Journal
AGRICULTURAL SCIENCES IN CHINA
Volume 10, Issue 10, Pages 1595-1602Publisher
ELSEVIER SCI LTD
DOI: 10.1016/S1671-2927(11)60156-9
Keywords
crop model; assimilation; Ensemble Kalman Filter algorithm; leaf area index
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Funding
- National Natural Science Foundation of China [40701120]
- Beijing Natural Science Foundation, China [4092016]
- Beijing Nova, China [2008B33]
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Data assimilation in agricultural remote sensing research is of great significance to integrate with remote sensing observations and model simulations for parameters estimation. The present investigation not only designed and realized the Ensemble Kalman Filtering algorithm (EnKF) assimilation by combing the crop growth model (CERES-Wheat) with remote sensing data, but also optimized and updated the key parameters (LAI) of winter wheat by using remote sensing data. Results showed that the assimilation LAI and the observation ones agreed with each other, and the R-2 reached 0.8315. So assimilation remote sensing and crop model could provide reference data for the agricultural production.
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