RT - Journal Article
T1 - Volumetric soil moisture estimation using Sentinel 1 and 2 satellite images
JF - kntu-jgit
YR - 2020
JO - kntu-jgit
VO - 7
IS - 4
UR - http://jgit.kntu.ac.ir/article-1-771-en.html
SP - 215
EP - 232
K1 - Soil Moisture
K1 - Sentinel 1 & 2
K1 - Artificial Neural Network
K1 - Support Vector Regression
K1 - Genetic Optimization Algorithm.
AB - 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.
LA eng
UL http://jgit.kntu.ac.ir/article-1-771-en.html
M3 10.29252/jgit.7.4.215
ER -