Support vector regression for high-resolution beach surface moisture estimation from terrestrial LiDAR intensity data

Jin, Junling; Verbeurgt, Jeffrey; Sloover, Lars De; Stal, Cornelis; Deruyter, Greet; Montreuil, Anne-Lise; Vos, Sander; Maeyer, Philippe De; Wulf, Alain De

doi:10.1016/j.jag.2021.102458

Cited by 6 publications

(5 citation statements)

References 77 publications

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“…However, the linear kernel model performed less well than SVR rbf and SVR quad with R, RMSE and Bias values of about 0.69, 0.06m 3 /m 3 and −0.01m 3 /m 3 , respectively. It is important to mention that our finding were similar to those reported in previous studies [60], [61] which demonstrated that the SVR rbf provides the most superior simulation results, followed by polynomial kernel and linear kernel. In a similar vein, our statistical metrics are in the range of the performance exhibited by the investigation of [43] when using SVR with several features extracted from Sentinel-1&2 and Radarsat-2 remote sensing data.…”

Section: ) Support Vector Regression and Tree-based Algorithmssupporting

confidence: 91%

“…Likewise, [62] showed that the SVR algorithm driven by backscatter at VV polorisation extracted from the active microwave Advanced Scatterometer (ASCAT) estimates correctly soil moisture and their results, which are in agreement with our finding, exhibit minor variations depending on the different training/testing configurations. Kindly note that, in addition to its robustness in estimation soil moisture, and compared with SVR quad, SVR rbf exhibits lower computational complexity and less computational time [61]. Consequently, among the three proposed SVR algorithms, only RBF kernel will be adopted for testing the transferability processes.…”

Section: ) Support Vector Regression and Tree-based Algorithmsmentioning

confidence: 99%

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Sentinel-1 Backscatter and Interferometric Coherence for Soil Moisture Retrieval in Winter Wheat Fields Within a Semiarid South-Mediterranean Climate: Machine Learning Versus Semiempirical Models

Ezzahar,

Chehbouni,

Ouaadi

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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This work aims to assess the effectiveness of machine learning (ML) algorithms and semi-empirical models for surface soil moisture (SSM) retrieval by exploring the Sentinel-1 backscatter and interferometric coherence data. Firstly, three commonly used categories of ML algorithms are evaluated using data gathered from diverse rainfed and irrigated wheat fields located in Morocco and Tunisia. Specifically, these algorithms include: artificial neural network (ANN), deep neural network (DNN), three support vector regression (SVR) models (radial basis function (SVR rbf), linear (SVR linear) and polynomial (SVR quad) kernels) and two tree-based methods (Random Forest (RF) and XGBoost). The comparison between predicted and measured SSM showed that the best retrieval results were obtained using Sentinel-1 data at VV polarization with R ranging between 0.68 and 0.76 and RMSE of 0.05m 3 /m 3 and 0.06m 3 /m 3 . Secondly, to further assess their transferability, the ANN, SVR rbf and XGBoost that demonstrated the most favorable results from each category were evaluated and compared against the coupled Water Cloud and Oh models (WCM), using a second dataset collected over a drip-irrigated wheat field in Morocco. Overall, the best retrieval results were achieved by ANN and SVR rbf with R and RMSE of 0.81 and 0.034m 3 /m 3 , respectively. In addition, their performances were consistent with that of WCM which yielded R and RMSE values of 0.81 and 0.04m 3 /m 3 , respectively. Finally, due to its good compromise between retrieval accuracy of SSM, processing time and simplicity, SVR rbf was chosen to generate high-resolution SSM maps from Sentinel-1 data over irrigated wheat fields.

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Section: ) Support Vector Regression and Tree-based Algorithmssupporting

confidence: 91%

Section: ) Support Vector Regression and Tree-based Algorithmsmentioning

confidence: 99%

Sentinel-1 Backscatter and Interferometric Coherence for Soil Moisture Retrieval in Winter Wheat Fields Within a Semiarid South-Mediterranean Climate: Machine Learning Versus Semiempirical Models

Ezzahar,

Chehbouni,

Ouaadi

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…SVR is suitable for nonlinearity and is resistant to outliers. This method has been applied to various hydrogeophysical problems such as hydraulic conductivity estimation from soil electrical spectra (Boadu, 2020) and soil moisture estimation from airborne and other datasets (Acharya et al, 2021;Achieng, 2019;Jin et al, 2021;Pasolli et al, 2011). SVR was performed using the R e1071 package (D. Meyer et al, 2021) function svm.…”

Section: Support Vector Regressionmentioning

confidence: 99%

Field evaluation of semi‐automated moisture estimation from geophysics using machine learning

Terry

Day‐Lewis

Lane

et al. 2023

Vadose Zone Journal

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Geophysical methods can provide three-dimensional (3D), spatially continuous estimates of soil moisture. However, point-to-point comparisons of geophysical properties to measure soil moisture data are frequently unsatisfactory, resulting in geophysics being used for qualitative purposes only. This is because (1) geophysics requires models that relate geophysical signals to soil moisture, (2) geophysical methods have potential uncertainties resulting from smoothing and artifacts introduced from processing and inversion, and (3) results from multiple geophysical methods are not easily combined within a single soil moisture estimation framework. To investigate these potential limitations, an irrigation experiment was performed wherein soil moisture was monitored through time, and several surface geophysical datasets indirectly sensitive to soil moisture were collected before and after irrigation: ground penetrating radar, electrical resistivity tomography (ERT), and frequency domain electromagnetics (FDEM). Data were exported in both raw and processed form, and then snapped to a common 3D grid to facilitate moisture prediction by standard calibration techniques, multivariate regression, and machine learning. A combination of inverted ERT data, raw FDEM, and inverted FDEM data was most informative for predicting soil moisture using a random regression forest model (one-thousand 60/40 training/test cross-validation folds produced root mean squared errors ranging from 0.025-0.046 cm 3 /cm 3 ). This cross-validated model was further supported by a separate evaluation using a test set from a physically separate portion of the study area. Machine learning was conducive to a semi-automated model-selection process that could be used for other sites and datasets to locally improve accuracy.

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“…Additionally, there are several machine learning algorithms such as, classification and regression trees (ART), Random Forest Regression (RFR) [11], support vector regression (SVR) [12], multiple linear regression modeling, boosted regression tree (BRT), artificial neural networks (ANN) [13] are used to predict soil moisture availability (Figure 1).…”

Section: Literature Reviewmentioning

confidence: 99%

Neural Network Model of Soil Moisture Forecast North Kazakhstan Region

Mimenbayeva,

Shaushenova,

Bekenova

et al. 2023

sjaitu

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Dealing with agriculture, it is valuable to know an amount of moisture in a soil and to know how to forecast the stored soil moisture within particular period. Forecasting the stored soil moisture works for planning an extent and structure of crop production areas and adjustment of plant-growing programs. Having known about an amount of moisture in one-meter soil and the depth of precipitation in a vegetation season shall help farmers to determine a seeding time, type of fertilizers depending on soil quality and to work out an irrigation schedule as well. In this regard, over the last few years some vigorous activities applied to machine training methods of the weather forecast have been launched in the world. The goal of present research is to develop an artificial neuron network which shall afford an opportunity to figure out a stored soil moisture prior to outgoing to winter in a short-term. North Kazakhstan Region agrometeorological measuring stations for the period from 2012 to 2022 were used in the course of the neuron network training. The Levenberg-Marquardt algorithm aimed at non-linear regression models optimization was chosen for network training. The algorithm includes sequential approximation of initial parameter values to a local optimum. The mean squared error (MSE) function and the correlation coefficient ensure accuracy and precision of forecasts. As a result, 7 neural networks under MATLAB environment using the Levenberg-Marquardt algorithm, with different input and output data, and with different number of learning iterations came to realization. Following analysis of the results, the choice was fallen on the ANN9 best network offering minimum error function and actual data maximum correlation. The neural network obtained fits for use to make efficient decisions in the North Kazakhstan region agricultural sector in the short term.

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Support vector regression for high-resolution beach surface moisture estimation from terrestrial LiDAR intensity data

Abstract: Highlights High-resolution beach surface moisture estimation from the LiDAR intensity data. No need to calibrate the intensity data in advance based on the Machine Learning. Detailed investigation of the impacts of SVR training samples’ size and density.

Cited by 6 publications

References 77 publications

Sentinel-1 Backscatter and Interferometric Coherence for Soil Moisture Retrieval in Winter Wheat Fields Within a Semiarid South-Mediterranean Climate: Machine Learning Versus Semiempirical Models

Sentinel-1 Backscatter and Interferometric Coherence for Soil Moisture Retrieval in Winter Wheat Fields Within a Semiarid South-Mediterranean Climate: Machine Learning Versus Semiempirical Models

Field evaluation of semi‐automated moisture estimation from geophysics using machine learning

Neural Network Model of Soil Moisture Forecast North Kazakhstan Region

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