Selecting the Optimal NDVI Time-Series Reconstruction Technique for Crop Phenology Detection. Issue 2 (2nd April 2016)

• Record Type: Journal Article
• Authors: Wei, Wei; Wu, Wenbin; Li, Zhengguo; Yang, Peng; Zhou, Qingbo
• Is Part Of: Intelligent automation & soft computing. Volume 22:Issue 2(2016)
• Page Start: 237
• Page End: 247
• Publication Date: 2016-04-02
• Subjects: NDVI -- time-series reconstruction -- crop phenology -- MODIS; Artificial intelligence -- Periodicals; Intelligent control systems -- Periodicals; 003.5
• DOI: 10.1080/10798587.2015.1095482
• Languages: English
• ISSNs: 1079-8587

Abstract

Abstract: A new scored method has been proposed in this study to evaluate the performances of different NDVI time-series reconstruction techniques. By giving a synthetic score to each of the candidates techniques based on two quantified criteria the optimal one is selected for the purpose of phenology detection. Three widely used techniques including Asymmetric Gaussian function fitting (AG), Double Logistic function fitting (DL) and Savitzky-Golay filtering (SG) are compared using NDVI time-series products from Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra satellite over cropland of Northeast China. The results show that AG approach outperforms the two others in our study area. Cropland NDVI values have been improved obviously after the reconstruction by AG. Spatial patterns of the crop phenology detected from the AG reconstructed NDVI time-series are reasonable. The errors of the derived crop phenology metrics are within an acceptable limit.