Vegetation effects on soil moisture estimation from ERS-2 SAR images

Said, Salim; Kothyari, Umesh C.; Arora, Manoj K.

doi:10.1080/02626667.2012.665608

Cited by 24 publications

(13 citation statements)

References 26 publications

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“…Champion (1991) and Champion and Guyot (1991) found that the LAI (m 2 /m 2 ) better represents the wheat canopy in the WCM than the VWC per unit volume (kg/m 3 ). Said et al (2012) compared the use of LAI (m 2 /m 2 ), VWC (kg/m 2 ), and HVE and found that the use of LAI as the vegetation descriptor allows the accurate simulation of the vegetation volume contribution (sugarcane, cherry, rice, and grassland).…”

Section: Radar Signal Modelingmentioning

confidence: 99%

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

Hajj¹,

Baghdadi²,

Zribi

et al. 2016

Remote Sensing of Environment

157

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Section: Radar Signal Modelingmentioning

confidence: 99%

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

Hajj¹,

Baghdadi²,

Zribi

et al. 2016

Remote Sensing of Environment

157

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“…The scattering coefficient of bare soil with HH and VV polarization is provided with the developed empirical relationship, and these values were subsequently used in Dubois model to calculate volumetric soil moisture content regardless of the roughness value. Said et al (2012) mapped soil moisture from ERS-2 SAR images by minimizing the effect of vegetation on the backscatter coefficient. These authors analyzed the performances of the prominent crop descriptors (i.e., crop height, leaf area index, and plant water content).…”

Section: Cropland Parameter Estimationmentioning

confidence: 99%

“…A semi-empirical water cloud model was used to eliminate the vegetation effects on the backscatter coefficient. The results showed that the water cloud model based on the LAI estimated the crop-covered backscatter coefficient more accurately than the other crop descriptors (Said et al 2012). Hajj et al (2016b) simulated a coupling scenario between SAR and optical images through the water cloud model.…”

Section: Cropland Parameter Estimationmentioning

confidence: 99%

Research advances of SAR remote sensing for agriculture applications: A review

Liu

Chen

Shao

et al. 2019

Journal of Integrative Agriculture

155

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Synthetic aperture radar (SAR) is an effective and important technique in monitoring crop and other agricultural targets because its quality does not depend on weather conditions. SAR is sensitive to the geometrical structures and dielectric properties of the targets and has a certain penetration ability to some agricultural targets. The capabilities of SAR for agriculture applications can be organized into three main categories: crop identification and crop planting area statistics, crop and cropland parameter extraction, and crop yield estimation. According to the above concepts, this paper systematically analyses the recent progresses, existing problems and future directions in SAR agricultural remote sensing. In recent years, with the remarkable progresses in SAR remote sensing systems, the available SAR data sources have been greatly enriched. The accuracies of the crop classification and parameter extraction by SAR data have been improved progressively. But the development of modern agriculture has put forwarded higher requirements for SAR remote sensing. For instance, the spatial resolution and revisiting cycle of the SAR sensors, the accuracy of crop classification, the whole phenological period monitoring of crop growth status, the soil moisture inversion under the condition of high vegetation coverage, the integrations of SAR remote sensing retrieval information with hydrological models and/or crop growth models, and so on, still need to be improved. In the future, the joint use of optical and SAR remote sensing data, the application of multi-band multi-dimensional SAR, the precise and high efficient modeling of electromagnetic scattering and parameter extraction of crop and farmland composite scene, the development of light and small SAR systems like those onboard unmanned aerial vehicles and their applications will be active research areas in agriculture remote sensing. This paper concludes that SAR remote sensing has great potential and will play a more significant role in the various fields of agricultural remote sensing.

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“…Several studies have reported different WCM parametrization techniques to model the total backscattering coefficients using the C-band data (Prevot et al, 1993;Dabrowska-Zielinska et al, 2007;Said et al, 2012). Dabrowska-Zielinska et al (2007) investigated the applicability of three different canopy descriptors viz., leaf area index (LAI), leaf water area index (LWAI) and vegetation water mass (VWM) and found that at 23 o incidence angle, the attenuation of the soil signal by the canopy was strongest in case of VWM.…”

Section: Introductionmentioning

confidence: 99%

“…Dabrowska-Zielinska et al (2007) investigated the applicability of three different canopy descriptors viz., leaf area index (LAI), leaf water area index (LWAI) and vegetation water mass (VWM) and found that at 23 o incidence angle, the attenuation of the soil signal by the canopy was strongest in case of VWM. In another experiment, Said et al (2012) attempted to identify the prominent descriptor among, LAI, canopy height, and plant water content (PWC) using ERS-2 SAR data, so that the vegetation effects on soil moisture estimation could be eliminated. Although several refined versions of WCM have been published over the years, the vertical heterogeneity of a vegetation cover has never been described solely using a vegetation descriptor.…”

Section: Introductionmentioning

confidence: 99%

Improved Parameterization of Water Cloud Model for Hybrid-Polarized Backscatter Simulation Using Interaction Factor

Chauhan

Srivastava

Patel

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The prime aim of this study was to assess the potential of semi-empirical water cloud model (WCM) in simulating hybrid-polarized SAR backscatter signatures (RH and RV) retrieved from RISAT-1 data and integrate the results into a graphical user interface (GUI) to facilitate easy comprehension and interpretation. A predominant agricultural wheat growing area was selected in Mathura and Bharatpur districts located in the Indian states of Uttar Pradesh and Rajasthan respectively to carry out the study. The three-date datasets were acquired covering the crucial growth stages of the wheat crop. In synchrony, the fieldwork was organized to measure crop/soil parameters. The RH and RV backscattering coefficient images were extracted from the SAR data for all the three dates. The effect of four combinations of vegetation descriptors (V1 and V2) viz., LAI-LAI, LAI-Plant water content (PWC), Leaf water area index (LWAI)-LWAI, and LAI-Interaction factor (IF) on the total RH and RV backscatter was analyzed. The results revealed that WCM calibrated with LAI and IF as the two vegetation descriptors simulated the total RH and RV backscatter values with highest R 2 of 0.90 and 0.85 while the RMSE was lowest among the other tested models (1.18 and 1.25 dB, respectively). The theoretical considerations and interpretations have been discussed and examined in the paper. The novelty of this work emanates from the fact that it is a first step towards the modeling of hybrid-polarized backscatter data using an accurately parameterized semi-empirical approach.

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Vegetation effects on soil moisture estimation from ERS-2 SAR images

Cited by 24 publications

References 26 publications

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

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

Research advances of SAR remote sensing for agriculture applications: A review

Improved Parameterization of Water Cloud Model for Hybrid-Polarized Backscatter Simulation Using Interaction Factor

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