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

物候学 遥感 植被(病理学) 时间序列 归一化差异植被指数 卫星 增强植被指数 计算机科学 地理 气候变化 环境科学 气象学 生态学 机器学习 航空航天工程 病理 工程类 生物 医学 植被指数
作者
Dongdong Kong,Tim R. McVicar,Mingzhong Xiao,Yongqiang Zhang,Jorge L. Peña‐Arancibia,Gianluca Filippa,Yuxuan Xie,Xihui Gu
出处
期刊:Methods in Ecology and Evolution [Wiley]
卷期号:13 (7): 1508-1527 被引量:63
标识
DOI:10.1111/2041-210x.13870
摘要

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 to extract vegetation phenological information from satellite‐derived VIs. 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, and , 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), 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, 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).
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
小兔狸花昕完成签到,获得积分20
1秒前
IRer79完成签到,获得积分10
3秒前
彭于晏应助noah采纳,获得10
3秒前
青年才俊发布了新的文献求助10
3秒前
zhu发布了新的文献求助10
4秒前
波波蛋完成签到,获得积分10
4秒前
5秒前
6秒前
7秒前
maguodrgon发布了新的文献求助10
10秒前
科研通AI5应助shell采纳,获得80
10秒前
波波蛋发布了新的文献求助10
10秒前
11秒前
九鹤完成签到,获得积分10
11秒前
jsy完成签到,获得积分10
11秒前
量子星尘发布了新的文献求助10
12秒前
旷野发布了新的文献求助10
12秒前
12秒前
Owen应助浮云521采纳,获得10
12秒前
浮游应助muming采纳,获得10
12秒前
酷炫的凤妖完成签到 ,获得积分10
13秒前
14秒前
14秒前
16秒前
16秒前
16秒前
17秒前
18秒前
浮游应助勤劳的莆采纳,获得10
18秒前
19秒前
霜降发布了新的文献求助10
20秒前
jsy发布了新的文献求助10
20秒前
daring完成签到,获得积分10
20秒前
20秒前
上官若男应助maguodrgon采纳,获得10
22秒前
pomfret发布了新的文献求助10
22秒前
wsx完成签到,获得积分10
23秒前
周学亮关注了科研通微信公众号
24秒前
曾峥发布了新的文献求助10
25秒前
26秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Zeolites: From Fundamentals to Emerging Applications 1500
Architectural Corrosion and Critical Infrastructure 1000
Early Devonian echinoderms from Victoria (Rhombifera, Blastoidea and Ophiocistioidea) 1000
By R. Scott Kretchmar - Practical Philosophy of Sport and Physical Activity - 2nd (second) Edition: 2nd (second) Edition 666
Energy-Size Reduction Relationships In Comminution 500
Principles Of Comminution, I-Size Distribution And Surface Calculations 500
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 内科学 生物化学 物理 计算机科学 纳米技术 遗传学 基因 复合材料 化学工程 物理化学 病理 催化作用 免疫学 量子力学
热门帖子
关注 科研通微信公众号,转发送积分 4941339
求助须知:如何正确求助?哪些是违规求助? 4207390
关于积分的说明 13077624
捐赠科研通 3986257
什么是DOI,文献DOI怎么找? 2182529
邀请新用户注册赠送积分活动 1198125
关于科研通互助平台的介绍 1110387