Eddy covariance serves as one the most effective techniques for long-term monitoring of
ecosystem fluxes, however long-term data integrations rely on complete timeseries …

Grassland gross primary productivity (GPP) is an important part of global terrestrial carbon
flux, and its accurate simulation and future prediction play an important role in …

In response to irregular data gaps in evapotranspiration (ET) data obtained using eddy
covariance (EC) methods, this study seeks to explore a high-precision interpolation method …

Continuous meteorological variable time series are highly demanded for various climate
related studies. Five statistical models were tested for application of temporal gaps filling in …

In situ evapotranspiration (ET) observations play an important role in validating remotely-
sensed ET models and products and analyzing eco-hydrological processes. The eddy …

Automated detection of the content of images remains a challenging problem in artificial
intelligence. Hence, continuous manual monitoring of restricted development zones is …

This study addresses the limitations of traditional statistical methods and artificial neural
networks (ANNs) in extracting relevant features from carbon-based cementitious composites …

Climate change is intensifying the hydrologic cycle and altering ecosystem function,
including water flux to the atmosphere through evapotranspiration (ET). ET is made up of …

This paper examines the potential of using adaptive weighting factors in the Health Index
(HI) method for transformer health diagnostics. Typically, HI methods rely on weighting …

This study investigated the accuracy of the Random Forest (RF) model in gap-filling the
sensible (H) and latent heat (LE) fluxes, by using the observation data collected at a site …