A review of reconstruction methods for remote-sensing-based time series data of vegetation and some examples
LI Weiguang1,2(), HOU Meiting3()
1. Hainan Meteorological Service, Haikou 570203, China 2. Key Laboratory of South China Sea Meteorological Disaster Prevention and Mitigation of Hainan Province, Haikou 570203,China 3. China Meteorological Administration Training Centre, Beijing 100081, China
Remote-sensing-based time series data of vegetation have been increasingly available with the accumulation of remote sensing data. These data are vital for ascertaining the changes in an ecosystem and analyzing relevant driving factors. However, some factors (e.g., cloud cover and instrument errors) restrict the observation quality of the vegetation products of remote sensing, creating data gaps in continuous and high-quality observation data. The data gaps can be filled based on the spatio-temporal dependence of the earth’s surface characteristics, which is called the spatio-temporal reconstruction of time series data. High-quality spatio-temporal reconstruction of time series data is an important prerequisite for the accurate extraction of changes in time series data. Taking the remote-sensing-based time series data of vegetation indices as examples, this study briefly reviewed the widely used reconstruction methods of time series data firstly. These methods generally include two steps: interpolation and smoothing. The interpolation can be divided into three major types, namely time-based, space-based, and spatio-temporal interpolation. Then, taking the simulated normalized vegetation index (NDVI) time series and actual GIMMS NDVI time series as examples, different proportions of data gaps in the two time series were created. Then, this study compared the effects of four types of data reconstruction methods (i.e., linear interpolation, singular spectrum analysis (SSA), Whittaker, and time series harmonic analysis (HANTS)) on the reconstruction results of the two time series. The results show that the four methods have their own advantages and disadvantages, and the Whittaker method showed relatively good performance overall. However, the performance of interpolation methods might vary within different regions, and thereby the data reconstruction methods need to be further verified.
李伟光, 侯美亭. 植被遥感时间序列数据重建方法简述及示例分析[J]. 自然资源遥感, 2022, 34(1): 1-9.
LI Weiguang, HOU Meiting. A review of reconstruction methods for remote-sensing-based time series data of vegetation and some examples. Remote Sensing for Natural Resources, 2022, 34(1): 1-9.
Roughgarden J, Running S W, Matson P A. What does remote sensing do for Ecology?[J]. Ecology, 1991, 72(6):1918-1922.
doi: 10.2307/1941546
[2]
Goward S N, Tucker C J, Dye D G. North American vegetation patterns observed with the NOAA-7 advanced very high resolution radiometer[J]. Vegetatio, 1985, 64(1):3-14.
doi: 10.1007/BF00033449
[3]
Sun Y, Frankenberg C, Jung M, et al. Overview of solar-induced chlorophyll fluorescence (SIF) from the orbiting carbon observatory-2:Retrieval,cross-mission comparison,and global monitoring for GPP[J]. Remote Sensing of Environment, 2018, 209:808-823.
doi: 10.1016/j.rse.2018.02.016
[4]
Skidmore A K, Pettorelli N, Coops N C, et al. Agree on biodiversity metrics to track from space[J]. Nature, 2015, 523(7561):403-405.
doi: 10.1038/523403a
[5]
Ben Abbes A, Bounouh O, Farah IR, et al. Comparative study of three satellite image time-series decomposition methods for vegetation change detection[J]. European Journal of Remote Sensing, 2018, 51(1):607-615.
doi: 10.1080/22797254.2018.1465360
Lyu Y, Zhang L, Yan H M, et al. Spatial and temporal patterns of changing vegetation and the influence of environmental factors in the Karst region of Southwest China[J]. Acta Ecologica Sinica, 2018, 38(24):8774-8786.
[7]
Cord A F, Brauman K A, Chaplin-Kramer R, et al. Priorities to advance monitoring of ecosystem services using earth observation[J]. Trends in Ecology and Evolution, 2017, 32(6):416-428.
doi: 10.1016/j.tree.2017.03.003
[8]
Wylie B K, Johnson D A, Laca E, et al. Calibration of remotely sensed,coarse resolution NDVI to CO2 fluxes in a sagebrush-steppe ecosystem[J]. Remote Sensing of Environment, 2003, 85(2):243-255.
doi: 10.1016/S0034-4257(03)00004-X
[9]
Ustin S L, Gamon J A. Remote sensing of plant functional types[J]. New Phytologist, 2010, 186(4):795-816.
doi: 10.1111/nph.2010.186.issue-4
[10]
Hilker T, Natsagdorj E, Waring R H, et al. Satellite observed widespread decline in Mongolian grasslands largely due to overgrazing[J]. Global Change Biology, 2014, 20(2):418-428.
doi: 10.1111/gcb.2014.20.issue-2
[11]
Jönsson P, Eklundh L. TIMESAT:A program for analyzing time-series of satellite sensor data[J]. Computers and Geosciences, 2004, 30(8):833-845.
doi: 10.1016/j.cageo.2004.05.006
[12]
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(1):106-115.
doi: 10.1016/j.rse.2009.08.014
[13]
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(2):310-317.
doi: 10.1109/JSTARS.4609443
[14]
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
[15]
Jamali S, Jönsson P, Eklundh L, et al. Detecting changes in vegetation trends using time series segmentation[J]. Remote Sensing of Environment, 2015, 156:182-195.
doi: 10.1016/j.rse.2014.09.010
[16]
Hufkens K, Basler D, Milliman T, et al. An integrated phenology modelling framework in R[J]. Methods in Ecology and Evolution, 2018, 9(5):1276-1285.
doi: 10.1111/mee3.2018.9.issue-5
[17]
Frantz D. FORCE-Landsat + Sentinel-2 analysis ready data and beyond[J]. Remote Sensing, 2019, 11(9):1124.
doi: 10.3390/rs11091124
[18]
Zhao K, Wulder M A, 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
[19]
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
[20]
Hird J N, McDermid G J. Noise reduction of NDVI time series:An empirical comparison of selected techniques[J]. Remote Sensing of Environment, 2009, 113(1):248-258.
doi: 10.1016/j.rse.2008.09.003
[21]
Cai Z, Jönsson P, Jin H, et al. Performance of smoothing methods for reconstructing NDVI time-series and estimating vegetation phenology from MODIS data[J]. Remote Sensing, 2017, 9(12):1271.
doi: 10.3390/rs9121271
[22]
Geng L, Ma M, Wang X, et al. Comparison of eight techniques for reconstructing multi-satellite sensor time-series NDVI data sets in the Heihe River basin,China[J]. Remote Sensing, 2014, 6(3):2024-2049.
doi: 10.3390/rs6032024
[23]
Liu R, Shang R, Liu Y, et al. Global evaluation of gap-filling approaches for seasonal NDVI with considering vegetation growth trajectory,protection of key point,noise resistance and curve stability[J]. Remote Sensing of Environment, 2017, 189:164-179.
doi: 10.1016/j.rse.2016.11.023
Geng L Y, Ma M G. Advance in method comparison of reconstructing remote sensing time series data sets[J]. Remote Sensing Technology and Application, 2014, 29(2):362-368.
Li R, Zhang X, Liu B, et al. Review on methods of remote sensing time-series data reconstruction[J]. Journal of Remote Sensing, 2009, 13(2):335-341.
[26]
Zhang Y, Song C, Band L E, et al. Reanalysis of global terrestrial vegetation trends from MODIS products:Browning or greening?[J]. Remote Sensing of Environment, 2017, 191:145-155.
doi: 10.1016/j.rse.2016.12.018
[27]
Mondal S, Jeganathan C. Mountain agriculture extraction from time-series MODIS NDVI using dynamic time warping technique[J]. International Journal of Remote Sensing, 2018, 39(11):3679-3704.
doi: 10.1080/01431161.2018.1444289
[28]
Joiner J, Yoshida Y, Anderson M, et al. Global relationships among traditional reflectance vegetation indices (NDVI and NDII),evapo-transpiration (ET),and soil moisture variability on weekly timescales[J]. Remote Sensing of Environment, 2018, 219:339-352.
doi: 10.1016/j.rse.2018.10.020
[29]
Jeganathan C, Dash J, Atkinson P M. Remotely sensed trends in the phenology of northern high latitude terrestrial vegetation,controlling for land cover change and vegetation type[J]. Remote Sensing of Environment, 2014, 143:154-170.
doi: 10.1016/j.rse.2013.11.020
[30]
Nestola E, Calfapietra C, Emmerton C A, et al. Monitoring grassland seasonal carbon dynamics,by integrating MODIS NDVI,proximal optical sampling,and eddy covariance measurements[J]. Remote Sensing, 2016, 8(3):260.
doi: 10.3390/rs8030260
[31]
Eilers P H C. A perfect smoother[J]. Analytical Chemistry, 2003, 75(14):3631-3636.
pmid: 14570219
[32]
Julien Y, Sobrino J A. Comparison of cloud-reconstruction metho-ds for time series of composite NDVI data[J]. Remote Sensing of Environment, 2010, 114(3):618-625.
doi: 10.1016/j.rse.2009.11.001
[33]
Shen M, Zhang G, Cong N, et al. Increasing altitudinal gradient of spring vegetation phenology during the last decade on the Qinghai-Tibetan Plateau[J]. Agricultural and Forest Meteorology, 2014, 189-190:71-80.
doi: 10.1016/j.agrformet.2014.01.003
[34]
Zhang X. Reconstruction of a complete global time series of daily vegetation index trajectory from long-term AVHRR data[J]. Remote Sensing of Environment, 2015, 156:457-472.
doi: 10.1016/j.rse.2014.10.012
[35]
Ganguly S, Friedl M A, Tan B, et al. Land surface phenology from MODIS:Characterization of the Collection 5 global land cover dynamics product[J]. Remote Sensing of Environment, 2010, 114(8):1805-1816.
doi: 10.1016/j.rse.2010.04.005
[36]
Kandasamy S, Baret F, Verger A, et al. A comparison of methods for smoothing and gap filling time series of remote sensing observations:Application to MODIS LAI products[J]. Biogeosciences, 2013, 10(6):4055-4071.
doi: 10.5194/bg-10-4055-2013
[37]
Liang S, Ma W, Sui X, et al. Extracting the spatiotemporal pattern of cropping systems from NDVI time series using a combination of the spline and HANTS algorithms:A case study for Shandong Province[J]. Canadian Journal of Remote Sensing, 2017, 43(1):1-15.
doi: 10.1080/07038992.2017.1252906
[38]
Pede T, Mountrakis G. An empirical comparison of interpolation methods for MODIS 8-day land surface temperature composites across the conterminous Unites States[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 142:137-150.
doi: 10.1016/j.isprsjprs.2018.06.003
[39]
Hmimina G, Dufrêne E, Pontailler J Y, et al. Evaluation of the potential of MODIS satellite data to predict vegetation phenology in different biomes:An investigation using ground-based NDVI measurements[J]. Remote Sensing of Environment, 2013, 132:145-158.
doi: 10.1016/j.rse.2013.01.010
[40]
Zhou J, Jia L, Menenti M. Reconstruction of global MODIS NDVI time series:Performance of harmonic analysis of time series (HANTS)[J]. Remote Sensing of Environment, 2015, 163:217-228.
doi: 10.1016/j.rse.2015.03.018
[41]
Julien Y, Sobrino J A. Optimizing and comparing gap-filling techniques using simulated NDVI time series from remotely sensed global data[J]. International Journal of Applied Earth Observation and Geoinformation, 2019, 76:93-111.
doi: 10.1016/j.jag.2018.11.008
[42]
Ghafarian Malamiri H R, Zare H, et al. Comparison of harmonic analysis of time series (HANTS) and multi-singular spectrum analysis (M-SSA) in reconstruction of long-gap missing data in NDVI time series[J]. Remote Sensing, 2020, 12(17):2747.
doi: 10.3390/rs12172747
[43]
Kondrashov D, Ghil M. Spatio-temporal filling of missing points in geophysical data sets[J]. Nonlin Processes Geophys, 2006, 13(2):151-159.
doi: 10.5194/npg-13-151-2006
[44]
Zhao J, Lange H, Meissner H.Gap-filling continuously-measured soil respiration data:A highlight of time-series-based methods[J]. Agricultural and Forest Meteorology, 2020, 285-286:107912.
doi: 10.1016/j.agrformet.2020.107912
[45]
Konik M, Kowalewski M, Bradtke K, et al. The operational method of filling information gaps in satellite imagery using numerical models[J]. International Journal of Applied Earth Observation and Geoinformation, 2019, 75:68-82.
doi: 10.1016/j.jag.2018.09.002
[46]
Miles V V, Esau I. Spatial heterogeneity of greening and browning between and within bioclimatic zones in northern west Siberia[J]. Environmental Research Letters, 2016, 11(11):115002.
doi: 10.1088/1748-9326/11/11/115002
[47]
Borak J S, Jasinski M F. Effective interpolation of incomplete satellite-derived leaf-area index time series for the continental United States[J]. Agricultural and Forest Meteorology, 2009, 149(2):320-332.
doi: 10.1016/j.agrformet.2008.08.017
[48]
Militino A F, Ugarte M D, Pérez-Goya U, et al. Interpolation of the mean anomalies for cloud filling in land surface temperature and normalized difference vegetation index[C]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(8):6068-6078.
[49]
Cao R, Chen Y, Shen M, et al. A simple method to improve the quality of NDVI time-series data by integrating spatiotemporal information with the Savitzky-Golay filter[J]. Remote Sensing of Environment, 2018, 217:244-257.
doi: 10.1016/j.rse.2018.08.022
[50]
Yan L, Roy D P. Spatially and temporally complete Landsat reflectance time series modelling:The fill-and-fit approach[J]. Remote Sensing of Environment, 2020, 241:111718.
doi: 10.1016/j.rse.2020.111718
[51]
Fang X, Zhu Q, Ren L, et al. Large-scale detection of vegetation dynamics and their potential drivers using MODIS images and BFAST:A case study in Quebec,Canada[J]. Remote Sensing of Environment, 2018, 206:391-402.
doi: 10.1016/j.rse.2017.11.017
[52]
Burrell A L, Evans J P, Liu Y. The impact of dataset selection on land degradation assessment[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 146:22-37.
doi: 10.1016/j.isprsjprs.2018.08.017
[53]
Pastor-Guzman J, Dash J, Atkinson P M. Remote sensing of mangrove forest phenology and its environmental drivers[J]. Remote Sensing of Environment, 2018, 205:71-84.
doi: 10.1016/j.rse.2017.11.009
[54]
Doktor D, Bondeau A, Koslowski D, et al. Influence of heterogeneous landscapes on computed green-up dates based on daily AVHRR NDVI observations[J]. Remote Sensing of Environment, 2009, 113(12):2618-2632.
doi: 10.1016/j.rse.2009.07.020
[55]
Holben B N. Characteristics of maximum-value composite images from temporal AVHRR data[J]. International Journal of Remote Sensing, 1986, 7(11):1417-1434.
doi: 10.1080/01431168608948945
[56]
Zeng L, Wardlow B D, 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
[57]
Eklundh L, Jönsson P. TIMESAT:A software package for time-series processing and assessment of vegetation dynamics[M]//Kuenzer C, Dech S,Wagner W.Remote sensing time series:Revealing land surface dynamics. Springer International Publishing, 2015:141-158.
[58]
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
[59]
Chen J, Jönsson P, Tamura M, et al. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter[J]. Remote Sensing of Environment, 2004, 91(3):332-344.
doi: 10.1016/j.rse.2004.03.014
[60]
Atzberger C, Eilers P H C. A time series for monitoring vegetation activity and phenology at 10-daily time steps covering large parts of South America[J]. International Journal of Digital Earth, 2011, 4(5):365-386.
doi: 10.1080/17538947.2010.505664
[61]
Melaas E K, Friedl M A, Zhu Z. Detecting interannual variation in deciduous broadleaf forest phenology using Landsat TM/ETM+ data[J]. Remote Sensing of Environment, 2013, 132:176-185.
doi: 10.1016/j.rse.2013.01.011
[62]
Padhee S K, Dutta S. Spatio-temporal reconstruction of MODIS NDVI by regional land surface phenology and harmonic analysis of time-series[J]. GIScience and Remote Sensing, 2019, 56(8):1261-1288.
doi: 10.1080/15481603.2019.1646977
[63]
Fang H, Baret F, Plummer S, et al. An overview of global leaf area index (LAI):Methods,products,validation,and applications[J]. Reviews of Geophysics, 2019, 57(3):739-799.
doi: 10.1029/2018RG000608
[64]
Verbesselt J, Hyndman R, Zeileis A, et al. Phenological change detection while accounting for abrupt and gradual trends in satellite image time series[J]. Remote Sensing of Environment, 2010, 114(12):2970-2980.
doi: 10.1016/j.rse.2010.08.003
2022, Vol. 34 
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