Estimation of optical parameters in a living tissue by solving the inverse problem of the multiflux radiative transfer

Fukshansky, Leonid; Fukshansky-Kazarinova, Nina; Remisowsky, Alexander Martinez v.

doi:10.1364/ao.30.003145

Cited by 76 publications

(19 citation statements)

References 20 publications

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“…N-flux models (Fukshansky et al, 1991;Richter and Fukshansky, 1996) are based on the Kubelka-Munk theory and consider the leaf as being a slab of diffusing and absorbing material. The advantage of this approximation is its simplicity.…”

Section: Leaf Optical Modelsmentioning

confidence: 99%

See 1 more Smart Citation

A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling

Dorigo

Zurita-Milla

Wit

et al. 2007

International Journal of Applied Earth Observation and Geoinfor

485

302

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Section: Leaf Optical Modelsmentioning

confidence: 99%

“…However, it is very difficult to link the overall absorption coefficient of the leaf with the specific absorption coefficients and the concentrations of the plant chemicals. A retrieval of concentrations of biochemicals by model inversion is therefore rather complex, if not even impossible (Fukshansky et al, 1991).…”

Section: Leaf Optical Modelsmentioning

confidence: 99%

A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling

Dorigo

Zurita-Milla

Wit

et al. 2007

International Journal of Applied Earth Observation and Geoinfor

485

302

View full text Add to dashboard Cite

“…Physical models can avoid such drawback. The commonly used leaf-scale physical models include PROSPECT [7], LIBERTY (Leaf Incorporating Biochemistry Exhibiting Reflectance and Transmittance Yields) [8], 2-flux Kubelka-Munk (K-M) model [9,10], 4-flux K-M model [11], Radiative Transfer Equation [12], and ray tracing. The scaling issue must be considered if leaf-scale models are used to predict biochemistry at the canopy scale.…”

Section: Introductionmentioning

confidence: 99%

Limited Effects of Water Absorption on Reducing the Accuracy of Leaf Nitrogen Estimation

Wang

Chen

et al. 2017

Remote Sensing

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Abstract:Nitrogen is an essential nutrient in many terrestrial ecosystems because it affects vegetation's primary production. Due to the variety of nitrogen-containing substances and the differences in their composition across species, statistical approaches are now dominant in remote sensing retrieval of leaf nitrogen content. Many studies remove spectral regions characterized by strong water absorptions before retrieving nitrogen content, because water is believed to mask the absorption features of nitrogen. The objectives of this study are to discuss the necessity of this practice and to explore how water absorption affects leaf nitrogen estimation. Spectral measurements and chemical analyses for Maize, Sawtooth Oak, and Sweetgum leaves were carried out in 2014. The leaf optical properties model PROSPECT5 was used to eliminate the influences of water on the measured reflectance spectra. The inversion accuracy of PROPECT5 for chlorophyll, carotenoid, water, and dry matter of Maize was also discussed. Measured, simulated, and water-removed spectra were used to: (1) find the optimal nitrogen-related spectral index; and (2) regress with the area-based leaf nitrogen concentration (LNC) using the partial least square regression technique (PLSR). Two types of spectral indices were selected in this study: Normalized Difference Spectral Index (NDSI) and Ratio Spectral Index (RSI). Additionally, first-order derivative forms of measured, simulated, and water-removed spectra were devised to search for the optimal spectral indices. Finally, species-specific optimal indices and cross-species optimal indices, as well as their root mean square errors (RMSE) and coefficients of determination (R 2 ), were obtained. The Ending Top Percentile (ETP), an indicator of the performance of cross-species optimal indices, was also calculated. PLSR was combined with leave-one-out cross validation (LOOCV) for each species. The predicted root mean square errors (RMSEP) and predicted R 2 were finally calculated. The results showed that chlorophyll, carotenoid, and water contents could be estimated with R 2 of 0.75, 0.59, and 0.69, respectively, which were acceptable for fresh leaves. The dry matter was retrieved with a relatively lower accuracy because of the fixed absorption coefficients adopted by PROSPECT5. The performances of species-specific optimal indices using water-free spectra were comparable to or worse than the corresponding indices derived with measured or simulated spectra. Compared with measured spectra, ETP did not change much after the effects of water were removed, and the R 2 between cross-species optimal spectral indices and area-based LNC for Sawtooth Oak and Sweetgum decreased while it remained almost the same for Maize, suggesting that the water-removed cross-species optimal indices were inferior to the corresponding optimal indices found without water removal. ETP was larger than 30% for all spectra, demonstrating the non-existence of common optimal NDSI or RSI for the three species. After water removal, the accuracy...

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“…Most early studies in the field deal with plane-parallel ge-ometry [20,24]. Inversion procedures were also developed in many more general yet still constrained geometries; see, for instance, [3,42,43,61] and the review papers [49,50].…”

Section: Introductionmentioning

confidence: 99%

Frequency Domain Optical Tomography Based on the Equation of Radiative Transfer

Ren

Bal

Hielscher³

2006

SIAM J. Sci. Comput.

137

112

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Abstract. Optical tomography consists of reconstructing the spatial distribution of absorption and scattering properties of a medium from surface measurements of transmitted light intensities. Mathematically, this problem amounts to parameter identification for the equation of radiative transfer (ERT) with diffusion-type boundary measurements. Because they are posed in the phase-space, radiative transfer equations are quite challenging to solve computationally. Most past works have considered the steady-state ERT or the diffusion approximation of the ERT. In both cases, substantial cross-talk has been observed in the reconstruction of the absorption and scattering properties of inclusions. In this paper, we present an optical tomographic reconstruction algorithm based on the frequency-domain ERT. The inverse problem is formulated as a regularized least-squares minimization problem, in which the mismatch between forward model predictions and measurements is minimized. The ERT is discretized by using a discrete ordinates method for the directional variables and a finite volume method for the spatial variables. A limited-memory quasi-Newton algorithm is used to minimize the least-squares functional. Numerical simulations with synthetic data show that the cross-talk between the two optical parameters is significantly reduced in reconstructions based on frequency-domain data as compared to those based on steady-state data.

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Estimation of optical parameters in a living tissue by solving the inverse problem of the multiflux radiative transfer

Cited by 76 publications

References 20 publications

A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling

A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling

Limited Effects of Water Absorption on Reducing the Accuracy of Leaf Nitrogen Estimation

Frequency Domain Optical Tomography Based on the Equation of Radiative Transfer

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