Assimilation of satellite data into agrohydrological models to improve crop yield forecasts

M. Vazifedoust, J.C. van Dam, W.G.M. Bastiaansen, R.A. Feddes

Research output: Contribution to journalArticleAcademicpeer-review

58 Citations (Scopus)


This paper addresses the question of whether data assimilation of remotely sensed leaf area index and/or relative evapotranspiration estimates can be used to forecast total wheat production as an indicator of agricultural drought. A series of low to moderate resolution MODIS satellite data of the Borkhar district, Isfahan (Iran) was converted into both leaf area index and relative evapotranspiration using a land surface energy algorithm for the year 2005. An agrohydrological model was then implemented in a distributed manner using spatial information of soil types, land use, groundwater and irrigation on a raster basis with a grid size of 250 m, i.e. moderate resolution. A constant gain Kalman filter data assimilation algorithm was used for each data series to correct the internal variables of the distributed model whenever remotely sensed data were available. Predictions for 1 month in advance using simulations with assimilation at a regional scale were very promising with respect to the statistical data (bias = ±10%). However, longer-term predictions, i.e. 2 months in advance, resulted in a higher bias between the simulated and statistical data. The introduced methodology can be used as a reliable tool for assessing the impacts of droughts in semi-arid regions.
Original languageEnglish
Pages (from-to)2523-2545
JournalInternational Journal of Remote Sensing
Issue number10
Publication statusPublished - 2009


  • soil-moisture
  • hydrological models
  • evapotranspiration
  • field

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