Estimating global GPP from the plant functional type perspective using a machine learning approach

The long-term monitoring of gross primary production (GPP) is crucial to
the assessment of the carbon cycle of terrestrial ecosystems. In this
study, a well-known machine learning model (Random Forest, RF) is
established to reconstruct the global GPP dataset named ECGC_GPP. The
model distinguished nine functional plant types, including C3 and C4
crops, using eddy fluxes, meteorological variables, and leaf area index as
training data of the RF model. Based on ERA5_Land and the corrected GEOV2
data, the global monthly GPP dataset at a 0.05-degree resolution from 1999
to 2019 was estimated. The results showed that the RF model could explain
74.81% of the monthly variation of GPP in the testing dataset, of which
the average contribution of Leaf Area Index (LAI) reached 41.73%. The
average annual and standard deviation of GPP during 1999–2019 were 117.14
± 1.51 Pg C yr-1, with an upward trend of 0.21 Pg C yr-2 (p <
0.01). By using the plant functional type classification, the
underestimation of cropland is improved. Therefore, ECGC_GPP provides
reasonable global spatial patterns and long-term trends of annual GPP.