The popularity of using various neural network models and deep learning-based models to
predict environmental temperament is increasing due to their ability to comprehend and …

In order to generate a probability density function (PDF) for fitting the probability distributions
of practical data, this study proposes a deep learning method which consists of two …

Satellite remote sensing can detect and predict subsurface temperature and salinity
structure within the ocean over large scales. In the era of big ocean data, making full use of …

The oceans are an important component of Earth's system and play a crucial role in climate
change through the coupled atmosphere–ocean process. Observations are fundamental for …

This study; i) investigates the suitability of two frequently employed machine learning
algorithms in remote sensing, namely, random forests (RFs) and support vector regression …

Soil plant analysis development (SPAD) values indicate the relative chlorophyll content in
leaves. Chlorophyll plays a vital role in wheat growth and fertilization management as a …

Subsurface ocean observations are sparse and insufficient, significantly constraining studies
of ocean processes. Retrieving high-resolution subsurface dynamic parameters from remote …

Internal solitary waves (ISW) are widely distributed worldwide and significantly affect the
ocean environment and offshore activities. ISW propagation speed is important for ISW …

Sea subsurface temperature, an essential component of aquatic wildlife, underwater
dynamics, and heat transfer with the sea surface, is affected by global warming in climate …

This paper proposes a convolutional neural network (CNN) method to estimate subsurface
temperature (ST) in the Pacific Ocean from a suite of satellite remote sensing …