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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, 2015, Vol. 12, No. 2, pp. 143-154

MODIS satellite data usage in operational agrometeorology

A.D. Kleshchenko1 , V.M. Lebedeva1 , T.A. Naidina1 , O.V. Savitskaya1 
1 National Research Institute of Agricultural Meteorology, Obninsk, Russia
Modis satellite data usage for tasks of operative assessment of grain crop condition and productivity is relevant in view of the possibility to obtain the satellite data on the entire territory of the Russian Federation during the growing season.
The satellite data usage technologies for operational agricultural production service, developed at National Research Institute on Agricultural Meteorology, are based on the relationship between the agricultural crop reflective characteristics and crop conditions and productivity.
Satellite information along with meteorological parameters are used in regression models for grain crops yield estimation on some districts of three departments of the Russian Federal Service for Hydrometeorology and Environmental Monitoring: North Caucasus, Privolzhsky and Central Chernozemnyh Areas, in most cases, the relative error does not exceed 15 %. The method of the spatial distribution of the expected grain crop yields on the territory of the Russian Federation with the satellite data usage is developed.
In dynamic models of plant production process, for operational grain yield forecasting, the satellite information is used for gas exchange calculation. Crop yield forecasts, with dynamic-statistical method usage, are produced within the established by Roshydromet time limits with the lead time from 1 to 4 months. Yield prediction techniques with NDVI data usage are developed for maize yield forecast for the 16 regions of the Southern, North-Caucasian, Central and Volga federal districts, for spring wheat and spring barley yield forecast – for Krasnodar Territory. The average yield forecasts error was 10–12 %.
Keywords: remote-sensed data, NDVI, state estimation, regression equations, dynamic model, crop yield forecast, plant growth process
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