A Hybrid Artificial Neural Network to Estimate Soil Moisture Using SWAT+ and SMAP Data

A Hybrid Artificial Neural Network to Estimate Soil Moisture Using SWAT+ and SMAP Data

In this work, we developed a data-driven framework to predict near-surface (0–5 cm) soil moisture (SM) by mapping inputs from the Soil & Water Assessment Tool to SM time series from NASA’s Soil Moisture Active Passive (SMAP) satellite for the period 1 January 2016–31 December 2018. We developed...

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Bibliographic Details
Main Authors: Katherine H. Breen, Scott C. James, Joseph D. White, Peter M. Allen, Jeffery G. Arnold
Format: Article
Language:English
Published: MDPI AG 2020-08-01
Series:Machine Learning and Knowledge Extraction
Subjects:
physics-guided machine learning
hybrid architecture
LSTM
data-driven science
Online Access:https://www.mdpi.com/2504-4990/2/3/16

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