Optical remotely sensed data are typically discontinuous, with missing values due to cloud
cover. Consequently, gap-filling solutions are needed for accurate crop phenology …

Image processing entered the era of artificial intelligence, and machine learning algorithms
emerged as attractive alternatives for time series data processing. Satellite image time series …

The availability of satellite optical information is often hampered by the natural presence of
clouds, which can be problematic for many applications. Persistent clouds over agricultural …

The ESA's forthcoming FLuorescence EXplorer (FLEX) mission is dedicated to the global
monitoring of the vegetation's chlorophyll fluorescence by means of an imaging spectrometer, …

For the last decade, Gaussian process regression (GPR) proved to be a competitive machine
learning regression algorithm for Earth observation applications, with attractive unique …

Monitoring cropland phenology from optical satellite data remains a challenging task due to
the influence of clouds and atmospheric artifacts. Therefore, measures need to be taken to …

The Free Core Nutation (FCN) is a free mode of the Earth's rotation caused by the different
material characteristics of the Earth's core and mantle. This causes the rotational axes of …

Vegetation indices (VIs) are widely used in optical remote sensing to estimate biophysical
variables of vegetated surfaces. With the advent of spectroscopy technology, spectral bands …

Thanks to the emergence of cloud-computing platforms and the ability of machine learning
methods to solve prediction problems efficiently, this work presents a workflow to automate …

Accurate Earth orientation parameter (EOP) predictions are needed for many applications,
eg, for the tracking and navigation of interplanetary spacecraft missions. One of the most …