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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]

Spaceborne L-band data have the potential to monitor flooded and irrigated areas. However, further studies are needed to assess in real cases the impact of flood-irrigated crops on soil moisture and ocean salinity (SMOS) and soil moisture active passive (SMAP) surface soil moisture (SSM) data. This letter demonstrates the ability of SMOS/SMAP SSM retrievals to quantify the fraction of flood-irrigated areas at the seasonal scale and at a 25-km resolution in the Telangana State in southern India. Over irrigated areas, both SMOS level 3 (L3) SSM and SMAP L3 enhanced SSM products present a bimodal annual cycle, with a peak of SSM during the monsoon (wet) season corresponding to rainfall and irrigation, and a peak during the dry season due to irrigation activities solely. The second peak is absent or has a very small amplitude in areas where rice represents a small fraction (typically below 5%–10%). More importantly, the amplitude of the second SSM peak is significantly correlated with the rice cover fraction within $25\times25$ km2 pixels ($R = 0.81$ for SMOS and 0.77 for SMAP), showing its potential to assess crop fraction and hence the water used for irrigation. The SMOS/SMAP L3 SSM peak during the dry period occurs several months before the harvest, constituting an indicator for rice stocks at the end of the season. However, the irrigation signature is absent from the SMAP level 4 SSM product derived from the assimilation of SMAP brightness temperatures (Tbs) in a land surface model, which indicates that the data assimilation scheme is inefficient to restitute irrigation information. [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]

In this letter, the results obtained with the last version (level 2, version 650) of SMOS retrieval algorithm are compared against independent measurements, over tropical forests. In particular, the climate research unit meteorological variables and data bases of forest height and forest biomass are considered. Comparisons with results obtained by AMSR-2 under similar conditions are also illustrated. Vegetation optical depth shows a generally good correlation with forest height and forest biomass, particularly in Africa and South America. Spatial and temporal trends of retrieved soil moisture follow trends of rainfall, particularly in regions of dry winter. [ABSTRACT FROM AUTHOR]

The Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions provide Level-1 brightness temperature (Tb) observations that are used for global soil moisture estimation. However, the nature of these Tb data differs: the SMOS Tb observations contain atmospheric and select reflected extraterrestrial (“Sky”) radiation, whereas the SMAP Tb data are corrected for these contributions, using auxiliary near-surface information. Furthermore, the SMOS Tb observations are multiangular, whereas the SMAP Tb is measured at 40° incidence angle only. This letter discusses how SMOS Tb, SMAP Tb, and radiative transfer modeling components can be aligned in order to enable a seamless exchange of SMOS and SMAP Tb data in soil moisture retrieval and assimilation systems. The aggregated contribution of the atmospheric and reflected Sky radiation is, on average, about 1 K for horizontally polarized Tb and 0.5 K for vertically polarized Tb at 40° incidence angle, but local and short-term values regularly exceed 5 K. [ABSTRACT FROM PUBLISHER]

In this letter, a monofrequent dielectric model for moist soils taking into account dependences on the temperature and texture is proposed, in the case of an electromagnetic frequency equal to 1.4 GHz. The proposed model is deduced from a more general model proposed by Mironov and Fomin (2009) that provides estimations of the complex relative permittivity (CRP) of moist soils as a function of frequency, temperature, moisture, and texture of soils. The latter employs the physical laws of Debye and Clausius–Mossotti and the law of ion conductance to calculate the CRP of water solutions in the soil. The parameters of the respective physical laws were determined by using the CRPs of moist soils measured by Curtis (1995) for a wide ensemble of soil textures (clay content from 0% to 76%), moistures (from drying at 105 ^\circ\C to nearly saturation), temperatures (10 ^\circ\C –40 ^\circ\C), and frequencies (0.3–26.5 GHz). This model has standard deviations of calculated CRPs from the measured values equal to 1.9 and 1.3 for the real and imaginary parts of CRP, respectively. In the model proposed in this letter, the respective standard deviations were decreased to the values of 0.87 and 0.26. In addition, the equations to calculate the complex dielectric permittivity as a function of moisture, temperature, and texture were represented in a simple form of the refractive mixing dielectric model, which is commonly used in the algorithms of radiometric and radar remote sensing to retrieve moisture in the soil. [ABSTRACT FROM PUBLISHER]

We evaluate a new 3-D numerical modeling approach for calculating the rough-surface scattering and emission of a soil layer. The approach relies on the use of Ansoft's numerical computation software High-Frequency Structure Simulator, which solves Maxwell's equations directly using the finite-element method. The interest of this approach is that it can be easily extended to studies of heterogeneous media. However, before being applied in this way, it must first be validated for the rough-surface case. In this letter, we perform this validation by comparing the results of rough-surface scattering and emission with the results of the method of moments (MoM) for a range of different roughness and permittivity conditions and with both Gaussian and exponential rough-surface autocorrelation functions. For the scattering case, we obtain results that are in agreement with the MoM to within approximately 1–3 dB for angles up to and including 40^\circ and 2–4 dB for angles from 50^\circ to 70 ^\circ. Agreement for emissivity is to within 3.2 and 3.6 K for low and high roughness conditions, respectively. We then illustrate the application of the new approach by calculating the emission of a two-layer system with rough surfaces, representing the soil-litter system in forests. [ABSTRACT FROM PUBLISHER]

With the recent launch of the Soil Moisture and Ocean Salinity (SMOS) mission, the passive microwave remote-sensing community is currently planning and undertaking airborne validation campaigns. Given the financial and logistical constraints on the size of validation area that can be covered by airborne simulators and the experiments underway that cover only a part of a satellite footprint, timely and scientifically sound advice on fractional footprint coverage requirements by campaigns for these low-resolution sensors is of paramount importance. Using high-resolution airborne data from an extensive airborne campaign in Southeast Australia, the fractional coverage requirement for L-band passive microwave satellite missions is assessed using a subsampling technique of flight lines through a passive microwave footprint. It is shown that minimum 50% coverage of the total footprint size will typically be required, given a spatial variability value of 20 K at 1-km resolution, to ensure that the footprint mean is estimated with an expected sampling error of less than 4 K, which is the design sensitivity of SMOS. [ABSTRACT FROM PUBLISHER]