Comparison of principal component inversion with VI-empirical approach for LAI estimation using simulated reflectance data

A simulated canopy reflectance dataset for a total of six channels in visible, near-infrared (NIR) and shortwave-infrared (SWIR) region, corresponding to Landsat Thematic Mapper (TM) was generated using the PROSAIL (PROSPECT+SAIL) model and a range of Leaf Area Index (LAI), soil backgrounds, leaf chlorophyll, leaf inclination and viewing geometry inputs. This dataset was used to develop and evaluate approaches for LAI estimation, namely, standard two-band nonlinear empirical vegetation index (VI)–LAI formulation (using Normalized Difference Vegetation Index/simple ratio (NDVI/SR)) and a multi-band principal component inversion (PCI) approach. The analysis indicated that the multi-band PCI approach had a smaller rms error (RMSE=0.380) than the NDVI and SR approaches (RMSE=2.28, 0.88), for an independently generated test dataset.

[1]  W. Verhoef.  Light scattering by leaf layers with application to canopy reflectance modelling: The SAIL model , 1984 .

[2]  D. E. Bowker,et al.  Spectral reflectances of natural targets for use in remote sensing studies , 1985 .

[3]  J. Harlan,et al.  Spectral estimation of Green leaf area index of oats , 1985 .

[4]  F. Baret,et al.  PROSPECT: A model of leaf optical properties spectra , 1990 .

[5]  F. Baret,et al.  Potentials and limits of vegetation indices for LAI and APAR assessment , 1991 .

[6]  R. Jackson,et al.  Multisite Analyses of Spectral-Biophysical Data for Wheat , 1992 .

[7]  J. C. Price,et al.  Estimating leaf area index from satellite data , 1993, IEEE Trans. Geosci. Remote. Sens..

[8]  S. Running,et al.  Forest ecosystem processes at the watershed scale: Sensitivity to remotely-sensed leaf area index estimates , 1993 .

[9]  Harald Krawczyk,et al.  Investigation of interpretation possibilities of spectral high-dimensional measurements by means of principal component analysis: a concept for physical interpretation of those measurements , 1993, Defense, Security, and Sensing.

[10]  S. Jacquemoud.  Inversion of the PROSPECT + SAIL Canopy Reflectance Model from AVIRIS Equivalent Spectra: Theoretical Study , 1993 .

[11]  Nadine Gobron,et al.  Optical remote sensing of vegetation: Modeling, caveats, and algorithms , 1995 .

[12]  M. S. Moran,et al.  Biophysical parameter estimations using multidirectional spectral measurements , 1995 .

[13]  A. Gitelson,et al.  Use of a green channel in remote sensing of global vegetation from EOS- MODIS , 1996 .

[14]  S. Running,et al.  Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active , 1998 .

[15]  Estimation of wheat leaf area index from IRS LISS-III data using Price model , 2000 .

[16]  S. Leblanc,et al.  A Shortwave Infrared Modification to the Simple Ratio for LAI Retrieval in Boreal Forests: An Image and Model Analysis , 2000 .

[17]  C. Bacour.  Design and analysis of numerical experiments to compare four canopy reflectance models , 2002 .

[18]  J. Chen,et al.  GeoComp-n, an advanced system for the processing of coarse and medium resolution satellite data. Part 2: Biophysical products for Northern ecosystems , 2002 .

[19]  S. Leblanc,et al.  Derivation and validation of Canada-wide coarse-resolution leaf area index maps using high-resolution satellite imagery and ground measurements , 2002 .

[20]  R. Reulke,et al.  Remote Sensing and Spatial Information Sciences , 2005 .

[21]  William J. Wilson,et al.  Multivariate Statistical Methods , 2005, Technometrics.

[22]  Vinay Kumar Dadhwal,et al.  Principal component inversion technique for the retrieval of leaf area index , 2005 .