Soil Moisture Estimation over Vegetated Agricultural Areas: Tigris Basin, Turkey from Radarsat-2 Data by Polarimetric Decomposition Models and a Generalized Regression Neural Network

Özerdem, Mehmet Siraç; Acar, Emrullah; Ekinci, Remzi

doi:10.3390/rs9040395

Cited by 24 publications

(16 citation statements)

References 35 publications

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“…This step was followed by the polarimetric decomposition techniques (Freeman-Durden and H/A/ α ) and 5 more polarimetric features (volumetric, surface and double scattering with entropy and anisotropy) were calculated. The techniques used in the study for the feature extraction stage are described in detail by [34].…”

Section: Producing Radarsat-2 Data Featuresmentioning

confidence: 99%

On a yearly basis prediction of soil water content utilizing sar data: a machine learning and feature selection approach

Acar¹,

Özerdem²

2020

Turk J Elec Eng & Comp Sci

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Soil water content (SWC) performs an important role in many areas including agriculture, drought cases, usage of water resources, hydrology, crop diseases and aerology. However, the measurement of the SWC over large terrains with standard computational techniques is very hard. In order to overcome this situation, remote sensing tools are preferred, which can produce much more successful results in less time than standard calculation techniques. Among all remote sensing tools, synthetic aperture radar (SAR) has a significant impact on determining SWC over large terrains. The main objective of this study is to predict SWC on a yearly basis over the vegetation-covered terrains with the aid of different machine learning techniques and SAR based Radarsat-2 data, which obtained in 2015 and 2016 years.The proposed system consists of several stages, respectively. In the feature extraction stage, the backscatter coefficients of different polarizations and the parameters obtained from different models of decomposition (Freeman-Durden and H/A/ α) were combined and nine polarimetric features were formed for each sample point. In the next stage, support vector regression (SVR), generalized regression neural network (GRNN) and adaptive neuro-fuzzy inference system (ANFIS) were employed for the prediction of SWC. In the last stage, a machine learning based feature selection was implemented to the obtained feature vectors for determining optimal feature sets. Finally, a feature set with 6 parameters was determined as most optimal feature set over the SWC prediction and a slightly better performance was observed thanks to this feature set compared to the other results.

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Section: Producing Radarsat-2 Data Featuresmentioning

confidence: 99%

On a yearly basis prediction of soil water content utilizing sar data: a machine learning and feature selection approach

Acar¹,

Özerdem²

2020

Turk J Elec Eng & Comp Sci

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“…The method was suggested for use in the hidden resources decomposition [25]. In the ELM, input layer weights and threshold values are randomly assigned and the output layer weights are calculated analytically [26]. In the literature, the images are usually classified into hidden sources based on changes in weight [27].…”

Section: Extreme Learning Machinesmentioning

confidence: 99%

An Efficient Noisy Pixels Detection Model for CT Images using Extreme Learning Machines

Çalışkan

Çevik

2018

Teh. vjesn.

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In this study, a new and rapid hidden resource decomposition method has been proposed to determine noisy pixels by adopting the extreme learning machines (ELM) method. The goal of this method is not only to determine noisy pixels, but also to protect critical structural information that can be used for disease diagnosis. In order to facilitate the diagnosis and also the treatment of patients in medicine, two-dimensional (2-D) images were calculated tomography (CT) which is obtained using medical imaging techniques. Utilizing a large number of CT images, promising results have been obtained from these experiments. The proposed method has shown a significant improvement on mean squared error and peak signal-to-noise ratio. The experimental results indicate that the proposed method is statistically efficient, and it has a good performance with a high learning speed. In the experiments, the results demonstrated that remarkable successive rates were obtained through the ELM method.

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“…On the other hand, the phase information contained in SAR images was decomposed into polarimetric parameters to evaluate the vegetation impact. The H/A/ α polarimetric decomposition (i.e., Cloude decomposition) model has been successfully employed in previous SMC retrieval [ 19 , 29 ]. For example, Özerdem et al [ 19 ] employed the H/A/ α method to decompose Radarsata-2 data to obtain the polarization characteristics inputted to the generalized regression neural network (GRNN) algorithm to retrieve SMC.…”

Section: Introductionmentioning

confidence: 99%

Soil Moisture Retrieval in Farmland Areas with Sentinel Multi-Source Data Based on Regression Convolutional Neural Networks

Liu

et al. 2021

Sensors

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As an important component of the earth ecosystem, soil moisture monitoring is of great significance in the fields of crop growth monitoring, crop yield estimation, variable irrigation, and other related applications. In order to mitigate or eliminate the impacts of sparse vegetation covers in farmland areas, this study combines multi-source remote sensing data from Sentinel-1 radar and Sentinel-2 optical satellites to quantitatively retrieve soil moisture content. Firstly, a traditional Oh model was applied to estimate soil moisture content after removing vegetation influence by a water cloud model. Secondly, support vector regression (SVR) and generalized regression neural network (GRNN) models were used to establish the relationships between various remote sensing features and real soil moisture. Finally, a regression convolutional neural network (CNNR) model is constructed to extract deep-level features of remote sensing data to increase soil moisture retrieval accuracy. In addition, polarimetric decomposition features for real Sentinel-1 PolSAR data are also included in the construction of inversion models. Based on the established soil moisture retrieval models, this study analyzes the influence of each input feature on the inversion accuracy in detail. The experimental results show that the optimal combination of R2 and root mean square error (RMSE) for SVR is 0.7619 and 0.0257 cm3/cm3, respectively. The optimal combination of R2 and RMSE for GRNN is 0.7098 and 0.0264 cm3/cm3, respectively. Especially, the CNNR model with optimal feature combination can generate inversion results with the highest accuracy, whose R2 and RMSE reach up to 0.8947 and 0.0208 cm3/cm3, respectively. Compared to other methods, the proposed algorithm improves the accuracy of soil moisture retrieval from synthetic aperture radar (SAR) and optical data. Furthermore, after adding polarization decomposition features, the R2 of CNNR is raised by 0.1524 and the RMSE of CNNR decreased by 0.0019 cm3/cm3 on average, which means that the addition of polarimetric decomposition features effectively improves the accuracy of soil moisture retrieval results.

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Soil Moisture Estimation over Vegetated Agricultural Areas: Tigris Basin, Turkey from Radarsat-2 Data by Polarimetric Decomposition Models and a Generalized Regression Neural Network

Cited by 24 publications

References 35 publications

On a yearly basis prediction of soil water content utilizing sar data: a machine learning and feature selection approach

On a yearly basis prediction of soil water content utilizing sar data: a machine learning and feature selection approach

An Efficient Noisy Pixels Detection Model for CT Images using Extreme Learning Machines

Soil Moisture Retrieval in Farmland Areas with Sentinel Multi-Source Data Based on Regression Convolutional Neural Networks

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