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This paper investigates the bistatic scattering from randomly corrugated surfaces with irregular grooves. For a given incident wave, the surface fields and the scattered field were computed by the method of moments in which the rooftop basis function was used to account for fast phase changes due to steep surface slopes. The total scattered field is decomposed into coherent and incoherent components to analyze their respective contributions. We found that, for randomly corrugated surfaces with irregular ridges or grooves, the coherent scattering is profound at several scattering angles with strong main lobes, whose beamwidths are strongly correlated with the ridge density. The numerical simulation has shown that the lobe angular shift away from the specular direction is quasi-linearly dependent on the ridge density, suggesting that the coherent scattering pattern substantially contains the surface geometric information. We expect this paper to offer deeper understanding of coherent imaging of rough surface and to help in designing a novel imaging system. [ABSTRACT FROM AUTHOR]

Surface soil moisture (SSM) plays significant roles in various scientific fields, including agriculture, hydrology, meteorology, and ecology. However, the spatial resolutions of microwave SSM products are too coarse for regional applications. Most current optical/thermal infrared SSM retrieval models cannot directly estimate the quantitative volumetric soil water content without establishing empirical relationships between ground-based SSM measurements and satellite-derived proxies of SSM. Therefore, in this paper, SSM is estimated directly from 5-km-resolution Chinese Geostationary Meteorological Satellite FY-2E data based on an elliptical-new SSM retrieval model developed from the synergistic use of diurnal cycles of land surface temperature (LST) and net surface shortwave radiation (NSSR). The elliptical-original model was constructed for bare soil and did not consider the impacts of different fractional vegetation cover (FVC) conditions. To optimize the elliptical-original model for regional-scale SSM estimates, it is improved in this paper by considering the influence of FVC, which is based on a dimidiate pixel model and a Moderate Resolution Imaging Spectroradiometer normalized difference vegetation index product. A preliminary validation of the model is conducted based on ground measurements from the counties of Maqu, Luqu, and Ruoergai in the source area of the Yellow River. A correlation coefficient (R) of 0.620, a root-mean-square error (RMSE) of 0.146 m3/m3, and a bias of 0.038 m3/m3 were obtained when comparing the in situ measurements with the FY-2E-derived SSM using the elliptical-original model. In contrast, the FY-2E-derived SSM using the elliptical-new model exhibited greater consistency with the ground measurements, as evidenced by an R of 0.845, an RMSE of 0.064 m3/m3, and a bias of 0.017 m3/m3. To provide accurate SSM estimates, high-accuracy FVC, LST, and NSSR data are required. To complement the point-scale validation conducted here, cross-comparisons with other existing SSM products will be conducted in the future studies. [ABSTRACT FROM AUTHOR]

Polarimetric coherence, which has the potential to reveal physical properties of scatterers, is an important source for polarimetric synthetic aperture radar (PolSAR) data investigation. Target structure and orientation relative to the PolSAR illumination direction are key factors affecting the polarimetric coherence degree. The relative orientation between a sensor and a target can be adjusted using the rotating processing along the radar's line of sight. The main idea of this paper is to extend the traditional polarimetric coherence at a given rotation state (θ = 0) to the rotation domain (θ ∈ [-π, π)) along the radar's line of sight for hidden information exploration. A visualization and characterization tool named as a polarimetric coherence pattern for two arbitrary polarization channels is proposed and developed. This interpretation tool is able to view the variation of polarimetric coherence in the rotation domain containing rich orientation diversity information which is seldom considered. A set of characterization features are derived to completely describe a polarimetric coherence pattern thereafter. Experimental studies with unmanned aerial vehicle SAR (UAVSAR) PolSAR data over crop areas have validated that polarimetric coherence patterns vary in terms of polarization combinations and crop types. The proposed characterization features show good potential to differentiate polarimetric responses from different land covers. Furthermore, a classification scheme combining the selected proposed features and the commonly used roll-invariant features is developed for quantitative and application investigation. Comparison studies with both UAVSAR and Airborne SAR (AIRSAR) data clearly demonstrate the superiority of the proposed classification to the conventional classification with only roll-invariant features. The overall classification accuracies for the seven and eleven land covers of UAVSAR and AIRSAR data are, respectively, increased from 90.21% and 93.87% to 95.12% and 94.63% by the proposed classification scheme. This paper also demonstrates the importance of and potential for utilizing the complementary advantages of roll-invariant features and the proposed roll-variant features. [ABSTRACT FROM AUTHOR]

A complete facet model for the backscattering from rough sea surface with breaking waves is proposed in this paper. In consideration of the spatial distribution of breaking waves on sea surface, the model is able to give a good interpretation to the “super events” under high sea states, such as sea spikes and high polarization ratios. Based on the proposed model, normalized radar cross section plots of sea surface under backscattering configuration are calculated and compared with measured data. The comparisons show that the proposed model is tractable to estimate the scattering from electrically large ocean surface under high sea states with accuracy and efficiency. In addition, the non-Gaussian statistics and spatial correlation properties of sea clutter are analyzed under different range resolutions and incident angles. The results show that high kurtosis value, which means a sea spike phenomenon, mostly happens in lower grazing angle and higher range resolution cases. The comparisons of simulated SAR images of sea surface with and without breaking waves also reveal that sea spikes and high polarization ratios are caused by breaking waves. All the simulation results prove that our model not only is able to explain the physical mechanism of the scattering but also can be applied to the analyses of statistical properties of sea clutter under high sea states. [ABSTRACT FROM PUBLISHER]

Microwave backscattering models play key roles in surface scattering modeling and soil moisture inversion in active microwave remote sensing. However, numerous evaluations indicate that significant discrepancies between the model simulations and radar observations remain, and these discrepancies are regarded to be attributed to inaccuracies in the models. What do such discrepancies originate from is unclear and has not been comprehensively analyzed. To this end, this paper presents an uncertainty analysis to explore the intrinsic reason for the discrepancies between the backscattering model simulations and radar observations. The probability distribution function and the corresponding statistical characteristics are introduced to describe the uncertainty in the model outputs. We find that the scale dependence of the key model inputs leads to significant uncertainties in the model inputs, and the uncertainties are transferred into the model outputs. Thus, the discrepancies between the model simulations and radar observations are intrinsically caused by the spatial scaling and related uncertainties of key model inputs. In short, the scale mismatch between the model inputs and remote sensing pixels is an intrinsic factor that causes the discrepancies between the model simulations and radar observations. This finding suggests that the scaling effect of model inputs should be carefully considered when using the backscattering models at the pixel scale, and equivalent inputs matched at the corresponding scales should be developed for remote sensing applications. Thus, this analysis insights into the scale dependence of inputs for backscattering models and suggests to provide scale-matched inputs where the models are applied at different scales. [ABSTRACT FROM AUTHOR]

The demand for microwave spectrum for commercial and industrial use has been increasing rapidly over the last decade, putting stress on the limited spectral resources for passive microwave remote sensing. Radio frequency interference from man-made sources is expected to become worse over the coming years. At 1.4 GHz, the SMAP mission has implemented and demonstrated advanced interference detection algorithms for its microwave radiometer. This scheme will not be feasible at higher microwave frequencies (above 6 GHz) due to much larger radiometer bandwidths used and the limited downlink data volume available to implement RFI filtering algorithms in the ground processing. In this paper, we present the design, development, and test of an advanced on-board interference detection and RFI filtering digital back-end that is capable of operation for a 1 GHz-radiometer bandwidth. We describe the combined RFI detection algorithms implemented in the digital backend’s firmware and the on-board RFI filtering of interference-corrupted data that will be necessary to limit downlink rate requirements for future high-frequency microwave missions. [ABSTRACT FROM AUTHOR]

In this paper, we develop a radar snow water equivalent (SWE) retrieval algorithm based on a parameterized forward model of bicontinuous dense media radiative transfer (Bic-DMRT). The algorithm is based on retrieving the absorption loss of the snowpack which is directly proportional to the SWE. In the algorithm, Bic-DMRT is first applied to generate a lookup table (LUT) of snowpack backscattering at X- and Ku-band. Regression training is applied to the LUT to transform the dual-frequency backscatter into functions of two parameters: the scattering albedo at X-band and SWE. The background scattering is subtracted from the SnowSAR data to give the volume scattering of snow. Classification of SnowSAR data is applied to provide a priori information. Based on the obtained volume scattering and the priori information, a cost function is established to find SWE. Performance of the retrieval algorithm was tested using three sets of airborne SnowSAR data acquired over mixed areas in Finland and open tundra landscape in Canada. It is shown that the retrieval algorithm has a root-mean-square error below 30 mm of SWE and a correlation coefficient above 0.64. [ABSTRACT FROM AUTHOR]

Calibration of microwave radiometers is a crucial task for reliable and accurate antenna temperature measurements in remote sensing applications. This paper describes a processing procedure for data calibration of noise-adding (NA) total power (TP) radiometers, without thermal stabilization, able to improve the radiometric resolution performance and keep good accuracy. This method, easily implementable, is based on the strong dependence of the radiometric gain on the internal physical temperature of the system. It provides a calibration of the output voltage measured in the TP mode exploiting the noise source power injection only every 30 min. The quality of the proposed procedure was assessed by means of three experiments carried out in different years and environmental conditions, using a low-cost NA TP radiometer operating at 12.65 GHz. The measurements show an uncertainty better than 0.7 K and, above all, a clear improvement in radiometric resolution (below 0.1 K). The radiometric resolution benefit is particularly appreciable in applications where the aim is the detection of small radiation power increments. Two experimental tests show how this method for data calibration effectively resolves the warm target counting inside the antenna footprint, while the same data measured with the standard NA procedure do not allow the same detection capability. [ABSTRACT FROM AUTHOR]

This paper uses a 3-D finite-difference time-domain method to accurately calculate the single-scattering properties of randomly oriented leaves and evaluate the influence of vegetation water content (VWC) on these properties at frequencies of 19.35 and 37.0 GHz. The studied leaves are assumed to be thin elliptical disks with two different sizes and have various VWC values. Although leaf moisture causes considerable absorption in the scattering process, the effective efficiencies of extinction and scattering of leaves essentially linearly increase with VWC, which is critical for forest remote sensing. Calculated asymmetry factors and phase functions also indicate that there is a significant amount of scattered energy at large scattering angles at microwave wavelengths. This paper can improve the modeling of the radiative transfer by vegetation canopies at the higher frequencies of the microwave spectrum, which is important for passive microwave remote sensing. [ABSTRACT FROM AUTHOR]

NASA's Soil Moisture Active Passive (SMAP) mission objective is global mapping of surface volumetric soil moisture at 10-km resolution every two to three days and with accuracy of 0.04 cm3 cm−3 (one sigma). In order to achieve this resolution and accuracy, the SMAP utilizes L-band radar and L-band radiometer measurements. The instruments share a rotating 6-m mesh reflector antenna that scans across a 1000-km swath in order to meet the required data refresh rate. The Level-2 Active–Passive soil moisture product (L2_SM_AP) at 9 km is retrieved from the disaggregated/downscaled brightness temperature obtained by merging of active and passive L-band observations. The baseline L2_SM_AP algorithm disaggregates the coarse-resolution (∼36 km) brightness temperatures of the SMAP L-band radiometer using the high-resolution (∼3 km) backscatter data from the SMAP L-band radar with unfocused synthetic aperture processing. The inversion of brightness temperature to estimate surface soil moisture is more mature when compared with inversions of radar backscatter. This is the primary driver of the brightness temperature disaggregation approach to the combined active–passive surface soil moisture product. Furthermore, this approach allows some consistency with the coarse-resolution radiometer-only surface soil moisture product since the disaggregated brightness temperatures sums to the radiometer measurement. The disaggregated brightness temperature contains instrument errors (∼0.7 dB for co-pol backscatter and ∼1.0 dB for cross-pol backscatter, and ∼1.3 K in brightness temperature) inherent in the radar and radiometer. Furthermore, the algorithm has two critical parameters that add uncertainty. Finally, correction of the land brightness temperature (used in the inversion) for water body contributions is a source of uncertainty. In this paper, we introduce analytical expressions for the SMAP downscaled brightness temperature due to all these sources of uncertainty. The expressions allow estimation of uncertainty (in kelvin) for each data granule of the SMAP L2_SM_AP product. Since the uncertainties depend on the given ground conditions, e.g., existing water body fraction and local algorithm parameters that depend on vegetation cover and landscape heterogeneity, it is necessary to evaluate the uncertainty for each data granule. In this paper, we show that the uncertainty expressions closely match Monte Carlo simulations with an overall difference of only ∼0.1 K. Whereas Monte Carlo estimates of uncertainty can only be afforded for a nominal case (such as those typically reported in Algorithm Theoretical Basis Documents as uncertainty tables), the analytical expressions allow uncertainty estimates for every data granule. The expressions are now used to provide uncertainty standard deviation of downscaled brightness temperature at 9 km in the SMAP L2_SM_AP product. These standard deviations are useful for the following: 1) guidance on the expected level of error in the estimate brightness temperature due to the downscaling process and 2) observation error in direct radiance data assimilation. [ABSTRACT FROM PUBLISHER]

WindSat has been providing accurate ocean vector winds (OVWs) for over a decade. WindSat makes polarimetric brightness temperature measurements of Earth looking forwards and backwards. However, because the overlap of these two swaths is relatively narrow, the benefit of two-look polarimetric (2LP) retrieval accuracy has not been utilized. This paper derives OVW from WindSat 2LP measurements using a radiative transfer model and maximum-likelihood estimation. The purpose of this paper is a comparison of WindSat 2LP wind direction accuracy with WindSat one-look, QuikSCAT, and Advanced Scatterometer (ASCAT) wind directions. Retrievals are compared with anemometer measurements on collocated moored buoys. Statistics are examined for both the first-ranked wind direction ambiguity and the selected ambiguity after median filtering initialized with a numerical weather prediction wind field. For winds above 8 m/s, WindSat 2LP retrievals have the most accurate first-ranked direction compared with all other sensors. For winds of 6–9 m/s, the standard deviation relative to buoys is 17°, and for 9–20 m/s, it is less than 10°. One-look standard deviations are nearly twice as large. At low winds, QuikSCAT provides the most accurate wind directions, for first-ranked and selected ambiguity. Thus, scatterometer and radiometer OVW measurements provide complementary capabilities. The accuracy of 2LP OVW is particularly relevant now that new internal calibration technology allows for a 360° conical scan of earth observations. Moreover, new low-cost designs would make it possible to affordably deploy a constellation of OVW sensors capable of providing accurate winds under a wide range of conditions, described herein. [ABSTRACT FROM PUBLISHER]

The newest soil moisture-dedicated satellite, the Soil Moisture Active Passive (SMAP) mission, provides global maps of soil moisture using concurrent L-band radar and radiometer acquisitions. To support the ongoing validation activities of SMAP soil moisture products, in this paper, we examined the retrieval accuracy of four SMAP soil moisture products by using well-calibrated and dense in situ measurements from the Little Washita Watershed network, one of the SMAP core validation sites with intensive ground sampling. The four SMAP products include the active (3 km), passive (36 km), active-passive (9 km), and the enhanced passive product which is a newly released soil moisture data set with a grid resolution of 9 km. Efforts on identifying the possible error sources of these products were also made for the purpose of improving the SMAP soil moisture algorithms. The results show that the passive and active-passive products can well capture the temporal dynamic of ground soil moisture with overall unbiased root-mean-square error (ubRMSE) values of 0.032 and 0.041 m3· m-3, respectively, which generally meet their mission requirement of 0.04 m3· m-3. In contrast, some irregular fluctuations exist in the active product, leading to an overall wet bias, which makes its accuracy a little poorer than its expected retrieval accuracy of 0.06 m3· m-3. The new enhanced passive product shows the lowest ubRMSE value of 0.026 m3 · m-3 though it underestimates in situ measurements with a bias of 0.059 m3 · m-3, revealing its great potential to substitute the active-passive product to provide global soil moisture measurements at a medium resolution of 9 km. The underestimation of SMAP surface temperature data may be one of the reasons that contribute to the dry bias of SMAP passive, active-passive, and enhanced passive products. The microwave polarization difference index and HV-polarized backscatter show good response to in situ soil moisture and may be considered in SMAP algorithms to further improve the accuracy of soil moisture retrievals. We expect that our findings can be fed back to improve the SMAP soil moisture algorithms and thus promote the application of SMAP soil moisture products in terrestrial water, energy, and carbon cycles. [ABSTRACT FROM AUTHOR]

Satellite and ground-based microwave radiometers are routinely used for the retrieval of liquid water path (LWP) under all atmospheric conditions. The retrieval of water vapor and LWP from ground-based radiometers during rain has proved to be a difficult challenge for two principal reasons: the inadequacy of the nonscattering approximation in precipitating clouds and the deposition of rain drops on the instrument’s radome. In this paper, we combine model computations and real ground-based, zenith-viewing passive microwave radiometer brightness temperature measurements to investigate how total, cloud, and rain LWP retrievals are affected by assumptions on the cloud drop size distribution (DSD) and under which conditions a nonscattering approximation can be considered reasonably accurate. Results show that until the drop effective diameter is larger than ~200 \mu \textm , a nonscattering approximation yields results that are still accurate at frequencies less than 90 GHz. For larger drop sizes, it is shown that higher microwave frequencies contain useful information that can be used to separate cloud and rain LWP provided that the vertical distribution of hydrometeors, as well as the DSD, is reasonably known. The choice of the DSD parameters becomes important to ensure retrievals that are consistent with the measurements. A physical retrieval is tested on a synthetic data set and is then used to retrieve total, cloud, and rain LWP from radiometric measurements during two drizzling cases at the atmospheric radiation measurement Eastern North Atlantic site. [ABSTRACT FROM PUBLISHER]

Reliable and efficient analysis of electromagnetic scattering by cylindrical components of vegetation is important for microwave remote sensing of vegetated terrain. It allows for the characterization of anistropicity of the effective permittivity for vegetation or tree canopy, where averaging operation over distribution of cylinder orientation is needed in general. In this paper, we propose a T-matrix formulation based on our virtual partition method (VPM) for the whole cylindric component of either homogeneous or inhomogeneous nature. Numerical simulations demonstrate that the proposed T-matrix preserves all the desirable features of the VPM method, including high fidelity prediction of the scattering amplitude function and fulfillment of energy conservation as well as the reciprocity theorem. More importantly, in the evaluation of averaging over orientation distribution, the proposed method is usually faster than the VPM by two orders of magnitude. The predicted effective permittivity for an exemplary orientation distribution shows appreciable difference from that of the infinite cylinder approximation. With its qualitatively characterized region of validity, the proposed method is expected to be helpful in multiband coherent scattering models of vegetated terrain. [ABSTRACT FROM PUBLISHER]

The NASA Soil Moisture Active Passive (SMAP) satellite mission was launched on January 31, 2015 to provide global mapping of high-resolution soil moisture and freeze–thaw state every 2–3 days using an L-band (active) radar and an L-band (passive) radiometer. The Level 2 radiometer-only soil moisture product (L2_SM_P) provides soil moisture estimates posted on a 36-km Earth-fixed grid using brightness temperature observations from descending passes. This paper provides the first comparison of the validated-release L2_SM_P product with soil moisture products provided by the Soil Moisture and Ocean Salinity (SMOS), Aquarius, Advanced Scatterometer (ASCAT), and Advanced Microwave Scanning Radiometer 2 (AMSR2) missions. This comparison was conducted as part of the SMAP calibration and validation efforts. SMAP and SMOS appear most similar among the five soil moisture products considered in this paper, overall exhibiting the smallest unbiased root-mean-square difference and highest correlation. Overall, SMOS tends to be slightly wetter than SMAP, excluding forests where some differences are observed. SMAP and Aquarius can only be compared for a little more than two months; they compare well, especially over low to moderately vegetated areas. SMAP and ASCAT show similar overall trends and spatial patterns with ASCAT providing wetter soil moistures than SMAP over moderate to dense vegetation. SMAP and AMSR2 largely disagree in their soil moisture trends and spatial patterns; AMSR2 exhibits an overall dry bias, while desert areas are observed to be wetter than SMAP. [ABSTRACT FROM PUBLISHER]

Theoretical modeling plays a significant role as forward and inverse problem in active and passive microwave remote sensing. Understanding the validity and limitations of the models is essential for model refinements and, perhaps more importantly, model applications. Motivated by these, this paper presents a comprehensive analysis of the scattering, both backscattering and bistatic scattering, and emission of rough soil surface predicted by the advanced integral equation model (AIEM), a well-established theoretical model. Numerically simulated data, covering a wide range of surface parameters, and in situ measurement data set of well-characterized bare soil surfaces were used to evaluate the performance of AIEM in predicting the scattering coefficient and microwave emissivity over a wide range of geometric parameters and ground surface conditions. The results show that the AIEM predictions are generally in good consistency with both numerical simulations and experiment measurements in terms of angular, frequency, and polarization dependences, except for some deviations in a few cases (e.g., at large incident angles and dry soil conditions). Extensive comparison confirms the effectiveness and practicability of AIEM for both scattering and emission of rough soil surface. Possible explanations for the discrepancy between the model prediction and data are given, together with suggestions for model usage and refinements. [ABSTRACT FROM AUTHOR]

Passive microwave remote sensing has experienced significant success for soil moisture (SM) inversion. However, quantifying the uncertainties caused by soil parameter sensitivities has not attracted sufficient attention. Although local sensitivity analysis (SA) has been used to describe parameter sensitivity in the past, it fails to quantify parameter sensitivities, especially interactions, for nonlinear microwave emission models. This paper presents a comprehensive evaluation that combines physically based emission models and various global SA algorithms to evaluate parameter sensitivity. All the algorithms exhibit highly consistent sensitivity measures, which means a reliable SA result is obtained. The results indicate that the sums of the main sensitivity indices of SM and surface roughness parameters—root-mean-square height (RMSH) and correlation length—are greater than 0.92 and 0.95 for emissivity and brightness temperature (TB), respectively. Furthermore, we find that: 1) the parameter probability distributions have little effect on the sensitivity measures; 2) the SM sensitivity decreases and the RMSH sensitivity increases as the frequency increases and the incidence angle decreases; and 3) the SM is more sensitive on V-polarized than on H-polarized emissivity and TB, while the RMSH is much more sensitive on the polarization index. The presented global SA quantitatively explains the optimal frequency, incidence angle, and polarization for SM inversion and extends the parameter SA for microwave emission models to a more general framework, as well as provides an implication for bare soil emission modeling and SM inversion. [ABSTRACT FROM PUBLISHER]

To retrieve soil moisture from L-band microwave radiometry, it is necessary to account for the effects of temperature within both vegetation and soil media. To compute the effective soil temperature TG, several simple formulations accounting for soil temperatures at the surface and at depth and surface soil moisture have been developed. However, the effects of the soil physical properties in terms of texture, density, or structure, which all may be important variables in the modeling of TG, have never been investigated. In this paper, several simple formulations of TG at L-band, accounting for or ignoring the effects of soil texture and density, were developed and compared based on a very large simulated data set. The best configurations and parameterizations of these simple formulations were computed and could be directly used for operational applications in future soil moisture retrieval studies. For instance, we showed that the use of the surface temperature in the estimation of TG can be significantly improved by using additional information on the soil temperature at depth (the average error in the estimation of TG decreased from ∼4 to ∼1.8 K). On the contrary, almost no improvement was obtained if air temperature was used instead of surface temperature. Also, it is shown that the use of additional information on the soil properties, mainly the soil clay content and density, led to improved results by about 0.2 K in the estimation of TG. The improvement was found to be larger for sandy and dry soils: simplified formulations accounting for soil properties are able to represent the fact that TG is closer to the soil temperature at depth for these soil conditions. [ABSTRACT FROM AUTHOR]

In this paper, the L-band Microwave Emission of the Biosphere (L-MEB) model used in the Soil Moisture and Ocean Salinity (SMOS) Level 2 Soil Moisture algorithm is calibrated using L-band (1.4 GHz) microwave measurements over a coniferous (Pine) and a deciduous (mixed/Beech) forest. This resulted in working values of the main canopy parameters optical depth (τ), single scattering albedo (ω), and structural parameters tt(H) and tt(V), besides the soil roughness parameters HR and NR. Using these calibrated values in the forward model resulted in a root mean-square error in brightness temperatures from 2.8 to 3.8 K, depending on data set and polarization. Furthermore, the relationship between canopy optical depth and leaf area index is investigated for the deciduous site. Finally, a sensitivity study is conducted for the focus parameters, temperature, soil moisture, and precipitation. The results found in this paper will be integrated in the operational SMOS Level 2 Soil Moisture algorithm and used in future inversions of the L-MEB model, for soil moisture retrievals over heterogeneous, partly forested areas. [ABSTRACT FROM AUTHOR]

This paper examines the use of simulated and measured canopy reflectance for chlorophyll estimation over crop canopies. Field spectral measurements were collected over corn and wheat canopies in different intensive field campaigns organized during the growing seasons of 2004 and 2005. They were used to test and evaluate several combined indices for chlorophyll determination using hyperspectral imagery (Compact Airborne Spectrographic Imager). Several index combinations were investigated using both PROSPECT-SAILH canopy simulated spectra and field-measured reflectances. The relationships between leaf chlorophyll content and combined optical indices have shown similar trends for both PROSPECT-SAILH simulated data and ground-measured data sets, which indicates that both spectral measurements and radiative transfer models hold comparable potential for the quantitative retrieval of crop foliar pigments. The data set used has shown that crop type had a clear influence on the establishment of predictive equations as well as on their validation. In addition to generating different predictive equations, corn and wheat data yielded contrasting agreement between estimated and measured chlorophyll contents even for the same predictive algorithm. Among the set of indices tested in this paper, index combinations like Modified Chlorophyll Absorption Ratio Index/Optimized Soil-Adjusted Vegetation Index (OSAVI), Triangular Chlorophyll Index/OSAVI, Moderate Resolution Imaging Spectrometer Terrestrial Chlorophyll Index/Improved Soil-Adjusted Vegetation Index (MSAVI), and Red-Edge Model/MSAVI seem to be relatively consistent and more stable as estimators of crop chlorophyll content. [ABSTRACT FROM AUTHOR]

Wind wave spectrum describes the quasi-periodic nature of the ocean surface oscillations and plays an indispensable role in the study of microwave electromagnetic scattering from sea surface. A reliable spectrum model suitable for radar cross section (RCS) predictions at different radar frequencies is desired. This paper evaluated the performances of five common spectrum models (i.e., Fung spectrum, Durden–Vesecky spectrum, Apel spectrum, Elfouhaily spectrum, and the newest version of Hwang spectrum, H18) on the normalized radar backscattering cross section (NRBCS) simulations based on advanced integral equation model (AIEM) at L-, C-, X-, and Ku-bands versus incidence angle, wind direction, and wind speed by comparing with the model and measured data for validation. These results indicate no single wave spectrum of them is satisfying for all the four radar frequencies, e.g., Apel and H18 spectra are better for L- and C-bands, Apel spectrum for X-band, and Elfouhaily and H18 spectra for Ku-band. Given this, three average composite spectrum models are constructed using different spectral models (i.e., all five spectra, Apel + Elfouhaily + H18, and Apel + H18) to simulate NRBCSs, similar to that of the individual spectrum model. It is concluded that the combination of Apel and H18 spectra overall performs best among the individual one and other composited spectra in like-polarized NRBCSs versus incidence angles, wind directions, and wind speeds, for wind speed greater than 30 m/s where the combination of the five spectra work well at Ku-band. [ABSTRACT FROM AUTHOR]

The dielectric properties of sea ice are important for both passive and active microwave remote sensing of sea ice. In this paper, we present a new technique for dielectric measurements of artificially grown sea ice in the frequency range between 0.3 and 12 GHz using an open-ended coaxial probe. To provide a solid contact between the probe and ice, we slightly submerge and then freeze the probe's flange in sea water in a cold laboratory with a preset temperature. Once the ice is formed, we conduct a measurement of the complex reflection coefficient in the cold room using a vector network analyzer. To calibrate the system, we propose a set of measurements from air, shorting block (short), and pure methanol to be conducted immediately after. Both the real and imaginary parts of the complex dielectric constant as functions of frequency are then derived using a coaxial probe inverse model fed by these data. X-ray microtomography analysis of our samples revealed that the ice formed under the described conditions has completely isotropic microstructure typical for the frazil layer of natural first-year sea ice. To evaluate the experimental system's accuracy, we conducted extensive test measurements of standard materials (saline water, methanol, butanol, and pure ice). We also demonstrate that our sea ice dielectric measurements are close to corresponding values previously reported in the literature. The proposed measurement technique is valuable for developing a sea ice dielectric mixture model at microwave frequencies for different temperatures and salinities. [ABSTRACT FROM PUBLISHER]

In this paper, a novel machine learning algorithm is presented for disaggregation of satellite soil moisture (SM) based on self-regularized regressive models (SRRMs) using high-resolution correlated information from auxiliary sources. It includes regularized clustering that assigns soft memberships to each pixel at a fine scale followed by a kernel regression that computes the value of the desired variable at all pixels. Coarse-scale remotely sensed SM was disaggregated from 10 to 1 km using land cover (LC), precipitation, land surface temperature, leaf area index, and in situ observations of SM. This algorithm was evaluated using multiscale synthetic observations in NC Florida for heterogeneous agricultural LCs. It was found that the rmse for 96% of the pixels was less than 0.02 m 3/m3. The clusters generated represented the data well and reduced the rmse by up to 40% during periods of high heterogeneity in LC and meteorological conditions. The Kullback–Leibler divergence (KLD) between the true SM and the disaggregated estimates is close to zero, for both vegetated and bare-soil LCs. The disaggregated estimates were compared with those generated by the principle of relevant information (PRI) method. The rmse for the PRI disaggregated estimates is higher than the rmse for the SRRM on each day of the season. The KLD of the disaggregated estimates generated by the SRRM is at least four orders of magnitude lower than those for the PRI disaggregated estimates, whereas the computational time needed was reduced by three times. The results indicate that the SRRM can be used for disaggregating SM with complex nonlinear correlations on a grid with high accuracy. [ABSTRACT FROM PUBLISHER]

The electromagnetic scattering by a dielectric cylinder of finite length is important in many applications, particularly for microwave remote sensing of vegetated terrain. Yet, not only a unified analytical solution is still elusive in providing the scattering cross sections but also other important aspects, such as the phase of the scattering amplitude function, energy conservation, and reciprocity relation, have been scantly touched in the literature. The treatment of a dielectric inhomogeneous cylinder of finite length brings forth new challenges. Taking on such challenges is the focus of this paper. The main plan of attack is to extend the virtual partition method, which is a T-matrix-based semi-analytical model, that we have previously proposed to treat scattering from a homogeneous dielectric cylinder of finite length, to the inhomogeneous cases. The effectiveness of the proposed method is validated numerically, including: 1) high-fidelity prediction of the copolarized and cross-polarized cross sections for arbitrary bistatic scattering configuration; 2) high-fidelity predictions of the phase of the scattering amplitude function; 3) verification of energy conservation; and 4) verification of the reciprocity theorem. [ABSTRACT FROM PUBLISHER]

In microwave remote sensing of the Earth's surface, the satellite signal holds information on both soil moisture and vegetation. This necessitates a correction for vegetation effects when retrieving soil moisture. This paper assesses the strengths and weaknesses of the existing vegetation correction as part of the Vienna University of Technology (TU-Wien) method for soil moisture retrieval from coarse-scale active microwave observations. In this method, vegetation is based on a multiyear climatology of backscatter variations related to phenology. To assess the plausibility of the correction method, we first convert the correction terms for retrievals from the Advanced Scatterometer (ASCAT) into estimates of vegetation optical depth \taua using a water-cloud model. The spatial and temporal behaviors of the newly developed \taua are compared with the optical depth retrieved from passive microwave observations with the land parameter retrieval model \taup. Spatial patterns correspond well, although low values for \taua are found over boreal forests. Temporal correlation between the two products is high $(R = 0.5)$, although negative correlations are observed in drylands. This comparison shows that \taua and thus the vegetation correction method are sensitive to vegetation dynamics. Effects of the vegetation correction on soil moisture retrievals are investigated by comparing retrieved soil moisture before and after applying the correction term to modeled soil moisture. The vegetation correction increases the quality of the soil moisture product. In areas of high interannual variability in vegetation dynamics, we observed a negative impact of the vegetation correction on the soil moisture, with a decrease in correlation up to 0.4. It emphasizes the need for a dynamic vegetation correction in areas with high interannual variability. [ABSTRACT FROM AUTHOR]

Multiyear ice (MYI) characteristics can be retrieved from passive or active microwave remote sensing observations. One of the algorithms that combine both observations to identify partial concentrations of ice types (including MYI) is the Environment Canada Ice Concentration Extractor (ECICE). However, cycles of warm–cold air temperature trigger wet–dry cycles of the snow cover on MYI surface. Under wet snow conditions, anomalous brightness temperature and backscatter, similar to those of first-year ice (FYI), are observed. This leads to misidentification of MYI as being FYI, hence decreasing the estimated MYI concentration suddenly. The purpose of this paper is to introduce a correction scheme to restore the MYI concentration under this condition. The correction is based on air temperature records. It utilizes the fact that the warm spell in autumn lasts for a short period of time (a few days). The correction is applied to MYI concentration retrievals from ECICE using an input of combined QuikSCAT and AMSR-E data, acquired over the Arctic region in a series of autumn seasons from 2003 to 2008. The correction works well by replacing anomalous MYI concentrations with interpolated ones. For September of the six years, it introduces over 0.1\times 10^6\ \mboxkm^2 MYI area, except for 2005. Due to the regional effect of warm air spells, the correction could be important in the operational applications where ice concentrations are crucial on small scale and mesoscale. [ABSTRACT FROM AUTHOR]

Soil surface roughness is a crucial factor affecting the land surface microwave emissivity. Presented in this paper is a semiempirical model that analytically accounts for both roughness attenuation and cross-polarization-mixing effects in the frequency range of 1–100 GHz. The model is based on the finite linear superposition of hyperbolic tangent (tanh) functions over the normalized surface roughness and radiative parameter space, which proves to be very flexible and efficient in handling the distinct asymptotic features of roughness effects at the low-frequency end and high-frequency end and the nonlinear structure in between. The model performance was analyzed with the ground-based reflectivity measurements collected from different sources. In comparison with the existing semiempirical models in the literature, the new tanh-based roughness model demonstrated higher accuracy and consistent performance in the frequency range of 1.4–100 GHz and $0^\circ\!\sim\! 60^\circ$ incident angles. [ABSTRACT FROM PUBLISHER]

Information on vegetation water content (VWC) is important in retrieving soil moisture using microwave remote sensing. It can be also used for other applications, including drought detection, bushfire prediction, and agricultural productivity assessment. Through the Soil Moisture Active Passive (SMAP) mission of the National Aeronautics and Space Administration, radar data may potentially provide the VWC information needed for soil moisture retrieval from the radiometer data acquired by the same satellite. In this paper, VWC estimation is tested using radar vegetation index (RVI) data from the third SMAP airborne Experiment. Comparing with coincident ground measurements, prediction equations for wheat and pasture were developed. While a good relationship was found for wheat, with r=0.49, 0.62,\ \mboxand\ 0.65 and root-mean-square error (\mboxRMSE)=0.42, 0.37,\ \mboxand\ 0.36 kg/m2, the relationship for pasture was poor, with r=-0.06,-0.14,\ \mboxand\ -0.002 and \mboxRMSE=0.15,0.15,\ \mboxand\ 0.15, kg/m2, for 10-, 30-, and 90-m resolutions, respectively. These results suggested that RVI is better correlated with VWC for vegetation types having a greater dynamic range. However, the results were not as good as those from a previous tower-based study ( $r=0.98$ and \mbox{RMSE}=0.12\ \mbox{kg/m}^2) over wheat. This is possibly due to spatial variation in vegetation structure and surface roughness not present in tower studies. Consequently, results from this study are expected to more closely represent those from satellite observations such as SMAP, where large variation in vegetation and environment conditions will be experienced. [ABSTRACT FROM PUBLISHER]

A physically based Bayesian passive microwave precipitation retrieval requires an accurate forward radiative transfer model along with realistic database representation of hydrometeors, atmospheric properties, and surface emission. NASA’s Global Precipitation Measurement (GPM) Mission provides an unprecedented opportunity for the development of such databases, matching a well-calibrated radiometer with dual-frequency radar. Early versions of passive microwave products from GPM utilized a physically constructed database in a Bayesian retrieval scheme, assumed ice particles to be spheres, and used Mie radiative transfer. A large body of recent work demonstrates that this is insufficient for retrieval at the GPM radiometer frequencies. In this paper, the retrieval is updated to use nonspherical particles. Simulated brightness temperature (Tb) agreement with observations is shown to be significantly improved across the high frequencies, decreasing biases significantly and increasing correlations to observed Tb. This is compared with a second identical retrieval performed with the assumption of spherical ice particles, and retrieval results are compared globally, seasonally, and instantaneously for a case study at the rain rate level. While not at the high level of improvement shown in Tb space, the precipitation retrieval is improved as compared to one using observed Tb in correlation, bias, and root-mean-square error. Reported improvements, while modest in magnitude, advance the retrieval to more physical consistency which allows for deeper insight into ice particle properties associated with precipitation. [ABSTRACT FROM AUTHOR]

In this paper, we experimentally analyzed the feasibility of estimating soil hydraulic properties from 1.4 GHz radiometer and 0.8-2.6 GHz ground-penetrating radar (GPR) data. Radiometer and GPR measurements were performed above a sand box, which was subjected to a series of vertical water content profiles in hydrostatic equilibrium with a water table located at different depths. A coherent radiative transfer model was used to simulate brightness temperatures measured with the radiometer. GPR data were modeled using full-wave layered medium Green's functions and an intrinsic antenna representation. These forward models were inverted to optimally match the corresponding passive and active microwave data. This allowed us to reconstruct the water content profiles, and thereby estimate the sand water retention curve described using the van Genuchten model. Uncertainty of the estimated hydraulic parameters was quantified using the Bayesian-based DREAM algorithm. For both radiometer and GPR methods, the results were in close agreement with in situ time-domain reflectometry (TDR) estimates. Compared with radiometer and TDR, much smaller confidence intervals were obtained for GPR, which was attributed to its relatively large bandwidth of operation, including frequencies smaller than 1.4 GHz. These results offer valuable insights into future potential and emerging challenges in the development of joint analyses of passive and active remote sensing data to retrieve effective soil hydraulic properties. [ABSTRACT FROM AUTHOR]

Applying passive microwave (PM) remote sensing to estimate mountain snow water equivalent (SWE) is challenging due in part to the large PM footprints and the high subgrid spatial variability of snow properties. In this paper, we linked the land surface model Simplified Simple Biosphere version 3.0 (SSiB3) with the radiative transfer model Microwave Emission Model of Layered Snowpacks, and we forced the coupled model with the disaggregated North American Data Assimilation System phase 2 (NLDAS-2) meteorological data to simulate the snow properties and the 36.5-GHz microwave brightness temperature (Tb) at a spatial resolution of 90 m. The modeled SWE and Tb were used to interpret the radiance observed by the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) and to explore the impact of snow spatial variability on the microwave radiance in a mountain environment. The modeling was carried out over the Upper Kern Basin, Sierra Nevada. We developed new methods for modeling the effect of large snowfall events on the snow grain size. We aggregated the modeled radiance to the satellite scale using the AMSR-E 36.5-GHz antenna sampling pattern. The methods were calibrated for water years (WYs) 2004-2006 and validated for WYs 2003, 2007, and 2008. The coefficient governing the grain growth rate was also calibrated. The modeling results showed that the new snow grain estimation scheme reduced the error in the modeled radiance by 55.2% during the calibration period. The Tb root-mean-square error was 3.1 K during the snow accumulation season for the validation period. The modeling results showed that, in the study area, the microwave signal saturated for SWE values between 0.3 and 0.5 m. It was found that the subfootprint-scale SWE variability has a significant impact on the saturation of spaceborne PM observations. The experiments demonstrate that this modeling system improves the accuracy of the radiance modeling, which is critical for estimating the mountain SWE via PM remote sensing either for informing direct retrieval algorithms or for data assimilation. We plan to use the modeling framework in future radiance assimilation studies. [ABSTRACT FROM AUTHOR]

Robust and effective detection of a marine target is a challenging task due to the complex sea environment and target's motion. A long-time coherent integration technique is one of the most useful methods for the improvement of radar detection ability, whereas it would easily run into the across range unit (ARU) and Doppler frequency migration (DFM) effects resulting distributed energy in the time and frequency domain. In this paper, the micro-Doppler (m-D) signature of a marine target is employed for detection and modeled as a quadratic frequency-modulated signal. Furthermore, a novel long-time coherent integration method, i.e., Radon-linear canonical ambiguity function (RLCAF), is proposed to detect and estimate the m-D signal without the ARU and DFM effects. The observation values of a micromotion target are first extracted by searching along the moving trajectory. Then these values are carried out with the long-time instantaneous autocorrelation function for reduction of the signal order, and well matched and accumulated in the RLCAF domain using extra three degrees of freedom. It can be verified that the proposed RLCAF can be regarded as a generalization of the popular ambiguity function, fractional Fourier transform, fractional ambiguity function, and Radon-linear canonical transform. Experiments with simulated and real radar data sets indicate that the RLCAF can achieve higher integration gain and detection probability of a marine target in a low signal-to-clutter ratio environment. [ABSTRACT FROM PUBLISHER]

The triplet Markov fields (TMF) model has been successfully applied to statistical segmentation of nonstationary images by introducing the auxiliary field, which represents the different stationarities of images. Commonly, the TMF adopts a four-nearest neighborhood. This limits the modeling ability for complex priors. Therefore, this paper suggests using a higher order neighborhood-based TMF (HN-TMF). In the HN-TMF, the autocovariance analysis is applied to reveal the local fluctuation at each site. The auxiliary field is then redefined based on the local fluctuation information to denote homogeneity or heterogeneity. Based on the auxiliary field, the local energy function in HN-TMF is constructed either in a homogeneous or heterogeneous way, and hence, the local structure can be embedded in the energy function to improve the prior modeling ability. Along with the newly constructed energy function, new initializations of HN-TMF parameters are given to fulfill the physical interpretation of the energy function. The experiments performed on both synthetic and real synthetic aperture radar images demonstrate the effectiveness of the proposed HN-TMF in both speckle noise reduction and heterogeneous region segmentation accuracy. [ABSTRACT FROM PUBLISHER]

The detection of radio-frequency interference (RFI) continues to be an important problem for satellite-based microwave radiometers. This paper introduces two new probability-distribution-based techniques for computing the likelihood that a given brightness temperature observation contains an RFI signal. These methods extend the spectral difference method already being employed for the detection of RFI signals, and they allow for simultaneous observation-by-observation RFI detection of both land- and sea-based brightness temperature observations. This paper starts by laying out the theoretical groundwork for both techniques. It will then expand upon that groundwork to detail its practical application in analyzing WindSat brightness temperature observations. Finally, this paper compares the resulting probability indices from the detailed algorithms with spectral difference and principle component analysis indices computed from WindSat observations for various geographic regions. These comparisons will show effective RFI signal detection by these new techniques for RFI signal strengths as low as 4–5 K. [ABSTRACT FROM AUTHOR]

Microwave (MW) satellite data are widely used as input in numerical weather prediction models and also in other applications such as climate monitoring and re-analysis. MW satellite data are prone to different problems, including geolocation errors. These data do not have a fine spatial resolution like visible and infrared data; therefore, the accuracy of their geolocation cannot be easily determined using the normal methods such as superimposing coastlines on the satellite images. Currently, no geolocation correction is performed on data from MW instruments aboard the satellites in the National Oceanic and Atmospheric Administration (NOAA) Polar Operational Environmental Satellite program. However, geolocation error can be a significant source of bias in the satellite measurements. In this paper, we investigated and corrected the geolocation errors of the observations from the Advanced Microwave Sounding Unit (AMSU)-A aboard NOAA-15 to NOAA-19, AMSU-B aboard NOAA-15 to NOAA-17, and Microwave Humidity Sounder (MHS) aboard NOAA-18 and NOAA-19. We used the difference between ascending and descending observations along the coastlines to quantify the geolocation errors in terms of the satellite attitudes (Euler angles), i.e., pitch, roll, and yaw. Then, new geographical coordinates and scan/local zenith angles were calculated using new attitudes. The results show that NOAA-15 AMSU-A2 instrument has a mounting error of about 1.2 ^\circ cross-track, and -0.5^\circ along-track, NOAA-16 AMSU-A1 and -A2 instruments have a mounting error of about -0.5^\circ along-track, and NOAA-18 AMSU-A2 instrument has a mounting error of more than -1^\circ along-track. [ABSTRACT FROM AUTHOR]

Whenever multitemporal synthetic aperture radar (SAR) images are available, precise calibration and perfect spatial registration are required to obtain a useful image for displaying changes that have occurred. SAR calibration is a very complex and sensitive problem; some errors may persist after calibration that interfere with subsequent steps in the data fusion and visualization process. Because of the strong histogram asymmetry of SAR images, due to the well-known non-Gaussian model of radar backscattering, traditional image preprocessing procedures cannot be used here. A novel specific preprocessing phase, the so-called “cross-calibration/normalization,” is proposed to solve this problem. This, in turn, facilitates image enhancement and the numerical comparison of different image takes together with data fusion and visualization processes. The proposed processing chain includes filtering, histogram truncation, and equalization steps applied in an adaptive way to the images in question. The design of the method and the experimental procedure is based on images from the Italian Cosmo/Skymed mission. Both Stripmap and Spotlight images are taken into account to test the algorithms at different spatial resolutions. This paper also presents an example application: the generation of a single flood picture, the so-called “fast-ready flood map,” from multitemporal SAR images. The maps are very quickly and automatically generated without user interaction to support the authorities in providing first aid to a population. Toward this end, RGB composition is used: pre-flood and post-flood images are combined into a color image to better identify the flooded areas in comparison with permanent water and other classes. [ABSTRACT FROM AUTHOR]

The present paper analyzes the effects of roughness on the surface emission at L-band based on observations acquired during a long-term experiment. At the Surface Monitoring of the Soil Reservoir Experiment site near Toulouse, France, a bare soil was plowed and monitored over more than a year by means of an L-band radiometer, profile soil moisture and temperature sensors, and a local weather station, accompanied by 12 roughness campaigns. The aims of this paper are the following: 1) to present this unique database and 2) to use this data set to investigate the semiempirical parameters for the roughness in L-band Microwave Emission of the Biosphere, which is the forward model used in the Soil Moisture and Ocean Salinity soil moisture retrieval algorithm. In particular, we studied the link between these semiempirical parameters and the soil roughness characteristics expressed in terms of standard deviation of surface height (σ) and the correlation length (LC). The data set verifies that roughness effects decrease the sensitivity of surface emission to soil moisture, an effect which is most pronounced at high incidence angles and soil moisture and at horizontal polarization. Contradictory to previous studies, the semiempirical parameter Qr was not found to be equal to 0 for rough conditions. A linear relationship between the semiempirical parameters N and σ was established, while NH and NV appeared to be lower for a rough (NH ~ 0.59 and NV ~ -0.3) than for a quasi-smooth surface. This paper reveals the complexity of roughness effects and demonstrates the great value of a sound long-term data set of rough L-band surface emissions to improve our understanding on the matter. [ABSTRACT FROM AUTHOR]

This paper describes an algorithm to simultaneously retrieve ocean surface salinity and wind from combined passive/active L-band microwave observations of sea surfaces. The algorithm takes advantage of the differing response of brightness temperatures and radar backscatter to salinity, wind speed, and direction. The algorithm minimizes the least square error (LSE) measure, signifying the difference between measurements and model functions of brightness temperatures and radar backscatter. Three LSE measures with different measurement combinations are tested. One of the LSE measures uses passive microwave data only with retrieval errors reaching 2 psu for salinity and 2 m/s for wind speed. The second LSE measure uses both passive and active microwave data for vertical and horizontal polarizations. The addition of active microwave data significantly improves the retrieval accuracy by about a factor of five. To mitigate the impact of Faraday rotation on satellite observations, we propose the third LSE measure using measurement combinations invariant under the Faraday rotation. For Aquarius, the expected root-mean-square SSS error will be less than 0.2 psu for low winds and increases to 0.3 psu at 25-m/s wind speed for warm waters, and the accuracy of retrieved wind speed will be high (about 1-2 m/s or lower). Our results suggest that combining passive and active microwave observations will allow retrieval of sea surface salinity along with the wind speed and direction. In particular, the LSE measure invariant under the Faraday rotation will be directly applicable to spaceborne missions, such as the NASA Aquarius and Soil Moisture Active Passive missions. [ABSTRACT FROM AUTHOR]

Soil moisture is the central focus of land surface and atmospheric modeling because it controls surface water and energy fluxes and consequently affects land–atmosphere interactions. Although global or regional satellite-derived surface soil moisture data sets are readily available, knowledge about assimilating them into numerical weather prediction (NWP) models is limited. The methods of assimilating soil moisture products in NWP models have several limitations, and they cannot be applied in near-real-time applications. As a result, this paper focuses on the development of a system [a Land Data Assimilation System coupled with a mesoscale Atmospheric model (LDAS-A)] that couples satellite land data assimilation with a mesoscale model to physically introduce land surface heterogeneities into the mesoscale model. The LDAS-A consists of a sequential LDAS that directly assimilates the lower frequency passive microwave brightness temperatures, and therefore, its use is feasible for near-real-time NWP applications. The LDAS-A was validated for the Tibetan Plateau using surface, radiosonde, and satellite observations. The simulation results show that the LDAS-A effectively improved the land surface variables (i.e., surface soil moisture and skin temperature) compared with the no-assimilation case and that it has the potential to correct uncertainties resulting from model initialization, model-specific parameters, and model forcing on a wider scale. The improved land surface conditions in the LDAS-A improve the land–atmosphere feedback mechanism, and the assimilated results provide better prediction of atmospheric profiles (i.e., potential temperature and specific humidity) than the no-assimilation case when compared with radiosonde soundings. Improvements in solar radiation, in addition to soil moisture, are necessary to introduce realistic land–atmosphere interactions into a mesoscale model. [ABSTRACT FROM AUTHOR]

In this paper, we describe and utilize polarization contrast techniques of the adaptive polarization difference imaging algorithm and its transient modification for through-wall microwave imaging (TWMI) applications. Originally developed for optical imaging and sensing of polarization information in nature, this algorithm is modified to serve for target detection purposes in a through-wall environment. The proposed techniques exploit the polarization statistics of the observed scene for the detection and identification of changes within the scene and are not only capable of mitigating and substantially removing the wall effects but also useful in detecting motion, when conventional Doppler techniques are not applicable. Applications of the techniques to several TWMI scenarios including both homogeneous and periodic wall cases are presented. [ABSTRACT FROM AUTHOR]

This paper develops two alternative approaches for downscaling passive microwave-derived soil moisture. Ground and airborne data collected over the Walnut Gulch experimental watershed during the Monsoon'90 experiment were used to test these approaches. These data consisted of eight micrometeorological stations (METFLUX) and six flights of the L-band Push Broom Microwave Radiometer (PBMR). For each PBMR flight, the 180-rn resolution L-band pixels covering the eight METFLUX sites were first aggregated to generate a 500-rn "coarse-scale" passive microwave pixel. The coarse-scale-derived soil moisture was then downscaled to the 180-m resolution using two different surface soil moisture indexes (SMIs): 1) the evaporative fraction (EF), which is the ratio of the evapotranspiration to the total energy available at the surface; and 2) the actual EF (AEF), which is defined as the ratio of the actual-to-potential evapotranspiration. It is well known that both SMIs depend on the surface soil moisture. However, they are also influenced by other factors such as vegetation cover, soil type, root-zone soil moisture, and atmospheric conditions. In order to decouple the influence of soil moisture from the other factors, a land surface model was used to account for the heterogeneity of vegetation cover, soil type, and atmospheric conditions. The overall accuracy in the downscaled values was evaluated to 3% (vol.) for EF and 2% (vol.) for AEF under cloud-free conditions. These results illustrate the potential use of satellite-based estimates of instantaneous evapotranspiration on clear-sky days for downscaling the coarse-resolution passive microwave soil moisture. [ABSTRACT FROM AUTHOR]

In this paper, microwave remote sensing, together with in situ moisture probes, is used to investigate temperature effects on the soil dielectric constant. Field and specific laboratory measurements were performed for different soil water content over a wide range of temperatures. The experimental results lead to the following evidences: 1) temperature effect is different for bound and free waters in soil; 2) bound-water relaxation frequency falls within the range of frequencies that are used by impedance soil moisture probes for field measurements; and 3) the increase of bound-water relaxation frequency with soil temperature interferes in a significant way with moisture measurements when bound-water fraction is important. These results have implications in field experimentation since most moisture sensors operate under 500 MHz and are affected by this phenomena of relaxation. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of supervised classification of remote sensing images in the presence of incomplete (nonexhaustive) training sets. The problem is analyzed according to two different perspectives: 1) description and recognition of a specific land-cover class by using single-class classifiers and 2) solution of multiclass problems with single-class classification techniques. In this framework, we analyze different one-class classifiers and introduce in the remote sensing community the support vector domain description method (SVDD). The SVDD is a kernel-based method that exhibits intrinsic regularization ability and robustness versus low numbers of high-dimensional samples. The SVDD technique is compared with other standard single-class methods both in problems focused on the recognition of a single specific land-cover class and in multiclass problems. For the latter, we properly define an easily scalable multiclass architecture capable to deal with incomplete training data. Experimental results, obtained on different kinds of data (synthetic, hyperspectral, and multisensor images), point out the effectiveness of the SVDD technique and provide important indications for driving the choice of the classification technique and architecture in the presence of incomplete training data. [ABSTRACT FROM AUTHOR]

The Atmospheric Radiation Measurement (ARM) Program maintains a suite of instruments in various locations to provide continuous monitoring of atmospheric parameters. Temperature and humidity retrievals are two of the key parameters used by the climate-modeling community. Accuracy in the spectroscopy adopted by the various radiative transfer models is crucial for obtaining accurate retrievals. While the accuracy of the spectroscopic parameters used for water-vapor retrievals is satisfactory, temperature retrievals continue to be affected by uncertainties in oxygen line parameters leading to discrepancies between the modeled and observed brightness temperatures. In this paper, we compare the model calculations in the oxygen-band channels with the measurements collected by the ARM-operated 12-channel Microwave Radiometer Profiler (MWRP). The dataset used spans a wide range of atmospheric temperature conditions, with ground temperatures varying between – 40 °C and +20 °C. Model calculations are performed by using line parameters from the high-resolution transmission molecular-absorption (HITRAN) database and from a set of newly published parameters. Our comparison shows that the newly published parameters agree more closely with the MWRP measurements and confirms the need to update the HITRAN database for the oxygen lines. We show the effect of line parameters on the retrievals of temperature, water vapor, and liquid water, and show that improved oxygen absorption is essential to reduce the clear-sky bias in the liquid-water path retrievals. [ABSTRACT FROM AUTHOR]

An improved oceanic rainfall algorithm based on a radiative transfer model that reduces many uncertainties of rainfall retrieval was developed using advanced microwave scanning radiometer for Earth Observing System data. Error models were embedded to quantify rainfall uncertainties and to reduce net uncertainties. Six channels (37, 18, and 10 GHz with dual polarization) were utilized in the algorithm. Several developments such as improvement of the freezing-level (FL) retrieval, a weighted average scheme, and enhanced offset correction were implemented in this paper. As a result, rain rate uncertainties were substantially reduced and quantified. To establish error models, drop-size-distribution uncertainty, beam filling error, data calibration uncertainty, and instrument noise were taken into account. These error models were used to compute proper weights of each channel to combine the six rain rates. The algorithm was evaluated with respect to the current operational algorithm (NASA Level 3 rainfall algorithm). It showed more reasonable mean FLs and rain rate estimation than the operational algorithm. Furthermore, pixel-by-pixel-based quantitative error estimates were conducted through the error model. [ABSTRACT FROM AUTHOR]

Limb sounding instruments provide high vertical resolution data on the temperature and composition of the atmosphere. Their data are, therefore, valuable for assimilating into general circulation models of the atmosphere. Direct assimilation of radiances from limb sounders is more complex in practice than from nadir sounders due to the need to know the tangent pressures of the measurements. This paper discusses the practical implications of tangent pressures in direct radiance assimilation of limb sounding radiances and demonstrates that a neural network can be used to find these tangent pressures for the Earth Observing System Microwave Limb Sounder with a root mean-square error of σ = 50 m, which is comparable with that in traditional retrieval techniques. [ABSTRACT FROM AUTHOR]

Brightness temperatures over the Amazon rainforest are obtained from the Advanced Microwave Sounding Units (AMSU-A and AMSU-B) instruments onboard three NOAA satellites (NOAA-15, -16, and -17, respectively) for the months of July, August, and October, 2002. The three AMSU-A instruments provided six daily measurements, separated by 2.5-5.5 h of the diurnal time intervals, over the Amazon rainforest region, and these measurements offer a unique opportunity for investigation of the diurnal variation of the brightness temperatures over the Amazon rainforests. The angular distributions of brightness temperatures over the Amazon rainforest are very stable and can be simulated with a radiative transfer model, which consists of an atmospheric radiative component and a rainforest-canopy model that treats the rainforest as a uniform layer with an effective canopy temperature. The simulated results agree well with the observations. The diurnal variation of brightness temperatures over the Amazon rainforest is simulated with a Fourier-series model. It shows that a second order of Fourier series can reproduce the observed pattern of diurnal variation of the brightness temperatures at zenith angles of 0°, 28.7°, and 58.1°, respectively. In a practical application, the coefficients of Fourier-series expansion can be used to generate the brightness temperatures as a function of diurnal hours. These results can be applied to postlaunch calibration of satellite-borne microwave radiometer with different equator crossing time. In addition, the results presented in this paper indicate that the Amazon rainforest can be used as a hot calibration reference target. The availability of a land calibration target is important for calibration and validation of spaceborne microwave radiometers. [ABSTRACT FROM AUTHOR]

Radio-frequency interference (RFI) is an increasingly serious problem for passive and active microwave sensing of the Earth. To satisfy their measurement objectives, many spaceborne passive sensors must operate in unprotected bands, and future sensors may also need to operate in unprotected bands. Data from these sensors are likely to be increasingly contaminated by RFI as the spectrum becomes more crowded. In a previous paper we reported on a preliminary investigation of RFI observed over the United States in the 6.9-GHz channels of the Advanced Microwave Scanning Radiometer (AMSR-E) on the Earth Observing System Aqua satellite. Here, we extend the analysis to an investigation of RFI in the 6.9- and 10.7-GHz AMSR-E channels over the global land domain and for a one-year observation period. The spatial and temporal characteristics of the RFI are examined by the use of spectral indices. The observed RI?! at 6.9 GHz is most densely concentrated in the United States, Japan, and the Middle East, and is sparser in Europe, while at 10.7 GHz the RFI is concentrated mostly in England, Italy, and Japan. Classification of RFI using means and standard deviations of the spectral indices is effective in identifying strong RFI. In many cases, however, it is difficult, using these indices, to distinguish weak RFI from natural geophysical variability. Geophysical retrievals using RFI-filtered data may therefore contain residual errors due to weak RFI. More robust radiometer designs and continued efforts to protect spectrum allocations will be needed in future to ensure the viability of spaceborne passive microwave sensing. [ABSTRACT FROM AUTHOR]

In this paper, we propose a soil dielectric constant retrieval scheme based on the use of the ratio of power densities scattered at HH and VV polarizations along the specular direction for different incidence angles and/or frequencies. The method relies on the minimum squares technique and is based on the observation that at variance with the backscattering case, in the specular case the small perturbation method (SPM) and the Kirchhoff approach both lead to the same expression of the copolarized ratio. Accordingly, it is expected that its evaluation is rather robust, to hold under a wide range of surface roughnesses. We present method-of-moments (MoM) simulations that confirm this expectation, and we test the validity of the overall retrieval scheme, as well as its sensitivity to measurement errors (calibration errors and fading or speckle noise), by applying the algorithm to data simulated by using the MoM. Theoretical and practical problems related to the implementation of measurements along the specular direction are also discussed. The proposed method is amenable of application for the new generation of bistatic remote sensing instruments. [ABSTRACT FROM AUTHOR]

In the numerical Maxwell-equation model (NMM3D) of rough-surface scattering, we solve Maxwell equations in three dimensions. to calculate emissivities for applications in passive microwave remote sensing of soil and ocean surfaces. The difficult cases for soil surfaces are with exponential correlation functions when the surfaces have fine-scale structures of large slopes. The difficulty for ocean surfaces is that because the emissivities are close to that of a flat surface, the emissivities have to be calculated accurately to correctly assess the rough-surface effects In this paper, the accuracies of emissivity calculations are improved by using Rao-Wilton--Glisson basis functions. We further use sparse matrix canonical method to solve the matrix equation of Poggio-Miller-Chang--Harrington-Wu integral equations. Energy conservation checks are provided for the simulations. Comparisons are made with results from the pulse basis function. Numerical results are illustrated for soil and ocean surfaces respectively with exponential correlation function and ocean spectrum. The emissivities of soil are illustrated at both L- and C-bands and at multiple incidence angles for the same physical roughness parameters. The brightness temperatures for ocean surfaces are illustrated for cases with various wind speeds. We compare results with those from the sparse matrix methods. Comparisons are also made with experimental emissivity measurements of soil surfaces. Parallel computation is also implemented. Lookup tables of emissivities based on NMM3D are provided. [ABSTRACT FROM AUTHOR]