Title
Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at Plot Scale.
Abstract
This paper presents an approach for retrieval of soil moisture content (SMC) by coupling single polarization C-band synthetic aperture radar (SAR) and optical data at the plot scale in vegetated areas. The study was carried out at five different sites with dominant vegetation cover located in Kenya. In the initial stage of the process, different features are extracted from single polarization mode (VV polarization) SAR and optical data. Subsequently, proper selection of the relevant features is conducted on the extracted features. An advanced state-of-the-art machine learning regression approach, the support vector regression (SVR) technique, is used to retrieve soil moisture. This paper takes a new look at soil moisture retrieval in vegetated areas considering the needs of practical applications. In this context, we tried to work at the object level instead of the pixel level. Accordingly, a group of pixels (an image object) represents the reality of the land cover at the plot scale. Three approaches, a pixel-based approach, an object-based approach, and a combination of pixel- and object-based approaches, were used to estimate soil moisture. The results show that the combined approach outperforms the other approaches in terms of estimation accuracy (4.94% and 0.89 compared to 6.41% and 0.62 in terms of root mean square error (RMSE) and R-2), flexibility on retrieving the level of soil moisture, and better quality of visual representation of the SMC map.
Year
DOI
Venue
2018
10.3390/rs10081285
REMOTE SENSING
Keywords
Field
DocType
soil moisture mapping,object-based image analysis,support vector regression,Sentinel 1&2,SAR,plot scale,the C-band,feature selection
Remote sensing,Water content,Geology
Journal
Volume
Issue
Citations 
10
8
1
PageRank 
References 
Authors
0.35
31
4
Name
Order
Citations
PageRank
Reza Attarzadeh110.35
Jalal Amini292.90
Claudia Notarnicola316640.15
f greifeneder4245.69