Bare Soil Surface Moisture Retrieval from Sentinel-1 SAR Data Based on the Calibrated IEM and Dubois Models Using Neural Networks

Mirsoleimani, Hamid Reza; Sahebi, Mahmod Reza; Baghdadi, Nicolas; Hajj, Mohammad El

doi:10.3390/s19143209

Cited by 65 publications

(42 citation statements)

References 32 publications

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“…Monitoring and studying the Pedosphere and the corresponding categories (e.g., soil, geology, and geomorphology) are prerequisites for sustainable development, especially in the climate modeling context [156]. Soil is the most significant component of the Pedosphere that has straight impacts on the surrounding environment and, thus, essential for biodiversity conservation and climate regulations [157]- [160]. The availability of RS datasets in GEE makes it an appealing platform for the Pedosphere studies at diverse scales.…”

Section: Pedospherementioning

confidence: 99%

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Amani¹,

Ghorbanian

Ahmadi

et al. 2020

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

597

216

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Remote Sensing (RS) systems have been collecting massive volumes of datasets for decades, managing and analyzing of which are not practical using common software packages and desktop computing resources. In this regard, Google has developed a cloud computing platform, called Google Earth Engine (GEE), to effectively address the challenges of big data analysis. In particular, this platform facilitates processing big geo data over large areas and monitoring the environment for long periods of time. Although this platform was launched in 2010 and has proved its high potential for different applications, it has not been fully investigated and utilized for RS applications until recent years. Therefore, this study aims to comprehensively explore different aspects of the GEE platform, including its datasets, functions, advantages/limitations, and various applications. For this purpose, 450 journal articles published in 150 journals between January 2010 and May 2020 were studied. It was observed that Landsat and Sentinel datasets were extensively utilized by GEE users. Moreover, supervised machine learning algorithms, such as Random Forest (RF), were more widely applied to image classification tasks. GEE has also been employed in a broad range of applications, such as Land Cover/land Use (LCLU) classification, hydrology, urban planning, natural disaster, climate analyses, and image processing. It was generally observed that the number of GEE publications has significantly increased during the past few years, and it is expected that GEE will be utilized by more users from different fields to resolve their big data processing challenges.

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Section: Pedospherementioning

confidence: 99%

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Amani¹,

Ghorbanian

Ahmadi

et al. 2020

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

597

216

View full text Add to dashboard Cite

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“…As the ultimate goal of this work was to evaluate SVR based mainly on statistical learning against semi-empirical and theoretical soil backscattering models, SVR was also driven by Sentinel-1 backscattering coefficient data for retrieving soil moisture retrieval. Usually, machine learning methods used to retrieve soil moisture were trained with backscattering coefficient data generated from backscattering models and validated using radar data [47,95]. However, the error associated with the use of these models can be directly translated into an error in soil moisture retrieval.…”

Section: Soil Moisture Inversionmentioning

confidence: 99%

“…To surmount the limitations of empirical, semi-empirical, and physical models, machine-learning approaches provide an alternative tool to solve prediction problems based on an analysis of the data that characterizes the system under study with only a limited number of assumptions about the physical behavior of this system [43,44]. Among these approaches, one can cite the artificial neural network algorithm [45][46][47] and the support vector machine (SVM) technique. The latter, which is based on statistical learning theory, has been applied to a variety of themes, such as the inversion and classification problems, and has attracted the attention of many researchers due to their prediction accuracy and modeling conveniences [48][49][50][51][52][53].…”

Section: Introductionmentioning

confidence: 99%

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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“…Several studies use the combination of neural networks and empirical models to map soil moisture [17][18][19]. Mirsoleimani et al [20] proposed to simulate backscattering dataset with IEM and WCM models to train neural networks, and then used the Sentinel-1 data to retrieve moisture. Although using the dataset simulated by the empirical model and combining with a neural network can meet the demand of water inversion, the problem of inconsistency exists between the simulated dataset and measured dataset.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of a Neural Network Model and Two Empirical Models for Estimating Soil Moisture Based on Sentinel-1 Sar Data

Li¹,

Yan²,

Chen³

et al. 2020

PIER C

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The objective of this paper is to propose an inversion model of soil moisture using a neural network and compare the performance of this method with two empirical models in soil moisture inversion. A wide dataset of backscattering coefficients extracted from Sentinel-1 images and in-situ soil surface parameter measurements (moisture content and roughness) are used. Since the available backscattering models have limited performances of describing the nonlinear relationship between soil parameters and backscatter coefficient, the retrieval of soil parameters from radar backscattering coefficient remains challenging. The proposed inversion method of a neural network is used for establishing this relationship. At the same time, two empirical models are employed to estimate the soil moisture for comparison. The results show that for most of the six measuring stations the inverted soil moisture with the neural network model has higher correlation coefficient with the in-situs oil moisture than those by the empirical models. Moreover, the neural network model inversion results under multipolarization input conditions are discussed in this paper. The results of stations 2, 4, and 5 show that R 2 of multi-polarization inputs are increased by 0.1928, 0.4821, and 0.2758, respectively, compared with those of single-polarization inputs.

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Bare Soil Surface Moisture Retrieval from Sentinel-1 SAR Data Based on the Calibrated IEM and Dubois Models Using Neural Networks

Cited by 65 publications

References 32 publications

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

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

Performance Evaluation of a Neural Network Model and Two Empirical Models for Estimating Soil Moisture Based on Sentinel-1 Sar Data

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