Abstract
The main objective of this study is to detect and quantify changes in the vegetation dynamics of each vegetation type at the global scale over the last 17 years. With recent advances in remote sensing techniques, it is now possible to study the Leaf Area Index (LAI) seasonal and interannual variability at the global scale and in a consistent way over the last decades. However, the coarse spatial resolution of these satellite-derived products does not permit distinguishing vegetation types within mixed pixels. Considering only the dominant type per pixel has two main drawbacks: the LAI of the dominant vegetation type is contaminated by spurious signal from other vegetation types and at the global scale, significant areas of individual vegetation types are neglected. In this study, we first developed a Kalman Filtering (KF) approach to disaggregate the satellite-derived LAI from GEOV1 over nine main vegetation types, including grasslands and crops as well as evergreen, broadleaf and coniferous forests. The KF approach permits the separation of distinct LAI values for individual vegetation types that coexist within a pixel. The disaggregated LAI product, called LAI-MC (Multi-Cover), consists of world-wide LAI maps provided every 10 days for each vegetation type over the 1999–2015 period. A trend analysis of the original GEOV1 LAI product and of the disaggregated LAI time series was conducted using the Mann-Kendall test. Resulting trends of the GEOV1 LAI (which accounts for all vegetation types) compare well with previous regional or global studies, showing a greening over a large part of the globe. When considering each vegetation type individually, the largest global trend from LAI-MC is found for coniferous forests (0.0419 m 2 m − 2 yr − 1 ) followed by summer crops (0.0394 m 2 m − 2 yr − 1 ), while winter crops and grasslands show the smallest global trends (0.0261 m 2 m − 2 yr − 1 and 0.0279 m 2 m − 2 yr − 1 , respectively). The LAI-MC presents contrasting trends among the various vegetation types within the same pixel. For instance, coniferous and broadleaf forests experience a marked greening in the North-East of Europe while crops and grasslands show a browning. In addition, trends from LAI-MC can significantly differ (by up to 50%) from trends obtained with GEOV1 by considering only the dominant vegetation type over each pixel. These results demonstrate the usefulness of the disaggregation method compared to simple ones. LAI-MC may provide a new tool to monitor and quantify tendencies of LAI per vegetation type all over the globe.
Highlights
Global environmental change is strongly dependent on land surface processes
This study presents an innovative method to disaggregate the total leaf area index (LAI) observed from satellites into LAIs of several vegetation types, including broadleaf, evergreen and coniferous forests, grasslands and summer/winter crops
All vegetation types have experienced greening over the last two decades at rates ranging from 0.026 m2m−2yr−1 for winter crops to 0.042 m2m−2yr−1 for coniferous forests
Summary
Global environmental change is strongly dependent on land surface processes. The leaf area index (LAI), defined as the area of green leaves per unit ground horizontal surface area [2,3], is a good indicator of the vegetation growth. As such it is an essential parameter for the description of the vegetation dynamics [4], crop monitoring [5] or climate change studies [6,7]. With recent advances in remote sensing techniques, it has become possible to study the LAI variations at the global scale and in a consistent way over the last decades using a variety of techniques (see, e.g., the review of Fang, H. and Jiang, C. et al [9])
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