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PROSAIL specifications


Unique identifier OMICS_19984
Software type Application/Script, Package/Module
Interface Command line interface, Graphical user interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages Fortran, MATLAB, Python, R, Other
Computer skills Advanced
Stability Stable
Maintained Yes




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  • person_outline Wout Verhoef
  • person_outline Stéphane Jacquemoud
  • person_outline Frédéric Baret
  • person_outline Cédric Bacour
  • person_outline Pablo Zarco-Tejada

Publication for PROSAIL

PROSAIL citations


Retrieval of Winter Wheat Leaf Area Index from Chinese GF 1 Satellite Data Using the PROSAIL Model

PMCID: 5948798
PMID: 29642395
DOI: 10.3390/s18041120

[…] area index (lai) is one of the key biophysical parameters in crop structure. the accurate quantitative estimation of crop lai is essential to verify crop growth and health. the prosail radiative transfer model (rtm) is one of the most established methods for estimating crop lai. in this study, a look-up table (lut) based on the prosail rtm was first used to estimate winter […]


Leaf Area Index Estimation Using Chinese GF 1 Wide Field View Data in an Agriculture Region

PMCID: 5539751
PMID: 28698464
DOI: 10.3390/s17071593

[…] index (ndvi) value. , the canopy radiative transfer model quantitatively described the physical relationship between lai and canopy spectral reflectances. the widely used coupled prospect and sail (prosail) model, which was easy to use, had general robustness and also had consistent performance in validation practices [,,] was selected to simulate the satellite observations of canopy […]


The Effect of Leaf Stacking on Leaf Reflectance and Vegetation Indices Measured by Contact Probe during the Season

PMCID: 5492110
PMID: 28538685
DOI: 10.3390/s17061202

[…] at a leaf level by a spectroradiometer then serve as an input in radiative transfer (rt) models at a leaf level (e.g., prospect []) and can be coupled with rt models at a canopy level (e.g., prosail [] or dart []) and provide better understanding of airborne remote sensing data in large-scale studies []. from this point of view, the effect of leaf stacking and overlapping […]


Evaluating Hyperspectral Vegetation Indices for Leaf Area Index Estimation of Oryza sativa L. at Diverse Phenological Stages

Front Plant Sci
PMCID: 5438995
PMID: 28588596
DOI: 10.3389/fpls.2017.00820

[…] lai estimation using spectral data: (1) the empirical relationship between spectral vegetation indices (vis) and biophysical variables and (2) inversion of canopy radiative transfer models, such as prosail model (). the latter strategy uses complicated models because they do not account for as much of the optimized variability caused by the large spatial coverage of biophysical variables (). […]


Assessing the Spectral Properties of Sunlit and Shaded Components in Rice Canopies with Near Ground Imaging Spectroscopy Data

PMCID: 5375864
PMID: 28335375
DOI: 10.3390/s17030578

[…] over chlorophylls []. to test the effects of variation in chlorophyll and carotenoid content on the absorption features in the blue region, we used the commonly used radiative transfer model prosail [,] and the continuum removal method to generate simulated continuum-removed reflectance. shows that the double-peak feature in the blue region is most significant at the lowest chlorophyll […]


Improved Accuracy of the Asymmetric Second Order Vegetation Isoline Equation over the RED–NIR Reflectance Space

PMCID: 5375736
PMID: 28245566
DOI: 10.3390/s17030450

[…] signal-to-noise ratio (snr) of the existing sensors. the accuracy improvements obtained in this study were validated using a radiative transfer model of a system of vegetation and soil layers, the prosail model [,]. after optimizing a single factor in the asymmetric approximation of the vegetation isoline equation, the error levels of the improved isoline equations are discussed by comparing […]

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PROSAIL institution(s)
Institut de Physique du Globe de Paris & Université Paris Diderot (UMR 7154), Géophysique spatiale et planétaire, Paris, France; National Aerospace Laboratory, NLR, Marknesse, Netherlands; INRA, Unité Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (UMR1114), Avignon, France; Laboratoire des Sciences du Climat et l'Environnement (CEA/CNRS/UVSQ), Gif-sur-Yvette, France; Instituto de Agricultura Sostenible, (IAS), Consejo Superior de Investigaciones Científicas (CSIC), Córdoba, Spain; Department of Global Ecology, Carnegie Institution of Washington, Stanford, CA, USA; Laboratoire Ecologie, Systématique et Evolution (UMR 8079), Université Paris-Sud, Orsay, France; Center for Spatial Technologies and Remote Sensing, Department of Land, Air, and Water Resources, University of California, Davis, CA, USA
PROSAIL funding source(s)
Supported by the space agencies (CNES, ESA, EU, NASA, NLR) and national remote sensing programs (INSU Programme National de Télédétection Spatiale, NASA Earth Observing System and Terrestrial Ecology programs, and the University of California's CalSpace program).

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