Retrieval of biophysical vegetation parameters using simultaneous inversion of high resolution remote sensing imagery constrained by a vegetation index

Retrieval of biophysical vegetation parameters using simultaneous inversion of high resolution... This study proposes a new method for inverting radiative transfer models to retrieve canopy biophysical parameters using remote sensing imagery. The inversion procedure is improved with respect to standard inversion, and achieves simultaneous inversion of leaf area index (LAI), soil reflectance (ρsoil), chlorophyll content (Ca+b) and average leaf angle (ALA). In this approach, LAI is used to constrain modelling conditions during the inversion process, providing information about the phenological state of each plot under study. Due to the small area of the vegetation plots used for the inversion procedure and in order to avoid redundant information and improve computation efficiency, existing plot segmentation was used. All retrieved biophysical parameters, except LAI, were assumed to be invariant within each plot. The proposed methodology, based on the combination of PROSPECT and SAILH models, was tested over 16 cereal fields and 51 plots, on two dates, which were chosen to ensure crop assessment at different phenological stages. Plots were selected to provide a wide range of LAI between 0 and 6. Field measurements of LAI, ALA and Ca+b were conducted and used as ground truth for validation of the proposed model-inversion methodology. The approach was applied to very high spatial resolution remote sensing data from the QuickBird 2 satellite. The inversion procedure was successfully applied to the imagery and retrieved LAI with R 2 = 0.83 and RMSE = 0.63 when compared to LAI2000 ground measurements. Separate inversions for barley and wheat yielded R 2 = 0.89 (RMSE = 0.64) and R 2 = 0.56 (RMSE = 0.61), respectively. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

Retrieval of biophysical vegetation parameters using simultaneous inversion of high resolution remote sensing imagery constrained by a vegetation index

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Publisher
Springer US
Copyright
Copyright © 2013 by Springer Science+Business Media New York
Subject
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
ISSN
1385-2256
eISSN
1573-1618
D.O.I.
10.1007/s11119-013-9315-8
Publisher site
See Article on Publisher Site

Abstract

This study proposes a new method for inverting radiative transfer models to retrieve canopy biophysical parameters using remote sensing imagery. The inversion procedure is improved with respect to standard inversion, and achieves simultaneous inversion of leaf area index (LAI), soil reflectance (ρsoil), chlorophyll content (Ca+b) and average leaf angle (ALA). In this approach, LAI is used to constrain modelling conditions during the inversion process, providing information about the phenological state of each plot under study. Due to the small area of the vegetation plots used for the inversion procedure and in order to avoid redundant information and improve computation efficiency, existing plot segmentation was used. All retrieved biophysical parameters, except LAI, were assumed to be invariant within each plot. The proposed methodology, based on the combination of PROSPECT and SAILH models, was tested over 16 cereal fields and 51 plots, on two dates, which were chosen to ensure crop assessment at different phenological stages. Plots were selected to provide a wide range of LAI between 0 and 6. Field measurements of LAI, ALA and Ca+b were conducted and used as ground truth for validation of the proposed model-inversion methodology. The approach was applied to very high spatial resolution remote sensing data from the QuickBird 2 satellite. The inversion procedure was successfully applied to the imagery and retrieved LAI with R 2 = 0.83 and RMSE = 0.63 when compared to LAI2000 ground measurements. Separate inversions for barley and wheat yielded R 2 = 0.89 (RMSE = 0.64) and R 2 = 0.56 (RMSE = 0.61), respectively.

Journal

Precision AgricultureSpringer Journals

Published: May 5, 2013

References

  • Object-based retrieval of biophysical canopy variables using artificial neural nets and radiative transfer models
    Atzberger, C
  • Atmospheric turbidity determined by the annual cycle of the aerosol optical depth over north-center Spain from ground (AERONET) and satellite (MODIS)
    Bennouna, YS; Cachorro, VE; Torres, B; Toledano, C; Berjón, A; Frutos, AM

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