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and Vegetation","monthly_downloads":510,"total_dependent_repos":2,"total_dependent_packages":0,"readme":"---\noutput: github_document\n---\n\n```{r, echo = FALSE}\nknitr::opts_chunk$set(\n  collapse = TRUE,\n  comment = \"#\",\n  fig.width = 10, fig.height = 5,\n  fig.align = \"center\",\n  fig.path  = \"man/Figure/\",\n  dev = 'svg'\n)\n```\n# phenofit  \n[![R-CMD-check](https://github.com/eco-hydro/phenofit/workflows/R-CMD-check/badge.svg)](https://github.com/eco-hydro/phenofit/actions)\n[![codecov](https://codecov.io/gh/eco-hydro/phenofit/branch/master/graph/badge.svg)](https://app.codecov.io/gh/eco-hydro/phenofit)\n[![License](https://img.shields.io/badge/license-GPL%20%28%3E=%202%29-brightgreen.svg?style=flat)](http://www.gnu.org/licenses/gpl-2.0.html)\n[![CRAN](https://www.r-pkg.org/badges/version/phenofit)](https://cran.r-project.org/package=phenofit)\n[![total](https://cranlogs.r-pkg.org/badges/grand-total/phenofit)](https://cran.r-project.org/package=phenofit)\n[![monthly](https://cranlogs.r-pkg.org/badges/phenofit)](https://cran.r-project.org/package=phenofit)\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.5150204.svg)](https://doi.org/10.5281/zenodo.5150204)\n\nA state-of-the-art **remote sensing vegetation phenology** extraction package: `phenofit`\n\n- `phenofit` combine merits of TIMESAT and phenopix\n- A simple and stable growing season dividing methods was proposed\n- Provide a practical snow elimination method, based on Whittaker\n- 7 curve fitting methods and 4 phenology extraction methods\n- We add parameters boundary for every curve fitting methods according to their ecological meaning.\n- `optimx` is used to select best optimization method for different curve fitting methods.\n\n\n***Task lists***\n\n- [x] Test the performance of `phenofit` in multiple growing season regions (e.g. the North China Plain);\n- [ ] Uncertainty analysis of curve fitting and phenological metrics;\n- [x] shiny app has been moved to [phenofit.shiny](https://github.com/eco-hydro/phenofit.shiny);\n- [x] Complete script automatic generating module in shinyapp;\n- [x] `Rcpp` improve double logistics optimization efficiency by 60%;\n- [x] Support spatial analysis;\n- [x] Support annual season in curve fitting;\n- [x] flexible fine fitting input ( original time-series or smoothed time-series by rough fitting).\n- [x] Asymmetric of Threshold method\n\n\u003c!-- ![title](man/Figure/Figure1_phenofit_flowchart.svg)\n\n*\u003cu\u003eFigure 1. The flowchart of phenology extraction in `phenofit`.\u003c/u\u003e* --\u003e\n\n# Installation\n\nYou can install phenofit from github with:\n\n```{r gh-installation, eval = FALSE}\n# install.packages(\"remotes\")\nremotes::install_github(\"eco-hydro/phenofit\")\n```\n\n# Note\n\nUsers can through the following options to improve the performance of phenofit in multiple growing \nseason regions:\n\n- Users can decrease those three parameters `nextend`, `minExtendMonth` and\n  `maxExtendMonth` to a relative low value, by setting option \n  `set_options(fitting = list(nextend = 1, minExtendMonth = 0, maxExtendMonth = 0.5))`.\n\n- Use `wHANTS` as the rough fitting function. Due to nature of fourier functions,\n  `wHANTS` is more stable for multiple growing seasons, but it is less flexible\n  than `wWHIT.` `wHANTS` is suitable for regions with the static growing season\n  pattern accoss multiple years, `wWHIT` is more suitable for regions with the\n  dynamic growing season pattern. \n  Dynamic growing season pattern is the most challenging task, which also means\n  that large uncertainty might be exists.\n\n- Use only one iteration in fine fitting procedure.\n\n# **References** \n\n\u003e [1] Kong, D., McVicar, T. R., Xiao, M., Zhang, Y., Peña-Arancibia, J. L., Filippa, G., Xie, Y., Gu, X. (2022). phenofit: An R package for extracting vegetation phenology from time series remote sensing. *Methods in Ecology and Evolution*, 13, 1508-1527. https://doi.org/10.1111/2041-210X.13870\n\n\u003e [2] Kong, D., Zhang, Y., Wang, D., Chen, J., \u0026 Gu, X. (2020). Photoperiod Explains the Asynchronization Between Vegetation Carbon Phenology and Vegetation Greenness Phenology. *Journal of Geophysical Research: Biogeosciences*, 125(8), e2020JG005636. https://doi.org/10.1029/2020JG005636\n\u003e\n\u003e [3] Kong, D., Zhang, Y., Gu, X., \u0026 Wang, D. (2019). A robust method for reconstructing global MODIS EVI time series on the Google Earth Engine. *ISPRS Journal of Photogrammetry and Remote Sensing*, 155, 13–24.\n\u003e\n\u003e [4] Kong, D., (2020). R package: A state-of-the-art Vegetation Phenology extraction package, `phenofit` version 0.3.1, \u003chttps://doi.org/10.5281/zenodo.5150204\u003e\n\u003e\n\u003e [5] Zhang, Q., Kong, D., Shi, P., Singh, V.P., Sun, P., 2018. Vegetation phenology on the Qinghai-Tibetan Plateau and its response to climate change (1982–2013). Agric. For. 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