Assimilating remote sensing observations of leaf area index and soil moisture for wheat yield estimates: An observing system simulation experiment

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dc.contributor.authorNearing, G. S.
dc.contributor.authorCrow, W. T.
dc.contributor.authorThorp, K. R.
dc.contributor.authorMoran, M. S.
dc.contributor.authorReichle, R. H.
dc.contributor.authorGupta, H. V.
dc.contributor.otherUniversity of Arizona
dc.contributor.otherUnited States Department of Agriculture (USDA)
dc.contributor.otherNational Aeronautics & Space Administration (NASA)
dc.contributor.otherNASA Goddard Space Flight Center
dc.contributor.otherUniversity of Alabama Tuscaloosa
dc.date.accessioned2018-10-11T22:02:42Z
dc.date.available2018-10-11T22:02:42Z
dc.date.issued2012-05-17
dc.description.abstractObserving system simulation experiments were used to investigate ensemble Bayesian state-updating data assimilation of observations of leaf area index (LAI) and soil moisture (theta) for the purpose of improving single-season wheat yield estimates with the Decision Support System for Agrotechnology Transfer (DSSAT) CropSim-Ceres model. Assimilation was conducted in an energy-limited environment and a water-limited environment. Modeling uncertainty was prescribed to weather inputs, soil parameters and initial conditions, and cultivar parameters and through perturbations to model state transition equations. The ensemble Kalman filter and the sequential importance resampling filter were tested for the ability to attenuate effects of these types of uncertainty on yield estimates. LAI and theta observations were synthesized according to characteristics of existing remote sensing data, and effects of observation error were tested. Results indicate that the potential for assimilation to improve end-of-season yield estimates is low. Limitations are due to a lack of root zone soil moisture information, error in LAI observations, and a lack of correlation between leaf and grain growth.en_US
dc.format.mimetypeapplication/pdf
dc.identifier.citationNearing, G., Crow, W., Thorp, K., Moran, M., Reichle, R., Gupta, H. (2012): Assimilating remote sensing observations of leaf area index and soil moisture for wheat yield estimates: An observing system simulation experiment. Water Resources Research, 48, W0552. DOI: 10.1029/2011WR011420
dc.identifier.doi10.1029/2011WR011420
dc.identifier.orcidhttps://orcid.org/0000-0001-7031-6770
dc.identifier.orcidhttps://orcid.org/0000-0001-9855-2839
dc.identifier.orcidhttps://orcid.org/0000-0001-5513-0150
dc.identifier.orcidhttps://orcid.org/0000-0001-9855-2839
dc.identifier.orcidhttps://orcid.org/0000-0001-9168-875X
dc.identifier.orcidhttps://orcid.org/0000-0002-8217-261X
dc.identifier.urihttp://ir.ua.edu/handle/123456789/4010
dc.languageEnglish
dc.language.isoen_US
dc.publisherAmerican Geophysical Union
dc.subjectENSEMBLE KALMAN FILTER
dc.subjectFERTILIZER-N
dc.subjectCROP YIELD
dc.subjectMODEL
dc.subjectRETRIEVAL
dc.subjectGROWTH
dc.subjectWATER
dc.subjectEnvironmental Sciences
dc.subjectLimnology
dc.subjectWater Resources
dc.subjectEnvironmental Sciences & Ecology
dc.subjectMarine & Freshwater Biology
dc.titleAssimilating remote sensing observations of leaf area index and soil moisture for wheat yield estimates: An observing system simulation experimenten_US
dc.typetext
dc.typeArticle

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