A method for quality management of vegetation phenophases derived from satellite remote sensing data. Issue 15 (3rd August 2021)

• Record Type: Journal Article
• Authors: Ruan, Yongjian; Zhang, Xinchang; Xin, Qinchuan; Sun, Ying; Ao, Zurui; Jiang, Xin
• Is Part Of: International journal of remote sensing. Volume 42:Issue 15(2021)
• Page Start: 5811
• Page End: 5830
• Publication Date: 2021-08-03
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2021.1931534
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: Remote sensing has become an important technique for monitoring vegetation phenology. The quality of remote-sensing images and derived products is key to successful extraction of vegetation phenophases. There is a need to develop quality management methods to evaluate the data uncertainty and assist the removal of the noises. This paper developed a shape quality assurance score threshold (SQAT) method which accounts for the trend in satellite-derived vegetation index associated with the process of vegetation growth. The proposed method was tested on six widely used methods for extracting vegetation phenophases. Results showed that the SQAT method can effectively identify noises in the vegetation index time series and improve the accuracies of estimated start of season (SOS) and end of season (EOS) of the six methods. After removal of identified noises, the Pearson correlation coefficient (r) averagely increased by 8% for SOS, and 11% for EOS. Regression analyses of vegetation phenophases between the PhenoCam observations and MCD12Q2 product showed that the proposed method performs better than the QA score of MCD12Q2 for quality management. This paper provides promising method for quality management; it has the potential to reduce the uncertainty of the vegetation index time series that can support studies of vegetation phenology monitoring.