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The Influence of Observation Conditions on the Accuracy of NDVI Vegetation Index Calculation from Earth Remote Sensing Data

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Abstract

The problem of calculating the normalized vegetation index (NDVI) from satellite data is considered. The index values calculated from the MODIS/Aqua radiometer data by the SeaDAS software package algorithm are compared with the values obtained at the La Crau research site (France) for 7 years. The site is located near the Mediterranean coast and is a flat field where grass grows. The measurements are carried out by the ROSAS automatic photometric station. Calculations show the proximity of satellite and field measurements: bias of 0.005 and standard deviation of 0.03. The algorithm used does not take into account the effect of aerosol on the NDVI value. However, the errors due to the lack of accounting for aerosol lie within the limits of the total calculation error. There is a slight dependence of the error on the zenith angle of the sun, which varied in the range from 20° to 70°. The bidirectional reflectance distribution function of the surface on the site is uniform, except for the directions close to the sunbeam. The measurements were far from the sunbeam zone. However, the accuracy of the calculation depends on the difference between the azimuth angle of the site survey and the azimuth to the sun. NDVI mismatches due to differences in the central wave numbers of different satellites also turned out to be significant. The comparison was made for the following radiometers: MODIS/Aqua, Landsat-8/OLI-1, Landsat-9/OLI-2, and Kanopus-V/MSS.

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REFERENCES

  1. Vasil’ev, A.I., Stremov, A.S., Kovalenko, V.P., and Mikheev, A.A., Methodology of Kanopus-V MSS and Landsat ETM+ basic product comparison, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2018, vol. 15, no. 4, pp. 36–48. https://doi.org/10.21046/2070-7401-2018-15-4-36-48

  2. Selin, V.A., Markov, A.N., Vasil’ev, A.I., and Korshunov, A.P., Geoinformation service “Bank of basic products,” Raketno-kosmicheskoe priborostroenie i informatsionnye sistemy, 2019, vol. 6, no. 1, pp. 40–48. https://doi.org/10.30894/issn2409-0239.2019.6.1.40.48

  3. Khailov, M.N. and Zaichko, V.A., Scientific and technical problems of collection, storage, processing, distribution and application of space geospatial information in the interests of Russian consumers, Distantsionnoe zondirovanie Zemli iz kosmosa v Rossii, 2020, no. 1, pp. 6–15. http://2020.raystudio.ru/media/pdf/dzz/dzz-2020-01_n.pdf. Accessed August 19, 2022.

  4. Albarakat, R. and Lakshmi, V., Comparison of normalized difference vegetation index derived from Landsat, MODIS, and AVHRR for the Mesopotamian Marshes between 2002 and 2018, Remote Sensing, 2019, vol. 11, p. 1245. https://doi.org/10.3390/rs11101245

    Article  ADS  Google Scholar 

  5. Chander, G., Markham, B.L., and Helder, D.L., Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors, Remote Sensing of Environment, 2009, vol. 113, pp. 893–903. https://doi.org/10.1016/j.rse.2009.01.007

    Article  ADS  Google Scholar 

  6. Czapla-Myers, J., McCorkel, J., Anderson, N., Thome, K., Biggar, S., Helder, D., Aaron, D., Leigh, L., and Mishra, N., The ground-based absolute radiometric calibration of Landsat 8 OLI, Remote Sensing, 2015, vol. 7, pp. 600–626, https://doi.org/10.3390/rs70100600

    Article  ADS  Google Scholar 

  7. Gumley, L., Descloitres, J., and Schmaltz, J., Creating reprojected true color MODIS images: A tutorial, Version 1.0.1, 2007. https://ftp.ssec.wisc.edu/pub/willemm/ Creating_Reprojected_True_Color_MODIS_Images_ A_Tutorial_process.pdf. Accessed July 31, 2022.

  8. Holben, B.N., Characteristics of maximum-value composite images from temporal AVHRR data, Int. J. Remote Sensing, 1986, vol. 7, no. 2, pp. 1417–1434. https://doi.org/10.1080/01431168608948945

    Article  ADS  Google Scholar 

  9. Huang, Sh., Tang, L., Hupy, J.P., Wang, Ya., and Shao, G., A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing, J. Forestry Research, 2021, vol. 32, no. 1, pp. 1–6.https://doi.org/10.1007/s11676-020-01155-1

    Article  Google Scholar 

  10. Huete, A., Justice, Ch., and Leeuwen, W., MODIS vegetation index (mod 13), Algorithm Theoretical Basis Document, University of Arizona, University of Virginia, 1999. https://modis.gsfc.nasa.gov/data/atbd/atbd_mod13.pdf.

  11. Jordan, C.F., Derivation of leaf-area index from quality of radiation on the forest floor, Ecology, 1969, vol. 50, no. 4, pp. 663–666. https://doi.org/10.2307/1936256

    Article  Google Scholar 

  12. Lee, K., Kim, K., Lee, S.-G., and Kim, Yo., Determination of the normalized difference vegetation index (NDVI) with top-of-canopy (TOC) reflectance from a KOMPSAT-3A image using orfeo toolBox (OTB) extension, ISPRS Int. J. Geo-Inf., 2020, vol. 9, p. 257. https://doi.org/10.3390/ijgi9040257

    Article  CAS  Google Scholar 

  13. Meygret, A., Santer, R., and Berthelot, B., ROSAS, a robotic station for atmosphere and surface characterization dedicated to on-orbit calibration, Proc. SPIE, 2011, vol. 8153, p. 815311. https://doi.org/10.1117/12.892759

    Article  Google Scholar 

  14. RadCalNet guidance site characterisation. CEOS reference: QA4EO-WGCV-RadCalNet-G2_v1. Version 1.0, 2018, https://www.radcalnet.org/documentation/RadCalNetGenDoc/G2-RadCalNetGuidance-SiteCharacterisation_V1.pdf.

  15. Rouse, J.W., Haas, R.Y., Schell, J.A., and Deering, D.W., Monitoring vegetation systems in the great plains with ERTS, 3rd ERTS Symp., NASA, Goddard Space Flight Center, 1973, vol. 1, sect. A, paper-A20, pp. 309–317. https://ntrs.nasa.gov/citations/19740022614. Accessed July 31, 2022.

  16. Rouse, J.W., Haas, R.Y., Schell, J.A., and Deering, D.W., Diurnal variation of NDVI from an unprecedented high-resolution geostationary ocean colour satellite, Remote Sensing Letters, 2013, vol. 4, no. 7, pp. 639–647. https://doi.org/10.1080/2150704X.2013.781285

    Article  Google Scholar 

  17. Wang, Sh., Yang, M., Li, J., Shen, Q., and Zhang, F., MODIS surface reflectance product (MOD09) validation for typical inland waters in China, Ocean Remote Sensing and Monitoring from Space: Proc. SPIE, 2014, vol. 9261, p. 92610F. https://doi.org/10.1117/12.2068628

    Article  ADS  Google Scholar 

  18. Xie, Y., Zhao, X., Li, L., and Wang, H., Calculating NDVI for Landsat7-ETM data after atmospheric correction using 6S model: A case study in Zhangye city, China, 18th Int. Conf. Geoinformatics, 2010, pp. 1–4. https://doi.org/10.1109/GEOINFORMATICS.2010.5567553

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Funding

This work was supported by the “Priorities 2030” program of the Ministry of Science and Higher Education of the Russian Federation.

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Correspondence to A. I. Aleksanin.

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Aleksanin, A.I., Timofeev, A.N. The Influence of Observation Conditions on the Accuracy of NDVI Vegetation Index Calculation from Earth Remote Sensing Data. Cosmic Res 61 (Suppl 1), S188–S194 (2023). https://doi.org/10.1134/S0010952523700521

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