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

Abstract

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 (Formula presented.) to extract vegetation phenological information from satellite-derived VIs. (Formula presented.) 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, (Formula presented.) and (Formula presented.), (Formula presented.) 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), (Formula presented.) 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, (Formula presented.) 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 https://zenodo.org/record/6425745 (Kong, 2022a).