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PROSPECT+SAIL models: A review of use for vegetation characterization

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TLDR
The combined PROSPECT leaf optical properties model and SAIL canopy bidirectional reflectance model, also referred to as PROSAIL, has been used for about sixteen years to study plant canopy spectral and directional reflectance in the solar domain this paper.
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This article is published in Remote Sensing of Environment.The article was published on 2009-09-01. It has received 1245 citations till now. The article focuses on the topics: Canopy.

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PROSPECT-4 and 5: Advances in the leaf optical properties model separating photosynthetic pigments

TL;DR: In this article, a new calibration and validation of the PROSPECT optical model is presented, which separates plant pigment contributions to the visible spectrum using several comprehensive datasets containing hundreds of leaves collected in a wide range of ecosystem types.
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Remote sensing for agricultural applications: A meta-review

TL;DR: In this paper, the authors present the agronomical variables and plant traits that can be estimated by remote sensing, and describe the empirical and deterministic approaches to retrieve them, and provide a synthesis of the emerging opportunities that should strengthen the role of remote sensing in providing operational, efficient and long-term services for agricultural applications.
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Review of studies on tree species classification from remotely sensed data

TL;DR: It is recommended that future research efforts focus stronger on the causal understanding of why tree species classification approaches work under certain conditions or – maybe even more important - why they do not work in other cases as this might require more complex field acquisitions than those typically used in the reviewed studies.
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Sources of variability in canopy reflectance and the convergent properties of plants

TL;DR: Current understanding of plant spectral properties with respect to sources of uncertainty at leaf to canopy scales is reviewed and a pattern that emerges suggests a synergy among the scattering effects of leaf-, stem- and canopy-level traits that becomes most apparent in the near-infrared (NIR) region.
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Advanced methods of plant disease detection. A review

TL;DR: Modern methods based on nucleic acid and protein analysis are described, which represent unprecedented tools to render agriculture more sustainable and safe, avoiding expensive use of pesticides in crop protection.
References
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Journal ArticleDOI

PROSPECT: A model of leaf optical properties spectra

TL;DR: In this paper, a radiative transfer model based on Allen's generalized plate model is proposed to represent the optical properties of plant leaves from 400 nm to 2500 nm, where spectral refractive index (n) and a parameter characterizing the leaf mesophyll structure (N) are used.
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Bidirectional reflectance spectroscopy: 1. Theory

TL;DR: In this article, an approximate analytic solution for the radiative transfer equation describing particulate surface light scattering, taking into account multiple scattering and mutual shadowing, was derived for the interpretation of reflectance spectroscopy of laboratory surfaces and the photometry of solar system objects.
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Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data

TL;DR: An algorithm based on the physics of radiative transfer in vegetation canopies for the retrieval of vegetation green leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FPAR) from surface reflectances was developed and implemented for operational processing prior to the launch of the moderate resolution imaging spectroradiometer (MODIS) aboard the TERRA platform in December of 1999 as discussed by the authors.
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Light scattering by leaf layers with application to canopy reflectance modeling: The SAIL model

TL;DR: In this article, the scattering and extinction coefficients of the SAIL canopy reflectance model were derived for the case of a fixed arbitrary leaf inclination angle and a random leaf azimuth distribution.
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Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture

TL;DR: In this paper, a combined modeling and indices-based approach is presented to predict the crop chlorophyll content from remote sensing data while minimizing LAI (vegetation parameter) influence and underlying soil background effects.
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