Retrieving crop leaf tilt angle from imaging spectroscopy data

Xiaochen C. Zou (Corresponding Author), Matti Mõttus

Research output: Contribution to journalArticleScientificpeer-review

14 Citations (Scopus)

Abstract

Leaf angle distribution (LAD) is an important vegetation parameter quantifying the structure of the canopy. Together with leaf area index, LAD is often assumed to fully describe the structure of herbaceous plant canopies such as field crops. To this date, only a few papers have addressed the direct estimation of LAD from near-nadir imaging spectroscopy data. Using simulations with the PROSAIL vegetation reflectance model and measurements of six field crops, we propose and evaluate two methods of determining leaf mean tilt angle (MTA), the central moment of LAD, from reflectance data in blue, red and near infrared wavebands. First, we noted that when canopy reflectance in blue (479 nm) is plotted against that in red (663 nm), the PROSAIL-simulated canopies with different MTAs occupied different areas in the plot. Similarly, the six crop species in the measured dataset were separated in the red–blue reflectance plane. To retrieve MTA from spectral reflectance measurements, we created rotated coordinate axes in the red–blue plane and aligned one of the axes with the largest change in MTA. The new coordinate, which was highly and near-linearly correlated with MTA (R2 = 0.97), was subsequently used to estimate MTA from airborne imaging spectroscopy data. The second MTA retrieval algorithm was based on earlier findings of a high correlation (R2 = 0.78) between MTA and reflectance at 748 nm. Using a lookup table created with PROSAIL, we inverted MTA from measured crop reflectance at this far red edge wavelength. We evaluated the two MTA retrieval methods using photographically measured species-specific MTA. For the red–blue method, MTA was estimated with a root mean square difference (RMSD) of 18.7° between field measured and spectroscopically estimated values. Retrieval from crop reflectance at 748 nm provided a RMSD of 11.4°. To demonstrate the generality of our methods, analyze their limitations, and explain the differences between the two retrieval algorithms, we give an analytical interpretation of the observed correlations using the spectral invariants theory.
Original languageEnglish
Pages (from-to)73-82
JournalAgricultural and Forest Meteorology
Volume205
DOIs
Publication statusPublished - 2015
MoE publication typeA1 Journal article-refereed

Fingerprint

leaf angle
tilt
reflectance
spectroscopy
image analysis
crop
crops
canopy
field crops
vegetation
herbaceous plants
methodology
leaf area index
canopy reflectance
wavelengths
spectral reflectance
nadir
herb
near infrared

Keywords

  • Imaging spectroscopy
  • Leaf inclination angle distribution
  • Crop
  • Radiative transfer model
  • Spectral invariant theory

Cite this

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title = "Retrieving crop leaf tilt angle from imaging spectroscopy data",
abstract = "Leaf angle distribution (LAD) is an important vegetation parameter quantifying the structure of the canopy. Together with leaf area index, LAD is often assumed to fully describe the structure of herbaceous plant canopies such as field crops. To this date, only a few papers have addressed the direct estimation of LAD from near-nadir imaging spectroscopy data. Using simulations with the PROSAIL vegetation reflectance model and measurements of six field crops, we propose and evaluate two methods of determining leaf mean tilt angle (MTA), the central moment of LAD, from reflectance data in blue, red and near infrared wavebands. First, we noted that when canopy reflectance in blue (479 nm) is plotted against that in red (663 nm), the PROSAIL-simulated canopies with different MTAs occupied different areas in the plot. Similarly, the six crop species in the measured dataset were separated in the red–blue reflectance plane. To retrieve MTA from spectral reflectance measurements, we created rotated coordinate axes in the red–blue plane and aligned one of the axes with the largest change in MTA. The new coordinate, which was highly and near-linearly correlated with MTA (R2 = 0.97), was subsequently used to estimate MTA from airborne imaging spectroscopy data. The second MTA retrieval algorithm was based on earlier findings of a high correlation (R2 = 0.78) between MTA and reflectance at 748 nm. Using a lookup table created with PROSAIL, we inverted MTA from measured crop reflectance at this far red edge wavelength. We evaluated the two MTA retrieval methods using photographically measured species-specific MTA. For the red–blue method, MTA was estimated with a root mean square difference (RMSD) of 18.7° between field measured and spectroscopically estimated values. Retrieval from crop reflectance at 748 nm provided a RMSD of 11.4°. To demonstrate the generality of our methods, analyze their limitations, and explain the differences between the two retrieval algorithms, we give an analytical interpretation of the observed correlations using the spectral invariants theory.",
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Retrieving crop leaf tilt angle from imaging spectroscopy data. / Zou, Xiaochen C. (Corresponding Author); Mõttus, Matti.

In: Agricultural and Forest Meteorology, Vol. 205, 2015, p. 73-82.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - Retrieving crop leaf tilt angle from imaging spectroscopy data

AU - Zou, Xiaochen C.

AU - Mõttus, Matti

PY - 2015

Y1 - 2015

N2 - Leaf angle distribution (LAD) is an important vegetation parameter quantifying the structure of the canopy. Together with leaf area index, LAD is often assumed to fully describe the structure of herbaceous plant canopies such as field crops. To this date, only a few papers have addressed the direct estimation of LAD from near-nadir imaging spectroscopy data. Using simulations with the PROSAIL vegetation reflectance model and measurements of six field crops, we propose and evaluate two methods of determining leaf mean tilt angle (MTA), the central moment of LAD, from reflectance data in blue, red and near infrared wavebands. First, we noted that when canopy reflectance in blue (479 nm) is plotted against that in red (663 nm), the PROSAIL-simulated canopies with different MTAs occupied different areas in the plot. Similarly, the six crop species in the measured dataset were separated in the red–blue reflectance plane. To retrieve MTA from spectral reflectance measurements, we created rotated coordinate axes in the red–blue plane and aligned one of the axes with the largest change in MTA. The new coordinate, which was highly and near-linearly correlated with MTA (R2 = 0.97), was subsequently used to estimate MTA from airborne imaging spectroscopy data. The second MTA retrieval algorithm was based on earlier findings of a high correlation (R2 = 0.78) between MTA and reflectance at 748 nm. Using a lookup table created with PROSAIL, we inverted MTA from measured crop reflectance at this far red edge wavelength. We evaluated the two MTA retrieval methods using photographically measured species-specific MTA. For the red–blue method, MTA was estimated with a root mean square difference (RMSD) of 18.7° between field measured and spectroscopically estimated values. Retrieval from crop reflectance at 748 nm provided a RMSD of 11.4°. To demonstrate the generality of our methods, analyze their limitations, and explain the differences between the two retrieval algorithms, we give an analytical interpretation of the observed correlations using the spectral invariants theory.

AB - Leaf angle distribution (LAD) is an important vegetation parameter quantifying the structure of the canopy. Together with leaf area index, LAD is often assumed to fully describe the structure of herbaceous plant canopies such as field crops. To this date, only a few papers have addressed the direct estimation of LAD from near-nadir imaging spectroscopy data. Using simulations with the PROSAIL vegetation reflectance model and measurements of six field crops, we propose and evaluate two methods of determining leaf mean tilt angle (MTA), the central moment of LAD, from reflectance data in blue, red and near infrared wavebands. First, we noted that when canopy reflectance in blue (479 nm) is plotted against that in red (663 nm), the PROSAIL-simulated canopies with different MTAs occupied different areas in the plot. Similarly, the six crop species in the measured dataset were separated in the red–blue reflectance plane. To retrieve MTA from spectral reflectance measurements, we created rotated coordinate axes in the red–blue plane and aligned one of the axes with the largest change in MTA. The new coordinate, which was highly and near-linearly correlated with MTA (R2 = 0.97), was subsequently used to estimate MTA from airborne imaging spectroscopy data. The second MTA retrieval algorithm was based on earlier findings of a high correlation (R2 = 0.78) between MTA and reflectance at 748 nm. Using a lookup table created with PROSAIL, we inverted MTA from measured crop reflectance at this far red edge wavelength. We evaluated the two MTA retrieval methods using photographically measured species-specific MTA. For the red–blue method, MTA was estimated with a root mean square difference (RMSD) of 18.7° between field measured and spectroscopically estimated values. Retrieval from crop reflectance at 748 nm provided a RMSD of 11.4°. To demonstrate the generality of our methods, analyze their limitations, and explain the differences between the two retrieval algorithms, we give an analytical interpretation of the observed correlations using the spectral invariants theory.

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KW - Leaf inclination angle distribution

KW - Crop

KW - Radiative transfer model

KW - Spectral invariant theory

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