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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, 2013, Vol. 10, No. 3, pp. 53-65

Simulation of the water regime of a vast agricultural region territory utilizing measurement data from geostationary meteorological satellites

E.L. Muzylev 1, A.B. Uspensky 2, Z.P. Startseva 1, E.V. Volkova 2, A.V. Kucharsky 2, S.A. Uspensky 2
1 Water Problem Institute of Russian Academy of Sciences, Moscow, Russia
2 Planeta State Research Center of Space Hydrometeorology, Moscow, Russia
The physical-mathematical model of water and heat exchange of vast vegetation covered land areas with atmosphere has been developed to calculate the water and heat regimes of these areas during vegetation season. The model is adjusted to utilize area-distributed estimates of the land surface and meteorological characteristics derived from satellite-based measurements of radiometers AVHRR / NOAA, MODIS / EOS Terra and Aqua, and SEVIRI / Meteosat-9 as the model parameters and variables. New developed and refined technologies to build such estimates based on thematic processing and analyzing named sensor measurement data are described. These technologies are adapted to the territory of the Central Black Earth Region of the European Russia with area of 227,300 km2. The procedures to assimilate obtained satellite-derived estimates in the model are also described. Using the model intended for utilization of satellite data there have been built the fields of soil water content, evapotraspiration and other water regime characteristics of the study region.
Keywords: моделирование характеристик водного режима, спутниковые данные, тематическая обработка, температура подстилающей поверхности, растительный покров, пространственная изменчивость, simulation of water regime characteristics, satellite data, thematic processing, land surface temperature, vegetation cover, spatial variability
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