Ruzbeh Akbar

A Combined Active–Passive Soil Moisture Estimation Algorithm With Adaptive Regularization in Support of SMAP

We present a method to combine same-resolution measurements of active radar and passive radiometer microwave remote sensing to build a framework for soil moisture estimation in support of the Soil Moisture Active and Passive (SMAP) mission. A unified active-passive soil moisture estimation algorithm is developed within a global optimization scheme, using a joint cost function with adaptive regularization, where, unlike traditional methods, both radar and radiometer measurements are utilized at the same time to retrieve soil moisture. Monte Carlo numerical simulations and optimization to retrieve soil moisture are performed for Corn, Soybean, and Grass landcover types for active-only, passive-only, and active-passive scenarios. These numerical experiments show that the proposed combined active-passive (CAP) soil moisture estimation approach outperforms either the single-sensor technique, particularly for higher vegetation water content (VWC) values (VWC > 3 kg/m2). For example, for the case of Corn with VWC of 5 kg/m2, retrieval error is reduced to 0.035 cm3/cm3 for the active-passive method from 0.08 cm3/cm3 for active scenarios. Furthermore, tests of this new algorithm on the Passive and Active Land S-band Sensor (PALS) Soil Moisture Experiment 2002 (SMEX02), as well as the Combined RadarRadiometer (ComRad) collocated active and passive data, demonstrate the applicability of this method to actual data, even with potentially inaccurate forward models and noisy data. Results indicate that the best soil moisture estimates over a large range of soil moisture (0.04-0.4 cm3/cm3) and vegetation (0-5 kg/m2) conditions are achievable when the adaptive regularization parameter γ is chosen to give slightly more weight to the radiometer forward model without discarding the complementary radar measurement points.

Automated L-Band Radar System for Sensing Soil Moisture at High Temporal Resolution

The ground-based University of Florida L-band Automated Radar System (UF-LARS) was developed to obtain observations of normalized radar backscatter (\mmbσ0) at high temporal resolution for soil moisture applications. The system was mounted on a 25 m manlift with capabilities of antenna positioning for multi-angle data acquisition and ranging. The RF subsystem of UF-LARS was based upon the established designs for ground-based scatterometers employing a vector network analyzer with simultaneous acquisition of V- and H-polarized returns. System integration and automated data acquisition were enabled using a software control system. Fifteen-minute observations of \mmb σ0 collected over a growing season of sweet-corn and bare soil conditions in North Central Florida, were used to study the sensitivity of \mmbσ0 to growing vegetation and near-surface (0-5 cm) soil moisture (\mmbSM0 - 5). On average, \mmb σ\mmbVV0 were observed to be 23% higher than \mmbσ\mmbHH0 during the mid- and late-stages of crop growth due to the vertical structure of stems. The correlation between 3-day observations of \mmbSM0 - 5 and \mmbσ\mmbVV0 reduced by 55% compared to those obtained for ≤ 30-min observations. These findings suggested that data set at high temporal frequencies can be used to develop more realistic and robust forward backscattering models.

Soil Moisture Profiles and Temperature Data from SoilSCAPE Sites, USA

This data set contains in-situ soil moisture profile and soil temperature data collected at 20-minute intervals at SoilSCAPE (Soil moisture Sensing Controller and oPtimal Estimator) project sites in four states (California, Arizona, Oklahoma, and Michigan) in the United States. SoilSCAPE used wireless sensor technology to acquire high temporal resolution soil moisture and temperature data at up to 12 sites over varying durations since August 2011. At its maximum, the network consisted of over 200 wireless sensor installations (nodes), with a range of 6 to 27 nodes per site. The soil moisture sensors (EC-5 and 5-TM from Decagon Devices) were installed at three to four depths, nominally at 5, 20, and 50 cm below the surface. Soil conditions (e.g., hard soil or rocks) may have limited sensor placement. Temperature sensors were installed at 5 cm depth at six of the sites. Data collection started in August 2011 and continues at eight sites through late 2016. The data enables estimation of local-scale soil moisture at high temporal resolution and validation of remote sensing estimates of soil moisture at regional (airborne, e.g. NASAs Airborne Microwave Observation of Subcanopy and Subsurface Mission - AirMOSS) and national (spaceborne, e.g. NASAs Soil Moisture Active Passive - SMAP) scales.

Radar-radiometer soil moisture estimation with joint physics and adaptive regularization in support of SMAP

A combined radar-radiometer soil moisture estimation framework is presented in this work, which utilizes both radar backscatter and radiometer brightness temperature using physics-based models that fundamentally couple the scattering and emission processes. A regularization or tuning parameter is introduced within the optimization algorithm to enable flexibility and adaptability. It is observed that by finding the right balance between radar and radiometer contributions within a “joint” cost function, best estimates over a larger range of surface soil moisture and roughness are achievable. Monte Carlo numerical simulations are performed to highlight how this novel Active-Passive method is capable of fully utilizing emission and scattering sensitivities to surface soil moisture.

A multi-objective optimization approach to combined radar-radiometer soil moisture estimation

With emphasis on physics-based techniques, a multi-objective optimization approach to combined radar-radiometer soil moisture estimation is presented in this work. Soil moisture estimation is demonstrated via application of this method to SMAP high resolution radar and coarse resolution radiometer data. Comparisons are then made with the SMAP baseline active-passive soil moisture output data product. A strong agreement between the two techniques, especially in capturing spatial distributions of soil moisture is observed.

Active and Passive Microwave Remote Sensing Synergy for Soil Moisture Estimation

Abstract The following chapter presents the concept of combined active-passive soil moisture estimation. By simultaneously taking advantage of radar and radiometer microwave remote sensing observations, it is possible to obtain both high accuracy and high-resolution global surface soil moisture predictions. This is mainly achieved by merging the complementary strengths, sensitivities to soil moisture, and spatial resolutions of active and passive observations within an integrated and unified retrieval framework.

A radar-radiometer surface soil moisture retrieval algorithm for SMAP

A soil moisture retrieval algorithm is presented whereby both radar and radiometer measurements are used simultaneously in an optimization scheme to retrieve for surface soil moisture in the presence of vegetation. Further, using fine-resolution radar measurements, a disaggregated brightness temperature product at the radar resolution is developed to reconcile the spatial resolution discrepancy between the two measurements. Numerical simulations are performed to synthesize SMAP data, and then via the method of Simulated Annealing, optimization is accomplished to retrieve high resolution soil moisture.

Estimation of Landscape Soil Water Losses from Satellite Observations of Soil Moisture

AbstractThis study presents an observation-driven technique to delineate the dominant boundaries and temporal shifts between different hydrologic regimes over the contiguous United States (CONUS). ...

Comparison of downscaling techniques for high resolution soil moisture mapping

Soil moisture impacts exchanges of water, energy and carbon fluxes between the land surface and the atmosphere. Passive microwave remote sensing at L-band can capture spatial and temporal patterns of soil moisture in the landscape. Both ESA and NASA have launched L-band radiometers, in the form of the SMOS and SMAP satellites respectively, to monitor soil moisture globally, every 3-day at about 40 km resolution. However, their coarse scale restricts the range of applications. While SMAP included an L-band radar to downscale the radiometer soil moisture to 9 km, the radar failed after 3 months and this initial approach is not applicable to developing a consistent long term soil moisture product across the two missions anymore. Existing optical-, radiometer-, and oversampling-based downscaling methods could be an alternative to the radar-based approach for delivering such data. Nevertheless, retrieval of a consistent high resolution soil moisture product remains a challenge, and there has been no comprehensive intercomparison of the alternate approaches. This research undertakes an assessment of the different downscaling approaches using the SMAPEx-4 field campaign data.

Decomposition of SMAP polarization ratio into surface soil moisture and vegetation dynamics

In this study we examined the linear decomposition and relationship between the SMAP observed Polarization Ratio into surface soil moisture and vegetation. Temporal linear regression, per each SMAP pixel, is performed to estimate the decomposition coefficients. Variances (explained variance) in PR is predominantly dominated by dynamics of surface soil moisture and degrades with increasing vegetation amount. Although PR, by itself, is high in arid and semi-arid regions, due to lack of moisture and vegetation dynamics, the explained variance is very small.