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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, 2018, Vol. 15, No. 1, pp. 169-182

Decision-making on the date of fodder harvesting based on remote sensing data of the Earth and mathematically tuned models

I.M. Mikhaylenko 1 , V.N. Timoshin 1 , V.D. Malygin 1 
1 Agrophysical Research Institute, Saint Petersburg, Russia
Accepted: 31.12.2017
DOI: 10.21046/2070-7401-2018-15-1-169-182
Scientific and methodological basis for the decision making system of the harvesting date made by the agronomic service on the basis of remote sensing data and ground measurements are presented. The task is completed on the example of perennial grass, which is a raw material for dairy cattle fodder. The essence of the decision is to find a compromise between the quantity and the quality of the harvested biomass. It corresponds to a minimum of the criterion, which is a weighted sum of squares of deviations from the given values of the yield of biomass and the index of its digestibility. To predict the criterion, dynamic models of biomass state parameters and indices of its quality are proposed. Current assessment of biomass state parameters is based on the remote sensing data. An optimal filtering algorithm based on dynamic models of biomass state parameters and supplemented by the optical measurement model is used for the data processing. At the same time, current estimates of the biomass state parameters become the initial conditions for predicting the criterion of decision on the date of harvesting.
Keywords: decisions on the date of harvesting, remote sensing data of the Earth, mathematical models, optimal estimations, biomass quality indicators of perennial grasses
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References:

  1. Antonov V. N., Sladkikh L. A., Monitoring sostoyaniya posevov i prognozirovaniye urozhaynosti yarovoy pshenitsy po dannym DZZ (Monitoring of the state of crops and forecasting the yield of spring wheat according to remote sensing data), Geomatika, 2009, No. 4, pp. 50–53.
  2. Bartalev S. A., Loupian E. A., Neishtadt I. A., Savin I. Yu., Klassifikatsiya nekotorykh tipov sel’skokhozyay­stvennykh posevov v yuzhnykh regionakh Rossii po sputnikovym dannym MODIS (Classification of some types of agricultural crops in southern regions of Russia according to MODIS satellite data), Issledovaniye Zemli iz kosmosa, 2006, No. 3, pp. 68–75.
  3. Kochubey S. M., Shadchina T. M., Kobets N. I., Spektral’nyye svoystva rasteniy kak osnova metodov distantsionnoy diagnostiki (Spectral properties of plants as a basis for remote diagnostic methods), Kiyev: Naukova dumka, 1990, 134 p.
  4. Mikhaylenko I. M., Upravleniye protsessom silosovaniya kormov iz mnogoletnikh trav (Management of silage forage from perennial grasses), Doklady RASKHN, 2006, No. 3, pp. 66–68.
  5. Mikhaylenko I. M., Osnovnyye zadachi otsenivaniya sostoyaniya posevov i pochvennoy sredy po dannym kosmicheskogo zondirovaniya (The main tasks of assessing the state of crops and soil environment from space probing data), Ekologicheskiye sistemy i pribory, 2011, No. 8, pp. 17–25.
  6. Mikhaylenko I. M., Kurashvili A. Ye., Upravleniye kachestvom kormov iz mnogoletnikh trav (Management of quality of forages from perennial grasses), Informatsionnyye tekhnologii, informatsionnyye izmeritel’nyye sistemy i pribory v issledovanii selskohozjastvenyh protsessov (Information technologies, information measuring systems and devices in the study of agricultural processes), Proc. Intern. Conf., Novosibirsk, 2003, pp. 106–115.
  7. Mikhaylenko I. M., Kurashvili A. Ye., Sistema monitoringa sostoyaniya mnogoletnikh trav v Leningrad­skoy oblasti (System for monitoring the condition of perennial grasses in the Leningrad Region), Sel’skokhozyaystvennyye vesti, 2004, No. 3 (58), pp. 24–27.
  8. Mikhaylenko I. M., Kurashvili A. Ye., Prognozirovaniye sostoyaniya travostoya v sisteme upravleniya kachestvom kormov v molochnom zhivotnovodstve (Forecasting the condition of the grass stand in the feed quality management system in dairy cattle breeding), Vestnik RSKHA, 2008, No. 2, pp. 10–13.
  9. Mikhaylenko I. M., Timoshin V. N., Danilova T. N., Matematicheskoye modelirovaniye sistemy “pochva – rasteniye – atmosfera” na primere mnogoletnikh trav (Mathematical modeling of the soil-plant-atmosphere system using the example of perennial grasses), Doklady Rossiyskoy akademii sel’skokhozyaystvennykh nauk, 2009, No. 4, pp. 61–64.
  10. Rachkulik V. I., Sitnikova M. V., Otrazhatel’nyye svoystva i sostoyaniye rastitel’nogo pokrova (Reflective properties and state of vegetation cover), Leningrad: Gidrometeoizdat, 1981, 287 p.
  11. Cherepanov A. S., Vegetatsionnyye indeksy: spravochnyye materially (Vegetation indices: reference materials), Geomatika, 2011, No. 2, pp. 98–102.
  12. Shagiyev B. R., Rogova T. V., Savel’yev A. A., Ispol’zovaniye vegetatsionnykh indeksov kak surrogatnykh faktorov dlya razrabotki regional’noy ekologicheskoy shkaly plodorodiya pochv (Use of vegetative indices as surrogate factors for the development of a regional ecological scale of soil fertility), Vestnik Udmurtskogo universiteta: Biologiya. Nauki o zemle, 2011, Issue 4, pp. 21–30.
  13. Crippen R. E., Calculating the Vegetation Index Faster, Remote Sensing of Environment, 1990, Vol. 34, pp. 71–73.
  14. Harmoney K. R., Moore K. J., George I. R., Determination of pasture biomass using four indirect methods, Agronomy Journal, 1997, Vol. 89, pp. 665–672.
  15. Hill M. O., Carey P. D., Prediction of yield in the Rothamsted Park Grass Experiment by Ellenberg indicator values, Journal of Vegetation Science, 1997, Vol. 8, pp. 579–586.
  16. Jordan C. F., Derivation of leaf area index from quality of light on the forest floor, Ecology, 1969, Vol. 50, pp. 663–666.
  17. Jouven M., Carrère P., Baumont R., Model predicting dynamics of biomass, structure and digestibility of herbage in managed permanent pastures. 1. Model description, Grass & Forage Science, 2006, Vol. 61, Issue 2, pp. 112–124.
  18. Kriegler F. J., Malila W. A., Nalepka R. F., Richardson W., Preprocessing transformations and their effects on multispectral recognition, In: Proceedings of the Sixth International Symposium on Remote Sensing of Environment, University of Michigan, 1969, Ann Arbor, MI, pp. 97–131.
  19. Lawrence R., Comparisons among Vegetation Indices and Bandwise Regression in a Highly Disturbed, Heterogeneous Landscape: Mount St. Helens, Washington, Remote Sensing of Environment, 1998, Vol. 64, pp. 91–102.
  20. Lillesand T. M., Kiefer R. W., Remote Sensing and Image Interpretation. 2nd edition, New York: John Wiley and Sons, 1987, 721 p.
  21. Mikhailenko I. M., Assessment of crop and soil state using satellite remote sensing data, International Journal of Information Technology & Operations Management, 2013, Vol. 1, No. 5, pp. 41–52.
  22. Rouse J. W., Haas R. H., Schell J. A., Deering D. W., Monitoring vegetation systems in the great plains with ERTS, Proc. Third ERTS-1 Symp., NASA SP-351, Washington DC, 1973, Vol. 1, pp. 309–317.
  23. Sanderson M. A., Rotz C. A., Fultz S. W., Rauburn E. B., Estimating forage mass with a commercial capacitance meter, rising plate meter, and pasture ruler, Agronomy Journal, 2001, Vol. 93, pp. 1281–1286.