Soil moisture retrieval over agricultural fields from multi-polarized and multi-angular RADARSAT-2 SAR data

Gherboudj, Imen; Magagi, Ramata; Berg, Aaron; Toth, Brenda

doi:10.1016/j.rse.2010.07.011

Cited by 181 publications

(120 citation statements)

References 32 publications

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“…The water cloud model [Attema andUlaby, 1978,Ulaby andWilson, 1985] attempts to model the radar backscatter contributions of the plant canopy by accounting for above ground plant water content. Backscatter contributions from soil surface and subsurface can then be separated from plant canopy [Gherboudj et al, 2011]. Active microwave satellites generally can acquire finer resolution data but have longer revisit times than passive microwave satellites.…”

Section: Remote Sensing Of Soil Moisturementioning

confidence: 99%

“…Validation measurements of soil moisture also need to be acquired near to the time of satellite data acquisition [Merzouki et al, 2011, Gherboudj et al, 2011. The ability to only monitor at discrete time instances and the need for data to calibrate and validate methods, limit the potential of remote sensing for agro-climatic monitoring.…”

Section: Remote Sensing Of Soil Moisturementioning

confidence: 99%

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Integrated sensing of soil moisture at the field-scale: Measuring, modeling and sharing for improved agricultural decision support

Phillips

Newlands

Liang

et al. 2014

Computers and Electronics in Agriculture

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Determining the best way to efficiently use limited water resources, for food and energydedicated crops, has become crucial due to the rise in extreme events (floods/droughts) and higher variability in rainfall attributed to global climate change. Changing climate conditions will require new crops to be adapted to a changing agricultural environment. Reliable information on seasonal trends in crop growth and evapotranspiration with associated uncertainty/confidence ranges is crucial to guide the development of new crops and management strategies to cope with future climate. Given that crop growth is strongly coupled to soil moisture, developing reliable growth curves requires a detailed understanding of soil moisture at the field-scale. Typically, it is impractical to collect soil samples to adequately assess soil moisture that represents both spatial distribution at the field-scale and temporal dynamics on the scale of a growing season (e.g. 110 days for cereals). A novel way to address soil moisture monitoring challenges is through an integrated, agro-ecosystems-level approach using an integrated sensing system that can link data from multiple platforms (wireless sensors, satellites, airborne imagery, near real-time climate stations). Assimilated data can then be fed into predictive models to generate reference crop growth curves and predict regionally-specific yield potentials. However, integrated sensing requires interagency cooperation, common data processing standards and long-term, timely access to data. Large databases need to be reusable by various organizations and accessible in the future, with comprehensive metadata. During the 2012 growing season a feasibility study was conducted which involved measuring field-scale soil moisture with sensor network technology. The experiment utilized radially-distributed sensors for tracking in-season soil moisture data was collect using both automated in-situ sensors and hand-held sensors. Box-plots of soil moisture data was collected with hand-held revealed an early season wet soil moisture regime and late season dry soil moisture regime. The data sampled on July 5, 2012 was selected for ii geostatistical analysis. Bayesian kriging models and Bayesian kriging models with polynomial trends using different combinations prior distributions for the range and nugget were tested. Models with first order polynomial trend, a reciprocal 2 prior distribution for the range and a reciprocal prior distribution for the nugget fit tended to predict the sample distributions the best. Soil moisture was predicted at a set of random point using ordinary kriging, universal kriging, the two Bayesian kriging models and the Bayesian kriging models with first and second order polynomial trends. Overall, the universal kriging and Bayesian trend models predicted similar data distributions. OpenGIS-compliant services and standards were utilized to provide long-term access to sensor data and construct corresponding metadata. Sensor Model Language, an inter-operable metadata format, ...

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Section: Remote Sensing Of Soil Moisturementioning

confidence: 99%

Section: Remote Sensing Of Soil Moisturementioning

confidence: 99%

Integrated sensing of soil moisture at the field-scale: Measuring, modeling and sharing for improved agricultural decision support

Phillips

Newlands

Liang

et al. 2014

Computers and Electronics in Agriculture

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“…High correlation was observed in the 25 th October 2013, when field was almost covered with vegetation. This is due to the fact that high incidence angle is sensitive to vegetation (Gherboudj et al, 2011), thus incorporating σ HH high (θ) is more suitable for retrieval of soil moisture when ground is covered with the vegetation.…”

Section: Topp's Modelmentioning

confidence: 99%

Soil Moisture Model with Multi Angle and Multi Polarisation Risat-1 Data

Rao

Sahadevan

Wadodkar

et al. 2014

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Multi dimensional data (multi frequency, incident angle and polarisation) measurements of σ 0 provided better estimates of soil moisture over those derived from single. This particular paper explains a new methodology for soil moisture estimation with the use of multi angle and multi polarisation RISAT-1 data. The roughness component was derived by correlating root mean square height with the differences of cross polarisation and like polarisation backscatter values (σ HV -σ HH ) and differences of low and high incidence backscatter values (σ HH high (θ) -σ HH Low). The derived roughness was inputted to the modified dubois model (MDM). The results show both the σ HV -σ HH & σ HH high (θ) -σ HH Low are sensitive to roughness. The derived soil moisture using the MDM model is shows reasonable correlation with ground soil moisture.

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“…In recent years, radar remote sensing with different wavelengths is used to monitor spatial and temporal variations in soil moisture. Many studies have been presented to retrieve soil moisture content from X-band (Aubert et al, 2011;Baghdadi et al, 2011;Zribi et al, 2012;Kseneman et al, 2012;Baghdadi et al, 2012;Kweon et al, 2012;Satalino et al, 2012) and C-band (Gherboudj et al, 2011;Moran et al, 2011;Pasolli et al, 2011;Merzouki et al, 2011;Livens & Verhoest, 2012;Jacome et al, 2013;Wang et al, 2013) SAR images; however, few studies were carried out using L-band (Paloscia et al, 2012;Balenzano et al, 2013) SAR data.…”

Section: Introductionmentioning

confidence: 99%

Preliminary Results of Estimating Soil Moisture Over Bare Soil Using Full-Polarimetric Alos-2 Data

Şekertekin

Marangoz

Abdikan

et al. 2016

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

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ABSTRACT:Synthetic Aperture Radar (SAR) imaging system is one of the most effective way for Earth observation. The aim of this study is to present the preliminary results about estimating soil moisture using L-band Synthetic Aperture Radar (SAR) data. Full-polarimetric (HH, HV, VV, VH) ALOS-2 data, acquired on 22.04.2016 with the incidence angle of 30.4 o , were used in the study. Simultaneously with the SAR acquisition, in-situ soil moisture samples over bare agricultural lands were collected and evaluated using gravimetric method. Backscattering coefficients for all polarizations were obtained and linear regression analysis was carried out with in situ moisture measurements. The best correlation coefficient was observed with VV polarization. Cross-polarized backscattering coefficients were not so sensitive to soil moisture content. In the study, it was observed that soil moisture maps can be retrieved with the accuracy about 14% (RMSE).

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Soil moisture retrieval over agricultural fields from multi-polarized and multi-angular RADARSAT-2 SAR data

Cited by 181 publications

References 32 publications

Integrated sensing of soil moisture at the field-scale: Measuring, modeling and sharing for improved agricultural decision support

Integrated sensing of soil moisture at the field-scale: Measuring, modeling and sharing for improved agricultural decision support

Soil Moisture Model with Multi Angle and Multi Polarisation Risat-1 Data

Preliminary Results of Estimating Soil Moisture Over Bare Soil Using Full-Polarimetric Alos-2 Data

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