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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]

P-band radiometry has been demonstrated to have a deeper sensing depth than L-band, making the consideration of multilayer microwave interactions necessary. In addition, the scattering and phase interference effects are different at the P-band, requiring a reconsideration of the need for coherent models. However, the impact remains to be clarified, and understanding the validity and limitations of these models at both L- and P-bands is crucial for their refinement and application. Therefore, two general categories of microwave emission models, including two stratified coherent models (Njoku and Wilhite) and four incoherent models (conventional tau-omega model and three multilayer models being zero-order, first-order, and incoherent solution), were intercompared for the first time on the same dataset. This evaluation utilized observations of L- and P-bands radiometry under different land cover conditions from a tower-based experiment in Victoria, Australia. Model estimations of brightness temperature (TB) were consistent with measurements, with the lowest root mean square error (RMSE) at P-band V-polarization under corn (2 K) and the highest RMSE at L-band H-polarization under bare soil (13 K). Coherent models performed slightly better than incoherent models under bare soil (3 K less RMSE), while the opposite was true under vegetated soil conditions (1 K less RMSE). Coherent and incoherent models showed maximum differences (3 K at P-band and 2 K at L-band), correlating strongly with soil moisture variations at 0–10 cm. Findings suggest that coherent and incoherent models performed similarly; thus, incoherent models may be preferable for estimating TB at L- and P-bands due to reduced computational complexity. [ABSTRACT FROM AUTHOR]

As an emerging technique, P-band (0.3–1 GHz) may improve soil moisture remote sensing compared to L-band (1.4 GHz) Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions, because of its greater moisture retrieval depth resulting from its longer wavelength. Consequently, a number of tower-based experiments were undertaken in VIC, Australia, to understand and quantify potential improvements. The study reported here has extended the evaluation of the tau-omega model to a scenario with a dense corn canopy whose vegetation water content (VWC) reached ~20 kg/m2, and compared the soil moisture retrieval performance at P- and L-band. Based on the locally calibrated parameters, the results from both the single-channel algorithm (SCA) and dual-channel algorithm (DCA) approaches presented a clear reduction in vegetation impact at the P-band compared to L-band. While the root-mean-square error (RMSE) for the P-band did not achieve the 0.04- $\text{m}^{3}/\text{m}^{3}$ target accuracy of SMOS and SMAP, i.e., 0.054 $\text{m}^{3}/\text{m}^{3}$ for the SCA and 0.074 $\text{m}^{3}/\text{m}^{3}$ for the DCA, this performance can be regarded as acceptable considering the extremely high VWC. In comparison, the RMSEs at L-band were larger than 0.1 $\text{m}^{3}/\text{m}^{3}$ for both the SCA and the DCA approaches. Additionally, DCA performed better in correlation coefficient and unbiased RMSE, while SCA performed better in RMSE at the P-band due to the larger bias when using DCA. Moreover, the calibrated vegetation parameters at the P-band were found to apply to broader conditions than those at the L-band, likely due to the reduced vegetation impact. [ABSTRACT FROM AUTHOR]

This article proposes a closed-form asymptotic solution for the bistatic coherent scattering of a multilayered rough surface structure based upon its effective dielectric constant (EDC) in specular direction. The EDC is modeled by establishing an equivalence of the coherent scattering between the multilayered rough surface structure and a half-space homogeneous medium. The scattering solution is then solved using the scalar Kirchhoff approximation (SKA) method. This new method is referred to as the SKA-EDC method, and it is applied to analyze the sensitivity of the EDC and coherent reflectivity to bare soils with realistic parameters at P- and L-bands. The result indicates that EDC can give different responses to the soil moisture variations with respect to the coherent reflectivity, enabling the potentials of root-zone soil moisture retrieval. At incidence angle smaller than 35°, EDC gives the same value for both polarizations, and the coherent reflectivity can show a significant response to soil at depths <15–50 cm at 0.80 GHz and <5–15 cm at 1.57 GHz, depending on soil moisture. The simulation also demonstrates that subsurface roughness has a trivial effect on the EDC and coherent reflectivity for three rough surfaces separated by continuously dielectric profiles. Subsurface can, thus, be assumed to be flat for reducing uncertainty in soil moisture inversion algorithms when the subsurface roughness is unknown. [ABSTRACT FROM AUTHOR]

The spaceborne aperture radar (SAR) technique based on a combination of P-band signals of opportunity (SoOp) reflectometry with a sparse array of receivers at low earth orbits (LEOs) and transmit signals from the United States Navy's Mobile User Objective System operating on a geosynchronous altitude has been analyzed. The design focuses on the forward-looking geometry near the specular direction, which allows a high surface reflectivity, in order to obtain adequate signal-to-noise ratio (SNR) with a moderate receiving antenna gain. The sparse array is utilized to sharpen the across-track resolution and reduce the iso-range ambiguity. The formulation for match filtering and illustrations of point target response are presented. This work shows that an array of five to seven receivers is able to achieve an across-track resolution of about 200 m in the outer portion of swath and about 1 km in the center part of swath. The along-track resolution can reach 10 m or better due to the feasibility of a long dwell time for Doppler filtering. We find that the sparse array allows the reduction of the iso-range ambiguity to a level of lower than 5% for a major portion of swath, ~70% or greater depending on the number of receivers and spacing. We have completed an SNR formulation, which can consistently account for both coherent and incoherent scattering regardless the spatial resolution. An analysis of SNR based on the Kirchhoff approximation for rough surface scattering has been performed. We find that it is possible to obtain a swath width of 100 km with an SNR of 5 dB or better for a constellation of seven satellites with a receiving antenna directivity of 15 dBi at a LEO altitude of 675 km for a wide range of surface roughness. Our study suggests the promise of the SoOpSAR concept for high-resolution remote sensing of land surfaces.

Effective water management in agriculture requires a comprehensive understanding of the distribution of water content throughout the soil profile to the root zone. This knowledge empowers farmers and water managers to make informed decisions regarding irrigation timing and quantity for optimizing crop growth. To estimate the soil moisture profile, this study utilized combined L- and P-band radiometry with four incoherent radiative transfer models, including three multi-layer models based on a zero-order (IZ), first order (IF) and incoherent solution (IS) approximation, and a uniform model (UM) model, as well as the stratified coherent Njoku model (NM). The impact of vegetation was considered through the conventional tau-omega model. Linear (Li) and second-order polynomial (Pn2) functions were used to represent the shape of the soil moisture profile. Observations from a tower-based experiment under various land cover conditions, including bare, bare-weed, grass, wheat and corn, were used. The root mean square error (RMSE) was calculated between the observed and estimated soil moisture profiles. The results revealed comparable RMSE values for all five radiative transfer models, with the Pn2 function outperforming the Li function in estimating the soil moisture of deeper layers. Regardless of the employed radiative transfer model, utilizing combined L- and P-band radiometry and employing the Pn2 function yielded RMSEs of 0.03 m3/m3, 0.08 m3/m3, and 0.1 m3/m3 over depths of 0–5 cm, 0–30 cm, and 0–60 cm, respectively. A comparison between the incoherent and stratified coherent Njoku radiative transfer models indicated that the latter slightly outperformed the former under the dry bare soil conditions, exhibiting a 0.003 m3/m3 lower RMSE at the surface while nearly equal performance at the bottom of the profile. Furthermore, the multi-layer incoherent radiative transfer models provided only a slightly better estimate than the UM model, especially for shallow layers, with the average RMSE over the entire profile being 0.002 m3/m3 lower. Consequently, the complexity of the multi-layer incoherent and coherent radiative transfer models is not justified for this small gain in performance. The depth for which the UM model provided a reasonable soil moisture estimate ranged from 1 cm (under wet corn) to 39 cm (under dry bare), and depended on the soil moisture profile gradient and soil moisture content values in the shallow layers. These important findings pave the way for estimating soil moisture profile on a global scale using combined L- and P-band radiometry from future satellite missions. • Combined L-band and P-band radiometry was used to estimate soil moisture profiles. • Second-order polynomial surpassed linear function in estimating deep soil moisture. • Multilayer coherent models slightly outperformed uniform model for shallow layers. • Differences between models were minor, particularly for second-order polynomial. • The uniform model estimates: 1 cm (wet corn) to 39 cm (dry bare). [ABSTRACT FROM AUTHOR]

Currently, near-surface soil moisture at a global scale is being provided using National Aeronautics and Space Administration’s (NASA’s) Soil Moisture Active Passive (SMAP) and European Space Agency’s (ESA’s) Soil Moisture and Ocean Salinity (SMOS) satellites, both of which utilize L-band (1.4 GHz; 21 cm wavelength) passive microwave remote sensing techniques. However, a fundamental limitation of this technology is that the water content can only be measured for approximately the top 5-cm layer of soil moisture, and only over low-to-moderate vegetation covered areas in order to meet the 0.04 $\text{m}^{3}/\text{m}^{3}$ target accuracy, limiting its applicability. Consequently, a longer wavelength radiometer is being explored as a potential solution for measuring soil moisture in a deeper surface layer of soil and under denser vegetation. It is expected that P-band (wavelength of 40 cm and frequency of 750 MHz) could potentially provide soil moisture information for the top $\sim 10$ -cm layer of soil, being one-tenth to one-quarter of the wavelength. In addition, P-band is expected to have higher soil moisture retrieval accuracy due to its reduced sensitivity to vegetation water content and surface roughness. To demonstrate the potential of P-band passive microwave soil moisture remote sensing, a short-term airborne field experiment was conducted over a center pivot irrigated farm at Cressy in Tasmania, Australia, in January 2017. First results showing a comparison of airborne P-band brightness temperature observations against airborne L-band brightness temperature observations and ground soil moisture measurements are presented. The P-band brightness temperature was found to have a similar but stronger response to soil moisture compared to L-band. [ABSTRACT FROM AUTHOR]

Active microwave-based retrieval of soil moisture in vegetated areas has uncertainties due to the sensitivity of the signal to both soil (dielectric constant and roughness) and vegetation (dielectric constant and structure) properties. A multi-frequency acquisition system would increase the number of observations that may constrain soil and/or vegetation parameter retrievals. In order to realize this constraint, an understanding of microwaves interaction with the surface and vegetation across frequencies is necessary. Different microwave frequencies have varied interactions with the soil–vegetation medium and increasing penetration into the soil and canopy with the decreasing frequency. In this study, we examine the contributions of different scattering mechanisms to coincident observations from two microwave frequencies (L and P) of airborne synthetic aperture radar instruments. We quantify contributions of surface, vegetation volume, and double-bounce scattering components. Results are analyzed and discussed to guide future multi-frequency retrieval algorithm designs. [ABSTRACT FROM AUTHOR]

Understanding the scattering mechanisms from the ground surface in the presence of different vegetation densities is necessary for the interpretation of P-band Synthetic Aperture Radar (SAR) observations and for the design of geophysical retrieval algorithms. In this study, a quantitative analysis of vegetation and soil scattering mechanisms estimated from the observations of an airborne P-band SAR instrument across nine different biomes in North America is presented. The goal is to apply a hybrid (model- and eigen-based) three component decomposition approach to separate the contributions of surface, double-bounce and vegetation volume scattering across a wide range of biome conditions. The decomposition makes no prior assumptions about vegetation structure. We characterize the dynamics of the decomposition across different North American biomes and assess their characteristic range. Impacts of vegetation cover seasonality and soil surface roughness on the contributions of each scattering mechanism are also investigated. Observations used here are part of the NASA Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) mission and data have been collected between 2013 and 2015. [ABSTRACT FROM AUTHOR]

At radar frequencies below 2 GHz, the mismatch between the 5 to 15 cm sensing depth of classical time domain reflectometry (TDR) probe soil moisture measurements and the radar penetration depth can easily lead to unreliable in situ data. Accurate quantitative measurements of soil water contents at various depths by classical methods are cumbersome and usually highly invasive. We propose an improved method for the estimation of vertical soil moisture profiles from multi-offset ground penetrating radar (GPR) data. A semi-automated data acquisition technique allows for very fast and robust measurements in the field. Advanced common mid-point (CMP) processing is applied to obtain quantitative estimates of the permittivity and depth of the reflecting soil layers. The method is validated against TDR measurements using data acquired in different environments. Depth and soil moisture contents of the reflecting layers were estimated with root mean square errors (RMSE) on the order of 5 cm and 1.9 Vol.-%, respectively. Application of the proposed technique for the validation of synthetic aperture radar (SAR) soil moisture estimates is demonstrated based on a case study using airborne L-band data and ground-based P-band data. For the L-band case we found good agreement between the near-surface GPR estimates and extended integral equation model (I2EM) based SAR retrievals, comparable to those obtained by TDR. At the P-band, the GPR based method significantly outperformed the TDR method when using soil moisture estimates at depths below 30 cm. [ABSTRACT FROM AUTHOR]

A proof-of-concept experiment was carried out to demonstrate the feasibility of retrieving snow water equivalent (SWE) using P-band signals of opportunity. The fundamental observation is the change in the phase of the reflected waveforms as related to the change in SWE. Through theoretical modeling it was found that the change in SWE was approximately linearly dependent on the change in phase. This was verified by retrieving SWE data collected and processed from a tower-based experiment at Fraser, CO, USA. A linear regression was performed on measured phase and in situ SWE. The correlation was found to be 0.94 and root mean square deviation was found to be 7.5 mm. [ABSTRACT FROM PUBLISHER]

At radar frequencies below 2 GHz, the mismatch between the 5 to 15 cm sensing depth of classical time domain reflectometry (TDR) probe soil moisture measurements and the radar penetration depth can easily lead to unreliable in situ data. Accurate quantitative measurements of soil water contents at various depths by classical methods are cumbersome and usually highly invasive. We propose an improved method for the estimation of vertical soil moisture profiles from multi-offset ground penetrating radar (GPR) data. A semi-automated data acquisition technique allows for very fast and robust measurements in the field. Advanced common mid-point (CMP) processing is applied to obtain quantitative estimates of the permittivity and depth of the reflecting soil layers. The method is validated against TDR measurements using data acquired in different environments. Depth and soil moisture contents of the reflecting layers were estimated with root mean square errors (RMSE) on the order of 5 cm and 1.9 Vol.-%, respectively. Application of the proposed technique for the validation of synthetic aperture radar (SAR) soil moisture estimates is demonstrated based on a case study using airborne L-band data and ground-based P-band data. For the L-band case we found good agreement between the near-surface GPR estimates and extended integral equation model (I2EM) based SAR retrievals, comparable to those obtained by TDR. At the P-band, the GPR based method significantly outperformed the TDR method when using soil moisture estimates at depths below 30 cm.

Freeze-thaw (FT) and moisture dynamics within the soil active layer are critical elements of boreal, arctic and alpine ecosystems, and environmental change assessments. We evaluated the potential for detecting dielectric changes within different soil layers using combined L- and P-band radar remote sensing as a prerequisite for detecting FT and moisture profile changes within the soil active layer. A two-layer scattering model was developed and validated for simulating radar responses from vertically inhomogeneous soil. The model simulations indicated that inhomogeneity in the soil dielectric profile contributes to both L- and P-band backscatter, but with greater P-band sensitivity at depth. The difference in L- and P-band responses to soil dielectric profile inhomogeneity appears suitable for detecting associated changes in soil active layer conditions. Additional evaluation using collocated airborne radar (AIRSAR) observations and in situ soil moisture measurements over alpine tundra indicates that combined L- and P-band SAR observations are sensitive to soil dielectric profile heterogeneity associated with variations in soil moisture and FT conditions. [ABSTRACT FROM AUTHOR]

In this paper, we report on the performance of a semiempirical algorithm for the retrieval of soil moisture (SM) under dense tropical forests using ultrahigh frequency (UHF) polarimetric synthetic aperture radar (SAR) data. The algorithm is a simplification of a 3-D coherent model of forest canopy based on the distorted Born approximation (DBA). The simplified model reduces the number of parameters and preserves the three dominant scattering mechanisms of volume, volume-surface, and surface for three polarized backscattering coefficients, i.e., σHH, σHV, and σVV, at UHF frequencies. The inversion process uses the Levenberg-Marquardt nonlinear least squares method to estimate the three model parameters: vegetation aboveground biomass, integrated SM up to a certain depth, and surface roughness. The performance of the inversion process is examined by first using simulation data where the initial values of the inversion process vary randomly and then using airborne UHF SAR data acquired in Costa Rica over La Selva Biological Station. The results with simulated data show that the inversion process is not significantly sensitive to initial values considering they are in the range of ±50% of the true value. A root-mean-square error (RMSE) of less than 4% can be achieved in retrieving the SM. The use of an alternate inversion approach without initial conditions using a genetic algorithm is less efficient (> 120 times longer time) and produces larger error with simulated data (RMSE = 11%) than the Levenberg-Marquardt estimation method. The inversion model simultaneously produces a biomass and SM distribution at 100-m spatial resolution. The RMSE of biomass estimation is 38 Mg/ha (15% relative error) when compared with 28 field plots. Over the plots where SM ground measurements are available, but not at the exact same day as the radar flight occurred, the total volumetric RMSE is 13.6%. However, only two ground measurements were very close to the flight day (three days apart), and for those, the SM estimate has about 3% absolute volumetric error. At the P-band, the SM sensing depth is inversely correlated with the SM allowing to map the spatial variations of SM close to the average root zone or hydrological active horizon of soils in tropical ecosystems. [ABSTRACT FROM PUBLISHER]

It has been over ten years since the successful launch of the first-ever dedicated satellite for global soil moisture monitoring; Soil Moisture and Ocean Salinity (SMOS). Looking towards the future, P-band (0.3–1 GHz) is a promising technique to replace or enhance the L-band (1.4 GHz) SMOS and SMAP (Soil Moisture Active Passive) missions because of an expected reduction in roughness and vegetation impact, leading to an improved soil moisture accuracy over rougher soil surfaces and more densely vegetated areas. Accordingly, this investigation evaluated the tau-omega model at P-band (0.75 GHz) using a tower-based experiment in Victoria, Australia, where brightness temperature observations were collected concurrently at P- and L-band over bare and wheat-covered flat and periodic soil surfaces. The potential to retrieve soil moisture without discriminating periodic and flat surfaces was investigated by applying the roughness and vegetation parameters calibrated for flat soil to retrieve the moisture of periodic soil. Results showed that P-band had a comparable RMSE across different roughness configurations (variations less than 0.016 m3/m3) for both bare and wheat-covered soil, while the L-band RMSE was only comparable for wheat-covered soil, indicating that periodic surfaces did not need to be discriminated in such scenarios. Conversely, a difference of 0.022 m3/m3 was observed for L-band with bare soil. A reduced vegetation impact was also demonstrated at P-band, with an RMSE of 0.029 m3/m3 achieved when completely ignoring the wheat existence with under 4-kg/m2 vegetation water content, whereas at L-band the RMSE increased to 0.063 m3/m3. This study therefore paves the way for a successful P-band radiometer mission for obtaining more accurate global soil moisture information. • The tau-omega model was evaluated at P-band for the first time. • P-band had an acceptable RMSE of 0.029 m3/m3 even if ignoring the wheat presence. • Periodic and flat soil can be modeled using the same parameters at P-band. • Periodic soil needed to be discriminated from flat soil at L-band when it was bare. • The SCA-V performed better than the DCA at both P- and L-band. [ABSTRACT FROM AUTHOR]

L-band passive microwave remote sensing is currently considered a robust technique for global monitoring of soil moisture. However, soil roughness complicates the relationship between brightness temperature and soil moisture, with current soil moisture retrieval algorithms typically assuming a constant roughness parameter globally, leading to a potential degradation in retrieval accuracy. This current investigation established a tower-based experiment site in Victoria, Australia. P-band (~40-cm wavelength/0.75 GHz) was compared with L-band (~21-cm wavelength/1.41 GHz) over random and periodic soil surfaces to determine if there is an improvement in brightness temperature simulation and soil moisture retrieval accuracy for bare soil conditions, due to reduced roughness impact when using a longer wavelength. The results showed that P-band was less impacted by random and periodic roughness than L-band, evidenced by more comparable statistics across different roughness conditions. The roughness effect from smooth surfaces (e.g., 0.8-cm root-mean-square height and 11.1-cm correlation length) could be potentially ignored at both P- and L-band with satisfactory simulation and retrieval performance. However, for rougher soil (e.g., 1.6-cm root-mean-square height and 6.8-cm correlation length), the roughness impact needed to be accounted for at both P- and L-band, with P-band observations showing less impact than L-band. Moreover, a sinusoidal soil surface with 10-cm amplitude and 80-cm period substantially impacted the brightness temperature simulation and soil moisture retrieval at both P- and L-band, which could not be fully accounted for using the SMOS and SMAP default roughness parameters. However, when retrieving roughness parameters along with soil moisture, the ubRMSE at P-band over periodic soil was improved to a similar level (0.01‐0.02 m3/m3) as that of smooth flat soil (0.01 m3/m3), while L-band showed higher ubRMSE over the periodic soil (0.03‐0.04 m3/m3) than over smooth flat soil (0.01 m3/m3). Accordingly, periodic roughness effects were reduced by using observations at P-band. • P-band observations were more representative of 0‐5 cm soil moisture than L-band. • Impact of random and periodic roughness was reduced at P- compared to L-band. • The SMOS and SMAP default parameters induced retrieval error for periodic surfaces. [ABSTRACT FROM AUTHOR]

Abstract: A scatterometer operating in P-band at 441MHz was used to estimate soil water content. This letter describes encouraging results of experiments that were undertaken in Israel at Yotvata and Ashalim experimental farms. The soil water saturation percent of the entire wetting and drying cycles created through irrigation of sandy agricultural soils were retrieved using the scatterometer, which yielded good agreement with gravimetric measurements of soil water content demonstrating that P-band provides relatively unambiguous estimates of the soil water saturation percent. [Copyright &y& Elsevier]