A Fusion Approach to Retrieve Soil Moisture With SAR and Optical Data

Prakash, Rishi; Singh, Dharmendra; Pathak, Nagendra Prasad

doi:10.1109/jstars.2011.2169236

Cited by 95 publications

(59 citation statements)

References 47 publications

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“…This vegetation parameter could be estimated from optical data. Therefore, the best way for soil moisture retrieval over areas covered by vegetation is to combine SAR and optical data [4][5][6][16][17][18][19][20]. Currently, the high temporal repetitiveness of Sentinel-1 and Sentinel-2 data makes it possible to combine SAR and optical data for soil moisture monitoring at time scale close to user requirements (weekly to daily depending on the applications).…”

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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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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“…Synthetic aperture radar (SAR) and optic data fusion is one of the most widely used approaches [28][29][30][31]. The integration of data acquired by optical sensors and SAR data may provide useful information for reducing ambiguity due to the presence of vegetation.…”

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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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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“…These results indicate that the surface scattering component is more highly correlated with SMC than the dihedral scattering component. At the same time, the maximum correlation coefficient (R 2 = 0.7035, R 2 D3_VZ3 = 0.8167), suggesting that the FD3 surface scattering component is strongly affected by vegetation. This finding also shows that the Y3 and VZ3 polarization decomposition methods are better than the FD3.…”

Section: Regression Modelsmentioning

confidence: 88%

“…In particular, surface SM has been estimated using a series of theoretical, empirical or semi-empirical models according to microwave remote sensing technology [2]. The characteristics of microwave remote sensing provide advantages to detect SM underlying vegetation, such as penetration, multi-polarization and all-time and all-weather observation capability [3,4].…”

Section: Introductionmentioning

confidence: 99%

A Soil Moisture Retrieval Method Based on Typical Polarization Decomposition Techniques for a Maize Field from Full-Polarization Radarsat-2 Data

et al. 2017

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Soil moisture (SM) estimates are important to research, but are not accurately predictable in areas with tall vegetation. Full-polarization Radarsat-2 C-band data were used to retrieve SM contents using typical polarization decomposition (Freeman-Durden, Yamaguchi and VanZly) at different growth stages of maize. Applicability analyses were conducted, including proportion, regression and surface scattering model analyses. Furthermore, the Bragg, the extended Bragg scattering model (X-Bragg) and improved surface scattering models (ISSM) were used to retrieve SM content. The results indicated that the VanZly decomposition method was the best. The proportion of surface scattering in the proportion analysis was highest (>52%), followed by that in the Yamaguchi method (>41%). The R 2 (>0.6144) between surface scattering and SM was significantly higher (R 2 < 0.4484) between dihedral scattering and SM in the regression analysis. The ISSM was better at different maize growth stages than the Bragg and X-Bragg models with a higher R 2 (>0.6599) and lower absolute error (AE) (<5.82) and root mean square error (RMSE) (<3.73). The best algorithm was obtained at the sowing stage (R 2 = 0.8843, AE = 3.13, RMSE = 1.76). In addition, the X-Bragg model provided better approximation of actual surface scattering without the measured data (better algorithm: R 2 = 0.8314, AE = 4.39, RMSE = 2.81).

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A Fusion Approach to Retrieve Soil Moisture With SAR and Optical Data

Cited by 95 publications

References 47 publications

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

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

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

A Soil Moisture Retrieval Method Based on Typical Polarization Decomposition Techniques for a Maize Field from Full-Polarization Radarsat-2 Data

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