLITE IMAGES USING GOOGLE EARTH ENGINE PLATFORM

Year 2024, Volume: 12 Issue: 4, 955 - 970, 01.12.2024
https://doi.org/10.36306/konjes.1531085

Abstract

In many models calculating solar radiation, a combination of physical measurements and mathematical models is used to achieve results close to reality. In these calculations, the slope values and shading effects in the region being analyzed are often disregarded. Mathematical models such as ArcGIS's Area Solar Radiation (ASR) can calculate shading effects on three-dimensional surfaces. When solar radiation models are computed in three dimensions, accounting for solar rays reflected from the ground, in addition to atmospheric reflections, will increase accuracy.
This study aimed to determine the surface reflectance coefficients that should be added in three-dimensional radiation models. In literature, general assumptions exist for surface reflectance coefficients, which represent very broad average values. However, this study aimed to establish precise albedo values for all land classes and surfaces. An area of approximately 1600 km² located in the mountainous region south of Karaman was chosen as the test area. This area was chosen in Karaman province because, as is known, this region has high solar energy potential. Sentinel 2A satellite images with a spatial resolution of 10 meters were used for both summer and winter seasons through the Google Earth Engine (GEE) platform. For the summer and winter applications, the albedo value for snowy surfaces was calculated as 0.86, while for light-colored buildings, it was 0.36 for summer and 0.28 for winter. Although examples were provided for some land classes, the study ultimately determined albedo values for all land surfaces without differentiation between classes.

References

  • G. L. Stephens, D. O'Brien, P. J. Webster, P. Pilewski, S. Kato, and J. L. Li, The albedo of Earth, Reviews of geophysics, 53(1), pp. 141-163, 2015.
  • G. Gürsoy, Pv, rüzgâr türbini ve batarya içeren hibrid enerji sistemlerinde iyileştirilmiş optimal ölçeklendirme, Master’s Thesis, Yıldız Technical University, Thesis Number. 406277, YÖK Thesis Center, 2015.
  • M. Dursun, Küresel güneş radyasyonun makine öğrenmesi yöntemleri ile tahmini: örnek bir uygulama, Gaziantep İslam Bilim ve Teknoloji Üniversitesi Graduate School of Education, Department of Electrical and Electronics Engineering, Institute of Science, Master's thesis, 2023.
  • Y. Özdemir, Uydu Tabanlı Kuadratik Model İle Türkiye’de Güneş Radyasyonu Dağılımının Belirlenmesi, Master's Thesis, Gazi University, Institute of Science, Ankara, 2012.
  • S. Ener Ruşen, 2018. Performance Evaluation of a Coupled Method for the Estimation of Daily Global Solar Radiation on a Horizontal Surface, Atmósfera, 31, pp. 347-354, 2018.
  • A. Kara, Global solar irradiance time series prediction using long short term memory network, Gazi University Journal of Science Part C: Design and Technology, 4, 7, 2019.
  • G. Arslan, B. Bayhan and K. Yaman, Mersin/Türkiye için ölçülen global güneş ışınımının yapay sinir ağları ile tahmin edilmesi ve yaygın ışınım modelleri ile karşılaştırılması, Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji,, pp. 7, 80-96, 2019.
  • S. Ghimire, R. C. Deo, N. Raj and J. Mi, Deep solar radiation forecasting with convolutional neural network and long short-term memory network algorithms, Applied Energy, 253, 113541, 2019.
  • B. Gao, X. Huang, J. Shi, Y. Tai and J. Zhang, Hourly forecasting of solar irradiance based on CEEMDAN and multi-strategy CNN-LSTM neural networks, Renewable Energy, 162, 1665-83, 2020.
  • S. A. Haider , M. Sajid, H. Sajid, E. Uddin and Y. Ayaz, Deep learning and statistical methods for short-and long-term solar irradiance forecasting for Islamabad, Renewable Energy, 198, pp. 51-60, 2022.
  • A. Angström, Solar and terrestrial radiation, Q. J. R. Meteorolog. Soc., pp. 50,121–125, 1924.
  • A. B. Karaveli, and B. G. Akınoğlu, Development of New Monthly Global and Diffuse Solar Irradiation Estimation Methodologies and Comparisons, Int. J. Green Energy, 15, pp. 325-346, 2018.
  • A. Bayyiğit, O. K. Çinici, and A. Acır, Tek Yüzeyli ve Çift Yüzeyli Fotovoltaik Panellerin Performans Analizi. Gazi University Journal of Science Part C: Design and Technology, 11(2), pp. 407-420, 2023.
  • X. Su, C. Luo, X. Chen and et al., Numerical modeling of all-day albedo variation for bifacial PV systems on rooftops and annual yield prediction in Beijing. Build. Simul, 17, pp. 955–964, 2024.
  • M. H. Aksoy and M. K. Çalık, Performance Investigation of Bifacial Photovoltaic Panels at Different Ground Conditions, KONJES, vol. 10, no. 3, pp. 704–718, 2022.
  • S. Bony and J. L. Dufresne, Marine boundary layer clouds at the heart of tropical cloud feedback uncertainties in climate models, Geophys. Res. Lett., 32, L20806, 2005.
  • J. Charney, W. J. Quirk, S. H. Chow and J. Kornfield, A comparative study of the effects of albedo change on drought in semi–arid regions, Journal of the atmospheric sciences, 34(9), 1366-1385. 1977.
  • A. K. Betts and J. H. Ball, Albedo over the boreal forest. Journal of Geophysical Research: Atmospheres, 102(D24), 28901-28909, 1997.
  • Z. Jin, T. P. Charlock, W. L. Smith Jr, and K. Rutledge, A parameterization of ocean surface albedo, Geophysical research letters, 31(22), 2004.
  • A. S. Gardner, and M. J. Sharp, A review of snow and ice albedo and the development of a new physically based broadband albedo parameterization, Journal of Geophysical Research: Earth Surface, 115(F1), 2010.
  • H. Akbari, H. D. Matthews and D. Seto, The long-term effect of increasing the albedo of urban areas. Environmental Research Letters, 7(2), 024004, 2012.
  • J. R. Hummel, and R. A. Reck, A global surface albedo model, Journal of Applied Meteorology and Climatology, 18(3), pp. 239-253, 1979.
  • A. Henderson‐Sellers, and M. F. Wilson, Surface albedo data for climatic modeling, Reviews of Geophysics, 21(8), , pp. 1743-1778, 1983.
  • A. Hall, The role of surface albedo feedback in climate, Journal of climate, 17(7), pp. 1550-156, 2004.
  • A. Donohoe and D. S. Battisti, Atmospheric and surface contributions to planetary albedo. Journal of Climate, 24(16), pp. 4402-4418, 2011.
  • M. Drusch, U. Del Bello, S. Carlier, O. Colin, V. Fernandez F. Gascon, B. Hoersch, C. Isola, P. Laberinti, P. Martimort and et al. Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services, Remote Sens. Environ. 120, pp. 25–36, 2012.
  • F. Gascon, C. Bouzinac, O. Thépaut, M. Jung, B. Francesconi, J. Louis, V. Lonjou, B. Lafrance, S. Massera, A. Gaudel-Vacaresse and et al. Copernicus Sentinel-2A Calibration and Products Validation Status, Remote Sens., 9, 584, 2017.
  • C. Revel, V. Lonjou, S. Marcq, C. Desjardins, B. Fougnie, C. Coppolani-Delle Luche, C. and X. Lenot, Sentinel-2A and 2B absolute calibration monitoring, European Journal of Remote Sensing, 52(1), pp. 122–137, 2019.
  • L. Yu, P. Gong, Google Earth as a virtual globe tool for Earth science applications at the global scale: Progress and perspectives, Int. J. Remote Sens., 33, pp. 3966–3986, 2012.
  • Q. Zhao, L. Yu, X. Li, D. Peng, Y. Zhang and P. Gong, Progress and Trends in the Application of Google Earth and Google Earth Engine, Remote Sens., 13, 3778, 2021.
  • T. Tao, S. Abades, S. Teng, Z. Y. X. Huang, L. Reino, B. J. W. Chen, Y. Zhang, C. Xu and J. C. Svenning, Macroecological factors shape local-scale spatial patterns in agriculturalist settlements, Proc. R. Soc. B Biol. Sci., 284, 2017.
  • H. Du, W. Cai, Y. Xu, Z. Wang, Y. Wang and Y. Cai, Quantifying the cool island effects of urban green spaces using remote sensing Data, Urban For. Urban Green,27, pp. 24–31, 2017.
  • A. Schneider, Monitoring land cover change in urban and pen-urban areas using dense time stacks of Landsat satellite data and a data mining approach, Remote Sens. Environ, 124, pp.689–704, 2012.
  • M. Akbar, S. Aliabadi, R. Patel and M. Watts, A fully automated and integrated multi-scale forecasting scheme for emergency preparedness. Environmental Modelling & Software, 39, pp- 24–38, 2013.
  • F. Giselle Murillo-Garcia, I. Alcantara-Ayala, F. Ardizzone, M. Cardinali, F. Fiourucci and F. Guzzetti, Satellite stereoscopic pair images of very high resolution: A step forward for the development of landslide inventories, Landslides, 12, pp. 277–291, 2015.
  • J. Zhang, D. R. Gurung, R. Liu, M. S. R. Murthy and F. Su, Abe Barek landslide and landslide susceptibility assessment in Badakhshan Province, Afghanistan. Landslides, 12, pp. 597–609, 2015.
  • A. Sharma, J. Wang and E. M. Lennartson, Intercomparison of MODIS and VIIRS fire products in Khanty-Mansiysk Russia: Implications for characterizing gas flaring from space, Atmosphere, 8, 95, 2017.
  • A. Hall, and X. Qu, Using the current seasonal cycle to constrain snow albedo feedback in future climate change, Geophys. Res. Lett., 33, L03502, doi:10.1029/2005GL025127, 2006.
  • B. Y. Liu, and R. C. Jordan, The interrelationship and characteristic distribution of direct, diffuse and total solar radiation, Solar energy, 4(3), pp.1-19, 1960.
  • D. Vernez, A. Milon, L. Vuilleumier, and J. L. Bulliard, Anatomical exposure patterns of skin to sunlight: relative contributions of direct, diffuse and reflected ultraviolet radiation, British Journal of Dermatology, 167(2), pp. 383-390, 2012.
  • A. Höpe, Diffuse reflectance and transmittance, In Experimental Methods in the Physical Sciences , Vol. 46, Academic Press, pp. 179-219, 2014.
  • S. Vanino and et al., Capability of Sentinel-2 data for estimating maximum evapotranspiration and irrigation requirements for tomato crop in Central Italy, Remote Sensing of Environment 215, , pp. 452–470, 2018.
  • D. J. Wojcicki, Derivation of the effective beam radiation incidence angle equations for diffuse and reflected solar radiation using a two dimensional approach, Solar Energy, 112, pp. 272-281, 2015.
  • ArcGIS Solar Radiation Documents. [Online]. Available: https://desktop.arcgis.com/en/arcmap/latest/tools/spatial-analyst-toolbox/modeling-solar-radiation.htm [Accessed Apr.10, 2024].
  • Wikipedia [Online]. Available: https://en.wikipedia.org/wiki/Google_Earth [Accessed Feb. 5, 2024].
  • ENEA, Italian National Agency for New Technologies, Energy and Sustainable Economic Development [Online]. Available: https://www.enea.it/en/ [Accessed Feb. 10, 2024].
Year 2024, Volume: 12 Issue: 4, 955 - 970, 01.12.2024
https://doi.org/10.36306/konjes.1531085

Abstract

References

  • G. L. Stephens, D. O'Brien, P. J. Webster, P. Pilewski, S. Kato, and J. L. Li, The albedo of Earth, Reviews of geophysics, 53(1), pp. 141-163, 2015.
  • G. Gürsoy, Pv, rüzgâr türbini ve batarya içeren hibrid enerji sistemlerinde iyileştirilmiş optimal ölçeklendirme, Master’s Thesis, Yıldız Technical University, Thesis Number. 406277, YÖK Thesis Center, 2015.
  • M. Dursun, Küresel güneş radyasyonun makine öğrenmesi yöntemleri ile tahmini: örnek bir uygulama, Gaziantep İslam Bilim ve Teknoloji Üniversitesi Graduate School of Education, Department of Electrical and Electronics Engineering, Institute of Science, Master's thesis, 2023.
  • Y. Özdemir, Uydu Tabanlı Kuadratik Model İle Türkiye’de Güneş Radyasyonu Dağılımının Belirlenmesi, Master's Thesis, Gazi University, Institute of Science, Ankara, 2012.
  • S. Ener Ruşen, 2018. Performance Evaluation of a Coupled Method for the Estimation of Daily Global Solar Radiation on a Horizontal Surface, Atmósfera, 31, pp. 347-354, 2018.
  • A. Kara, Global solar irradiance time series prediction using long short term memory network, Gazi University Journal of Science Part C: Design and Technology, 4, 7, 2019.
  • G. Arslan, B. Bayhan and K. Yaman, Mersin/Türkiye için ölçülen global güneş ışınımının yapay sinir ağları ile tahmin edilmesi ve yaygın ışınım modelleri ile karşılaştırılması, Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji,, pp. 7, 80-96, 2019.
  • S. Ghimire, R. C. Deo, N. Raj and J. Mi, Deep solar radiation forecasting with convolutional neural network and long short-term memory network algorithms, Applied Energy, 253, 113541, 2019.
  • B. Gao, X. Huang, J. Shi, Y. Tai and J. Zhang, Hourly forecasting of solar irradiance based on CEEMDAN and multi-strategy CNN-LSTM neural networks, Renewable Energy, 162, 1665-83, 2020.
  • S. A. Haider , M. Sajid, H. Sajid, E. Uddin and Y. Ayaz, Deep learning and statistical methods for short-and long-term solar irradiance forecasting for Islamabad, Renewable Energy, 198, pp. 51-60, 2022.
  • A. Angström, Solar and terrestrial radiation, Q. J. R. Meteorolog. Soc., pp. 50,121–125, 1924.
  • A. B. Karaveli, and B. G. Akınoğlu, Development of New Monthly Global and Diffuse Solar Irradiation Estimation Methodologies and Comparisons, Int. J. Green Energy, 15, pp. 325-346, 2018.
  • A. Bayyiğit, O. K. Çinici, and A. Acır, Tek Yüzeyli ve Çift Yüzeyli Fotovoltaik Panellerin Performans Analizi. Gazi University Journal of Science Part C: Design and Technology, 11(2), pp. 407-420, 2023.
  • X. Su, C. Luo, X. Chen and et al., Numerical modeling of all-day albedo variation for bifacial PV systems on rooftops and annual yield prediction in Beijing. Build. Simul, 17, pp. 955–964, 2024.
  • M. H. Aksoy and M. K. Çalık, Performance Investigation of Bifacial Photovoltaic Panels at Different Ground Conditions, KONJES, vol. 10, no. 3, pp. 704–718, 2022.
  • S. Bony and J. L. Dufresne, Marine boundary layer clouds at the heart of tropical cloud feedback uncertainties in climate models, Geophys. Res. Lett., 32, L20806, 2005.
  • J. Charney, W. J. Quirk, S. H. Chow and J. Kornfield, A comparative study of the effects of albedo change on drought in semi–arid regions, Journal of the atmospheric sciences, 34(9), 1366-1385. 1977.
  • A. K. Betts and J. H. Ball, Albedo over the boreal forest. Journal of Geophysical Research: Atmospheres, 102(D24), 28901-28909, 1997.
  • Z. Jin, T. P. Charlock, W. L. Smith Jr, and K. Rutledge, A parameterization of ocean surface albedo, Geophysical research letters, 31(22), 2004.
  • A. S. Gardner, and M. J. Sharp, A review of snow and ice albedo and the development of a new physically based broadband albedo parameterization, Journal of Geophysical Research: Earth Surface, 115(F1), 2010.
  • H. Akbari, H. D. Matthews and D. Seto, The long-term effect of increasing the albedo of urban areas. Environmental Research Letters, 7(2), 024004, 2012.
  • J. R. Hummel, and R. A. Reck, A global surface albedo model, Journal of Applied Meteorology and Climatology, 18(3), pp. 239-253, 1979.
  • A. Henderson‐Sellers, and M. F. Wilson, Surface albedo data for climatic modeling, Reviews of Geophysics, 21(8), , pp. 1743-1778, 1983.
  • A. Hall, The role of surface albedo feedback in climate, Journal of climate, 17(7), pp. 1550-156, 2004.
  • A. Donohoe and D. S. Battisti, Atmospheric and surface contributions to planetary albedo. Journal of Climate, 24(16), pp. 4402-4418, 2011.
  • M. Drusch, U. Del Bello, S. Carlier, O. Colin, V. Fernandez F. Gascon, B. Hoersch, C. Isola, P. Laberinti, P. Martimort and et al. Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services, Remote Sens. Environ. 120, pp. 25–36, 2012.
  • F. Gascon, C. Bouzinac, O. Thépaut, M. Jung, B. Francesconi, J. Louis, V. Lonjou, B. Lafrance, S. Massera, A. Gaudel-Vacaresse and et al. Copernicus Sentinel-2A Calibration and Products Validation Status, Remote Sens., 9, 584, 2017.
  • C. Revel, V. Lonjou, S. Marcq, C. Desjardins, B. Fougnie, C. Coppolani-Delle Luche, C. and X. Lenot, Sentinel-2A and 2B absolute calibration monitoring, European Journal of Remote Sensing, 52(1), pp. 122–137, 2019.
  • L. Yu, P. Gong, Google Earth as a virtual globe tool for Earth science applications at the global scale: Progress and perspectives, Int. J. Remote Sens., 33, pp. 3966–3986, 2012.
  • Q. Zhao, L. Yu, X. Li, D. Peng, Y. Zhang and P. Gong, Progress and Trends in the Application of Google Earth and Google Earth Engine, Remote Sens., 13, 3778, 2021.
  • T. Tao, S. Abades, S. Teng, Z. Y. X. Huang, L. Reino, B. J. W. Chen, Y. Zhang, C. Xu and J. C. Svenning, Macroecological factors shape local-scale spatial patterns in agriculturalist settlements, Proc. R. Soc. B Biol. Sci., 284, 2017.
  • H. Du, W. Cai, Y. Xu, Z. Wang, Y. Wang and Y. Cai, Quantifying the cool island effects of urban green spaces using remote sensing Data, Urban For. Urban Green,27, pp. 24–31, 2017.
  • A. Schneider, Monitoring land cover change in urban and pen-urban areas using dense time stacks of Landsat satellite data and a data mining approach, Remote Sens. Environ, 124, pp.689–704, 2012.
  • M. Akbar, S. Aliabadi, R. Patel and M. Watts, A fully automated and integrated multi-scale forecasting scheme for emergency preparedness. Environmental Modelling & Software, 39, pp- 24–38, 2013.
  • F. Giselle Murillo-Garcia, I. Alcantara-Ayala, F. Ardizzone, M. Cardinali, F. Fiourucci and F. Guzzetti, Satellite stereoscopic pair images of very high resolution: A step forward for the development of landslide inventories, Landslides, 12, pp. 277–291, 2015.
  • J. Zhang, D. R. Gurung, R. Liu, M. S. R. Murthy and F. Su, Abe Barek landslide and landslide susceptibility assessment in Badakhshan Province, Afghanistan. Landslides, 12, pp. 597–609, 2015.
  • A. Sharma, J. Wang and E. M. Lennartson, Intercomparison of MODIS and VIIRS fire products in Khanty-Mansiysk Russia: Implications for characterizing gas flaring from space, Atmosphere, 8, 95, 2017.
  • A. Hall, and X. Qu, Using the current seasonal cycle to constrain snow albedo feedback in future climate change, Geophys. Res. Lett., 33, L03502, doi:10.1029/2005GL025127, 2006.
  • B. Y. Liu, and R. C. Jordan, The interrelationship and characteristic distribution of direct, diffuse and total solar radiation, Solar energy, 4(3), pp.1-19, 1960.
  • D. Vernez, A. Milon, L. Vuilleumier, and J. L. Bulliard, Anatomical exposure patterns of skin to sunlight: relative contributions of direct, diffuse and reflected ultraviolet radiation, British Journal of Dermatology, 167(2), pp. 383-390, 2012.
  • A. Höpe, Diffuse reflectance and transmittance, In Experimental Methods in the Physical Sciences , Vol. 46, Academic Press, pp. 179-219, 2014.
  • S. Vanino and et al., Capability of Sentinel-2 data for estimating maximum evapotranspiration and irrigation requirements for tomato crop in Central Italy, Remote Sensing of Environment 215, , pp. 452–470, 2018.
  • D. J. Wojcicki, Derivation of the effective beam radiation incidence angle equations for diffuse and reflected solar radiation using a two dimensional approach, Solar Energy, 112, pp. 272-281, 2015.
  • ArcGIS Solar Radiation Documents. [Online]. Available: https://desktop.arcgis.com/en/arcmap/latest/tools/spatial-analyst-toolbox/modeling-solar-radiation.htm [Accessed Apr.10, 2024].
  • Wikipedia [Online]. Available: https://en.wikipedia.org/wiki/Google_Earth [Accessed Feb. 5, 2024].
  • ENEA, Italian National Agency for New Technologies, Energy and Sustainable Economic Development [Online]. Available: https://www.enea.it/en/ [Accessed Feb. 10, 2024].
There are 46 citations in total.

Details

Primary Language English
Subjects Solar Energy Systems, Geographical Information Systems (GIS) in Planning
Journal Section Research Article
Authors

Mehmet Alper Yıldız 0000-0002-2845-072X

Hakan Karabörk 0000-0001-7387-7004

Selmin Ener Rüşen 0000-0003-3389-5739

Publication Date December 1, 2024
Submission Date August 12, 2024
Acceptance Date October 15, 2024
Published in Issue Year 2024 Volume: 12 Issue: 4

Cite

IEEE M. A. Yıldız, H. Karabörk, and S. Ener Rüşen, “DETERMINATION OF SOLAR REFLECTION COEFFICIENTS (ALBEDO) FROM SATELLITE IMAGES USING GOOGLE EARTH ENGINE PLATFORM”, KONJES, vol. 12, no. 4, pp. 955–970, 2024, doi: 10.36306/konjes.1531085.