Potential of Sentinel-1 Radar Data for the Assessment of Soil and Cereal Cover Parameters

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

doi:10.3390/s17112617

Cited by 128 publications

(128 citation statements)

References 57 publications

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“…As synthesized by several studies, there are different factors affecting the radar backscatter, as the C-band represents an integration of the backscatter from the ground attenuated by the canopy layer, volume scattering, and stem-ground interactions (usually negligible for wheat) [54,65,66].…”

Section: Sentinel-1 Datamentioning

confidence: 99%

Sentinel-1 Data for Winter Wheat Phenology Monitoring and Mapping

et al. 2019

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The ability of Synthetic Aperture Radar (SAR) Sentinel-1 data to detect the main wheat phenological phases was investigated in the Bekaa plain of Lebanon. Accordingly, the temporal variation of Sentinel-1 (S1) signal was analyzed as a function of the phenological phases’ dates observed in situ (germination; heading and soft dough), and harvesting. Results showed that S1 data, unlike the Normalized Difference Vegetation Index (NDVI) data, were able to estimate the dates of theses phenological phases due to significant variations in S1 temporal series at the dates of germination, heading, soft dough, and harvesting. Particularly, the ratio VV/VH at low incidence angle (32–34°) was able to detect the germination and harvesting dates. VV polarization at low incidence angle (32–34°) was able to detect the heading phase, while VH polarization at high incidence angle (43–45°) was better than that at low incidence angle (32–34°), in detecting the soft dough phase. An automated approach for main wheat phenological phases’ determination was then developed on the western part of the Bekaa plain. This approach modelled the S1 SAR temporal series by smoothing and fitting the temporal series with Gaussian functions (up to three Gaussians) allowing thus to automatically detect the main wheat phenological phases from the sum of these Gaussians. To test its robustness, the automated method was applied on the northern part of the Bekaa plain, in which winter wheat is harvested usually earlier because of the different weather conditions. The Root Mean Square Error (RMSE) of the estimation of the phenological phases’ dates was 2.9 days for germination, 5.5 days for heading, 5.1 days soft dough, 3.0 days for West Bekaa’s harvesting, and 4.5 days for North Bekaa’s harvesting. In addition, a slight underestimation was observed for germination and heading of West Bekaa (−0.2 and −1.1 days, respectively) while an overestimation was observed for soft dough of West Bekaa and harvesting for both West and North Bekaa (3.1, 0.6, and 3.6 days, respectively). These results are encouraging, and thus prove that S1 data are powerful as a tool for crop monitoring, to serve enhanced crop management and production handling.

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Section: Sentinel-1 Datamentioning

confidence: 99%

Sentinel-1 Data for Winter Wheat Phenology Monitoring and Mapping

et al. 2019

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“…In a recent study, Bousbih et al [31] investigate the sensitivity of the Sentinel-1 radar signal to soil moisture and surface roughness. For a bare soil, results showed that the radar signal in the C-band increases as soil moisture increases (5 vol.…”

Section: Real Datasetmentioning

confidence: 99%

Potential of Sentinel-1 Images for Estimating the Soil Roughness over Bare Agricultural Soils

Baghdadi

Hajj

Choker

et al. 2018

Water

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Abstract:The purpose of this study is to analyze the potential of Sentinel-1 C-band SAR data in VV polarization for estimating the surface roughness (Hrms) over bare agricultural soils. An inversion technique based on Multi-Layer Perceptron neural networks is used. It involves two steps. First, a neural network (NN) is used for estimating the soil moisture without taking into account the soil roughness. Then, a second neural network is used for retrieving the soil roughness when using as an input to the network the soil moisture that was estimated by the first network. The neural networks are trained and validated using simulated datasets generated from the radar backscattering model IEM (Integral Equation Model) with the range of soil moisture and surface roughness encountered in agricultural environments. The inversion approach is then validated using Sentinel-1 images collected over two agricultural study sites, one in France and one in Tunisia. Results show that the use of C-band in VV polarization for estimating the soil roughness does not allow a reliable estimate of the soil roughness. From the synthetic dataset, the achievable accuracy of the Hrms estimates is about 0.94 cm when using the soil moisture estimated by the NN built with a priori information on the moisture volumetric content "mv" (accuracy of mv is about 6 vol. %). In addition, an overestimation of Hrms for low Hrms-values and an underestimation of Hrms for Hrms higher than 2 cm are observed. From a real dataset, results show that the accuracy of the estimates of Hrms in using the mv estimated over a wide area (few km 2 ) is similar to that in using the mv estimated at the plot scale (RMSE about 0.80 cm).

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“…The classification process was used to discriminate between irrigated and non-irrigated fields. Two classifications were used (Bousbih et al, 2018). The first one concerns the SVM classification to delineate between the irrigated and rainfed agricultural fields, using the means and variance soil moisture time series.…”

Section: Approach Based On Satellite Productsmentioning

confidence: 99%

“…The retrieved accuracy of estimated volumetric soil moisture is about 5 vol.%. Based on estimated moisture products, two methodologies are considered to map irrigated areas , Bousbih et al, 2018. An analysis of different metrics (mean, variance, correlation length, etc.)…”

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confidence: 99%

Surface Moisture and Irrigation Mapping at Agricultural Field Scale Using the Synergy Sentinel-1/Sentinel-2 Data

Zribi¹,

Baghdadi²,

Bousbih³

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Soil moisture plays a key role in various processes at the soil-vegetation-atmosphere interface, such as evapotranspiration, infiltration and runoff. In this study, we firstly propose a global analysis of Sentinel-1 (S1) & Sentinel-2 (S2) data potential to retrieve soil moisture. Two approaches are tested. The first one is based on neural network approach; it uses Integral Equation Model (IEM) coupled to Water Cloud Model for vegetation cover backscattering simulation . The second approach considers change detection methodology. It estimates change of soil moisture with the driest and highest moisture levels, and also change of moisture between successive radar acquisitions . The proposed approaches are validated over three agricultural regions, south of France, Urgell (Spain) and Merguellil (Tunisia). In these different sites, important ground campaigns have been realized over reference fields with different types of measurements (soil moisture and roughness, Leaf area Index of vegetation cover). The retrieved accuracy of estimated volumetric soil moisture is about 5 vol.%. Based on estimated moisture products, two methodologies are considered to map irrigated areas , Bousbih et al., 2018. An analysis of different metrics (mean, variance, correlation length, etc.) of radar signal time series and surface parameters (moisture and NDVI) are tested. The proposed classification of irrigated areas is based on a combination of Support Vector Machine (SVM) and decision tree methodologies. For Urgell and Merguellil sites, a mapping of irrigated fields is proposed. The accuracy of mapping is higher than 75% for the two studied sites.

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Potential of Sentinel-1 Radar Data for the Assessment of Soil and Cereal Cover Parameters

Cited by 128 publications

References 57 publications

Sentinel-1 Data for Winter Wheat Phenology Monitoring and Mapping

Sentinel-1 Data for Winter Wheat Phenology Monitoring and Mapping

Potential of Sentinel-1 Images for Estimating the Soil Roughness over Bare Agricultural Soils

Surface Moisture and Irrigation Mapping at Agricultural Field Scale Using the Synergy Sentinel-1/Sentinel-2 Data

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