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The L-band radiometer onboard the soil moisture active passive (SMAP) mission is used to retrieve sea surface salinity (SSS) over global ocean. In the Arctic seas, one of the major challenges of SSS remote sensing is the presence of sea ice. This paper proposes a data-driven ice correction (IC) algorithm which extracts emission from the water portion of measured brightness temperature (TB) in scenes mixed with water and ice. Emission of the ice portion was removed based on estimation according to the ice fraction (fice) in the satellite footprint and ice signature derived from surrounding pixels. The IC algorithm is applied to SMAP TB data to obtain TB with IC (TBIC), which are used for SSS retrieval using the standard JPL SMAP CAP processing system. We show that the algorithm is most effective near the ice edge, thereby increasing the fice threshold for possible SSS retrieval to 15% from the current 3% without IC. SMAP SSS are validated using in situ salinity collected during NASA's Ocean Melting Greenland (OMG) mission from 2016 to 2020 along the Greenland coast. The number of collocations between OMG and SMAP daily gridded salinity increased by more than 30% with IC. The statistical analysis shows a similar retrieval accuracy with or without IC, with the standard deviation of the difference between OMG and SMAP of 1.41 psu (with IC) and 1.42 psu (without IC). The bias-adjusted SMAP SSS depicts salinity patterns and gradients around Greenland consistent with OMG measurements.

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

Recent lunar observations have concluded that significant water ice deposits may exists in the regolith, especially in the permanently shadowed regions near the lunar poles. Detection and utilization of these reserves is very important to sustain the continuous human presence on the Moon. This paper, using analytical models and numerical simulations, demonstrates that wideband microwave radiometers are promising instruments for exploring underground water ice deposits on the Moon as their brightness temperature spectra are highly sensitive to water presence in the regolith.

Brightness temperature data from wave Imaging Radiometer using Aperture Synthesis (MIRAS) on board the European Space Agency's (ESA) Soil Moisture and Ocean Salinity (SMOS) mission have been used to derive the thickness of thin sea ice for the Arctic freeze-up period. To control the long-term geophysical quality for level 3 SMOS sea ice thickness products we derive a regional extent parameter that can be compared to independent standard ice extent products such as the NSIDC sea ice index. This metric allows to identify first-order quality problems such as data gaps and to observe the evolution of the Arctic sea ice growth in key regions. The regionalized SMOS sea ice thickness extent corresponds in general well with the corresponding NSIDC Sea Ice Index. The occurrence of severe RFI problems has so far mainly been limited to the initial period of the SMOS measurements during the season 2010/2011. Otherwise the comparison does not reveal any significant quality problems of the SMOS sea ice thickness data.

Accurately measuring the vertical temperature profiles of ice sheets and glaciers can provide critical data for numerical ice sheet models used in global sea level projections. Ice-penetrating radars and radiometers have been utilized independently in remote sensing campaigns to measure englacial ice temperatures. Here we show that by combining these two instruments, which have complimentary sensitivities to lower and upper depths of the ice column, respectively, the overall uncertainty in temperature profile retrievals can be significantly reduced. We present unified simulations of radar attenuation and radiometer brightness temperature as functions of depth and frequency to demonstrate the value in this combined active/passive remote sensing approach.

The antarctic region is an essential component of Earth's climate system and heat balance. Therefore, continuous monitoring of various cryospheric parameters (Sea-Ice Extent - SIE, and Snow Water Equivalent - SWE, among others) over Antarctica is essential to understand the state of global climate processes. This study presents a framework to detect and analyse the SIE and SWE using enhanced resolution data products from ISRO’ s (Indian Space Research Organisation) Scatterometer Satellite (SCATSAT -1) level-4 (operating at Ku-band, 13.5 GHz) data products. This framework is based on Artificial Neural Network (ANN) and Post-Classification Comparison (PCC) to detect the multitemporal variations using SCATSAT-1 images (sigma-naught, gamma-naught, and brightness temperature). For validation purposes, the classified maps and change maps are compared with Advanced Microwave Scanning Radiometer (AMSR2) derived SIE and SWE polar gridded datasets. Statistical analysis has confirmed the effectiveness (more than 88.75% accuracy) of the proposed framework with SCATSAT-1. We conclude that the ANN-PCC is a powerful technique to estimate the spatiotemporal dynamics of surface melting and freezing over Antarctica.

This paper presents a study of snowpack thermal emissions at long wavelengths and over a wide frequency band. Brightness temperature measurements of a snow layer are reported and used to estimate the travel time through the layer. The Ultra-Wideband Software-Defined Radiometer (UWBRAD) and the Wideband Autocorrelation Radiometer (WiBAR) were deployed at the Keweenaw Research Center (KRC) from February to April 2020 to demonstrate these techniques. Results on snowpack brightness temperature and thickness measurements are presented and discussed.

Arctic sea surface temperature is an important parameter for the research of climate change monitoring. Satellite remote sensing provides an important means of sea surface temperature (SST) observation, especially when there are few buoy and ship data in the Arctic. The Visible and Infrared Scanning Radiometer (VIRR) onboard the Fengyun-3C (FY-3C) can be used to observe SST. In this paper, intercalibration between VIRR and MODIS is performed to obtain corrected brightness temperature (BT) via double difference method, then Bayesian cloud detection and optimal estimation algorithms based on VIRR corrected BT are used to obtain Arctic VIRR SST. In this paper, only the data in March 2016 are processed, the bias of matchups between VIRR SST and AVHRR SST is -0.24°C, the standard deviation is 0.57°C, the robust standard deviation is 0.45°C. In the future, the data of long time series will then be processed for a complete assessment.

Modern microwave radiometers have demonstrated the feasibility of monitoring surface salinity from space and also the need for better accuracy in cold water. Accuracy could be improved by adding measurements at lower frequencies (lower than the measurement at 1.4 GHz currently used) and closer to the peak in sensitivity of brightness temperature to changes in salinity. Proposals to accomplish this have focused on wide bandwidth receivers which include at the low end frequencies close to the peak in sensitivity. This strategy involves trade-offs, some obvious such as radio frequency interference (RFI) when operating outside the protected band at 1.4 GHz and the loss of spatial resolution at lower frequencies. Others stemming from the interdependence of the retrieval of salinity on water temperature and surface roughness are more subtle. The objective of this manuscript is to examine this interdependence and its implications to future wide bandwidth instruments for remote sensing of salinity from space.

This paper discusses the utilization of the Global Precipitation Measurement (GPM) constellation as a single multi-frequency radiometer to profile subsurface properties of the polar firn. Initial analyses focusing on the Concordia station in Antarctica have demonstrated that GPM brightness temperature measurements can be successfully simulated across a wide frequency spectrum below 100 GHz and are sensitive to important physical properties of the firn from its surface down to deep isothermal ice. Therefore, the GPM constellation provides an excellent opportunity for characterizing the polar firn with broad spatiotemporal coverage.

In recent years many studies concerning the monitoring of volcanic activity have been carried out to develop ever more accurate and refine methods which allow to face the emergencies related to an eruption event. In our work we present different approaches for the volcanic ash cloud detection and retrieval using Sentinel-3 Sea and Land Surface Temperature Radiometer (SLSTR) data. As test case the SLSTR image collected on Raikoke volcano the 22 June 2019 at 00:07 UTC has been considered. A neural network based algorithm able to detect and distinguish volcanic and meteorological clouds, and the underlying surfaces, has been implemented and compared with two consolidated approaches: the RGB (Red-Green-Blue) and the Brightness Temperature Difference procedures. For the ash retrieval parameters (aerosol optical depth, effective radius and ash mass), three different methods have been compared: the reliable and consolidated LUT p (Look Up Table) procedure, the very fast VPR (Volcanic Plume Retrieval) algorithm and a neural network based model.

The relationship between the radio brightness temperature and the electrophysical characteristics of the soil is known, which implies the possibility of solving inverse problems, namely: determining the electrophysical characteristics of the soil and its state by the measured parameters of its radio-thermal radiation [1]. The comparison of L-band radiometer and off-ground ultra-wideband ground penetrating radar (GPR) to map surface soil moisture at the field scale over bare soil was firstly performed in [2], [3]. In this work, the integration of an active chirp GPR with a radiometer is proposed, i.e. combining various sensors into a single complex, which has higher characteristics in terms of accuracy, information content and reliability in comparison with individual instruments.

Passive microwave radiometers provide brightness temperature (TB) measurements in a large spectral range with relatively high temporal resolution. Because of their significant role in environmental studies, it is imperative to have a higher temporal resolution and more consistent datasets. This research aims to build a TB diurnal cycle at the Global scale. The shape (amplitude and phase) of the diurnal cycle for each month is obtained by merging several days of Global Precipitation Measurement (GPM) Microwave Imager (GMI) TB measurements because the acquisition times of GMI vary from day to day. This preliminary shape will later be improved by using the other sensors with daily fixed acquisition times such as three Special Sensor Microwave Imager/Sounder (SSMIS) and Advanced Microwave Scanning Radiometer 2 (AMSR2). The final product is a highly frequent diurnal cycle that can help prediction of the time of the freeze thaw transition states.

The tipping-curve method is widely used to provide the cold calibration point for calibration of ground-based radiometers. Statistical analysis of the opacity air mass pairs have been used as measures of the tip curve quality, but the choice of acceptance criteria has been subjective. An analysis of the tipping curve acceptance criteria was accomplished by simulation of a total power radiometer under non stratified conditions due to perturbations of water vapor content. Relationships between the root mean square error (RMSE) in zenith brightness temperature and regression coefficient were examined. While the RMSE decreased with increasing regression coefficient, a stringent acceptance criteria did not ensure accuracy likely due to the presence of net horizontal gradients. On the other hand, for radiometers with multiple channels the standard deviation of brightness temperature measurements of a reference black body, also known as spectral consistency, was found to be a robust indicator of calibration accuracy.

Meteorological satellite “Meteor-M” No. 2-2 with a MTVZA-GY microwave radiometer onboard was launched on a circular sun-synchronous orbit on July 8, 2019. Radiometer carries out conical scanning at the incidence angle of 65° and receives the outgoing radiation of the Earth at 31 channels at frequencies from 10 to about 190 GHz. Swath width is 2500/1500 km. The parameters of the ocean, land surface, and troposphere are retrieved from brightness temperatures with vertical and horizontal polarizations T B (ν) measured at imager frequencies ν = 6.9, 10.65, 18.7, 23.8, 31.5, 36.5, 42.0, 48.0, and 91,65 GHz. Measurements at sounder frequencies (10 channels in the oxygen absorption band 52–58 GHz and three channels near the strong resonance line of water vapor 183.31 GHz) provide information on air temperature and humidity of various layers in the upper troposphere - middle stratosphere. Manifestations of synoptic scale dynamic atmospheric phenomena on global T B charts at imager frequencies visualize their structure and development. The time series of brightness temperatures of the South polar area at sounder frequencies allowed to detect and trace the evolution of rare phenomenon - Sudden Stratospheric Warming over Antarctica in August-September 2019.

A dual polarization super-heterodyne radiometer operating between 85 and 100 GHz has been developed and used for surface characterization. The instrument can detect changes from dry to icy road in many environmental conditions (night, fog, rain) by measuring the combination of reflected/transmitted radiation from the surface at two orthogonal polarizations. The difference in brightness temperature between those polarizations is an accurate tool to distinguish ice from dry road surfaces. Results from measurement of dry asphalt and ice layers on asphalt are presented in this paper.

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

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.

An ultra-wideband radiometer was built and deployed in the Arctic to monitor the microwave emissions of sea ice from a stationary platform on an ice floe. The experiment was performed as part of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in the far north of the Laptev Sea. Brightness temperature data were acquired over the spectrum 0.5-2 GHz during the first phase of the expedition. The outcome of the experiment is expected to provide new insights into the advantages of wideband radiometry for the remote sensing of Arctic sea ice properties.

Accurate knowledge of soil and crop conditions such as water content, biomass, and phenology is crucial in agriculture for estimating growth and productivity. Remote sensing observations at microwave frequencies are sensitive to various soil and crop characteristics. For example, both the active (radar) and passive (radiometer) microwave sensors measure radiation quantities that are functions of soil and vegetation dielectric constant and exhibit sensitivities to soil moisture (SM) and vegetation water content (VWC). In addition to SM, the quantities are also influenced by other land surface parameters such temperature, soil surface roughness, vegetation geometry. Brightness temperature (TB) is more sensitive to SM and less sensitive to surface roughness and vegetation geometry compared to radar backscattering ( $\sigma^{0}$ ). Satellite passive observations have been widely used because of their high temporal resolutions (frequency of every 3 days), but these observations are available at coarse resolutions of about 25-40km. In contrast, satellite active observations from synthetic aperture radar (SAR) provide finer resolution (lower than 3 km), but low temporal resolution. However, with the recent availabilities of both Radarsat-2 and Sentinel-1, we have almost weekly observations combining both observations. In addition, upcoming launch of NISAR mission in 2021 will provide unprecedented opportunities to harness multifrequency observations at L-, S-, and C-bands. In this study, we investigate sensitivities of such multifrequency observations to soil and vegetation water content using current C-band SAR data and ground-based L-band data (in preparation for NISAR) obtained from field experiments. Active L-band observations are the most sensitive to SM variations even when the biomass in agriculture fields such as corn is high; in contrast, active C-band observations are more sensitive to vegetation.

We introduce a recently developed cost effective, low mass, portable L-band radiometer for aerial applications on drones or for ground-based applications. The radiometer has been tested on a multicopter drone and for a winter seasons measuring snow at the Davos-Laret Remote Sensing Field Site in Switzerland. The radiometer measures dual-linear polarizations, has a brightness temperature resolution of about 0.15 K for 1 sec integration time, and the antenna has a half-power full beamwidth of 37 degrees. The mass of the combined system is 3.8 kg and the power consumption is less than 3 W. We present the antenna and radiometer instrument, and initial results from these test phases.

We present and discuss the details of the SMAP radiometer calibration that is used in the NASA/RSS ocean surface salinity algorithm. The values for antenna spillover, noise diode and reflector emissivity are fine-tuned to give accurate antenna and brightness temperature values for the global ocean and the Amazon rain forest. The calibration is dynamically adjusted to account for small calibration drifts over time. An adjustment of the thermal model for the reflector temperature is necessary in order to mitigate biases that occur when the SMAP spacecraft goes in and out of solar eclipse. An important test of the reflector emission is an analysis of SMAP brightness temperature differences between ascending and descending swaths. The radiometric calibration accuracy of the NASA/RSS SMAP salinity data set is better than 0.1 Kelvin, which is necessary for accurate ocean surface salinity retrievals.

Chinese ocean salinity satellite is a mission designed for global ocean salinity monitoring. Two main payloads equipped onboard the satellite are the Interferometric Microwave Radiometer (IMR) and the Microwave Imager Combined Active and Passive (MICAP) which both have L-band radiometer and adopt aperture synthesis technology. A simulation is designed according to the payloads' configurations and consists of three parts: the brightness temperature generator, the radiometer system and the parameters retrieval. Preliminary results, which contain IMR and MICAP's brightness temperature resolution, spatial resolution, and parameter retrieval accuracy, are presented.

Snow plays an important role in global hydrological cycle and climate system. In addition, snow as a key variable factor is important indicator to climate change. In recent years, applying passive microwave remote sensing to retrieval snow depth has made greatly progress. Snow depth retrieval algorithms and snow products can provide spatial and temporal information on snow cover distribution, which is an important data source to snow monitor. Based on the survey project of snow cover characteristics and snow distribution in Northeast China, Advanced Microwave Scanning Radiometer 2 (AMSR2) brightness temperature data and MODIS product data are used in this paper. Original resolution brightness temperature observations are used in AMSR2 algorithm, except for the 89GHz channel which is resampled according to 36GHz footprint, and brightness temperature corrections made on the original measurements rather than using the aggregated brightness temperature observations. The original algorithm takes advantage of the expanded range of channels available on the AMSR2 instrument compared with the SSM/I and SMMR, and it undertakes a forest correction. However, the original AMSR2 snow depth retrieval algorithm greatly overestimate measured snow depth, especially in forest areas where is a challenge to retrieval snow depth, the error between 20 to 30cm. In order to improve the retrieval accuracy of snow depth in Northeast China, an improved snow depth retrieval algorithm in forest areas is proposed in this paper, the improved snow depth retrieval algorithm in forest areas is SD f = 1/ln(pol 36 ) × (Tb 18V − Tb 36V )/(1 − fd × 0.6). The results proved that the error is less than 5cm after using the improved algorithm, the root mean square error and deviation are 4.82cm, 3.55cm, respectively.

L-band microwave radiometers (1.4 GHz) are relied in the field of remote sensing to retrieve and analyze land surface characteristics. However, the existing ground based L-band microwave radiometers are not portable and requires heavy maintenance. This paper focuses on introducing a new terrestrial hand held L-band microwave radiometer to measure the passive microwave data over bare ground surface. Soil temperature, brightness temperature (TB), emissivity and soil moisture of the bare ground is measured and the relationships between the measured values are evaluated for the present study.

Radio Frequency Interference (RFI) is a well-documented problem for passive remote sensing of the Earth at L-band even though the measurements are made in the protected band centered at 1.413 GHz. Consequently, filtering for RFI is an important early step in the processing of measurements made by the SMAP (Soil Moisture Active/Passive) radiometer. However, the filtered data still include regions with suspiciously high antenna temperatures. One possible cause of these “hot spots” is interference not fully detected during RFI filtering. This paper presents some evidence supporting this hypothesis and describes an algorithm to identify these “hot spots” so that they can be removed from the measurements. The impact of removing these “hot spots” is generally small, but evidence is presented that the brightness temperature and soil moisture improve when the hot spots are removed.

The Radio Experiment for the Analysis of Cosmic Hydrogen (REACH) is a new project to perform radiometric measurements in the 50–200 MHz frequency range, with the purpose of realising a confident detection of the red-shifted 21 cm line signature pertaining to the Epoch of Reionisation (EoR) and Cosmic Dawn (CD). The brightness temperature of this signal is several orders of magnitude lower than those of the galactic and extra-galactic foregrounds, necessitating a radiometer design that is capable of extracting the 21 cm signal with minimal distortion due to systematic effects in the receiver instrumentation. The REACH project is an international collaboration between numerous research institutions, with prototype deployment for late 2019 planned in the Karoo region of South Africa.

In this study, we present a research to transfer the merits of SMAP (Soil Moisture Active Passive) to AMSR2 (Advanced Microwave Scanning Radiometer 2) with using machine learning method-artificial neural network. The surface soil moisture (SSM) products of SMAP were set as the reference data, while brightness temperature (TB) of various channels and the microwave vegetation index (MVI) obtained or derived from AMSR2 were input into an Artificial Neural Network (ANN). During training period (2015–2017), the ANN product (NNsm) can reproduce the SMAP SSM accurately, with a correlation coefficient (CC) of 0.74, Root Mean Square Error (RMSE) of 0.033 m3/m3, and Bias of −0.00008 m3/m3. It was found that machine learning method failed to provide reliable SSM over moderate vegetated areas where SMAP works well. With these trained networks, we developed a global soil moisture data set (named as NNsm) using AMSR2 TB from 2012 to 2018. Comparing to the in situ SM observations from all SCAN (Soil Climate Analysis Network) sites (named as SCANsm), NNsm has a good agreement with CC = 0.44, RMSE = 0.113 m3/m3 and Bias = 0.030 m3/m3, which is much better than those of the AMSR2 SSM products from JAXA and LPRM.

Continuous monitoring of physical parameters such as soil moisture (SM) is crucial to improve food sustainability and risks mitigation. Because of the spatio-temporal variations in SM, satellite observations are an excellent tool to monitor its dynamic. Among different operational satellite missions, the NASA SMAP mission offers a unique opportunity to monitor SM worldwide thanks to its frequency band of operation (L-band). The main goal of this mission was the disaggregation of brightness temperature (T B ) at 36 km to 9km using backscatter images at 3 km. Although the SMAP mission was designed to collect simultaneously radar/radiometer observations, due to a failure in the radar in July 2015, only the radiometer continues working. Among different options to compensate the lack of information from the L-band radar, the exploitation of C-band active information with L-band passive observations to disaggregate T B and produce SM at fine resolution has been proposed. Recently, the NASA SMAP team delivered a new SM product using this approach. However, this new product has not been validated over forested areas yet. In this work, we present the results of implementing a disaggregation algorithm based on the baseline SMAP downscaling approach over a tropical forest located in Southern Mexico, using information from the SMAP radiometer and Sentinel-1 images.

The Moon is a good calibration source for the millimeter-wave radiometer onboard the Feng Yun-4 Millimeter (FY-4M) satellite. The temperature profiles of the lunar surface can be obtained by solving the one-dimensional heat conduction equation with the thermo-physical properties of regolith. Then, the loss tangent is fitting using the Chinese Chang’e-2 (CE-2) 37GHz brightness temperatures (TB) data against TiO2 abundances, which is derived from Clementine five-bands multispectral data. The calculated surface temperatures and the TBs are validated by Diviner infrared data and CE-2 37GHz TB data. With the theoretical analysis of the radiative transfer, the TBs at FY-4M frequencies of lunar center area is obtained for calibration. In addition, the abnormal thermal emission at "cold spots" should be excluded for calibration.

The Soil Moisture Active Passive (SMAP) mission was designed to acquire and combine L-band radar and radiometer measurements for the estimation of soil moisture (SM) with an average ubRMSE of no more than 0.04 m3/m3 volumetric accuracy in the top 5 cm for vegetation with water content of less than 5 kg/m2.Currently, a single-channel algorithm that uses the V polarized brightness temperature (SCA-V) is used to retrieve SM satisfying the defined requirements. Even though other alternatives were tested, SCA-V proved to be the best option for the retrieval of SM. In this work, we show that by choosing suitable roughness parameters, the use of two polarizations (H and V), mixed dual-channel algorithm (MDCA), and an additional constraint, regularized DCA (RDCA), not only provides retrieved SM that satisfies the aforementioned requirement but also allows for the retrieval of vegetation optical depth (VOD).

Using a three-dimensional radiative transfer model combined with cloud-resolving model output, we simulate active and passive sensor observations of clouds and precipitaiton. This combination of tools allows us to diagnose the contributions of various hydrometeor types. Radar multiple scattering is most closely associated with the presence of graupel. At W-band, massive amounts multiple scattering in deep convection can decorrelate the reflectivity profile from the vertical structure, but for less intense events, multiple scattering could be a useful indicator of riming. For passive sensors, polarization differences at 166 GHz indicate the presence of horizontally-aligned frozen particles with pronounced aspect ratios, while high concentrations of more isotropic aggregates and graupel dampen the polarization difference while also contributing to the lowest brightness temperature depressions. The insights into remote sensing measurements will facilitate the development of improved algorithms and advanced sensors.

The application of passive microwave soil moisture products is limited by the poor spatial resolution. In this study, a spatially-based regression method was tested using the PLMR(Polarimetric L-band Multibeam Radiometer) observation data acquired in the Heihe Watershed Allied Telemetry Experimental Research (HiWATER). The land surface temperature data were used as the auxiliary data to perform downscaling soil moisture and brightness temperature. The root-mean-square errors (RMSEs) of brightness temperature and soil moisture are 5.5 K and 0.021 cm3/cm3 between the calculated values and refenced values, respectively. This method provided a potential way for the downscaling of passive microwave brightness temperature and soil moisture.

The NASA SMAP (Soil Moisture Active Passive) mission provides a global coverage of soil moisture measurements based on its L-band microwave radiometer every 2-3 days at about 40 km resolution. The soil moisture retrieval algorithms model the brightness temperature as a function of soil moisture, surface conditions and vegetation. External data sources inform the algorithms about the surface conditions and vegetation, which enable the retrieval of soil moisture. The inversion process contains uncertainties related to radiometer measurements, forward model assumptions and ancillary data sources. This study focuses on the uncertainties that depend on the seasonal evolution of the surface conditions and vegetation. The study compares the SMAP and core validation site (CVS) soil moisture values over a period of four years to extract the evolution of performance metrics over time. The analysis showed that most CVS that include managed agriculture exhibit significant time-dependent seasonal bias. This bias was linked to seasonal temperature cycle, which is a proxy to several features that can cause seasonally dependent errors in the SMAP product.

Radio Frequency Interference (RFI) at 18.7 GHz in and around the continental United States observed by passive microwave radiometers such as the Global Precipitation Measurement (GPM) Microwave Imager (GMI) has been noted in multiple publications. A large part of this RFI arises from reflected geosynchronous direct broadcast satellite transmissions. The interference yields surprisingly high artificial brightness temperature values, ranging from a few Kelvin up to over 1000 Kelvin. Some of the highest levels and most direct reflections occur over land from specular reflections from smooth surfaces such as lakes and rivers. Over the ocean surrounding the United States, the reflections are generally less directional, occurring at higher glint angles and often with lower peak levels than over land. The interference corrupts the GMI measurements about 13% of the time over the ocean around the US, and over 5% of the time over the affected land areas.

Global Satellite Mapping of Precipitation (GSMaP) is a system to produce global surface precipitation field with 0.1 degree and 1 hour resolution from geostationary infrared imager and microwave radiometer in low earth orbit (Ushio et al. 2019, Mega et al. 2018). On this system, the input data sets are precipitation rate retrieved from the microwave radiometers and infrared images to compute the moving vector fields. Based on the moving vector fields calculated from the successive IR images, precipitation fields are propagated and refined on the Kalman filter model which uses the relationship between the infrared brightness temperature and surface precipitation rate. Because of this methodology, basically the resolution of the GSMaP products is limited on the resolution of the geostationary infrared images.