ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa


Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2015, Vol. 12, No. 2, pp. 173-182

Analysis of spring wheat yield forecasts based on time series of statistical data and integrated vegetation indices

L.F. Spivak1 , I.S. Vitkovskaya2 , M.J. Batyrbayeva2 , A.M. Kauazov2 
1 Dubna International University, Dubna, Russia
2 National Center of Space Research and Technologies, Almaty, Kazakhstan
The article is devoted to comparative analysis of the results of spring wheat yield forecast based on multi-year statistical data series and integrated vegetation indices derived from remote sensing data. A comparison is performed by an example of the Akmola Region of Kazakhstan. Forecast of grain yield is a priority for Kazakhstan, because its main cultivated areas are situated in the zone of risky agriculture, and often suffer from drought. In recent years, remote sensing data have become widely used for monitoring and control of agricultural production in many countries. Space monitoring of crops in Kazakhstan has been conducted since 2000. A technology for accurate determining the area and estimating current status of crops was developed. At the same time the accuracy of long-term forecasts of crops productivity is still poor. The results of forecasts using trend equations and probability models are presented. The accuracy of the forecasts is estimated. An adaptive method of pre-sowing forecast of spring wheat yield is proposed. The forecast is calculated in view of the priori probability and patterns of alternation of favorable, normal and dry vegetation seasons. The article describes the local factors which can improve the accuracy of the forecast. For the Akmola Region the main factors affecting the yield of spring wheat are: type of the current cycle of solar activity, the time and rate of snow cover melting, weather conditions in prior years.
Keywords: forecast yield, spring wheat, remote sensing, vegetation indices, time series
Full text


  1. Bartalev S.A., Loupian E.A., Issledovaniya i razrabotki IKI RAN po razvitiyu metodov sputnikovogo monitoringa rastitel'nogo pokrova (R&D on methods for satellite monitoring of vegetation by the Russian Academy of Sciences’ Space Research Institute), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2013, Vol.10, No. 1, pp.197–214.
  2. Kogan F., Kussul' N.N., Adamenko T.I., Skakun S.V., Kravchenko A.N., Krivobok A.A., Shelestov A.Yu., Kolotii A.V., Kussul' O.M., Lavrenyuk A.N., Sravnitel'nyi analiz rezul'tatov regressionnykh i biofizicheskikh modelei v zadache prognozirovaniya urozhainosti ozimoi pshenitsy (Comparative analysis of regression and biophysical models for the problem of forecasting the yield of winter wheat), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2013, Vol.10, No. 1, pp. 215–227.
  3. Savin I.Yu.. Bartalev S.A., Loupian E.A.. Tolpin V.A., Khvostikov S.A., Prognozirovanie urozhinosti sel'skokhozyaistvennykh kul'tur na osnove sputnikovykh dannykh: vozmozhnosti i perspektivy (Crop yield forecasting based on satellite data: opportunities and Perspectives), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2010, Vol.7, No.3, pp.275–285.
  4. Spivak L.F., Vitkovskaya I.S., Batyrbaeva M.Zh., Muratova N.R., Kauazov A.M., Kosmicheskii monitoring zasukh v Kazakhstane: analiz mnogoletnikh dannykh distantsionnogo zondirovaniya (Space Monitoring of Droughts in Kazakhstan: Multi-Year Remote Sensing Data Analysis), Zemlya iz kosmosa: naibolee effektivnye resheniya, 2012, No. 12, pp.15–23.
  5. Gitelson A., Kogan F., Zakarin E., Spivak L., Lebed L., Using AVHRR Data for Quantitative Estimation of Vegetation Conditions: Calibration and Validation, Advances in Space Research, 1998,Vol. 22 ( 5), pp. 673–676.
  6. Kogan F., Gitelson A., Zakarin E., Spivak L., Lebed L., AVHRR-based spectral vegetation index for quantitative assessment of vegetation state and productivity: Calibration and validation, Photogrammetric engineering and remote sensing, 2003, Vol. 69(8), pp. 899–906.
  7. Spivak L., Vitkovskaya I., Batyrbayeva M., Terekhov A., The experience of land cover change detection by satellite data, Frontiers of Earth Science, 2012, Vol. 6(2), pp.140–146.