Global Gap-Free MERIS LAI Time Series (2002–2012)

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 (R 2 : 0.88) of agreement on a global scale.

Inconsistencies of interannual variability and trends in long‐term satellite leaf area index products

“…MERIS LAI product, acquired from https://centaurus.caf.dlr.de:8443/, is generated every 10 days in 1/360°spatial resolution (Tum et al, 2016). The algorithm is based on the training of a neural network over a database of simulated top-of-atmosphere radiances, using a coupled leaf-canopy-atmosphere radiative transfer model (Bacour, Baret, B eal, Weiss, & Pavageau, 2006).…”

“…Aiming at this goal, we examined their trends, interannual variabilities, and uncertainty variations. We also compared the four long-term LAI products with other widely used but short-term products: GEOV1 ; MERIS (Tum et al, 2016); MODIS Collection 5 (C5) (Shabanov et al, 2005); and MODIS Collection 6 (C6) (Yan et al, 2016). Consequently, three time periods were investigated separately: 30-year long-term period (1982( ), pre-MODIS period (1982( -1999, and overlap period (2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011) (Pedelty et al, 2007), and 1 km resolution Terra/MODIS surface reflectance datasets (MOD09) since 2001 (Xiao et al, 2016).…”

Forecasting GRACE Data over the African Watersheds Using Artificial Neural Networks

“…For example, short gaps in TWS records could hinder the analysis and interpretation of temporal variability by introducing errors in the extracted amplitude and phase of the annual cycle, non-seasonal residual signals, as well as in secular trends and associated significance levels [39][40][41] and could also increase the level of uncertainty in the spectral modeling of temporal variability and cyclicity [42]. Long gaps, on the other hand, could obscure the temporal patterns of TWS data and consequently distort the results of any statistical analysis [38,43].…”

Validation of Earth Observation Time-Series: A Review for Large-Area and Temporally Dense Land Surface Products

“…The majority of reviewed studies (61) facilitated more than one validation method. Hereby, a variety of studies showed, in particular, the combination of an intercomparison of products with a comparison to reference data [9,21,35,48,53,54,57,59,84,97,[120][121][122][123][124]129,133,135,139,[141][142][143][144][145]. This dual approach can provide an indication of general remote sensing performance for a particular research area or, on the other hand, illustrate improvements of novel methods in relation to existing products [35,71,136].…”