Abstract
This article describes the principles used to generate global gap-free Leaf Area Index (LAI) time series from 2002–2012, based on MERIS (MEdium Resolution Imaging Spectrometer) full-resolution Level1B data. It is produced as a series of 10-day composites in geographic projection at 300-m spatial resolution. The processing chain comprises geometric correction, radiometric correction, pixel identification, LAI calculation with the BEAM (Basic ERS & Envisat (A)ATSR and MERIS Toolbox) MERIS vegetation processor, re-projection to a global grid and temporal aggregation selecting the measurement closest to the mean value. After the LAI pre-processing, we applied time series analysis to fill data gaps and to filter outliers using the technique of harmonic analysis (HA) in combination with mean annual and multiannual phenological data. Data gaps are caused by clouds, sensor limitations due to the solar zenith angle (<10°), topography and intermittent data reception. We applied our technique for the whole period of observation (July 2002–March 2012). Validation, carried out with VALERI (Validation of Land European Remote Sensing Instruments) and BigFoot data, revealed a high degree (R2 : 0.88) of agreement on a global scale.
Highlights
The understanding of Earth surface processes, including water, carbon and nitrogen cycles, as well as climate and resource assessment applications, requires information about the seasonal development and current state of vegetation
The Leaf Area Index (LAI) was calculated for each individual MERIS FR L1B product in satellite coordinates, and a nearest neighbor re-projection was applied to map the data to a fixed global geographic grid (WGS 84) of the same 300-m resolution as the MERIS-FR inputs
Temporal aggregation to 10-day LAI composites is performed using all valid acquisitions within 10-day periods by selecting the most representative value as the sample that is closest to the temporal average value estimated over the compositing period [63]
Summary
The understanding of Earth surface processes, including water, carbon and nitrogen cycles, as well as climate and resource assessment (agriculture and forest production) applications, requires information about the seasonal development and current state of vegetation. Like land cover classification or plant physiological parameters with applicable spatio-temporal resolution, are mandatory when applying Earth system process models [1]. Vegetation indices, such as the frequently-used Normalized Difference Vegetation Index (NDVI), can be directly linked to the photosynthetic capacity of plant canopies [2,3,4].
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