Soil hydrological processes (SHP) support ecosystems, modulate the impact of climate
change on terrestrial systems and control feedback mechanisms between water, energy and …

Agriculture provides humanity with food, fibers, fuel, and raw materials that are paramount
for human livelihood. Today, this role must be satisfied within a context of environmental …

We propose a new architecture based on a fully connected feed-forward Artificial Neural
Network (ANN) model to estimate surface soil moisture from satellite images on a large …

The amount of surface soil moisture (SSM) is a crucial ecohydrological natural resource that
regulates important land surface processes. It affects critical land–atmospheric phenomena …

Floods are among the most destructive natural disasters, which are highly complex to model.
The research on the advancement of flood prediction models contributed to risk reduction …

Deep learning (DL), a new generation of artificial neural network research, has transformed
industries, daily lives, and various scientific disciplines in recent years. DL represents …

Soil moisture observations are of broad scientific interest and practical value for a wide
range of applications. The scientific community has made significant progress in estimating …

The application of machine learning (ML) techniques in various fields of science has
increased rapidly, especially in the last 10 years. The increasing availability of soil data that …

The hydrologic community has experienced a surge in interest in machine learning in recent
years. This interest is primarily driven by rapidly growing hydrologic data repositories, as …

The cyber-agricultural system (CAS) represents an overarching framework of agriculture that
leverages recent advances in ubiquitous sensing, artificial intelligence, smart actuators, and …