Soil moisture estimation from ERS/SAR data: toward an operational methodology

Hégarat‐Mascle, Sylvie Le; Zribi, Mehrez; Alem, F.; Weisse, A.; Loumagne, C.

doi:10.1109/tgrs.2002.806994

Cited by 145 publications

(79 citation statements)

References 17 publications

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“…It can be seen in Table 3 that the optimal values for the shape and compactness parameters have less variation than the scale parameter for the studied areas. The variation intervals are [0-0.2], [0.4-0.6], and [2][3][4][5][6][7][8][9][10][11][12][13][14][15] for the shape, compactness, and scale parameters, respectively. Object size and consequently the scale parameter appears to have wider variations than those of the shape parameters in the five study areas.…”

Section: Resultsmentioning

confidence: 99%

“…These sensors are well-suited for soil moisture estimation at regional and global scales. Although active sensors, such as ERS, RADARSAT, ASAR/ENVISAT, PALSAR/ALOS, TerraSAR-X, and Cosmo-SkyMed, are appropriate for soil moisture estimations at a high spatial resolution (better than 30 m) to provide a diagnosis suited to agricultural watershed areas, their accuracy is not as high as the passive sensors [11][12][13][14][15]. Recently, operational monitoring of soil moisture was made possible by the launch of the Sentinel-1 satellite in regular temporal coverage (6 days for Europe when both the A and B satellites are considered) together with a spatial resolution of 10 m and at no cost to the users.…”

Section: Introductionmentioning

confidence: 99%

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Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at Plot Scale

et al. 2018

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This paper presents an approach for retrieval of soil moisture content (SMC) by coupling single polarization C-band synthetic aperture radar (SAR) and optical data at the plot scale in vegetated areas. The study was carried out at five different sites with dominant vegetation cover located in Kenya. In the initial stage of the process, different features are extracted from single polarization mode (VV polarization) SAR and optical data. Subsequently, proper selection of the relevant features is conducted on the extracted features. An advanced state-of-the-art machine learning regression approach, the support vector regression (SVR) technique, is used to retrieve soil moisture. This paper takes a new look at soil moisture retrieval in vegetated areas considering the needs of practical applications. In this context, we tried to work at the object level instead of the pixel level. Accordingly, a group of pixels (an image object) represents the reality of the land cover at the plot scale. Three approaches, a pixel-based approach, an object-based approach, and a combination of pixel-and object-based approaches, were used to estimate soil moisture. The results show that the combined approach outperforms the other approaches in terms of estimation accuracy (4.94% and 0.89 compared to 6.41% and 0.62 in terms of root mean square error (RMSE) and R 2 ), flexibility on retrieving the level of soil moisture, and better quality of visual representation of the SMC map.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at Plot Scale

et al. 2018

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“…With regard to agricultural areas, measurements have been performed on contrasting surfaces (bare soil and vegetated fields), allowing the distinction between the parameters related to soil and those related to crops. Following the soil contribution, influences from different levels of topsoil moisture, surface roughness and texture on backscattering coefficients have been widely analyzed since the 1980s [15][16][17][18][19][20] showing the importance of the sensor configurations (wavelength, incidence angle or polarization states). Studies addressing the sensitivities of vegetation parameters are mainly based on data acquired during specific phenological stages, at local scale [16,21,22].…”

Section: Introductionmentioning

confidence: 99%

Monitoring Wheat and Rapeseed by Using Synchronous Optical and Radar Satellite Data—From Temporal Signatures to Crop Parameters Estimation

Fieuzal¹,

Baup²,

Marais-Sicre³

2013

ARS

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This paper investigates the sensitivity of multi-temporal SAR data acquired at different frequencies (X-, C-and L-bands), polarizations (HH, VV, VH and HV) and incidence angles (from 24˚ to 53˚) during the growing season of two winter crops (rapeseed and wheat). This study was part of a multi-sensor crop-monitoring experiment that was performed from February to November 2010 (MCM'10). During the experiment, dense series of satellite data were acquired in microwave, optical and thermal domains (more than 150 images were provided by TerraSAR-X, Radarsat-2 Alos, Formosat-2, Spot-4/5 and Landsat-5/7) were synchronous with ground measurements over an agricultural area located in southwestern France, near Toulouse. An angular normalization of radar signals is first performed for each crop type at X-and C-bands by using a dense temporal satellite series and the complementarity provided by microwave and optical data. The results show that the angular sensitivity of radar backscatter decreases with the increase of the vegetation index (from 0.4 dB.˚− 1 over bare soils to 0.05 dB.˚− 1 for fully vegetated fields). Lower angular sensitivity is observed at X-band (compared to C-band), and for the cross-polarized signal. Analyses of the temporal signatures of the radar backscatter show a well-marked signal dynamic at X-, C-and L-bands, depending on the crops and theirs associated phenological stages. During the stems elongation of wheat while the NDVI increases of 0.2, a dynamic of 10 dB is observed at X-band and at C-band with VV polarization. Interesting behaviors are also observed during the crop senescence with an increase of several dB (depending on the sensor configuration), while the NDVI decreases of 0.5. Over rapeseed, cross-polarized backscatters offer promising dynamic of 6 dB during the seed development, while the NDVI saturates at maximum values. The use of radar signals, in complement of optical, for crop parameters monitoring is achieved in terms of leaf area index and crop height estimations. Over rapeseed, best correlations between crop parameters and radar signals are obtained at C-band, by combining co-and cross-polarized backscatters (R 2 > 0.61). Over wheat, best results are achieved by using X-band data (R 2 > 0.64).

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“…However, extensive studies have been conducted to retrieve soil moisture by using mono-or multi-polarization C-band SAR data (e.g. Alvarez-Mozos et al, 2006Baghdadi et al, 2002aBaghdadi et al, , 2006aRahman et al, 2008;Le Hégarat et al, 2002;Lievens et al, 2011;Mattia et al, 2009;Moran et al, 2004;Paloscia et al, 2008;Satalino et al, 2002;Srivastava et al, 2003;Zribi and Deschambre, 2002). The availability of RADARSAT-2 data (C-band, ∼5.3 GHz) should enable improvements and increase the ability to retrieve soil parameters, based on RADARDAT's capability of providing images in full polarization.…”

Section: Introductionmentioning

confidence: 99%

Estimation of soil parameters over bare agriculture areas from C-band polarimetric SAR data using neural networks

Baghdadi

Cresson

Hajj

et al. 2012

Hydrol. Earth Syst. Sci.

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Abstract. The purpose of this study was to develop an approach to estimate soil surface parameters from C-band polarimetric SAR data in the case of bare agricultural soils. An inversion technique based on multi-layer perceptron (MLP) neural networks was introduced. The neural networks were trained and validated on a noisy simulated dataset generated from the Integral Equation Model (IEM) on a wide range of surface roughness and soil moisture, as it is encountered in agricultural contexts for bare soils. The performances of neural networks in retrieving soil moisture and surface roughness were tested for several inversion cases using or not using a-priori knowledge on soil parameters. The inversion approach was then validated using RADARSAT-2 images in polarimetric mode. The introduction of expert knowledge on the soil moisture (dry to wet soils or very wet soils) improves the soil moisture estimates, whereas the precision on the surface roughness estimation remains unchanged. Moreover, the use of polarimetric parameters α 1 and anisotropy were used to improve the soil parameters estimates. These parameters provide to neural networks the probable ranges of soil moisture (lower or higher than 0.30 cm 3 cm −3 ) and surface roughness (root mean square surface height lower or higher than 1.0 cm). Soil moisture can be retrieved correctly from C-band SAR data by using the neural networks technique. Soil moisture errors were estimated at about 0.098 cm 3 cm −3 without a-priori information on soil parameters and 0.065 cm 3 cm −3 (RMSE) applying a-priori information on the soil moisture. The retrieval of surface roughness is possible only for low and medium values (lower than 2 cm). Results show that the precision on the soil roughness estimates was about 0.7 cm. For surface roughness lower than 2 cm, the precision on the soil roughness is better with an RMSE about 0.5 cm. The use of polarimetric parameters improves only slightly the soil parameters estimates.

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Soil moisture estimation from ERS/SAR data: toward an operational methodology

Cited by 145 publications

References 17 publications

Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at Plot Scale

Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at Plot Scale

Monitoring Wheat and Rapeseed by Using Synchronous Optical and Radar Satellite Data—From Temporal Signatures to Crop Parameters Estimation

Estimation of soil parameters over bare agriculture areas from C-band polarimetric SAR data using neural networks

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