Assimilation of satellite data into agrohydrological models to improve crop yield forecasts
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
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
Soil Physics, Ecohydrology and Groundwater Management Group, Department of Environmental Sciences, Wageningen University, The Netherlands
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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