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. 5, pp. 183-193

Estimation of the spatial distribution of spring barley yield (Krasnoyarsk Territory) from ground and satellite spectrophotometric data

I.Yu. Botvich 1 , D.V. Emelyanov 1 , A.A. Larko 1 , N.O. Malchikov 1 , V.K. Ivchenko 2 , T.N. Demyanenko 2 , A.P. Shevyrnogov 1 
1 Institute of Biophysics SB RAS, Krasnoyarsk, Russia
2 Krasnoyarsk State Agrarian University, Krasnoyarsk, Russia
Accepted: 08.08.2019
DOI: 10.21046/2070-7401-2019-16-5-183-193
The paper presents a method for estimating the spatial distribution of spring barley yield, implemented based on the use of optical ground and satellite spectral data (PlanetScope with a spatial resolution of 3 meters). This approach is highly relevant for the development of precision farming technologies. Yield mapping is carried out on the basis of the data on spatial distribution of actual yield and spatial distribution of spectral optical characteristics. A feature of the method is the use of the integral values of vegetation indices (NDVI, MSAVI2, ClGreen) at various stages of crop development. Testing of the method was performed on the basis of stationary field experience, when traditional agriculture (deep plowing) was compared with resource-saving technologies (flat-cut, surface treatments and direct seeding at zero tillage). As a result, a method for estimating the spatial distribution of spring barley yield, implemented using optical ground and satellite spectral data (PlanetScope with a spatial resolution of 3 meters) was developed. The prediction of barley yields at the end of July on the basis of a linear regression model was performed, the values of the integral under the NDVI curve in different periods of time were used as parameters. The type of a multiple linear model for predicting barley with 7 variables was established (the coefficient of determination is 0.73; the root-mean-square error is 1.5). The spatial distribution of barley yield by satellite (PlanetScope) and ground data was mapped. The resulting yield maps will be used when planning work for the next year.
Keywords: precision farming, crop yield, growing season, spectroradiometer, barley, types of tillage, PlanetScope
Full text

References:

  1. Akinchin A. V., Levshakov L. V., Linkov S. A., Kim V. V., Gorbunov V. V., Informatsionnye tekhnologii v sisteme tochnogo zemledeliya (Information technology in precision agriculture), Vestnik Kurskoi gosudarstvennoi sel’skokhozyaistvennoi akademii, 2017, No. 9, pp. 16–21.
  2. Antonov S. A., Rol’ geoinformatsionnykh tekhnologii i dannykh distantsionnogo zondirovaniya Zemli dlya otsenki sostoyaniya i produktivnosti agrolandshaftov (The role of geographic information technologies and remote sensing of the Earth for the assessment of the state and productivity of agrolandscapes), Izvestiya Orenburgskogo gosudarstvennogo agrarnogo universiteta, 2018, Vol. 73, No. 5, pp. 10–14.
  3. Bobkova Yu. A., Lobkov V. T., Ispol’zovanie tekhnologii tochnogo zemledeliya pri sozdanii agrokhimicheskikh kartogramm (Using technologies of precision farming for creation agrochemical cartograms), Vestnik Orlovskogo gosudarstvennogo agrarnogo universiteta, 2017, Vol. 67, No. 4, pp. 25–31.
  4. Bondur V. G., Gorokhovskii K. Yu., Ignat’ev V. Yu., Murynin A. B., Gaponova E. V., Metod prognozirovaniya urozhainosti po kosmicheskim nablyudeniyam za dinamikoi razvitiya vegetatsii (The method of forecasting yield productivity based on space observation for dynamics of vegetation development), Izvestiya vysshikh uchebnykh zavedenii. Geodeziya i aerofotosʺemka, 2013, No. 6, pp. 61–68.
  5. Botvich I. Yu., Pisman T. I., Kononova N. A., Shevyrnogov A. P., Sezonnaya dinamika rastitel’nosti zalezhnykh zemel’ Krasnoyarskoi lesostepi po nazemnym i sputnikovym dannym (Seasonal dynamics of fallow land vegetation in Krasnoyarsk forest steppe according to ground and satellite data), Issledovaniya Zemli iz kosmosa, 2018, No. 6, pp. 39–51.
  6. Kazarov K. R., Soldatov Yu. I., Trofimova T. A., Povyshenie tochnosti kartirovaniya urozhainosti pri uborke zernovykh kul’tur kombainom John Deere 9670 STS (Increasing the mapping yield accuracy when harvesting grain crops with a Jonh Deere 9670 STS), Sovremennye nauchno-prakticheskie resheniya XXI veka (Modern scientific and practical solution of the XXI century), Proc. Intern. Scientific-Practical Conf., 21–22 Dec. 2016, Voronezh, 2016, pp. 299–304.
  7. Kolesnik D. E., Kolesnikov V. N., Kartirovanie urozhainosti kak element sistemy tochnogo zemledeliya (Yield mapping as an element of precision farming system), Sovremennye tendentsii razvitiya tekhnologii i tekhnicheskikh sredstv v sel’skom khozyaistve (Modern trends in the development of technologies and technical means in agriculture), Proc. Intern. Scientific-Practical Conf. Dedicated to the 80th Anniversary of A. P. Tarasenko, 10 Jan. 2017, Voronezh, 2017, pp. 190–194.
  8. Rudashko A. A., Prokhorova Yu. S., Yarmolenko A. S., Model’ urozhainosti sel’skokhozyaistvennykh kul’tur kak bazis tsifrovoi karty v tekhnologii tochnogo zemledeliya (Model of the agricultural crops yield as a basis of digital map in the technology of precision farming), Izvestiya vysshikh uchebnykh zavedenii. Geodeziya i aerofotosʺemka, 2013, No. 4, pp. 86–90.
  9. Tolpin V. A., Bartalev S. A., Burtsev M. A., Efremov V. Yu., Loupian E. A., Mazurov A. A., Matveev A. M., Proshin A. A., Flitman E. V., Otsenka sostoyaniya sel’skokhozyaistvennykh kul’tur na osnove mezhgodovoi dinamiki s ispol’zovaniem dannykh MODIS (Estimation of the agricultural crops condition based on annual dynamics using MODIS data), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2007, Vol. 4, No. 2, pp. 380–389.
  10. Shishov L. L., Tonkonogov V. D., Lebedeva I. I., Gerasimova M. I., Klassifikatsiya i diagnostika pochv Rossii (Soil classification and diagnostics in Russia), Smolensk: Oikumena, 2004, 341 p.
  11. Dall’Olmo G., Gitelson A. A., Rundquista D. C., Leavitt B., Barrow T., Holz J. C., Assessing the potential of SeaWiFS and MODIS for estimating chlorophyll concentration in turbid productive waters using red and near-infrared bands, Remote Sensing of Environment, 2005, Vol. 96, pp. 176–187.
  12. Gitelson A. A., Stark R., Grits U., Rundquist D., Kaufman Y., Derry D., Vegetation and soil lines in visible spectral space: a concept and technique for remote estimation of vegetation fraction, Intern. J. Remote Sensing, 2002, Vol. 23, pp. 2537–2562.
  13. Jayawardhana W. G. N. N., Chathurangeb V. M. I., Extraction of agricultural phenological parameters of Sri Lanka using MODIS, NDVI time series data, Procedia Food Science, 2016, Vol. 6, pp. 235–241.
  14. Jordan C. F., Derivation of leaf-area index from quality of light on the forest floor, Ecology, 1969, Vol. 50, pp. 663–666.
  15. Qi J., Chehbouni A., Huete A. R., Kerr Y. H., Sorooshian S., A modified soil adjusted vegetation index, Remote Sensing of Environment, 1994, Vol. 48, pp. 119–126.
  16. Zhou F., Zhang A., Townley-Smith L., A data mining approach for evaluation of optimal time-series of MODIS data for land cover mapping at a regional level, ISPRS J. Photogrammetry and Remote Sensing, 2013, Vol. 83, pp. 114–129.