Neural network models for soil moisture forecasting from remotely sensed measurements
Neural network models for forecasting soil moisture from satellite data.
Abstract
The study proposes the use of artificial neural networks to predict soil moisture from remotely sensed data.
Using data-driven models such as MLP, LSTM and ANFIS, it analyses time series and meteorological variables to support smart irrigation and water resource management in agriculture.
Authors
Andrea Marini, Loris Francesco Termite, Alberto Garinei, Marcello Marconi, Lorenzo Biondi
Attachments
Publication date: 22/06/2020 Last updated: 05/10/2026 09:28