Soil Moisture and Irrigation Mapping in A Semi-Arid Region, Based on the Synergetic Use of Sentinel-1 and Sentinel-2 Data

Bousbih, Safa; Zribi, Mehrez; Hajj, Mohammad El; Baghdadi, Nicolas; Lili-Chabaane, Zohra; Gao, Qi; Fanise, Pascal

doi:10.3390/rs10121953

Cited by 118 publications

(84 citation statements)

References 56 publications

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“…Apart from optical data, recent studies have started exploiting the potential of SAR (synthetic aperture radar) data to map different agricultural irrigated areas [20][21][22][23][24]. The assumption established for using SAR data relies on the fact that radar remote sensing is sensitive to the water content of soil due to the increase in the dielectric constant with the increase of the soil water content.…”

Section: Introductionmentioning

confidence: 99%

Mapping Irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain

et al. 2019

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Mapping irrigated plots is essential for better water resource management. Today, the free and open access Sentinel-1 (S1) and Sentinel-2 (S2) data with high revisit time offers a powerful tool for irrigation mapping at plot scale. Up to date, few studies have used S1 and S2 data to provide approaches for mapping irrigated plots. This study proposes a method to map irrigated plots using S1 SAR (synthetic aperture radar) time series. First, a dense temporal series of S1 backscattering coefficients were obtained at plot scale in VV (Vertical-Vertical) and VH (Vertical-Horizontal) polarizations over a study site located in Catalonia, Spain. In order to remove the ambiguity between rainfall and irrigation events, the S1 signal obtained at plot scale was used conjointly to S1 signal obtained at a grid scale (10 km × 10 km). Later, two mathematical transformations, including the principal component analysis (PCA) and the wavelet transformation (WT), were applied to the several SAR temporal series obtained in both VV and VH polarization. Irrigated areas were then classified using the principal component (PC) dimensions and the WT coefficients in two different random forest (RF) classifiers. Another classification approach using one dimensional convolutional neural network (CNN) was also performed on the obtained S1 temporal series. The results derived from the RF classifiers with S1 data show high overall accuracy using the PC values (90.7%) and the WT coefficients (89.1%). By applying the CNN approach on SAR data, a significant overall accuracy of 94.1% was obtained. The potential of optical images to map irrigated areas by the mean of a normalized differential vegetation index (NDVI) temporal series was also tested in this study in both the RF and the CNN approaches. The overall accuracy obtained using the NDVI in RF classifier reached 89.5% while that in the CNN reached 91.6%. The combined use of optical and radar data slightly enhanced the classification in the RF classifier but did not significantly change the accuracy obtained in the CNN approach using S1 data.

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Section: Introductionmentioning

confidence: 99%

Mapping Irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain

et al. 2019

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“…With the launch of the Sentinel-1 constellation, the opportunity has arisen for the development of operational algorithms, and several very high spatial resolution products have been proposed to meet agricultural needs at the scale of individual plots. However, this improvement in spatial resolution has been achieved at the expense of lower temporal resolutions (typically corresponding to a 6-day repeat cycle) [47][48][49][50]. The two main approaches used to analyze the raw data are known as the neural network and change detection techniques.…”

Section: Introductionmentioning

confidence: 99%

Estimating 500-m Resolution Soil Moisture Using Sentinel-1 and Optical Data Synergy

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Zribi

Albergel

et al. 2020

Water

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The aim of this study is to estimate surface soil moisture at a spatial resolution of 500 m and a temporal resolution of at least 6 days, by combining remote sensing data from Sentinel-1 and optical data from Sentinel-2 and MODIS (Moderate-Resolution Imaging Spectroradiometer). The proposed methodology is based on the change detection technique, applied to a series of measurements over a three-year period (2015 to 2018). The algorithm described here as “Soil Moisture Estimations from the Synergy of Sentinel-1 and optical sensors (SMES)” proposes different options, allowing information from vegetation densities and seasonal conditions to be taken into account. The output from this algorithm is a moisture index ranging between 0 and 1, with 0 corresponding to the driest soils and 1 to the wettest soils. This methodology has been tested at different test sites (South of France, Central Tunisia, Western Benin and Southwestern Niger), characterized by a wide range of different climatic conditions. The resulting surface soil moisture estimations are compared with in situ measurements and already existing satellite-derived soil moisture ASCAT (Advanced SCATterometer) products. They are found to be well correlated, for the African regions in particular (RMSE below 6 vol.%). This outcome indicates that the proposed algorithm can be used with confidence to estimate the surface soil moisture of a wide range of climatically different sites.

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“…Several authors [2][3][4][5][6] are developing techniques that allow SAR data to be applied to the estimation of soil moisture. They have also proposed various applications that can be used to interpret soil moisture patterns (for example irrigation mapping).…”

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confidence: 99%

“…Ezzahar et al [2] studied several different surface scattering models, leading to the development of a Sentinel-1 inversion method, based on the SVM machine learning technique, which can be used to map soil moisture. Bousbih et al [3] also proposed a method based on the use of Sentinel-1 data for the estimation of soil moisture. This approach relies on the inversion of the semi-empirical Water Cloud Model (WCM), and combines radar data with multispectral (visible and near infrared) Sentinel-2 data, in order to characterize the radar scattering properties of the vegetation cover.…”

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confidence: 99%