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This article investigated the possibility of remotely sensing the soil moisture profile in thawed soil from multi-angular dual-polarized brightness temperature (TB) observations at P-band frequencies of 750 MHz and 409 MHz using a modified Burke model. Moreover, it was found that an excellent agreement (coefficient of determination R2 = 0.999 and root-mean-square error (RMSE) no more than RMSE = 0.6 K) could be achieved between the Njoku coherent brightness temperature model and the modified Burke model by introducing a reflectivity from the air-soil interface that takes into account the phases of the multiple re-reflected waves in the underlying layers. Based on the modified Burke model, the depths from which apparent moisture and temperature could be retrieved in a dielectrically-inhomogeneous, non-isothermal soil were investigated, being approximately ten times less than the depth for which apparent soil temperature could be retrieved. In general, the thickness of the emitting layer depends on the TB look angle and polarization, along with the moisture and temperature profiles of the soil. It was also shown that due to the effect of the Brewster angle, the H-polarization of TB was twice as sensitive (4 K/1%) to changes in volumetric soil moisture than V-polarization (1.9 K/1%). Based on multi-angular (10°-50°) observations of TB at H- and V-polarizations, a method of moisture profile retrieval in the top 5–15 cm soil (depending on surface moisture) was proposed using an exponential fitting function, the parameters of which are found in the course of solving the inverse problem. A decrease in the sensing frequency from 750 MHz to 409 MHz makes it possible to increase the accuracy of soil moisture profiles retrieval by a factor of two, being from RMSE = 1.6% (R2 = 0.946) to RMSE = 0.85% (R2 = 0.982) in the top 15 cm layer of soil. The conducted investigation shows the promise of using P-band observations of TB for soil moisture profile retrieval. [ABSTRACT FROM AUTHOR]

In this paper, the possibilities of remote sensing of moisture profiles in the arable layer were theoretically and experimentally studied based on the nadir measurements of reflection coefficients at three frequencies of 1.26 GHz, 796 MHz and 641 MHz. The reflection coefficients were measured by the impulse method during natural cycles of evaporation and moistening of an arable layer at the agricultural field being under steam, located at 56°05ʹN, 92°40ʹ E in the area of the Minino village, Krasnoyarsk region, the Russian Federation. The soil moisture profiles were retrieved in the course of solving the inverse problem, in which the reflection coefficients at different frequencies acted as an informative sign. The root-mean-square error and the determination coefficient (R2) between retrieved and measured moisture values in the topsoil thickness of 0.15 m were 3.3% and 0.79, respectively. In the course of theoretical calculations, it was shown that in practice, it is impossible to predict the sensing depth of the arable layer without preliminary information on the form of moisture profile. Moreover, the sensing depth depends not only on the form of soil moisture profile but also on frequency. In this regard, it is impossible to correlate the effective soil moisture, retrieved from single-frequency measurements of the reflection coefficient in the approximation of homogeneous topsoil, with the specific thickness of topsoil. The study shows the promise of developing multi-frequency radar systems for remote sensing of soil moisture profiles in the arable layer, the potential of which can be realized on lightweight unmanned area vehicle (UAV) platforms. [ABSTRACT FROM AUTHOR]

Nutrients loads in aquatic systems are dynamic and highly influenced by changing the land, soil, and atmospheric conditions. This study enhances water quality modeling by providing novel nitrate transport models using remotely-sensed geospatial data, allowing for dynamic predictions of nitrate loads in watersheds. One factor at a time, sensitivity analysis was employed in the classical nitrate transport model to incorporate the impacts of 1) nitrates in the soil moisture profiles 2) wet deposition of nitrates and 3) the synergistic effects of multiple atmospheric and soil effects on nitrate-nitrogen in catchments. The study found that the effects of soil moisture profiles were dominant than the wet deposition in the evaluation of nitrate-nitrogen in catchments. The addition of nitrates from soil moisture profile, wet deposition and both together effectively increased the annual average nitrates in the Fish River catchment from 0.180 kg/ha to 0.187 kg/ha, 0.396 kg/ha and 0.381 kg/ha respectively. Their additions consistently increased the nitrate loads from spring to winter seasons but exhibited different seasonal trends for soils such as silty sand and fine sand. The models developed in this study can be utilized in water quality assessment tools for effective dynamic predictions of nutrients loads into water bodies. [ABSTRACT FROM AUTHOR]

P-band radar remote sensing applied during the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) mission has shown great potential for estimation of root zone soil moisture. When retrieving the soil moisture profile (SMP) from P-band radar observations, a mathematical function describing the vertical moisture distribution is required. Because only a limited number of observations are available, the number of free parameters of the mathematical model must not exceed the number of observed data. For this reason, an empirical quadratic function (second order polynomial) is currently applied in the AirMOSS inversion algorithm to retrieve the SMP. The three free parameters of the polynomial are retrieved for each AirMOSS pixel using three backscatter observations (i.e., one frequency at three polarizations of Horizontal-Horizontal, Vertical-Vertical and Horizontal-Vertical). In this paper, a more realistic, physically-based SMP model containing three free parameters is derived, based on a solution to Richards' equation for unsaturated flow in soils. Evaluation of the new SMP model based on both numerical simulations and measured data revealed that it exhibits greater flexibility for fitting measured and simulated SMPs than the currently applied polynomial. It is also demonstrated that the new SMP model can be reduced to a second order polynomial at the expense of fitting accuracy. [ABSTRACT FROM AUTHOR]

Ground-Penetrating Radar (GPR) has recently become a powerful geophysical technique to characterize soil moisture at the field scale. We developed a data assimilation scheme to simultaneously estimate the vertical soil moisture profile and hydraulic parameters from time-lapse GPR measurements. The assimilation scheme includes a soil hydrodynamic model to simulate the soil moisture dynamics, a full-wave electromagnetic wave propagation model, and petrophysical relationship to link the state variable with the GPR data and a maximum likelihood ensemble assimilation algorithm. The hydraulic parameters are estimated jointly with the soil moisture using a state augmentation technique. The approach allows for the direct assimilation of GPR data, thus maximizing the use of the information. The proposed approach was validated by numerical experiments assuming wrong initial conditions and hydraulic parameters. The synthetic soil moisture profiles were generated by the Hydrus-1D model, which then were used by the electromagnetic model and petrophysical relationship to create 'observed' GPR data. The results show that the data assimilation significantly improves the accuracy of the hydrodynamic model prediction. Compared with the surface soil moisture assimilation, the GPR data assimilation better estimates the soil moisture profile and hydraulic parameters. The results also show that the estimated soil moisture profile in the loamy sand and silt soils converge to the 'true' state more rapidly than in the clay one. Of the three unknown parameters of the Mualem-van Genuchten model, the estimation of n is more accurate than that of α and Ks. The approach shows a great promise to use GPR measurements for the soil moisture profile and hydraulic parameter estimation at the field scale. [ABSTRACT FROM AUTHOR]

• Assimilation of satellite derived SM profile into crop modeling framework using an EnKF. • A fully coupled Microwave-Thermal Infrared driven SM profiles were developed for assimilation. • Crop model absolute error reduced from 36 % to nearly 29 % with data assimilation. • Overall, the assimilation of RDSMP into the crop model reduced the open-loop model errors by nearly 2.5 times (23 % vs 58 %). Global food security is one of the most pressing issues of the current century, particularly for developing nations. Agricultural simulation models can be a key component in testing new technologies, seeds and cultivars etc., however, inaccurate input information in addition to model related errors adds to model uncertainties. Satellite observations of soil moisture (SM), vegetation index etc. can be assimilated into crop models to reduce input and model related uncertainties. The goal of this study was to determine if assimilation of satellite-driven SM profiles can improve crop model yield estimations in a commonly used crop model while reducing the reliance on field management information at regional scales. Toward reaching this goal, this study utilized satellite derived microwave and thermal-infrared coupled SM profiles assimilated into a crop model via the Ensemble Kalman Filter over parts of the Southeastern United States from 2006 to 2010. The gridded Decision Support System for AgroTechnology Transfer (GriDSSAT) model was used to test and verify the SM profile assimilation methodology in two specific modes: (a) first using the best state-of-the art climate inputs available without data assimilation ("open-loop"); (b) then enhancing the open-loop model by assimilating into the model Remote sensing Driven SM Profile (RDSMP) data where available. The National Agricultural Statistical Services (NASS) reported yield data at county levels were used for comparison and validation purposes. For non-irrigated regions, the GriDSSAT model simulation (rain-fed) showed an overall absolute error of nearly 36 % in comparison with the reported NASS yields, whereas the error in crop yields after data assimilation was only 29 %. Over irrigated counties, the GriDSSAT model simulation (irrigated) showed an error of nearly 24 % while using the data assimilation model simulation without irrigation information also showed overall error of ∼23 %. Compared to the open-loop model simulation and improvement in simulated crop yields of nearly 2.5 times was found over irrigated regions (23 % vs 58 %). [ABSTRACT FROM AUTHOR]