4.6 Article

phenofit: An R package for extracting vegetation phenology from time series remote sensing

期刊

METHODS IN ECOLOGY AND EVOLUTION
卷 13, 期 7, 页码 1508-1527

出版社

WILEY
DOI: 10.1111/2041-210X.13870

关键词

cloud contamination; R language; satellite data; time series reconstruction; vegetation indices; vegetation phenology

类别

资金

  1. National Natural Science Foundation of China [4210011820]
  2. Fundamental Research Funds for the Central Universities
  3. China University of Geosciences (Wuhan) [CUG2106107]

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This article introduces an open-source R package called phenofit$$ phenofit $$ for extracting vegetation phenological information from satellite-derived vegetation indices (VIs). phenofit$$ phenofit $$ adopts state-of-the-art phenology extraction methods and provides flexible input and output options, practical growing season division function, and robust performance.
Satellite-derived vegetation indices (VIs) provide a way to analyse vegetation phenology over decades globally. However, these data are often contaminated by different kinds of optical noise (e.g. cloud, cloud shadow, snow, aerosol), making accurate phenology extraction challenging. We present an open-source state-of-the-art R package called phenofit$$ phenofit $$ to extract vegetation phenological information from satellite-derived VIs. phenofit$$ phenofit $$ adopts state-of-the-art phenology extraction methods, such as a weight updating function for reducing optical noise contamination, a growing season division function for separating the VI time series into different vegetation cycles, and rough and fine fitting functions for reconstructing VI time series. They work together to make phenology extraction from frequently contaminated VIs easier and more accurate. Compared against other widely used phenology extraction tools, for example, TIMESAT$$ \mathrm{TIMESAT} $$ and phenopix$$ phenopix $$, phenofit$$ phenofit $$ provides flexible input and output options, a practical growing season division function, rich curve fitting and phenology extraction functions, and robust performance under different kinds of optical noise. In addition to working with VIs from mesoscale satellites (e.g. MODIS and AVHRR), phenofit$$ phenofit $$ can also reconstruct vegetation time series and extract phenology using other sources, such as VIs from high-resolution optical satellites (e.g. Sentinel-2 and Landsat) and radar satellites (e.g. Sentinel-1), vegetation greenness indices from digital cameras and gross primary production estimations from eddy-covariance sites. As such, phenofit$$ phenofit $$ can contribute to the study of ecological process dynamics and assist in effective modelling of global change impacts on vegetation, as caused by climate variability and human intervention. Code and data of case studies are available at (Kong, 2022a).

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