Deep Learning Forecasts of Soil Moisture: Convolutional Neural Network and Gated Recurrent Unit Models Coupled with Satellite-Derived MODIS, Observations and Synoptic-Scale Climate Index Data

Deep Learning Forecasts of Soil Moisture: Convolutional Neural Network and Gated Recurrent Unit Models Coupled with Satellite-Derived MODIS, Observations and Synoptic-Scale Climate Index Data

Remotely sensed soil moisture forecasting through satellite-based sensors to estimate the future state of the underlying soils plays a critical role in planning and managing water resources and sustainable agricultural practices. In this paper, Deep Learning (DL) hybrid models (i.e., CEEMDAN-CNN-GRU...

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Bibliographic Details
Main Authors: A. A. Masrur Ahmed, Ravinesh C Deo, Nawin Raj, Afshin Ghahramani, Qi Feng, Zhenliang Yin, Linshan Yang
Format: Article
Language:English
Published: MDPI AG 2021-02-01
Series:Remote Sensing
Subjects:
deep learning algorithm
MODIS
gated recurrent unit
satellite models of soil moisture
Online Access:https://www.mdpi.com/2072-4292/13/4/554

Internet

https://www.mdpi.com/2072-4292/13/4/554

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