Independent System Calibration of Sentinel-1B

Schwerdt, Marco; Schmidt, Kersten; Ramon, Núria Tous; Klenk, Patrick; Yague-Martinez, Nestor; Prats-Iraola, Pau; Zink, Manfred; Geudtner, Dirk

doi:10.3390/rs9060511

Cited by 69 publications

(72 citation statements)

References 13 publications

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“…In addition, the soil contribution was simulated using the IEM modified by Baghdadi et al [15,17]. The results showed that the parameterized WCM simulates the backscattering coefficients in the VV and VH polarizations with an accuracy (Root Mean Square Error "RMSE") of approximately 1.4 dB (R 2 approximately 0.60), which is close to the S1 measurement accuracy [51,52].…”

Section: Radar Backscattering Modelmentioning

confidence: 88%

“…To make the synthetic database of backscattering coefficients more realistic (i.e., similar to real SAR data), the error corresponding to Sentinel-1 radiometric accuracy was added to the simulated backscattering coefficients. This error is approximately 0.70 dB (3σ) for the VV polarization and 1.0 dB (3σ) for the VH polarization [52]. Thus, we considered two absolute zero-mean Gaussian random additive noises with a standard deviation of ±0.7 and ±1.0 dB to be added to the simulated backscattering coefficients in the VV and VH, respectively.…”

Section: Synthetic Databasementioning

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: 88%

Section: Synthetic Databasementioning

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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“…The correlation coefficient R2 is equal to 0.55 for VV and 0.63 for VH with RMSE (Root Mean Square Error) on the predicted backscattering coefficients of 1.55 dB in VV and 1.30 dB in VH. The RMSE obtained on the WCM predictions close to 1.5 dB are slightly higher than the Sentinel-1 precision [32,33]. Indeed, the radiometric accuracy for all measurement modes of Sentinel-1 is within 1 dB (3σ) and its sensitivity expressed by the noise equivalent sigma naught is −22 dB or higher [32].…”

Section: Water Cloud Model Validationmentioning

confidence: 93%

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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“…In order to make the IEM simulations more realistic, Sentinel-1 radiometric error was added to simulated backscattering coefficients. In Schwerdt et al [20], this error is about 0.70 dB (3σ) for VV polarization. Accordingly, zero-mean Gaussian random noise with a standard deviation of ±0.70 dB was added to simulated backscattering coefficients in VV polarization.…”

Section: Synthetic Datasetmentioning

confidence: 99%

“…The standard acquisition mode of Sentinel-1 corresponds to acquisitions in both VV and VH polarizations. Since the VH polarization produces low signal levels over bare soils and is strongly affected by noise (radiometric accuracy is about 1.0 dB (3σ) for VH [20], it is less commonly used for the analysis of surface parameters. Accordingly, only VV was used.…”

mentioning

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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Independent System Calibration of Sentinel-1B

Cited by 69 publications

References 13 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

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

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