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<title>prospect: an R package to link leaf optical properties
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PROSPECT</title>
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the leaf optical properties model separating photosynthetic pigments.
Remote Sensing of Environment, 112(6), 3030–3043.
https://doi.org/10.1016/j.rse.2008.02.012</unstructured_citation>
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<volume>193</volume>
<doi>10.1016/j.rse.2017.03.004</doi>
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S. D., &amp; Jacquemoud, S. (2017). PROSPECT-D: Towards modeling leaf
optical properties through a complete lifecycle. Remote Sensing of
Environment, 193, 204–215.
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<article_title>Estimating leaf mass per area and equivalent
water thickness based on leaf optical properties: Potential and
limitations of physical modeling and machine learning</article_title>
<author>Féret</author>
<journal_title>Remote Sensing of Environment</journal_title>
<volume>231</volume>
<doi>10.1016/j.rse.2018.11.002</doi>
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Berveiller, D., Bendoula, R., Hmimina, G., Cheraiet, A., Oliveira, J.
C., Ponzoni, F. J., Solanki, T., De Boissieu, F., Chave, J., Nouvellon,
Y., Porcar-Castell, A., Proisy, C., Soudani, K., Gastellu-Etchegorry,
J.-P., &amp; Lefèvre-Fonollosa, M.-J. (2019). Estimating leaf mass per
area and equivalent water thickness based on leaf optical properties:
Potential and limitations of physical modeling and machine learning.
Remote Sensing of Environment, 231, 110959.
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<article_title>PROSPECT-PRO for estimating content of
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constituents</article_title>
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(1990). PROSPECT: A model of leaf optical properties spectra. Remote
Sensing of Environment, 34(2), 75–91.
https://doi.org/10.1016/0034-4257(90)90100-Z</unstructured_citation>
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<doi>10.1016/j.rse.2008.01.026</doi>
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F., Bacour, C., Zarco-Tejada, P. J., Asner, G. P., François, C., &amp;
Ustin, S. L. (2009). PROSPECT+ SAIL models: A review of use for
vegetation characterization. Remote Sensing of Environment, 113,
S56–S66.
https://doi.org/10.1016/j.rse.2008.01.026</unstructured_citation>
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spectroscopy</article_title>
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<journal_title>Remote Sensing of Environment</journal_title>
<volume>177</volume>
<doi>10.1016/j.rse.2016.02.029</doi>
<cYear>2016</cYear>
<unstructured_citation>Jay, S., Bendoula, R., Hadoux, X.,
Féret, J.-B., &amp; Gorretta, N. (2016). A physically-based model for
retrieving foliar biochemistry and leaf orientation using close-range
imaging spectroscopy. Remote Sensing of Environment, 177, 220–236.
https://doi.org/10.1016/j.rse.2016.02.029</unstructured_citation>
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<article_title>Retrieval of leaf biochemical parameters
using PROSPECT inversion: A new approach for alleviating ill-posed
problems</article_title>
<author>Li</author>
<journal_title>IEEE Transactions on Geoscience and Remote
Sensing,</journal_title>
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<volume>49</volume>
<doi>10.1109/TGRS.2011.2109390</doi>
<cYear>2011</cYear>
<unstructured_citation>Li, P., &amp; Wang, Q. (2011).
Retrieval of leaf biochemical parameters using PROSPECT inversion: A new
approach for alleviating ill-posed problems. IEEE Transactions on
Geoscience and Remote Sensing, 49(7), 2499–2506.
https://doi.org/10.1109/TGRS.2011.2109390</unstructured_citation>
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<article_title>Spectral subdomains and prior estimation of
leaf structure improves PROSPECT inversion on reflectance or
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<volume>252</volume>
<doi>10.1016/j.rse.2020.112176</doi>
<cYear>2021</cYear>
<unstructured_citation>Spafford, L., Maire, G. le,
MacDougall, A., Boissieu, F. de, &amp; Féret, J.-B. (2021). Spectral
subdomains and prior estimation of leaf structure improves PROSPECT
inversion on reflectance or transmittance alone. Remote Sensing of
Environment, 252, 112176.
https://doi.org/10.1016/j.rse.2020.112176</unstructured_citation>
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<article_title>Unified optical-thermal four-stream radiative
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Sensing</journal_title>
<issue>6</issue>
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Su, Z. (2007). Unified optical-thermal four-stream radiative transfer
theory for homogeneous vegetation canopies. IEEE Transactions on
Geoscience and Remote Sensing, 45(6), 1808–1822.
https://doi.org/10.1109/TGRS.2007.895844</unstructured_citation>
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