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The IMERG data product is an optimal combination of precipitation estimates from the Global Precipitation Mission (GPM), making use of a variety of data types, primarily data from various spaceborne passive instruments. Previous versions of the IMERG product have been extensively validated by comparisons with gauge data and ground-based radars over land. However, IMERG rain rates, especially sub-daily, over open ocean are less validated due to the scarcity of comparison data, particularly with the relatively new Version 07. To address this issue, we consider IMERG V07 30-min data acquired in tropical cyclones over open ocean. We perform two tasks. The first is a straightforward comparison between IMERG precipitation rates and those retrieved from the GPM Dual-frequency Precipitation Radar (DPR). From this, we find that IMERG and DPR are close at low rain rates, while, at high rain rates, IMERG tends to be lower than DPR. The second task is the assessment of IMERG's ability to represent or detect structures commonly seen in tropical cyclones, including the annular structure and concentric eyewalls. For this, we operate on IMERG data with many machine learning algorithms and are able to achieve a 96% classification accuracy, indicating that IMERG does indeed contain TC structural information. [ABSTRACT FROM AUTHOR]

The passive and active L-band system (PALS) is an airborne microwave instrument suite that combines a separate radar and radiometer airborne instrument with a common antenna. PALS has been used for salinity, wind speed, and soil moisture (SM) measurements, which is critical for calibration and validation of the SM active passive satellite brightness temperature measurements. The combined 1.413 GHz radiometer and 1.26 GHz radar measurements can provide high-accuracy SM measurements over vegetated areas. The new upgraded version of the PALS instrument employs a novel single-chain receiver topology and combines the radar and radiometer instruments. The reduced-noise single-chain receiver PALS instrument was deployed on a DC-3 aircraft for SM measurement in the summer of 2019.

The IMERG data product is an optimal combination of precipitation estimates from the Global Precipitation Mission (GPM), making use of a variety of data types, primarily data from various spaceborne passive instruments. Previous versions of the IMERG product have been extensively validated by comparisons with gauge data and ground-based radars over land. However, IMERG rain rates, especially sub-daily, over open ocean are less validated due to the scarcity of comparison data, particularly with the relatively new Version 07. To address this issue, we consider IMERG V07 30-min data acquired in tropical cyclones over open ocean. We perform two tasks. The first is a straightforward comparison between IMERG precipitation rates and those retrieved from the GPM Dual-frequency Precipitation Radar (DPR). From this, we find that IMERG and DPR are close at low rain rates, while, at high rain rates, IMERG tends to be lower than DPR. The second task is the assessment of IMERG’s ability to represent or detect structures commonly seen in tropical cyclones, including the annular structure and concentric eyewalls. For this, we operate on IMERG data with many machine learning algorithms and are able to achieve a 96% classification accuracy, indicating that IMERG does indeed contain TC structural information.

Subsurface electromagnetic sensing techniques that can measure material properties of hidden layers are useful for applications such as security screening. A system combining active (radar) and passive (radiometer) sensing into one unit, or a pactive sensor, can address drawbacks of extant single mode sensors by reducing measurement ambiguity and resolving features better, thereby leading to improved identification of the hidden layer. This work investigates a technique to noninvasively extract the complex permittivity and thickness of hidden dielectrics using a pactive sensor. A proof-of-concept demonstration of an optimization-based inversion technique is used to extract the properties of an unknown, hidden layer in a multi-layered dielectric structure. The technique uses active data to estimate dielectric constant and thickness, and passive data to estimate loss tangent. The experimental setup is selected to represent a simplified single-voxel security scenario consisting of multiple dielectric layers backed by a human phantom layer (heated water). Tests using K-band prototype radar and radiometer systems yielded results with less than 5% error in the extracted dielectric constant, loss tangent and thickness of the unknown layer.

This article extends the application of an existing change-detection-based, time-series soil moisture retrieval algorithm to nonconcurrent active and passive measurements from WindSat/AMSR2 and the Soil Moisture Active Passive radar, which was active from late April until mid-July of 2015. A time series of L-band radar backscatter observations was used to populate an underdetermined matrix equation whose optimal solution was derived via a bounded linear least squares estimator, and whose bounds were derived from a time series of radiometer-derived soil moisture estimates (taken by either WindSat or AMSR2). Surface soil moisture estimates are compared with in-situ measurement probes, which were treated as ground truth. Error statistics and time-series results for the validation sites are presented here and conclusions derived therefrom. The overall RMSE and unbiased RMSE for the retrieval algorithm, taken across all reference pixels considered in the study, were 0.070 $\mathbf{m}^{3}/\mathbf{m}^{3}$ and 0.067 $\mathbf{m}^{3}/\mathbf{m}^{3}$, respectively, when using WindSat to constrain the algorithm. When using AMSR2 to constrain the algorithm, the RMSE and unbiased RMSE were 0.093 $\mathbf{m}^{3}/\mathbf{m}^{3}$ and 0.090 $\mathbf{m}^{3}/\mathbf{m}^{3}$, respectively.

An active–passive microwave retrieval algorithm for simultaneous determination of soil surface roughness parameters [vertical root-mean-square (RMS) height (${s}$) and horizontal correlation length (${l}$)] is presented for bare soils. The algorithm is based on active–passive microwave covariation, including the improved Integral Equation Method (I2EM), and is tested with global soil moisture active passive (SMAP) observations. The estimated retrieval results for ${s}$ and ${l}$ are overall consistent with values in the literature, indicating the validity of the proposed algorithm. Sensitivity analyses showed that the developed roughness retrieval algorithm is independent of permittivity for ${\varepsilon }_{s} > 10$ [-]. Furthermore, the physical model basis of this approach (I2EM) allows the application of different autocorrelation functions (ACF), such as Gaussian and exponential ACFs. Global roughness retrieval results confirm bare areas in deserts such as Sahara or Gobi. However, the type of ACF used within roughness parameter estimation is important. Retrieval results for the Gaussian ACF describe a rougher surface than retrieval results for the exponential ACF. No correlations were found between roughness results and the amount of precipitation or the soil texture, which could be due to the coarse spatial resolution of the SMAP data. The extension of this approach to vegetated soils is planned as an add-on study. [ABSTRACT FROM AUTHOR]

Subject and purpose. The main factor which affects the development of modern marine radars and increase of their efficiency is the correct justification of the concept of construction and selection of technical requirements for the development of new devices. The purpose of this work is to analyze the existing methods of marine radar construction, generalize the results of long-term radio physical researches in different regions of the World Ocean and in the Black Sea water area as well, and on their basis to develop the concept for applied complex radar for surface situation control and navigation. Methods and methodology. During the development of construction concept and analysis of the offered radar features the method of mathematical modeling was used. Results. The concept for the complex radar system construction for surface situation control and navigation consisting of 3-cm radar and 8-mm radar with radiometer device is proposed. When choosing 3-cm wave range, the results of long-term radio physical researches conducted by Usikov IRE NASU in different regions of the World Ocean and the Black Sea water area were considered. The usage of 8-mm wave range allows one to carry out radar observation in the near-field zone where signals of 3-cm radar interfere and create strong reflections from the sea surface. The necessity to use 8-mm wave range radiometer is caused by the fact that reduction of the effective scattering cross section due to applied radio absorbing materials leads to increasing of object’s own radio brightness radiation. Therefore to detect barely visible (produced under Stealth technology) and low-sized targets the radiometer which allows to increase target contrast can be used. Conclusion. Conclusions on practical importance of the offered concept for construction of complex radar for surface situation control and navigation are made.

The Tropical Rainfall Measuring Mission (TRMM) satellite was launched in 1997, and the observations continued for more than 17 years. The features of TRMM observation were as follows: (a) it followed a non-sun synchronized orbit that enabled the diurnal variation of precipitation to be investigated; (b) it carried a precipitation radar and microwave and infrared radiometers, along with two instruments of opportunity in the form of a lighting sensor and a radiation budget sensor; and (c) it worked as a standard reference for precipitation measurements for other spaceborne microwave radiometers, which enabled global rain maps to be developed. For science, TRMM provided precise and accurate rain distributions over tropical and subtropical regions. The rainfall results are primarily essential for the study of precipitation climatologies, whereas the three-dimensional images of precipitation systems enabled the study of the global characteristics of precipitation systems. Technologically, the spaceborne rain radar onboard TRMM demonstrated the effectiveness of radars in space, whereas the combination with other rain observation instruments showed its effectiveness as a calibration source. Multi-satellite rain maps in which TRMM was the reference standard have been developed, and they became prototypes of the multi-satellite Earth observation systems. On the basis of the great success of TRMM, the Global Precipitation Measurement (GPM) was designed to expand TRMM's coverage to higher latitudes. The core satellite of GPM is equipped with a dual-frequency precipitation radar (DPR) and a microwave radiometer. DPR comprises a Kuband radar (KuPR) and a Ka-band radar (KaPR) and can discriminate solid from liquid precipitation. The period of the precipitation measurement with spaceborne radars extended to more than 23 years, which may make it possible to detect the change of precipitation climatology related to change in the global environment. Although TRMM's and GPM's accomplishments are very broad, this paper attempts to highlight Japan's contributions to the science of these missions. [ABSTRACT FROM AUTHOR]

The Global Precipitation Measurement (GPM) Dual-Frequency Precipitation Radar (DPR) (Ku- and Ka-band, or 14 and 35 GHz) provides the capability to resolve the precipitation structure under moderate to heavy precipitation conditions. In this manuscript, the use of near-coincident observations between GPM and the CloudSat Profiling Radar (CPR) (W-band, or 94 GHz) are demonstrated to extend the capability of representing light rain and cold-season precipitation from DPR and the GPM passive microwave constellation sensors. These unique triple-frequency data have opened up applications related to cold-season precipitation, ice microphysics, and light rainfall and surface emissivity effects.

The Radar Vegetation Index (RVI) is a well-established microwave metric of vegetation cover. The index utilizes measured linear scattering intensities from co- and cross-polarization and is normalized to ideally range from 0 to 1, increasing with vegetation cover. At long wavelengths (L-band) microwave scattering does not only contain information coming from vegetation scattering, but also from soil scattering (moisture & roughness) and therefore the standard formulation of RVI needs to be revised. Using global level SMAP L-band radar data, we illustrate that RVI runs up to 1.2, due to the pre-factor in the standard formulation not being adjusted to the scattering mechanisms at these low frequencies. Improvements on the RVI are subsequently proposed to obtain a normalized value range, to remove soil scattering influences as well as to mask out regions with dominant soil scattering at L-band (sparse or no vegetation cover). Two purely vegetation-based RVIs (called RVII and RVIII), are obtained by subtracting a forward modeled, attenuated soil scattering contribution from the measured backscattering intensities. Active and passive microwave information is used jointly to obtain the scattering contribution of the soil, using a physics-based multi-sensor approach; simulations from a particle model for polarimetric vegetation backscattering are utilized to calculate vegetation-based RVI-values without any soil scattering contribution. Results show that, due to the pre-factor in the standard formulation of RVI the index runs up to 1.2, atypical for an index normally ranging between zero and one. Correlation analysis between the improved radar vegetation indices (standard RVI and the indices with potential improvements RVII and RVIII) are used to evaluate the degree of independence of the indices from surface roughness and soil moisture contributions. The improved indices RVII and RVIII show reduced dependence on soil roughness and soil moisture. All RVI-indices examined indicate a coupled correlation to vegetation water content (plant moisture) as well as leaf area index (plant structure) and no single dependency, as often assumed. These results might improve the use of polarimetric radar signatures for mapping global vegetation.

A robust physics-based combined active–passive (C-AP), or active–passive, surface soil moisture and roughness estimation methodology is presented. Soil moisture and roughness retrieval is performed via optimization, i.e., minimization, of a joint objective function, which constrains similar resolution radar and radiometer observations simultaneously. A data-driven and noise-dependent regularization term has also been developed to automatically regularize and balance corresponding radar and radiometer contributions to achieve optimal soil moisture retrievals. It is shown that in order to compensate for measurement and observation noise, as well as forward model inaccuracies, in C-AP estimation, surface roughness can be considered a free parameter. Extensive Monte Carlo numerical simulations and assessment using field data have been performed both to evaluate the algorithm’s performance and to demonstrate soil moisture estimation. Unbiased root mean squared errors range from 0.18 to 0.03 cm3/cm3 for two different land-cover types of corn and soybean. In summary, in the context of soil moisture retrieval, the importance of consistent forward emission and scattering development is discussed and presented. [ABSTRACT FROM PUBLISHER]

In this paper, we introduce the combined active/passive (CAP) data product for the Soil Moisture Active Passive mission. We develop the algorithms for a radiometer-only salinity product, a radar-only vector wind product, and a CAP vector wind and salinity product. We show that the performance of the radiometer-only salinity product nears but is still inferior to the Aquarius salinity accuracy performance when aggregated on a monthly timescale. Then, we show that the radar-only vector wind product has reasonable accuracy away from the nadir track while suffering from inadequate measurement geometry in the middle of the swath. Finally, we demonstrate that the CAP salinity and vector wind performance is superior to individual algorithms and provides wind vectors nearly as good as RapidScat for low-to-moderate winds and possibly superior to traditional scatterometers for wind speeds larger than 12.5 m/s. [ABSTRACT FROM PUBLISHER]

The L-band passive microwave data from the Soil Moisture Active Passive (SMAP) observatory are investigated for remote sensing of ocean surface winds during severe storms. The surface winds of Joaquin derived from the real-time analysis of the Center for Advanced Data Assimilation and Predictability Techniques at Penn State support the linear extrapolation of the Aquarius and SMAP geophysical model functions (GMFs) to hurricane force winds. We apply the SMAP and Aquarius GMFs to the retrieval of ocean surface wind vectors from the SMAP radiometer data to take advantage of SMAP's two-look geometry. The SMAP radiometer winds are compared with the winds from other satellites and numerical weather models for validation. The root-mean-square difference (RMSD) with WindSat or Special Sensor Microwave Imager/Sounder is 1.7 m/s below 20-m/s wind speeds. The RMSD with the European Center for Medium-Range Weather Forecasts direction is 18° for wind speeds between 12 and 30 m/s. We find that the correlation is sufficiently high between the maximum wind speeds retrieved by SMAP with a 60-km resolution and the best track peak winds estimated by the National Hurricane Center and the Joint Typhoon Warning Center to allow them to be estimated by SMAP with a correlation coefficient of 0.8 and an underestimation by 8%–18% on average, which is likely due to the effects of spatial averaging. There is also a good agreement with the airborne Stepped-Frequency Radiometer wind speeds with an RMSD of 4.6 m/s for wind speeds in the range of 20–40 m/s. [ABSTRACT FROM PUBLISHER]

While it has been proved that multiple scattering in the microwave frequencies has to be accounted for in precipitation retrieval algorithms, the effects of the random arrangements of drops in space has seldom been investigated. The fact is, a single rain drop size distribution (RDSD) corresponds with many actual 3D distributions of those rain drops and each of those may a priori absorb and scatter radiation in a different way. Each spatial configuration is equivalent to any other in terms of the RDSD function, but not in terms of radiometric characteristics, both near and far from field, because of changes in the relative phases among the particles. Here, using the T-matrix formalism, we investigate the radiometric variability of two ensembles of 50 different 3D, stochastically-derived configurations from two consecutive measured RDSDs with 30 and 31 drops, respectively. The results show that the random distribution of drops in space has a measurable but apparently small effect in the scattering calculations with the exception of the asymmetry factor.

The objective of the NASA Soil Moisture Active Passive (SMAP) mission is to provide global measurements of soil moisture and freeze/thaw states. SMAP integrates L-band radar and radiometer instruments as a single observation system combining the respective strengths of active and passive remote sensing for enhanced soil moisture mapping. Airborne instruments are a key part of the SMAP validation program. Here, we present an airborne campaign in the Rur catchment, Germany, in which the passive L-band system Polarimetric L-band Multi-beam Radiometer and the active L-band system F-SAR of DLR were flown simultaneously on six dates in 2013. The flights covered the full heterogeneity of the area under investigation, i.e., the main land cover types and all experimental monitoring sites. Here, we used the obtained data sets as a test bed for the analysis of three active–passive fusion techniques: 1) estimation of soil moisture by passive sensor data and subsequent disaggregation by active sensor backscatter data; 2) disaggregation of passive microwave brightness temperature by active microwave backscatter and subsequent inversion to soil moisture; and 3) fusion of two single-source soil moisture products from radar and radiometer. Results indicate that the regression parameters $\beta$ are dependent on the radar vegetation index. The best performance was obtained by the fusion of radiometer brightness temperatures and radar backscatter, which was able to reach the same accuracy as single-source coarse-scale radiometer soil moisture retrieval but on a higher spatial resolution. [ABSTRACT FROM AUTHOR]

The impact of ocean waves on L-band brightness temperatures and backscatter from the ocean surface was analyzed using Aquarius data. Matchups of Aquarius data with significant wave height (SWH) from National Oceanic and Atmospheric Administration (NOAA) WaveWatch3 reanalysis and the ocean surface wind are generated. We perform the analysis using two different wind speed products: special sensor microwave imager/sounder (SSMI/S) and National Center for Environmental Prediction (NCEP) operational data. Interestingly, the influence of SWH manifests itself differently for these two wind speed products. Conditionally, averaged normalized radar cross-section (\sigma _0) and brightness temperatures (\textT\text{B}) by NCEP wind speed show strong influence by SWH over the entire range of NCEP wind speeds with a larger impact at lower wind speeds. However, performing the same analysis conditioned on the SSMI/S wind speed, the conclusion becomes entirely different: the SWH effects appear small at low wind speeds (< 5\,\textm\texts^ - 1) and increase with increasing wind speed. The apparent contradiction is a result of the differing characteristics of NCEP and SSMI/S wind speeds. NCEP wind is an atmospheric model estimate of surface wind velocity, whereas the SSMI/S wind is a microwave remote sensing product, representing the characteristics of sea surface roughness. The impact of SWH needs to be considered when the SSMI/S wind speed is assimilated into the numerical weather prediction models, such as the NCEP. [ABSTRACT FROM PUBLISHER]

We present a method to combine same-resolution measurements of active radar and passive radiometer microwave remote sensing to build a framework for soil moisture estimation in support of the Soil Moisture Active and Passive (SMAP) mission. A unified active-passive soil moisture estimation algorithm is developed within a global optimization scheme, using a joint cost function with adaptive regularization, where, unlike traditional methods, both radar and radiometer measurements are utilized at the same time to retrieve soil moisture. Monte Carlo numerical simulations and optimization to retrieve soil moisture are performed for Corn, Soybean, and Grass landcover types for active-only, passive-only, and active-passive scenarios. These numerical experiments show that the proposed combined active-passive (CAP) soil moisture estimation approach outperforms either the single-sensor technique, particularly for higher vegetation water content (VWC) values (VWC > 3 kg/m2). For example, for the case of Corn with VWC of 5 kg/m2, retrieval error is reduced to 0.035 cm3/cm3 for the active-passive method from 0.08 cm3/cm3 for active scenarios. Furthermore, tests of this new algorithm on the Passive and Active L- and S-band Sensor (PALS) Soil Moisture Experiment 2002 (SMEX02), as well as the Combined Radar- Radiometer (ComRad) collocated active and passive data, demonstrate the applicability of this method to actual data, even with potentially inaccurate forward models and noisy data. Results indicate that the best soil moisture estimates over a large range of soil moisture (0.04-0.4 cm3/cm3) and vegetation (0-5 kg/m2) conditions are achievable when the adaptive regularization parameter γ is chosen to give slightly more weight to the radiometer forward model without discarding the complementary radar measurement points. [ABSTRACT FROM AUTHOR]

The purpose of this study is to present statistics related to the integration of cloud and rain liquid water and the profiles for different cloud types and regimes. From 2010 to 2012, the CHUVA project collected information regarding cloud and rain characteristics in different precipitation regimes in Brazil. CHUVA had four field campaigns between 2010 and 2011, located in the North, Northeast and Southeast regions of Brazil, covering the semi-arid, Amazon, coastal and mountain regions. The synergy of several instruments allowed us to classify rain events and describe the cloud processes regionally. Microwave radiometers, LiDAR, radar, and disdrometers were employed in this study. The rain type classification was made using vertical profiles of reflectivity (VPR) and polarimetric variables from dual polarization radar (XPOL). The integrated liquid water (ILWC) for non-precipitating clouds was retrieved with a microwave ground-based radiometer using a neural network. For rainy conditions, the profiles from the rain liquid water content (LWCR) and their integrated (ILWR) properties were estimated by Micro Rain Radar (MRR) and XPOL VPRs. For non-precipitating clouds, the ILWC values were larger for the sites in tropical regions, in particular near the coast, than for Southeast Brazil. For rainy cases, distinct LWCR profiles were observed for different rain classifications and regions. The differences are small for low rain rates and a distinction between different rainfall regimes is more evident for high rain rates. Vale and Belém clouds present the deepest layers and largest convective rain rates. The clouds in the Southeast region of Brazil (Vale do Paraíba) and North region (Belém) showed the largest reflectivity in the mixed and glaciated layers, respectively. In contrast, the Northeast coastal site (e.g. Fortaleza) showed larger values in the warm part of the clouds. Several analyses are presented, describing the cloud processes and the differences among the cloud types, rain rates and regions. [ABSTRACT FROM AUTHOR]

The L-band passive and active microwave geophysical model functions (GMFs) of ocean surface winds from the Aquarius data are derived. The matchups of Aquarius data with the Special Sensor Microwave Imager (SSM/I) and National Centers for Environmental Prediction (NCEP) winds were performed and were binned as a function of wind speed and direction. The radar HH GMF is in good agreement with the PALSAR GMF. For wind speeds above 10 \m\cdot\s^-1, the L-band ocean backscatter shows positive upwind–crosswind (UC) asymmetry; however, the UC asymmetry becomes negative between about 3 and 8 \m\cdot\s^-1. The negative UC (NUC) asymmetry has not been observed in higher frequency (above C-band) GMFs for ASCAT or QuikSCAT. Unexpectedly, the NUC symmetry also appears in the L-band radiometer data. We find direction dependence in the Aquarius TBV, TBH, and third Stokes data with peak-to-peak modulations increasing from about a few tenths to 2 K in the range of 10–25- \m\cdot\s^-1 wind speed. The validity of the GMFs is tested through application to wind and salinity retrieval from Aquarius data using the combined active–passive algorithm. Error assessment using the triple collocation analyses of SSM/I, NCEP, and Aquarius winds indicates that the retrieved Aquarius wind speed accuracy is excellent, with a random error of about 0.75 \m\cdot\s^-1. The wind direction retrievals also appear reasonable and accurate above 10 \m\cdot\s^-1. The results of the error analysis indicate that the uncertainty of the GMFs for the wind speed correction of vertically polarized brightness temperatures is about 0.14 K for wind speed up to 10 \m\cdot\s^-1. [ABSTRACT FROM AUTHOR]

Scattering results from snowflakes generated from aggregates comprised of 6-branch bullet rosette crystals are compared with those obtained from spherical or spheroidal ice-air mixed phase particles. The scattering parameters of the aggregates are computed by the discrete dipole approximation (DDA) numerical approach at frequencies from 10.65 to 183.31 GHz (where the particular frequencies match those of the Global Precipitation Measurement (GPM) Microwave Imager (GMI) and Dual-frequency Precipitation Radar (DPR)). Two mass density models are used to specify the snow density of the simple geometrical particles. In one model, the density is prescribed as a function of the maximum dimension of the aggregates (variable snow density model); in the other the density is independent of particle size (fixed snow density model). Comparisons of the results indicate that the scattering parameters of large complex aggregates differ significantly from those obtained from the equivalent-mass spherical/spheroidal-shaped particles when variable snow density is assumed. In contrast to the variable snow density model, the scattering properties of the aggregates are fairly well reproduced by the fixed snow density model. Although the results from a fixed snow densities between 0.2 and 0.3 g/cm³ reveal good overall agreement with those from the aggregates, the value of 0.2 g/cm³ yields the best agreement for the frequencies less than or equal to 35.6 GHz while the model results with a density set to 0.3 g/cm³ show the best match with the aggregates at frequencies higher than 35.6 GHz. Moreover, scattering parameters of the randomly-oriented spheroidal ice-air mixtures tend to agree better with those from the aggregates than do spherical snow particles. It is anticipated that these findings will have direct implication for the development of the GPM algorithms for estimates of precipitation rate. [ABSTRACT FROM PUBLISHER]

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]

Several soil moisture and sea salinity campaigns, including airborne L-band radiometer measurements, have been carried out in preparation for the European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission. The radiometer used in this context is fully polarimetric and is capable of detecting radio frequency interference (RFI) using the kurtosis method. Analyses have shown that the kurtosis method generally detects RFI in an efficient manner, particularly concerning pulsed, low duty cycle signals, but it has some shortcomings when it comes to more continuous wave signal types. Hence, other detection methods have been investigated as well. In particular, inspection of the third and fourth Stokes parameters shows promising results-possibly as a complement to the kurtosis method. The kurtosis method, however, cannot be used with SMOS data. Since SMOS is fully polarimetric, the third and fourth Stokes parameter method is an option, and a first assessment using a fully polarimetric SMOS data set looks promising. Finally, a variable incidence angle signature algorithm is introduced, and the possibility of using this as an RFI indicator is discussed. [ABSTRACT FROM PUBLISHER]

A robust and simple algorithm is developed to merge L-band radiometer retrievals and L-band radar observations to obtain high-resolution (9-km) soil-moisture estimates from data of the NASA Soil Moisture Active and Passive (SMAP) mission. The algorithm exploits the established accuracy of coarse-scale radiometer soil-moisture retrievals and blends this with the fine-scale spatial heterogeneity detectable by radar observations to produce a high-resolution optimal soil-moisture estimate at 9 km. The capability of the algorithm is demonstrated by implementing the approach using the airborne Passive and Active L-band System (PALS) instrument data set from Soil Moisture Experiments 2002 (SMEX02) and a four-month synthetic data set in an Observation System Simulation Experiment (OSSE) framework. The results indicate that the algorithm has the potential to obtain better soil-moisture accuracy at a high resolution and show an improvement in root-mean-square error of 0.015–0.02- \cm^3/\cm^3 volumetric soil moisture over the minimum performance taken to be retrievals based on radiometer measurements resampled to a finer scale. These results are based on PALS data from SMEX02 and a four-month OSSE data set and need to be further confirmed for different hydroclimatic regions using airborne data sets from prelaunch calibration/validation field campaigns of the SMAP mission. [ABSTRACT FROM PUBLISHER]

This paper introduces a new concept for a smart wireless sensor web technology for optimal measurements of surface-to-depth profiles of soil moisture using in-situ sensors. The objective of the technology, supported by the NASA Earth Science Technology Office Advanced Information Systems Technology program, is to enable a guided and adaptive sampling strategy for the in-situ sensor network to meet the measurement validation objectives of spaceborne soil moisture sensors. A potential application for this technology is the validation of products from the Soil Moisture Active/Passive (SMAP) mission. Spatially, the total variability in soil-moisture fields comes from variability in processes on various scales. Temporally, variability is caused by external forcings, landscape heterogeneity, and antecedent conditions. Installing a dense in-situ network to sample the field continuously in time for all ranges of variability is impractical. However, a sparser but smarter network with an optimized measurement schedule can provide the validation estimates by operating in a guided fashion with guidance from its own sparse measurements. The feedback and control take place in the context of a dynamic physics-based hydrologic and sensor modeling system. The overall design of the smart sensor web—including the control architecture, physics-based hydrologic and sensor models, and actuation and communication hardware—is presented in this paper. We also present results illustrating sensor scheduling and estimation strategies as well as initial numerical and field demonstrations of the sensor web concept. It is shown that the coordinated operation of sensors through the control policy results in substantial savings in resource usage. [ABSTRACT FROM PUBLISHER]

L-band microwave backscatter and brightness temperature of sea surfaces acquired using the Passive/Active L-band Sensor during the High Ocean Wind campaign are reported in terms of their dependence on ocean surface wind speed and direction. We find that the L-band VV, HH, and HV radar backscatter data increase by 6-7 dB from 5 to 25 m/s wind speed at a 45° incidence angle. The data suggest the validity of Phased Array type L-band Synthetic Aperture Radar (PALSAR) HH model function between 5 and 15 m/s wind speeds, but show that the extrapolation of PALSAR model at above 20 m/s wind speeds overpredicts A0 and α1 coefficients. There is wind direction dependence in the radar backscatter with about 4 dB differences between upwind and crosswind observations at 24 m/s wind speed for VV and HH. The passive brightness temperatures show about a 5-K change for TV and a 7-K change for TH for a wind speed increasing from 5 to 25 m/s. Circle flight data suggest a wind direction response of about 1-2 K in TV and TH at 14 and 24 m/s wind speeds. The L-band microwave data show excellent linear correlation with the surface wind speed with a correlation better than 0.95. The results support the use of L-band radar data for estimating the wind-driven excess brightness temperature of sea surfaces. The data also support the applications of L-band microwave signals for high-resolution (kilometer scale) observation of ocean surface winds under high wind conditions (10-28 m/s). [ABSTRACT FROM AUTHOR]

A change detection algorithm has been developed in order to obtain high-resolution soil moisture estimates from future Soil Moisture Active and Passive (SMAP) L-band radar and radiometer observations. The approach combines the relatively noisy 3-km radar backscatter coefficients and the more accurate 36-km radiometer brightness temperature into an optimal 10-km product. In preparation for the SMAP mission, an observation system simulation experiment (OSSE) and field experimental campaigns using the Passive and Active L- and S-band Airborne Sensor (PALS) have been conducted. We use the PALS airborne observations and OSSE data to test the algorithm and develop an error budget table. When applied to four-month OSSE data, the change detection method is shown to perform better than direct inversion of the radiometer brightness temperatures alone, improving the root mean square error by 2% volumetric soil moisture content. The main assumptions of the algorithm are verified using PALS data from the soil moisture experiments held during June--July 2002 (Soil Moisture Experiment 2002) in Iowa. The algorithm error budget is estimated and shown to meet SMAP science requirements. [ABSTRACT FROM AUTHOR]

The Cassini RADAR is a multimode instrument used to map the surface of Titan, the atmosphere of Saturn, the Saturn ring system, and to explore the properties of the icy satellites. Four different active mode bandwidths and a passive radiometer mode provide a wide range of flexibility in taking measurements. The scatterometer mode is used for real aperture imaging of Titan, high-altitude (around 20 000 km) synthetic aperture imaging of Titan and Iapetus, and long range (up to 700 000 km) detection of disk integrated albedos for satellites in the Saturn system. Two SAR modes are used for high- and medium-resolution (300-1000 m) imaging of Titan's surface during close flybys. A high-bandwidth altimeter mode is used for topographic profiling in selected areas with a range resolution of about 35 m. The passive radiometer mode is used to map emission from Titan, from Saturn's atmosphere, from the rings, and from the icy satellites. Repeated scans with differing polarizations using both active and passive data provide data that can usefully constrain models of surface composition and structure. The radar and radiometer receivers show very good stability, and calibration observations have provided an absolute calibration good to about 1.3 dB. Relative uncertainties within a pass and between passes can be even smaller. Data are currently being processed and delivered to the planetary data system at quarterly intervals one year after being acquired. [ABSTRACT FROM AUTHOR]

In the present study, remote sensing of soil moisture is carried out using the Passive and Active L- and S-band airborne sensor (PALS). The data in this paper were taken from five days of overflights near Chickasha, OK during the 1999 Southern Great Plains (SGP99) experiment. Presently, we analyze the collected data to understand the relationships between the observed signals (radiometer brightness temperature and radar backscatter) and surface parameters (surface soil moisture, temperature, vegetation water content, and roughness). In addition, a radiative transfer model and two radar backscatter models are used to simulate the PALS observations. An integration of observations, regression retrievals, and forward modeling is used to derive the best estimates of soil moisture under varying surface conditions. [ABSTRACT FROM AUTHOR]

To improve precipitation estimation accuracy, new methods, which are able to merge different precipitation measurement modalities, are necessary. In this study, we propose a deep learning method to merge rain gauge measurements with a ground-based radar composite and thermal infrared satellite imagery. The proposed convolutional neural network, composed of an encoder–decoder architecture, performs a multiscale analysis of the three input modalities to estimate simultaneously the rainfall probability and the precipitation rate value with a spatial resolution of 2 km. The training of our model and its performance evaluation are carried out on a dataset spanning 5 years from 2015 to 2019 and covering Belgium, the Netherlands, Germany and the North Sea. Our results for instantaneous precipitation detection, instantaneous precipitation rate estimation, and for daily rainfall accumulation estimation show that the best accuracy is obtained for the model combining all three modalities. The ablation study, done to compare every possible combination of the three modalities, shows that the combination of rain gauges measurements with radar data allows for a considerable increase in the accuracy of the precipitation estimation, and the addition of satellite imagery provides precipitation estimates where rain gauge and radar coverage are lacking. We also show that our multi-modal model significantly improves performance compared to the European radar composite product provided by OPERA and the quasi gauge-adjusted radar product RADOLAN provided by the DWD for precipitation rate estimation. [ABSTRACT FROM AUTHOR]