The saturation of spectral reflectance within densely vegetated regions is a renowned
challenge that has precluded the optimal use of broad-band remotely sensed data and its …

Grasslands cover one third of the earth's terrestrial surface and are mainly used for livestock
production. The usage type, use intensity and condition of grasslands are often unclear …

Having the ability to make accurate assessments of above ground biomass (AGB) at high
spatial resolution is invaluable for the management of dryland forest resources in areas at …

Random Forest (RF) is a machine learning technique that has been proved to be highly
accurate in several agricultural applications. However, to yield prediction, how much this …

Grassland degradation has accelerated in recent decades in response to increased climate
variability and human activity. Rangeland and grassland conditions directly affect forage …

The study of grasslands using machine learning (ML) methods combined with
proximal/remote sensing data (RS) has been steadily increasing in the last decades …

This paper provides an overview of the recent developments in remote sensing technology
and machine learning algorithms for estimating important biophysical and biochemical …

The speedy development of UAV (Unmanned Aerial Vehicle) has provided more data
choices for crop yield estimation. In most cases, spectral information derived from …

Aboveground biomass (AGB) of shrub communities in the desert is a basic quantitative
characteristic of the desert ecosystem and an important index to measure ecosystem …

With an increasing volume and dimensionality of Earth observation data, enhanced
integration of machine-learning methodologies is needed to effectively analyze and utilize …