The NOAA-21 ATMS, launched in November 2022, collected two-dimensional lunar scan data on March 10, 2023, during its commissioning phase at a Moon phase angle of 34°. The raw data were calibrated using MiCalPS, a microwave calibration and geolocation tool developed at the University of Maryland. Analysis showed that NOAA-21’s lunar antenna gain is generally lower than NOAA-20’s due to sampling rate differences. After correcting for beam pointing errors, disk-averaged lunar brightness temperatures (TDISKB,Moon) were derived for 23–183 GHz. These were compared to NOAA-20 data at 0° phase angle and predictions from the microwave lunar radiative transfer model (ML-RTM). The observed model differences were: −3.3 K (K band), 0.01 K (V), 2.0 K (W), −2.3 K (low-G), and 1.6 K (high-G), consistent with predicted phase delay trends. Further observations at varying Moon phases are recommended to enhance MLRTM validation.

Space-borne microwave sounding instruments have become vital data sources for weather prediction and climate change studies. Among the various radiometer configurations, the total power microwave radiometer is particularly appealing for current and future operational satellites due to its superior sensitivity and simple design. However, its performance is vulnerable to degradation caused by receiver gain fluctuations, electronic 1/f noise, and other time varying receiver characteristics. For Numerical Weather Prediction (NWP) users, 1/f noise introduces inter-channel correlations, complicating the assimilation of affected observations and reducing their accuracy. Addressing this noise issue in ground data processing system is essential to enhance the utility of microwave sounding data. This paper focuses on the characterization and mitigation of noise in current and future microwave sounding instruments, with particular emphasis on the impact of 1/f noise. Various methods are applied to quantitatively characterize noise features in both frequency and time domains. Additionally, the influence of calibration parameters on 1/f noise are analyzed. Based on these findings, we propose a mitigation algorithm for reducing noise during the on-orbit calibration of microwave sounding instruments, aiming to improve the quality of retrieved data for operational use.

Understanding the vertical distribution of soil moisture is crucial for making informed decisions in various applications, ranging from precision agriculture to hydrological modeling. Four machine learning algorithms, including random forest, extreme gradient boosting, deep learning, and support vector regression were employed to estimate the soil moisture profile from collected tower-based L-band and P-band brightness temperature observations in Victoria, Australia. The results showed that random forest outperformed the other algorithms, with root mean square error (RMSE) values of 0.03, 0.04, and 0.06 m3/m3for depths of 0-5 cm, 0-30 cm, and 0-60 cm, respectively

To better understand the interactions of satellite C-band radar with the soil-snow-vegetation continuum in a spatio-temporal context and to provide a novel approach for snow depth retrieval from Sentinel-1 observations, a coupled radiative transfer model was developed. This model combines a snow, soil and vegetation radiative transfer model to simulate the Sentinel-1 observations over the Alps, and can be inversed to obtain estimates of snow depth. Performance will be assessed at 1 km spatial resolution for the winter of 2017-2018, across a wide range of elevations, local incidence angles and total accumulated snow, using several performance measures (Pearson correlation and MAE).

A physical-statistical framework to estimate Snow Water Equivalent (SWE) and Snow depth (SD) from SAR measurements was implemented and applied to SnowSAR flight-line data collected during the SnowEx’2017 field campaign in Grand Mesa, Colorado, USA and averaged to 90 m resolution. The physical (radar) model is used to describe the relationship between snowpack conditions and volume backscatter. The statistical model is a Bayesian inference model that seeks to estimate the joint probability distribution of volume backscatter measurements, SWE and SD and physical model parameters. To reduce the number of physical parameters, the snowpack is represented by two layers only. Retrievals compare well with pit observations with good performance in deep snow and residual errors less than 8% for SnowSAR incidence angles > 30°.

As an emerging technique, P-band (0.3-1 GHz) may improve soil moisture remote sensing compared to L-band (1.4 GHz) SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active Passive) missions, because of its greater moisture retrieval depth resulting from its longer wavelength. Consequently, a number of tower-based experiments were undertaken in Victoria, Australia, to understand and quantify potential improvements. The study reported here has extended the evaluation of the tau-omega model to a scenario with a dense corn canopy whose vegetation water content reached ~20 kg/m2, and compared the soil moisture retrieval performance at P- and L-band. Based on the locally calibrated parameters, the results from both the SCA (Single Channel Algorithm) and DCA (Dual Channel Algorithm) approaches presented a clear reduction in vegetation impact at P-band compared to L-band. While the root-mean-square error (RMSE) for P-band did not achieve the 0.04-m3/m3target accuracy of SMOS and SMAP, i.e., 0.054 m3/m3for the SCA and 0.074 m3/m3for the DCA, this performance can be regarded as acceptable considering the extremely high vegetation water content. In comparison, the RMSEs at L-band were larger than 0.1 m3/m3for both the SCA and the DCA approaches. Additionally, DCA performed better in correlation coefficient and unbiased RMSE, while SCA performed better in RMSE at P-band due to the larger bias when using DCA. Moreover, the calibrated vegetation parameters at P-band were found to apply to broader conditions than those at L-band, likely due to the reduced vegetation impact.

Snow water equivalent (SWE) is an important characteristic of a terrestrial hydrological cycle that needs to be retrieved in any global snow satellite mission. Many retrieval algorithms have been proposed based on microwave backscattering of snow packs. And X and Ku bands have been a focus on many of these past and future missions. In this paper we analyse the airborne X(9.6 GHz) and Ku (17.2 GHz) band data of the SnowSAR 2017 campaign and the University of Massachusetts InSAR Ku (13.3 GHz) band data using the bi-continuous dense media radiative transfer (DMRT) model. In-situ measurements of density, temperature and specific surface area (SSA) from the snow pits are used as physical parameters and are used in estimating the numerical parameters ($\zeta$) and$b$which characterizes the model. The background effects such as rough surface scattering are also removed from the airborne data and only the volume scattering is analyzed. Overcoming limitations in other models such as the sticky sphere model, the bi-continuous media model gives a more realistic representation of snow microstructure and has a weaker frequency dependence.

The US National Oceanic and Atmospheric Administration (NOAA) is currently in the process of exploring and evaluating options for its next-generation satellite observing systems. The Low Earth Orbit (LEO) portion, in particular, is heavily relied upon by the world's Numerical Weather Prediction (NWP) centers, which in turn, rely heavily on microwave sounders. Observations from these passive microwave sensors are the highest-impact observations ingested by NWP models. These passive observations are also the most vulnerable to man-made radio frequency interference (RFI). At the same time, the communications industry is expanding its deployment of new wireless services in bands that are nearby (sometimes even overlapping). New sensor options such as cubesats as well as advancements in digital back end technology present both challenges and new tools in the RFI-related tradespace. Exploring some of these options and the associated tradeoffs is the topic of this paper.

A summer 2021 European airborne field campaign-the Land surface Interactions with the Atmosphere over the Iberian Semi-arid Environment (LIAISE) campaign-presented an opportunity to explore passive soil moisture sensing with footprints as small as 100x200m, contributing a key measurement to LIAISE and providing a rare opportunity to gain detailed insight into the water/energy/carbon exchanges at such plot-scale resolution over a$17 \mathrm{x}5$km area. NASA Goddard's Scanning L-band Active Passive (SLAP) sensor-an airborne simulator of the Soil Moisture Active Passive (SMAP) satellite-made nine soil moisture flights near Lleida, Spain during 15–29 July. We present soil moisture imagery and histograms spanning irrigated and non-irrigated land and their response to a precipitation event followed by a drydown.

Backscattering from rough soil surface has important applications to the remote sensing of soil moisture and snow water equivalent (SWE), To extend simulations to Ku-band, we have performed full wave simulations up tokh=15. Results are simulated for various profiles and show that the scattering at C-, X-, and Ku-bands is influenced by both scales of centimeters roughness and millimeter roughness. Comparisons are made between constant ratios rough surface and constant correlation length rough surfaces. The results are illustrated for both VV and HH polarizations. Simulation results are in good agreement with X band measurement data as a function of incidence angle. An illustration is used to show how the results can be used for theoretical models of rough surface scattering in remote sensing of snow water equivalent.

Passive microwave sounders provide the highest-impact observations ingested by major numerical weather prediction (NWP) forecast models. The Advanced Technology Microwave Sounder (ATMS), built by Northrop Grumman, Azusa, CA, USA, is the latest operational microwave sounder series being launched by the United States to provide both temperature and water vapor soundings of the atmosphere. The first ATMS was launched on the Suomi National Polar-orbiting Partnership (SNPP) satellite in 2011. This article focuses on the details of the on-orbit performance characterization of the second ATMS, which launched on November 18, 2017, on the Joint Polar Satellite System-1 (JPSS-1) satellite. After successful commissioning, JPSS-1 was renamed National Oceanic and Atmospheric Administration (NOAA)-20 (N-20). We present performance characterizations from prelaunch and postlaunch tests, including the thermal vacuum (TVAC) campaign, and postlaunch activities that contribute to the radiance data products. Significant improvements were found for reflector emissivity,$1/f$noise performance, antenna beam efficiency, interchannel noise correlation, and scan drive bearing design. New geolocation and pointing algorithms were evaluated. The N-20 ATMS has the same channel set, polarizations, scan geometry, and calibration approach as the SNPP ATMS. The N-20 ATMS meets all performance requirements with margin.

The Advanced Technology Microwave Sounder (ATMS) is a passive microwave radiometer for the current generation of polar-orbiting meteorological satellites operated by the National Oceanic and Atmospheric Administration (NOAA). The first two ATMS instruments are manifested onboard the Suomi National Polar-orbiting Partnership (S-NPP) and NOAA-20 satellites. Several critical changes have been made to ATMS operational calibration algorithm since March 2017. The calibration processing has been revised from a Rayleigh–Jeans approximated algorithm to a full radiance algorithm in order to reduce the error introduced by the approximation over cold radiances in the higher frequency channels. In addition, based on the lessons learned from S-NPP and NOAA-20 postlaunch calibration/validation tests, some major improvements have been made in the updated operational algorithm. These include reflector emission and antenna pattern corrections. Details of the radiance-based ATMS on-orbit calibration are documented in this report, and results of prelaunch calibration error budget analysis and postlaunch calibration accuracy evaluation are also presented for reference.

Microwave sounders in low Earth orbit (LEO) are a foundation of the current NWP satellite observation system, providing daily global coverage and all-weather capability. The current Joint Polar-orbiting Satellite System (JPSS) will fly the Advanced Technology Microwave Sounder (ATMS) series through the conclusion of JPSS at the end of the 2030s. Studies are currently under way to define what comes after JPSS, including exploration of options for future microwave sounders. New options are becoming available—smaller and cheaper sounders, satellite buses and launchers—and changing the landscape. Exploring some of these options and the associated tradeoffs is the topic of this paper.

Radio frequency interference (RFI) is a growing issue affecting passive microwave Earth remote sensing. NASA's Soil Moisture Active Passive (SMAP) mission included a dedicated detection subsystem specifically to deal with RFI at 1.4 GHz. The recent auctioning of 24 GHz spectrum in the USA for new International Mobile Telephony (“5G”) systems adjacent to the 23.8 GHz sounding band is an example of a new type of RFI: wider in bandwidth, harder to distinguish from natural signals, and with the potential to impact global weather forecasting. This new type of RFI will require much more powerful detection hardware and new algorithms.

Reported here are disk-integrated Moon surface microwave brightness temperature ($Tb$) retrievals covering the frequency range of 23–183 GHz. Full Moon observations obtained from the advanced technology microwave sounder (ATMS) onboard the NOAA-20 satellite during a special spacecraft pitch–maneuver operation forms the basis of the retrievals. Instrument nonlinearity, Earth sidelobe contamination, cosmic background radiation, and reflector thermal emission corrections are applied to the observations to obtain accurate values of the Moon’s$Tb$at all frequencies. The measured full Moon$Tb$ranges from ~240 to 293 K with frequency increases from 23 to 183 GHz. A clear frequency trend is detected when the brightness temperature increases.

Currently, near-surface soil moisture at a global scale is being provided using National Aeronautics and Space Administration's (NASA's) Soil Moisture Active Passive (SMAP) and European Space Agency's (ESA's) Soil Moisture and Ocean Salinity (SMOS) satellites, both of which utilize L-band (1.4 GHz; 21 cm wavelength ) passive microwave remote sensing techniques. However, a fundamental limitation of this technology is that the water content can only be measured for approximately the top 5-cm layer of soil moisture, and only over low-to-moderate vegetation covered areas in order to meet the 0.04 m3/m3target accuracy, limiting its applicability. Consequently, a longer wavelength radiometer is being explored as a potential solution for measuring soil moisture in a deeper surface layer of soil and under denser vegetation. It is expected that P-band ( wavelength of 40 cm and frequency of 750 MHz) could potentially provide soil moisture information for the top ~10-cm layer of soil, being one-tenth to one-quarter of the wavelength. In addition, P-band is expected to have higher soil moisture retrieval accuracy due to its reduced sensitivity to vegetation water content and surface roughness. To demonstrate the potential of P-band passive microwave soil moisture remote sensing, a short-term airborne field experiment was conducted over a center pivot irrigated farm at Cressy in Tasmania, Australia, in January 2017. First results showing a comparison of airborne P-band brightness temperature observations against airborne L-band brightness temperature observations and ground soil moisture measurements are presented. The P-band brightness temperature was found to have a similar but stronger response to soil moisture compared to L-band.

A NOAA-20 (N20) Advanced Technology Microwave Sounder (ATMS) active geolocation (GEO) test was performed around January 2018 with 24 preselected coastline crossing scenes. After a comprehensive analysis of ATMS stare data and the corresponding Visible Infrared Imaging Radiometer Suite (VIIRS) data, the ATMS pitch, roll, and yaw pointing angle errors are found from the nadir perpendicular, the nadir oblique shallow angle, and the off-nadir perpendicular coastline crossing data, respectively. In this study, we first determine the ATMS radiometric coastline crossing time by using the ATMS radiometric count data. Since the coastline can be located anywhere within one ATMS field of view (FOV) from the passive (regular scanning) GEO data, depending on the scan starting time, it is not a valid assumption for the inflection point being the same as the coastline location. Consequently, using the passive GEO data to validate the sensor’s on-orbit pointing angle performance is limited. After finding the ATMS radiometric coastline crossing time from the ATMS data, we compare it with the VIIRS effective time stamp. Specifically, the VIIRS M3, M4, and M5 (true color) and M15 and M16 bands (thermal) data have a much smaller footprint size. Using the differences of the ATMS and VIIRS (effective) coastlines crossing times, the N20 ATMS pitch, roll, and yaw pointing angle errors are found to be −0.09°, −0.24°, and 0.28°, respectively. To determine ATMS GEO properly, these on-orbit pointing errors need to be corrected, adding to the ATMS sensor data record (SDR) processing coefficient table, and passed on to the operational GEO processing code.

The fourth and fifth Soil Moisture Active Passive Experiments (SMAPEx-4 and -5) were conducted at the beginning of the SMAP operational phase, May and September 2015, to: 1) evaluate the SMAP microwave observations and derived soil moisture (SM) products and 2) intercompare with the Soil Moisture and Ocean Salinity (SMOS) and Aquarius missions over the Murrumbidgee River Catchment in the southeast of Australia. Airborne radar and radiometer observations at the same microwave frequencies as SMAP were collected over SMAP footprints/grids concurrent with its overpass. In addition, intensive ground sampling of SM, vegetation water content, and surface roughness was carried out, primarily for validation of airborne SM retrieval over six ~ 3 km × 3 km focus areas. In this study, the SMAPEx-4 and -5 data sets were used as independent reference for extensively evaluating the brightness temperature and SM products of SMAP, and intercompared with SMOS and Aquarius under a wide range of SM and vegetation conditions. Importantly, this is the only extensive airborne field campaign that collected data while the SMAP radar was still operational. The SMAP radar, radiometer, and derived SM showed a high agreement with the SMAPEx-4 and -5 data set, with a root-mean-squared error (RMSE) of ~3 K for radiometer brightness temperature, and an RMSE of ~ 0.05 m3 for the radiometer-only SM product. The SMAP radar backscatter had an RMSE of 3.4 dB, while the retrieved SM had an RMSE of 0.11 m3/m3 when compared with the SMAPEx-4 data set.

Soil Moisture is an important geophysical variable that needs reliable quantification for applications in hydrology, meteorology and agriculture. L-band radiometry has proved to be one of the best methods in soil moisture estimation using microwave signals. However, they provide measurements that correspond to a shallow depth of 5 cm and are also affected by the presence of overlaying vegetation and roughness. In contrast, P-band radiometry is expected to provide moisture information on a deeper layer of soil. Moreover, these lower frequency measurements are expected to be less affected by soil roughness and vegetation contributions. Consequently, this pilot study uses the Polarimetric P-band Multibeam Radiometer (PPMR) at 740 MHz to evaluate the response of the P-band radiometer over a realistic range of surface conditions at the field scale. A preliminary framework of P-band Microwave Emission of the Biosphere (P-MEB) has been developed as a forward model that simulates brightness temperature from soil moisture and other ancillary data collected from the field. This paper presents the model for the bare soil condition observed during June 2018 to August 2018. The results show that H-polarised PPMR data has better correlation to the soil moisture over a depth of 10 cm than the V-polarized PPMR data. A model is under improvement by incorporating a more suitable effective temperature formulation.

The Advanced Technology Microwave Sounder (ATMS) is a satellite-based microwave radiometer that provides temperature and humidity sounding observations from low Earth orbit. The instrument utilizes 22 channels that cover a frequency range of 23 to 183 GHz. The first ATMS instrument was launched in 2011 on the Suomi National Polar-orbiting Partnership (S-NPP) satellite and the second ATMS was launched in 2017 on the Joint Polar Satellite System-1 (JPSS-1) satellite (now NOAA-20); both on-orbit ATMS instruments are currently operational. This paper will describe the pre-launch performance of the third ATMS instrument, designated for the JPSS-2 satellite, during ground testing and calibration.

For the on-orbit calibration of passive microwave radiometers, instrument non-linearity is a major error source, causing a scene-temperature-dependent error if not being properly corrected. non-linearity results from the intrinsic feature of the square-law detector and amplifiers used in total-power microwave radiometers, and can only be accurately characterized through the ground-based Thermal Vacuum Test (TVAC). The ground-based non-linearity characterization is then used in the calibration algorithm to attempt to remove this error source. Evaluation results for current operational microwave-sounding instruments show that the magnitude of the non-linearity error varies from channel to channel and from instrument to instrument, with maximum changes of several tenths of kelvins to several kelvins. While the different responses of the detector and amplifier may explain the non-linearity differences in different instruments, errors in TVAC tests could also increase the uncertainty in the non-linearity assessment. Therefore, accurate knowledge of error sources in the TVAC test and their corrections are important for a reliable and accurate non-linearity measurement. In this paper, major error sources in the TVAC test are studied and identified for the NOAA-20 Advanced Technology Microwave Sounder. Correction methods are developed by combining the pre-launch TVAC test and post-launch deep-space-scan test data sets. An on-orbit evaluation method is also proposed to validate the ground-measured instrument non-linearity.

Seasonal snow on land is an important factor in water resources, natural disasters, water security, and weather and climate. While many types of sensors are sensitive to snow water equivalent or snow depth, no single sensor type works across the range of snow conditions and confounding factors (primarily forests and complex terrain) experienced globally. An attractive potential solution is to leverage existing and planned satellites with applicable sensors in a distributed spacecraft mission configuration. In this paper, we examine the basic question of the spatial-temporal coverage achievable by various combinations of satellites and sensors. The results will inform discussions on the advantages of specific future sensors, sensor parameters, as well as requirements for algorithms and models to help bridge the space-time gaps.

An ensemble of land surface models and forcing data was developed to assess variability in SWE estimation over North America. In this study, the ensemble output was used to assess how SWE uncertainty impacts streamflow estimation. The analysis was conducted by major basins of North America over the 2009-2017 time period.

There are multiple existing microwave radiative transfer models (RTMs) to simulate the brightness temperature (Tb) of snowpacks. It is still challenging to have consistent Tb responses from RTMs due to individual physical formulations of the snowpack scattering process. This article examines three of the widely-used multi-layer RTMs: 1) the microwave emission model of layered snowpacks (MEMLS); 2) the dense media radiative transfer based on the quasi-crystalline approximation (QCA) of Mie scattering of densely packed sticky spheres (DMRT-QMS); and 3) the Helsinki University of Technology (HUT) model. Interestingly, these models yield slightly different Tb responses when driven by the same physical snowpack properties. Tb variations, dependent on the choice of RTMs, are then evaluated to improve the understanding of model differences in microwave emission from a snowpack. We first perform a sensitivity study of the Tb predictions from the three RTMs as a function of snow grain sizes, densities, and depths. While Tb from all three RTMs decreases with increasing snow grain sizes, it is found that a scaling factor is required to have the same amount of Tb attenuation for small grain sizes within the Rayleigh scattering regime. For larger grain sizes, however, a scaling coefficient is not enough to match the model outputs due to the different scattering assumptions of the RTMs. For a single snow layer with increasing snow depths and densities, all three RTMs exhibit Tb attenuations arising from the increase in path lengths and optical depths. Further evaluations are conducted by feeding the three RTMs with the output of a snow physics model driven by in situ weather forcing in a coupled simulation. Outputs of this coupled model include snowpack physical properties and Tbs. By using snow stratigraphy observations, another set of Tb simulation is also conducted with RTMs driven by in situ snowpit observations. The snow physics outputs from the coupled case are compared against in situ snow stratigraphy observations. And both Tb simulations are compared against ground-based microwave observations from the European Space Agency (ESA) Nordic Snow Radar Experiment (NoSREx) 2009-2012. For three consecutive years, the in situ driven Tbs have 21.0-K root-mean-squared error (RMSE) while the coupled simulations have 24.7-K RMSE. However, after isolating the dry snow period and excluding diurnal melting snow conditions, 12.2- and 6.3-K RMSEs are achieved from in situ and coupled cases, respectively, in the 2011 water year.

Soil moisture measurement using L-band radiometry is now widely accepted as the state-of-art remote sensing approach, and has been adopted by both the SMOS and SMAP soil moisture dedicated satellite missions. However, it suffers from the shallow depth of its soil moisture measurement, and the confounding effects of vegetation and soil roughness on soil moisture retrieval. P-band, which is a longer wavelength measurement, provides the potential to retrieve deeper soil moisture information, and to do so more accurately due to reduced soil roughness and vegetation effects. This paper presents some pioneering work on the use of P-band for soil moisture retrieval. The Polarimetric P-band Multibeam Radiometer (PPMR) used in this research operates at 740 MHz / wavelength of 40 cm. It is used together with the Polarimetric L-band Multibeam Radiometer (PLMR) which operates at 1.4 GHz / wavelength of 21 cm. The PPMR and PLMR are mounted onto a 10m high tower in an agricultural farm located at Cora Lynn, Victoria. This paper outlines the initial set up for the study and the experimental plan for understanding PPMR's performance, along with some initial data.

The second Advanced Technology Microwave Sounder (ATMS) recently launched November 2017 on the Joint Polar Satellite System-l satellite (JPSS-l), now re-named NOAA-20. It joins the first ATMS flight unit aboard the Suomi NPP (S-NPP) satellite, as well as older sounders-the Advanced Microwave Sounding Units A & B (AMSU-A/B) and Microwave Humidity Sounder (MHS)-on polar-orbiting operational weather satellites. Together, these sounders provide critical all-weather temperature and humidity profile information for Numerical Weather Prediction (NWP) models. This paper presents results from a number of special post-launch tests used to characterize the instrument and provide unique calibration information. These special tests-long stares, alternate techniques for lunar intrusion mitigation and geolocation, spacecraft maneuvers, special scan modes, comparisons with NWP models-require nonstandard modes of operation or data analysis, and can only be conducted during commissioning, prior to the start of regular forecast observations.

Permanently frozen and seasonally frozen soils occur over a large portion of the Earth's land surface. Changes in the freeze/thaw state of the land surface reflects major changes in thermal and hydraulic properties as well as acting as a “switch” for many ecological processes. In short, soil freeze/thaw state is a fundamental land surface variable in the water and energy cycles, and it connects to the carbon cycle. Surface freeze/thaw state is observable by passive and active microwave sensors. For example, NASA's Soil Moisture Active Passive (SMAP) mission includes a freeze/thaw data product. Such satellite sensing offers routine all-season and all-weather global observations of soil freeze/thaw state with the application of suitable algorithms. We describe early finding from the SLAPex Freeze/Thaw campaign, believed to be the first airborne campaign of its type, focusing on soil freeze/thaw.

The first NASA SnowEx campaign was held in February 2017 over Grand Mesa, Colorado, covering both open and forested areas. The Belspo SNOPOST project aims at using the collected SnowEx data to enhance snow estimates using the Level 1 remote sensing data together with land surface modeling and to document the limitations of snow remote sensing where needed. A preliminary spatiotemporal analysis of in situ and satellite remote sensing data, and modeling estimates of snow will be presented.

A fundamental limitation of current soil moisture remote sensing technology is that can only provide moisture information on the top 5 cm layer of soil at most, being one-tenth to one-quarter of the wavelength (21 cm at L-band; 1.4 GHz) using the current SMAP and SMOS soil moisture dedicated missions of NASA and ESA. Consequently, we have developed an airborne passive microwave sensing capability at P-band to develop a new state-of-the-art satellite concept that will provide soil moisture data for the top 10 cm layer of soil using radiometer observations at P-band (40 cm; 750 MHz). Not only would P-band provide soil moisture information on a soil layer thickness that more closely relates to that affecting crop and pasture growth, but it is expected to produce greater spatial coverage with improved accuracy to that from L-band. This is because P-band should be less affected by surface roughness conditions and have a reduced attenuation by the overlaying vegetation. This paper describes a series of small airborne field experiments at P-band, and presents some early results of P-band passive microwave observations in comparison with L-band and K-band passive microwave from initial trial flights.

Accurate global observations from space are critical for global climate change study. However, atmospheric temperature trend derived from spaceborne microwave instruments remains a subject of debate, due mainly to the uncertainty in characterizing the long-term drift of instrument calibration. Thus, a highly stable target with a well-known microwave radiation is required to evaluate the long-term calibration stability. This paper develops a new model to simulate the lunar emission at microwave frequencies, and the model is then used for monitoring the stability of the Advanced Technology Microwave Sounder (ATMS) onboard Suomi NPP satellite. It is shown that the ATMS cold space view of lunar radiation agrees well with the model simulation during the past five years and this instrument is capable of serving the reference instrument for atmospheric temperature trending studies, and connecting the previous generation of microwave sounders from NOAA-15 to the future Joint Polar Satellite System Microwave Sounder onboard NOAA-20 satellite.

SnowEx is a winter airborne and field campaign designed to measure snow-water equivalent in forested landscapes. A major focus of Year 1 (2016-17) of NASA's SnowEx campaign will be an extensive field program involving dozens of participants from U.S. government agencies and from many universities and institutions, both domestic and foreign. Along with other instruments, two infrared (IR) sensors will be flown on a Naval Research Laboratory P-3 aircraft. Surface temperature is a critical input to hydrologic models and will be measured during the SnowEx mission. A Quantum Well Infrared Photodetector (QWIP) IR imaging camera system will be flown along with a KT-15 remote thermometer to aid in the calibration of the IR image data. Together, these instruments will measure surface temperature of snow and ice targets to an expected accuracy of <;1°C.

SnowEx is a multi-year airborne snow campaign with the primary goal of addressing the question: How much water is stored in Earth's terrestrial snow-covered regions? Year 1 (2016-17) focused on the distribution of snow-water equivalent (SWE) and the snow energy balance in a forested environment. The year 1 primary site was Grand Mesa and the secondary site was the Senator Beck Basin, both in western, Colorado, USA. Nine sensors on five aircraft made observations using a broad range of sensing techniques-active and passive microwave, and active and passive optical/infrared - to determine the sensitivity and accuracy of these potential satellite remote sensing techniques, along with models, to measure snow under a range of forest conditions. SnowEx also included an extensive range of ground truth measurements - in-situ manual samples, snow pits, ground based remote sensing measurements, and sophisticated new techniques. A detailed description of the data collected will be given and some preliminary results will be presented.

The Advanced Technology Microwave Sounder (ATMS) is a space-based, cross-track radiometer for operational atmospheric temperature and humidity sounding, utilizing 22 channels over a frequency range from 23 to 183 GHz. The ATMS for the Joint Polar Satellite System-1 has undergone two rounds of rework in 2014-2015 and 2016, following performance issues discovered during and following thermal vacuum chamber (TVAC) testing at the instrument and observatory level. Final shelf-level testing, including measurement of pass band characteristics and spectral response functions, was completed in December 2016. Final instrument-level TVAC testing and calibration occurred during February 2017. Here we will describe the instrument-level TVAC calibration process, and illustrate with results from the final TVAC calibration effort.

Scanning L-band Active/Passive (SLAP) is an airborne remote sensing instrument developed at NASA Goddard Space Flight Center specifically as an airborne simulator of the Soil Moisture Active/Passive (SMAP) satellite instrument suite, for remote sensing of soil moisture, freeze-thaw state, ocean salinity, sea ice, and other physical phenomena that display characteristics at microwave L-band. This paper will present example observations from a recent soil freeze-thaw campaign in Winnipeg, Manitoba, Canada, as well as detail enhancements made to the SLAP RFI processor beyond the capabilities of the SMAP RFI processor.

The L-band (1 - 2 GHz) microwave remote sensing has been widely acknowledged as the most promising method to monitor regional to global soil moisture. Consequently, the Soil Moisture Active Passive (SMAP) satellite applied this technique to provide global soil moisture every 2 to 3 days. To verify the performance of SMAP, the fourth and fifth campaign of SMAP Experiments (SMAPEx-4 & -5) were carried out at the beginning of the SMAP operational phase in the Murrumbidgee River catchment, southeast Australia. The airborne radar and radiometer observations together with ground sampling on soil moisture, vegetation water content, and surface roughness were collected in coincidence with SMAP overpasses. The SMAPEx-4 & -5 data sets will benefit to SMAP post-launch calibration and validation under Australian land surface conditions.

This study investigates the sensitivity of multispectral reflectivity to changing snow correlation lengths. Mätzler's ice-lamellae radiative transfer model was implemented and tested to evaluate the reflectivity of snow correlation lengths at multiple frequencies from the ultraviolet (UV) to the microwave bands. The model reveals that, in the UV to infrared (IR) frequency range, the reflectivity and correlation length are inversely related, whereas reflectivity increases with snow correlation length in the microwave frequency range. The model further shows that the reflectivity behavior can be mainly attributed to scattering rather than absorption for shallow snowpacks. The largest scattering coefficients and reflectivity occur at very small correlation lengths (~10-5m) for frequencies higher than the IR band. In the microwave range, the largest scattering coefficients are found at millimeter wavelengths. For validation purposes, the ice-lamella model is coupled with a multilayer snow physics model to characterize the reflectivity response of realistic snow hydrological processes. The evolution of the coupled model simulated reflectivities in both the visible and the microwave bands is consistent with satellite-based reflectivity observations in the same frequencies. The model results are also compared with colocated in situ snow correlation length measurements (Cold Land Processes Field Experiment 2002-2003). The analysis and evaluation of model results indicate that the coupled multifrequency radiative transfer and snow hydrology modeling system can be used as a forward operator in a data-assimilation framework to predict the status of snow physical properties, including snow correlation length.

Microwave emission models are a critical component of snow water equivalent retrieval algorithms applied to passive microwave measurements. Several such emission models exist, but their differences need to be systematically compared. This paper compares the basic theories of two models: the multiple-layer Helsinki University of Technology (HUT) model and the microwave emission model of layered snowpacks (MEMLS). By comparing the mathematical formulation side by side, three major differences were identified: 1) by assuming that the scattered intensity is mostly (96%) in the forward direction, the HUT model simplifies the radiative transfer equation in 4π space into two one-flux equations, whereas MEMLS uses a two-flux theory; 2) the HUT scattering coefficient is much larger than the one of MEMLS; and 3) MEMLS considers the trapped radiation inside snow due to internal reflection by a six-flux model, which is not included in HUT. Simulation experiments indicate that the large scattering coefficient of the HUT model compensates for its large forward scattering ratio to some extent, but the effects of one-flux simplification and the trapped radiation still result in different TBsimulations between the HUT model and MEMLS. The models were compared with observations of natural snow cover at Sodankylä, Finland; Churchill, Canada; and Colorado, USA. No optimization of the snow grain size was performed. It shows that the HUT model tends to underestimate TBfor deep snow. MEMLS with the physically based improved Born approximation performed best among the models, with a bias of -1.4 K and a root-mean-square error of 11.0 K.

The Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions provide Level-1 brightness temperature (Tb) observations that are used for global soil moisture estimation. However, the nature of these Tb data differs: the SMOS Tb observations contain atmospheric and select reflected extraterrestrial (“Sky”) radiation, whereas the SMAP Tb data are corrected for these contributions, using auxiliary near-surface information. Furthermore, the SMOS Tb observations are multiangular, whereas the SMAP Tb is measured at 40° incidence angle only. This letter discusses how SMOS Tb, SMAP Tb, and radiative transfer modeling components can be aligned in order to enable a seamless exchange of SMOS and SMAP Tb data in soil moisture retrieval and assimilation systems. The aggregated contribution of the atmospheric and reflected Sky radiation is, on average, about 1 K for horizontally polarized Tb and 0.5 K for vertically polarized Tb at 40° incidence angle, but local and short-term values regularly exceed 5 K.

The Soil Moisture and Ocean Salinity (SMOS) satellite marks the commencement of dedicated global surface soil moisture missions, and the first mission to make passive microwave observations at L-band. On-orbit calibration is an essential part of the instrument calibration strategy, but on-board beam-filling targets are not practical for such large apertures. Therefore, areas to serve as vicarious calibration targets need to be identified. Such sites can only be identified through field experiments including both in situ and airborne measurements. For this purpose, two field experiments were performed in central Australia. Three areas are studied as follows: 1) Lake Eyre, a typically dry salt lake; 2) Wirrangula Hill, with sparse vegetation and a dense cover of surface rock; and 3) Simpson Desert, characterized by dry sand dunes. Of those sites, only Wirrangula Hill and the Simpson Desert are found to be potentially suitable targets, as they have a spatial variation in brightness temperatures of${<}{4}~{\rm K}$under normal conditions. However, some limitations are observed for the Simpson Desert, where a bias of 15 K in vertical and 20 K in horizontal polarization exists between model predictions and observations, suggesting a lack of understanding of the underlying physics in this environment. Subsequent comparison with model predictions indicates a SMOS bias of 5 K in vertical and 11 K in horizontal polarization, and an unbiased root mean square difference of 10 K in both polarizations for Wirrangula Hill. Most importantly, the SMOS observations show that the brightness temperature evolution is dominated by regular seasonal patterns and that precipitation events have only little impact.

The Suomi NPP spacecraft pitchover maneuver revealed an ATMS scan angle-dependent bias when viewing deep space, which is a homogenous and unpolarized source that fills the entire ATMS Field of Regard. Reflector emissivity was investigated as a possible root cause. The emissivity is polarization dependent, which results in a scan-dependent bias with the quasi-vertical channels having a different bias shape than the quasi-horizontal channels. The normal emissivity was empirically estimated by minimizing the scan bias during the pitchover maneuver, and the reflector's temperature was derived from ATMS telemetry. The model, calibration change, and estimated normal emissivity were verified using the ATMS thermal vacuum test data. Reflector emissitivies from approximately 0.2% to 0.4% were derived, resulting in brightness temperature corrections of up to 0.5 K.

A simplified atmospheric radiative transfer model at L-band has been developed for the Soil Moisture Active/Passive (SMAP) forward brightness temperature (level 1) simulator. The upwelling and downwelling brightness temperatures and the total loss factor of the atmosphere are modeled as polynomial functions of pressure, temperature, and water vapor density near the Earth's surface, as well as incidence angle. The model has been developed and verified by using global radiosonde data, and the model error is within the 0.1 K error budget (atmosphere portion) of the SMAP brightness temperature (Level 1B) product.

A suite of sensors scheduled to fly onboard the NPOESS Preparatory Project (NPP) satellite in 2011 will continue the Sensor Data Records (SDRs) provided by operational and research missions over the last 40 years. The Cross-track Infrared and Microwave Sounding Suite (CrIMSS), consisting of the Cross-track Infrared Sounder (CrIS) and the first space-based, Nyquist-sampled cross-track microwave sounder, the Advanced Technology Microwave Sounder (ATMS), will provide atmospheric vertical profile information to improve numerical weather and climate modeling. The ability of ATMS to sense temperature and moisture profile information in the presence of non-precipitating clouds complements the high vertical resolution of CrIS. Furthermore, the ability of ATMS to sense scattering of cold cosmic background radiance from the tops of precipitating clouds allows the retrieval of precipitation intensities with useful accuracies over most surface conditions. This paper will present several assessments of the performance of ATMS. Prelaunch testing of ATMS has characterized the principal calibration parameters and has enabled predictions of on-orbit performance with high levels of confidence. Also to be discussed is the planned on-orbit characterization of ATMS, which will further improve both the measurement quality and the understanding of various error contributions.

From a campaign conducted in 2002, hourly L-band H polarized TB(Brightness temperature) measurements are available for five episodes distributed over the corn growth cycle. In this study, fitting the τ-ω model to the TBmeasurements shows that the empirical parameter b, defining the optical depth or canopy opacity (r), and its dependence towards the incidence and azimuth angles both change during the growth cycle. The b found for the early growth stage is about three times larger than expected based on the literature, while near peak biomass and at senescence its value is about half. Moreover, the soil moisture dependence of the roughness and crop row orientation are found to be important uncertainties in the TBsimulations. The latter effect is particularly significant at senescence.

NASA's planned SMAP mission will utilize a radar operating in a band centered on 1.26 GHz and a co-observing radiometer operating at 1.41 GHz to measure surface soil moisture. Both the radar and radiometer sub-systems are susceptible to radio frequency interference (RFI). Any significant impact of such interference requires mitigation in order to avoid degradation in the SMAP science products. Studies of RFI detection and mitigation methods for both the radar and radiometer are continuing in order to assess the risk to mission products and to refine the performance achieved.

Large-scale snow hydrology has been studied via spaceborne passive microwave (PM) measurements for decades. Forward and inverse radiative transfer (RT) models of snow are utilized in this context but typically neglect snow stratigraphy. Our objective in this paper is to characterize the expected model error in PM brightness temperature (Tb) predictions due to neglecting stratigraphy over a range of snow cover conditions. For 191 snowpits ranging from prairie to alpine, we performed side-by-side RT model runs including and ignoring stratigraphy via mass-weighted averages across stratigraphic layers; error was estimated by comparing the two RT model runs. Neglecting stratigraphy at 37 GHz led to approximately 10-K root mean square error (RMSE) for moderately deep (alpine) snow cover and to approximately 5-K RMSE for shallower (prairie) snow. RMSE across all types of snow was 1.67 and 26.9 K at 18.7 and 89 GHz, respectively. At 37 GHz, there was a low bias for deep snowpacks and a high bias for moderate-to-shallow snowpacks. Bias magnitude bias was dependent on vertical grain size variability. Based on these results and estimates of sensitivity of Tbto snow depth, we estimated that snow depth RMSE due to neglecting stratigraphy approaches 50%.

A microwave radiance assimilation (RA) scheme for the retrieval of snow physical state variables requires a snowpack physical model (SM) coupled to a radiative transfer model. In order to assimilate microwave brightness temperatures (Tbs) at horizontal polarization (h-pol), an SM capable of resolving melt-refreeze crusts is required. To date, it has not been shown whether an RA scheme is tractable with the large number of state variables present in such an SM or whether melt-refreeze crust densities can be estimated. In this paper, an RA scheme is presented using the CROCUS SM which is capable of resolving melt-refreeze crusts. We assimilated both vertical (v) and horizontal (h) Tbs at 18.7 and 36.5 GHz. We found that assimilating Tb at both h-pol and vertical polarization (v-pol) into CROCUS dramatically improved snow depth estimates, with a bias of 1.4 cm compared to -7.3 cm reported by previous studies. Assimilation of both h-pol and v-pol led to more accurate results than assimilation of v-pol alone. The snow water equivalent (SWE) bias of the RA scheme was 0.4 cm, while the bias of the SWE estimated by an empirical retrieval algorithm was -2.9 cm. Characterization of melt-refreeze crusts via an RA scheme is demonstrated here for the first time; the RA scheme correctly identified the location of melt-refreeze crusts observed in situ.

The 19 papers in this special issue were originally presented at MicroRad 2010, held in Washington, DC from March 1 to 4, 2010.

Merging microwave radiances and modeled estimates of snowpack states in a data assimilation scheme is a potential method for snowpack characterization. A radiance assimilation scheme for snow requires a land surface model (LSM) coupled to a radiative transfer model (RTM). In this paper, we explore the degree of model fidelity required in order for radiance assimilation to yield benefits for snowpack characterization. Specifically, we characterize the uncertainty of Microwave Emission Model for Layered Snowpacks (MEMLS) radiance predictions by quantifying model accuracy and sensitivity to the following: (1) the LSM snowpack layering scheme and (2) the properties of the snow layers, including melt-refreeze ice layers. MEMLS was consistent with the measured brightness temperatures at 18.7 and 36.5 GHz with a bias (mean absolute error) of 0.1 K (3.1 K) for the vertical polarization and 3.4 K (9.3 K) for the horizontal polarization. An error in the predictions at horizontal polarization is due to uncertainty in ice-layer properties. It was found that in order for predicted brightness temperatures from the coupled LSM and RTM to be adequate for radiance assimilation purposes, the following must be satisfied: (1) the LSM snowpack layering scheme must accurately represent the stratigraphic snowpack layers; (2) dynamics of melt-refreeze ice layers must be modeled explicitly, and the predicted density of melt-refreeze layers must be accurate within ; and (3) the MEMLS correlation length must be predicted within 0.016 mm, or effective optical grain diameter must be predicted within 0.045 mm. Recommendations for future field measurements are made.

The National Airborne Field Experiment 2005 (NAFE'05) and the Campaign for validating the Operation of Soil Moisture and Ocean Salinity (CoSMOS) were undertaken in November 2005 in the Goulburn River catchment, which is located in southeastern Australia. The objective of the joint campaign was to provide simulated Soil Moisture and Ocean Salinity (SMOS) observations using airborne L-band radiometers supported by soil moisture and other relevant ground data for the following: (1) the development of SMOS soil moisture retrieval algorithms; (2) developing approaches for downscaling the low-resolution data from SMOS; and (3) testing its assimilation into land surface models for root zone soil moisture retrieval. This paper describes the NAFE'05 and CoSMOS airborne data sets together with the ground data collected in support of both aircraft campaigns. The airborne L-band acquisitions included 40 km × 40 km coverage flights at 500-m and 1-km resolution for the simulation of a SMOS pixel, multiresolution flights with ground resolution ranging from 1 km to 62.5 m, multiangle observations, and specific flights that targeted the vegetation dew and sun glint effect on L-band soil moisture retrieval. The L-band data were accompanied by airborne thermal infrared and optical measurements. The ground data consisted of continuous soil moisture profile measurements at 18 monitoring sites throughout the 40 km × 40 km study area and extensive spatial near-surface soil moisture measurements concurrent with airborne monitoring. Additionally, data were collected on rock coverage and temperature, surface roughness, skin and soil temperatures, dew amount, and vegetation water content and biomass. These data are available at www.nafe.unimelb.edu.au.

A numerical technique based on genetic algorithms (GAs) is used to invert the equations of an electromagnetic model based on dense-medium radiative transfer theory (DMRT) to retrieve snow depth, mean grain size, and fractional volume from microwave brightness temperatures. In order to study the sensitivity of the GA to its parameters, the technique is initially tested on simulated microwave data with and without adding a random noise. A configuration of GA parameters is selected and used for the retrieval of snow parameters from both ground-based observations and brightness temperatures recorded by the Advanced Microwave Scanning Radiometer-EOS (AMSR-E). Retrieved snow parameters are then compared with those measured on ground. Although more investigation is required, results suggest that the proposed technique is able to retrieve snow parameters with good accuracy

Electromagnetic models can be used for understanding the interaction between electromagnetic waves and matter, interpreting experimental data, and retrieving geophysical parameters. Comparing the results of different snow models, when driven with the same set of input parameters, can benefit remote sensing of snow. Microwave brightness temperatures of snow at 19 and 37 GHz for six different classes of snow (prairie, tundra, taiga, alpine, maritime, and ephemeral) are simulated by means of four different electromagnetic models: the Helsinki University of Technology snow emission model, the microwave emission model of layered snowpacks, a dense-medium radiative-transfer theory model, and a strong fluctuation theory model. The frequency behavior of the extinction coefficients obtained with the different models between 5 and 90 GHz is also studied. The four models are also driven with inputs derived from snow-pit data, and the outputs are compared with ground-based measurements of brightness temperatures at 18.7 and 36.5 GHz. Significant differences among the brightness temperatures and the extinction coefficients simulated with the four models in the cases of the six classes of snow are observed. Moreover, no particular model is found to be able to systematically reproduce all of the experimental data. The results highlight the need to more closely examine the relationships relating mean grain size and correlation length, introduce multiple layers in each model, and to perform controlled laboratory measurements on materials with well-known electromagnetic properties in order to improve the understanding of the causes of the observed differences and to improve model performance

The ability of electromagnetic models to accurately predict microwave emission of a snowpack is complicated by the need to account for, among other things, nonindependent scattering by closely packed snow grains, stratigraphic variations, and the occurrence of wet snow. A multilayer dense medium model can account for the first two effects. While microwave remote sensing is well known to be capable of binary wet/dry discrimination, the ability to model brightness as a function of wetness opens up the possibility of ultimately retrieving a percentage wetness value during such hydrologically significant melting conditions. In this paper, the first application of a multilayer dense medium radiative transfer theory (DMRT) model is proposed to simulate emission from both wet and dry snow during melting and refreezing cycles. Wet snow is modeled as a mixture of ice particles surrounded by a thin film of water embedded in an air background. Melting/refreezing cycles are studied by means of brightness temperatures at 6.7, 19, and 37 GHz recorded by the University of Michigan Truck-Mounted Radiometer System at the Local Scale Observation Site during the Cold Land Processes Experiment-1 in March 2003. Input parameters to the DMRT model are obtained from snow pit measurements carried out in conjunction with the microwave observations. The comparisons between simulated and measured brightness temperatures show that the electromagnetic model is able to reproduce the brightness temperatures with an average percentage error of 3% (~8 K) and a maximum relative percentage error of around 8% (~20 K)

Recent availability of high-speed analog to digital converters (ADC) has enabled the development of digital radiometers for Earth remote sensing. In the unprotected C band, undesired radio frequency interferences (RFI) have been observed and mitigation efforts are currently being carried out in the research community. They mainly consist of detection and filtering in the time or frequency domain. Because RFI can be very small and mistaken as geophysical signals, an additional approach could rely on the inversion of a model describing internal noise, RFI and geophysical data processed through the radiometer. Such an approach demands a thorough understanding of receiver internal noise statistics. As a first step, we propose to model the system by taking advantage of the time series record available at the digital back end of a receiver. The experimental setup includes an X band benchtop radiometer serving as a standard analog gain chain, and various inputs fed into the radiometer front end. The digital output of an eight-bit ADC following the analog chain was recorded using a logic analyzer. An exploratory data analysis enabled us to select a priori valid data. Statistical signal processing techniques encompassed loglog relationships, classical spectral estimation (smoothed periodogram and windowed autocovariance), and autoregressive modeling.

We present an analytical calibration approach for passive microwave polarimeters that is applicable where the instrument can be partitioned into distinct, functional radio-frequency blocks. The methodology is focused on polarimetric system characterization, not polarimetric measurements. It requires characterization of each major internal functional subsystem with a vector network analyzer to obtain a closed-form transfer function. The goals of this approach are to provide a transfer function describing the system in its entirety and to isolate the contribution of each subsystem to the uncertainty in the final modified Stokes parameters. Notably, the approach does not assume ideal polarization isolation in the radiometer system. A significant benefit of this approach is that the cascaded transfer functions serve as a realistic instrument simulator revealing where improvements in component performance would have greatest benefit for system performance over the dynamic range of the instrument. This systems-focused approach is applied to the National Aeronautics and Space Administration Goddard Space Flight Center polarimetric Airborne C-band Microwave Radiometer (ACMR), whose architecture allows the necessary subsystem partitioning. The characteristics of each subsystem were extensively measured, converted to a transfer function, and imported into the overall closed-form system model. Inversion of the system model and error analysis inherent to this calibration approach are illustrated by a full Stokes parameter retrieval for a senescent cornfield.

Passive microwave remote sensing is sensitive to the quantity and distribution of water in soil and vegetation. During summer 2000, the Microwave Geophysics Group at the University of Michigan conducted the 7thRadiobrighness Energy Balance Experiment (REBEX-7) over a corn canopy in Michigan. Long time series of brightness temperatures, soil moisture and micrometeorology on the plot scale were taken. This paper addresses the calibration of the NASA GSFC polarimetric airborne C band microwave radiometer (ACMR) that participated in REBEX-7. Passive polarimeters are typically calibrated using an end-to-end approach based upon a standard artificial target or a well-known geophysical target. Analyzing the major internal functional subsystems offers a different perspective. The primary goal of this approach is to provide a transfer function that not only describes the system in its entirety but also accounts for the contributions of each subsystem toward the final modified Stokes parameters. This approach also serves as a realistic instrument simulator, a useful tool for future designs. The ACMR architecture can be partitioned into several functional subsystems. Each subsystem was extensively measured and the estimated parameters were imported into the overall system model. We will present the results of polarimetric antenna measurements, the instrument model as well as four Stokes observations from REBEX-7 using a first order inversion

The NASA Cold-land Processes Field Experiment-1 (CLPX-1) involved several instruments in order to acquire data at different spatial resolutions. Indeed, one of the main tasks of CLPX-1 was to explore scaling issues associated with microwave remote sensing of snowpacks. To achieve this task, microwave brightness temperatures collected at 18.7, 36.5, and 89 GHz at LSOS test site by means of the University of Tokyo's Ground Based Microwave Radiometer-7 (GBMR-7) were compared with brightness temperatures recorded by the NOAA Polarimetric Scanning Radiometer (PSR/A) and by SSM/I and AMSR-E radiometers. Differences between different scales observations were observed and they may be due to the topography of the terrain and to observed footprints. In the case of satellite and airborne data, indeed, it is necessary to consider the heterogeneity of the terrain and the presence of trees inside the observed scene becomes a very important factor. Also when comparing data acquired only by the two satellites, differences were found. Different acquisition times and footprint positions, together with different calibration and validation procedures, can be responsible for the observed differences.

Microwave brightness temperatures at 18.7, 36.5, and 89 GHz collected at the Local-Scale Observation Site (LSOS) of the NASA Cold-Land Processes Field Experiment in February, 2003 (third Intensive Observation Period) were simulated using a Dense Media Radiative Transfer model (DMRT), based on the Quasi Crystalline Approximation with Coherent Potential (QCA-CP). Inputs to the model were averaged from LSOS snow pit measurements, although different averages were used for the lower frequencies vs. the highest one, due to the different penetration depths and to the stratigraphy of the snowpack. Mean snow particle radius was computed as a best-fit parameter. Results show that the model was able to reproduce satisfactorily brightness temperatures measured by the University of Tokyo's Ground Based Microwave Radiometer system (GBMR-7). The values of the best-fit snow particle radii were found to fall within the range of values obtained by averaging the field-measured mean particle sizes for the three classes of Small, Medium and Large grain sizes measured at the LSOS site

Soil moisture is a critical state variable in land surface hydrology. Large-scale soil moisture mapping based on microwave remote sensing would be valuable in many different practical and theoretical applications, and a real potential exists for new space missions in the near future which well utilize simultaneous active/passive microwave measurements for global soil moisture retrieval. This paper discusses the experiment for the retrieval of soil moisture using radar and radiometric measurements. It was shown that combinations of simultaneous radar and radiometer data can enhance soil moisture retrievals, especially in the presence of dynamic vegetation.

Passive microwave polarimeters, or polarimetric radiometers, are important tools for Earth remote sensing. They have found utility in ocean surface wind-vector remote sensing and polarization basis rotation systems for compensating instrument or ionospheric induced rotation. The hybrid coupler-based polarimeter is specific class of polarimeter that utilizes a 180-degree hybrid coupler to affect a correlation between the received vertical and horizontal polarization signals. This type of polarimeter requires at least four calibration states for complete calibration. One state must be a polarized signal, such as the application of a correlated noise source. The phase balance of the noise source directly determines the quality of the calibration.

The authors' point-scale Land Surface Process/Radiobrightness (LSP/R) model for a prairie grassland in the northern Great Plains was adapted to winter wheat-stubble within the region of the Southern Great Plains 1997 (SGP'97) Hydrology Experiment. The model maintains running estimates of near-surface soil moisture and stored water in soil and vegetation when forced by weather, and predicts the microwave brightness of the terrain. LSP/R model predictions were compared with the field observations recorded during SGP'97. The model captures canopy and soil temperatures very well, with the maximum mean and variance of the difference between the model and field temperatures being 1.06 K and 3.28 K/sup 2/, respectively. It yields reasonable predictions for the moisture in deeper layers of the soil, but its predictions for the moisture in the upper layers are low by /spl sim/2.3% by volume. These underpredictions of near-surface soil moisture result in higher H-pol brightnesses at 19 GHz than those observed.