Derived Optimal Linear Combination Evapotranspiration - DOLCE v2.1

DOLCE v2.1 is an extension of DOLCE V2.0, the main differenc eis that ERA5-Land data from 1981-2018 was used for 2.1 and 2000-2018 for 2.0.
DOLCE V2 is a new hybrid Evapotranspiration (ET) dataset derived by merging 11 available global ET datasets. These include BACI, FLUXCOM RS, FLUXCOM MET, ERA5-land, GLEAM v3.3a, GLEAM v3.3b,  PML-CSIRO, PLSH, MOD16, SEBS, and SRB-GEWEX. The contribution of each dataset to DOLCE V2 is based on its ability to match field observations as well as its dependence to the other parent datasets. DOLCE V2 provides time-variant estimates of its uncertainty errors, which are consistent with its agreement with field observations.
DOLCE V2 and its previous version DOLCE V1 use the same merging technique and provide monthly ET estimates and associated uncertainties over the global land. There are several improvements implemented in DOLCE V2, these include (DOLCE V2 vs DOLCE V1):
Employing a wider range of parent datasets (11 parents vs 6 parent datasets)
Incorporating more field observations to constrain the merging technique (260 sites vs 160 sites)
Finer spatial resolution (0.25° vs 0.5°).
Longer temporal coverage (1980-2018 vs 2000-2009).

Datasets employed to derive DOLCE V2: BACI
FLUXCOM RS
FLUXCOM MET
ERA5-land
GLEAM v3.3a
GLEAM v3.3b
PML-CSIRO
PLSH
MOD16
SEBS
SRB-GEWEX.
Field observations from flux tower networks including:
FLUXNET2015-tier1 and tier2
LaThuile Free Fair Use 
CarboEurope
AmeriFlux
ARM
AsiaFlux
Oak Ridge
Ozflux

The final output is a monthly, 0.25-degree dataset of terrestrial Evapotranspiration and its error estimates over 1980-2018. The dataset is provided as yearly NETCDF4 files
We used RStudio with R version 3.6.1 (2019-07-05) Copyright (C) 2019 The R Foundation for Statistical ComputingPlatform: x86_64-w64-mingw32/x64 (64-bit)

The validity of the script and the outputs were tested by:
Visual assessment of the results
Comparison with results from similar studies
Validation with in-situ observations
Using statistical metrics (detailed in the related manuscript).