Verification of multispectral data processing for the Sentinel-2A bands, field ASD FieldSpec® 3FR and UAV with the DJI STS-VIS

  • Stanislav Dugin Scientific Centre for Aerospace Research of the Earth Institute of Geological Science National Academy of Sciences of Ukraine, Kyiv, Ukraine
  • Oksana Sybirtseva Scientific Centre for Aerospace Research of the Earth Institute of Geological Science National Academy of Sciences of Ukraine, Kyiv, Ukraine
  • Stanislav Golubov Scientific Centre for Aerospace Research of the Earth Institute of Geological Science National Academy of Sciences of Ukraine, Kyiv, Ukraine
  • Yelizaveta Dorofey Scientific Centre for Aerospace Research of the Earth Institute of Geological Science National Academy of Sciences of Ukraine, Kyiv, Ukraine
Keywords: spectral imaging, vegetation indices, orthophotoplane, correlation between the terrestrial and remote sensing measurements

Abstract

The study of plant cover have been performed by the hyperspectral remote sensing method using ASD FieldSpec® 3FR and DJI STS-VIS measurements. The orthophotoplans are compiled for the test plots of interest at the spatial resolution of 2.5 cm. The substantial correlation for the results of terrestrial verification for the satellite image data in the range of Sentinel-2A bands are confirmed. 15 vegetation indices for the Sentinel-2А wavelength bands were drawn at the Pearson correlation coefficient r > 0.97, with a maximum value of the correlation error of 0.07.

References

Ivanova, V. M., Kalinina, V. N., Neshumova, L. A., Reshetnikova, O. I. (1981). Mathematical statistics. Moscow: Vyssch. schk. (in Russian).

Lyalko, V. I., Popov, M. O. (Eds.) (2006). Multispectral methods of remote sensing of the Earth in the problems of natureuse. Kyiv: Nauk. dumka. (in Ukrainian).

Lyalko, V. I., Shportiuk, Z. M., Sybirtseva ,O. M., Duhin, S. S. (2014). Application of hyperspectral indices fordetermination of changes in grass cover by spectrometricdata. Dop. NAN Ukrainy. 4, 105–111. https://doi.org/10.15407/dopovidi2014.04.105 (in Ukrainian).

Clevers, J., Kooistra, L., Marnix, V. D. B. (2017). Using Sentinel-2 data for retrieving LAI and leaf and canopy chlorophyll content of a potato crop. Remote Sens. 9, 405. https://doi.org/10.3390/rs9050405

Clevers, J. G. P. W., Kooistra, L., Salas, E. A. L. (2004). Study of heavy metal contamination in river floodplains using the red-edge positioning in spectroscopic data. Int. J. Remote Sens. 25, 1–13. https://doi.org/10.1080/01431160310001654473

Dash, J., Curran, P. J. (2004). The MERIS terrestrialchlorophyll index. Int. J. Remote Sens. V. 25, iss. 23. 5403–5413. https://doi.org/10.1080/0143116042000274015

Dotzler, S., Hill, J., Buddenbaum, H., Stoffels, J. (2015). The Potential of EnMAP and Sentinel-2 Data for Detecting Drought Stress Phenomena in Deciduous Forest Communities. Remote Sens. 7 (10), 14227–14258. https://doi.org/10.3390/rs71014227

Frampton, W.J., Dash, J., Watmough, Gary R. and Milton, E.J. (2013) Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation. ISPRS Journal of Photogrammetry and Remote Sensing, 82, 83-92. https://doi.org/10.1016/j.isprsjprs.2013.04.007

Gitelson, A. A., Kaufman, Y. J., Merzlyak, M. N. (1996). Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens. Environ. 58, 289–298. https://doi.org/10.1016/s0034-4257(96)00072-7

Guyot, G., Baret, F. (1988). Utilisation de la haute resolution spectrale pour suivre I’etat des couverts vegetaux. In: Proceedings, 4th International Colloquium ‘‘Spectral Signatures of Objects in Remote Sensing’’, Aussois, 8–22January 1988. Paris: ESA, Publ. SP-287, 279–286.

Harris, А., Dash, J. (2011). A new approach for estimating northern peatland gross primary productivity using a satellite-sensor-derived chlorophyll index. J. Geophys.Research. 116, G4. https://doi.org/10.1029/2011jg001662

Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sens. Environ. 25 (3), 295–309. https://doi.org/10.1016/0034-4257(88)90106-x

Jiang, Z., Huete, A. R, Didan, K, Miura, T. (2008).Development of a two band enhanced vegetation index without a blue band. Remote Sens. Environ. 112 (10), 3833–3845. https://doi.org/10.1016/j.rse.2008.06.006

Kang, Yu.; Gnyp, Martin Leon; Lei, Gao; Yuxin, Miao; Xinping, Chen & Georg, Bareth. (2015). Estimate Leaf Chlorophyll of Rice Using Reflectance Indices and Partial Least Squares. Schweizerbart’sche Verlagsbuchhandlung, Stuttgart, (PFG) Photogrammetrie - Fernerkundung - Geoinformation. 2015 (1). 45-54. https://doi.org/10.1127/pfg/2015/0253

Merzlyak, M. N., Gitelson, A. A., Chivkunova, O. B., Rakitin,V. Yu. (1999). Non-destructive optical detection ofpigment changes during leaf senescence and fruit ripening. Physiologia Plantarum. 106 (1), 135–141. https://doi.org/10.1034/j.1399-3054.1999.106119.x

Radoux, J., Chom , G., Jacques, D. C., Waldner, F., Bellemans, N., Matton, N., Lamarche, C., D’Andrimont, R. & Defourny, P. (2016). Sentinel-2’s Potential for Sub-Pixel Landscape Feature Detection. Remote Sens. 8 (6), 488–516. https://doi.org/10.3390/rs8060488

Rouse, J. W., Haas, Jr. R. H., Schell, J. A., Deering, D. W. (1973). Monitoring the vernal advancement and retrogradation (greenwave effect) of natural vegetation. Prog. Rep. RSC. 19781.

Zarco-Tejada, P. J., Berjon, A., Lopez Lozano, R., Miller, J. R., Martin, P., Cachorro, V., Gonzalez, M. R. & Frutos, A. (2005). Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulating in a row-structured discontinuous canopy. Remote Sens. Environ. 99 (3), 271–287. https://doi.org/10.1016/j.rse.2005.09.002

Verification of multispectral data processing for the Sentinel-2A bands, field ASD FieldSpec® 3FR and UAV with the DJI STS-VIS

Section
Earth observation data applications: Challenges and tasks