Журнал

About Editorial Board Information for authors and reviewers Publication ethics Contacts User Account: send / review a manuscript
Archive
Volume 23, 2026
Number 1
Volume 22, 2025
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 21, 2024
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 20, 2023
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 19, 2022
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 18, 2021
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 17, 2020
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 16, 2019
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 15, 2018
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 14, 2017
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 13, 2016
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 12, 2015
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 11, 2014
Number 1 Number 2 Number 3 Number 4
Volume 10, 2013
Number 1 Number 2 Number 3 Number 4
Volume 9, 2012
Number 1 Number 2 Number 3 Number 4 Number 5
Volume 8, 2011
Number 1 Number 2 Number 3 Number 4
Volume 7, 2010
Number 1 Number 2 Number 3 Number 4
Issue 6, 2009
Volume 1 Volume 2
Issue 5, 2008
Volume 1 Volume 2
Issue 4, 2007
Volume 1 Volume 2
Issue 3, 2006
Volume 1 Volume 2
Issue 2, 2005
Volume 1 Volume 2
Issue 1, 2004
Volume 1
Search
Find:
русский
ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2026, V. 23, No. 1, pp. 9-28

Implementation of mathematical algorithms for converting RGB images from UAVs to ultra-high-spatial resolution albedo of a surface using satellite data as illustrated by the example of the Pleistocene Park landfill

N.A. Petrov 1 , I.A. Repina 2, 3, 4 , V.M. Stepanenko 1, 3 , M.I. Varentsov 1, 2, 3 , D.G. Chechin 2 , V.Yu. Slobodyan 5 , M.V. Zimin 1, 6 , N.S. Zimov 7 
1 Lomonosov Moscow State University, Moscow, Russia
2 A.M. Obukhov Institute of Atmospheric Physics RAS, Moscow, Russia
3 Research Computing Center of Lomonosov Moscow State University, Moscow, Russia
4 Maykop State Technological University, Maykop, Russia
5 JSC Institute of Environmental Survey, Planning & Assessment, Moscow, Russia
6 Institute of Geography RAS, Moscow, Russia
7 Pacific Geographical Institute FEB RAS, Vladivostok, Russia
Accepted: 30.09.2025
DOI: 10.21046/2070-7401-2026-23-1-9-28
Restoring the surface albedo with ultra-high spatial resolution is an urgent task for detailed calculations of the components of the radiation and thermal balance. This paper discusses methods for recovering the surface albedo in the shortwave spectrum range from RGB-images (color system: Red, Green, Blue) from unmanned aerial vehicles (UAVs) using two implemented mathematical algorithms and satellite image processing data. The mathematical formulation of the problem is related to solving the inverse problem of reconstructing the integral of the spectral function of reflected radiation and normalizing it on the basis of known reference values of the surface albedo retrieved from satellite data. The article describes two implemented mathematical algorithms that differ in the way they solve the inverse problem (iterative and non-iterative methods) and also indicates the areas of their applicability. Thus, a significant limitation of the methods is the requirement of uniform illumination conditions (shortwave downward radiation) across the territory. The algorithms were tested in cold (snowy) and warm (snowless) periods based on nadir surveys of orthophotoplanes from UAVs of the territory of the Pleistocene Park landfill (Sakha Republic, Russia). The algorithms were normalized at reference sites using the Landsat-8, -9 and Sentinel-2 satellite data. The obtained surface albedo values have a high spatial resolution equal to the resolution of the orthophotoplane. Optimal conditions for using the algorithms in the presence of homogeneous dense and continuous clouds, as well as negative effects of shadows from direct solar radiation, have been identified. Thus, this paper shows the possibility of using RGB-signal conversion algorithms to multiply the spatial resolution of surface albedo obtained from satellite images. In the future, it is planned to refine the algorithms and test them on a larger statistical sample.
Keywords: RGB-images, surface albedo, UAV, orthophotoplane, satellite images, mathematical algorithms
Full text

References:

  1. Alieva A. J., Alieva Kh. S., Ashrafov M. G., Mustafazade N. Kh., Optimization of albedometric measurements using an unmanned aerial vehicle, Measuring. Monitoring. Management. Control, 2024, No. 4 (50), pp. 68–75 (in Russian), DOI: 10.21685/2307-5538-2024-4-8.
  2. Valiev I. V., Voloboy A. G., Denisov E. Yu., Ershov S. V., Pozdnyakov S. G., Transformation of XYZ into spectrum for surface properties, Trudy Yubileinoi 25-i Mezhdunarodnoi konferentsii GraphiCon’2015 (25th Anniversary of Intern. Conf. GraphiCon’2015), Protvino: Institut fiziko-tekhnicheskoi informatiki, 2015, pp. 209–213 (in Russian).
  3. Judd D. B., Wyszecki G., Color in business, science and industry, New York: John Wiley and Sons, 1975, 553 p.
  4. Zhdanov D. D., Potemin I. S., Spectrum construction from RGB triplet in spectral simulation tasks, Trudy 20-i Mezhdunarodnoi konferentsii po komp’yuternoi grafike i zreniyu GraphiCon’2010 (The 20th Conf. Proc. Computer Graphics and Vision GraphiCon’2010), Saint Petersburg: Sankt-Peterburgskii gosudarstvennyi universitet informatsionnykh tekhnologii, mekhaniki i optiki, 2010, pp. 144–147 (in Russian).
  5. Zhuravski D. M., Ivanov B. V., Kaschin S. V., Kuprikov N. M., The method of albedo remote measuring using photorecording equipment, Issledovanie Zemli iz kosmosa, 2018, No. 1, pp. 52–59 (in Russian), DOI: 10.7868/S0205961418010050.
  6. Zavalishin N. N., The model of dependence of the surface atmospheric temperature on the Earth albedo and thermal inertia of hydrosphere, Optika atmosfery i okeana, 2010, V. 23, No. 6, pp. 480–484 (in Russian).
  7. Korneva I. A., Semenov S. M., Surface temperature response to variations in atmospheric albedo: Estimating the radiation effect, Russian meteorology and hydrology, 2016, V. 41, No. 5, pp. 307–311 (in Russian).
  8. Lykossov V. N., Glazunov A. V., Kulyamin D., Mortikov E. V., Stepanenko V. M., Superkomp’yuternoe modelirovanie v fizike klimaticheskoi sistemy: uchebnoe posobie (Supercomputer Modeling in Physics of Climate System: Study guide), Moscow University Press, 2012, p. 408 (in Russian).
  9. Timofeev Yu. M., Vasiliev A. V., Teoreticheskie osnovy atmosfernoi optiki (Theoretical fundamentals of atmospheric optics), Saint Petersburg: Nauka, 2003, 475 p. (in Russian).
  10. Afanasiev V., Ignatenko A., Voloboy A., The simple method of the RGB to spectrum conversion for tasks of physically based rendering, Scientific Visualization, 2015, V. 7, No. 4, pp. 20–26.
  11. Agisoft Metashape user manual: professional edition, version 1.5, Agisoft LLC, 2019, 139 p.
  12. Andres-Anaya P., Sanchez-Aparicio M., Del Pozo S. et al., New methodology for estimating surface albedo in heterogeneous areas from satellite imagery, Applied Sciences, 2023, V. 14, Iss. 1, Article 75, DOI: 10.3390/app14010075.
  13. Angelini L. P., Biudes M. S., Machado N. G. et al., Albedo and temperature models for surface energy balance fluxes and evapotranspiration using SEBAL and Landsat 8 over Cerrado-Pantanal, Brazil, Sensors, 2021, V. 21, Iss. 21, Article 7196, https://doi.org/10.3390/s21217196.
  14. Baldinelli G., Bonafoni S., Rotili A., Albedo retrieval from multispectral Landsat 8 observation in urban environment: Algorithm validation by in situ measurements, IEEE J. Selected Topics in Applied Earth Observations and Remote Sensing, 2017, V. 10, Iss. 10, pp. 4504–4511, DOI: 10.1109/JSTARS.2017.2721549.
  15. Bartmiński P., Siłuch M., Mapping the albedo of the active surface at different stages of the growing season using data from various sources, Remote Sensing Applications: Society and Environment, 2022, V. 28, Iss. 3, Article 100818, https://doi.org/10.1016/j.rsase.2022.100818.
  16. Bonafoni S., Sekertekin A., Albedo retrieval from Sentinel-2 by new narrow-to-broadband conversion coefficients, IEEE Geoscience and Remote Sensing Letters, 2020, V. 17, Iss. 9, pp. 1618–1622, DOI: 10.1109/LGRS.2020.2967085.
  17. Canisius F., Wang S., Croft H. et al., A UAV-based sensor system for measuring land surface albedo: Tested over a boreal peatland ecosystem, Drones, 2019, V. 3, Iss. 1, Article 27, https://doi.org/10.3390/drones3010027.
  18. Cao C., Lee X., Muhlhausen J. et al., Measuring landscape albedo using unmanned aerial vehicles, Remote Sensing, 2018, V. 10, Iss. 11, Article 1812, https://doi.org/10.3390/rs10111812.
  19. Corripio J. G., Snow surface albedo estimation using terrestrial photography, Intern. J. Remote Sensing, 2024, V. 25, Iss. 24, pp. 5705–5729, DOI: 10.1080/01431160410001709002.
  20. Dumont M., Arnaud Y., Six D., Corripio J. G., Détermination de l’albédo de surface des glaciers à partir de photographies terrestres, La Houille Blanche, 2009, V. 95, Iss. 2, pp. 102–108, DOI: 10.1051/lhb:2009021.
  21. D’Urso G., Calera Belmonte A., Operative approaches to determine crop water requirements from Earth observation data: Methodologies and applications, AIP Conf. Proc., 2006, V. 852, No. 1, pp. 14–25, https://doi.org/10.1063/1.2349323.
  22. Fan Y., Yu J., Liu W., Cross-comparison of snow albedo products derived from satellite (Sentinel-2 and Landsat-8) optical data, IOP Conf. Series: Earth and Environmental Science, 2021, V. 658, No. 1, Article 012048, DOI: 10.1088/1755-1315/658/1/012048.
  23. Fischer W., Thomas C. K., Zimov N., Göckede M., Grazing enhances carbon cycling but reduces methane emission during peak growing season in the Siberian Pleistocene Park tundra site, Biogeosciences, 2022, V. 19, No. 6, pp. 1611–1633, https://doi.org/10.5194/bg-19-1611-2022.
  24. Glassner A. S., How to derive a spectrum from an RGB triplet, IEEE Computer Graphics and Applications, 1989, V. 9, No. 4, pp. 95–99.
  25. Grassmann H., Zur theorie der farbenmischung, Annalen Der Physik, 1853, V. 165, Iss. 5, pp. 69–84.
  26. Hansen J. E., Takahashi T., Climate processes and climate sensitivity, Geophysical Monograph Series, 1984, V. 5, Article 29.
  27. Jiménez-Muñoz J. C., Sobrino J. A., Skoković D. et al., Land surface temperature retrieval methods from Landsat-8 thermal infrared sensor data, IEEE Geoscience Remote Sensing Letters, 2014, V. 11, Iss. 10, pp. 1840–1843, https://doi.org/10.1109/LGRS.2014.2312032.
  28. Knap W., Reijmer C., Oerlemans J., Narrowband to broadband conversion of Landsat TM glacier albedos, Intern. J. Remote Sensing, 1999, V. 20, Iss. 10, pp. 2091–2110, DOI: 10.1080/014311699212362.
  29. Latham J., Rasch P., Chen C.-C. et al., Global temperature stabilization via controlled albedo enhancement of low-level maritime clouds, Philosophical Trans. of Royal Soc. A: Mathematical, Physical and Engineering Sciences, 2008, V. 366, No. 1882, pp. 3969–3987, DOI: 10.1098/rsta.2008.0137.
  30. Lebourgeois V., Bégué A., Labbé S. et al., Can commercial digital cameras be used as multispectral sensors? A crop monitoring test, Sensors, 2008, V. 8, Iss. 11, pp. 7300–7322, DOI: 10.3390/s8117300.
  31. Lenton T. M., Vaughan N. E., The radiative forcing potential of different climate engineering options, Atmospheric Chemistry and Physics, 2009, V. 9(1), pp. 5539–5561, DOI: 10.5194/acp-9-5539-2009.
  32. Li Z., Erb A., Sun Q. et al., Preliminary assessment of 20-m surface albedo retrievals from Sentinel-2A surface reflectance and MODIS/VIIRS surface anisotropy measures, Remote Sensing of Environment, 2018, V. 217, Iss. G4, pp. 352–365, DOI: 10.1016/j.rse.2018.08.025.
  33. Lin X., Wu S., Chen B. et al., Estimating 10-m land surface albedo from Sentinel-2 satellite observations using a direct estimation approach with Google Earth Engine, ISPRS J. Photogrammetry and Remote Sensing, 2022, V. 194, pp. 1–20, DOI: 10.1016/j.isprsjprs.2022.09.016.
  34. Phiri D., Simwanda M., Salekin S. et al., Sentinel-2 data for land cover/use mapping: A review, Remote Sensing, 2020, V. 12, Iss. 14, Article 2291, https://doi.org/10.3390/rs12142291.
  35. Pielke R. A., Sr., Marland G., Betts R. A. et al., The influence of land-use changes and landscape dynamics on the climate system: relevance to climate-change policy beyond the radiative effect of greenhouse gases, Philosophical Trans. of Royal Soc. A: Mathematical, Physical and Engineering Sciences, 2002, V. 360, Iss. 1797, pp. 1705–1719, DOI: 10.1098/rsta.2002.1027.
  36. Segarra J. B., Buchaillot M. L., Araus J. L., Kefauver S. C., Remote sensing for precision agriculture: Sentinel-2 improved features and applications, Agronomy, 2020, V. 10, Iss. 5, Article 641, DOI: 10.3390/agronomy10050641.
  37. Smits B., An RGB-to-spectrum conversion for reflectances, J. Graphics Tools, 1999, V. 4, Iss. 4, P. 11–22, DOI: 10.1080/10867651.1999.10487511.
  38. Stokes M. A., Anderson M., Chandrasekar S., Motta R., A standard default color space for the Internet — sRGB, http://www.w3.org, 1996, http://www.w3.org/Graphics/Color/sRGB.html.
  39. Sun Y., Fracchia F. D., Calvert T. W., Drew M. S., Deriving spectra from colors and rendering light interference, IEEE Computer Graphics and Applications, 1999, V. 19, Iss. 4, pp. 61–67.
  40. van der Meer F. D., van der Werff H. M. A., van Ruitenbeek F. J. A., Potential of ESA’s Sentinel-2 for geological applications, Remote Sensing of Environment, 2014, V. 148, pp. 124–133, DOI: 10.1016/j.rse.2014.03.022.
  41. Vanino S., Nino P., Die Michele C. et al., Capability of Sentinel-2 data for estimating maximum evapotranspiration and irrigation requirements for tomato crop in Central Italy, Remote Sensing of Environment, 2018, V. 215, pp. 452–470, DOI: 10.1016/j.rse.2018.06.035.
  42. Wang Z., Erb A. M., Schaaf C. B. et al., Early spring post-fire snow albedo dynamics in high latitude boreal forests using Landsat-8 OLI data, Remote Sensing of Environment, 2016, V. 185, pp. 71–83, DOI: 10.1016/j.rse.2016.02.059.
  43. Wu S., Lin X., Bian Z. et al., Satellite observations reveal a decreasing albedo trend of global cities over the past 35 years, Remote Sensing of Environment, 2024, V. 303, Article 114003, DOI: 10.1016/j.rse.2024.114003.
  44. Wyman C., Sloan P., Shirley P., Simple analytic approximations to the CIE XYZ color matching functions, J. Computer Graphics Techniques, 2013, V. 2, No. 2, pp. 1–11.
  45. Zimov S. A., Pleistocene Park: Return of the mammoth’s ecosystem, Science, 2005, V. 308, No. 5723, P. 796–798, DOI: 10.1126/science.1113442.