Reconstruction of Time Series Data of Environmental Parameters: Methods and Application

  • Data Center for Resources and Environmental Sciences, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China

Received date: 2011-02-16

  Revised date: 2011-06-26

  Online published: 2011-08-23


The satellite-derived environmental parameters play important roles in global change and regional resources and environment researches. Atmosphere effects and sensor limitations often lead to data products of inherently variable quality. The main goals of time series data reconstruction are to remove cloud affected observations and create gapless dataset at a prescribed time with multiple spatio-temporal interpolation and statistical methods. The conventional reconstructing algorithm include threshold method, temporal filtering method, and nonlinear simulation, etc., and mainly applied on satellite derived vegetation index, leaf area index and surface energy balance parameters. Although methods mentioned above have been applied in related researches and received good results, they suffer from their own drawbacks which limit their use. The algorithm of MVC and MVI ignores the remarkable anisotropy of reflectivity and VI, which may conceal details of vegetation with too large interval and lead to the result of reconstructed VI data from MVC and MVI remain a lot of noises. The BISE and other threshold-based methods may make the extracted temporal information unreliable. The Fourier analysis algorithm is sensitive to spurious peaks of the VI trend line, so it may depart from the truth a lot. Much attention should be paid to data assimilation based approaches which couple remote sensing retrieval model with surface processing model.

Cite this article

JIANG Dong, FU Jingying, HUANG Yaohuan, ZHUANG Dafang . Reconstruction of Time Series Data of Environmental Parameters: Methods and Application[J]. Journal of Geo-information Science, 2011 , 13(4) : 439 -446 . DOI: 10.3724/SP.J.1047.2011.00439


[1] 王浩, 王建华, 汪党献. 现代环境下的水资源综合评价体系及其方法研究[J]. 水科学进展,2003,14(增刊):110-117.

[2] 贾仰文,王浩. 分布式流域水文模拟研究进展及其展望[J]. 水科学进展,2003,14(增刊):118-123.

[3] 杨大文, 李翀, 倪广恒,等. 分布式水文模型在黄河流域的应用[J].地理学报,2004,59(1):143-154.

[4] Mark R, Rick C, Mick C, et al. Long-term Studies of Vegetation Dynamics[J]. Science, 2001, 293(5530): 650-655.

[5] Defries R S, Townshend J R G. NDVI-derived Land-cover Classification at a Global-scale[J]. International Journal of Remote Sensing, 1994, 15(17): 3567-3586.

[6] Zhou L, Tucker C J, Kaufmann R, et al. Variations in Northern Vegetation Activity Inferred from Satellite Data of Vegetation Index during 1981 to 1999[J]. Journal of Geophysical Research, 2001, 106(17): 20069-20083.

[7] 陈民锋,郎兆新. 基于自适应遗传算法的油田产量灰色预测模型[J]. 系统工程学报, 2003,18(6):541-546.

[8] 王彬. 时间序列预测模型在压缩机零件测试中的运用[J].计算机工程与应用,2008,44(17):228-230.

[9] 陈子丹."3S"技术在水文应用中的思考[J].水文,2006, 26(3):69-71.

[10] Sakamoto T, Van Nguyen N, Ohno H, et al. Spatio-temporal Distribution of Rice Phenology and Cropping Systems in the Mekong Delta with Special Reference to the Seasonal Water Flow of the Mekong and Bassac Rivers[J]. Remote Sensing of Environment, 2006, 100(1): 1-16.

[11] Viovy N, Arino O, Belward A S. The Best Index Slope Extraction (BISE): A Method for Reducing Noise in NDVI Time-series[J]. International Journal of Remote Sensing, 1992,13(8): 1585-1590.

[12] Lovell J L and Graetz R D. Filtering Pathfinder AVHRR Land NDVI Data for Australia[J]. International Journal of Remote Sensing, 2001, 22(13): 2649-2654.

[13] 顾娟,李新,黄春林.NDVI 时间序列数据重建方法述评[J].遥感技术与应用,2006,21(4):391-395.

[14] Huete A, Justice C, Liu H. Development of Vegetation and Soil Indices for MODIS-EOS[J]. Remote Sensing of Environment,1994,49:224-234.

[15] Chen J, Jnsson 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(3/4): 332-344.

[16] 黄耀欢,江东,周芹. 基于S-G滤波的MODIS-EVI时间序列数据重构[J]. 武汉大学学报信息科学版, 2009,34(12):1440-1443.

[17] Park J. G, Tateishi Ryutaro, Matsuoka Masayuki. A Proposal of the Temporal Window Operation (TWO) Method to Remove High-frequency Noises in AVHRR NDVI Time Series Data[J]. Journal of the Japan Society of Photogrammetry and Remote Sensing,1999,38(5): 36-47.

[18] 李 松,刘力军,谷晨. 混沌时间序列预测模型的比较研究[J]. 计算机工程与应用, 2009,45(32):53-56.

[19] Hou Y L, Luo D. A Decision Model Based on Grey Rough Sets Integration with Incomplete Information[J]. Chinese Quarterly Journal of Mathematics, 2009,1:121-123.

[20] Roerink G, Menenti M and Verhoef W. Reconstructing Cloudfree NDVI Composites Using Fourier Analysis of Time Series[J]. International Journal of Remote Sensing, 2000, 21(9): 1911-1917.

[21] 李儒,张霞,刘波,张兵. 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.

[22] Kondrashov D and Ghil M. Spatio-temporal Filling of Missing Points in Geophysical Data Sets[J]. Nonlinear Processes in Geophysics, 2006, 13: 151-159.

[23] Schafer J L, Graham J W. Missing Data: Our View of the State of the Art[J]. Psychol. Methods. 2002, 7: 147-177.

[24] Kumar S V, Reichle R H, Peters-Lidard C D, Koster R D, Xiwu Zhan, Crow W T, Eylander J B, Houser P R. A Land Surface Data Assimilation Framework Using the Land Information System: Description and Applications[J]. Advances in Water Resources, 2008,31:1419-1432.

[25] Gu J, Li X, Huang C L, Okin G S. A Simplified Data Assimilation Method for Reconstructing Time-series MODIS NDVI Data[J]. Advances in Space Research, 2009, 44:501-509.

[26] 周剑,王根绪,李新,等. 数据同化算法在青藏高原高寒生态系统能量-水分平衡分析中的应用[J]. 地球科学进展,2008,23(9):965-973.

[27] Alavi N, Warland J S, Berg A A. Filling Gaps in Evapotranspiration Measurements for Water Budget Studies: Evaluation of a Kalman Filtering Approach[J]. Agricultural and Forest Meteorology, 2006,141:57-66.

[28] Beck P S A, Atzberger C, Hogda K A, et al. Improved Monitoring of Vegetation Dynamics at Very High Latitudes: A New Method Using MODIS NDVI[J]. Remote Sensing of Environment, 2006, 100(3): 321-334.

[29] 王丹,姜小光,唐伶俐,等.利用时间序列傅立叶分析重构无云NDVI图像[J].国土资源遥感,2005,2:29-32.

[30] 于信芳,庄大方.基于MODIS/NDVI数据的东北森林物候期监测[J].资源科学, 2006, 28(4):111-117.

[31] Barr A G, Black T A, Hogg E H, Kljun N, Morgenstern K, Nesic Z. Inter-annual Variability in the Leaf Area Index of a Boreal Aspen-hazelnut Forest in Relation to Net Ecosystem Production[J]. Agricultural and Forest Meteorology, 2004, 126(3-4), 237-255.

[32] Fang H L, Liang S L, Townshend J R, Dickinson R E. Spatially and Temporally Continuous LAI Data Sets Based on an Integrated Filtering Method: Examples from North America[J]. Remote Sensing of Environment, 2008, 112(1): 75-93.

[33] Wang D D, Liang S L. Singular Spectrum Analysis for Filling Gaps and Reducing Uncertainties of Modis Land Products[J]. Geoscience and Remote Sensing Symposium, IGARSS 2008. IEEE International, 2008,5:558-561.

[34] 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 Meterology, 2009, 149:320-332.

[35] 马寨璞,井爱芹.动态最优插值方法及其同化应用研究[J].河北大学学报(自然科学版),2004, 24(6):574-580.

[36] 朱江,徐迎春,王赐震,等.海温数值预报资料同化试验: 客观分析的最优插值法试验[J].海洋学报,1995,17(6):9-20.

[37] Beckers J M, Rixen M. EOF Calculations and Data Filling from Incomplete Oceanographic Data Sets[J]. Journal of Atmospheric and Oceanic Technology,2003,20(12):1839-1856.

[38] 盛峥,石汉青,丁又专.利用DINEoF方法重构缺测的卫星遥感海温数据[J]. 海洋科学进展,2009,27(2):243-249.

[39] Neteler M. Estimating Daily Land Surface Temperatures in Mountainous Environments by Reconstructed MODIS LST Data[J]. Remote Sens. 2010, 2: 333-351.

[40] 徐自为,刘绍民,徐同仁,等. 涡动相关仪观测蒸散量的插补方法比较[J]. 地球科学进展. 2009,24(4):372-382.

[41] Yu F, Price K P, Ellis J, et al. Satellite Observations of the Seasonal Vegetation Growth in Central Asia: 1982-1990[J].Photogrammetric Engineering & Remote Sensing, 2004,70(4): 461-469.