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

Rasmus Houborg, Matthew McCabe, Alessandro Cescatti, Feng Gao, Mitchell Schull, Anatoly Gitelson

Research output: Contribution to journalArticle

55 Citations (Scopus)

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.

LanguageEnglish
Pages203-221
Number of pages19
JournalRemote Sensing of Environment
Volume159
Early online date19 Jan 2015
DOIs
Publication statusPublished - 15 Mar 2015
Externally publishedYes

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Chlorophyll
Landsat
leaf area index
chlorophyll
canopy reflectance
canopy
leaves
reflectance
Satellites
maize
corn
inversion
optical properties
Time series
Remote sensing
optical property
biosphere
foliage
remote sensing
Optical properties

Cite this

Houborg, Rasmus ; McCabe, Matthew ; Cescatti, Alessandro ; Gao, Feng ; Schull, Mitchell ; Gitelson, Anatoly. / Joint leaf chlorophyll content and leaf area index retrieval from Landsat data using a regularized model inversion system (REGFLEC). In: Remote Sensing of Environment. 2015 ; Vol. 159. pp. 203-221.
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Joint leaf chlorophyll content and leaf area index retrieval from Landsat data using a regularized model inversion system (REGFLEC). / Houborg, Rasmus; McCabe, Matthew; Cescatti, Alessandro; Gao, Feng; Schull, Mitchell; Gitelson, Anatoly.

In: Remote Sensing of Environment, Vol. 159, 15.03.2015, p. 203-221.

Research output: Contribution to journalArticle

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AB - 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.

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