Журнал

About Editorial Board Information for authors and reviewers Publication ethics Contacts User Account: send / review a manuscript
Archive
Volume 22, 2025
Number 1 Number 2
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, 2025, Vol. 22, No. 2, pp. 319-325

Towards assessment of georeferencing accuracy for VIIRS (Suomi NPP) products

D.E. Plotnikov 1 , P.A. Kolbudaev 1 , A.M. Matveev 1 , A.A. Proshin 1 
1 Space Research Institute RAS, Moscow, Russia
Accepted: 13.02.2025
DOI: 10.21046/2070-7401-2025-22-2-319-325
This communication describes recent results related to assessment of geolocation accuracy of VIIRS (Visible Infrared Imaging Radiometer Suite) (Suomi NPP) products, both original (VNP09GA product) and new composite time series created on their basis at Space Research Institute of the Russian Academy of Sciences (VIIRS HiTSE product). Georeferencing accuracy of both products in Red and NIR bands with a spatial resolution of 500 meters has been assessed using MSI (Multispectral Instrument) (Sentinel-2A/B) imagery in the same bands with a spatial resolution of 10 meters as high-resolution reference data. The assessment was carried out based on a set of 6×6 km reference sites covering Russia with a quasi-regular grid. To assess the geolocation accuracy, the previously developed HiResByLowRes method was used that allows one to identify the exact position of a low-resolution pixel by analyzing a series of coarsening results of a highly detailed reference image at various positions of the sliding window used for coarsening. It was found that mean absolute reference error for VNP09GA product was 166.6 and 167.4 meters in Red and NIR bands, respectively (standard deviation was 210.6 and 208.5 meters, respectively), with a maximum absolute reference error of 740 meters, whereas for the VIIRS HiTSE product it was 88.6 and 85 meters (standard deviation 104.5 and 100.0 meters) with a maximum absolute error of 320 meters. Thus, new data on actual geolocation accuracy of the studied products were presented and weighted LOWESS interpolation of VNP09GA time series was shown not only to provide cloud-free daily imagery, but also to reduce on average by half the error of georeferencing of the initial products.
Keywords: VIIRS, Suomi NPP, Sentinel-2, MSI, HiTSE, LOWESS, geographic reference
Full text

References:

  1. Gavrilyuk E. A., Plotnikova A. S., Plotnikov D. E., Land cover mapping of the Pechora-Ilych Nature Reserve and its vicinity based on reconstructed multitemporal Landsat satellite data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2018, V. 15, No. 5, pp. 141–153 (in Russian), DOI: 10.21046/2070-7401-2018-15-5-141-153.
  2. Elkina E. S., Plotnikov D. E., Dunaeva E. A., Discovering the possibility for irrigated lands identification with remote sensing data over Republic of Crimea based on spectral-temporal and thermal features, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2024, V. 21, No. 5, pp. 379–386 (in Russian), DOI: 10.21046/2070-7401-2024-21-5-379-386.
  3. Kolbudaev P. A., Plotnikov D. E., Matveev A. M., SmisGeoCorrHiResByLowRes. Certificate of state registration of software No. 2021682066 (RU), Reg. 29.12.2021 (in Russian).
  4. Loupan E. A., Proshin A. A., Burcev M. A. et al., Experience of development and operation of the IKI-Monitoring center for collective use of systems for archiving, processing and analyzing satellite data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2019, V. 16, No. 3, pp. 151–170 (in Russian), DOI: 10.21046/2070-7401-2019-16-3-151-170.
  5. Plotnikov D. E., Khvostikov S. A., Bartalev S. A., Method for automated crop types mapping using remote sensing data and a plant growth simulation model, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2018, V. 15, No. 4, pp. 131–141 (in Russian), DOI: 10.21046/2070-7401-2018-15-4-131-141.
  6. Plotnikov D. E., Elkina E. S., Dunaeva E. A. et al., Development of the method for automatic winter crops mapping by means of remote sensing aimed at crops state assessment over the Republic of Cremia, Tauride Bull. Agricultural Science, 2020, No. 1(21), pp. 64–83 (in Russian), DOI: 10.33952/2542-0720-2020-1-21-64-83.
  7. Plotnikov D. E., Kolbudaev P.A, Loupian E. A., An automatic method for subpixel registration of KMSS-M imagery based on coarse-resolution actualized reference, Computer Optics, 2022, V. 46, No. 5, pp. 818–827 (in Russian), DOI: 10.18287/2412-6179-CO-1098.
  8. Plotnikov D. E., Boymatov Yu. Sh., Elkina E. S. et al., Evaluation of the effectiveness of multiseasonal machine learning models for real-time recognition of winter crops over large areas, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2024, V. 21, No. 5, pp. 116–129 (in Russian), DOI: 10.21046/2070-7401-2024-21-5-116-129.
  9. Plotnikov D. E., Zhou Z., Kolbudaev P. A., Loupian E. A., Matveev A. M., Zimin M. V., Zhukov B. S., Kondratieva T. V., Lebedev S. V., Development and assessment of Leaf Area Index of Russian vegetation cover based on multi-angular observations of KMSS (Meteor-M) and neural network inversion of PROSAIL model, Computer Optics, 2025, V. 49, No. 3 (in print).
  10. Savin I.Yu., Bartalev S. A., Loupian E. A., Tolpin V. A., Medvedeva M. A., Plotnikov D. E., Satellite monitoring of vegetation affected by drought (using drought 2010 in Russia as an example), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2011, V. 8, No. 1, pp. 150–162 (in Russian).
  11. Sereda I. I., Denisov P. V., Troshko K. A. et al., The unique situation of winter crops development observed from remote sensing data in the European territory of Russia in October 2020, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2020, V. 17, No. 5, pp. 304–310 (in Russian), DOI: 10.21046/2070-7401-2020-17-5-304-310.
  12. Troshko K. A., Denisov P. A., Loupian E. A. et al., The state of grain crops in the European part of Russia and Siberia in June 2021 based on remote sensing data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2021, V. 18, No. 3, pp. 325–331, DOI: 10.21046/2070-7401-2021-18-3-325-331.
  13. Shabanov N. V., Bartalev S. A., Eroshenko F. V., Plotnikov D. E., Development of capabilities for remote sensing estimate of Leaf Area Index from MODIS data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2018, V. 15, No. 4, pp. 166–178 (in Russian), DOI: 10.21046/2070-7401-2018-15-4-166-178.
  14. Plotnikov D., Kolbudaev P., Matveev A. et al., Daily surface reflectance reconstruction using LOWESS on the example of various satellite systems, 8 th Intern. Conf. “Information Technology and Nanotechnology” (ITNT-2022), IEEE Xplore, 2022, 5 p., DOI: 10.1109/ITNT55410.2022.9848630.
  15. Plotnikov D., Kolbudaev P., Matveev A. et al., Accuracy assessment of atmospheric correction of KMSS-2 Meteor-M #2.2 data over northern Eurasia, Remote Sensing, 2023, V. 15, Iss. 18, Article 4395, DOI: 10.3390/rs15184395.
  16. Waldner F., Schucknecht A., Lesiv M. et al., Conflation of expert and crowd reference data to validate global binary thematic maps, Remote Sensing of Environment, 2019, V. 221, pp. 235–246, DOI: 10.1016/j.rse.2018.10.039.