Журнал

About Editorial Board Information for authors and reviewers Publication ethics Contacts User Account: send / review a manuscript
Archive
Volume 21, 2024
Number 1 Number 2 Number 3 Number 4 Number 5
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, 2019, Vol. 16, No. 6, pp. 143-154

Interpolation algorithm for the recovery of long satellite data time series of vegetation cover observation

T.S. Miklashevich 1 , S.А. Bartalev 1 , D.E. Plotnikov 1 
1 Space Research Institute RAS, Moscow, Russia
Accepted: 25.11.2019
DOI: 10.21046/2070-7401-2019-16-6-143-154
The importance of remote sensing methods based on time series analysis has increased in connection with the advent of the possibility of Earth surface regular satellite observation at high frequency. Analysis of Earth reflective characteristics dynamics is often used for land cover seasonal changes monitoring. Snow cover, atmospheric haze, clouds and shadows from clouds often impede continuous observation of vegetation. Image preprocessing allows filtering satellite observation data distorted due to adverse shooting conditions and hardware noise. This leads to time series gaps without, however, complete exclusion of distorted measurements. The paper presents an algorithm for recovery of long satellite data time series of vegetation cover observation, which is universal, with regard to the input data, in providing the possibility of restoring the information inaccessible for direct observation. Long time series allow continuous monitoring of the vegetation cover, both during vegetation and at rest in areas with unstable or short snow cover, including assessment of its long-term dynamics under the influence of various factors.
Keywords: remote sensing, time series, satellite data, vegetation indices, interpolation, data recovery, MODIS
Full text

References:

  1. Bartalev S. A., Egorov V. A., Zharko V. O., Loupian E. A., Plotnikov D. E., Khvostikov S. A., Shabanov N. V., Sputnikovoe kartografirovanie rastitel’nogo pokrova Rossii (Land cover mapping over Russia using Earth observation data), Moscow: IKI RAN, 2016, 208 p.
  2. Egorov V. A., Il’in V. O., Loupian E. A., Mazurov A. A., Proshin A. A., Flitman E. V., Vozmozhnosti postroeniya avtomatizirovannykh sistem obrabotki sputnikovykh dannykh na osnove programmnogo kompleksa XV_SAT (The possibility of building automated systems for satellite data processing on the basis of software XV_SAT), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2004, Issue 1, pp. 431–436.
  3. Loupian E. A., Proshin A. A., Burtsev M. A., Balashov I. V., Bartalev S. A., Efremov V. Yu., Kashnitskii A. V., Mazurov A. A., Matveev A. M., Sudneva O. A., Sychugov I. G., Tolpin V. A., Uvarov I. A., Tsentr kollektivnogo pol’zovaniya sistemami arkhivatsii, obrabotki i analiza sputnikovykh dannykh IKI RAN dlya resheniya zadach izucheniya i monitoringa okruzhayushchei sredy (IKI center for collective use of satellite data archiving,process-ing and analysis systems aimed at solving the problems of environmental study and monitoring), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2015, Vol. 12, No. 5, pp. 263–284.
  4. Plotnikov D. E., Miklashevich T. S., Bartalev S. A., Vosstanovlenie vremennykh ryadov dannykh distantsionnykh izmerenii metodom polinomial’noi approksimatsii v skol’zyashchem okne peremennogo razmera (Using local polynomial approximation within moving window for remote sensing data time-series smoothing and data gaps recovery), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2014, Vol. 11, No. 2, pp. 103–110.
  5. Plotnikov D. E., Kolbudaev P. A., Bartalev S. A., Loupian E. A., Avtomaticheskoe raspoznavanie ispol’zuemykh pakhotnykh zemel’ na osnove sezonnykh vremennykh serii vosstanovlennykh izobrazhenii Landsat (Automated annual cropland mapping from reconstructed time series of Landsat data), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2018, Vol. 15, No. 2, pp. 112–127.
  6. Bannari A., Morin D., Bonn F., Huete A. R., A review of vegetation indices, Remote Sensing Reviews, 1995, Vol. 13, Issue 1, pp. 95–120.
  7. Cleveland W. S., Robust Locally Weighted Regression and Smoothing Scatterplots, J. American Statistical Association, 1979, Vol. 74, No. 368, pp. 829–836.
  8. Ding C., Liu X., Huang F., Temporal Interpolation of Satellite-Derived Leaf Area Index Time Series by Introducing Spatial-Temporal Constraints for Heterogeneous Grasslands, Remote Sensing, 2017, Vol. 9(9), 968, available at: https://doi.org/10.3390/rs9090968.
  9. Gao F., Masek J., Schwaller M., Hall F., On the Blending of the Landsat and MODIS Surface Reflectance: Predicting Daily Landsat Surface Reflectance, IEEE Trans. Geoscience and Remote Sensing, 2006, Vol. 44, No. 8, pp. 2207–2218.
  10. Gerber F., Furrer R., Schaepman-Strub G., de Jong R., Schaepman M. E., Predicting missing values in spatio-temporal satellite data, e-prints, 2016, 35 p., arXiv:1605.01038.
  11. Moreno-Martinez A., Maneta M., Camps-Valls G., Martino L., Robinson N. P., Allred B. W., Running S. W., Interpolation and Gap Filling of Landsat Reflectance Time Series, IGARSS 2018 ― 2018 IEEE Intern. Geoscience and Remote Sensing Symp., Proc., Valencia, 2018, pp. 349–352.
  12. Olsson L., Eklundh L., Fourier-series for analysis of temporal sequences of satellite sensor imagery, Intern. J. Remote Sensing, 1994, Vol. 15, Issue 18, pp. 3735–3741.
  13. Roerink G. J., Menenti M., Verhoef W., Reconstructing cloudfree NDVI composites using Fourier analysis of time series, Intern. J. Remote Sensing, 2000, Vol. 21, No. 29, pp. 1911–1917.
  14. Savitzky A., Golay M. J. E., Smoothing and Differentiation of Data by Simplified Least Squares Procedures, Analytical Chemistry, 1964, Vol. 36, Issue 8, pp. 1627–1639.
  15. Sellers P. J., Dickinson R. E., Randall D. A., Betts A. K., Hall F. G., Berry J. A., Collatz G. J., Denning A. S., Mooney H. A., Nobre C. A., Sato N., Field C. B., Henderson-Sellers A., Modeling the exchanges of energy, water, and carbon between continents and the atmosphere, Science, 1997, Vol. 275, No. 5299, pp. 502–509.
  16. Verger A., Baret F., Weiss M., Kandasamy S., Vermote E., The CACAO method for smoothing, gap filling, and characterizing seasonal anomalies in satellite time series, IEEE Trans. Geoscience and Remote Sensing, 2013, Vol. 51, No. 4, pp. 1963–1972.
  17. Viovy N., Arino O., Belward A. S., The best index slope extraction (BISE) ― A method for reducing noise in NDVI time-series, Intern. J. Remote Sensing, 1992, Vol. 13, No. 8, pp. 1585–1590.
  18. Wu M., Wu C., Huang W., Niu Z., Wang C., Li W., Hao P., An improved high spatial and temporal data fusion approach for combining Landsat and MODIS data to generate daily synthetic Landsat imagery, Information Fusion, 2016, No. 31, pp. 14–25.
  19. Zhou J., Jia L., Menenti M., Gorte B., On the performance of remote sensing time series reconstruction methods ― A spatial comparison, Remote Sensing of Environment, 2016, Vol. 187, pp. 367–384.
  20. Zhu X., Helmer E. H., Chen J., Liu D., An Automatic System for Reconstructing High-Quality Seasonal Landsat Time-Series, Remote Sensing: Time Series Image Processing. Taylor and Francis Series in Imaging Science, Boca Raton, CRC Press, 2018, 263 p.