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Zusammenfassung:
This paper proposes a processing chain for the derivation of global Leaf Area Index (LAI),
Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fraction Vegetation Cover (FVC),
and Canopy water content (CWC) maps from 15-years of MODIS data exploiting the capabilities
of the Google Earth Engine (GEE) cloud platform. The retrieval chain is based on a hybrid method
inverting the PROSAIL radiative transfer model (RTM) with Random forests (RF) regression. A major
feature of this work is the implementation of a retrieval chain exploiting the GEE capabilities using
global and climate data records (CDR) of both MODIS surface reflectance and LAI/FAPAR datasets
allowing the global estimation of biophysical variables at unprecedented timeliness. We combine
a massive global compilation of leaf trait measurements (TRY), which is the baseline for more realistic
leaf parametrization for the considered RTM, with large amounts of remote sensing data ingested by
GEE. Moreover, the proposed retrieval chain includes the estimation of both FVC and CWC, which are
not operationally produced for the MODIS sensor. The derived global estimates are validated over the
BELMANIP2.1 sites network by means of an inter-comparison with the MODIS LAI/FAPAR product
available in GEE. Overall, the retrieval chain exhibits great consistency with the reference MODIS
product (R2 = 0.87, RMSE = 0.54 m2/m2 and ME = 0.03 m2/m2 in the case of LAI, and R2 = 0.92,
RMSE = 0.09 and ME = 0.05 in the case of FAPAR). The analysis of the results by land cover type
shows the lowest correlations between our retrievals and the MODIS reference estimates (R2 = 0.42
and R2 = 0.41 for LAI and FAPAR, respectively) for evergreen broadleaf forests. These discrepancies
could be attributed mainly to different product definitions according to the literature. The provided
results proof that GEE is a suitable high performance processing tool for global biophysical variable
retrieval for a wide range of applications.
