Please wait a minute...
Remote Sensing for Natural Resources    2021, Vol. 33 Issue (4) : 19-25     DOI: 10.6046/zrzyyg.2020382
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
Download: PDF(2942 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    

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:;
Issue Date: 23 December 2021
E-mail this article
E-mail Alert
Articles by authors
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.
URL:     OR
分类 界定 常用方法 处理
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
BFAST R [15],2010年 12
TimeStats IDL https: // [16],2011年 0
SPIRITS 1.5.2 C,JAVA https: // [17],2014年 1
BeeBox JavaScript,HTML5,JAVA,
Perl,IDL,R [18],2016年 1
Phenor R R https: // [3],2018年 1
pyPhenology Python https: // [19],2018年 0
CropPhenology R https: // [20],2018年 2
BEAST C/FORTRAN https: // [30],2019年 1
FORCE 2.1 C/C++ [4],2019年 1
Earth Engine App JavaScript/Python https: // [21],2019年 1
EO Time Series Viewer Python https: // [22],2020年 2
DATimeS MATLAB [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
[1] 梁顺林, 程洁, 贾坤, 等. 陆表定量遥感反演方法的发展新动态[J]. 遥感学报, 2016,20(5):875-898.
[1] Liang S L, Cheng J, Jia K, et al. Recent progress in land surface quantitative remote sensing[J]. Journal of Remote Sensing, 2016,20(5):875-898.
[2] 赵忠明, 孟瑜, 岳安志, 等. 遥感时间序列影像变化检测研究进展[J]. 遥感学报, 2016,20(5):1110-1125.
[2] Zhao Z M, Meng Y, Yue A Z, et al. Review of remotely sensed time series data for change detection[J]. Journal of Remote Sensing, 2016,20(5):1110-1125.
[3] Hufkens K, Basler D, Milliman T, et al. An integrated phenology modelling framework in R[J]. Methods in Ecology and Evolution, 2018,9:1276-1285.
doi: 10.1111/mee3.2018.9.issue-5 url:
[4] Frantz D. Force-Landsat+Sentinel-2 analysis ready data and beyond[J]. Remote Sensing, 2019,11:1124.
doi: 10.3390/rs11091124 url:
[5] Chao Z, Liu N, Zhang P, et al. Estimation methods developing with remote sensing information for energy crop biomass:A comparative review[J]. Biomass and Bioenergy, 2019,122:414-425.
doi: 10.1016/j.biombioe.2019.02.002 url:
[6] 万昌君, 吴小丹, 林兴稳. 遥感数据时空尺度对地理要素时空变化分析的影响[J]. 遥感学报, 2019,23(6):1064-1077.
[6] Wan C J, Wu X D, Lin X W. Impact of spatial and temporal scales of remote sensing data on the spatiotemporal change in geographic elements[J]. Journal of Remote Sensing, 2019,23(6):1064-1077.
[7] Dong Y, Peng C. Principled missing data methods for researchers[J]. SpringerPlus, 2013,2(1):1-17.
doi: 10.1186/2193-1801-2-1 url:
[8] Moreno-Martínez Á, Izquierdo-Verdiguier E, Maneta M P, et al. Multispectral high resolution sensor fusion for smoothing and gap-filling in the cloud[J]. Remote Sensing of Environment, 2020,247:111901.
doi: 10.1016/j.rse.2020.111901 pmid: 32943798
[9] Chao Z, Wang L, Che M, et al. Effects of different urbanization levels on land surface temperature change:Taking Tokyo and Shanghai for example[J]. Remote Sensing, 2020,12:2022.
doi: 10.3390/rs12122022 url:
[10] Zeng L, Wardlow B, Xiang D, et al. A review of vegetation phenological metrics extraction using time-series,multispectral satellite data[J]. Remote Sensing of Environment, 2020,237:111511.
doi: 10.1016/j.rse.2019.111511 url:
[11] Smith W, Dannenberg M, Yan D, et al. Remote sensing of dryland ecosystem structure and function:Progress,challenges,and opportunities[J]. Remote Sensing of Environment, 2019,233:111401.
doi: 10.1016/j.rse.2019.111401 url:
[12] Chen J, Boccelli D. Real-time forecasting and visualization toolkit for multi-seasonal time series[J]. Environmental Modelling and Software, 2018,105:244-256.
doi: 10.1016/j.envsoft.2018.03.034 url:
[13] Jönsson P, Cai Z, Melaas E, et al. A method for robust estimation of vegetation seasonality from Landsat and Sentinel-2 time series data[J]. Remote Sensing, 2018,10(4):635.
doi: 10.3390/rs10040635 url:
[14] Jönsson P, Eklundh L. TIMESAT:A program for analyzing time-series of satellite sensor data[J]. Computers and Geosciences, 2004,30:833-845.
doi: 10.1016/j.cageo.2004.05.006 url:
[15] Verbesselt J, Hyndman R, Newnham G, et al. Detecting trend and seasonal changes in satellite image time series[J]. Remote Sensing of Environment, 2010,114:106-115.
doi: 10.1016/j.rse.2009.08.014 url:
[16] Udelhoven T. Timestats:A software tool for the retrieval of temporal patterns from global satellite archives[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2011,4:310-317.
doi: 10.1109/JSTARS.4609443 url:
[17] Eerens H, Haesen D, Rembold F, et al. Image time series processing for agriculture monitoring[J]. Environmental Modelling and Software, 2014,53:154-162.
doi: 10.1016/j.envsoft.2013.10.021 url:
[18] Arundel J, Winter S, Gui G, et al. A web-based application for beekeepers to visualise patterns of growth in floral resources using MODIS data[J]. Environmental Modelling and Software, 2016,83:116-125.
doi: 10.1016/j.envsoft.2016.05.010 url:
[19] Taylor S. pyPhenology:A python framework for plant phenology modelling[J]. Journal of Open Source Software, 2018,3(28):827.
doi: 10.21105/joss url:
[20] Araya S, Ostendorf B, Lyle G, et al. CropPhenology:An R package for extracting crop phenology from time series remotely sensed vegetation index imagery[J]. Ecological Informatics, 2018,46:45-56.
doi: 10.1016/j.ecoinf.2018.05.006 url:
[21] Li H, Wan W, Fang Y, et al. A Google Earth Engine-enabled software for efficiently generating high-quality user-ready Landsat mosaic images[J]. Environmental Modelling and Software, 2019,112:16-22.
doi: 10.1016/j.envsoft.2018.11.004 url:
[22] Jakimow B, van der Linden S, Thiel F, et al. Visualizing and labeling dense multi-sensor earth observation time series:The EO time series viewer[J]. Environmental Modelling and Software, 2020,125:104631.
doi: 10.1016/j.envsoft.2020.104631 url:
[23] Belda S, Pipia L, Morcillo-Pallarés P, et al. DATimeS:A machine learning time series GUI toolbox for gap-filling and vegetation phenology trends detection[J]. Environmental Modelling and Software, 2020,127:104666.
doi: 10.1016/j.envsoft.2020.104666 url:
[24] Dragoni D, Schmid H, Wayson C, et al. Evidence of increased net ecosystem productivity associated with a longer vegetated season in a deciduous forest in southcentral Indiana,USA[J]. Global Change Biology, 2011,17(2):886-897.
doi: 10.1111/gcb.2010.17.issue-2 url:
[25] White M, De Beurs K, Didan K, et al. Intercomparison,interpretation,and assessment of spring phenology in North America estimated from remote sensing for 1982 to 2006[J]. Global Change Biology, 2009,15:2335-2359.
doi: 10.1111/gcb.2009.15.issue-10 url:
[26] Julien Y, Sobrino J. Global land surface phenology trends from GIMMS database[J]. International Journal of Remote Sensing, 2009,30:3495-3513.
doi: 10.1080/01431160802562255 url:
[27] Tan B, Morisette J, Wolfe R, et al. An enhanced TIMESAT algorithm for estimating vegetation phenology metrics from MODIS data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2011,4:361-371.
doi: 10.1109/JSTARS.4609443 url:
[28] Broich M, Huete A, Paget M, et al. A spatially explicit land surface phenology data product for science,monitoring and natural resources management applications[J]. Environmental Modelling and Software, 2015,64:191-204.
doi: 10.1016/j.envsoft.2014.11.017 url:
[29] Berra E, Gaulton R, Barr S. Assessing spring phenology of a temperate woodland:A multiscale comparison of ground,unmanned aerial vehicle and Landsat satellite observations[J]. Remote Sensing of Environment, 2019,223:229-242.
doi: 10.1016/j.rse.2019.01.010 url:
[30] Zhao K, Wulder M, Hu T, et al. Detecting change-point,trend,and seasonality in satellite time series data to track abrupt changes and nonlinear dynamics:A Bayesian ensemble algorithm[J]. Remote Sensing of Environment, 2019,232:111181.
doi: 10.1016/j.rse.2019.04.034 url:
[31] Belda S, Pipia L, Morcillo-Pallarés P, et al. Optimizing Gaussian process regression for image time series gap-filling and crop monitoring[J]. Agronomy, 2020,10:618.
doi: 10.3390/agronomy10050618 url:
[32] Verrelst J, Malenovský Z, Van der Tol C, et al. Quantifying vegetation biophysical variables from imaging spectroscopy data:A review on retrieval method[J]. Surveys in Geophysics, 2019,40:589-629.
doi: 10.1007/s10712-018-9478-y
[33] Reichstein M, Campas-Valls G, Stevens B, et al. Deep learning and process understanding for data driven Earth system science[J]. Nature, 2019,566:195-204.
doi: 10.1038/s41586-019-0912-1 url:
[34] 曹沛雨, 张雷明, 李胜功, 等. 植被物候观测与指标提取方法研究进展[J]. 地球科学进展, 2016,31(4):365-376.
[34] Cao P Y, Zhang L M, Li S G, et al. Review on vegetation phenology observation and phenological index extraction[J]. Advanced Earth Science, 2016,31(4):365-376.
[1] HUANG Xiaoyu, WANG Xuemei, KAWUQIATI Baishan. Inversion of soil salinity of an oasis in an arid area based on Landsat8 OLI images[J]. Remote Sensing for Natural Resources, 2023, 35(1): 189-197.
[2] LI Xianfeng, YUAN Zhengguo, DENG Weihua, YANG Liyuan, ZHOU Xueying, HU Lili. Spatial downscaling methods for the 2-meter air temperature grid data based on multiple machine learning models[J]. Remote Sensing for Natural Resources, 2023, 35(1): 57-65.
[3] Xun ZHOU, Yuebin WANG, Suhong LIU, Peixin YU, Xikai WANG. A machine learning algorithm for automatic identification of cultivated land in remote sensing images[J]. Remote Sensing for Land & Resources, 2018, 30(4): 68-73.
[4] YU Le, CAO Kai, WU Yang, ZHANG Deng-rong. Using Cross-sensor Image Learning for CBERS CCD Bands Simulation[J]. REMOTE SENSING FOR LAND & RESOURCES, 2011, 23(3): 48-53.
Full text



Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech