{"id":49802210,"url":"https://github.com/jbferet/prosail","last_synced_at":"2026-05-12T15:36:33.400Z","repository":{"id":154824542,"uuid":"414969406","full_name":"jbferet/prosail","owner":"jbferet","description":"R package dedicated to the PROSAIL canopy reflectance model. The package allows running PROSAIL in direct and inverse modes, with various inversion strategies. 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reflectance using the model PROSAIL (PROSPECT+SAIL).\n\n[![licence](https://img.shields.io/badge/Licence-MIT-blue.svg)](https://www.r-project.org/Licenses/MIT)\n[![Build Status](https://gitlab.com/jbferet/prosail/badges/master/pipeline.svg)](https://gitlab.com/jbferet/prosail/pipelines/latest)\n[![status](https://joss.theoj.org/papers/993d57e5a349d7365161dda9cccfd58f/status.svg)](https://joss.theoj.org/papers/993d57e5a349d7365161dda9cccfd58f)\n\n# 1 Requirements\n\n## Install `remotes`\n\nInstall the package `remotes`:\n\n```\ninstall.packages(\"remotes\")\n```\n\n## Install `prospect`\n\nInstall the package `prospect` with the following command line in R session:\n```\nremotes::install_github('jbferet/prospect')\n```\n\n## Install liquidSVM (optional)\n\n`prosail` uses Support Vector Regression (SVR) for hybrid inversion. \n\nThe default SVM implementation is currently based on the package \n[`liquidSVM`](http://pnp.mathematik.uni-stuttgart.de/isa/steinwart/software/R/documentation.html).\n\n[`liquidSVM`](https://arxiv.org/pdf/1702.06899v1.pdf) provides very efficient and \nfully-integrated hyper-parameter selection, multithreading and GPU support. \nHowever, this package is not maintained anymore and may cause difficulties during \nthe installation. \n\nTo install `liquidSVM`, please follow installation instructions provided in the \n[documentation webpage](http://pnp.mathematik.uni-stuttgart.de/isa/steinwart/software/R/documentation.html). \n\n### !!! WINDOWS USERS !!!\n\nOnce `liquidSVM` is installed, you will need to add the 32bit DLL into the R library. \nThis `i386` directory should be downloaded [here](https://gitlab.com/jbferet/myshareddata/-/tree/master/LiquidSVM_32bits) \nand copied into the local directory on your computer, where the binary codes of liquidSVM are installed:\n\n`Path_For_My_R_distribution/library/liquidSVM/libs/`.\n\n### If liquidSVM installation fails\n\n`liquidSVM` is a suggested package, so the installation of `prosail` should \nsucceed even without `liquidSVM`. \nTwo main functions using `liquidSVM` as default may be impacted: \n`train_prosail_inversion` and `prosail_hybrid_train`. \n\nIf `liquidSVM` is not properly installed, `prosail` automatically switches to \nthe `ksvm` function of the [`kernlab`](https://cran.r-project.org/web/packages/kernlab/kernlab.pdf) \npackage with nu regression and RBF kernel. \nThis is also accessible if setting `method \u003c- 'nu-svr'` when calling \n`train_prosail_inversion` and `prosail_hybrid_train`. \n\nAn SVM implementation based on the R package [`caret`](https://topepo.github.io/caret/) \nis also available, with linear (`method \u003c- 'nu-svr'`) or RBF \n(`method \u003c- 'nu-svr'`) kernel.\n\n\u003cspan style=\"color:red\"\u003e**WARNING : **\u003c/span\u003e \n**optimal performances are obtained with liquidSVM.**\n**caret implementation may need significantly longer time for training and application stages.**\n\n# 2 Install `prosail`\n\nThe package `prosail` can then be installed with the following command line in R session:\n```\nremotes::install_github('jbferet/prosail')\n```\n\n\u003cspan style=\"color:red\"\u003e**WARNING : **\u003c/span\u003e \n**many functions have been renamed in v3.0 of the package.**\n**The documentation has been updated accordingly.**\n**Please refer to the documentation for additional information.**\n\n\n# 3 Tutorial\n\n\u003c!-- README.md is generated from README.Rmd. Please edit that file --\u003e\n\n\u003c!-- ```{r include = FALSE} --\u003e\n\u003c!-- knitr::opts_chunk$set( --\u003e\n\u003c!--   collapse = TRUE, --\u003e\n\u003c!--   comment = \"#\u003e\", --\u003e\n\u003c!--   fig.path = \"man/figures/README-\", --\u003e\n\u003c!--   out.width = \"100%\" --\u003e\n\u003c!-- ) --\u003e\n\u003c!-- ``` --\u003e\n\nThe tutorial vignettes start \n[here](https://jbferet.gitlab.io/prosail/articles/prosail1.html).\n\n\n# 4 Acknowledgments / Fundings\n\nThis research was supported by the Agence Nationale de la Recherche ([ANR](https://anr.fr/en/open-calls-and-preannouncements/), France) through the young researchers project **BioCop** (ANR-17-CE32-0001)\n\nWe thank [Ingo Steinwart](ingo.steinwart@mathematik.uni-stuttgart.de) and [Philipp Thomann](philipp.thomann@mathematik.uni-stuttgart.de) (Institute for Stochastics and Applications, University of Stuttgart, Germany) for the development of the package `liquidSVM`.\n\n\n# 5 Citation\n\nIf you use **prosail**, please consider citing the following references when appropriate :\n\n### PROSPECT\n\nFéret, J.-B. \u0026 de Boissieu, F. (2024). `prospect`: an R package to link leaf optical properties with their chemical and structural properties with the leaf model PROSPECT. Journal of Open Source Software, 9(94), 6027, https://doi.org/10.21105/joss.06027\n\nFéret J-B, Gitelson AA, Noble SD \u0026 Jacquemoud S, 2017. PROSPECT-D: Towards modeling leaf optical properties through a complete lifecycle. Remote Sensing of Environment, 193, 204–215. https://doi.org/10.1016/j.rse.2017.03.004\n\nFéret J-B, Berger K, de Boissieu F \u0026 Malenovský Z, 2021. PROSPECT-PRO for estimating content of nitrogen-containing leaf proteins and other carbon-based constituents. Remote Sensing of Environment, 252, 112173. https://doi.org/10.1016/j.rse.2020.112173\n\n### PROSAIL\nJacquemoud S, Verhoef W, Baret F, Bacour C, Zarco-Tejada PJ, Asner GP, François C \u0026 Ustin SL, 2009. PROSPECT+ SAIL models: A review of use for vegetation characterization. Remote Sensing of Environment, 113:S56–S66. https://doi.org/doi:10.1016/j.rse.2008.01.026\n\nBerger K, Atzberger C, Danner M, D’Urso G, Mauser W, Vuolo F \u0026 Hank T 2018. Evaluation of the PROSAIL Model Capabilities for Future Hyperspectral Model Environments: A Review Study. Remote Sensing, 10:85. https://doi.org/10.3390/rs10010085\n\n### 4SAIL \u0026 4SAIL2\nVerhoef W \u0026 Bach H, 2007. Coupled soil–leaf-canopy and atmosphere radiative transfer modeling to simulate hyperspectral multi-angular surface reflectance and TOA radiance data. Remote Sensing of Environment, 109:166-182. https://doi.org/10.1016/j.rse.2006.12.013\n\nVerhoef W, Jia L, Xiao Q \u0026 Su Z, 2007. Unified optical-thermal four-stream radiative transfer theory for homogeneous vegetation canopies. IEEE Transactions in Geosciences and Remote Sensing, 45:1808–1822. https://doi.org/10.1109/TGRS.2007.895844\n\n### liquidSVM\nSteinwart I \u0026 Thomann P (2017). liquidSVM: A Fast and Versatile SVM package. [__ArXiv e-prints 1702.06899__](https://doi.org/10.48550/arXiv.1702.06899), http://www.isa.uni-stuttgart.de/software\n\n### SOILSPECT\nJacquemoud S, Baret F, Hanocq J-F, 1992. Modeling spectral and bidirectional soil reflectance. Remote Sensing of Environment, 41, 123–132. https://doi.org/10.1016/0034-4257(92)90072-R\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjbferet%2Fprosail","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjbferet%2Fprosail","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjbferet%2Fprosail/lists"}