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Assimilation of Remote Sensing and Crop Model for LAI Estimation Based on ensemble kalman filter

Agricultural Sciences in China 2011年第10期10卷 1595-1602页

作者： LI Rui LI Cun-jun DONG Ying-ying LIU Feng WANG Ji-hua YANG Xiao-dong PAN Yu-chun Beijing Research Center for Information Technology in Agriculture Beijing 100097 P.R.China Information Engineering Institute Capital Normal University Beijing 100048 P.R. China Chengdu Dawan High School Chengdu 610300 P.R.China

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 R2 reached 0.8315. So assimilation remote sensing and crop model could provide reference data for the agricultural production.

关键词： crop model assimilation ensemble kalman filter algorithm leaf area index

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A better carbon-water flux simulation in multiple vegetation types by data assimilation

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Forest Ecosystems 2022年第1期9卷 131-145页

作者： Qiuyu Liu Tinglong Zhang Mingxi Du Huanlin Gao Qingfeng Zhang Rui Sun College of Natural Resources and Environment Northwest A&F UniversityYanglingShaanxi712100China Institute of Environment Sciences Department of Biology SciencesUniversity of Quebec at MontrealMontrealH3C 3P8Canada Department of Geography McGill UniversityMontrealQuebecH3A 0B9Canada Faculty of Geographical Science Beijing Normal UniversityBeijing100875China

Background:The accurate estimation of carbon-water flux is critical for understanding the carbon and water cycles of terrestrial ecosystems and further mitigating climate *** simulations and observations have been widely used to research water and carbon cycles of terrestrial *** the advantages and limitations of each method,combining simulations and observations through a data assimilation technique has been proven to be highly promising for improving carbon-water flux ***,to the best of our knowledge,few studies have accomplished both parameter optimization and the updating of model state variables through data assimilation for carbon-water flux simulation in multiple vegetation *** little is known about the variation of the performance of data assimilation for carbon-water flux simulation in different vegetation ***:In this study,we assimilated leaf area index(LAI)time-series observations into a biogeochemical model(Biome-BGC)using different assimilation algorithms(ensemble kalman filter algorithm(EnKF)and unscented kalman filter(UKF))in different vegetation types(deciduous broad-leaved forest(DBF),evergreen broad-leaved forest(EBF)and grassland(GL))to simulate carbon-water ***:The validation of the results against the eddy covariance measurements indicated that,overall,compared with the original simulation,assimilating the LAI into the Biome-BGC model improved the carbon-water flux simulations(R^(2)increased by 35%,root mean square error decreased by 10%;the sum of the absolute error decreased by 8%)but more significantly,improved the water flux simulations(R^(2)increased by 31%,root mean square error decreased by 18%;the sum of the absolute error decreased by 16%).Among the different forest types,the data assimilation techniques(both EnKF and UKF)achieved the best performance towards carbon-water flux in EBF(R^(2)increased by 44%,root mean square error decreased by 24%;the sum of the absolute error d

关键词： Biome-BGC model Leaf area index Evapotranspiration Net ecosystem CO_(2)exchange ensemble kalman filter algorithm Unscented kalman filter

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