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17 pages, 13 figures, 1 table.-- Data availability: The product (Martínez et al., 2019) is freely distributed on the BEC (Barcelona Expert Center) web page (http://bec.icm.csic.es/, last access: 25 January 2022) with the DOI number https://doi.org/10.20350/digitalCSIC/12620 (Martínez et al., 2019) and on the Digital CSIC server: https://digital.csic.es/handle/10261/219679 (last access: 25 January 2022). Data can be downloaded from the FTP service: http://bec.icm.csic.es/bec-ftp-service/ (last access: 25 January 2022). The maps are distributed in the standard grid EASE-Grid 2.0, which has a spatial resolution of 25 km. In addition to the product validated in this work (L3 with temporal resolution of 9 d), L3 products having a temporal resolution of 3 and 18 d and the L2 product are available. These Arctic SSS products cover the period from 2011 to 2019.-- This work represents a contribution to the CSIC Thematic Interdisciplinary Platform PTI Teledetect and PolarCSIC. Argo data were collected and made freely available by the International Argo program and the national programs that contribute to it (https://argo.ucsd.edu, https://www.ocean-ops.org, last access: 25 January 2022). The Argo program is part of the Global Ocean Observing System, Measuring salinity from space is challenging since the sensitivity of the brightness temperature (TB) to sea surface salinity (SSS) is low (about 0.5 K psu−1), while the SSS range in the open ocean is narrow (about 5 psu, if river discharge areas are not considered). This translates into a high accuracy requirement of the radiometer (about 2–3 K). Moreover, the sensitivity of the TB to SSS at cold waters is even lower (0.3 K psu−1), making the retrieval of the SSS in the cold waters even more challenging. Due to this limitation, the ESA launched a specific initiative in 2019, the Arctic+Salinity project (AO/1-9158/18/I-BG), to produce an enhanced Arctic SSS product with better quality and resolution than the available products. This paper presents the methodologies used to produce the new enhanced Arctic SMOS SSS product (Martínez et al., 2019) . The product consists of 9 d averaged maps in an EASE 2.0 grid of 25 km. The product is freely distributed from the Barcelona Expert Center (BEC, http://bec.icm.csic.es/, last access: 25 January 2022) with the DOI number https://doi.org/10.20350/digitalCSIC/12620 (Martínez et al., 2019). The major change in this new product is its improvement of the effective spatial resolution that permits better monitoring of the mesoscale structures (larger than 50 km), which benefits the river discharge monitoring, This work has been carried out as part of the ESA Arctic+Salinity project (AO/1-9158/18/I-BG), which permitted the production of the database, and the Ministry of Economy and Competitiveness, Spain, through the National R&D Plan under L-BAND project ESP2017-89463-C3-1-R. [...] With the funding support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), of the Spanish Research Agency (AEI)

Active and passive microwave characteristics of diurnal soil freeze-thaw transitions and their relationships are crucial for developing retrieval algorithms of the soil liquid water content (θliq) and freeze/thaw state, which, however, have been less explored. This study investigates these microwave characteristics and relationships via analysis of ground-based measurements of brightness temperature (TB) and backscattering coefficients (σ⁰) in combination with simulations performed with the Tor Vergata discrete radiative transfer model. Both an L-band (1.4 GHz) radiometer ELBARA-III and a wide-band (1-10 GHz) scatterometer are installed in a seasonally frozen Tibetan meadow ecosystem to measure diurnal variations of TB and copolarized σ⁰ at both hh (σhh⁰) and vv (σ vv⁰) polarizations. Analysis of measurements collected between December 2017 and March 2018 shows that 1) diurnal cycles are observed in both TB and σ ⁰ due to the change in surface θliq caused by diurnal soil freeze-thaw transitions; 2) a negatively linear relationship is found between e and σ⁰ regardless of frequency, polarization combinations, and observation angles; 3) slopes (β ) of linearly fit equations between eH and σhh⁰ decrease with increasing observation angles of ELBARA-III, while the ones between eV and σvv⁰ increase with increasing observation angles; and 4) correlations between e and σ⁰ increase with decreasing microwave frequency of σ⁰ measurements and ELBARA-III observation angles, and magnitudes of diurnal σ⁰ cycles also increase with decreasing microwave frequency. Moreover, the calibrated Tor Vergata model shows capability to reproduce both diurnal e and σ⁰ variations as well as to quantify their relationships at different frequencies and observation angles.

In order to cover the warm end of Earth-scene brightness temperature (TB) range of passive microwave radiometers, intercalibration over warm scenes is necessary. This article presents a methodology to intercalibrate the microwave radiation imager (MWRI) on the Chinese second-generation meteorological satellite Fengyun 3C (FY-3C) with the Global Precipitation Measurement (GPM) Microwave Imager (GMI) over the warm scenes of dense forests using the double-difference (DD) method. Based on the microwave radiative transfer model (RTM), an intercalibration method is developed, in which a modified land surface emissivity (LSE) model for dense forests is proposed. The forests with optically thick canopy are identified in terms of polarization TB differences and normalized difference vegetation index (NDVI) extracted from the latest vegetation product of Moderate-Resolution Imaging Spectroradiometer (MODIS). The matching TBs between FY-3C MWRI and GMI over dense forest warm scenes are collected and analyzed together with the TBs over ocean surfaces obtained by Zeng and Jiang (2020). The results show that: 1) FY-3C MWRI's observations are generally underestimated, and the intercalibration biases are polynomial functions of observations; 2) the intercalibration biases at the warm end are relatively smaller than those at the cold end; and 3) the calibration in the ascending orbits (MWRIA) is relatively better than that in the descending orbits (MWRID). At the tropical rain forest scene TBs defined in this work, the intercalibration biases (mean ± standard deviation at the mean) in the FY-3C MWRI channels of 10 V, 10 H, 18 V, 18 H, 23 V, 36 V, 36 H, 89 V, and 89 H are, respectively, -1.3 ± 0.7, -1.9 ± 1.1, 1.6 ± 0.6, 2.5 ± 0.8, -0.2 ± 0.5, -2.0 ± 0.6, -2.4 ± 0.7, -0.2 ± 0.6, and -0.1 ± 0.6 K for the ascending orbits, while they are, respectively, -4.0 ± 0.8, -5.4 ± 1.2, -1.4 ± 0.7, -1.2 ± 0.8, -2.9 ± 0.5, -4.9 ± 0.7, -5.5 ± 0.7, -2.7 ± 0.8, and -2.3 ± 0.7 K for the descending orbits. The in-orbit calibration coefficients of GMI are successfully transferred to FY-3C MWRI.

The synergy of active and passive microwave measurements has attracted considerable attention in recent years since they offer complementary information on the characteristics of the observed target (e.g., soil moisture), which motivates the launch of NASA's Soil Moisture Active Passive (SMAP) mission. An assumption of a near-linear relationship between active and passive measurements has been made in the SMAP active-passive baseline algorithm, which is essential to downscale coarse-resolution radiometer brightness temperature (TB) using high-resolution radar backscatter (σ⁰) but has not yet been fully tested under a wide range of ground conditions. Motivated by this, we first examined the validity of the linear assumption by using concurrent and coincident SMAP active and passive observations under diverse environmental factors (e.g., land cover, climate types, terrain and its complexity, soil texture, vegetation coverage, soil moisture, and its dynamics). We also adopted SMAP enhanced TB to evaluate the performance of the disaggregated TB at the same grid resolution of 9 km. The results reveal there is a generally good linear relationship between σ⁰ (no matter in dB or in linear unit) and TB at a global scale. There is no significant difference in the correlation among the four polarization combinations ( $σ ⁰_{hh}$ versus TB $_{h}$ , $σ⁰_{hh}$ versus TB $_{v}$ , $σ⁰_{vv}$ versus TB $_{h}$ , and $σ⁰_{vv}$ versus TB $_{v}$ ) with the $σ⁰_{vv}$ and TB $_{h}$ combination displaying an overall slightly higher correlation. The linear relationship between σ⁰ and TB is significantly affected by environmental factors. Particularly in bare soils and densely vegetated areas (e.g., large forest fraction and vegetation coverage), and arid and polar climate zones, the linear correlation between active and passive measurements worsens, whereas it is favorable in moderate vegetation and soil moisture as well as large soil moisture dynamic conditions. Interestingly, the linear correlation generally decreases as sand content increases while increases with the increase of clay content. The absolute linear correlation coefficient is higher with larger soil moisture dynamics. When compared to SMAP enhanced TB, it shows the linear assumption may have more influence on the correlation (i.e., temporal evolution) of downscaled TB than its absolute accuracy. These findings can enhance the understanding of the geophysical relationship between radar and radiometer signatures, and thus benefit active-passive joint algorithms for future satellite missions.

The Microwave Radiation Imager (MWRI) sensors aboard on the Chinese FengYun-3 (FY-3) series satellites have a great potential for long-term study of sea ice distribution. This study corrected the newly released FY-3D MWRI brightness temperature (TB) data with the Advanced Microwave Scanning Radiometer 2 (AMSR2) TB data and used an Arctic Radiation and Turbulence Interaction Study Sea Ice (ASI) dynamic tie points algorithm to derive the sea ice concentration (SIC) from these corrected MWRI TB data. We assessed the accuracy of our MWRI-ASI SIC product by comparing with the published SIC products at different spatial resolution and by validating with the Moderate Resolution Imaging Spectroradiometer (MODIS) and Sentinel-1 data. We find that MWRI-ASI SIC has the smallest difference with AMSR2-ASI SIC with the mean absolute difference (MAD) of 6.8%, and the differences mainly occur at the marginal ice zone regions. The MWRI-ASI displays the highest difference with Sea Ice Index and OSI-430-b at 25 km with the mean MAD of 11.8%. MADs between MWRI-ASI SIC and SIC products from other sensors are below 10% except for late June to early October, when those are higher. Compared with the MODIS SIC, MWRI-ASI SIC outperforms other SIC products at the same resolution, with the mean bias of -2.1% and mean RMSD of 13.5%. Although MWRI-ASI tends to under-estimate high SIC, it can capture details of large leads, ice edge, and fragmented ice area. Our MWRI-ASI SIC product at 12.5 km is promising to integrate into long-term sea ice records.

For synthetic aperture radiometer, radio frequency interference (RFI) seriously contaminates the observed data and affects the quality of the brightness temperature (BT) image. CLEAN algorithm is usually used to mitigate the Gibbs effect generated by RFI. However, the closely distributed RFI sources cannot be well mitigated by the original CLEAN algorithm. This letter proposes an improved adaptive CLEAN algorithm to enhance the performance of RFI mitigation, especially the closely distributed RFI sources. The loop factor is forced to synchronize with residual BT of RFI for every iteration to avoid over mitigation. Over mitigation can result in a BT vacancy area or an extra BT point generated at the position of RFI. Soil Moisture and Ocean Salinity (SMOS) L1B real data are applied to verify the effectiveness of the improved CLEAN algorithm. The simulation shows that the improved CLEAN algorithm can be competent in eliminating isolated and closely distributed RFIs better than the classic CLEAN algorithm, and the over mitigation problem is solved by the proposed method.

A ground-based ultra-wideband radiometer operating at 540, 900, 1380, and 1740 MHz was used to measure microwave thermal emissions from an Arctic sea ice floe as part of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) Expedition. The instrument was deployed on a drifting ice floe near 86°N, 120°E in leg 1 of the expedition (December 2019) and observed second-year ice (potentially with refrozen melt ponds) that experienced new ice growth at its base over a ten-day period. Measured circularly polarized brightness temperatures were compared with the predictions of a radiative transfer (RT) model for a layered medium consisting of ocean, growing new ice, desalinated remnant second-year ice/refrozen melt pond, and snow layers. Characteristics of the sea ice composition used in the model were determined from in-situ measurements. Comparisons of the measured and modeled wideband brightness temperatures showed good agreement consistently over the observation period and for various off-nadir observation angles. The results demonstrate the capabilities of 0.5-2 GHz microwave radiometry for observing sea ice properties and also show the impact of a saline ice layer at the ice bottom on the measured brightness temperature.

The Ultra-Wideband Software Defined Microwave Radiometer (UWBRAD) is a wideband radiometer operating from 0.5 to 2 GHz for remote sensing of polar ice sheet temperature profiles. Small-scale (cm to m) fluctuations in firn density in the upper portion of the ice sheet significantly impact observed brightness temperatures. Previously, a fully coherent model based on solving Maxwell’s equations for thousands of layers throughout the entire ice sheet was developed. Density profiles in the model are described as the sum of a smooth average density profile with a spatially correlated random process that represents density fluctuations. In this article, we develop a “partially coherent” implementation of the coherent model that captures the impact of variations in ice density on predicted brightness temperatures while improving computational efficiency. The partially coherent model divides the ice sheet into blocks. Within each block, the coherent model is applied to take into account coherence among the contributions of closely spaced layers. A Monte Carlo procedure is used to calculate the average block reflection and transmission parameters. Between adjacent blocks, interactions are assumed to be incoherent, and the radiative transfer theory is used to incoherently cascade block parameters. Results of the partially coherent model are in good agreement with the fully coherent model and also with Soil Moisture Ocean Salinity (SMOS) and UWBRAD brightness temperature observations.

FengYun-3C (FY-3C) is the second-generation polar-orbiting meteorological satellite in China. As one of the most important microwave payloads deployed onboard FY-3C, microwave radiation imager (MWRI) has been continuously observing radiance originating from land and sea surface and supports numerical weather predictions at regional and global scales. With a long-term monitoring of the cold-end reference, MWRI onboard FY-3C was found suffering from discontinuous observations due to its temporary power-OFF/power-ON in the years of 2015 and 2018, resulting in anomalous brightness temperature (TB) jump up to 2–3 K in magnitude at some channels. Analysis of the operating status of FY-3C MWRI indicated that deviation of the receiver system from its optimal operating status affects the nonlinearity characteristics significantly. A correction algorithm, which relates nonlinearity to the autogain control (AGC) operating voltage of the sensor, is thus proposed on the basis of on-ground thermal/vacuum (T/V) test results in the prelaunch phase in order to mitigate the calibration anomalies of FY-3C MWRI. Nonlinearity correction of FY-3C MWRI is further corroborated by comparisons of simultaneously overlapping observations of global precipitation measurement (GPM) microwave imager (GMI). The results demonstrated that after nonlinearity correction the performance of FY-3C MWRI has been improved at all channels except for the 36-V channel where technical failures have been detected since October 2016.

The absolute radiometric accuracy of thermal infrared channels of the Advanced Geosynchronous Radiation Imager (AGRI) onboard the FengYun-4A (FY4A) satellite was assessed with field experiment data in a vicarious calibration on 18, 2019, and August 20, 2019, in Lake Qinghai. Continuous water temperature records were collected by an unmanned surface vehicle equipped with a radiometer during the experimental period. We proposed a spectral matching method based on atmospheric transfer code to create consistent equivalent spectral radiance, and the results predicted by atmospheric transfer simulation were convolved with instrument response functions to obtain predicted brightness temperature for comparison with satellite-derived temperature. Our results indicated that AGRI had an average temperature bias of 0.12 K with an RMSE of 0.17 and 0.61 K with an RMSE of 0.22 K in band 12 and band 13 on August 18, while these biases decreased to −0.01 K with an RMSE of 0.13 and −0.48 K with an RMSE of 0.17 K on August 20, respectively. This radiometric accuracy indicated that AGRI TIR bands are well functional compared with their preflight requirement of less than 1 K. The uncertainty analysis also suggested that atmospheric conditions would alter the calibration accuracy by influencing the transmittance and path thermal radiance. Meanwhile, cloud cover also requires more attention to select the appropriate concurrent satellite pixels to reduce the possible cloud edge effects. It is important and necessary for FY4A AGRI to conduct more intense automatic observations and obtain more data to improve the accuracy of radiometric calibration and monitor the operational status of satellite instruments.

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

Objectives: To predict meteorological phenomena such as rain events from sky status during rainy and non-rainy periods. Methods: The method used here is based on brightness temperature ratio measurement at two different frequencies, namely 23.8 GHz and 30 GHz respectively, using ground based dual frequency radiometric data. The ratios of brightness temperature readings obtained by the dual-frequency radiometer at the two above mentioned frequencies are calculated for each simultaneously-taken pair of measurements. Data obtained by the authors for the year 2009 at Cachoeira Paulista in Brazil has been used for analysis. Findings: The major results obtained from the analysis of data collected over a continuous period of seven months are used to construct corresponding histograms and cumulative count graphs of brightness temperature ratios. The histograms and graphs clearly show three peak values that could be interpreted as thresholds between clear sky, cloudy sky and rainy sky conditions respectively. Novelty: The study implements detection of rain events from sky status during rainy and non-rainy periods using peak brightness temperature values obtained from graphs generated using the observation data. The outlined technique can therefore be used to clearly determine sky conditions and accurately predict rain phenomena. The ratio of brightness temperatures at the two frequencies is a unique parameter which is critical to the successful estimation of rain from sky status. The results agree well with multi-channel radiometric data obtained by other researchers at lower frequencies. Keywords: sky status; microwave; millimeter wave; dual frequency radiometer; brightness temperature; rainfall

An accuracy of reproduction of daily variations in the ocean–atmosphere system brightness temperature in the areas of development and movement of tropical hurricanes in the Caribbean Sea and Gulf of Mexico is analyzed. The analysis is based on the data of single and group satellite microwave radiometer measurements. The results are obtained using archival measurement data of SSM/I radiometers from the F11, F13, F14, and F15 DMSP satellites during the period of existence of tropical hurricanes Bret and Wilma. An example is given to demonstrate the use of daily brightness temperatures obtained from DMSP satellites for monitoring the development and propagation of hurricane Wilma.

Based on the spatiotemporally weighted two-step method (STW-TSM), the spatiotemporal characteristics of the residual microwave brightness temperature (MBT) with the Mw7.9 Wenchuan earthquake on May 12, 2008 are revealed by satellite data from the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) sensor. Two significant MBT positive anomalies are found to be exactly in spatial accordance with two geological Quaternary zones, and the detailed geometric information of the MBT positive anomaly is found to be correlated with the microwave frequencies. After eliminating other possible influential factors, including surface temperature, vegetation index, land-surface roughness, and surface soil moisture under the conditions of space, time, and magnitude, and according to the microwave radiative transfer model, the dielectric variation in the ground surface is suggested to be the primary contributor of the MBT positive anomaly. The positive-hole (P-hole) theory is applied to interpret the geological preference of the MBT positive anomalies through a chain process: crustal stress enhancing—P-hole producing and flowing down stress gradients—surface P-hole accumulating—dielectric constant decreasing—and microwave radiation increasing. The stress-resulting effect of the dielectric decrease on MBT the increase provides a novel mechanism for microwave remote-sensing monitoring of crustal stress field alteration, earthquake preparation, and upcoming shocks. This research has a particular significance for searching potential georelations between the tectonic earthquake preparation and the abnormal satellite MBT.

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 $\text{m}^{3}/\text{m}^{3}$ target 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 $\sim 10$ -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.

Compared with traditional real aperture microwave radiometers, one-dimensional synthetic aperture microwave radiometers have higher spatial resolution. In this paper, we proposed to retrieve sea surface temperature using a one-dimensional synthetic aperture microwave radiometer that operates at frequencies of 6.9 GHz, 10.65 GHz, 18.7 GHz and 23.8 GHz at multiple incidence angles. We used the ERA5 reanalysis data provided by the European Centre for Medium-Range Weather Forecasts and a radiation transmission forward model to calculate the model brightness temperature. The brightness temperature measured by the spaceborne one-dimensional synthetic aperture microwave radiometer was simulated by adding Gaussian noise to the model brightness temperature. Then, a backpropagation (BP) neural network algorithm, a random forest (RF) algorithm and two multiple linear regression algorithms (RE1 and RE2) were developed to retrieve sea surface temperature from the measured brightness temperature within the incidence angle range of 0°–65°. The results show that the retrieval errors of the four algorithms increase with the increasing Gaussian noise. The BP achieves the lowest retrieval errors at all incidence angles. The retrieval error of the RE1 and RE2 decrease first and then increase with the incidence angle and the retrieval error of the RF is contrary to that of RE1 and RE2.

Calibration is a key issue for quantitative application of meteorological satellite data. The complex space environment may cause many uncertainties in data calibration. A highly stable and reliable calibrator in flight is needed. Because the Moon has no atmosphere and no environmental variation, the physical and chemical properties of its surface are stable in the long term. The Moon might be an ideal candidate for in-flight thermal calibration. In advanced satellite-borne microwave remote sensing such as NOAA-18, the deep space view (DSV) of the microwave humidity sounder (MHS) has viewed the Moon many times every year. Using the thermal-physical properties of the lunar regolith derived from the Diviner infrared (IR) brightness temperature (TB) data, we solve the one-dimensional heat conduction equation to obtain the temperature profile of the near side of the lunar regolith medium. The loss tangents of the regolith medium are retrieved from microwave TB data of the Chinese satellite Chang’e-2. The integrated radiative transfer equation is used to simulate the weighted disk-average TB of the lunar surface for the MHS channels at 89, 157, and 183 GHz for the year 2011. The Moon is taken as an extended circular target. The simulated TBs are used to correct the full width at half maximum (FWHM) fitted with the MHS counts. We analyze the influences of the distance between the satellite and the Moon, the lunar phase angle, and the FWHM of the radiometer on the inverted FWHM. The corrected TB data are compared with the simulation. This paper presents a new method for thermal calibration of spaceborne in-flight microwave and millimeter-wave radiometers with the weighted disk-average TB of the lunar surface.

This study proposes the use of a Spectral Linear Mixing Model (SLMM) on passive microwave data for mapping the concentration and area of young and/or first-year ice in the oceanic region located in...

Satellite-based passive microwave observations provide the best available continuous observational estimates of global snow water storage due to their broad geographic footprint and low sensitivity to clouds and precipitation. However, these observations are subject to substantial uncertainty due to the complex radiative properties of snow and from interference in forested areas. Physical radiative transfer models can be leveraged to improve the fidelity of these observations and as a data-assimilation tool. In this article, the Dense Media Radiative Transfer model with Multiple Layers (DMRT-ML) is used to simulate snow brightness temperatures from data collected from snow pits excavated during a two-day-long field study performed a temperate forest in the Northeast United States. The simulations are evaluated against surface-based radiometer observations collected at the snow pits. The DMRT-ML is configured with varying complexity to determine the snowpack characteristics most essential toward simulating brightness temperature within a temperate forest with complicated snow stratigraphy. In general, the single-layer configurations were not sufficiently complex to accurately simulate snow brightness temperature without significant tuning. The most accurate simulation was a two-layer configuration with a prescribed ice layer separating the snow layers. This simulation had a root-mean-square error $< $15 K for the 37-GHz frequency. More complicated snowpack stratigraphy configurations did not substantively improve the results over the two-layer model configuration. The DMRT-ML was also used to examine differences between redundant datasets of density and grain size. It was determined that similar snow data collection and radiative transfer model configuration techniques are critical to ensure cross-study comparability.

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.

This study presents a correction algorithm to remove the backlobe intrusion from the hot-load reflector of microwave radiation imager (MWRI) on-board China FengYun-3C (FY-3C) meteorological satellite. Discontinuities in the radiometric gain of MWRI, due to inaccurate backlobe spillover of the hot-load reflector, have been diagnosed. A physical correction method, relating the radiometric gain difference between two distinct backlobe scenes to the spillover of the hot-load reflector, is thus established to estimate the in-orbit spillover factor of the MWRI hot-load reflector. Meanwhile, it is found that brightness temperature (TB) at each 1° × 1° grid could not address the backlobe pattern appropriately, leading to abrupt changes in the radiometric gain occurring near the coastlines. To better represent the backlobe spillover, the effective backlobe TB is now averaged over a 4° × 4° area from advanced microwave scanning radiometer-observed TBs, which is now in a finer resolution of 0.25° × 0.25°. With the adjusted spillover for the hot-load reflector, anomalous behaviors of the radiometric gain are mitigated effectively and the improvement at the coastlines is pronounced, inducing TB variation up to 2 K in magnitude at the 10.65 GHz channel. Although the algorithm presented in this study is only restricted to channel frequencies up to 23.8 GHz due to its accuracy limitation, it could tackle similar issues for those microwave radiometers not capable of in-orbit backlobe maneuvers and receiving the radiances inevitably from the outer edge of the hot-load or cold-sky reflectors.

This article analyzes seasonal and interannual dependences of brightness temperature in different zones of the Gulf of Ob according to data from the MIRAS radiometer (SMOS satellite). It is shown that, while the brightness temperature dynamics of the freshwater zone of the Gulf of Ob (the estuary and the central zone) is similar to that of freshwater lakes, this dynamics in the seawater zone is different. The authors explain this difference by an increase in water salinity and, consequently, an increase in radiation absorption in the lower ice layer, which is in contact with the water surface. The regularities of seasonal variations of brightness temperature in different zones of the Gulf of Ob and related ice-cover phases can be used to assess salinity and water mixing in large estuaries of the Arctic in winter based on satellite microwave radiometry.

Surface snowmelt in the pan-Antarctic region, including the Antarctic ice sheet (AIS) and sea ice, is crucial to the mass and energy balance in polar regions and can serve as an indicator of climate change. In this study, we investigate the spatial and temporal variations in surface snowmelt over the entire pan-Antarctic region from 2002 to 2017 by using passive microwave remote sensing data. The stable orbits and appropriate acquisition times of the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) and the Advanced Microwave Scanning Radiometer 2 (AMSR2) enable us to take full advantage of daily brightness temperature (Tb) variations to detect surface snowmelt. The difference between AMSR-E/2 ascending and descending 36.5 GHz Tb values in vertical polarization (DAV36) was utilized to map the pan-Antarctic region snowmelt, as this method is unaffected by snow metamorphism. We evaluated the DAV36 algorithm against ground-based measurements and further improved the method over the marginal sea ice zone by excluding the effect of open water. Snowmelt detected by AMSR-E/2 data was more extensive and persistent than that detected by the Special Sensor Microwave/Imager (SSM/I) data. Continuous melt onset (CMO) ranged from August in the marginal sea ice zone to January in the Antarctic inland, and the early transient melt events occurred several days to more than 2 months earlier. The pan-Antarctic region CMO was significantly correlated (R=0.54, p) with the summer Southern Annular Mode (SAM). The decreased AIS melt extent was very likely linked (R=-0.82, p) with the enhanced summer SAM.

The synthesis aperture interferometric technology provides a promising passive imaging approach to the radiometer for reconstructing the brightness temperature distribution of the scene. However, the application of this technology may be limited by the large number of antennas, receivers, and analog-to-digital converters (ADCs) required in the radiometer. In order to reduce the complexity of the hardware requirements, a new passive microwave imaging technique based on compressed sensing (CS) is proposed in this letter. Different from the existing CS-based imaging methods that randomly select part of samples measured by all receivers, the proposed method develops a new practicable measurement matrix that employs the samples measured by partial receivers to recover the brightness temperature image. Numerical simulations and experiments are performed to demonstrate the effectiveness of the proposed method for reducing the hardware resources and the data amount while maintaining the image quality.

The mirrored aperture synthesis radiometer (MASR) has been proposed as a promising technique for Earth observation. However, considering the system imperfections, the cosine visibility function and the brightness temperature distribution do not constitute a cosine transform pair. The method to improve the quality of reconstructed brightness temperature images for a practical MASR has not yet been developed. In this article, the complete formulation of cosine visibility functions in a practical MASR is derived to better understand the measurement of a MASR with non-negligible effects of the system imperfections. The array factor of a practical MASR is analyzed. Then, the alias-free field of view (FOV) for a MASR is presented, and an iterative method is proposed to reconstruct the brightness temperature image in the alias-free FOV to reduce the computational burden. The numerical simulation and experimental results demonstrate that the proposed method can be applied to practical MASRs and that the reconstruction quality is significantly improved by the proposed method. Finally, the H-matrix inversion method is discussed as an alternative reconstruction method for a practical MASR. Compared with the H-matrix inversion method, the proposed method is more convenient for practical applications.

The relationship between the dielectric and radio brightness characteristics of aqueous solutions of (NH4)2[PdCl4] at low concentrations at 298.15 K were considered. The dielectric relaxation parameters representing the disturbance of the H-bond network in the transition from water to solution were calculated from experimental data on the complex permittivity near the maximum of dispersion of water and solutions. These data and the Fresnel equations were used to find the quasi-optical coefficients and radio brightness characteristics of aqueous solutions in the centimeter and millimeter bands. The radio brightness characteristics of model solutions were determined with a high-sensitivity radiometer at a frequency of 61.2 GHz. The radio brightness temperature in the model solutions decreases in the transition from water to solution according to both experimental and calculated data. It was shown that the intrinsic radiation of the solution reflects the contributions of both dipole and ionic losses, which still remain significant in the millimeter band. They may even determine the difference in the signs of the changes in the radio brightness. Basics of a new approach to studying solutions containing complex ions based on their intrinsic radiation in the millimeter band were developed.

The improvement of cloud modelling for global and regional climate and weather studies requires comprehensive information on many cloud parameters. This information is delivered by remote observations of clouds from ground-based and space-borne platforms using different methods and processing algorithms. Cloud liquid water path (LWP) is one of the main obtained quantities. Previously, the measurements of LWP by the SEVIRI and AVHRR satellite instruments provided the evidences of the systematic differences between LWP values over land and water areas in Northern Europe. An attempt is made to detect such differences by means of ground-based microwave observations performed near the coastline of the Gulf of Finland in the vicinity of St. Petersburg, Russia. The microwave radiometer RPG HATPRO located 2.5 km from the coastline is functioning in the angular scanning mode and is probing the air portions over land (at elevation angle 90°) and over water area (at 7 elevation angles in the range 4.8°–30°). The problem of the LWP horizontal gradient detection is examined in the measurement domain: the brightness temperatures of the microwave radiation measured at different elevation angles in the 31.4 GHz and 22.24 GHz spectral channels are analysed and compared with the corresponding values which were calculated under the assumption of horizontal homogeneity of the atmosphere. Several specific cases, selected on the basis of the analysis of the satellite observations by the SEVIRI instrument were considered in detail including: clear-sky conditions, the presence of clouds over the radiometer and at the same time the absence of clouds over the Gulf of Finland, and overcast conditions over the radiometer and over the opposite shore of the Gulf of Finland. The influence of the land-sea LWP difference on the brightness temperature values in the 31.4 GHz spectral channel has been demonstrated and the following features have been detected: (1) an interfering systematic signal is present in the 31.4 GHz channel which can attributed to the humidity horizontal gradient; (2) clouds over the opposite shore of the Gulf of Finland mask the LWP gradient effect. Preliminary results of the retrieval of LWP over water by statistical regression method are presented. These monthly averaged results are compared to the corresponding values derived from the satellite observations by the SEVIRI instrument. The agreement between satellite and ground-based results is very good for warm season in terms of temporal behaviour if systematic difference is neglected.

Passive microwave (PM) sensors on satellite can monitor sea ice distribution with their strengths of daylight- and weather-independent observations. Microwave Radiation Imager (MWRI) sensor aboard on the Chinese FengYun-3D (FY-3D) satellites was launched in 2017 and provides continuous observation for Arctic sea ice since then. In this study, sea ice concentration (SIC) product is derived from brightness temperature (TB) data of MWRI, based on an Arctic Radiation and Turbulence Interaction Study Sea Ice (ASI) dynamic tie points algorithm. Our product is inter-compared with a published MWRI SIC product by the Enhanced NASA Team (NT2) algorithm, and three Advanced Microwave Scanning Radiometer 2 (AMSR2) SIC products by the ASI, Bootstrap (BST) and NT2 algorithm. Results show that MWRI SIC are generally higher than AMSR2 SIC and the median of monthly SIC differences are larger in summer. Regional analysis indicates that the smaller differences between AMSR2 SIC and MWRI-ASI SIC occur in the higher SIC areas, and the biases are within ±5% in the Beaufort Sea, Chukchi Sea, East Siberian Sea, Canadian Archipelago Sea and Central Arctic Sea. There is the smallest SIC difference in the Central Arctic Sea with the biases of −0.77%, −0.60%, and 0.19% for AMSR2-ASI, AMSR2-BST and AMSR2-NT2, respectively. The trends of MWRI and AMSR2 sea ice extent and sea ice area are consistent with correlation coefficients all greater than 0.997. Besides, mean SIC, sea ice extent and sea ice area of MWRI-ASI are closer to those of AMSR2 than those of MWRI-NT2.

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

Microwave brightness temperature (Tb) and backscatter ( $\sigma ^{0}$ ) observations from the Soil Moisture and Ocean Salinity (SMOS) mission, Soil Moisture Active Passive (SMAP) mission, Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument, and Advanced Scatterometer (ASCAT) instrument provide a wealth of operationally available satellite data for soil moisture retrieval and data assimilation purposes. To assist the synergistic and efficient use of such techniques, the soil moisture information content in the respective Level-1 observations needs to be determined. Within this context, we compare L-, C-, and X-band Tb and $\sigma ^{0}$ signatures of the above-named sensors to in situ SM observations in Spain, Australia, and USA. We find that L-band Tb observations from SMOS and SMAP show the best overall performance given the considered diagnostics (correlation, anomaly correlation, and sensitivity), while all sensors provide significant soil moisture information. This finding is consistent across the analyzed incidence angle range for SMOS (25°–60°). The results are discussed with respect to physical processes governing the dynamics of Tb and $\sigma ^{0}$ , noting dependencies on vegetation seasonality and land surface temperature.

The additional microwave radiation from underground rock mass (lithosphere) that is caused by tectonic activity and alternating crustal stress has been shown to be a detectable electromagnetic signal via satellite remote sensing. However, the emission of microwave radiation produced inside the lithosphere will be affected by its overburden matter, such as sand, soil, water, and vegetation, and this effect is unknown. In this article, we use a C-band microwave radiometer to detect the variations in the microwave brightness temperature ( $T_{B}$ ) of rock samples in the process of axial loading. For the rock samples, the surface was bare and covered with dry sand or humid sand. The experimental detection illustrates that the dry sand unexpectedly allows more stress-associated additional radiation ( $\Delta T_{B}$ ) to be received by the radiometer, while humid sand shows significant extinction of $\Delta T_{B}$ . Both the radiative transfer theory and the stress-activated positive hole hypothesis are applied to interpret the mechanisms of the effect of dry and humid sand layers on $\Delta T_{B}$ . The outflow of stress-activated positive holes is from inside the loaded rock, and hence their accumulation beneath the surface of the sand layer is expected to reduce the local dielectric permittivity of sand, thereby making the detected $\Delta T_{B}$ by the microwave radiometer to be larger than that extrapolated theoretically in accordance with radiative transfer theory. This article is valuable for understanding the diverse $T_{B}$ anomalies that have been observed prior to tectonic earthquakes, and it facilitates the evaluation of the potential application of $\Delta T_{B}$ to seismic monitoring and earthquake prediction.

The soil moisture active passive (SMAP) mission was designed to acquire L-band radiometer measurements for the estimation of soil moisture (SM) with an average ubRMSD of not more than 0.04 $\text{m}^{3}/\text{m}^{3}$ volumetric accuracy in the top 5 cm for vegetation with a water content of less than 5 kg/ $\text{m}^{2}$ . Single-channel algorithm (SCA) and dual-channel algorithm (DCA) are implemented for the processing of SMAP radiometer data. The SCA using the vertically polarized brightness temperature (SCA-V) has been providing satisfactory SM retrievals. However, the DCA using prelaunch design and algorithm parameters for vertical and horizontal polarization data has a marginal performance. In this article, we show that with the updates of the roughness parameter $h$ and the polarization mixing parameters $Q$ , a modified DCA (MDCA) can achieve improved accuracy over DCA; it also allows for the retrieval of vegetation optical depth (VOD or $\tau$ ). The retrieval performance of MDCA is assessed and compared with SCA-V and DCA using four years (April 1, 2015 to March 31, 2019) of in situ data from core validation sites (CVSs) and sparse networks. The assessment shows that SCA-V still outperforms all the implemented algorithms.