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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, 2020, Vol. 17, No. 2, pp. 114-122

Within-field variability estimation based on variogram analysis of satellite data for precision agriculture

V.P. Yakushev 1 , V.M. Bure 2, 1 , O.A. Mitrofanova 1, 2 , E.P. Mitrofanov 1, 2 , A.F. Petrushin 1 , S.Yu. Blokhina 1 , V.V. Yakushev 1 
1 Agrophysical Research Institute, Saint Petersburg, Russia
2 St. Petersburg State University, Saint Petersburg, Россия
Accepted: 17.02.2020
DOI: 10.21046/2070-7401-2020-17-2-114-122
The method for estimating the variability of canopy parameters in a specific agricultural area based on variogram analysis of satellite data is presented. A geostatistical model of the agricultural field heterogeneity that represents an indicator of within-field variability and consists of a sum of macro-, meso- and micro-components provides the basis of the proposed method. It is assumed that the estimation of the transition to precision agriculture technologies based on the analysis of nugget dispersion is the most effective when the within-field variation of the indicator has considerable contribution to the general picture of the field heterogeneity. The paper considers an example of a computational experiment in which the initial data are Sentinel-2 satellite images (processing level L2A, survey date 06.23.2019), covering the territory of the Detskoselskiy farm in Leningrad Region. A comparative assessment of the within-field heterogeneity of two randomly selected farm fields was carried out by the proposed method using satellite data to determine the prospects of precision agrochemical application technologies based on NDVI values. Statistical programming language R was used for data processing and analysis.
Keywords: variogram analysis, precision agriculture, within-field heterogeneity, geostatistical model
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References:

  1. Dem’yanov V. V., Savel’yeva E. A., Geostatistika: teoriya i praktika (Geostatistics: theory and practice), Moscow: Science Publ., 2010, 327 p.
  2. Tochnoe sel’skoe khozyaistvo (Precision Agriculture), D. Shpaar, A. V. Zakharenko, V. P. Yakushev (eds.), Saint Petersburg, 2009, 397 p.
  3. Yakushev V. P., Zhukovskii E. E., Petrushin A. F., Yakushev V. V. (2010a), Variogrammnyi analiz prostranstvennoi neodnorodnosti sel’skokhozyaistvennykh polei dlya tselei tochnogo zemledeliya (Variogram analysis of the spatial heterogeneity of agricultural fields for precision agriculture), Saint Petersburg: ARI, 2010, 52 p.
  4. Yakushev V. P., Kanash E. V., Konev A. A., Kovtyukh S. N., Lekomtsev P. V., Matveenko D. A., Petrushin A. F., Yakushev V. V., Bure V. M., Rusakov D. V., Osipov Yu. A. (2010b), Teoreticheskie i metodicheskie osnovy vydeleniya odnorodnykh tekhnologicheskikh zon dlya differentsirovannogo primeneniya sredstv khimizatsii po opticheskim kharakteristikam poseva (Theoretical and methodological bases of the homogeneous technological zone delineation for differentiation of agrochemicals application based on the optical characteristics of crop canopy), Saint Petersburg: Agrofizicheskii institut, 2010, 60 p.
  5. Afanasyev R. A., Use of the regularities of within-field variability of arable soil fertility in precision agrotechnologies, Science J. Volgograd State University. Natural sciences, 2015, Vol. 11, No. 1, pp. 42–51.
  6. Balaguer A., Ruiz L. A., Hermosilla T., Recio J. A., Definition of a comprehensive set of texture semivariogram features and their evaluation for object-oriented image classification, Computers and Geosciences, 2010, Vol. 36, No. 2, pp. 231–240.
  7. Balaguer-Beser A., Ruiz L. A., Hermosilla T., Recio J. A., Using semivariogram indices to analyse heterogeneity in spatial patterns in remotely sensed images, Computers and Geosciences, 2013, Vol. 50, pp. 115–127.
  8. Galioto F., Raggi M., Viaggi D., Assessing the potential economic viability of precision irrigation: a theoretical analysis and pilot empirical evaluation, Water, 2017, Vol. 9, No. 12, pp. 990–1009.
  9. Garrigues S., Allard D., Baret F., Morisette J. (2008), Multivariate quantification of landscape spatial heterogeneity using variogram models, Remote Sensing of Environment, 2008, Vol. 112, No 1, pp. 216–230.
  10. Garrigues S., Allard D., Baret F. (2008b), Modeling temporal changes in surface spatial heterogeneity over an agricultural site, Remote Sensing of Environment, 2008, Vol. 112, No. 2, pp. 588–602.
  11. Godwin R. J., Richards T. E., Wood G. A., Welsh J. P., Knight S. M., An economic analysis of the potential for precision farming in UK cereal production, Biosystems Engineering, 2003, Vol. 84, Issue 4, pp. 533–545.
  12. Huang Y., Yin X., Ye G., Lin J., Huang R., Wang N., Wang L., Sun Y., Spatio-temporal variation of landscape heterogeneity under influence of human activities in Xiamen City of China in recent decade, Chinese Geographical Science, 2013, Vol. 23, No. 2, pp. 227–236.
  13. Korotchenya V., Digital agriculture and agricultural production efficiency: exploring prospects for Russia, Revista ESPACIOS, 2019, Vol. 40, No. 22, pp. 22–35.
  14. Lausch A., Pause M., Doktor D., Preidl S., Schulz K., Monitoring and assessing of landscape heterogeneity at different scales, Environmental Monitoring and Assessment, 2013, Vol. 185, No. 11, pp. 9419–9434.
  15. Mulla D. J., Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps, Biosystems Engineering, 2013, Vol. 114, Issue 4, pp. 358–371.
  16. Oliveira Silveira E. M., Mello J. M., Acerbi F. W. J., Reis A. A., Withey K. D., Ruiz L. A., Characterizing landscape spatial heterogeneity using semivariogram parameters derived from NDVI images, Cerne, 2017, Vol. 23, No. 4, pp. 413–422.
  17. Powers R. P., Hermosilla T., Coops N. C., Chen G., Remote sensing and object-based techniques for mapping fine-scale industrial disturbances, Intern. J. Applied Earth Observation and Geoinformation, 2015, Vol. 34, pp. 51–57.
  18. Qiu B., Zeng C., Cheng C., Tang Z., Gao J., Sui Y., Characterizing landscape spatial heterogeneity in multisensor images with variogram models, Chinese Geographical Science, 2013, Vol. 24, No. 3, pp. 1–11.
  19. Sylvester-Bradley R., Lord E., Sparkes D. L., Scott R. K., Wiltshire  J. J. J., Orson J., An analysis of the potential of precision farming in Northern Europe, Soil Use and Management, 1999, Vol. 15, Issue 1, pp. 1–8.
  20. Van Meirvenne M., Is the soil variability within the small fields of Flanders structured enough to allow precision agriculture, Precision Agriculture, 2003, Vol. 4, Issue 2, pp. 193–201.
  21. Wu X., Peng J., Shan J. E., Cui W., Evaluation of semivariogram features for object-based image classification, Geo-spatial Information Science, 2015, Vol. 18, No. 4, pp. 159–170.
  22. Yue A., Zhang C., Yang J., Su W., Yun W. E., Zhu D., Texture extraction for object-oriented classification of high spatial resolution remotely sensed images using a semivariogram, Intern. J. Remote Sensing, 2013, Vol. 34, No. 11, pp. 3736–3759.