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


Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2017, Vol. 14, No. 3, pp. 185-195

About the influence of weeds on spring barley NDVI determined from MODIS satellite data

I.Yu. Savin 1, 2 , P.A. Dokukin 2 , Yu.I. Verniuk 2, 1 , A.V. Zhogolev 1 
1 V.V. Dokuchaev Soil Science Institute, Moscow, Russia
2 Agrarian-Technological Institute of RUDN, Moscow, Russia
Accepted: 14.03.2017
DOI: 10.21046/2070-7401-2017-14-3-185-195
A comparison of field data on aboveground barley biomass on a certain plots in Tula Region with NDVI MODIS values presented by the internet service VEGA was conducted. The crop fractions, as well as total weight of crop and weeds aboveground biomass, were used as the indicators of crop status. As a result of investigations it was found that NDVI in the middle of the growing season is predefined by the combined effect of cultivated plants and weeds, and at the end of the season the role of weeds becomes predominant. Theoretically, NDVI for this period can be used as a proxy of general weedness of crops. In the case of NDVI usage as a predictor of crop yields it seems better to use index values, received during the first half of the vegetative season. Based on specifics of crop phenology and physiology, one can conclude that the same results can be received not only for barley, but also for wheat, triticale, and rye. The results obtained are valid only for the areas with the same agro-technologies and crop rotations. They should be considered when using the VEGA data for monitoring crop status at the level of individual fields and when predicting crop yield.
Keywords: MODIS, vegetative index, Tula Region, crop weedness, crop yield prediction
Full text


  1. Bereza O.V., Strashnaya A.I., Loupian E.A., O vozmozhnosti prognozirovaniya urozhainosti ozimoi pshenitsy v Srednem Povolzh'e na osnove kompleksirovaniya nazemnykh i sputnikovykh dannykh (On the possibility to predict the yield of winter wheat in the Middle Volga region on the basis of integration of land and satellite data), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2015, Vol. 12, No. 1, pp. 18−30.
  2. Zasorennost' posevov sel'skokhozyaistvennykh kul'tur i bor'ba s sornoi rastitel'nost'yu (Weedness of crops and struggle against weeds), Stavropol': SNIISKh, 1986, 205 p.
  3. Zakharenko V.A., Tendentsii izmeneniya poter' urozhaya sel'skokhozyaistvennykh kul'tur ot vrednykh organizmov v zemledelii v usloviyakh reformirovaniya ekonomiki Rossii (Tendencies in crop yield lost due to pests and weeds in conditions of transit of economy in Russia), Agrokhimiya, 1997, No 3, pp. 67−74.
  4. Loupian E.A., Savin I.Yu., Bartalev S.A., Tolpin V.A., Balashov I.V., Plotnikov D.E., Sputnikovyi servis monitoringa sostoyaniya rastitel'nosti (“Vega”) (Satellite Service for Vegetation Monitoring VEGA), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2011, Vol. 8, No. 1, pp. 190−198.
  5. Savin I.Yu., Bartalev S.A., Loupian E.A., Tolpin V.A., Khvostikov S.A., Prognozirovanie urozhainosti 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.
  6. Sineshchekov V.E., Vasil'eva N.V., Fitosanitarnaya situatsiya v zernovykh agrotsenozakh pri minimizatsii obrabotki pochvy (Phytosanitary status of grain agrocoenosis at minimal soil treatment), Novosibirsk: FGBNU “SibNIIZiKh”, 2015, 96 p.
  7. Tolpin V.A., Bartalev S.A., Efremov V.Yu., Loupian E.A., Savin I.Yu., Flitman E.V., Vozmozhnosti informatsionnogo servera SDMZ APK (Capabilities of the informational server SDMZ), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2010, Vol. 7, No. 2, pp. 221−232.
  8. Tolpin V.A., Balashov I.V., Loupian E.A., Savin I.Yu., Sputnikovyi servis “Vega” (Satellite service “Vega”), Zemlya iz kosmosa, 2011, No. 9, Vesna, pp. 32−37.
  9. Bala S.K., Islam A.S., Correlation between potato yield and MODIS-derived vegetation indices, International Journal of Remote Sensing, 2009, Vol. 30, Issue 10, pp. 2491−2507.
  10. Becker-Reshef I., Justice C., Sullivan M., Vermote E., Tucker C., Anyamba A., Small J., Monitoring Global Croplands with Coarse Resolution Earth Observations: The Global Agriculture Monitoring (GLAM) Project, Remote Sensing, 2010, Vol. 2 (6), pp. 1589−1609.
  11. Benedetti R., Rossinni P., On the use of NDVI profiles as a tool for agricultural statistics: the case study of wheat yield estimate and forecast in Emilia Romagna, Remote Sensing of Environment, 1993, Vol. 45, pp. 311−326.
  12. Bouman B.A.M., Uenk D., Haverkort, A.J., Estimation of ground cover of potato by reflectance measurements, Potato Research, 1992, Vol. 35, pp. 111−125.
  13. Brown R.J., Bernier M., Fedosejevs G., Skretkowicz L., NOAA-AVHRR Crop Condition Monitoring, Canadian Journal of Remote Sensing, 1982, No. 8, pp. 107−117.
  14. Esquerdo J.C.D.M., Zullo J.J., Antunes J.F.G., Use of NDVI/AVHRR Time Series Profiles for Soybean Crop Monitoring in Brazil, International Journal of Remote Sensing, 2011, Vol. 32 (13), pp. 3711−3727.
  15. Groten S.M.E., NDVI crop monitoring and early yield assessment of Burkina Faso, International Journal of Remote Sensing, 1993, Vol. 14 (8), pp. 1495−1515.
  16. Kogan F.N., Global Drought and Flood-Watch from NOAA Polar-orbiting Satellites, Advances in Space Research, 1998, Vol. 21, No. 3, pp. 477−480.
  17. Liu W.T., Kogan F., Monitoring Brazilian soybean production using NOAA/AVHRR based vegetation condition indices, International Journal of Remote Sensing, 2002, Vol. 23 (6), pp. 1161−1179.
  18. Methodology of the MARS Crop Yield Forecasting System. Eur Rep 21291 EN/1-4, Luxembourg: OPOCE, 2008, 438 p.
  19. Quarmby N.A., Milnes M., Hindle T., Silicos N., The use of multitemporal NDVI measurements from AVHRR data for crop yield estimation and prediction, International Journal of Remote Sensing, 1993, Vol. 14, pp. 199−210.
  20. Rasmussen M.S., Operational Yield forecast using AVHRR NDVI data: reduction of environmental and inter-annual variability, International Journal of Remote Sensing, 1997, Vol. 18, No. 5, pp. 1059−1077.
  21. Rembold F., Atzberger C., Savin I., Rojas O., Using low resolution satellite imagery for yield prediction and yield anomaly detection, Remote Sensing, 2013, Vol. 5, No. 4, pp. 1704−1733.
  22. Royer A., Genovese G., Methodology of the MARS Crop Yield Forecasting System. Vol. 3. Remote Sensing Information, Data Processing and Analysis, Luxembourg: OPOCE, 2004, 459 p.
  23. Savin I., Crop yield prediction with SPOT VGT in Mediterranean and Central Asian countries, ISPRS Archives XXXVI-8/W48 Workshop proceedings: Remote sensing support to crop yield forecast and area estimates. Commission VIII, WG VIII/10, Stresa: OPOCE, 2007, pp. 130−134.
  24. Savin I.Yu., Negre T., Agro-meteorological Monitoring in Russia and Central Asian Countries, Ispra: OPOCE, 2006, 214 p.
  25. The State of Food and Agriculture 2016 (SOFA): Climate change, agriculture and food security, Rome: FAO, 2016, 190 p.
  26. Unganai L.S., Kogan F.N., Drought monitoring and Corn yield estimation in Southern Africa from AVHRR data, Remote Sensing of Environment, 1998, Vol. 63, pp. 219−232.
  27. Wu B., Meng J., .Li Q, Yan N., Du X., Zhang M., Remote sensing-based global crop monitoring: experiences with China's CropWatch system, International Journal of Digital Earth, 2014, Vol. 7, No. 2, pp. 113−137.
  28. Yang C., Everitt J.H., Bradford J.M., Escobar D.E., Mapping grain sorghum growth and yield variations using airborne multispectral digital imagery, Transactions of ASAE, 2000, Vol. 43, No. 6, pp. 1927−1938.