Paper Info
Title
Soil Moisture Content Retrieval from Remote Sensing Data by Artificial Neural Network Based on Sample Optimization
Abstract
Soil moisture content (SMC) plays an essential role in geoscience research. The SMC can be retrieved using an artificial neural network (ANN) based on remote sensing data. The quantity and quality of samples for ANN training and testing are two critical factors that affect the SMC retrieving results. This study focused on sample optimization in both quantity and quality. On the one hand, a sparse sample exploitation (SSE) method was developed to solve the problem of sample scarcity, resultant from cloud obstruction in optical images and the malfunction of in situ SMC-measuring instruments. With this method, data typically excluded in conventional approaches can be adequately employed. On the other hand, apart from the basic input parameters commonly discussed in previous studies, a couple of new parameters were optimized to improve the feature description. The Sentinel-1 SAR and Landsat-8 images were adopted to retrieve SMC in the study area in eastern Austria. By the SSE method, the number of available samples increased from 264 to 635 for ANN training and testing, and the retrieval accuracy could be markedly improved. Furthermore, the optimized parameters also improve the inversion effect, and the elevation was the most influential input parameter.
Year
DOI
Venue
2022
10.3390/s22041611
SENSORS
Keywords
DocType
Volume
soil moisture content, artificial neural network, sample optimization, synthetic aperture radar, optical remote sensing image
Journal
22
Issue
ISSN
Citations 
4
1424-8220
0
PageRank 
References 
Authors
0.34
0
10
Name
Order
Citations
PageRank
Qixin Liu101.35
Xingfa Gu25436.00
Xinran Chen301.35
Faisal Mumtaz401.01
Yan Liu5213.88
Chunmei Wang600.68
Yu Tao715.45
Yin Zhang83492281.04
Dakang Wang901.01
Yulin Zhan1001.35