Please wait a minute...
Remote Sensing for Natural Resources    2022, Vol. 34 Issue (1) : 1-9     DOI: 10.6046/zrzyyg.2021071
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
Download: PDF(3718 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    

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.

Keywords remote sensing of vegetation      time series      data reconstruction      interpolation      smoothing     
ZTFLH:  TP79  
Corresponding Authors: HOU Meiting     E-mail:;
Issue Date: 14 March 2022
E-mail this article
E-mail Alert
Articles by authors
Weiguang LI
Meiting HOU
Cite this article:   
Weiguang LI,Meiting HOU. A review of reconstruction methods for remote-sensing-based time series data of vegetation and some examples[J]. Remote Sensing for Natural Resources, 2022, 34(1): 1-9.
URL:     OR
Fig.1  Comparison of reconstruction results of simulated NDVI time series
线性插值 SSA Whittaker HANTS 线性插值 SSA Whittaker HANTS
10 <0.000 1 0.096 6 <0.000 1 0.045 6 0.012 6 0.105 4 0.000 8 0.061 2
20 <0.000 1 0.004 8 <0.000 1 0.045 4 0.014 7 0.004 0 0.000 8 0.067 7
30 <0.000 1 0.077 5 <0.000 1 0.043 6 0.015 0 0.085 3 0.001 0 0.073 1
40 <0.000 1 0.102 3 <0.000 1 0.023 8 0.024 1 0.110 0 0.002 4 0.049 2
Tab.1  Comparison of RMSE for different data reconstruction methods based on simulated NDVI time series
Fig.2  Comparison of different reconstruction methods for NDVI time series
Fig.3  Local detailed comparison of different reconstruction methods for NDVI time series
线性插值 SSA Whittaker HANTS 线性插值 SSA Whittaker HANTS
10 <0.000 1 0.034 1 0.007 8 0.044 4 0.048 2 0.052 3 0.044 0 0.055 5
20 <0.000 1 0.035 8 0.026 1 0.042 3 0.056 5 0.043 0 0.056 6 0.061 9
30 <0.000 1 0.034 0 0.000 9 0.042 3 0.058 4 0.046 1 0.060 4 0.057 3
40 <0.000 1 0.031 2 <0.000 1 0.040 4 0.061 0 0.045 3 0.066 0 0.061 2
Tab.2  Comparison of RMSE for different data reconstruction methods based on NDVI time series
[1] 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 url:
[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 url:
[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 url:
[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 url:
[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 url:
[6] 吕妍, 张黎, 闫慧敏, 等. 中国西南喀斯特地区植被变化时空特征及其成因[J]. 生态学报, 2018, 38(24):8774-8786.
[6] 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 url:
[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 url:
[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 url:
[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 url:
[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 url:
[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 url:
[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 url:
[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 url:
[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 url:
[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 url:
[17] Frantz D. FORCE-Landsat + Sentinel-2 analysis ready data and beyond[J]. Remote Sensing, 2019, 11(9):1124.
doi: 10.3390/rs11091124 url:
[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 url:
[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 url:
[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 url:
[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 url:
[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 url:
[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 url:
[24] 耿丽英, 马明国. 长时间序列NDVI数据重建方法比较研究进展[J]. 遥感技术与应用, 2014, 29(2):362-368.
[24] 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.
[25] 李儒, 张霞, 刘波, 等. 遥感时间序列数据滤波重建算法发展综述[J]. 遥感学报, 2009, 13(2):335-341.
[25] 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 url:
[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 url:
[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 url:
[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 url:
[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 url:
[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 url:
[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 url:
[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 url:
[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 url:
[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 url:
[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 url:
[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 url:
[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 url:
[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 url:
[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 url:
[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 url:
[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 url:
[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 url:
[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 url:
[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 url:
[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 url:
[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 url:
[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 url:
[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 url:
[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 url:
[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 url:
[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 url:
[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 url:
[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 url:
[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 url:
[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 url:
[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 url:
[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 url:
[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 url:
[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 url:
[1] QIN Le, HE Peng, MA Yuzhong, LIU Jianqiang, YANG Bin. Change detection of satellite time series images based on spatial-temporal-spectral features[J]. Remote Sensing for Natural Resources, 2022, 34(4): 105-112.
[2] YU Wen, GONG Huili, CHEN Beibei, ZHOU Chaofan. Spatial-temporal evolution characteristics and prediction of land subsidence in the eastern plain of Beijing[J]. Remote Sensing for Natural Resources, 2022, 34(4): 183-193.
[3] MAO Kebiao, YAN Yibo, CAO Mengmeng, YUAN Zijin, QIN Zhihao. Reconstruction of surface temperature data and analysis of spatial and temporal changes in North America[J]. Remote Sensing for Natural Resources, 2022, 34(4): 203-215.
[4] DONG Jihong, MA Zhigang, LIANG Jingtao, LIU Bin, ZHAO Cong, ZENG Shuai, YAN Shengwu, MA Xiaobo. A comparative study of the identification of hidden landslide hazards based on time series InSAR techniques[J]. Remote Sensing for Natural Resources, 2022, 34(3): 73-81.
[5] ZENG Hui, REN Huazhong, ZHU Jinshun, GUO Jinxin, YE Xin, TENG Yuanjian, NIE Jing, QIN Qiming. Impacts of the Syrian Civil War on vegetation[J]. Remote Sensing for Natural Resources, 2022, 34(3): 121-128.
[6] LI Zhu, FAN Hongdong, GAO Yantao, XU Yaozong. DS-InSAR-based monitoring and analysis of a long time series of surface deformation in the fire area of the Wuda coal field[J]. Remote Sensing for Natural Resources, 2022, 34(3): 138-145.
[7] LUO Hongjian, MING Dongping, XU Lu. Time series calculation of remote sensing ecological index based on GEE[J]. Remote Sensing for Natural Resources, 2022, 34(2): 271-277.
[8] HUANG Pei, PU Junwei, ZHAO Qiaoqiao, LI Zhongjie, SONG Haokun, ZHAO Xiaoqing. Research progress and development trend of remote sensing information extraction methods of vegetation[J]. Remote Sensing for Natural Resources, 2022, 34(2): 10-19.
[9] SHI Feifei, GAO Xiaohong, XIAO Jianshe, LI Hongda, LI Runxiang, ZHANG Hao. Classification of wolfberry planting areas based on ensemble learning and multi-temporal remote sensing images[J]. Remote Sensing for Natural Resources, 2022, 34(1): 115-126.
[10] SONG Qi, FENG Chunhui, MA Ziqiang, WANG Nan, JI Wenjun, PENG Jie. Simulation of land use change in oasis of arid areas based on Landsat images from 1990 to 2019[J]. Remote Sensing for Natural Resources, 2022, 34(1): 198-209.
[11] LAI Peiyu, HUANG Jing, HAN Xujun, MA Mingguo. An analysis of impacts from water impoundment in Three Gorges Dam Project on surface water in Chongqing area base on Google Earth Engine[J]. Remote Sensing for Natural Resources, 2021, 33(4): 227-234.
[12] YU Bing, TAN Qingxue, LIU Guoxiang, LIU Fuzhen, ZHOU Zhiwei, HE Zhiyong. Land subsidence monitoring based on differential interferometry using time series of high-resolution TerraSAR-X images and monitoring precision verification[J]. Remote Sensing for Natural Resources, 2021, 33(4): 26-33.
[13] SUN Chao, CHEN Zhenjie, WANG Beibei. Expansion monitoring of construction land based on SAR time series: A case study of Xinbei District, Changzhou[J]. Remote Sensing for Land & Resources, 2020, 32(4): 154-162.
[14] WANG Dejun, JIANG Qigang, LI Yuanhua, GUAN Haitao, ZHAO Pengfei, XI Jing. Land use classification of farming areas based on time series Sentinel-2A/B data and random forest algorithm[J]. Remote Sensing for Land & Resources, 2020, 32(4): 236-243.
[15] WANG Lingyu, CHEN Quan, WU Yue, ZHOU Zhongfa, DAN Yusheng. Accurate recognition and extraction of karst abandoned land features based on cultivated land parcels and time series NDVI[J]. Remote Sensing for Land & Resources, 2020, 32(3): 23-31.
Full text



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