  The accurate estimation of terrestrial water and vegetation is a grand
challenge in hydrometeorology. Many previous studies developed land data
assimilation systems (LDASs) and provided global-scale land surface datasets by
integrating numerical simulation and satellite data. However, vegetation
dynamics has not been explicitly solved in these land reanalysis datasets. Here
we present the newly developed land reanalysis dataset, ECoHydrological Land
reAnalysis (ECHLA). ECHLA is generated by sequentially assimilating C- and X-
band microwave brightness temperature satellite observations into a land
surface model which can explicitly simulate the dynamic evolution of vegetation
biomass. The ECHLA dataset provides semi-global soil moisture from surface to
1.95m depth, Leaf Area Index (LAI), and vegetation water content and is
available from 2003 to 2010 and from 2013 to 2019. We assess the performance of
ECHLA to estimate soil moisture and vegetation dynamics by comparing the ECHLA
dataset with independent satellite and in-situ observation data. We found that
our sequential update by data assimilation substantially improves the skill to
reproduce the seasonal cycle of vegetation. Data assimilation also contributes
to improving the skill to simulate soil moisture mainly in the shallow soil
layers (0-0.15m depth). The ECHLA dataset will be publicly available and
expected to contribute to understanding terrestrial ecohydrological cycles and
water-related natural disasters such as drought.
