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

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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.
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Document Type: Research Article

Affiliations: 1: Soil Physics, Ecohydrology and Groundwater Management Group, Department of Environmental Sciences, Wageningen University, The Netherlands,Water Engineering Group, Department of Agriculture and Natural Resources, Guilan University, Iran 2: Soil Physics, Ecohydrology and Groundwater Management Group, Department of Environmental Sciences, Wageningen University, The Netherlands 3: Water Watch, Generaal Foulkesweg 28A, 6703 BS, Wageningen, The Netherlands,Department of Civil Engineering, Delft University of Technology, The Netherlands

Publication date: 2009-01-01

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