Despite great progress in simulating multiphysics problems using the numerical
discretization of partial differential equations (PDEs), one still cannot seamlessly incorporate …

Various forms of machine learning (ML) methods have historically played a valuable role in
environmental remote sensing research. With an increasing amount of “big data” from earth …

The global volume of digital data is expected to reach 175 zettabytes by 2025. The volume,
variety and velocity of water-related data are increasing due to large-scale sensor networks …

Plant phenotype plays an important role in genetics, botany, and agronomy, while the
currently popular methods for phenotypic trait measurement have some limitations in …

Mangrove ecosystems provide critical goods and ecosystem services to coastal
communities and contribute to climate change mitigation. Over four decades, remote …

Interest in statistical analysis of remote sensing data to produce measurements of
environment, agriculture, and sustainable development is established and continues to …

Time-series remote sensing data offer a rich source of information that can be used in a wide
range of applications, from monitoring changes in land cover to surveillance of crops …

Over the last few years, Deep learning (DL) approaches have been shown to outperform
state-of-the-art machine learning (ML) techniques in many applications such as vegetation …

The Synthetic Aperture Radar (SAR) time series allows describing the rice phenological
cycle by the backscattering time signature. Therefore, the advent of the Copernicus Sentinel …

Abstract Land-Use/Land-Cover Time-Series Classification (LULC-TSC) is an important and
challenging problem in terrestrial remote sensing. Detecting change-points, dividing the …