Study WP5.2 Impacts and Evaluation of Vegetation Phenology Changes on Observed and Modelled Land-Atmosphere Processes
Description
This Study is led by Daniele Peano from CMCC. Additional contributors to this Study are Debbie Hemming and Rob King from the Met Office.
The main CCI ECVs used in this Study are Vegetation, Snow, Water Vapour, Land Surface Temperature, Biomass, Land Cover, and Soil Moisture.
This Study ran from September 2023 until August 2025.
The Study comprises two parts. The first will occur during the development phase of the Vegetation Parameters_cci project and, through interaction with the CMUG team, will provide testing and feedback on preliminary LAI (Leaf Area Index) and FAPAR (Fraction of Absorbed Photosynthetically Active Radiation) data. The second involves analysis of the relationships between phenology and land-atmosphere processes by defining a core set of phenology indicators at the global and habitat scale, quantifying the influence of phenology on land-atmosphere interactions, and comparisons with model and observed values.
Results and conclusions
Plants provide a critical interface between the atmosphere and the ground; moving water and carbon, and perform an essential buffer on heat fluxes. Understanding the health and growing cycles of vegetation is an important process in evaluating their inclusion in climate models and working to mitigate climate change from increasing CO2 emissions. One method for this is through satellite observations, specifically of leaf area index (LAI) which describes the area of green leaves present.
State-of-the-art land surface models commonly use LAI to compute leaf-related processes. Consequently, LAI can be used to determine the many phenology metrics describing the growing season such as vegetation onset, length of growing season, and peak of growth. These measures can be combined with other essential climate variables such as land surface temperature (LST), soil moisture, above ground biomass (AGB), and snow cover to understand processes occurring in a region’s vegetation. This includes plant health, recovery from extreme events such as droughts and heat waves, crop monitoring, and to evaluate if these processes are captured correctly in climate models such as those from CMIP6.
The European Space Agency's Climate Change Initiative (CCI) LAI data (CCI_LAI) developed by the Vegetation Parameters CCI project is a brand-new dataset based on observations from multiple satellite platforms combined to give a high frequency, every five days, high resolution, 1 km gridboxes, dataset. The work of the Met Office and Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC) looks to understand and exploit this new dataset in understanding vegetation processes through links to other CCI datasets and observations.
Initial work with CCI_LAI data was to understand methods to use the higher temporal frequency and aggregate the LAI values across larger areas such as those that are representative of biomes. This used the uncertainty information in the CCI_LAI product to create weighted averages of LAI across a region. This also required an understanding of how to filter the LAI values based on flags in the product indicating how well the underlying retrieval has performed. An example of the effects of aggregating the LAI data is given in Figure 1.
The process of understanding the CCI_LAI data’s features and behaviour continued with a further look at how to smooth and fill gaps to allow phenology metrics to be calculated. This is an important step because existing LAI datasets typically have an 8-15 day frequency, and the later work in phenology metrics builds on techniques developed for these lower frequency datasets. Using techniques from other LAI datasets processing chains, the CCI_LAI is processed to create timeseries that can be used in phenology calculations. Figure 2 shows the effects of this process from taking the original CCI_LAI data at one site, and applying the filtering, filling gaps and smoothing the resulting data. The process is not perfect, and further techniques are under review.
