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

遥感学报 Pub Date : 2021-11-09 DOI:10.34133/2021/9842830
H. Fang, Yao Wang, Yinghui Zhang, Sijia Li
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引用次数: 3

Abstract

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.
全球GEOV2和MODIS叶面积指数(LAI)的长期变化及其不确定性:对产品稳定性的洞察
叶面积指数是了解全球植被变化的重要气候变量。长期卫星LAI产品已应用于许多全球植被变化研究。然而,这些LAI产品包含各种不确定性,这些不确定性在当前的研究中没有得到充分考虑。本研究的目的是探讨全球LAI产品的不确定性及其不确定性变化。研究了两个全球LAI数据集——欧洲Geoland2版本2(GEOV2)和中分辨率成像光谱仪(MODIS)(2003-2019)。分析嵌入产品质量层中的定性质量标志(QQFs)和定量质量指标(QQIs),以识别质量剖面中的时间异常。结果表明,从2003年到2019年,全球GEOV2(0.042/10a)和MODIS(0.034/10a)LAI值稳步上升。GEOV2的全球LAI不确定性(0.016/10a)和相对不确定性(0.3%/10a)也逐渐增加,尤其是在4月至10月的生长季节。木本生物群落的不确定性增加幅度大于草本生物群落。相比之下,MODIS LAI产品的不确定性在研究期间保持稳定。GEOV2显示的不确定性增加部分归因于产品系列中的传感器偏移。需要进一步的算法增强来提高交叉传感器的性能。本研究强调了研究LAI不确定性和不确定性变化的重要性。本文揭示的LAI产品和产品质量的时间变化对全球植被变化研究具有重要意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
遥感学报
遥感学报 Social Sciences-Geography, Planning and Development
CiteScore
3.60
自引率
0.00%
发文量
3200
期刊介绍: The predecessor of Journal of Remote Sensing is Remote Sensing of Environment, which was founded in 1986. It was born in the beginning of China's remote sensing career and is the first remote sensing journal that has grown up with the development of China's remote sensing career. Since its inception, the Journal of Remote Sensing has published a large number of the latest scientific research results in China and the results of nationally-supported research projects in the light of the priorities and needs of China's remote sensing endeavours at different times, playing a great role in the development of remote sensing science and technology and the cultivation of talents in China, and becoming the most influential academic journal in the field of remote sensing and geographic information science in China. As the only national comprehensive academic journal in the field of remote sensing in China, Journal of Remote Sensing is dedicated to reporting the research reports, stage-by-stage research briefs and high-level reviews in the field of remote sensing and its related disciplines with international and domestic advanced level. It focuses on new concepts, results and progress in this field. It covers the basic theories of remote sensing, the development of remote sensing technology and the application of remote sensing in the fields of agriculture, forestry, hydrology, geology, mining, oceanography, mapping and other resource and environmental fields as well as in disaster monitoring, research on geographic information systems (GIS), and the integration of remote sensing with GIS and the Global Navigation Satellite System (GNSS) and its applications.
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