Víztudományi és Vízbiztonsági Nemzeti Laboratórium(RRF-2.3.1-21-2022-00008) Támogató:
NKFIH
Szakterületek:
Föld- és kapcsolódó környezettudományok
16 different satellite soil moisture (SM) datasets (passive, active, combined, and
model data) were compared at the European scale. We hypothesized that SM should be
reflected by a variety of environmental factors, such as topography, hydroclimatology,
soil characteristics, and biomass. Robust correlation was used to explore the relationship
among the satellite data products, and the Recursive Feature Elimination method combined
with the Random Forest Regression (RFR) algorithm was used to find the most important
variables. Variations in SM-values were analyzed using extended triple collocation
analysis (ETC), while the accuracy metrics of the RFR models were summarized through
UMAP dimension reduction. The result showed that generally, correlations among the
SM products were low ( r < 0.5) with some exceptions. GLDAS had the weakest correlation
with the other SM products. Using SM as the dependent variable in regression models,
model testing showed that GLDAS’s SM was explained with the highest accuracy based
on the Nash-Sutcliffe Efficiency (0.631), followed by the SMOPS (0.624). SSM demonstrated
the lowest environmental influence (NSE: 0.288). Using UMAP, ETC, it was determined
that SMOPS exhibited superior performance in terms of error variance and model accuracy;
however, based on the ETC results, GRD.P was deemed the most suitable option. Results
called the attention of varying SM values by products, being biased by various environmental
factors and the applied technology of the satellites.
