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While many microwave studies related to tree emission have been undertaken, a few have considered the effect of phenological change on the emission from coniferous trees. The permittivity of vegetation tissue is known to be influenced by water content, while the water content and phase is sensitive to temperature in particular at temperatures below freezing. In addition to temperature, canopy-intercepted snow might also modify the tree emission and transmissivity in the microwave range. In this paper, a season-long experiment was designed to quantify the effect of snow accumulation and temperature on the observed microwave transmissivity from tree. A ground-based, upward-pointing multifrequency radiometer was used to monitor the microwave emissivity of a single coniferous tree at a site in Northern Finland. Radiometer measurements were combined with measurements of the canopy-intercepted snow cover and tree skin temperature. This paper presents two important findings. First, the tree transmissivity was strongly correlated with tree skin temperature under subzero temperature conditions, but uncorrelated with skin temperature changes above freezing. Second, although the tree transmissivity was slightly affected by the snow accumulation on the tree canopy, the overall influence on tree emission was statistically insignificant in this paper. [ABSTRACT FROM AUTHOR]

This paper discusses the intersatellite radiometric calibration for microwave sounders in support of NASA’s Global Precipitation Measurement (GPM) mission. This paper is an extension of previous GPM Intersatellite Calibration Working Group work, which assesses the robustness of the Central Florida Remote Sensing Laboratory “double difference” technique for sounders intercalibration. In this paper, we will analyze the intercalibration between the Advanced Technology Microwave Sounder on board the National Polar-orbiting Operational Environmental Satellite System Preparatory Project, the Microwave Humidity Sounder on board the MetOp-A and MetOp-B and NOAA-18 and NOAA-19, and the Sondeur Atmosphérique du Profil d’Humidité Intertropicale par Radiométrie sounder onboard the Megha-Tropiques satellite. The sensitivity of the calibration to the geophysical parameters and incidence angles is investigated using two years data. Here, it is observed that a reliable intercalibration can be made only when the path lengths of the two sensors are similar, so when matching the scan angles for the two sensors, the sensitivity of the biases upon the water vapor profiles is significantly reduced. [ABSTRACT FROM AUTHOR]

This paper presents an evaluation of the quality of data from the MicroWave Humidity Sounder-2, flown on the FY-3C polar orbiting satellite, and first results of trials assimilating the data in all-sky conditions in the European Centre for Medium Range Weather Forecasts’ assimilation system. This instrument combines traditional microwave humidity sounding capabilities at 183 GHz with new channels at 118 GHz, which have never before been available from space. The aim of this paper is twofold: to contribute to the calibration/validation of this satellite by evaluating the data and to test for the first time the impact of assimilating 118-GHz sounding channels in a global numerical weather prediction system. MWHS-2 was first evaluated by comparing observation minus short-range forecast statistics to similar instruments, which indicated that the quality was generally similar. Adding the 118- and 183-GHz channels to the full operational observing system led to small improvements in the short-range (~12 h) forecast accuracy for both sets of channels, and improvements in the 2–4-day wind forecast accuracy for the 183-GHz channels. Short-range forecasts were improved for global humidity in particular when assimilating the 183-GHz channels (0.5% improvement) and for cloud and low-level wind in the Southern Hemisphere extra-tropics when assimilating the 118-GHz channels (by, respectively, 0.5% and 0.2%). In the absence of other atmospheric observations, assimilating the 118-GHz channels improved the global forecast accuracy, but the overall impact was less than for the 183-GHz channels, or for equivalent Advanced Microwave Sounding Unit-A channels. [ABSTRACT FROM AUTHOR]

Shore-based coherent microwave radar and high-frequency (HF) surface wave radar are two components of a rapidly emerging set of technologies suitable for ocean wave remote sensing. To investigate their differences, this paper describes and analyzes the relationship between the water particle velocity and the wave height spectrum based on linear wave theory which underpins the algorithms developed for the analysis of data collected by coherent microwave radar. The backscatter mechanism which addresses the interaction of the HF radio waves with the ocean surface waves, as well as the empirical method adopted in our HF radar is also presented. The wave characteristics observed by the shore-based coherent S-band radar [Microwave Ocean Remote SEnsor (MORSE)] are analyzed. A multifrequency HF (MHF) radar based on a circular receiving array, which is capable of sensing waves up to 100-km offshore, is also introduced. An intercomparison of the wave height measurements obtained from the MORSE, MHF radar, and wave buoy is made. The comparison indicates that the wave heights measured by the MORSE and the MHF radar are consistent with the buoy-derived wave heights, with the root-mean-square differences (RMSDs) of 0.27 and 0.37 m, respectively. [ABSTRACT FROM AUTHOR]

Microwave radiometers measure weak thermal emission from the Earth, which is broadband in nature. Radio frequency interference (RFI) originates from active transmitters and is typically narrow band, directional, and continuous or intermittent. The Global Precipitation Measurement (GPM) Microwave Imager (GMI) has seen RFI caused by ocean reflections from direct broadcast and communication satellites in the shared 18.7-GHz allocated band. This paper focuses on the use of a complex signal kurtosis algorithm to detect direct broadcast satellite (DBS) signals at 18.7 GHz. An experiment was conducted in August 2017 at the Harvest oil platform, located about 10 km off the coast of central California. Data were collected for direct and ocean reflected DBS transmissions in the K- and Ku-bands from a commercial geostationary satellite. Results are presented for the complex kurtosis performance for a five-channel quadrature phase-shift keying (QPSK) signal versus the seven-channel case. As the spectrum becomes more occupied, detector performance decreases. Filtering of RFI in the fully occupied spectrum is very difficult, and detection using the complex kurtosis detector is only possible for very large interference-to-noise ratio (INR) values at −5 dB and higher. This corresponds to over 100 K in a real system such as GMI; therefore, other detection approaches might be more appropriate. [ABSTRACT FROM AUTHOR]

This paper utilizes the model regression difference method to identify sun glint contamination on Global Precipitation Measurement Microwave Imager (GMI) data over the ocean based on observations from 2015 to 2016. The spatial distribution characteristics and the critical angles of the sun glint flags are analyzed in depth. It is found that the GMI measurements with horizontal and vertical polarizations at 10.65 GHz over the ocean are sometimes contaminated by the solar radiation reflected by the sea surface. The sun glint contamination has also been detected over high reflected land surface. The intensity and locations of the contamination are related to the sun glint angle. Only those GMI field of views with smaller sun glint angles are easily contaminated. The closer the sun glint angle is to 0°, the stronger the magnitude of the contamination. The GMI observations at other channels are not contaminated mainly because sun glint is most pronounced at 10 GHz. There are too strong constraints and tossing out of too many useful data in current GMI sun glint algorithms. The suggested critical angles of the sun glint flags for 10.65GHz is 20° to reduce false flagging. By applying the model regression difference method, the error in brightness temperature caused by sun glint can be corrected. The Tropical Rainfall Measuring Mission Microwave Imager (TMI) observations at 10.65 GHz are also contaminated by the reflected solar radiation from the ocean, and the intensity and locations of the contamination are similar to those of the GMI. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a novel approach to enhance the spatial resolution of single-pass microwave data collected by mesoscale sensors. The proposed rationale is based on an $L^{p}$ -minimization approach with a variable $p$ exponent. The algorithm automatically adapts the $p$ exponent to the region of the image to be reconstructed. This approach allows taking benefit of the advantages of both the regularization in Hilbert ($p = 2$) and Banach ($1< p< 2$) spaces. Experiments are undertaken considering the microwave radiometer and refer to both actual and simulated data collected by the special sensor microwave imager (SSM/I). Results demonstrate the benefits of the proposed method in reconstructing abrupt discontinuities and smooth gradients with respect to conventional approaches in Hilbert or Banach spaces. [ABSTRACT FROM AUTHOR]

This paper presents an electromagnetic modeling of temporal variations of microwave backscatter from rice canopy based on radiative transfer theory to understand the complex microwave scattering mechanisms of rice crops at different growth stages. Model validation is made by a comparison with field measurements from four independent campaigns by ground-based scatterometers and spaceborne synthetic aperture radar (SAR). Then, the frequency responses and angular dependence are examined, followed by an analysis of parameter sensitivity and uncertainty to microwave backscatter. The validation shows promising results for the physically based model in assessing radar response of rice plant in its full-growth stage. It is found that radar scattering of rice canopy is highly dependent on the growing stages of rice plants, in addition to radar configurations, such as frequency, polarization, and incident angle. Moreover, backscattering of rice canopy is more dependent on stems than on leaves, and as rice plants grow, increasing stems and leaves tend to promote HH- and VH-polarized scattering, while first promoting and then reducing VV-polarized scattering. Moreover, the greatest uncertainty takes place at the late growth stage for copolarization and at the early stages for cross-polarization. The main contributions of this paper can be summarized as follows: 1) the establishment of a rice canopy microwave backscattering model using radiative transfer theory adapted to all the stages in the phenological cycle of rice; 2) the validation of the performance of the proposed model with numerous independent measurements from spaceborne SAR and ground-based scatterometers from different countries and regions; 3) the investigation of the interactions between microwave backscatter signatures and rice canopy growth variables over growth stages; and 4) the identification of the dominant influential parameters of the rice scattering model and determination model uncertainty. [ABSTRACT FROM PUBLISHER]

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]

Wind scatterometer measurements are collected over an irregular grid, and processing is required to generate backscatter images on an Earth-centered grid. The most common algorithms used for this are “drop in the bucket” (DIB) and variations of the scatterometer image reconstruction (SIR) algorithm. These algorithms are also used for radiometer brightness temperature imaging. The Backus–Gilbert (BG) algorithm has been used for radiometer imaging but has not been applied to scatterometer backscatter imaging. In this paper, the application of BG to scatterometer backscatter imaging is explored and its performance is compared to DIB and SIR. Like SIR, optimally tuned BG is capable of producing higher resolution images than DIB, though its noise performance is slightly inferior to SIR’s. While BG and SIR produce similar results for radiometer data, the higher relative noise level of scatterometer data increases the differences between the SIR and BG algorithm performance, and limits the performance of BG relative to SIR in scatterometer imaging. Comparison of the SIR and BG algorithms in scatterometer imaging offers important insights into the inversion/reconstruction problem. [ABSTRACT FROM AUTHOR]

This paper describes the results obtained by the application of an innovative environmental monitoring technique able to estimate rainfall intensity in real time by processing the attenuation of microwave satellite link signal measured by low cost sensors. The satellite that has been used during our work, Turksat 42° E, belongs to the plethora of satellites operating for television and radio channel broadcasting. Each sensor exploits off the shelf components, is equipped with a radio frequency power-measuring unit, and provides connectivity to the server over a wide area network. A validation of the approach with a field comparison experiment at the urban scale, comprising three measurement sites equipped with such sensors, was established since autumn 2016 in the municipality of Genoa, Italy. Point-scale rainfall intensity measurements made by two calibrated tipping-bucket rain gauges constitute the reference for the comparative analysis of the microwave sensors performance. The dynamic calibration of the rain gauges was carried out by using an automatic calibration rig and the measurements have been processed with advanced algorithms to reduce counting errors. The experimental setup allowed a full characterization of the microwave signal trends as a function of different precipitation. The results showed a strong correlation between the microwave signal attenuation and the reference rainfall observations and demonstrated the possibility to retrieve ten-minute rain accumulations from the microwave links by adopting a proper electromagnetic model. The comparison between the different measuring systems is performed by computing the time series statistics and the frequency of the rain conditions for each precipitation event. [ABSTRACT FROM AUTHOR]

A rain-gauge-adjusted algorithm for global satellite mapping of precipitation (GSMaP) that estimates the surface precipitation rate with resolutions of 0.1° and 1 h over the globe is described herein. Precipitation is one of the most important parameters of the earth’s system, and its global distribution and changes are essential data for modeling the water cycle, maintaining ecosystems, increasing agricultural production, improving weather forecasting precision, and implementing flood warning systems. In the Global Precipitation Measurement project, integrated products of high-resolution mapping of precipitation, obtained from microwave measurements made by a constellation satellite and infrared radiometers in geostationary orbit, are developed and supplied to the public. However, these high-resolution products, such as GSMaP_MVK, sometimes underestimate surface precipitation, introducing large errors into hydrological modeling. This paper combines the global gauge data set with GSMaP_MVK, using a new algorithm [gauge-adjusted GSMaP (GSMaP_Gauge)], described and evaluated herein using local radar and rain-gauge data sets. This algorithm outperforms other GSMaP products in all validation tests. [ABSTRACT FROM AUTHOR]

Active and passive low-frequency microwave measurements from a number of space- and airborne instruments are used to estimate soil moisture. Each of the sensing approaches has distinct advantages and disadvantages. There is increasing interest in combining active and passive measurements in order to realize the advantages and alleviate the disadvantages. In order to combine active and passive measurements, their covariations with respect to soil moisture need to be known. The covariation is dependent on how the active and passive microwaves interact with vegetation canopy and soil surface. In this paper, we introduce a physics-based model for the covariation of active and passive microwaves over soil surfaces with vegetation cover. The analytical form for a covariation function is derived which depends on the scattering and absorption of microwaves by soil and vegetation with different orientations, structures, and water contents. The main finding is that the covariation function $\beta $ is related to the roughness and vegetation losses in the two measurements. An increase in soil roughness or in vegetation cover leads to less negative values of $\beta $ , which is pronounced for dense and moist vegetation. Both the soil and vegetation components introduce a polarization dependence of $\beta $ that is caused by polarization-induced differences in soil scattering and oriented plant structures. The forward modeled covariations are plotted together with statistically derived covariation estimates from two months of global active and passive L-band observations of the Soil Moisture Active Passive mission. The physically modeled and statistically derived estimates of covariation are comparable in magnitude and scale. [ABSTRACT FROM AUTHOR]

The ground level rainfall at a given time is modeled as a 2-D spatial random process ${r}(\textit {x, y})$ , the rain field. Existing measurement equipment, such as rain gauges, weather stations, or recently proposed microwave links, samples ${r}(\textit {x, y})$ spatially in specific points or along lines. Given these samples, our purpose is to reconstruct ${r}(\textit {x, y})$. In this paper, we study the question: “under what conditions can a given topology of ground measurements guarantee reconstructability of the rain field?” Based on the assumption that rain fields are sparse, we present a statistical approach to this problem by first characterizing the statistics of the measurements, and then answering the question by applying methods from compressed sensing theory, and in particular Donoho and Tanner’s phase transition diagram for sparse recovery. We conclude by suggesting a solution in a form of a simple diagram, allowing one to evaluate the potential reconstruction of ${r}(\textit {x, y})$ in different resolutions without the need for computations. [ABSTRACT FROM AUTHOR]

In this paper, a simple algorithm is proposed to perform the nowcasting of rainfall in the tropical region. The algorithm applies global positioning system-derived precipitable water vapor (PWV) values and its second derivative for the short-term prediction of rainfall. The proposed algorithm incorporates the seasonal dependency of PWV values for the prediction of a rain event in the coming 5 min based on the past 30 min of PWV data. This proposed algorithm is based on the statistical study of four-year PWV and rainfall data from a station in Singapore and is validated using two-year independent data for the same station. The results show that the algorithm can achieve an average true detection rate and a false alarm rate of 87.7% and 38.6%, respectively. To analyze the applicability of the proposed algorithm, further validations are done using one-year data from one independent station from Singapore and two-year data from one station from Brazil. It is shown that the proposed algorithm performs well for both the independent stations. For the station from Brazil, the average true detection and false alarm rates are around 84.7% and 37%, respectively. All these observations suggest that the proposed algorithm is reliable and works well with a good detection rate. [ABSTRACT FROM AUTHOR]

In this paper, a new computer-time effective iterative method is proposed to enhance the spatial resolution of microwave radiometer data in both Hilbert and Banach spaces. The reconstruction method is based on the conjugate gradient (CG) method. CG is a well-known method in electromagnetics, where it is commonly used to address inverse problems in Hilbert spaces. However, the method has never been applied to inverse problems related to spatial resolution enhancement of microwave remotely sensed measurements. In this paper, CG is adapted to the microwave radiometer case and exploited to reconstruct the brightness field at enhanced spatial resolution. Then, CG is, for the first time, extended to Banach spaces by means of duality maps and then applied to enhance the radiometer spatial resolution. Experiments undertaken on both simulated and actual radiometer data confirm the soundness of the proposed approach in Banach spaces and demonstrate that CG is able to provide a reconstruction accuracy similar to the conventional Landweber method, but with a significantly reduced processing time. [ABSTRACT FROM AUTHOR]

Simulating and forecasting river discharge using satellite information is one of the most economical ways of measuring discharge, especially in remote areas where in situ gauges are too expensive to install, maintain, and operate. However, satellite signals are affected by climatic factors, such as clouds, mist, dust storms, and smoke from large forest fires. Hence, a reliable method of preparing and treating space-based signals is necessary. In this paper, a method is introduced to remove inaccurate and out-of-range signals having a low correlation with the in situ discharge measurements, using a combination of data classification and outlier detection procedures. To forecast future river discharge using space-based signals, the signal data set should not contain any gaps. Therefore, we introduced a procedure for the missing signals to be estimated by a model calibrated using the measured discharge. This procedure is illustrated using a case study for the White River, near Boston, MA, USA, that involved using a ddata set of three years’ daily signals of the passive microwave information from the Advanced Microwave Scanning Radiometer for Earth Observing System satellite, which are obtained from the difference between thermal emission of wet and dry land surfaces, and the in situ discharge measurements of the river. The best model was selected from 6200 available models using the Pareto front. The optimum model detected 339 samples as outliers and eliminated them from the data set. Subsequently, the signals were calibrated with the in situ information, and the results indicated the superior accuracy in simulating White River discharge using satellite information. [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]

Haiyang-2 (HY-2) is the first marine dynamic environmental satellite of China, which was launched on August 16, 2011. The scanning microwave radiometer (RM) onboard HY-2 has low-frequency channels with the capability of observing sea surface temperature (SST) from space. In this paper, the Level 2A (L2A) SST products of HY-2 RM are evaluated. The global HY-2 RM L2A SST products are compared with the buoy SST measurements, WindSat SST, and National Oceanic and Atmospheric Administration Optimum Interpolation (OI) weekly SST products for the period from January 2012 to December 2014. The collocations of HY-2 RM, WindSat, and buoy SST data are generated with the spatial window of 0.25° and the temporal window of 0.5 h. The biases are −0.45 °C (RM minus buoy) and −0.41 °C (RM minus WindSat) and the corresponding standard deviations are 1.73 °C and 1.72 °C. The comparisons of the weekly averaged HY-2 RM and OI SST show that the biases of each week difference are from −1.06 °C to 0.48 °C with the mean value of −0.30 °C. The standard deviations of the SST difference are from 0.83 °C to 1.47 °C with the mean value of 1.05 °C. The relationships between SST difference and the sea surface and atmospheric parameters, such as wind speed, wind direction, SST, and water vapor are investigated. [ABSTRACT FROM PUBLISHER]

Microwave Humidity Sounder (MWHS) onboard the Chinese FengYun-3C satellite has a total of 12 channels. Eight of these channels are located near the 118-GHz oxygen absorption band for probing atmospheric temperature and humidity fields from space. While the water vapor sounding channels near 183 GHz have been extensively studied in the past, we report the first satellite observations at 118 GHz. In this paper, the MWHS calibration accuracy is assessed by comparing the satellite observations with simulations. Using the collocated Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) radio occultation (RO) data in clear-sky conditions as inputs to Community Radiative Transfer Model, MWHS brightness temperatures are simulated for the five upper level sounding channels 2–9 located near the 118-GHz oxygen absorption band. For quality control of clear-sky radiance, a new cloud index is first developed based on the two MWHS window channels 1 and 10. Monthly mean biases of the antenna brightness temperature observations for the 118-GHz sounding channels are quantified by using more than 2000–5000 collocated COSMIC and MWHS data from August 2014 to February 2015. It is found that the bias of MWHS data relative to COSMIC RO simulation is dependent on channel and ranges within ±1.5 K. The standard deviation from the bias is the largest at MWHS channel 2, indicating a larger variability of the measurements of channel 2 than the other channels. [ABSTRACT FROM PUBLISHER]

The Soil Moisture and Ocean Salinity (SMOS) satellite provides retrievals of soil moisture in roughly the upper 5 cm with a 30–50-km resolution and a mission accuracy requirement of 0.04 cm3/cm-3. These observations can be used to improve land surface model (LSM) soil moisture states through data assimilation (DA). In this paper, SMOS soil moisture retrievals are assimilated into the Noah LSM via an Ensemble Kalman Filter within the National Aeronautics and Space Administration Land Information System. Bias correction is implemented using cumulative distribution function (cdf) matching, with points aggregated by either land cover or soil type to reduce the sampling error in generating the cdfs. An experiment was run for the warm season of 2011 to test SMOS DA and to compare assimilation methods. Verification of soil moisture analyses in the 0–10-cm upper layer and the 0–1-m root zone was conducted using in situ measurements from several observing networks in central and southeastern United States. This experiment showed that SMOS DA significantly increased the anomaly correlation of Noah soil moisture with station measurements from 0.45 to 0.57 in the 0–10-cm layer. Time series at specific stations demonstrates the ability of SMOS DA to increase the dynamic range of soil moisture in a manner consistent with station measurements. Among the bias correction methods, the correction based on soil type performed best at bias reduction but also reduced correlations. The vegetation-based correction did not produce any significant differences compared with using a simple uniform correction curve. [ABSTRACT FROM PUBLISHER]

Previous studies have shown that thermal infrared anomalies can be detected in the crust rocks in satellite infrared images before an earthquake. However, thermal infrared remote sensing is easily affected by weather conditions because of the short wavelength of infrared radiation. In this paper, instead of infrared radiation, we focus on the microwave radiation characteristics of loaded rock. First, a microwave observation system was built to observe the loading process of rocks in an outdoor environment with a cold sky background. Then, the microwave radiation changes in the loaded granite samples during elastic deformation and fracturing stages were analyzed. The experiments yielded the following results. First, the microwave brightness temperature has a linear positive correlation with the load in the elastic deformation stage of the granite samples, and lateral pressure accelerates the changes in the microwave radiation. Second, the microwave brightness temperature usually decreases as the rock develops layered fractures but increases as the rock develops surface fractures, which significantly alter the surface morphology and roughness. The mechanisms responsible for the changes in microwave radiation during the rock deformation and fracturing processes are discussed. This study demonstrates the potential ability to use microwave-sensing satellites to observe seismogenic processes and earthquakes. [ABSTRACT FROM AUTHOR]

The Advanced Technology Microwave Sounder (ATMS) on board the Suomi National Polar-orbiting Partnership (NPP) satellite is a total power radiometer and scans across the track with a range of ±52.77° from nadir. It has 22 channels and measures the microwave radiation at either quasi-vertical or quasi-horizontal polarization from the Earth's atmosphere. The ATMS scanning reflector is made of beryllium coated with gold and can have an emission due to the surface roughness. During prelaunch phase, an estimate of the reflector emissivity was not explored. In this paper, a new methodology is developed to assess the antenna emission from the ATMS pitch-over observations. It is found that the antenna emission is significant and dominates the scan-angle-dependent features in the ATMS antenna temperatures. Retrieved emissivity from K- to G-bands ranges from 0.002 to 0.006. An error model was also developed to assess the impact of antenna emissivity to calibration accuracy of antenna temperature products. Simulation results show that the calibration error is scene temperature dependent and can be as large as 2.5 K for space view. [ABSTRACT FROM PUBLISHER]

Vertically and horizontally inhomogeneous distributions of hydrometeors are often observed in precipitating clouds. The 3-D characteristics can then cause errors in the passive microwave rainfall measurements with the current off-nadir viewing sensors' specifications. This result is due to the fact that the same surface rainfall could be associated with different amounts of hydrometeors depending on the sensors' viewing paths. In this paper, we confirmed that the plane-parallel radiative treatment to the atmosphere leaves a notable deficiency in the microwave radiometric signatures, particularly at the higher frequency channels for different viewing directions when largely inhomogeneous precipitating clouds are accompanied by significant ice particles. The mean differences between the two brightness temperature fields with two opposite azimuthal viewing directions were up to approximately 40 °K for the vertically polarized channel at 85.5 GHz in the case study. The impact of the 3-D effect on the passive microwave rainfall estimations was also examined by synthetic retrievals employing a Bayesian methodology. The results showed that the uncertainty in the rainfall estimations due to the 3-D effect depended on the viewing directions considered in the a priori information. It was also found that taking more viewing angles or the azimuth angles in the a priori information into consideration tended to moderate the retrieval difference that resulted from the different viewing directions. In addition, the retrieval uncertainty related to the 3-D effect appeared to be more significant for heavy rainfall cases with large amounts of ice particles, as expected. [ABSTRACT FROM PUBLISHER]

Passive microwave sensors have better capability of penetrating forest layers to obtain more information from forest canopy and ground surface. For forest management, it is useful to study passive microwave signals from forests. Passive microwave sensors can detect signals from needleleaf, broadleaf, and mixed forests. The observed brightness temperature of a mixed forest can be approximated by a linear combination of the needleleaf and broadleaf brightness temperatures weighted by their respective abundances. For a mixed forest observed by an N-band microwave radiometer with horizontal and vertical polarizations, there are 2 N observed brightness temperatures. It is desirable to infer 4 N $+$ 2 unknowns: 2 N broadleaf brightness temperatures, 2 N needleleaf brightness temperatures, 1 broadleaf abundance, and 1 needleleaf abundance. This is a challenging underdetermined problem. In this paper, we devise a novel method that combines microwave unmixing with video segmentation for inferring broadleaf and needleleaf brightness temperatures and abundances from mixed forests. We propose an improved Otsu method for video segmentation to infer broadleaf and needleleaf abundances. The brightness temperatures of needleleaf and broadleaf trees can then be solved by the nonnegative least squares solution. For our mixed forest unmixing problem, it turns out that the ordinary least squares solution yields the desired positive brightness temperatures. The experimental results demonstrate that the proposed method is able to unmix broadleaf and needleleaf brightness temperatures and abundances well. The absolute differences between the reconstructed and observed brightness temperatures of the mixed forest are well within 1 K. [ABSTRACT FROM AUTHOR]

Most of the inverse problems arising in applied electromagnetics come from an underdetermined direct problem, this is the case, for instance, of spatial resolution enhancement. This implies that no unique inverse operator exists; therefore, additional constraints must be imposed on the sought solution. When dealing with microwave remote sensing, among the possible choices, the minimum $p$–norm constraint, with $1 < p \leq 2$, allows obtaining reconstructions in Hilbert $(p=2)$ and Banach $(1 < p < 2) $ subspaces. Recently, it has been experimentally proven that reconstructions in Banach subspaces mitigate the oversmoothing and the Gibbs oscillations that typically characterize reconstructions in Hilbert subspaces. However, no fair intercomparison among the different reconstructions has been done. In this paper, a mathematical framework to analyze reconstructions in Hilbert and Banach subspaces is provided. The reconstruction problem is formulated as the solution of a $p$–norm constrained minimization problem. Two signals are considered that model abrupt and spot-like discontinuities. The study, undertaken in both the noise-free and the noisy cases, demonstrates that $l^p$ reconstructions for $1 < p < 2$ significantly outperform the $l^2$ ones when spot-like discontinuities are considered; when dealing with abrupt discontinuities, $l^2$ and $l^p$ reconstructions are characterized by similar performance; however, $l^p$ reconstructions exhibit oscillations when the background is not properly accounted for. [ABSTRACT FROM PUBLISHER]

Microwave remote sensing has been widely acknowledged as the most promising technique to measure the spatial distribution of near-surface soil moisture. However, due to a strong incidence angle dependence in microwave radiometer and radar data, airborne observations typically have an across-track variation in incidence angle that needs to be normalized to a fixed angle for the purposes of data visualization and aggregation to spatial resolutions that mimic spaceborne data. There are two normalization methods commonly used, often resulting in a noticeable stripe pattern along the flight direction. This paper develops a 2-D cumulative distribution function (CDF)-based normalization method, which normalizes the variable-angle observations to a reference angle by matching the CDF of observations for each nonreference angle, using the information content from multiple partially overlapped swaths. The performance of this method is tested using an airborne microwave radiometer and radar observations collected during three Australian field experiments. The normalization results show that the stripe pattern problem over heterogeneous land surfaces when not any prior knowledge of land surface types is primarily attributed to the linearity of the commonly used normalization methods, and that the nonlinear 2-D CDF-based method produced the least noticeable stripe pattern and the highest normalization accuracy when compared with independent data. Compared with the two linear methods, a root-mean-squared error improvement of up to 2 K was obtained using 1-km radiometer data, and a correlation coefficient improvement of 0.2 and RMSE improvement of ∼0.2 dB were achieved for the 7-m resolution radar data. [ABSTRACT FROM PUBLISHER]

The dual-frequency precipitation radar onboard the Global Precipitation Measurement (GPM) core satellite has reflectivity measurements at two independent frequencies, i.e., Ku-band and Ka-band. Dual-frequency retrieval algorithms have been developed traditionally through forward, backward, and recursive approaches. However, these algorithms suffer from a “dual value” problem when they retrieve median volume diameter D0 from a dual-frequency ratio (DFR) in the rain region. It has been shown in the literature that a linear constraint of the drop-size distribution along the rain profile is a reasonable assumption to avoid the “dual value” problem. In this paper, a hybrid method is proposed to retrieve DSDs by combining the forward method and the linear constraint. The forward method is applied to ice and melting ice regions, whereas the linear constraint is applied to the rain region. The method is evaluated using data-based simulation. Different error sources, including sensitivity of snow density, system bias, and attenuation from nonprecipitating particles, are considered. The hybrid method is compared with the surface reference with weak constraint method and the Hitschfeld-Bordan DFR method and shows reasonable comparisons, particularly for medium-to-heavy precipitation. Retrieval examples for Hurricane Earl are shown using the hybrid method. [ABSTRACT FROM PUBLISHER]

The Soil Moisture and Ocean Salinity (SMOS) satellite was launched by the European Space Agency on November 2, 2009. Its payload, i.e., Microwave Imaging Radiometer with Aperture Synthesis, which is a 2-D L-band interferometric radiometer, measures the brightness temperatures (BTs) in the protected 1400-1427-MHz band. Although this band was preserved for passive measurements, numerous radio frequency interferences (RFIs) are clearly visible in SMOS data. One method to get rid of these interferences is to create a synthetic signal as close as possible to the measured interference and subtract it from the instrument visibilities. In this paper, we describe an approach to create such a signal and on how to use it for geolocalization of the emitters. Then, different methods for assessing the quality of the mitigation are introduced. Due to the complexity of estimating the effects of mitigation globally, it is finally proposed to use mitigation results to create flag maps about the estimated RFI impact, to be associated with BT measurements. [ABSTRACT FROM PUBLISHER]