A Comparison Between the Algorithms for Removing Cloud Pixel from MODIS NDVI Time Series Data
LIANG Shou-zhen 1,2， SHI Ping 1， XING Qian-guo 1
(1. Yantai Institute of Coastal Zone Research， Chinese Academy of Sciences， Yantai 264003， China； 2. Graduate University of Chinese Academy of Sciences， Beijing 100039， China)
Abstract：Although composite data present lower atmospheric contamination than raw time series, MODIS NDVI products are still contaminated by clouds, especially when cloud cover lasts longer than the composite period. e.g., in the rainy season. The long-time cloud cover will weaken the application of MODIS NDVI time series data. To remove the effect of these clouds from NDVI data and reconstruct high-quality NDVI data, the authors propose three algorithms for cloud removal, namely SPLINE function, HANTS and Savizky-Golay. The capabilities of the three algorithms in cloud removal was compared with each other in this study, with the MODIS NDVI time series data in Shandong province serving as the test data. The results show that the three algorithms can remove the effect of cloud from NDVI time series data effectively, with each algorithm having its own advantages and disadvantages. For the algorithm of SPLINE function, the result of cloud removal mainly depends on the quality of cloud data and sometimes extreme values will occur; this algorithm fails to change the values of pixels which have not been contaminated atmospherically. When HANTS and Savizky-Golay algorithms are used, most of the pixels will lose their original values, and the parameters have to be determined after conducting many experiments because there is no objective rule to set them.
梁守真, 施平, 邢前国. MODIS NDVI时间序列数据的去云算法比较[J]. 国土资源遥感, 2011, 23(1): 33-36.
LIANG Shou-Zhen, SHI Peng, XENG Qian-Guo. A Comparison Between the Algorithms for Removing Cloud Pixel from MODIS NDVI Time Series Data. REMOTE SENSING FOR LAND & RESOURCES, 2011, 23(1): 33-36.
［1］顾娟,李新,黄春林.NDVI时间序列数据集重建方法述评［J］.遥感技术与应用，2006,21(4):391-395.［2］Roerink G J,Menenti M,Verhoef W.Reconstructing Cloud Free NDVI Composites Using Fourier Analysis of Time Series［J］.International Journal of Remote Sensing,2000,21(9):1911-1917.［3］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.［4］Mucher C A,de Badts E P J.Global Land Cover 2000:Evaluation of the SPOT VEGTATION Sensor for Land Use Mapping［R］.Wageningen Alterra:Green World Research，2002.［5］Chen J,Jnsson P,Tamura M,et al.A Simple Method for Reconstructing a High-quality NDVI Time-series Dataset Based on the Savitzky-Golay Filter ［J］.Remote Sensing of Environment,2004,91:332-344［6］Malingreau J P.Global Vegetation Dynamics:Satellite Observations over Asia［J］.International Journal of Remote Sensing,1986,7:1121-1146［7］Kogan F,Sullivan J,Carry R,et al.Post-pinatubo Vegetation Index in Central Africa［J］.Geocarto International,1994,9:63-66.［8］Savitzky A,Golay M J E.Smoothing and Differentiation of Data by Simplified Least Squares Procedures ［J］.Analytical Chemistry,1964,36:1627-1639.
2011, Vol. 23 
