scholarly journals A Semiprognostic Phenology Model for Simulating Multidecadal Dynamics of Global Vegetation Leaf Area Index

2020 ◽  
Vol 12 (7) ◽  
Author(s):  
Qinchuan Xin ◽  
Xuewen Zhou ◽  
Nan Wei ◽  
Hua Yuan ◽  
Zurui Ao ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Area Index ◽  
Phenology Model ◽  
Global Vegetation

Improving the Consistency and Continuity of MODIS 8 Day Leaf Area Index Products

2012 ◽  
Vol 58 (2) ◽  
pp. 141-146 ◽  
Author(s):  
Sivasathivel Kandasamy ◽  
Philippe Neveux ◽  
Aleixandre Verger ◽  
Samuel Buis ◽  
Marie Weiss ◽  
...  
Keyword(s):  
Time Series ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Singular Spectrum Analysis ◽  
Area Index ◽  
Standard Methods ◽  
Error Metrics ◽  
Mode Decomposition ◽  
Low Pass ◽  
Global Vegetation

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.

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
Keyword(s):  
Leaf Area Index ◽  
Product Quality ◽  
Leaf Area ◽  
Vegetation Change ◽  
Temporal Variations ◽  
Area Index ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Global Vegetation

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.

scholarly journals Non-Gaussian data assimilation of satellite-based leaf area index observations with an individual-based dynamic global vegetation model

2017 ◽  
Vol 24 (3) ◽  
pp. 553-567 ◽  
Author(s):  
Hazuki Arakida ◽  
Takemasa Miyoshi ◽  
Takeshi Ise ◽  
Shin-ichiro Shima ◽  
Shunji Kotsuki
Keyword(s):  
Data Assimilation ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Model Parameters ◽  
Vegetation Model ◽  
Dynamic Global Vegetation Model ◽  
Area Index ◽  
The Real ◽  
Global Vegetation ◽  
The Impact

Abstract. We developed a data assimilation system based on a particle filter approach with the spatially explicit individual-based dynamic global vegetation model (SEIB-DGVM). We first performed an idealized observing system simulation experiment to evaluate the impact of assimilating the leaf area index (LAI) data every 4 days, simulating the satellite-based LAI. Although we assimilated only LAI as a whole, the tree and grass LAIs were estimated separately with high accuracy. Uncertain model parameters and other state variables were also estimated accurately. Therefore, we extended the experiment to the real world using the real Moderate Resolution Imaging Spectroradiometer (MODIS) LAI data and obtained promising results.

scholarly journals Non-Gaussian data assimilation of satellite-based Leaf Area Index observations with an individual-based dynamic global vegetation model

2016 ◽  
Author(s):  
Hazuki Arakida ◽  
Takemasa Miyoshi ◽  
Takeshi Ise ◽  
Shin-ichiro Shima ◽  
Shunji Kotsuki
Keyword(s):  
Data Assimilation ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Model Parameters ◽  
Vegetation Model ◽  
Dynamic Global Vegetation Model ◽  
Area Index ◽  
The Real ◽  
Global Vegetation ◽  
The Impact

Abstract. We newly developed a data assimilation system based on a particle filter approach with the Spatially Explicit Individual-Based Dynamic Global Vegetation Model (SEIB-DGVM). We first performed an idealized observing system simulation experiment to evaluate the impact of assimilating the leaf area index (LAI) data every 4 days, assuming the satellite-based LAI. Although we assimilated only LAI as a whole, the forest and grass LAIs were estimated separately with high accuracy. Uncertain model parameters and other state variables were also estimated accurately. Therefore, we extended the experiment to the real world using the real Moderate Resolution Imaging Spectroradiometer (MODIS) LAI data, and obtained promising results.

Sign in / Sign up

Export Citation Format

Share Document