This study provided a comprehensive evaluation of eight machine learning regression
algorithms for forest aboveground biomass (AGB) estimation from satellite data based on …

Increasing numbers of explanatory variables tend to result in information redundancy and
“dimensional disaster” in the quantitative remote sensing of forest aboveground biomass …

Forest biomass is a major store of carbon and plays a crucial role in the regional and global
carbon cycle. Accurate forest biomass assessment is important for monitoring and mapping …

Forest aboveground biomass (AGB) plays an important role in the study of the carbon cycle
and climate change in the global terrestrial ecosystem. AGB estimation based on remote …

Accurately quantifying the aboveground biomass (AGB) of forests is crucial for
understanding global change-related issues such as the carbon cycle and climate change …

Biomass is one significant biophysical parameter of a forest ecosystem, and accurate
biomass estimation on the regional scale provides important information for carbon-cycle …

Remote sensing–based forest aboveground biomass (AGB) estimation has been
extensively explored in the past three decades, but how to effectively combine different …

Rapid and accurate estimation of forest biomass are essential to drive sustainable
management of forests. Field-based measurements of forest above-ground biomass (AGB) …

Feature selection (FS) can increase the accuracy of forest aboveground biomass (AGB)
prediction from multiple satellite data and identify important predictors, but the role of FS in …

A simpler, unbiased, and comprehensive random forest (RF) model is needed to improve
the accuracy of aboveground biomass (AGB) estimation. In this study, data were obtained …