Temporal Analysis and Prediction of Soil Surface Temperature using Infrared Biological Temperature Data

Noise-Augmented Boosting Framework for Accurate Soil Temperature Prediction

Soil temperature prediction is important for farming, climate research, and environmental modeling. This research proposes an ensemble prediction method for soil temperature prediction on a daily basis using lag feature and Gaussian noise. In the proposed framework, the ensemble algorithms Extreme Gradient Boosting (XG...

Deep learning prediction of multi-depth soil temperature using climatic variables in eastern Hungary

Three predictive approaches were applied: linear regression, random forest, and a deep neural network to predict soil temperature at depths of 5 and 50 cm in Central Europe, specifically eastern Hungary, highlighting soil temperature as a sensitive indicator of environmental change and demonstrating the value of deep l...

A novel hybrid remote sensing–machine learning framework for multiday soil moisture forecasting

The proposed method performed better than the conventional LSTM algorithm in all forecasting scenarios and showed robust performance even at a 7-day forecasting lead time, showing promise for applications in short-range soil moisture prediction and environmental monitoring studies.

Machine Learning-Based Prediction of Soil Moisture in Sikkim's High-Rainfall Zones Using Multimodal Remote Sensing Data

The strong predictive performance demonstrated the effectiveness of integrating high-resolution Sentinel-2 imagery data with ANN for accurate, scalable, and efficient soil moisture estimation in high-relief, data-sparse environments.

High-resolution surface soil moisture estimation over tropical agricultural land leveraging Sentinel-2 products and neural networks

Predicting stream water temperature: A data-driven approach highlighting the impact of riparian vegetation

Abstract Stream water temperature strongly influences aquatic ecosystem health, affecting dissolved oxygen, species distributions, and thermal stress for sensitive taxa. We present a data-driven framework to predict water temperatures across rivers in the Canton of Vaud, Switzerland, explicitly quantifying the influenc...