[Home ] [Archive]   [ فارسی ]  
:: Main :: About :: Current Issue :: Archive :: Search :: Submit :: Contact ::
Main Menu
Home::
Journal Information::
Articles archive::
For Authors::
For Reviewers::
Registration::
Contact us::
Site Facilities::
::
Search in website

Advanced Search
..
Receive site information
Enter your Email in the following box to receive the site news and information.
..
:: Volume 7, Issue 4 (3-2020) ::
jgit 2020, 7(4): 215-232 Back to browse issues page
Volumetric soil moisture estimation using Sentinel 1 and 2 satellite images
Sadegh Ranjbar , Mahdi Akhoondzadeh *
University of Tehran
Abstract:   (3043 Views)
Surface soil moisture is an important variable that plays a crucial role in the management of water and soil resources. Estimating this parameter is one of the important applications of remote sensing. One of the remote sensing techniques for precise estimation of this parameter is data-driven models. In this study, volumetric soil moisture content was estimated using data-driven models, support vector regression (SVR) and multi-layer perceptron artificial neural network (ANN-MLP) method. The parameters of the two models are optimized by the Genetic optimization algorithm. Estimation of volumetric soil moisture content with the two top models was performed using two types of radar image (Sentinel 1) and optics image (Sentinel 2), in which optimized optics image bands were identified by the Genetic optimization algorithm. After estimating the volumetric soil moisture map, four outputs of the two methods are compared. The best estimate of the volumetric soil moisture content has been achieved by the support vector regression (SVR) method with the Sentinel 1 image. The worst estimate of the volumetric soil moisture content has been achieved by the multi-layer perceptron artificial neural network (ANN-MLP) method with the Sentinel 2 image. The accuracy of this study was calculated by the square of correlation coefficient of the measured volumetric soil moisture content and the estimated volumetric soil moisture content, which the best and worst correlation coefficients, respectively, 0.659 for Sentinel1 image using support vector regression method and 0.409 for Sentinel2 image using multilayer perceptron neural network method have been calculated. The root mean square error (RMSE) is also used to calculate the error of the methods. The lowest and highest errors were calculated by 0.291  for Sentinel1 image with support vector regression and 0.4612  for Sentinel2 image with Multilayer Perceptron Artificial Neural Network.
Keywords: Soil Moisture, Sentinel 1 & 2, Artificial Neural Network, Support Vector Regression, Genetic Optimization Algorithm.
Full-Text [PDF 2116 kb]   (1319 Downloads)    
Type of Study: Research | Subject: RS
Received: 2019/07/20 | Accepted: 2019/12/10 | Published: 2020/03/19
Send email to the article author



XML   Persian Abstract   Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Ranjbar S, Akhoondzadeh M. Volumetric soil moisture estimation using Sentinel 1 and 2 satellite images. jgit 2020; 7 (4) :215-232
URL: http://jgit.kntu.ac.ir/article-1-771-en.html


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 7, Issue 4 (3-2020) Back to browse issues page
نشریه علمی-پژوهشی مهندسی فناوری اطلاعات مکانی Engineering Journal of Geospatial Information Technology
Persian site map - English site map - Created in 0.07 seconds with 35 queries by YEKTAWEB 4645