Vol. 29, issue 07, article # 6

Ostrikov V. N., Plakhotnikov O. V., Kirienko A. V., Smirnov S. I. Estimation of nitrogen and potassium content in plant biomass for atmospheric corrected hyperspectral remote sensing data. // Optika Atmosfery i Okeana. 2016. V. 29. No. 07. P. 566-571. DOI: 10.15372/AOO20160706 [in Russian].
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Abstract:

An indirect estimation of nitrogen and potassium on experimental agrophysical fields for the two wheat varieties "Ester" and "Trizo" by mathematical analysis of the results of aircraft hyperspectral survey in the range from 450 to 900 nm is considered. After pre-processing of the received signals and performing external calibration (atmospheric correction) of image (conversion from signal space to the spectral brightness coefficients) nitrogen and potassium content is estimated by comparison of the remote data processing results with laboratory measurements of the percentage composition of the analyzed chemical elements in plant stems on test plots. Method of subpixel analysis is used for calculations where as reference are considered two middle spectrums calculated on the plots that corresponded to the maximal content of nitrogen and potassium. The quality of the result is controlled by comparing the concentrations of these substances and estimations of concentrations for those plots that are presented in the image herewith their spectral vectors are not used as reference. The results showed a significant dependence of the accuracy of the estimations on the type of probed culture.

Keywords:

aircraft hyperspectral survey, spectral identification, subpixel method, thematic treatment of hyperspectral data

References:

  1. Hatfield J.L. Precision agriculture and environmental quality: Challenges for research and education. U.S. Department of Agriculture in Ames, Iowa, 2000. 15 p.
  2. Tolpin V.A., Lupjan E.A., Bartalev S.A., Plotnikov D.E., Matveev A.M. Vozmozhnosti analiza sostojanija sel'skohozjajstvennoj rastitel'nosti s ispol'zovaniem sputnikovogo servisa «VEGA» // Optika atmosf. i okeana. 2014. V. 27, N 7. P. 581–586.
  3. Kanash E.V., Osipov Yu.A. Optical signals of oxidative stress in crops physiological state diagnostics // Precision agriculture. Wageningen, Netherlands. 2009. P. 81–89.
  4. Yakushev V.P., Kanash E.V. Evaluation of plants nitrogen status by colorimetric characteristics of crop presented in digital images // Precision Agriculture / Ed. by J.V. Stafford, Ampthill, UK. Paper presented at 8th European Conf. on Precision Agricultire, 2011, Prague, Czech Republic, 11–14 July. P. 341–351.
  5. Mihajlenko I.M. Nauchno-metodicheskie i algoritmicheskie osnovy ocenivanija produktivnogo i sanitarnogo sostojanija posevov po dannym DZZ // Materialy Vseros. nauch. konf. (s mezhdunarodnym uchastiem) «Primenenie sredstv distancionnogo zondirovanija Zemli v sel'skom hozjajstve». Sankt-Peterburg, 16–17 september, 2015. P. 37–40.
  6. Kanash E.V. Osnovnye harakteristiki agrofitocenozov dlja deshifrirovanija spektral'nyh dannyh distancionnogo zondirovanija // Materialy Vseros. nauch. konf. (s mezhdunarodnym uchastiem) «Primenenie sredstv distancionnogo zondirovanija Zemli v sel'skom hozjajstve». Sankt-Peterburg, 16–17 september, 2015. P. 25–28.
  7. Ostrikov V.N., Plahotnikov O.V., Kikot' A.V. Ocenka spektral'nogo razreshenija apparatury giperspektral'noj s#emki po nabljudenijam fraungoferovyh linij // Mehanika, upravlenie i informatika. 2012. N 9. P. 272–276.
  8. Ostrikov V.N., Plakhotnikov O.V. Correlation between hyperspectral imagery preprocessing and the quality of thematic analysis // Izvestiya. Atmos. Ocean. Phys. 2014. V. 50, N 9. P. 889–891.
  9. Ostrikov V.N., Plakhotnikov O.V. Calibration of hyperspectral data aviation mode according with accompanying groundbased measurements of standard surfaces of observed scenes // Izvestiya. Atmos. Ocean. Phys. 2014. V. 50, N 9. P. 1016–1019.
  10. Ostrikov V.N., Kirienko A.V. Navigacionno-korreljacionnaja korrekcija izobrazhenij, iskazhennyh vzaimnymi sdvigami strok // Informacionno-izmeritel'nye i upravljajushhie sistemy. 2009. V. 7, N 7. P. 52–57.
  11. Ostrikov V.N., Plahotnikov O.V., Kirienko A.V., Shulika K.M. Kalibrovka dannyh giperspektral'noj apparatury aviacionnoj s#emki dlja provedenija distancionnogo spektral'nogo analiza sostojanija sel'skohozjajstvennyh kul'tur // Materialy Vseros. nauch. konf. (s mezhdunarodnym uchastiem) «Primenenie sredstv distancionnogo zondirovanija Zemli v sel'skom hozjajstve». Sankt-Peterburg, 16–17 september, 2015. P. 20–24.
  12. Protasov K.T., Protasov K.K. Algoritm raspoznavanija obrazov po dannym giperspektral'noj s#emki // Optika atmosf. i okeana. 2014. V. 27, N 7. P. 601–604.
  13. Belov V.V., Afonin S.V. Ot fizicheskih osnov, teorii i modelirovanija k tematicheskoj obrabotke sputnikovyh izobrazhenij. Tomsk: Izd-vo IOA SO RAN, 2005. 266 p.
  14. Brandt S. Statistical and computational methods in data analysis. New York: INC, 1970. 312 p.