Artificial intelligence (AI) plays an essential role in agricultural mapping. It reduces costs and
time and increases efficiency in agricultural management activities, which improves the food …

Corn yields vary spatially and temporally in the plots as a result of weather, altitude, variety,
plant density, available water, nutrients, and planting date; these are the main factors that …

In agriculture, crop biomass is a vital indicator of the overall health of an ecosystem. Recent
developments in synthetic aperture radar (SAR) technologies have promoted the application …

The water cloud model (WCM) can be inverted to estimate leaf area index (LAI) using the
intensity of backscatter from synthetic aperture radar (SAR) sensors. Published studies have …

Remote sensing data are considered as one of the primary data sources for precise
agriculture. Several studies have demonstrated the excellent capability of radar and optical …

The main problem posed by Polarimetric Synthetic Aperture Radar (PolSAR) image
classification in remote sensing is the ability to develop classifiers that can substantially …

Above ground biomass is an important crop biophysical parameter for monitoring crop
condition and determining crop productivity, in particular if linked with phenological growth …

This study presents a demonstration of the applicability of machine learning techniques for
the retrieval of crop height in corn fields using space-borne PolSAR (Polarimetric Synthetic …

The magnitude of ecosystem services provided by winter cover crops is linked to their
performance (ie, biomass and associated nitrogen content, forage quality, and fractional …

Crop classification using remote sensing data has emerged as a prominent research area in
recent decades. Studies have demonstrated that fusing synthetic aperture radar (SAR) and …