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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.

Passive microwave brightness temperature (TB) radiometers are widely used to retrieve several atmospheric and surface parameters such as precipitation, soil moisture, freeze and thaw, water vapor, air temperature profile, and land surface emissivity. Since TBs are measured at different microwave frequencies with various instruments, incident angles, footprints, spatial resolutions, and radiometric characteristics, a combination of data from different microwave sensors could be inconsistent. For this reason, this study primarily uses the nonsynchronous Global Precipitation Measurement (GPM) Microwave Imager (GMI) measurements to construct the diurnal cycle of TBs for each month. This diurnal pattern could be used as a point of reference to validate and calibrate the diurnal cycle of TBs from fusing the measurements of other sensors with daily fixed acquisition times (SSM/I, SSMIS, AMSR2, and etc.). The data from these sensors should also be merged and harmonized in order to build a comprehensive global diurnal cycle of passive microwave TBs. This highly frequent diurnal cycle will eventually help predict the emissivity estimation and potentially further advance the accurate prediction of the estimated time of the freeze/thaw (FT) transition states. Global comparison of TBs obtained from different sensors shows a moderate variance with a significant dependence on land cover type. The results of this study can enhance the temporal detection of freeze and thaw which is more helpful during the transition times when multiple FT changes may occur within a day.

The method of flood monitoring based on Soil Moisture Active Passive (SMAP) radiometer data is put forward. The SMAP L1C enhanced brightness temperature product with 9 km spatial resolution is higher than others passive microwave data, such as AMSR-E, ATMS and SSM/I. In order to study the SMAP’s potential ability, the case of flood induced by Hurricane Harvey occurred in 2017 in United State was selected to apply the SMAP data to monitor the inundated water. The result showed that the flood area can be reflected based on SWAP data, and H polarization data is more sensitive to the V polarization data. The mixed pixel separation method is applied to estimate the water fraction and provide more detailed sub-pixel scale water information. The SMAP L1C enhanced brightness temperature product with 9 km spatial resolution can provide more detailed flood information than previous passive microwave data. By compare the result with Fengyun-3 Microwave Radiation Imager (FY-3/MWRI) data and NASA's Earth Observing System Moderate Resolution Imaging Spectroradiometer (EOS/MODIS) data, there are better accordance with the SMAP data, which prove the effect of SMAP data for flood monitoring.

Spaceborne microwave radiometers with high frequency channels (150 – 183 GHz) are important for retrieving many geophysical parameters such as snowfall, ice water path, and atmospheric water vapor profiles. In order to obtain accurate retrievals from the brightness temperature measurements, the radiometers must be properly calibrated. Several methods have been developed to analyze the on-orbit calibration, including comparisons with radiative transfer models, comparisons with radiosonde profiles, and cross-calibration with similar radiometers. This paper introduces a new method to calibrate high frequency channels that utilizes TB histograms. This new method gives an independent approach that can be used by itself to analyze radiometer calibration or in conjunction with other methods to corroborate results.

The states of the Earth surface in terms of high-latitude freeze and thaw (FT) cycles significantly impact many physical applications that include biogeochemical transitions, hydrological phenomena, and ecosystem evolution. We have shown that land surface emissivity estimates have great potential for use in the detection of FT states since that parameter primarily depends on surface characteristics instead of on direct use of brightness temperatures. This study aims to investigate the potential of merging passive microwave sensors and their land surface emissivity estimates from Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E), Special Sensor Microwave Imager (SSM/I), AMSR2, and the Global Precipitation Measurement (GPM) Microwave Imager (GMI) to provide high temporal resolution (sub-daily) FT states. This factor is of critical importance and usage, primarily during the transitions between freeze and thaw that frequently occur at sub-daily time-frames in spring seasons. Data fusion techinques were used to construct diurnal estimates in order to accurately predicting the exact time of the freeze-thaw transition for a variety of land cover types and geographical regions. The results revealed that emissivity difference values between low and high frequencies (such as 10.7 GHz and 89GHz) at horizontal polarization from multiple platforms have a strong correlation with ground-based soil temperature diurnal values at 5-cm depth. Evaluation of the proposed approach with independent ground observations from year 2015 to 2017 showed that the data fusion of land surface emissivities in high-latitudes was able to notably capture the frequent FT transitions.

Conventional satellite intercalibration for microwave imaging radiometers involves a linear two-point calibration approach, over cold ocean scenes and warm ends of the scene brightness temperatures, traditionally over tropical rain forest. Unfortunately, this approach ignores the possibility of non-linear radiometer transfer function, which is often important. This paper discusses a new tool developed to identify large-scale homogeneous desert scenes as possible third calibration targets with brightness temperatures that fall between the conventional cold and warm earth scenes. Previously a prototype “ShowMask” procedure was developed over continental Australia using 10, 18 and 37 GHz Vertical and Horizontal polarized brightness temperatures from GPM Microwave Imager (GMI) data. This paper focus on extending this tool to global scale and including 89 GHz channel in the analysis. Further, by applying the mask, we examine intercalibration results over selected desert targets using GMI-TMI and GMI-AMSR-2 match-ups. Initial results indicate a scene temperature dependence of calibration factor particularly for horizontally polarized channels.

Since the problem of reconstructing the radiometric brightness temperature images in synthetic aperture imaging radiometers has been mathematically demonstrated to be ill-posed, it needs to be regularized to overcome its ill-conditioned properties. The LSQR iteration algorithm is proposed to retrieve brightness temperature images so as to provide a unique and stable solution. The performance is compared with the conventional minimum norm regularization based on the FPIR system. The simulation results show that the LSQR iteration algorithm can effectively reduce the reconstruction error, compared with the minimum norm regularization.

In this paper we summarize the effort to enhance the SMAP radiometer data. The applied technique is based on the Backus-Gilbert theory which is the classical estimation method in microwave radiometry. We show details of our implementation and summarize the assessment of the SMAP L1C_TB_E product.

The Tropical Rainfall Measurements Mission (TRMM) Microwave Imager (TMI), has served as the radiometric transfer standard for the TRMM constellation radiometers for over 17 years. The final legacy processing for the TMI is in progress for the TMI 1B11 Tb Version-8 (V8), which will be released in late 2017. This paper presents a radiometric evaluation of the Tb differences between the current TMI Version-7 (V7) and the newly derived V8 dataset. For this evaluation, we perform an inter-satellite radiometric calibration (XCAL) between the Global Precipitation Mission Microwave Imager (GMI) Tb product and TMI Tb products for both V7 and V8. The results of XCAL using different datasets are compared to assess the TMI V8 calibration biases relative to V7.

The Hurricane Imaging Radiometer (HIRAD) is an experimental C-band airborne microwave radiometer developed by NASA Marshall Space Flight Center (MSFC) to provide hurricane's surface wind speed and rain rate. It is intended to expand the current NOAA and US Air Force hurricane surveillance capability by extending the operational Stepped Frequency Microwave Radiometer (SFMR) measurements to a wide-swath hurricane image. This paper discusses the evaluation of HIRAD radiometric brightness temperature images on a relatively uniform ocean scene during the unmanned Global Hawk scientific flight over the Pacific Ocean in 2012. The objective of this analysis is to assess the accuracy and stability of HIRAD Tb measurement. This objective is accomplished by comparing HIRAD measured and theoretical Tbs.

The Earth-scene brightness temperature (TB) seen by satellite radiometers is generally cold over the ocean due to low emissivity, warm over land with high emissivity, and with a large dynamic range dependent on frequency and polarization. Calibration at either cold or warm end cannot fully characterize the calibration dependence on the full TB dynamic range. We have developed calibration methods using both warm and cold reference TB tie points and applied them to the Global Precipitation Measurement (GPM) mission particularly for the GPM Microwave Imager (GMI). The two-end method characterizes the GMI calibration dependence on TB and enables the development of TB-dependent intercalibration correction tables for GPM.

The ray tracing method based on space meshing is employed for microwave radiation imaging in this paper. With the scene models and the tracing codes, the correctness and the effectiveness of the space meshing ray tracing method is verified by comparing the reconstructive images and the actual output ones for the radiometer imaging problems in three dimension scenes.

SMOS is the acronym for the Soil Moisture and Ocean Salinity mission by the European Space Agency (ESA) [1]. Its single payload, the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS), was successfully launched in November 2009 to provide a continuous flow of fully polarimetric brightness temperature images. After more than five years of operation, SMOS has proven to be highly useful for a variety of scientific applications related to soil moisture over land, ocean salinity and winds over ocean, as well as specific studies over the ice covered surfaces [2][3]. Along these years of operation, SMOS calibration and imaging algorithms have undergone a continuous evolution to further improve the accuracy of the retrieved geophysical parameters. These improvements are particularly useful in Ocean Salinity applications where accuracy and stability requirements are very demanding and spatial and temporal averaging is required to achieve SMOS mission objectives. This paper analyzes the impact on ocean images of the so-called “floor error”, an image reconstruction artifact that, currently, is significant on the 3rd and 4th Stokes parameters. Error mitigation techniques are evaluated by computing spatial bias over the ocean and also by providing a 4th Stokes global error map over the ocean that takes into account SMOS full spatial and temporal averaging capabilities.

A constellation of disparate radiometers is inherent to the Global Precipitation Measurement (GPM) mission concept. The task of the Intersatellite Calibration Working group is to generate adjustments to make the measurements of all these radiometers physically consistent. A key role of the GPM Microwave Imager (GMI) on the GPM Core satellite is to serve as a transfer standard among the constellation radiometers. The TRMM Microwave Imager (TMI) has served this role during the development phase and for interim corrections early in the GPM mission. The stability of GMI appears to be very good and a physically based calibration has been generated that appears to be accurate at the 1K level or better.

We describe the design and performance of a 180 GHz correlation radiometer suitable for remote sensing. The radiometer provides continuous comparisons between a the observed signal and a reference load to provide stable radiometric baselines. The radiometer was assembled and tested using parts from the GeoSTAR-II instrument and is fully compatible with operation in a synthetic aperture radiometer or as a standalone technology for use in microwave sounding and imaging . This new radiometer was tested over several days easily demonstrating the required 6 hour stability requirement for observations of mean brightness temperature for a geostationary instrument.

Two-dimensional microwave imaging radiometers (TDMIR) are potential powerful instruments for high resolution observation of planetary surfaces. The prototype of TDMIR based on minimum-redundancy linear array and mechanical scanning is described. This paper deals with the reconstruction of radiometric brightness temperature maps from TDMIR measurements. Due to the inherent physical properties of passive radiometer imaging and associated hardware, the images by TDMIR have poor contrast and low signal to noise ratio. Standard segmentation algorithms are unable to detect objects. Algorithms for improving the brightness temperature image based on data fusion are presented. First, we remove the noise by subtracting the background, then use CLEAN method to detect object. Experimental results show that the proposed radiometer can give brightness temperature image of natural scenes and improve image quality of the radiometer for object detection.

As a second generation of China's polar meteorological satellite, the satellites of FY-3A and FY-3B launched in 2008 and 2010, respectively, carried 11 sensors, including microwave sensors such as microwave temperature sounder (MWTS), microwave humidity sounder (MWHS) and microwave radiometer imager (MWRI) etc. To fuse the MWRI and AMSR-E data and promote MWRI applicability, brightness temperature (T b ) of multi-channel MWRI are first simulated based on a radiative transfer (RT) model, and the difference between the technical features of MWRI and AMSR-E for T b observations, correspondingly, are analyzed. Data validation of MWRI and inter-comparison with AMSR-E data are discussed for monitoring drought, precipitation and flooding happened during the periods. It lays applicability of MWRI in future operational services.

On one hand, the SMOS mission is an ESA project aimed at global monitoring of surface Soil Moisture and Ocean Salinity from radiometric L-band observations. The single payload of the mission is MIRAS, a Microwave Imaging Radiometer with Aperture Synthesis. It has been successfully lofted into orbit on November 2nd, 2009. On the other hand, AQUARIUS/SAC-D mission is a partnership between NASA and CONAE for monitoring sea surface salinity from space. The observatory includes AQUARIUS, an L-band radiometer/radar combination and the mission is scheduled for launch on June 9th, 2011. This work is concerned with the synergy between both instruments. It is shown how the brightness temperature maps retrieved from MIRAS interferometric measurements can be resampled down to the ground resolution achieved by the three beams of AQUARIUS without introducing any artifact.

Snow disasters are common in pastoral areas of China's northern, which is the large and wide range of hazards of natural disasters, and it is also the one of the main meteorological disasters that constrain sustainable development of livestock in pastoral areas. Passive microwave remote sensing of snow is used as all-weather monitoring data, which is an important supplement about the changes of snow depth and the spatial and temporal distribution of the visible data. By passive microwave remote sensing of snow at home and abroad in recent years, having an overview of research progress, and meanwhile the characteristic parameters were compared with the common passive microwave sensors SMMR (Scanning Microwave Scanning Radiometer), AMSR-E (Advanced Microwave Scanning Radiometer-EOS) and MWRI (Microwave Radiation Imager) each other. this paper collected data of SSM/ I daily brightness temperature and corresponding measured ground snow depth from 79 meteorological stations in the study area from October to next March during 1995 to 1996, 2000 to 2001 and 2005 to 2006. And snow depth model in the eastern Qinghai-Tibet Plateau was inversed, then the model was applied to estimate snow area and snow depth of three quarters of the snow cover in the study area. The results showed that: 3 months areas of snow season and snow cover are the first increase in ten days in December to next January to the maximum, then decrease; snow depth of the snow season is based on the range to 10 cm above the snow, the snow of 5 ∼ 10 cm in the snow season of 3 mainly distributes in Yushu and Guoluo states in the south of QINGHAI; by the sight of the snow depth data of the snow season of 3 , the mid-three-level areas of snow depth change in different seasons basically followed the trend of first increase and then decrease, including the snow area of snow depth 10 cm of snow area change having Minimum. Currently, the use of passive microwave data can basically meet the range of large-scale snow cover monitoring requirements, but the representative of the measured values of snow depth at weather stations is not still strong, the data resolution (25km) is so small that it is needed further validating to the brightness temperature threshold on the snow tree removed the rain of affecting snow deep inversion, cold desert and frozen soil factors, and these factors will affect the accuracy of snow cover monitoring. With the accumulation of experimental data and the extensive application of remote sensing data of higher resolution AMSR-E and MWRI, It is expected to further improve the monitoring method and the monitoring model so as to improve the accuracy of snow cover monitoring.

The Corbella's revision of the fundamental equation of the interferometric aperture synthesis radiometry is verified using airborne and spaceborne data. This experimental verification uses the measurements of the airborne instrument HUT-2D from the Aalto University (Helsinki), and the unique spaceborne microwave imaging radiometer by aperture synthesis (MIRAS) from the ESA's Soil Moisture and Ocean Salinity (SMOS) mission. The data acquired with those sensors support the Corbella's revision of the visibility function. The revised function predicts that visibilities depend on the contrast between the target's brightness temperature and the backward noise of the receivers emitted through the antennas.

The SMOS (Soil Moisture and Ocean Salinity) Mission is the second of the ESApsilas Living Planet Programme Earth Explorer Opportunity Missions and it is scheduled for launch on November 2008. Its objective is to provide global and frequent Soil Moisture (SM) and Sea Surface Salinity (SSS) maps. SMOSpsila single payload is the Microwave Imaging Radiometer by Aperture Synthesis (MIRAS) sensor, an L-band two-dimensional aperture synthesis interferometric radiometer. To help in the retrieval process, auxiliary data must be used in combination with the brightness temperatures measured by MIRAS. The output products of SMOS at Level 3 will be SSS remote measurements with global coverage and an accuracy of 0.1-0.4 psu (practical salinity units) over 100 x 100 - 200 times 200 km2 in 30 - 10 days. In this study pseudo SMOS Level 3 Products have been obtained in order to test the impact at Level 3 of introducing ARGO salinity measurements in the SMOS data processing chain. To do so: 1) The Ocean Parallelise (OPA) Model has been run to provide geophysical parameters; 2) The SMOS End-to-end Processor Simulator (SEPS) has been used to compute the brightness temperatures as measured by the MIRAS; 3) The SMOS Level 2 Processor Simulator (SMOS-L2PS) has been applied to retrieve SSS values for each point and overpass. To asses also the possible impact of the coastal vicinity effect, two different zones have been simulated; the first one in open ocean and the second one in a coastal region, near the Canary islands (Spain) where SMOS and Aquarius CAL/VAL activities are foreseen. The results for both simulation scenarios are presented and discussed.

Synthetic aperture imaging radiometers (SAIR) are powerful instruments for high-resolution observation of the Earth surface at low microwave frequencies. This article deals with the impact of correlators and receivers failures on the reconstruction process which aims at inverting the interferometric data for retrieving the radiometric brightness temperature distribution of the scene under observation. Numerical simulations are carried out for the SMOS space mission, a project led by the European Space Agency and devoted to the remote sensing of soil moisture and ocean salinity from a low orbit platform.

The vicarious cold calibration method of Ruf has been used to assess the calibration of the TMI, WindSat, SSM/I F13 and SSM/I F14 microwave radiometers using data from the GPM Inter-Calibration Working Group. Significant scan position dependent biases are seen for TMI (as large as 1 K) and for WindSat (as large as 5 K)-scan position dependent biases in SSM/I data were removed prior to processing. These biases are thought to be due to obstructions in the edge of scan field of view from the given instrument and its spacecraft. WindSat vertically polarized data also show a linear decrease in vicarious cold calibration brightness temperatures with scan position. SSM/I F13 and F14 vicarious cold brightness temperatures differ by an amount consistent with a ~0.2deg offset in their relative Earth incidence angles.

Ground calibration of the calibration subsystems of MIRAS (Microwave Imaging Radiometer using Aperture Synthesis) has been performed. The MIRAS instrument is the payload of European Space Agency's (ESA) Soil Moisture and Ocean Salinity (SMOS) mission. The calibration subsystems are Calibration Subsystem (CAS), which is a noise distribution network, and Noise Injection Radiometer (NIR), which measures the noise levels of CAS and the average incident brightness temperature. This paper presents the used measurement approaches, related uncertainties, and the calibration results for the NIR and CAS. The results show that in spite of uncertainties, the characterization methods allow accurate ground calibration of the two subsystems. The performance of both subsystems meet the requirements.

The SMOS (Soil Moisture and Ocean Salinity) Mission is the second of the ESA's Living Planet Programme Earth Explorer Opportunity Missions and it is scheduled for launch in 2008. Its objective is to provide global and frequent soil moisture (SM) and sea surface salinity (SSS) maps. SMOS' single payload is the Microwave Imaging Radiometer by Aperture Synthesis (MIRAS) sensor, an L-band two-dimensional aperture synthesis interferometric radiometer. To help in the retrieval process, auxiliary data will be used in combination with the brightness temperatures measured by MIRAS. In the salinity retrieval case, the main data sources are the sea state, the sea surface temperature, and the salinity values (even at low density sampling), which would allow a more accurate retrieval and a better quality product. One of the obvious candidates of auxiliary data is the ARGO buoy array. In this study, two different algorithms are proposed to use the ARGO salinity measurements in the retrieval procedure and provide coherent SSS maps with these measurements: The first algorithm is referred to as the "external brightness temperature calibration" [1] and aims at eliminating the bias introduced by the instrument errors and the image reconstruction process at the brightness temperature level; The second algorithm is referred to as the "external salinity calibration", and aims at correcting the biases introduced by the retrieval algorithm [2] as well, due to an imperfect dielectric constant model and an imperfect sea state parameterization in the emissivity model. Using the SMOS End-to-end Performance Simulator (SEPS) [3] as a source of SMOS-like brightness temperatures and the ARGO data as the source of auxiliary parameters, a series of simulations have been performed both in open ocean, and in coastal regions using a SMOS Level 2 Processor Simulator (L2PS) developed by the Universitat Politecnica de Catalunya (UPC). The performances of both algorithms are compared, and simulation results are presented and discussed.

Three Flight Models (FM) of the reference ra- diometer of European Space Agency's SMOS mission have been developed and tested. SMOS stands for Soil Moisture and Ocean Salinity. The reference radiometer is a Noise Injection Radiometer and hence named as NIR; the NIR FM's have been developed by Elektrobit Microwave Ltd. in collaboration with Laboratory of Space Technology of Helsinki University of Technology, which has acted as a subcontractor. The NIRs will be integrated into the MIRAS (Microwave Imaging Radiometer Using Aperture Synthesis) payload in 2006. The MIRAS is the sole instrument onboard SMOS satellite. MIRAS has 66 regular receiver units (called LICEF) and three NIR units. The purpose of NIR subsystem is (1) to provide precise measurement of the average brightness temperature scene for absolute calibration of the MIRAS image map, (2) to measure the noise temperature level of internal active calibration sources for individual receiver calibration (named Calibration Subsystem, CAS), and (3) to form interferometer baselines, so-called mixed baselines, with the regular receiver units. The performance of the NIR is a decisive factor of the overall MIRAS performance. In this paper we present the design of the NIR flight models, the test campaign results, and both ground and orbital calibration techniques including the ground calibration results.

 The SMOS (Soil Moisture and Ocean Salinity) Mission was selected in May 1999 by the European Space Agency to provide global and frequent soil moisture and sea surface salinity maps. SMOS has a sun-synchronous polar dawn-dusk orbit and its single payload is MIRAS (Microwave Imaging Radiometer by Aperture Synthesis): a Lband 2D aperture synthesis interferometric radiometer. The array boresight is tilted 32.5o upwards in the orbital plane to maximize the angular excursion of the observed pixels. This study describes the practical implementation of the Sun effects (direct and diffuse scattering models) into the SMOS End-to-end Performance Simulator, a technique to cancel these effects in the brightness temperature image generated by the instrument, and the residual errors that can be expected.

Using interferometric synthetic aperture radiometer to measure the brightness temperature distribution of the Earth is a relative new technique for the microwave earth observation. Steady progresses of this technology have been achieved in both one-dimensional and two-dimensional cases since 1990's. The typical instruments are ESTAR and MIRAS. The CAS airborne X-band synthetic aperture radiometer is a thinned array microwave imaging radiometer, developed by National Microwave Remote Sensing Laboratory (NMRS Lab), Center for Space Science and Applied Research (CSSAR) and Chinese Academy of Sciences (CAS). The system integration of the instrument was accomplished in the Jan, 2004, and the first flying experiment was held in Apr, 2004. In this paper, both the system configuration and the experimental results of the instrument are described in detail

An L-band noise injection radiometer (NIR) has been designed and implemented by Helsinki University ofTech- nology Laboratory ofSpace Technology f or the SMOS (Soil Moisture and Ocean Salinity) mission ofESA (1). The work is performed as a part of ESA's MIRAS Demonstrator Pilot Project-2 (MDPP-2) under a subcontract for EADS-CASA. Other partners in the MDPP-2 NIR project are Toikka Engineering Ltd. and Ylinen Electronics Ltd. NIR will work as a part ofthe MIRAS (Microwave Imaging Radiometer Using Aperture Synthesis) instrument. Its main purpose is (1) to provide precise measurement ofthe average brightness temperature scene for absolute calibration of the MIRAS image map and (2) to measure the noise temperature level ofthe internal active calibration source f or individual receiver calibration. The performance of NIR is a decisive factor ofthe MIRAS perf ormance. The challenge in the implemented, so-called blind correlation, method is the fact that there is additional noise in the correlated signal due to using the noise injection method. The main objective ofthis paper is to demonstrate the f ofthis technique. I. INTRODUCTION The precision of a noise injection radiometer is based on comparing the measured signal to two reference sources, the noise temperatures of which are known. This will remove the effect of the receiver gain and offset variations. The length of the noise pulse is then proportional to the antenna temperature (2). NIR will also be used in the MIRAS array as a regular receiver unit for interferometric image creation. In addition to measuring the horizontally and vertically polarized antenna noise temperature and the calibration net- work noise temperature, the MDPP-2 NIR was designed to provide fully polarimetric measurement capability. The Stokes parameters are retrieved using the same correlator, which the MIRAS uses for solving the correlation for the interferometric image creation. The so-called modified Stokes parameters are defined under the Rayleigh-Jeans approximation as (3)

The paper presents the results of sea-surface microwave brightness measurements carried out on the Black Sea coast in 1999-2001. The sea-surface brightness temperature was measured as a function of the view angle at wind speed ranged from 0.5 to 7 m/s. The experiment was conducted from a pier in the South Department of the Shirshov Institute of Oceanology using microwave radiometers at wavelengths of 0.8 and 0.3 cm. For the first time, it was noted that brightness temperature followed wind speed with some delay. The gravity-capillary waves spectrum retrieved from the brightness temperature provided evidence that this delay may be explained by short wind waves.

Up to now, no space mission has attempted to measure the Sea Surface Salinity (SSS) due to technological challenges. The European Space Agency's SMOS (Soil Moisture and Ocean Salinity) mission, to be launched in 2007, aims at the generation of global SSS maps with a spatial and temporal resolution adequate for climatic and ocean general circulation studies. The sensor aboard SMOS is an L-band interferometric radiometer with full polarimetric capability called MIRAS (Microwave Imaging Radiometer by Aperture Synthesis). At low frequencies the brightness temperature measured by a radiometer presents maximum sensitivity to surface salinity, and optimal conditions are found at L-band (1400 MHz-1427 MHz), which it is a band protected for passive measurements. Even though the sensitivity is maximum at this frequency, it is not very high: 0.5 K/psu for sea temperature of 20/spl deg/C, decreasing to 0.25 K/psu for 0/spl deg/C. Furthermore, other variables influence the brightness temperature, as the sea roughness (mainly caused by the wind speed), sea foam, and also sea surface temperature. When SMOS will be in orbit it will be necessary to have accurate measurements of the sea roughness and surface temperature as much coincident as possible -both in time and space- to SMOS acquisitions, because errors on these ancillary data produce large errors on the retrieved salinity. This paper presents a study of the effect on the retrieved salinity of inaccuracies in the wind speed and wave height values. It compares the retrieved salinities when using different sources of these ancillary data: satellite measurements and meteorological models. It also investigates the possibility of allowing the algorithm to retrieve an effective wind speed, in addition to the sea surface salinity.

Presents some results of complex aircraft experiment conducted to study sea surface in the NW Pacific near the Kamchatka Peninsula. The present experiment was conducted using a /sup /spl Deltak-spectrometer (dual-frequency scatterometer) simultaneously with a scatterometer and a radiometer for the measurement of radar backscatter and brightness temperature variations. The application of these instruments made it possible to reconstruct the current velocity picture and temperature fronts in the shelf area and to connect these phenomena with underwater bottom topography. Zones of internal wave packets were identified and estimates were made of the influence of currents associated with internal waves on the surface wave spectra transformation. >

A numerical simulator has been developed to assess various design implications for a digital correlator used by a synthetic aperture imaging radiometer. Of particular interest are the effects of the number of bits used in the correlation and the accuracy requirements of automatic gain control circuitry needed to maintain digitization threshold levels relative to signal power levels. The simulator permits control of the type of digitization, the digitization thresholds with respect to noise power, the relative power levels of the radiometer system noise and brightness temperature signal, and the degree of correlation between the two antenna signals which are being correlated. A number of case studies will be presented and radiometer design implications discussed. >

The development and testing of an imaging total-power millimeter-wave radiometer operating at 10.7, 19.35, 37.1, and 85.5 GHz are described. The instrument, referred to as the advanced microwave precipitation radiometer (AMPR), is designed to be flown onboard NASA's ER-2 high-altitude research aircraft in support of atmospheric precipitation and storm-related field measurements. A unique feature of the AMPR antenna system is the multifrequency feedhorn for operation at 19.35. 37.1. and 85.5 GHz. This antenna feedhorn offers dual polarization output at all three frequencies, although initially only one polarization is being used at each frequency. A lens is used with the feedhorn to obtain half-power beamwidths of 7.9 degrees , 4.1 degrees , and 1.8 degrees at 19, 37, and 85 GHz. respectively. Three major subsystems of the AMPR are the multifrequency antennas, the four-channel downconverters, and the imaging scanner. These subsystems are combined to provide four radiometric data outputs which interface with NASA's data acquisition system. Calibration of the AMPR allows one to perform absolute temperature measurements, which is necessary to obtain brightness temperature data from precipitation in the atmosphere. >

A multiband microwave polarimetric scanning radiometer (PSR) was operating during January-March, 1997 over the Labrador Sea and the Atlantic Ocean aboard the NASA/WFF P-3B aircraft. Conically-scanned brightness temperatures were observed over open ocean for a variety of wind speeds and cloud conditions. Presented are several illustrations and applications of data obtained during the Labrador Sea experiment. Marine winds were measured.

A unique complement of passive and active microwave imaging and sensing instruments for observing ocean surface emission and scattering signatures were integrated onto the NASA Wallops Flight Facility's Orion P-3B aircraft (N426NA) for the purpose of studying the signature of ocean surface winds. The complement included: (i) a four-band (X, K, Ka, and W) tri-polarimetric scanning radiometer (PSR) (ii) a C-band ocean surface scatterometer (CSCAT), (iii) a Ka-band conical-scanning; polarimetric radiometer (KASPR), (iv) a nadir-viewing Ka-band polarimetric radiometer, (KAPOL) (v) a 21- and 31-GHz zenith-viewing cloud and water vapor radiometer (CWVR), and (vi) a radar ocean wave spectrometer (ROWS). The above Ocean Winds Imaging (OWI) complement was flown during January-March, 1997 over the Labrador Sea. Conically-scanned brightness temperature and backscatter imagery were observed over open ocean for a variety of wind speeds and cloud conditions. Presented herein are the results of a preliminary intercomparison of data from several of the OWI instruments.

For a spaceborne scanning radiometer the main requirements are given in terms of radiometric resolution, spectral resolution and spatial resolution. The radiometer uncertainty principle affirms that the above resolutions are interrelated, and it is not possible to optimize one of them without degrading one or both of the other two ones (for a given scanning and orbital configuration). The purpose of this paper is therefore to identify an optimization strategy which accounts for the global performance of the channels system and to describe the relevant implementation applied on MIMR, which is a 20 channel, 6 frequency passive radiometric system receiving both vertically and horizontally linearly polarized radiation at 6.8, 10.65, 18.7, 23.8, 36.5, and 89 GHz.

A microwave radiometer designed for remote sensing infers geophysical information from the thermal emission from natural targets. The most useful frequencies span 6 GHz to 37 GHz, and incorporate a scanning antenna to provide radiometric maps of the Earth surface and the atmospheric burden. This paper focuses on radiometric imaging systems, and the observational value offered by these systems since they were first flown on spacecraft nearly 30 years ago.

The author is developing a demonstration radiometer for two-dimensional passive microwave imaging based on supersynthesis technique. For confirmation of the supersynthesis procedure and the sensitivity of the radiometer, one-dimensional imaging experiments were made using a microwave interferometer in 6 GHz band. Rotary scanning is adopted for simplicity of operation instead of linear scanning, which are fundamentals of the platform movement. Two kinds of targets were used including a point target and an extended target. The images are retrieved by synthesis. The peaked image of the point target shows the efficient process of the synthesis and the fine contrast of the building wall and the sky in the extended target implies high sensitivity of the radiometer for detecting lower brightness temperature than 300 K.

Millimeter-wave Imaging Radiometer (MIR) data (ranging in frequency from 89 GHz to 325 GHz) collected from NASA ER-2 flights over Alaska in April 1995, are used to identify clouds, vegetation type, and snow cover. The procedure used is as follows: 1.) Determine whether a purely MIR-based cloud detection scheme is possible over a snow-covered surface; 2.) Analyze the influence of changing vegetation type on the brightness temperatures; and 3.) Compare completely snow-covered scenes with partially snow-covered and snow-free regions for cloudy and clear sky periods to determine whether varying snow conditions affect the MIR data. Results show that the determination of cloudy pixels over a snow-covered surface is not possible using a simple brightness temperature threshold technique. Furthermore, it is concluded that while no statistical discrimination between specific vegetation classes can be made, statistical significance is obtained when the vegetation is grouped into two classes only, for example vegetated and barren. It is also shown that the state of the snow cover (complete coverage; melting; or patchy) has a distinct affect on these results.