Volumetric soil moisture estimation using Sentinel 1 and 2 satellite images

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Ranjbar Sadegh; Akhoondzadeh Mahdi

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Journal: ENGINEERING JOURNAL OF GEOSPATIAL INFORMATION TECHNOLOGY Year:2020 | Volume:7 | Issue:4 Page(s): 215-232

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Title

Volumetric soil moisture estimation using Sentinel 1 and 2 satellite images

Author(s)

Ranjbar Sadegh | Akhoondzadeh Mahdi | Issue Writer Certificate

Keywords

Soil MoistureQ1

Sentinel 1 & 2Q1

Artificial Neural NetworkQ2

Support Vector RegressionQ1

Genetic Optimization AlgorithmQ2

Abstract

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 (ANNMLP) 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( 𝑚 3 𝑚 3) for Sentinel1 image with Support Vector Regression and 0. 4612( 𝑚 3 𝑚 3) for Sentinel2 image with Multilayer Perceptron Artificial Neural Network.

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APA: Copy

Ranjbar, Sadegh, & Akhoondzadeh, Mahdi. (2020). Volumetric soil moisture estimation using Sentinel 1 and 2 satellite images. ENGINEERING JOURNAL OF GEOSPATIAL INFORMATION TECHNOLOGY, 7(4 ), 215-232. SID. https://sid.ir/paper/230156/en

Vancouver: Copy

Ranjbar Sadegh, Akhoondzadeh Mahdi. Volumetric soil moisture estimation using Sentinel 1 and 2 satellite images. ENGINEERING JOURNAL OF GEOSPATIAL INFORMATION TECHNOLOGY[Internet]. 2020;7(4 ):215-232. Available from: https://sid.ir/paper/230156/en

IEEE: Copy

Sadegh Ranjbar, and Mahdi Akhoondzadeh, “Volumetric soil moisture estimation using Sentinel 1 and 2 satellite images,” ENGINEERING JOURNAL OF GEOSPATIAL INFORMATION TECHNOLOGY, vol. 7, no. 4 , pp. 215–232, 2020, [Online]. Available: https://sid.ir/paper/230156/en

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