Soil moisture retrieval over irrigated grassland using X-band SAR data

Hajj, Mohammad El; Baghdadi, Nicolas; Zribi, Mehrez; Belaud, Gilles; Cheviron, Bruneau; Courault, Dominique; Charron, François

doi:10.1016/j.rse.2016.01.027

Cited by 157 publications

(104 citation statements)

References 67 publications

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“…The soil contribution is a function of the soil moisture and roughness. The vegetation contribution and the attenuation are computed using one or more vegetation parameters, such as the leaf area index, NDVI, and vegetation water content [25][26][27]29,30,49,50].…”

Section: Radar Backscattering Modelmentioning

confidence: 99%

“…The direct vegetation contribution and the attenuation are computed using one or more vegetation descriptors. Several studies showed that the use of the NDVI (Normalized Differential Vegetation Index) as the only vegetation descriptor allows for computation of the vegetation effects on the total backscattered coefficients with good accuracy [26][27][28]. The parametrization of the WCM was provided in several studies for different SAR configurations and crop types [27,29].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, [26] parameterized the WCM for a radar signal in the C-band and for various crop types (grassland, wheat, and cereals). Using the WCM, several studies estimated the soil moisture from the C-and X-band SAR with an accuracy better than 8 vol % [27][28][29][30][31][32][33]. However, soil moisture estimates could be inaccurate for plots with well-developed vegetation cover because the vegetation contribution dominates the soil contribution in some cases of vegetation.…”

Section: Introductionmentioning

confidence: 99%

“…The results showed that HH polarization is the most relevant for soil moisture estimates (accuracy approximately 6 vol % even for dense grassland cover). El Hajj et al [27] developed an inversion technique based on the neural networks technique to invert the WCM for soil moisture estimation. First, the WCM was used to generate synthetic datasets of backscattering coefficients in the X-band (incidence angle~30 • ) for a wide range of soil moisture (mv between 5 and 45 vol %) and vegetation conditions (NDVI between 0 and 1).…”

Section: Introductionmentioning

confidence: 99%

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Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution over Agricultural Areas

et al. 2017

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Abstract:Soil moisture mapping at a high spatial resolution is very important for several applications in hydrology, agriculture and risk assessment. With the arrival of the free Sentinel data at high spatial and temporal resolutions, the development of soil moisture products that can better meet the needs of users is now possible. In this context, the main objective of the present paper is to develop an operational approach for soil moisture mapping in agricultural areas at a high spatial resolution over bare soils, as well as soils with vegetation cover. The developed approach is based on the synergic use of radar and optical data. A neural network technique was used to develop an operational method for soil moisture estimates. Three inversion SAR (Synthetic Aperture Radar) configurations were tested: (1) VV polarization; (2) VH polarization; and (3) both VV and VH polarization, all in addition to the NDVI information extracted from optical images. Neural networks were developed and validated using synthetic and real databases. The results showed that the use of a priori information on the soil moisture condition increases the precision of the soil moisture estimates. The results showed that VV alone provides better accuracy on the soil moisture estimates than VH alone. In addition, the use of both VV and VH provides similar results, compared to VV alone. In conclusion, the soil moisture could be estimated in agricultural areas with an accuracy of approximately 5 vol % (volumetric unit expressed in percent). Better results were obtained for soil with a moderate surface roughness (for root mean surface height between 1 and 3 cm). The developed approach could be applied for agricultural plots with an NDVI lower than 0.75.

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Section: Radar Backscattering Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution over Agricultural Areas

et al. 2017

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“…SAR remote sensing was widely used to estimate the surface soil moisture in agricultural areas (e.g., [1][2][3][4][5][6][7][8][9]). Over bare soil, the estimation of soil moisture is performed using either a physical (e.g., the Integral Equation Model [10] or statistical (e.g., Baghdadi [11], Dubois [12], and Oh models [13]), with an accuracy better than 6 vol % [3,14,15].…”

Section: Introductionmentioning

confidence: 99%

Calibration of the Water Cloud Model at C-Band for Winter Crop Fields and Grasslands

et al. 2017

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Abstract:In a perspective to develop an inversion approach for estimating surface soil moisture of crop fields from Sentinel-1/2 data (radar and optical sensors), the Water Cloud Model (WCM) was calibrated from C-band Synthetic Aperture Radar (SAR) data and Normalized Difference Vegetation Index (NDVI) values collected over crops fields and grasslands. The soil contribution that depends on soil moisture and surface roughness (in addition to SAR instrumental parameters) was simulated using the physical backscattering model IEM (Integral Equation Model). The vegetation descriptor used in the WCM is the NDVI because it can be directly calculated from optical images. A large dataset consisting of radar backscattered signal in Vertical transmit and Vertical receive (VV) and Vertical transmit and Horizontal receive (VH) polarizations with wide range of incidence angle, soil moisture, surface roughness, and NDVI-values was used. It was collected over two agricultural study sites. Results show that the soil contribution to the total radar backscattered signal is lower in VH than in VV because VH is more sensitive to vegetation cover. Thus, the use of VH alone or in addition to VV for retrieving the soil moisture is not advantageous in presence of well-developed vegetation cover.

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Coupling Radar and Optical Data for Soil Moisture Retrieval over Agricultural Areas

Hajj¹,

Baghdadi²,

Zribi³

et al. 2018

QGIS and Applications in Agriculture and Forest

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Soil moisture retrieval over irrigated grassland using X-band SAR data

Cited by 157 publications

References 67 publications

Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution over Agricultural Areas

Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution over Agricultural Areas

Calibration of the Water Cloud Model at C-Band for Winter Crop Fields and Grasslands

Coupling Radar and Optical Data for Soil Moisture Retrieval over Agricultural Areas

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