A semi-empirical backscattering model at L-band and C-band for a soybean canopy with soil moisture inversion

Roo, R.D. De; Du, Yang; Ulaby, F. T.; Dobson, M.C.

doi:10.1109/36.917912

Cited by 164 publications

(74 citation statements)

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“…The WCM is the most commonly used model to estimate soil moisture in the presence of vegetation cover because it uses a simple formulation and consequently, it could be easily inverted [21,29,[31][32][33][45][46][47][48]. In the WCM, the total backscattered signal is equal to the sum of vegetation contribution and soil contribution attenuated by the vegetation layer.…”

Section: Radar Backscattering Modelmentioning

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%

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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%

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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“…This semi-empirical model is widely used because it can be easily performed in an inversion scheme to estimate soil moisture and vegetation parameters [5,6,[25][26][27].…”

Section: Radar Signal Modelingmentioning

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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“…Recently, Betbeder et al [45] showed that the Shannon entropy is highly correlated with the fragmentation of hedgerows. The Pauli decomposition components corresponding to the three diagonal in : IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, n° 99, 2015. p. [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15] elements of the coherency matrix ( [46]), each have a specific scattering mechanism: (HH+VV) represents the surface scattering, (HV+VH) is associated with volume scattering, and (HH-VV) indicates doublebounce scattering. Surface scattering dominates when the radar signal is primarily reflected from the canopy top.…”

mentioning

confidence: 99%

Coupling SAR C-Band and Optical Data for Soil Moisture and Leaf Area Index Retrieval Over Irrigated Grasslands

Baghdadi

Hajj

Zribi

et al. 2016

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

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A semi-empirical backscattering model at L-band and C-band for a soybean canopy with soil moisture inversion

Cited by 164 publications

References 10 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 SAR C-Band and Optical Data for Soil Moisture and Leaf Area Index Retrieval Over Irrigated Grasslands

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