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Remote Sensing for Natural Resources    2021, Vol. 33 Issue (4) : 19-25     DOI: 10.6046/zrzyyg.2020382
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Brief review of vegetation phenological information extraction software based on time series remote sensing data
CHAO Zhenhua(), CHE Mingliang(), HOU Shengfang
School of Geographic Science, Nantong University, Nantong 226007, China
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Abstract  

Vegetation phenology reflects the interactions between the physiological and ecological processes of vegetation and environmental changes and thus it is practically significant to research and develop the software used to extract the vegetation phenological information based on time series remote sensing data. The existing pieces of software mainly include those developed by foreign R&D staff based on specific remote sensing data. They integrate different methods for data smoothing and reconstruction and serve different users. The analysis and comparison of the functions and characteristics of the existing pieces of software will assist users to select more targeted software and can also provide references for the R&D of the software for vegetation phenology monitoring. This paper first briefly introduces the monitoring principles of vegetation phenological information using remote sensing data and commonly used data smoothing methods for the reconstruction of time series remote sensing data. Then it summarizes multiple pieces of professional software for vegetation phenology monitoring that integrate the reconstruction methods and phenological information extraction methods. Most especially, it introduces the software TIMESAT, SPIRITS, and DATimeS in detail and compares and analyzes their functions and characteristics. Finally, it puts forward the prospect of developing localization application software with user-friendly graphical user interfaces according to the development of remote sensing big data and the demand for vegetation phenology monitoring.
Keywords residual noise      time-series reconstruction      TIMESAT      SPIRITS      machine learning      DATimeS     
ZTFLH:  TP75  
Corresponding Authors: CHE Mingliang     E-mail: chaozhenhua@ntu.edu.cn;dawnche@ntu.edu.cn
Issue Date: 23 December 2021
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Zhenhua CHAO
Mingliang CHE
Shengfang HOU
Cite this article:   
Zhenhua CHAO,Mingliang CHE,Shengfang HOU. Brief review of vegetation phenological information extraction software based on time series remote sensing data[J]. Remote Sensing for Natural Resources, 2021, 33(4): 19-25.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2020382     OR     https://www.gtzyyg.com/EN/Y2021/V33/I4/19
分类 界定 常用方法 处理
窗口
经验平滑方法 基于经验知识或假设,该类方法是在时间序列的一个局部时间窗口内进行操作 最大值合成、局部加权回归、最佳指数斜率提取、时间窗口操作、均值迭代滤波、迭代插值、变权滤波、非线性自动化符合平滑器 局部
曲线拟合方法 该类方法采用数学函数将植被指数时间序列曲线拟合到指定函数,使用了数学函数逼近植被生长的时间序列轨迹且不需预先确定的阈值或经验约束 非对称高斯、Logistic函数拟合、Whitaker、二次函数拟合、局部调整三次样条、高阶年样条、参数双精度、双曲正切模型、S-G(Savitzky-Golay)滤波器、时空S-G、基于形状先验的方法 局部
数据变换方法 该类方法通过数学运算将时间序列分解为周期性、趋势性和不规则(如噪声)成分,如傅里叶变换和小波分析 快速傅里叶变换、经验模态分解、时间序列谐波分析、离散傅里叶变换、非经典高阶傅里叶变换、小波滤波器 全局
Tab.1  Common data smoothing methods
软件 开发语言 免费使用 软件网址 文献及见刊年 引用数①
TIMESAT MATLAB,FORTRAN 是 http://web.nateko.lu.se/timesat/timesat.asp [14],2004年 40
BFAST R 是 http://r-forge.r-project.org/projects/bfast/ [15],2010年 12
TimeStats IDL 是 https: //dl.acm.org/profile/81430677185 [16],2011年 0
SPIRITS 1.5.2 C,JAVA 是 https: //mars.jrc.ec.europa.eu/asap/download.php [17],2014年 1
BeeBox JavaScript,HTML5,JAVA,
Perl,IDL,R		 是 http://www.tern.org.au/ [18],2016年 1
Phenor R R 是 https: //github.com/khufkens/phenor_manuscript [3],2018年 1
pyPhenology Python 是 https: //github.com/openjournals/joss-reviews/issues/827 [19],2018年 0
CropPhenology R 是 https: //github.com/SofanitAraya/CropPhenology [20],2018年 2
BEAST C/FORTRAN 否 https: //github.com/zhaokg/Rbeast [30],2019年 1
FORCE 2.1 C/C++ 是 http://force.feut.de [4],2019年 1
Earth Engine App JavaScript/Python 是 https: //code.earthengine.google.com/ [21],2019年 1
EO Time Series Viewer Python 是 https: //bitbucket.org/jakimowb/eo-time-series-viewer [22],2020年 2
DATimeS MATLAB 是 http://artmotoolbox.com [23],2020年 1
Tab.2  List of available software for extracting vegetation phenology information with remote sensing time series data
Fig.1  History of TIMESAT software
Fig.2  TIMESAT3.3 menu system
Fig.3  Framework for SPIRITS main functions
Fig.4  Hierarchical design of DATimeS
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