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

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

Increasing attention is being paid to the monitoring of global change, and remote sensing is an important means for acquiring global observation data. Due to the limitations of the orbital altitude, technological level, observation platform stability and design life of artificial satellites, spaceborne Earth observation platforms cannot quickly obtain global data. The Moon-based Earth observation (MEO) platform has unique advantages, including a wide observation range, short revisit period, large viewing angle and spatial resolution, thus, it provides a new observation method for quickly obtaining global Earth observation data. At present, the MEO platform has not yet entered the actual development stage, and the relevant parameters of the microwave sensors have not been determined. In this work, to explore whether a microwave radiometer is suitable for the MEO platform, the land surface temperature (LST) distribution at different times is estimated and the design parameters of the Moon-based microwave radiometer (MBMR) are analyzed based on the LST retrieval. Results show that the antenna aperture size of a Moon-based microwave radiometer is suitable for 120 m, and the bands include 18.7, 23.8, 36.5 and 89.0 GHz, each with horizontal and vertical polarization. Moreover, the optimal value of other parameters, such as the half-power beam width, spatial resolution, integration time of the radiometer system, temperature sensitivity, scan angle and antenna pattern simulations are also determined.

The long-term stability of microwave radiometers (MWR) on-board altimetry missions is critical to reduce the uncertainty on the global mean sea level estimate. Harmonization and homogenization steps are applied to MWR observations in that perspective. The Sentinel-3 tandem phase provides a unique opportunity to quantify the uncertainties on the “zero-bias line” homogenization approach defined by Bennartz et al. (2020). Initially developed to improve the performance of the wet tropospheric correction retrieval, it is used here to provide a common reference for the inter-calibration between Sentinel-3A and Sentinel-3B MWR. A simplified version of the “zero-bias line” approach, a linear correction depending on brightness temperatures, allows to strongly reduce the bias between the two radiometers for both channels (about 0.5 K) and the standard deviation of the difference (0.3 K). The full version of the approach adding a dependency on wind speed has improved the quality of the WTC retrieval (Bennartz et al. 2020) but degrades the performance of the homogenization. It is thus recommended to apply the simplified version of this approach in the processing of fundamental data record. The quantification of the uncertainties on the homogenization approach is only possible due to the ideal configuration of the Sentinel-3 tandem phase. The same dataset and the same metrics could be used to assess other approaches.

In this study, an existing combination approach that maximizes temporal correlations is used to combine six passive microwave satellite soil moisture products from 1998 to 2015 to assess its added value in long-term applications. Five of the products used are included in existing merging schemes such as the European Space Agency’s essential climate variable soil moisture (ECV) program. These include the Special Sensor Microwave Imagers (SSM/I), the Tropical Rainfall Measuring Mission (TRMM/TMI), the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) sensor on the National Aeronautics and Space Administration’s (NASA) Aqua satellite, the WindSAT radiometer, onboard the Coriolis satellite and the soil moisture retrievals from the Advanced Microwave Scanning Radiometer 2 (AMSR2) sensor onboard the Global Change Observation Mission on Water (GCOM-W). The sixth, the microwave radiometer imager (MWRI) onboard China’s Fengyun-3B (FY3B) satellite, is absent in the ECV scheme. Here, the normalized soil moisture products are merged based on their availability within the study period. Evaluation of the merged product demonstrated that the correlations and unbiased root mean square differences were improved over the whole period. Compared to ECV, the merged product from this scheme performed better over dense and sparsely vegetated regions. Additionally, the trends in the parent inputs are preserved in the merged data. Further analysis of FY3B’s contribution to the merging scheme showed that it is as dependable as the widely used AMSR2, as it contributed significantly to the improvements in the merged product.

Ocean surface whitecaps manifest surface wave breaking. Most of the whitecap data reported in the literature are based on optical observations through photographic or video recording. The air in whitecaps modifies the dielectric properties of microwave emissions and scattering. Therefore, whitecap information is intrinsic to microwave signals. This paper discusses a method to retrieve the ocean surface whitecap coverage from microwave radiometer signals.

Based on the optimal interpolation (OI) algorithm, a daily fusion product of high-resolution global ocean columnar atmospheric water vapor with a resolution of 0.25° was generated in this study from multisource remote sensing observations. The product covers the period from 2003 to 2018, and the data represent a fusion of microwave radiometer observations, including those from the Special Sensor Microwave Imager Sounder (SSMIS), WindSat, Advanced Microwave Scanning Radiometer for Earth Observing System sensor (AMSR-E), Advanced Microwave Scanning Radiometer 2 (AMSR2), and HY-2A microwave radiometer (MR). The accuracy of this water vapor fusion product was validated using radiosonde water vapor observations. The comparative results show that the overall mean deviation (Bias) is smaller than 0.6 mm; the root mean square error (RMSE) and standard deviation (SD) are better than 3 mm, and the mean absolute deviation (MAD) and correlation coefficient (R) are better than 2 mm and 0.98, respectively.

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

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

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

Ground-based microwave radiometers are increasingly used in operational meteorology and nowcasting. These instruments continuously measure the spectra of downwelling atmospheric radiation in the range 20–60 GHz used for the retrieval of tropospheric temperature and water vapor profiles. Spectroscopic uncertainty is an important part of the retrieval error budget, as it leads to systematic bias. In this study, we analyze the difference between observed and simulated microwave spectra obtained from more than four years of microwave and radiosonde observations over Nizhny Novgorod (56.2° N, 44° E). We focus on zenith-measured and elevation-scanning data in clear-sky conditions. The simulated spectra are calculated by a radiative transfer model with the use of radiosonde profiles and different absorption models, corresponding to the latest spectroscopy research. In the case of zenith-measurements, we found a systematic bias (up to ~2 K) of simulated spectra at 51–54 GHz. The sign of bias depends on the absorption model. A thorough investigation of the error budget points to a spectroscopic nature of the observed differences. The dependence of the results on the elevation angle and absorption model can be explained by the basic properties of radiative transfer and by cloud contamination at elevation angles.

This paper describes the Passive microwave Neural network Precipitation Retrieval algorithm for climate applications (PNPR-CLIM), developed with funding from the Copernicus Climate Change Service (C3S), implemented by ECMWF on behalf of the European Union. The algorithm has been designed and developed to exploit the two cross-track scanning microwave radiometers, AMSU-B and MHS, towards the creation of a long-term (2000–2017) global precipitation climate data record (CDR) for the ECMWF Climate Data Store (CDS). The algorithm has been trained on an observational dataset built from one year of MHS and GPM-CO Dual-frequency Precipitation Radar (DPR) coincident observations. The dataset includes the Fundamental Climate Data Record (FCDR) of AMSU-B and MHS brightness temperatures, provided by the Fidelity and Uncertainty in Climate data records from Earth Observation (FIDUCEO) project, and the DPR-based surface precipitation rate estimates used as reference. The combined use of high quality, calibrated and harmonized long-term input data (provided by the FIDUCEO microwave brightness temperature Fundamental Climate Data Record) with the exploitation of the potential of neural networks (ability to learn and generalize) has made it possible to limit the use of ancillary model-derived environmental variables, thus reducing the model uncertainties’ influence on the PNPR-CLIM, which could compromise the accuracy of the estimates. The PNPR-CLIM estimated precipitation distribution is in good agreement with independent DPR-based estimates. A multiscale assessment of the algorithm’s performance is presented against high quality regional ground-based radar products and global precipitation datasets. The regional and global three-year (2015–2017) verification analysis shows that, despite the simplicity of the algorithm in terms of input variables and processing performance, the quality of PNPR-CLIM outperforms NASA GPROF in terms of rainfall detection, while in terms of rainfall quantification they are comparable. The global analysis evidences weaknesses at higher latitudes and in the winter at mid latitudes, mainly linked to the poorer quality of the precipitation retrieval in cold/dry conditions.

Offshore of many coastal regions, the ocean surface wind varies in speed and direction throughout the day, owing to forcing from land/sea temperature differences and orographic effects. Far offshore, both diurnal and semidiurnal wind vector variability has been noted in the Tropical Atmosphere Ocean-TRIangle Trans-Ocean buoy Network (TAO-TRITON) mooring data in the tropical Pacific Ocean. In this manuscript, the tropical diurnal wind variability is examined with microwave radiometer-derived winds from the Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement (GPM), merged with RapidScat and other scatterometer data. Since the relationship between wind speed and its zonal and meridional components is nonlinear, this manuscript describes an observationally based methodology to merge the radiometer and scatterometer-based wind estimates as a function of observation time, to generate a multi-year dataset of diurnal wind variability. Compared to TAO-TRITON mooring array data, the merged satellite-derived wind components fairly well replicate the semidiurnal zonal wind variability over the tropical Pacific but generally show more variability in the meridional wind components. The meridional component agrees with the associated mooring location data in some locations better than others, or it shows no clear dominant diurnal or semidiurnal mode. Similar discrepancies are noted between two forecast model reanalysis products. It is hypothesized that the discrepancies amongst the meridional winds are due to interactions between surface convergence and convective precipitation over tropical ocean basins.

Identifying volcanic activity on worlds with optically thick atmospheres with passive microwave radiometry has been proposed as a means of skirting the atmospheric interference that plagues near infrared observations. By probing deeper into the surface, microwave radiometers may also be sensitive to older flows and thus amenable for investigations where repeat observations are infrequent. In this investigation we explore the feasibility of this tactic using data from the Soil Moisture Active Passive (SMAP) mission in three case studies: the 2018 Kilauea eruption, the 2018 Oct-Nov eruption at Fuego, and the ongoing activity at Erta Ale in Ethiopia. We find that despite SMAP’s superior spatial resolution, observing flows that are small fractions of the observing footprint are difficult to detect—even in resampled data products. Furthermore, the absorptivity of the flow, which can be temperature dependent, can limit the depths to which SMAP is sensitive. We thus demonstrate that the lower limit of detectability at L-band (1.41 GHz) is in practice higher than expected from first principles.

Confidence in the use of Earth observations for monitoring essential climate variables (ECVs) relies on the validation of satellite calibration accuracy to within a well-defined uncertainty. The gap analysis for integrated atmospheric ECV climate monitoring (GAIA-CLIM) project investigated the calibration/validation of satellite data sets using non-satellite reference data. Here, we explore the role of numerical weather prediction (NWP) frameworks for the assessment of several meteorological satellite sensors: the advanced microwave scanning radiometer 2 (AMSR2), microwave humidity sounder-2 (MWHS-2), microwave radiation imager (MWRI), and global precipitation measurement (GPM) microwave imager (GMI). We find departures (observation-model differences) are sensitive to instrument calibration artefacts. Uncertainty in surface emission is identified as a key gap in our ability to validate microwave imagers quantitatively in NWP. The prospects for NWP-based validation of future instruments are considered, taking as examples the microwave sounder (MWS) and infrared atmospheric sounding interferometer-next generation (IASI-NG) on the next generation of European polar-orbiting satellites. Through comparisons with reference radiosondes, uncertainties in NWP fields can be estimated in terms of equivalent top-of-atmosphere brightness temperature. We find NWP-sonde differences are consistent with a total combined uncertainty of 0.15 K for selected temperature sounding channels, while uncertainties for humidity sounding channels typically exceed 1 K.

The purpose of this paper is to present a new method for early detection of forest fires, especially in forest zones prone to fires using microwave remote sensing and information-modeling tools. A decision-making system is developed as a tool for operational coupled analysis of modeling results and remote sensing data. The main operating structure of this system has blocks that calculate the moisture of forest canopy, the soil-litter layer, and the forest physical temperature using the observed brightness temperature provided by the flying platform IL-18 equipped with passive microwave radiometers of 1.43, 13.3 and 37.5 GHz frequencies. The hydrological parameters of the forest are assessed with both a developed regional hydrological model and remote sensing observations. The hydrological model allows for the detection of fire-prone zones that are subject to remote sensing when modeling results are corrected and thermal temperatures are evaluated. An approach for the real time forest fires classification via daytime remote sensing observations is proposed. The relative theoretical and experimental results presented here have allowed us to use a new approach to forests monitoring during periods of potential fire. A decision-making algorithm is presented that aims at analyzing data flows from radiometers located on the remote sensing platform to calculate the probability of forest fire occurring in geographical pixels. As case study, the state of forest fires that occurred in Siberia in 2019 using microwave remote sensing measurements conducted by a flying IL-18 laboratory is presented. This remote sensing platform is equipped with optical and microwave tools that allow the optical and microwave images of the observed forest areas. The main operating frequencies of microwave radiometers are 1.43, 13.3 and 37.5 GHz. Microwave radiometers provide data on water content in the forest canopy and on litter and physical temperatures. Based on the long-term measurements made in Siberia, the possible improvement of the proposed decision-making system for future relevant studies is discussed in detail. The basic idea of cost-effective monitoring of forested areas consists of a two-stage exploration of fire risk zones. The first monitoring stage is performed using the hydrological model of the study area to identify low moisture areas of the forest canopy and litter. The second stage of monitoring is conducted using the remote sensing platform only in the local fire-dangerous areas in order to more precisely identify the areas prone to fire and to detect and diagnose real burning zones. The developed algorithm allows the calculation of physical temperatures and the detection of temperature anomalies based on measured brightness temperatures. Finally, the spatial distribution of the probability of forest fire occurrence is given as an example of the decision-making system along with a comparison of this distribution with the satellite images provided by the EOSDIS Land data.

Passive microwave measurements from satellites have been used to identify the signature of hail in intense thunderstorms. The scattering signal of hailstones is typically observed as a strong depression of upwelling brightness temperatures from the cloud to the satellite. Although the relation between scattering signal and hail diameter is often assumed linear, in this work a logistic model is used which seems to well approximate the complexity of the radiation extinction process by varying the hail cross-section. A novel probability-based method for hail detection originally conceived for AMSU-B/MHS and now extended to ATMS, GMI, and SSMIS, is presented. The measurements of AMSU-B/MHS were analyzed during selected hailstorms over Europe, South America and the US to quantify the extinction of radiation due to the hailstones and large ice aggregates. To this aim, a probabilistic growth model has been developed. The validation analysis based on 12-year surface hail observations over the US (NOAA official reports) collocated with AMSU-B overpasses have demonstrated the high performance of the hail detection method in distinguishing between moderate and severe hailstorms, fitting the seasonality of hail patterns. The flexibility of the method allowed its experimental application to other microwave radiometers equipped with MHS-like frequency channels revealing a high level of portability.

Improvements in radiometric calibration are needed to achieve the desired accuracy and stability of satellite-based microwave-radiometer observations intended for the production of climate data records. Linearity, stability and traceability of measurements to an SI-unit standard should be emphasized. We suggest radiometer design approaches to achieve these objectives in a microwave calibration-reference instrument. Multi-year stability would be verified by comparison to radio-occultation measurements. Data from such an instrument could be used for climate studies and also to transfer its calibration to weather-satellite instruments. With the suitable selection of an orbit, a climatology of the diurnal variation in the measured parameters could be compiled, which would reduce uncertainties in climate trends inferred from earlier microwave radiometers over past decades.

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

We present an analysis of information content for sea surface temperature (SST) retrieval from the Advanced Microwave Scanning Radiometer 2 (AMSR2). We find that SST uncertainty of ∼0.37 K can be achieved within an optimal estimation framework in the presence of wind, water vapour and cloud liquid water effects, given appropriate assumptions for instrumental uncertainty and prior knowledge, and using all channels. We test all possible combinations of AMSR2 channels and demonstrate the importance of including cloud liquid water in the retrieval vector. The channel combinations, with the minimum number of channels, that carry most SST information content are calculated, since in practice calibration error drives a trade-off between retrieved SST uncertainty and the number of channels used. The most informative set of five channels is 6.9 V, 6.9 H, 7.3 V, 10.7 V and 36.5 H and these are suitable for optimal estimation retrievals. We discuss the relevance of microwave SSTs and issues related to them compared to SSTs derived from infra-red observations.

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

Due to the difficulty in accurately interpreting surface emissivity spectra, problems remain in the application of passive microwave satellite observations over land surfaces. This study develops a parameterized soil surface emissivity model to quantify the microwave emissivity accurately and rapidly for Gaussian-correlated rough surfaces. We first analyze the sensitivity of surface emissivity to parameters using the advanced integral equation model (AIEM) simulated data. On the basis of the analysis and previous empirical models, two function factors that consider the polarization dependence of surface reflectivity are developed in the parameterized soil surface emissivity model. These factors also comprehensively account for the effects of surface roughness, soil moisture, and incident angle. A comparison with the AIEM simulated data indicates that the absolute error of effective reflectivity estimated by the parameterized soil surface emissivity model is small with a magnitude of 10−2. Validation through experimental measurements suggests that a good agreement could be obtained. The parameterized soil surface emissivity model is applied to simulate satellite measurements of the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E). Compared with the commonly-used microwave land emissivity model developed by Weng et al. (2001), the simulation results using the parameterized soil surface emissivity model yield a lower root-mean-square error (RMSE) and the overall errors are reduced, particularly for horizontal polarization. The newly-developed parameterized soil surface emissivity model should be useful in improving our understanding and modeling the measurements of passive microwave radiometers.

The observed brightness temperatures (Tb) at 37 GHz from typical moderate density dry snow in mid-latitudes decreases with increasing snow water equivalent (SWE) due to volume scattering of the ground emissions by the overlying snow. At a certain point, however, as SWE increases, the emission from the snowpack offsets the scattering of the sub-nivean emission. In tundra snow, the Tb slope reversal occurs at shallower snow thicknesses. While it has been postulated that the inflection point in the seasonal time series of observed Tb V 37 GHz of tundra snow is controlled by the formation of a thick wind slab layer, the simulation of this effect has yet to be confirmed. Therefore, the Dense Media Radiative Transfer Theory for Multi Layered (DMRT-ML) snowpack is used to predict the passive microwave response from airborne observations over shallow, dense, slab-layered tundra snow. Airborne radiometer observations coordinated with ground-based in situ snow measurements were acquired in the Canadian high Arctic near Eureka, NT, in April 2011. The DMRT-ML was parameterized with the in situ snow measurements using a two-layer snowpack and run in two configurations: a depth hoar and a wind slab dominated pack. With these two configurations, the calibrated DMRT-ML successfully predicted the Tb V 37 GHz response (R correlation of 0.83) when compared with the observed airborne Tb footprints containing snow pits measurements. Using this calibrated model, the DMRT-ML was applied to the whole study region. At the satellite observation scale, observations from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) over the study area reflected seasonal differences between Tb V 37 GHz and Tb V 19 GHz that supports the hypothesis of the development of an early season volume scattering depth hoar layer, followed by the growth of the late season emission-dominated wind slab layer. This research highlights the necessity to consider the two-part emission characteristics of a slab-dominated tundra snowpack at 37 GHz Tb.

In this paper, we implemented the in-situ observation of surface soil moisture (SSM), vegetation water content (VWC), and microwave brightness temperatures. By analyzing this in-situ observation dataset and the numerical simulation, we investigated the source of the uncertainty of the current algorithms for Advanced Microwave Scanning Radiometer for Earth observation system (AMSR-E) and AMSR2 to retrieve SSM and vegetation dynamics. Our findings are: (1) the microwave radiative transfer at C-band and X-band is not strongly affected by the shape of vegetation and the existing algorithm can be applied to a wide variety of plant types; (2) the diversity of surface soil roughness significantly affects the indices which are used by the current algorithms and addressing the uncertainty of surface soil roughness is necessary to improve the retrieval algorithms; (3) At C-band, SSM of the homogeneous vegetated land surfaces can be detected only when their VWC is less than approximately 0.25 (kg/m2); (4) the state-of-the-art Radiative Transfer Model (RTM) can predict our observed dataset although we have some biases in simulating brightness temperatures at a higher frequency. The new in-situ observation dataset produced by this study can be the guideline for both developers and users of passive microwave land observations to consider the uncertainties of their products.