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    Joint leaf chlorophyll content and leaf area index retrieval from Landsat data using a regularized model inversion system (REGFLEC)

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    RSE_Houborg_etal_2015_for_upload.pdf
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    Description:
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    Type
    Article
    Authors
    Houborg, Rasmus cc
    McCabe, Matthew cc
    Cescatti, Alessandro
    Gao, Feng
    Schull, Mitchell A.
    Gitelson, Anatoly A.
    KAUST Department
    Water Desalination and Reuse Research Center (WDRC)
    Biological and Environmental Sciences and Engineering (BESE) Division
    Environmental Science and Engineering Program
    Earth System Observation and Modelling
    Date
    2015-01-19
    Online Publication Date
    2015-01-19
    Print Publication Date
    2015-03
    Permanent link to this record
    http://hdl.handle.net/10754/564093
    
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    Abstract
    Leaf area index (LAI) and leaf chlorophyll content (Chll) represent key biophysical and biochemical controls on water, energy and carbon exchange processes in the terrestrial biosphere. In combination, LAI and Chll provide critical information on vegetation density, vitality and photosynthetic potentials. However, simultaneous retrieval of LAI and Chll from space observations is extremely challenging. Regularization strategies are required to increase the robustness and accuracy of retrieved properties and enable more reliable separation of soil, leaf and canopy parameters. To address these challenges, the REGularized canopy reFLECtance model (REGFLEC) inversion system was refined to incorporate enhanced techniques for exploiting ancillary LAI and temporal information derived from multiple satellite scenes. In this current analysis, REGFLEC is applied to a time-series of Landsat data.A novel aspect of the REGFLEC approach is the fact that no site-specific data are required to calibrate the model, which may be run in a largely automated fashion using information extracted entirely from image-based and other widely available datasets. Validation results, based upon in-situ LAI and Chll observations collected over maize and soybean fields in central Nebraska for the period 2001-2005, demonstrate Chll retrieval with a relative root-mean-square-deviation (RMSD) on the order of 19% (RMSD=8.42μgcm-2). While Chll retrievals were clearly influenced by the version of the leaf optical properties model used (PROSPECT), the application of spatio-temporal regularization constraints was shown to be critical for estimating Chll with sufficient accuracy. REGFLEC also reproduced the dynamics of in-situ measured LAI well (r2 =0.85), but estimates were biased low, particularly over maize (LAI was underestimated by ~36 %). This disparity may be attributed to differences between effective and true LAI caused by significant foliage clumping not being properly accounted for in the canopy reflectance model (SAIL). Additional advances in the retrieval of canopy biophysical and leaf biochemical constituents will require innovative use of existing remote sensing data within physically realistic canopy reflectance models along with the ability to exploit the enhanced spectral and spatial capabilities of upcoming satellite systems.
    Sponsors
    The research undertaken here was funded by the King Abdullah University of Science and Technology (KAUST). We appreciate the data provided by the Center for Advanced Land Management Information Technologies (CALMIT) and the Carbon Sequestration Program, University of Nebraska-Lincoln. Anatoly Gitelson acknowledge support of Marie Curie International Incoming Fellowship.
    Publisher
    Elsevier BV
    Journal
    Remote Sensing of Environment
    DOI
    10.1016/j.rse.2014.12.008
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.rse.2014.12.008
    Scopus Count
    Collections
    Articles; Biological and Environmental Sciences and Engineering (BESE) Division; Environmental Science and Engineering Program; Water Desalination and Reuse Research Center (WDRC)

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