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 Bo...

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Bibliographic Details
Published in:International journal of remote sensing 2009-01, Vol.30 (10), p.2523-2545
Main Authors: Vazifedoust, M., van Dam, J. C., Bastiaanssen, W. G. M., Feddes, R. A.
Format: Article
Language:English
Subjects:
Agronomy. Soil science and plant productions
Applied geophysics
Biological and medical sciences
Earth sciences
Earth, ocean, space
evapotranspiration
Exact sciences and technology
field
Fundamental and applied biological sciences. Psychology
Generalities. Biometrics, experimentation. Remote sensing
hydrological models
Internal geophysics
Remote sensing
soil-moisture
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Summary: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.
ISSN:0143-1161
1366-5901
DOI:10.1080/01431160802552769