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Across spatial and temporal scales, sea-ice motion has implications on ship navigation, the sea-ice thickness distribution, sea ice export to lower latitudes and re-circulation in the polar seas, among others. Satellite remote sensing is an effective way to monitor sea-ice drift globally and daily, especially using the wide swaths of passive microwave missions. Since the late 1990s, many algorithms and products have been developed for this task. Here, we investigate how processing sea-ice drift vectors from the intersection of individual swaths of the Advanced Microwave Scanning Radiometer 2 (AMSR2) mission compares to today's status-quo (processing from daily averaged maps of brightness temperature). We document that the swath-to-swath (S2S) approach results in many more (two orders of magnitude) sea-ice drift vectors than the daily-maps (DM) approach. These S2S vectors also validate better when compared to trajectories of on-ice drifters. For example, the RMSE of the 24 hour Arctic sea-ice drift is 0.9 km for S2S vectors, and 1.3 km for DM vectors from the 36.5 GHz imagery of AMSR2. Through a series of experiments with actual AMSR2 data and simulated Copernicus Imaging Microwave Radiometer (CIMR) data, we study the impact that geo-location uncertainty and imaging resolution have on the accuracy of the sea-ice drift vectors. We conclude by recommending that a swath-to-swath approach is adopted for the future operational Level-2 sea-ice drift product of the CIMR mission. We outline some potential next steps towards further improving the algorithms, and making the user community ready to fully take advantage of such a product.

Global warming is one of the most serious problems we are facing in the 21st Century. Sea ice has an important role of reflecting the solar radiation back into space. However, once sea ice started to melt, the ice-free water would absorb the solar radiation and amplify global warming in the Arctic region. Thus, importance of sea ice monitoring is increasing. Since longer wavelength microwave can penetrate clouds, passive microwave radiometers on-board satellites are powerful tools for monitoring the global distribution of sea ice on daily basis. The Advanced Passive Microwave Scanning Radiometer AMSR2 which was launched by JAXA in May 2012 on-board GCOM-W satellite provides brightness temperature data that are used to estimate sea ice concentration, the fundamental parameter that is used to monitor the sea ice cover. JAXA is providing AMSR2 sea ice concentration data, derived using ASMR2 Bootstrap Algorithm as a standard product of AMSR2, as a means to communicate how the sea ice cover is changing. This paper describes the advantages of AMSR2 in calculating sea ice concentration and evaluate the accuracy of the sea ice concentration in the Sea of Okhotsk by comparing the result with simultaneously collected MODIS data. The result suggested that under normal winter condition, the RMSE of the AMSR2 sea ice concentration could be less than 10%.

Land surface temperature (LST) is a key parameter and plays an important role in hydrology, ecology, environment, and biogeochemistry. It is difficult for the existing satellites to acquire global LST with spatial and temporal consistency. The Moon-based Earth observation platform with a long life, large coverage can observe continuously the Earth, and obtain the global-scale LST. At present, various approaches for retrieving LST from passive microwave remote sensing data have been developed for the satellite remote sensing data with small and constant viewing zenith angle, however, the Moon-based Earth observation platform located outside the Earth's ionosphere has the viewing zenith angle of 0-90°. In this study, a modified physical-based method of LST retrieval from passive microwave data was developed for wide viewing zenith angles, and the LST and emissivity can be simultaneously estimated through the analysis of various atmospheric and ionosphere parameters. Three types of data, including the FengYun-3B satellite microwave radiation imager data, the multichannel Advanced Microwave Scanning Radiometer data, and Moon-based microwave radiation simulation data with the viewing zenith angles of 52-53°, 55°, and 0-90°, respectively, were used to retrieve LST. Results show that the estimation accuracy of LST decreases with the increase of viewing zenith angle. The 23.8 and 36.5 GHz brightness temperature is optimum for the LST estimation under a large-scale viewing zenith angle, and the root mean square errors of the LST are 5.18, 5.44, and 4.79 K, respectively.

Microwave Imager Combined Active and Passive (MICAP), which is a package of active and passive microwave instruments including L/C/K-band radiometers and L-band scatterometer, has been approved to be taken onbord the Chinese Ocean Salinity Mission. The L-band one-dimensional synthetic aperture radiometer (L-Rad) is the key part of MICAP to measure sea surface salinity (SSS). Since radio frequency interference (RFI) is reported as a serious threat to L-band radiometry, the RFI detection and mitigation techniques must be carefully designed before launch. However, these techniques need to be developed based on the knowledge of how RFI affects complex correlation, visibility function, and reconstructed brightness temperature. This paper presents a time-domain signal modeling method for the simulation of interferometric measurement under RFI’s presences, and a simulation system for L-Rad is established accordingly. Several RFI cases are simulated with different RFI types, parameters, and positions, and the RFI characteristics upon L-Rad’s measurement are discussed. The proposed simulation system will be further dedicated to the design of RFI processing strategy onboard MICAP.

Ground-based multichannel microwave radiometers (GMRs) can observe the atmospheric microwave radiation brightness temperature at K-bands and V-bands and provide atmospheric temperature and humidity profiles with a relatively high temporal resolution. Currently, microwave radiometers are operated in many countries to observe the atmospheric temperature and humidity profiles. However, a theoretical analysis showed that a radiometer can be used to observe solar radiation. In this work, we improved the control algorithm and software of the antenna servo control system of the GMR so that it could track and observe the sun and we use this upgraded GMR to observe solar microwave radiation. During the observation, the GMR accurately tracked the sun and responded to the variation in solar radiation. Furthermore, we studied the feasibility for application of the GMR to measure the absolute brightness temperature (TB) of the sun. The results from the solar observation data at 22.235, 26.235, and 30.000 GHz showed that the GMR could accurately measure the TB of the sun. The derived solar TB measurements were 9950 ± 334, 10,351 ± 370, and 9217 ± 375 K at three frequencies. In a comparison with previous studies, we obtained average percentage deviations of 9.1%, 5.3%, and 4.5% at 22.235, 26.235, and 30.0 GHz, respectively. The results demonstrated that the TB of the sun retrieved from the GMR agreed well with the previous results in the literature. In addition, we also found that the GMR responded to the variation in sunspots and a positive relationship existed between the solar TB and the sunspot number. According to these results, it was demonstrated that the solar observation technique can broaden the field usage of GMR.

形態: カラー図版あり, Physical characteristics: Original contains color illustrations, Accepted: 2020-11-01, 資料番号: PA2110066000

An advanced algorithm for atmospheric water vapor column (WVC) retrieval from the Advanced Microwave Scanning Radiometer (AMSR) measurements over the Arctic sea ice (SI) and open ocean waters is presented. The algorithm is built on the physical modeling of the brightness temperature (BT) of the microwave radiation of the SI–open ocean–atmosphere system at the AMSR frequencies and polarizations. The BTs are calculated using a data set of the SI, atmospheric, and oceanic parameters changing in the range of their natural variability in the Arctic, and using the SI microwave emission coefficients varied according to the published experimental data. The inverse operator explores neural networks (NNs), trained on an ensemble of modeled BTs. The algorithm is applied both to the AMSR-E and to the AMSR2 measurement data. Validation of the algorithm is performed with radiosonde (r/s) WVC measurements from the four Arctic coastal stations at different SI conditions during 2014–2017. The results of the application of the new algorithm to satellite radiometer measurements are also compared with the Era-Interim reanalysis WVC, as well as with other satellite WVC products, based on the data of the Moderate Resolution Imaging Spectrometer (MODIS) and on the data of the Advanced Microwave Sounding Unit-B (AMSU-B) for 2008 and 2015. To justify the usage of the Era-Interim WVC as a reference data set for the algorithm accuracy estimation in the Arctic area, Era-Interim WVC is also compared with the r/s WVC measurements.

The self-designed HaiYang-2B (HY-2B) satellite was launched on 24 October 2018 in China at 22:57 UT in a 99.34&deg, inclination sun-synchronous orbit. The Scanning Microwave Radiometer (SMR) on the core observatory has the capability to provide near-real-time multi-channel brightness temperature (Tb) observations, which are designed mainly for improving the level of marine forecasting and monitoring, serving the development and utilization of marine resources. After internal calibration and ocean calibration, the first effort to retrieve land surface snow parameters was performed in this study, which obtained extremely low accuracy both in snow extent and snow mass. Accordingly, land inter-sensor calibration was carried out between SMR and the Advanced Microwave Scanning Radiometer 2 (AMSR2) in order to broaden the research and application of SMR data on the Earth&rsquo, s land surface. Finally, we evaluated the consistency of the snow extent and snow mass derived from the initial and land-calibrated SMR data. The results indicated that a systematic SMR cold deviation whose magnitude depends on the channel is present for all the compared channels. After intercalibration, the conformity of the snow extent and snow mass were substantially improved compared to before, the relative bias of the snow extent and snow mass decreased from &minus, 49.97% to 2.97% and from &minus, 51.71% to 3.01%, respectively.

Accurate representation of the ocean-atmosphere coupling in weather, wave and climate models requires reliable estimates of air-sea surface fluxes of momentum, heat and mass. Whitecap fraction (W) usually quantifies the enhancement of the surface fluxes due to wave breaking. Satellite-based passive remote sensing of W from ocean surface brightness temperatures ( T B s) observes open ocean surface fluxes at low spatial resolution. Radiometric surface observations at higher resolution are necessary to monitor the complex environment in the coastal zone and in polar regions. We assess the feasibility of using the millimeter-wave frequencies (89 to 150 GHz) to observe whitecaps. We evaluate the derivative of the T B with respect to W as a measure for the observation of W. We describe the models and data used to evaluate the T B sensitivity to W for different instrumental and environmental conditions. Atmospheric absorption limits the ability to observe the surface at millimeter-wave frequencies. We find that the T B sensitivity to W at 89 GHz may be sufficient to support limited W retrieval from observations at altitudes below 1 km and that the T B sensitivity at 113 and 150 GHz is not sufficient. Clear skies, and low to moderate atmospheric humidity favor whitecap observations.

In this study, we address the variations of bare soil surface microwave brightness temperatures and evaluate the performance of a dielectric mixing model over the desert of Kuwait. We use data collected in a field survey and data obtained from NASA Soil Moisture Active Passive (SMAP), European Space Agency Soil Moisture and Ocean Salinity (SMOS), Advanced Microwave Scanning Radiometer 2 (AMSR2), and Special Sensor Microwave/Imager (SSM/I). In situ measurements are collected during two intensive field campaigns over bare, flat, and homogeneous soil terrains in the desert of Kuwait. Despite the prevailing dry desert environment, a large range of soil moisture values was monitored, due to precedent rain events and subsequent dry down. The mean relative difference (MRD) is within the range of &plusmn, 0.005 m3&middot, m&minus, 3 during the two sampling days. This reflects consistency of soil moisture in space and time. As predicted by the model, the higher frequency channels (18 to 19 GHz) demonstrate reduced sensitivity to surface soil moisture even in the absence of vegetation, topography and heterogeneity. In the 6.9 to 10.7 GHz range, only the horizontal polarization is sensitive to surface soil moisture. Instead, at the frequency of 1.4 GHz, both polarizations are sensitive to soil moisture and span a large dynamic range as predicted by the model. The error statistics of the difference between observed satellite brightness temperature (Tb) (excluding SMOS data due to radio frequency interference, RFI) and simulated brightness temperatures (Tbs) show values of Root Mean Square Deviation (RMSD) of 5.05 K at vertical polarization and 4.88 K at horizontal polarization. Such error could be due to the performance of the dielectric mixing model, soil moisture sampling depth and the impact of parametrization of effective temperature and roughness.

The Canadian Arctic Archipelago (CAA) presents unique challenges to the determination of melt onset (MO) using remote sensing data. High spatial resolution data is required to discern melt onset among the islands and narrow waterways of the region. Current passive microwave retrievals use daily averaged 19 GHz and 37 GHz data from the multi-channel microwave radiometer (SMMR) and/or the special sensor microwave/imager (SSM/I). The development of a new passive microwave melt onset method capable of using higher resolution data is desirable. The new passive microwave melt onset method described here, named the Dynamic Threshold Variability Method (DTVM), uses higher resolution data from the 37 GHz vertically-polarized channel from the advanced microwave scanning radiometers (AMSR-E and AMSR-2). The DTVM MO detection methodology differs from previously presented passive microwave Arctic MO methods in that it does not use a fixed threshold of a brightness temperature parameter. Instead, the DTVM determines MO dates based on the distribution of dates corresponding to the exceedance of a range of brightness temperature variability thresholds. The method also uses swath data instead of daily averaged brightness temperatures, which is found to lead to improved melt detection. Two current passive microwave MO methods are compared and evaluated for applicability in the CAA alongside the DTVM. The DTVM provides MO dates at a higher spatial resolution than earlier methods in addition to higher correlation with MO dates from surface air temperature (SAT) reanalyses. It is found that, for some years, MO dates in the CAA exhibit a latitudinal dependence, while in other years the MO dates in the CAA are relatively uniform across the domain.

Land surface temperature (LST) is an important parameter that directly affects the water and heat balance between the Earth surface and atmosphere. Mapping the LST distribution at continuous temporal and wide spatial scales is very helpful for researching many physical and biochemical processes. Remotely sensed instruments are the key players in these studies. Passive microwave remotely sensed data have the advantage of retrieving the land parameters under nearly all weather conditions because of the power to penetrate clouds. In this study, a practical two-stage algorithm, which uses single-frequency and double-polarization passive microwave brightness temperature observations, is presented to retrieve the LST over China. The vertically polarized land surface emissivity (LSE) at 18.7 GHz is first estimated by a parameterization relationship with the polarization ratio (PR), which is defined as the ratio of the horizontal to the vertical brightness temperatures at the same frequency. Subsequently, the LST is retrieved using the estimated LSE by ignoring the atmospheric effect. The evaluation of the simulated data shows an RMSE of 1.45 K, which is very encouraging. The cross validations by the satellite thermal infrared products are from daily to monthly time scales. The daily accuracy is 3.04 K and the bimonthly accuracy is 4.43 K. A high positive bias in arid and semiarid regions and a negative bias in frequently cloudy regions are noticeable in both comparisons. These biases do not reveal the uncertainties of this LST retrieval algorithm; on the contrary, the retrieved the Advanced Microwave Scanning Radiometer − EOS (AMSR-E) LST appears to be more reasonable compared to the thermal infrared LST under certain circumstances.

This work presents an overview of the potential of microwave indices obtained from multi-frequency/polarization radiometry in detecting the characteristics of land surfaces, in particular soil covered by vegetation or snow and agricultural bare soils. Experimental results obtained with ground-based radiometers on different types of natural surfaces by the Microwave Remote Sensing Group of IFAC-CNR starting from ‘80s, are summarized and interpreted by means of theoretical models. It has been pointed out that, with respect to single frequency/polarization observations, microwave indices revealed a higher sensitivity to some significant parameters, which characterize the hydrological cycle, namely: soil moisture, vegetation biomass and snow depth or snow water equivalent. Electromagnetic models have then been used for simulating brightness temperature and microwave indices from land surfaces. As per vegetation covered soils, the well-known tau-omega (τ-ω) model based on the radiative transfer theory has been used, whereas terrestrial snow cover has been simulated using a multi-layer dense-medium radiative transfer model (DMRT). On the basis of these results, operational inversion algorithms for the retrieval of those hydrological quantities have been successfully implemented using multi-channel data from the microwave radiometric sensors operating from satellite.

Accurately measuring and monitoring the thickness distribution of thin ice is crucial for accurate estimation of ocean–atmosphere heat fluxes and rates of ice production and salt flux in ice-affected oceans. Here we present results from helicopter-borne brightness temperature (TB) measurements in the Southern Ocean in October 2012 and in the Sea of Okhotsk in February 2009 carried out with a portable passive microwave (PMW) radiometer operating at a frequency of 36 GHz. The goal of these measurements is to aid evaluation of a satellite thin-ice thickness algorithm which uses data from the spaceborne Advanced Microwave Scanning Radiometer–Earth Observing System AMSR-E) or the Advanced Microwave Scanning Radiometer-II (AMSR-II). AMSR-E and AMSR-II TB agree with the spatially collocated mean TB from the helicopter-borne measurements within the radiometers’ precision. In the Sea of Okhotsk in February 2009, the AMSR-E 36GHz TB values are closer to the mean than the modal TB values measured by the helicopter-borne radiometer. In an Antarctic coastal polynya in October 2012, the polarization ratio of 36GHz vertical and horizontal TB is estimated to be 0.137 on average. Our measurements of the TB at 36 GHz over an iceberg tongue suggest a way to discriminate it from sea ice by its unique PMW signature.

For four decades, satellite-based passive microwave sensors have provided valuable snow water equivalent (SWE) monitoring at a global scale. Before continuous long-term SWE records can be used for scientific or applied purposes, consistency of SWE measurements among different sensors is required. SWE retrievals from two passive sensors currently operating, the Special Sensor Microwave Imager Sounder (SSMIS) and the Advanced Microwave Scanning Radiometer 2 (AMSR2), have not been fully evaluated in comparison to each other and previous instruments. Here, we evaluated consistency between the Special Sensor Microwave/Imager (SSM/I) onboard the F13 Defense Meteorological Satellite Program (DMSP) and SSMIS onboard the F17 DMSP, from November 2002 to April 2011 using the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) for continuity. Likewise, we evaluated consistency between AMSR-E and AMSR2 SWE retrievals from November 2007 to April 2016, using SSMIS for continuity. The analysis is conducted for 1176 watersheds in the North Central U.S. with consideration of difference among three snow classifications (Warm forest, Prairie, and Maritime). There are notable SWE differences between the SSM/I and SSMIS sensors in the Warm forest class, likely due to the different interpolation methods for brightness temperature (Tb) between the F13 SSM/I and F17 SSMIS sensors. The SWE differences between AMSR2 and AMSR-E are generally smaller than the differences between SSM/I and SSMIS SWE, based on time series comparisons and yearly mean bias. Finally, the spatial bias patterns between AMSR-E and AMSR2 versus SSMIS indicate sufficient spatial consistency to treat the AMSR-E and AMSR2 datasets as one continuous record. Our results provide useful information on systematic differences between recent satellite-based SWE retrievals and suggest subsequent studies to ensure reconciliation between different sensors in long-term SWE records.

Two experimental brightness temperature data sets are revisited: A ground-based campaign of terrestrial snow in 1985 and an airborne research program of snow on lake ice in 2007–2014. Both data sets were obtained in southern Finland using multi-frequency microwave radiometers. In this paper both the overall behavior of the brightness temperature during the ground-based campaign and results from the intensive data collection period on April 12–13 (melt/freeze cycle) are discussed. Additionally, selected results from the airborne snow/lake ice program are compared with theoretical values. Two theoretical models are used for simulation.

The Advanced Microwave Scanning Radiometer 2 (AMSR2) on board the Global Change Observation Mission-Water 1 launched in May 2012 provides brightness temperature at two different spatial resolutions globally with an average temporal resolution of two days. Surface soil moisture is retrieved using land parameter retrieval model (LPRM) and level-3 brightness temperature data (10.65- and 36.5-GHz channels). The present LPRM implementation has AMSR2 brightness temperatures only with dielectric and vegetation parameters derived from 10.65- and 36.5-GHz channels, respectively. Retrieved soil moisture in the Indian region for 0.25 degrees and 0.1 degrees grid cells is validated and compared over several sites with in situ measurements. It is observed that in comparing the LPRM retrieved soil moisture with field measurements over various sites, 0.1 degrees grid performed relatively better over 0.25 degrees grid.

The development and continuity of consistent long-term data records from similar overlapping satellite observations is critical for global monitoring and environmental change assessments. We developed an empirical approach for inter-calibration of satellite microwave brightness temperature (Tb) records over land from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and Microwave Scanning Radiometer 2 (AMSR2) using overlapping Tb observations from the Microwave Radiation Imager (MWRI). Double Differencing (DD) calculations revealed significant AMSR2 and MWRI biases relative to AMSR-E. Pixel-wise linear relationships were established from overlapping Tb records and used for calibrating MWRI and AMSR2 records to the AMSR-E baseline. The integrated multi-sensor Tb record was largely consistent over the major global vegetation and climate zones; sensor biases were generally well calibrated, though residual Tb differences inherent to different sensor configurations were still present. Daily surface air temperature estimates from the calibrated AMSR2 Tb inputs also showed favorable accuracy against independent measurements from 142 global weather stations (R2 ≥ 0.75, RMSE ≤ 3.64 °C), but with slightly lower accuracy than the AMSR-E baseline (R2 ≥ 0.78, RMSE ≤ 3.46 °C). The proposed method is promising for generating consistent, uninterrupted global land parameter records spanning the AMSR-E and continuing AMSR2 missions.

Space-borne passive microwave radiometers are widely used to retrieve information in snowy regions by exploiting the high sensitivity of microwave emission to snow properties. For the Antarctic Plateau, many studies presenting retrieval algorithms or numerical simulations have assumed, explicitly or not, that the subpixel-scale heterogeneity is negligible and that the retrieved properties were representative of whole pixels. In this paper, we investigate the spatial variations of brightness temperature over a range of a few kilometers in the Dome C area. Using ground-based radiometers towed by a vehicle allowing measurements with meter resolution, we collected brightness temperature transects at 11, 19 and 37 GHz at horizontal and vertical polarizations. The most remarkable observation was a series of regular undulations of the signal with a significant amplitude of up to 10 K at 37 GHz and a quasi-period of 30–50 m. In contrast, the variability at longer length scales seemed to be weak in the investigated area and the mean brightness temperature was close to AMSR-E and WindSat satellite observations for all the frequencies and polarisations. To establish a link between the snow characteristics and undulation-scale variations of microwave emission, we collected detailed snow grain size and density profiles to run the DMRT-ML microwave emission model at two points where opposite extrema of brightness temperature were observed. The numerical simulations revealed that the difference in density of the upper first meter of the snowpack explained most of the brightness temperature variations. In addition, we found in the field that these variations of density were linked to the hardness of the snowpack. Areas of hard snow – probably formed by the wind – were clearly visible and covered as much as 39% of the investigated area. Their brightness temperature was higher than in normal areas. This result implied that the microwave emission measured by satellites over Dome C is more complex than expected and very likely depends on the areal proportion of the two different types of areas having distinct snow properties.

A new methodology to derive the spatial distribution of clay pans from satellite microwave data is presented. Soil moisture has a different temporal signature in clay pans compared with other soils, which is directly reflected in the satellite-observed brightness temperatures. Three years of Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) 6.9 GHz microwave observations were compiled and analysed over continental Australia to identify clay pans. This led to the development of a brightness temperature variance index (BTVI), which shows a strong spatial correspondence to an existing soil texture map and the ability to map clay pans for semi-arid regions. This simple method emphasizes the potential use of passive microwave remote sensing for soil type mapping. © 2013 Taylor & Francis.

Soil moisture plays an important role in understanding climate change and hydrology, and L-band passive microwave radiometers have been verified as effective tools for monitoring soil moisture. This paper proposes a novel, simplified algorithm for bare surface soil moisture retrieval using L-band radiometer. The algorithm consists of two sub-algorithms: a surface emission model and a soil moisture retrieval model. In analyses of the advanced integral equation model (AIEM) simulated database, the surface emission model was developed to diminish the effects of surface roughness using dual-polarization surface reflectivity. The soil moisture retrieval model, which was calibrated using the Dobson simulated database, is based on the relationship between the adjusted real refractive index N r and the volumetric soil moisture. Soil moisture can be determined via a numerical solution that uses several freely available input parameters: dual-polarization microwave brightness temperature, surface temperature, and the contents of sand and clay. The results showed good agreement with the input soil moisture values simulated by the AIEM model, with root mean square errors (RMSEs) lower than 3% at all incidence angles. The algorithm was then verified based on data from the four-year L-band experiments conducted at Beltsville Agricultural Research Center (BARC) test sites, achieving RMSEs of 4.3% and 3.4% at 40° and 50°, respectively. These results indicate that the simplified algorithm proposed in this paper shows a very good accuracy in soil moisture retrieval. Additionally, the algorithm exhibits a better performance for the large incidence angle radiometers in L-band such as those produced by the Soil Moisture Active and Passive (SMAP).

Measurements of the Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the Global Change Observation Mission–Water 1 (GCOM-W1) satellite at 6.925 and 7.3 GHz and both linear polarizations over tropical cyclones (TCs) during 2012–2014 are used to derive a new geophysical function relating the brightness temperature to the sea surface wind speed (SWS) in extreme conditions. Similar sensitivity to the SWS at close C-band frequencies allowed correcting for the atmospheric contributions to the microwave radiance and estimating the brightness temperature $(T_{B})$ at the surface under TCs, combining theoretical modeling and measured $T_{B}$ analyses. Estimated oceanic $T_{B}$ 's were regressed against the wind speeds from the Best Track Archive to derive a new geophysical model function for the wind speed excess emissivity at AMSR2 C-band microwave frequencies.

Consistent negative polarization differences (i.e. differences between the vertical and the horizontal brightness temperature) are observed when looking at precipitating systems by ground-based radiometers at slant angles. These signatures can be partially explained by one-dimensional radiative transfer computations that include oriented non-spherical raindrops. However some cases are characterized by polarization values that exceed differences expected from one-dimensional radiative transfer. A three-dimensional fully polarized Monte Carlo model has been used to evaluate the impact of the horizontal finiteness of rain shafts with different rain rates at 10, 19, and 30 GHz. The results show that because of the reduced slant optical thickness in finite clouds, the polarization signal can strongly differ from its one-dimensional counterpart. At the higher frequencies and when the radiometer is positioned underneath the cloud, significantly higher negative values for the polarization are found which are also consistent with some observations. When the observation point is located outside of the precipitating cloud, typical polarization patterns (with troughs and peaks) as a function of the observation angle are predicted. An approximate 1-D slant path radiative transfer model is considered as well and results are compared with the full 3-D simulations to investigate whether or not three-dimensional effects can be explained by geometry effects alone. The study has strong relevance for low-frequency passive microwave polarimetric studies.

For anticipated synergistic approaches of the L-band radiometer on the Soil Moisture and Ocean Salinity (SMOS) mission with higher frequency microwave radiometers such as the Advanced Microwave Scanning Radiometer (AMSR) (C-band), a reanalysis has been performed on the frequency dependence of the linear relationship between vegetation optical depth (/spl tau//sub o/) and vegetation water content (W), given by /spl tau//sub o/=b/spl middot/W. Insight into the frequency dependence of the b-factor is important for the retrieval of surface moisture from dual- or multifrequency microwave brightness temperature observations from space over vegetation-covered regions using model inversion techniques. The b-values presented in the literature are based on different methods and approaches. Therefore, a direct comparison is not straightforward and requires a critical analysis. This paper confirms that when a large frequency domain is considered, the b-factor is inversely proportional to the power of the wavelength b=c/(/spl lambda/)/sup x/, which is in line with theoretical considerations. It was found that different canopy types could be separated into different groups, each with a different combination of values for log(c) and x, which characterize the linearized relationship log(b)=log(c)-x/spl middot/log(/spl lambda/). A comparison of ratios b/sub C//b/sub L/ (with C and L denoting C- and L-band, respectively) also resulted in basically the same groups.

Hyperspectral indices from the Compact Airborne Spectrographic Imager (CASI 550), together with land surface temperatures from the Thermal Airborne Spectrographic Imager (TASI 600) and brightness temperatures from a L-band radiometer were combined in a semi-empirical model to obtain soil moisture at very high spatial resolution (3.5 m). The airborne imagery was acquired in an experiment performed over the Soil Moisture Measurement Stations Network of the University of Salamanca (REMEDHUS) in 2012, including intensive field measurements. The feasibility of the hyperspectral optical bands from CASI acting as soil moisture proxy is tested through the cross-correlation between every possible two-CASI bands combination and in situ soil moisture. Next, the potential of combining the selected hyperspectral indices with thermal and microwave observations for soil moisture mapping is explored, synergistically with thermal and microwave observations, for obtaining soil moisture.

A new cloud dynamics and radiation database (CDRD) precipitation retrieval algorithm for satellite passive microwave (PMW) radiometer measurements has been developed. It represents a modification to and an improvement upon the conventional cloud radiation database (CRD) algorithms, which have always been prone to ambiguity. This part 2 paper of a series describes the methodology of the algorithm and the modeling verification analysis involved in creating a synthetic CDRD database for the Europe/Mediterranean basin region. This is followed by a proof-of-concept analysis, which demonstrates that the underlying CDRD theory based on use of meteorological parameters for reducing retrieval ambiguity is valid. This paper uses a regional/mesoscale model, applied in cloud resolving model (CRM) mode, to produce a large set of numerical simulations of precipitating storms and extended precipitating systems. The simulations are used for selection of millions of meteorological/microphysical vertical profiles within which surface rainfall is identified. For each of these profiles, top-of-atmosphere brightness temperature (TB) vectors are calculated (the vector dimension associated with the number of relevant cm-mm wavelengths and polarizations), based on an elaborate radiative-transfer equation (RTE) model system (RMS) coupled to the CRM. This entire body of simulation information is organized into the CDRD database, then used as a priori knowledge to guide a physical Bayesian retrieval algorithm in obtaining rainfall and associated precipitation parameters from the PMW satellite observations. We first prove the physical validity of our CRM-RMS simulations, by showing that the simulated TBs are in close agreement with observations. Agreement is demonstrated using dual-channel-frequency TB manifold sections, which quantify the degree of overlap between the simulated and observed TBs extracted from the full manifolds. Nevertheless, the salient result of this paper is a pro- f that the underlying CDRD theory is valid, found by combining subdivisions of the invoked meteorological parameter ranges of values and showing that such meteorological partitioning associates itself with distinct microphysical profiles. It is then shown that these profiles give rise to similar TB vectors, proving the existence of ambiguity in a CRD-type algorithm. Finally, we show that the CDRD methodology provides significant improvements in reducing retrieval ambiguity and retrieval error, especially for land surface backgrounds where contrasts are typically small between the rainfall TB signatures and surface emission signatures.

A multilayer neural network has been developed to retrieve IPWV over land from brightness temperatures measured by the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) on board the Aqua satellite. Our study was optimized over two areas in Northern and Central Italy. Good agreements on the order of 0.24 cm and 0.33 cm rms, respectively, were found between neural network retrievals and ECMWF IPWV data for clear-sky. In the presence of clouds, an rms of the order of 0.38 cm was found for both areas.

This work deals with mapping snow water equivalent as well as soil moisture at low resolution from multifrequency microwave radiometers. The algorithm developed and implemented in this work produces the spatial distribution at regional scale of snow depth (SD) and of soil moisture (SMC) of snow free areas by using the brightness temperatures of the Advanced Multifrequency Scanning Radiometer (AMSR-E).

The Radio frequency Interference Survey of Earth (RISE) is a new type of instrument used to survey and characterize the presence of Radio Frequency Interference (RFI) that can affect microwave radiometers. It consists of a combined microwave radiometer and kurtosis spectrometer with broad frequency coverage and high temporal and spectral resolution. A K-Band airborne version has been built and flown across southeast Michigan. A kurtosis detector is included in RISE to reliably detect the presence of RFI, even at very low levels, and to aid in its characterization. A radiometer is included to measure the impact of the RFI on observed brightness temperature.

International audience; This paper presents the African Monsoon Multidisciplinary Analysis (AMMA) Land Surface Models Intercomparison Project (ALMIP) for Microwave Emission Models (ALMIP-MEM). ALMIP-MEM comprises an ensemble of simulations of C-band brightness temperatures over West Africa for 2006. Simulations have been performed for an incidence angle of 55°, and results are evaluated against C-band satellite data from the Advanced Microwave Scanning Radiometer on Earth Observing System (AMSR-E). The ensemble encompasses 96 simulations, for 8 Land Surface Models (LSMs) coupled to 12 configurations of the Community Microwave Emission Model (CMEM). CMEM has a modular structure which permits combination of several parameterizations with different vegetation opacity and soil dielectric models. ALMIP-MEM provides the first intercomparison of state-of-the-art land surface and microwave emission models at regional scale. Quantitative estimates of the relative importance of land surface modeling and radiative transfer modeling for the monitoring of low-frequency passive microwave emission on land surfaces are obtained. This is of high interest for the various users of coupled land surface microwave emission models. Results show that both LSMs and microwave model components strongly influence the simulated top of atmosphere (TOA) brightness temperatures. For most of the LSMs, the Kirdyashev opacity model is the most suitable to simulate TOA brightness temperature in best agreement with the AMSR-E data. When this best microwave modeling configuration is used, all the LSMs are able to reproduce the main temporal and spatial variability of measured brightness temperature. Averaged among the LSMs, correlation is 0.67 and averaged normalized standard deviation is 0.98.

This letter is focused on the microwave signature characterization of the Greenland ice sheet. Such characterization is carried out by exploiting the S- and Ku-band brightness temperatures measured by the radar altimeter RA-2 when it operates as a radiometer during the ENVISAT Commissioning Phase for the purpose of calibrating the receiver. Despite the poor radiometric resolution and the calibration issues, this activity represented a unique opportunity to gather brightness temperatures at frequencies that are not available from current spaceborne microwave radiometers. The analysis of the passive RA-2 data investigates the influence of terrain height and of the temperature of the snow layers on the brightness temperatures at RA-2 bands. The effect of the different penetration depths of the electromagnetic radiation at S- and Ku-bands is also pointed out. Measurements from the Advanced Microwave Scanning Radiometer for the Earth Observing System are used to complement the data provided by RA-2 and to verify their reliability.

An alternative to thermal infrared satellite sensors for measuring land surface temperature (Ts) is presented. The 37 GHz vertical polarized brightness temperature is used to derive Ts because it is considered the most appropriate microwave frequency for temperature retrieval. This channel balances a reduced sensitivity to soil surface characteristics with a relatively high atmospheric transmissivity. It is shown that with a simple linear relationship, accurate values for Ts can be obtained from this frequency, with a theoretical bias of within 1 K for 70% of vegetated land areas of the globe. Barren, sparsely vegetated, and open shrublands cannot be accurately described with this single channel approach because variable surface conditions become important. The precision of the retrieved land surface temperature is expected to be better than 2.5 K for forests and 3.5 K for low vegetation. This method can be used to complement existing infrared derived temperature products, especially during clouded conditions. With several microwave radiometers currently in orbit, this method can be used to observe the diurnal temperature cycles with surprising accuracy. © 2009 by the American Geophysical Union.

A simulation study to quantify the influence of the topography on the measurements performed by a satellite microwave radiometer is accomplished in this work. The Northern Italy region (including Alps) is considered and the information on the relief, extracted from a digital elevation model (DEM), is exploited. We have developed a simulator of satellite microwave radiometric observations of mountainous scenes operating at different frequencies and observation angles. The simulation shows that the changes in the local observation angle generally tend to decrease the antenna temperature. The effect of the rotation of the polarization plane attenuates (for horizontal polarization) or enlarges (vertical polarization) this decrease. Facets illuminated by radiation from surrounding elevated terrain enhance their brightness temperature with respect to surfaces which scatter atmospheric downward radiation. At horizontal polarization, this results in a general overestimation of the brightness temperature with respect to that measured observing a flat terrain. At vertical polarization, both under and overestimation may occur.

International audience; The antenna temperature measured by a microwave radiometer is converted in brightness temperature (TB) by removing the different contributions that do not come from the main lobe of the antenna. Among them, the Earth contribution in the sidelobes may be significant as for the Environmental Satellite mission due to the antenna position on the platform. In such a case, simple corrections commonly applied on previous altimetry missions are inadequate, and a more accurate correction should be determined. We propose in this paper a new method based on global seasonal tables of contamination. This allows application of an accurate sidelobe correction in space and time in the retrieved TB computation

This paper deals with the development of a microwave, noise-adding radiometer, purposely designed for the fire detection in forest environments. The sensor operates at 12.65GHz and exploits commercial TV-SAT components such as a parabolic dish and a low-noise down-converter. First, an electromagnetic model is presented in order to estimate the radiometric contrast (increase of the brightness temperature with respect to the background) due to a fire covered by vegetation. Then, the design of the sensor is addressed, underlining the key technologies that allow the required performances to be attained at low industrial costs.

Satellite data are the most suitable tools for monitoring time and spatial variations of snow covered areas and for studying snow characteristics on a global scale. Current knowledge of the microwave emission from the deep ice sheet in Antarctica is limited by the lack of low-frequency satellite sensors and by their inadequate knowledge of the physical effects governing microwave emission at wavelengths exceeding 5 cm. On the other hand, in addition to the interest related to climatic changes and to glaciological and hydrological applications, there is growing interest, on the part of the remote sensing community, in using the Antarctic and, in particular, the Dome-C plateau where the Concordia station is located, for calibrating and validating data of satellite-borne microwave and optical radiometers. This is because of the size, structure, spatial homogeneity, and thermal stability of this area. With a view to the future launches of two new low-frequency spaceborne sensors Soil Moisture and Ocean Salinity mission and Aquarius, an experiment was carried out at Dome-C, thanks to financial support from European Space Agency, aimed at evaluating the stability and the absolute value of the L- and C-band brightness temperature Tb. This paper presents a report on the experimental campaign, the characteristics of the radiometric measurements, and on the main results. The C-band Tb data indicated a diurnal cycle amplitude of a few kelvin. It was confirmed that this takes place as a consequence of observed variability in the physical temperature of the top 4 m of the snowpack around the mean surface value of -24degC. In contrast, the L-band data indicated extremely stable Tb values of 192.32 K (1sigma=0.18 K) and 190.77 K (1sigma=0.57 K) at thetas=45deg and thetas=56deg, respectively

Surface melting duration and extent of the Antarctic coasts and ice-shelves is a climatic indicator related to the summer temperature and radiative budget. Surface melting is easily detectable by remote sensing using passive microwave observations. The preliminary goal of this study is to extend to 26 years an existing data set of surface melting [Torinesi, O., Fily, M., Genthon, C. (2003), Interannual variability and trend of the Antarctic summer melting period from 20 years of spaceborne microwave data, J. Climate, 16(7), pp. 1047–1060] by including the most recent years of observation. These data come from 4 microwave sensors (the Scanning Multichannel Microwave Radiometer (SMMR) and three Special Sensor Microwave Imager (SSM/I)) observing the surface at different hours of the day. Since surface melting varies throughout the day as the air temperature or the radiation, the interannual melting extent and duration time series are biased by sensor changes. Using all the sensors simultaneously available since 2002, we were able to model the diurnal variations of melting and use this hourly model to correct the long-term time series. This results in an unbiased 26-year long time series better suited for climate analysis. The cooling trend found by Torinesi et al. using uncorrected time series for the 1980–1999 period is confirmed but the decreasing rate is weaker after correction. Furthermore, extending the series up to summer 2004–2005 reveals recent changes: the last 2 summers have been particularly warmer over all the East Antarctica compared to the 10 previous years, thus ending the cold period of the 1990s. The trend over 1980–2005 is no longer toward cooling but complex climatic variations appear. The time series are available at http://www.lgge.obs.ujf-grenoble.fr/~picard/melting/ .

A study of the melting cycle of snow was carried out by using ground based microwave radiometers, which operated continuously 24 hours/day from end of March to mid May in 2002 and from mid February to early May in 2003. The experiment took place in the eastern Italian Alps and included micro-meteorological and conventional snow measurements as well. The measurements confirmed the high sensitivity of microwave emission at 19 and 37 GHz to the melting-refreezing cycles of snow. Moreover, micro-meteorological data made it possible to simulate the snow density, temperature and liquid water content through a hydrological snowpack model and provided additional insight into these processes. Simulations obtained with a two layer electromagnetic model based on the Strong Fluctuation Theory and driven by the output of the hydrological snowpack model were consistent with experimental data and allowed interpretation of both the variation of microwave emission in the melting and refreezing phases and in separating the contributions of the upper and lower layers of snow as well as of the underlying ground surface.

The SeaWinds scatterometer onboard the QuikSCAT satellite simultaneously measures the polarized microwave brightness temperature of the ocean/ atmosphere. QuikSCAT Radiometer (QRad) brightness temperatures are used to infer instantaneous oceanic rain rates using a statistical retrieval algorithm that has been developed using collocated QRad brightness temperatures with TRMM Microwave Imager (TMI) rain rate measurements. The algorithm produces earth-located instantaneous rain rate binned in 0.5 hour universal time windows and produced on a 50-km earth grid. The orbit of QuikSCAT allows independent rain temporal sampling that can be used to improve the estimation of global rainfall in 3 hour windows, which is a goal of the future Global Precipitation Mission (GPM). In this paper, QRad retrieved rain rate examples are presented and comparisons are made with the TRMM 3B42RT near real time product. Results demonstrate that QRad rain measurements agree well with these independent microwave rain observations and superior to the visible/infrared rain estimates, which demonstrates the utility of adding QRad to the 3B42RT product.

It is now well established that Synthetic Aperture Imaging Radiometers (SAIR) promise to be powerful sensors for high-resolution observations of the Earth at low microwave frequencies. Within this context, the European Space Agency (ESA) is currently developing the SMOS mission. This propagation of modelling errors within a reconstruction process that attempts to retrieve the brightness temperature of a scene under observation from interferometric measurements depends on the knowledge of the values of the parameters involved in the modelling of the instrument. This contribution describes an approach to characterize these modelling parameters, once the instrument has been launched into space, with an accuracy such that the propagation of errors through the reconstruction process is still under control.

The soil microwave effective temperature (T/sub e/) is an important parameter which improves the accuracy of the soil surface moisture derived from low frequency microwave radiometric observations. A new semi-empirical model of T/sub e/ at L- and C-bands is proposed. The model is based on the following inputs: the air temperature (T/sub a/), a deep soil temperature (T/sub d/), and the microwave brightness temperature measured at X-band (/spl lambda//spl sime/3 cm) and V polarization (T/sub BXV/). Unlike other approaches based on the surface temperature (T/sub s/), the proposed model can be implemented without being dependent on the clear sky conditions required to measure T/sub s/ with a spaceborne infrared radiometer. However, the proposed model may also use T/sub s/ when available. The model was designed from a large data set simulated by a physical model for smooth bare soil. The model of T/sub e/ was then successfully validated with experimental data acquired during a ground based experiment with the multifrequency PORTOS radiometer. This model designed from smooth soil data was successfully tested on rough bare soil using experimental data.

Soil moisture and temperature vertical profiles vary quickly during the day and may have a significant influence on the soil microwave emission. The objective of this work is to quantify such an influence and the consequences in soil moisture estimation from microwave radiometric information. The analysis is based on experimental data collected by the ground-based PORTOS radiometer at 1.4, 5.05, and 10.65 GHz and data simulated by a coherent model of microwave emission from layered media [Wilheit model (1978)]. In order to simulate diurnal variations of the brightness temperature (TB), the Wilheit model is coupled to a mechanistic model of heat and water flows in the soil. The Wilheit model is validated on experimental data and its performances for estimating TB are compared to those of a simpler approach based on a description of the soil media as a single layer (Fresnel model). When the depth of this single layer (hereafter referred to as the sampling depth) is determined to fit the experimental data, similar accuracy in TB estimation is found with both the Wilheit and Fresnel models. The soil microwave emission is found to be strongly affected by the diurnal variations of soil moisture and temperature profiles. Consequently, the TB sensitivity to soil moisture and temperature profiles has an influence on the estimation, from microwave observations, of the surface soil moisture in a surface layer with a fixed depth (05): the accuracy of θs retrievals and the optimal sampling depth depends both on the variation in soil moisture and temperature profile shape.