Soil moisture prediction using a support vector regression

Lee, Danhyang; Kim, Gwangseob; Lee, Kyeong Eun

doi:10.7465/jkdi.2013.24.2.401

Cited by 3 publications

(2 citation statements)

References 14 publications

(9 reference statements)

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“…Additionally, [48], have used this technique for retrieving soil moisture from remote sensing data by combining backscatter from the Tropical Rainfall Measuring Mission (TRMM), and a normalized difference vegetation index (NDVI) from an advanced very high resolution radiometer (AVHRR), and they revealed that this technique performs better for soil moisture retrieval than the multivariate linear regression model (MLR). Recently, [59], confirmed the accuracy of the SVR model to predict soil moisture from soil temperature, NDVI, rainfall, and soil moisture observed the day before. To our knowledge, although SVR has been used in past studies for soil moisture retrieval, none of the studies have tested this algorithm with high resolution radar data.…”

Section: Introductionmentioning

confidence: 71%

Evaluation of Backscattering Models and Support Vector Machine for the Retrieval of Bare Soil Moisture from Sentinel-1 Data

et al. 2019

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The main objective of this work was to retrieve surface soil moisture (SSM) by using scattering models and a support vector machine (SVM) technique driven by backscattering coefficients obtained from Sentinel-1 satellite images acquired over bare agricultural soil in the Tensfit basin of Morocco. Two backscattering models were selected in this study due to their wide use in inversion procedures: the theoretical integral equation model (IEM) and the semi-empirical model (Oh). To this end, the sensitivity of the SAR backscattering coefficients at V V ( σ v v ∘ ) and V H ( σ v h ∘ ) polarizations to in situ soil moisture data were analyzed first. As expected, the results showed that over bare soil the σ v v ∘ was well correlated with SSM compared to the σ v h ∘ , which showed more dispersion with correlation coefficients values (r) of about 0.84 and 0.61 for the V V and V H polarizations, respectively. Afterwards, these values of σ v v ∘ were compared to those simulated by the backscatter models. It was found that IEM driven by the measured length correlation L slightly underestimated SAR backscatter coefficients compared to the Oh model with a bias of about − 0.7 dB and − 1.2 dB and a root mean square (RMSE) of about 1.1 dB and 1.5 dB for Oh and IEM models, respectively. However, the use of an optimal value of L significantly improved the bias of IEM, which became near to zero, and the RMSE decreased to 0.9 dB. Then, a classical inversion approach of σ v v ∘ observations based on backscattering model is compared to a data driven retrieval technic (SVM). By comparing the retrieved soil moisture against ground truth measurements, it was found that results of SVM were very encouraging and were close to those obtained by IEM model. The bias and RMSE were about 0.28 vol.% and 2.77 vol.% and − 0.13 vol.% and 2.71 vol.% for SVM and IEM, respectively. However, by taking into account the difficultly of obtaining roughness parameter at large scale, it was concluded that SVM is still a useful tool to retrieve soil moisture, and therefore, can be fairly used to generate maps at such scales.

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Section: Introductionmentioning

confidence: 71%

Evaluation of Backscattering Models and Support Vector Machine for the Retrieval of Bare Soil Moisture from Sentinel-1 Data

et al. 2019

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“…In recent years, the SVR-based models have been able to offer accurate assessments regarding the geotechnical problems (e.g., [34][35][36][37]). The phenomena related to soil environments as well as the earthquake-induced loadings are highly complex [38][39][40].…”

Section: -Introductionmentioning

confidence: 99%

A regression-based approach to predict crest settlement of embankment dams under earthquake shaking

2018

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Settlement of the embankment dams are among the major damages caused by earthquakes, eventually leading to dam instability. Therefore, accurate assessment of the seismic settlement of embankment dams is of particular concern. This study aims to evaluate the settlement of embankment dams subjected to earthquake loads using regression-based methods. A wideranging real data on crest settlement of embankment dams caused by earthquakes was analyzed.Yield acceleration of dam (a y ), maximum horizontal earthquake acceleration (a max ), fundamental period of dam body (T d ), predominant period of earthquake (T p ), and earthquake magnitude (M w ) were considered as the most influential parameters affecting the seismic crest settlement of embankment dams. Using support vector regression (SVR) method as well as multiple linear regression (MLR) method, two models were developed to estimate earthquake induced settlement of embankment dams. Subsequently, sensitivity analysis was conducted in order to assess the behavior of the proposed models under different conditions. Finally, the accuracy of the proposed models was compared with the existing relationship for estimation of earthquake induced crest settlement of embankment dams. Although both MLR-and SVR-based models have an acceptable accuracy in estimation of the crest settlement of embankment dams under earthquake loading, the SVR-based model has a higher accuracy.

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Sequence-to-Sequence Learning for Prediction of Soil Temperature and Moisture

Tang

Yin

2022

IEEE Geosci. Remote Sensing Lett.

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Soil moisture prediction using a support vector regression

Cited by 3 publications

References 14 publications

Evaluation of Backscattering Models and Support Vector Machine for the Retrieval of Bare Soil Moisture from Sentinel-1 Data

Evaluation of Backscattering Models and Support Vector Machine for the Retrieval of Bare Soil Moisture from Sentinel-1 Data

A regression-based approach to predict crest settlement of embankment dams under earthquake shaking

Sequence-to-Sequence Learning for Prediction of Soil Temperature and Moisture

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