Mapping surface roughness and soil moisture using multi-angle radar imagery without ancillary data

Rahman, M.; Moran, M. Susan; Thoma, David P.; Bryant, Ray B.; Collins, Chandra D. Holifield; Jackson, Thomas J.; Orr, Barron J.; Tischler, M. A.

doi:10.1016/j.rse.2006.10.026

Cited by 134 publications

(93 citation statements)

References 10 publications

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“…Generally, in experimental studies, there is no relationship between these two independent parameters, however, recent studies have offered empirical, semi-empirical and theoretical approaches for deriving CL directly from a measurement of h RM S and to parameterize radar scattering models like the Integral Equation Model (IEM) for surface roughness requiring only the measurement of h RM S [19][20][21]. Rahman et al, (2008) demonstrate regional-scale mapping of surface roughness and soil moisture (using a multi-angle approach and the Integral Equation Model (IEM) retrieval algorithm for sparsely vegetated landscapes), eliminating the need for field measurements [22]. A recent review of state-of-the-art with respect to measuring, analysis and modeling spatio-temporal dynamics of soil moisture at the field scale, Vereeeken et al, (2014) finds that ground-based and high-resolution satellite RS data of soil moisture is well suited for near real-time management of agricultural fields and operational, agricultural decision-making, but that more modeling research needs to be placed to understand more complex model-based data collection and adaptive sampling strategies.…”

Section: Broad Range Of Model Assumptions and Predictive Accuracymentioning

confidence: 99%

Statistical Modeling of Soil Moisture, Integrating Satellite Remote-Sensing (SAR) and Ground-Based Data

Hosseini¹,

Newlands

Dean

et al. 2015

Remote Sensing

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We present a flexible, integrated statistical-based modeling approach to improve the robustness of soil moisture data predictions. We apply this approach in exploring the consequence of different choices of leading predictors and covariates. Competing models, predictors, covariates and changing spatial correlation are often ignored in empirical analyses and validation studies. An optimal choice of model and predictors may, however, provide a more consistent and reliable explanation of the high environmental variability and stochasticity of soil moisture observational data. We integrate active polarimetric satellite remote-sensing data (RADARSAT-2, C-band) with ground-based in-situ data across an agricultural monitoring site in Canada. We apply a grouped step-wise algorithm to iteratively select best-performing predictors of soil moisture. Integrated modeling approaches may better account for observed uncertainty and be tuned to different applications that vary in scale and scope, while also providing greater insights into spatial scaling (upscaling and downscaling) of soil moisture variability from the field-to regional scale. We discuss several methodological extensions and data requirements to enable further statistical modeling and validation for improved agricultural decision-support.Remote Sens. 2015, 7 2753

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Section: Broad Range Of Model Assumptions and Predictive Accuracymentioning

confidence: 99%

Statistical Modeling of Soil Moisture, Integrating Satellite Remote-Sensing (SAR) and Ground-Based Data

Hosseini¹,

Newlands

Dean

et al. 2015

Remote Sensing

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“…Most field studies and models assume bare soil conditions to measure soil moisture with radar remote sensing and modeling backscatter contributions of the soil surface and subsurface [Dubois et al, 1995, Fung et al, 1992, Merzouki et al, 2011, Oh, 2004, Oh et al, 1992, Oh et al, 2002, Rahman et al, 2008, Verhoest et al, 2007, Zribi and Dechambre, 2003]. …”

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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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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“…While soil moisture can be observed at single point using gravimetric or Time Domain Reflectometry (TDR) probing, it is difficult to cover wide areas with this method. Thus, this necessitates the use of satellite remote sensing, particularly in the microwave region of the electromagnetic spectrum [4] [5]. Passive Microwave remote sensing produces coarse spatial resolution imagery, whose soil moisture retrievals can further be improved by combining passive and active products [6] [7].…”

Section: Introductionmentioning

confidence: 99%

“…Sensitivity of radar backscattering to soil moisture and roughness has been demonstrated in different papers [5] [11] [12] considering the variation in dielectric constant of wet (~80) and dry (~6) soils. Challenges in discriminating the contribution from soil moisture and roughness [13] still remain.…”

Section: Introductionmentioning

confidence: 99%

Influence of Vegetation Cover on the Oh Soil Moisture Retrieval Model: A Case Study of the Malinda Wetland, Tanzania

Kirimi¹,

Kuria²,

Thonfeld³

et al. 2016

ARS

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Mapping surface roughness and soil moisture using multi-angle radar imagery without ancillary data

Cited by 134 publications

References 10 publications

Statistical Modeling of Soil Moisture, Integrating Satellite Remote-Sensing (SAR) and Ground-Based Data

Statistical Modeling of Soil Moisture, Integrating Satellite Remote-Sensing (SAR) and Ground-Based Data

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

Influence of Vegetation Cover on the Oh Soil Moisture Retrieval Model: A Case Study of the Malinda Wetland, Tanzania

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