Paper Info
Title
Multi-Temporal Convolutional Neural Networks For Satellite-Derived Soil Moisture Observation Enhancement
Abstract
In this work, we propose a novel Convolutional Neural Network architecture for increasing the low spatial resolution SMAP radiometer based soil moisture estimations from 36 km to 3 km resolution by using time-series of observations from both SMAP's radiometer and Sentinel-1 radar. By simultaneously extracting features from both current low-resolution input and residuals between high and low resolution at previous time instances, the proposed network is capable of accurately estimating soil moisture using coarse resolution observations. Experimental results on three different locations demonstrate that the proposed scheme is able to estimate soil moisture with accuracy in the range of the requirements set by the SMAP science team.
Year
DOI
Venue
2020
10.1109/IGARSS39084.2020.9323790
IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM
Keywords
DocType
Citations 
Soil Moisture, Deep Learning, Observation Fusion, SMAP, Sentinel-1
Conference
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Grigorios Tsagkatakis112221.53
Mahta Moghaddam201.01
P. Tsakalides3954120.69