scholarly journals Long-Term Variation of Global GEOV2 and MODIS Leaf Area Index (LAI) and Their Uncertainties: An Insight into the Product Stabilities

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Hongliang Fang ◽  
Yao Wang ◽  
Yinghui Zhang ◽  
Sijia Li

Leaf area index (LAI) is an essential climate variable that is crucial to understand the global vegetation change. Long-term satellite LAI products have been applied in many global vegetation change studies. However, these LAI products contain various uncertainties that are not been fully considered in current studies. The objective of this study is to explore the uncertainties in the global LAI products and the uncertainty variations. Two global LAI datasets—the European Geoland2 Version 2 (GEOV2) and Moderate Resolution Imaging Spectroradiometer (MODIS) (2003-2019)—were investigated. The qualitative quality flags (QQFs) and quantitative quality indicators (QQIs) embedded in the product quality layers were analyzed to identify the temporal anomalies in the quality profile. The results show that the global GEOV2 (0.042/10a) and MODIS (0.034/10a) LAI values have steadly increased from 2003 to 2019. The global LAI uncertainty (0.016/10a) and relative uncertainty (0.3%/10a) from GEOV2 have also increased gradually, especially during the growing season from April to October. The uncertainty increase is larger for woody biomes than for herbaceous types. Contrastingly, the MODIS LAI product uncertainty remained stable over the study period. The uncertainty increase indicated by GEOV2 is partly attributed to the sensor shift in the product series. Further algorithm enhancement is necessary to improve the cross-sensor performance. This study highlights the importance of studying the LAI uncertainty and the uncertainty variation. Temporal variations in the LAI products and the product quality revealed herein have significant implications on global vegetation change studies.

2015 ◽  
Vol 7 (2) ◽  
pp. 1300-1319 ◽  
Author(s):  
Yelu Zeng ◽  
Jing Li ◽  
Qinhuo Liu ◽  
Yonghua Qu ◽  
Alfredo Huete ◽  
...  

2020 ◽  
Vol 12 (7) ◽  
Author(s):  
Qinchuan Xin ◽  
Xuewen Zhou ◽  
Nan Wei ◽  
Hua Yuan ◽  
Zurui Ao ◽  
...  

2011 ◽  
Vol 151 (5) ◽  
pp. 565-574 ◽  
Author(s):  
Michael Sprintsin ◽  
S. Cohen ◽  
K. Maseyk ◽  
E. Rotenberg ◽  
J. Grünzweig ◽  
...  

2012 ◽  
Vol 58 (2) ◽  
pp. 141-146 ◽  
Author(s):  
Sivasathivel Kandasamy ◽  
Philippe Neveux ◽  
Aleixandre Verger ◽  
Samuel Buis ◽  
Marie Weiss ◽  
...  

Improving the Consistency and Continuity of MODIS 8 Day Leaf Area Index Products Time Series Analysis of Leaf Area Index (LAI) is vital to the understanding of global vegetation dynamics. The LAI time series derived from satellite observations are usually not complete and noisy due to cloud contamination and uncertainties in the retrieval techniques. In this paper, the continuity and consistency of the MODIS 8 day LAI products are improved using a method based on Caterpillar Singular Spectrum Analysis. The proposed method is compared with other standard methods: Savitzky-Golay filter, Empirical Mode Decomposition, Low Pass filtering and Asymmetric Gaussian fitting. The experiment demonstrates the smoothing and gapfilling ability of the developed method, which is more robust across the biomes both in terms of root mean square error metrics and bias metrics as compared to the standard methods.


2007 ◽  
Vol 6 (12) ◽  
pp. 1437-1443 ◽  
Author(s):  
Peng YANG ◽  
Wen-bin WU ◽  
Hua-jun TANG ◽  
Qing-bo ZHOU ◽  
Jin-qiu ZOU ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document