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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.

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

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.

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.

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 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 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%.

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.

Microwave remote sensing is the most promising tool for monitoring near-surface soil moisture distributions globally. With the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions in orbit, considerable efforts are being made to evaluate derived soil moisture products via ground observations, microwave transfer simulation, and independent remote sensing retrievals. Due to the large footprint of the satellite radiometers of about 40 km in diameter and the spatial heterogeneity of soil moisture, minimum sampling densities for soil moisture are required to challenge the targeted precision. Here we use 400 m resolution simulations with the regional Terrestrial System Modeling Platform (TerrSysMP) and its coupling with the Community Microwave Emission Modelling platform (CMEM) to quantify the maximum sampling distance allowed for soil moisture and brightness temperature validation. Our analysis suggests that an overall sampling distance of finer than 6 km is required to validate the targeted accuracy of 0.04 cm3 cm−3 with a 70 % confidence level in SMOS and SMAP estimates over typical mid-latitude European regions. The maximum allowed sampling distance depends on the land-surface heterogeneity and the meteorological situation, which influences the soil moisture patterns, and ranges from about 6 to 17 km for a 70 % confidence level for a typical year. At the maximum allowed sampling distance on a 70 % confidence level, the accuracy of footprint-averaged soil moisture is equal to or better than brightness temperature estimates over the same area. Estimates strongly deteriorate with larger sampling distances. For the evaluation of the smaller footprints of the active and active–passive products of SMAP the required sampling densities increase; e.g., when a grid resolution of 3 km diameter is sampled by three sites of footprints of 9 km sampled by five sites required, only 50 %–60 % of the pixels have a sampling error below the nominal values. The required minimum sampling densities for ground-based radiometer networks to estimate footprint-averaged brightness temperature are higher than for soil moisture due to the non-linearities of radiative transfer, and only weakly correlated in space and time. This study provides a basis for a better understanding of the sometimes strong mismatches between derived satellite soil moisture products and ground-based measurements.

This paper considers existing and promising satellite microwave radiometry systems suitable for the evaluation of geophysical (hydrological) parameters of atmosphere, ocean, and land. A comparative analysis is provided for data sets available for end-users. Algorithms and tools for processing and visualization of satellite data are discussed. The capabilities of modern satellite systems to perform specific tasks of remote sensing are described using the example of a river flood in the Altai region in 2014. Monitoring soil moisture of upper layers of soil on floodplains combined with meteorological forecasts allows assessment of the probability of river flooding at certain areas using values of maximum soil moisture capacity. The effect of changes in the physical properties of ice during its destruction is discussed. This effect has been discovered by analyzing the dynamics of daily satellite measurements of brightness temperatures. It can be considered as a harbinger of ice condition changes of large freshwater bodies. The analysis of brightness temperature seasonal variations is presented using the example of Lake Big Bear (Canada).

Synthetic aperture imaging radiometers (SAIRs) are powerful instruments for high-resolution Earth observation by use of small-aperture antennas sparsely arranged to achieve a large-aperture antenna. High-precision reconstruction algorithm is one of the key contents of SAIRs. Owing to the ill-posed problem and band-limited physical characteristic, there is a still large residual error for traditional regularization methods. It should be noted that the prior information like the lower and upper bounds of the brightness temperature distributions has not been utilized in the reconstruction procedure, especially for the open ocean with relatively small brightness temperature difference. In order to reduce the reconstruction error in SAIRs, a reconstruction method based on active set algorithm is presented by solving the least squares problems with lower and upper bounds. The simulation experiment results show that the proposed method can more effectively reduce the reconstruction error and better improve the accuracy of retrieved brightness temperature distributions in SAIRs than the band-limited regularization method, demonstrating the effectiveness of the proposed method.

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.

Chinese FY-4M is a new-generation geosynchronous satellite, which will be launched in the near future. It is equipped with multichannel millimeter wave radiometers, which operate in the range of 50–430 GHz. This satellite will increase revisit time and significantly improve the monitoring and weather variation forecasting capabilities. Calibration is an important issue for the quantitative analysis and application of brightness temperature observations. Calibration is first conducted in a thermal/vacuum chamber prior to the launch. A hot target onboard the satellite is used to provide relative irradiance for onboard calibration, and the cold cosmos is used as a cold target. However, complicated space conditions may result in many uncertainties and instabilities during calibration. A new method is required to evaluate the performance of the instruments during long-term operation. Because of the non-existence of atmospheric and other environmental interferences, the lunar surface maintains its physical and chemical stability. Thus, it is an ideal source for thermal calibration. The earliest ground-based observation of thermal microwave emission of the Moon dates back to the early 1940s. At that time, the temperature profile and dielectric constant of the lunar surface had not been studied in detail. Thus, interpreting the observed microwave brightness temperature was difficult. In recent years, many studies of the lunar surface have been performed owing to the advances in satellite-borne remote sensing programs. The Diviner Lunar Radiometer Experiment onboard the Lunar Reconnaissance Orbiter investigated the global solar reflectance and mid-IR irradiance. These infrared measurements contribute to the quantitative description of the thermophysical properties of the superficial lunar regolith media. Chinese probes, Chang’e-1 and Chang’e-2, carried four-channel microwave radiometers to observe the microwave thermal emission of the lunar regolith media, which may provide information on superficial dielectric properties of the lunar surface. These advances created a foundation for the millimeter channel observation of the lunar surface. In this study, a real-time model of the sub-solar position and total irradiance of the lunar surface was developed. The altitude of FY-4M is 35800 km. The distance between the Moon and the Earth is between 363400 km (perigee) and 405400 km (apogee). This large distance enables nadir observation from the FY-4M radiometer toward the center region of the lunar equator. The observation beam angle ranges from 0.097° to 0.027°. The diameters of the observation regions at different channels are 750 km (55 GHz), 480 km (89, 118 GHz), 300 km (166, 183 GHz), and 210 km (425 GHz) at apogee. Based on the surface albedo derived from the Clementine data and the constrained thermophysical parameters of the lunar regolith media obtained from the Diviner infrared brightness temperature, the one-dimensional thermal transfer equation is solved to obtain the physical temperature profile of the lunar regolith media. Using the 37-GHz brightness temperature from Chang’e-2 observations, the loss tangent of the regolith media is inverted. Using the fluctuation dissipation theorem, the brightness temperature of the lunar surface in FY-4M millimeter channels is calculated. The thermophysical profile of the regolith can be validated using the Diviner infrared brightness temperature data. The calculated temperature at a depth of 1.3 m is consistent with the measurements obtained in the Apollo heat flow experiment. The surface temperature and millimeter brightness temperature on the lunar equator (0°N, 0°E) during the entire year of 2010 are simulated. The peak value of the brightness temperature is attained after that of the surface physical temperature peak value. In the low frequency channel, the penetration depth increases, its peak value is typically small, and the peak appears later. This study thus presents an effective model for FY-4M calibration and data analysis.

The Soil Moisture Active Passive (SMAP) mission is designed to acquire L -band radiometer measurements for the estimation of soil moisture with 0.04 m3/m3 volumetric accuracy in the top 5 cm for vegetation with water content of less than 5 kg/m2. In regions near the coast or near inland bodies of water, the signal measured by the SMAP radiometer contains emissions from land and water, resulting in errors in the soil moisture estimation. In this article, the effort to extract the brightness temperature (TB) according to the land fraction or water fraction (depending on the center of the footprint location) from the affected SMAP measurements was addressed. A single pixel correction algorithm was applied and its performance was evaluated over simulated data. A data-driven approach for the estimation of land and water TB for data correction was developed. The correction algorithm was then applied to real data and its performance was assessed over the SMAP soil moisture retrievals. We showed that the single pixel algorithm is an effective and computationally efficient algorithm for removing land or water TB contamination from the SMAP data.

An ultrawideband radiometer was used to measure microwave brightness temperature spectra over Arctic sea ice in the Lincoln Sea near the north coast of Greenland. Spectra over the range of 0.5–2 GHz were compared to thermal infrared images collected during the airborne campaign and also compared to nearly concurrent Sentinel-1 C-band synthetic aperture radar (SAR) data. Based on those comparisons, spectral signatures were associated with thick multiyear ice and thin ice. A radiative transfer (RT) model consisting of a homogeneous slab of sea ice bounded by sea water and air was then used to invert the spectra for sea ice thickness and salinity. Inferred thicknesses were consistent with ice thickness climatology for ice floes in the Lincoln Sea. Salinities are higher than expected which may be a consequence of neglecting surface and volume scattering contributions in the models.

We have developed an algorithm for thin ice detection under winter conditions using the Advanced Microwave Scanning Radiometer 2 (AMSR2) radiometer high-frequency brightness temperature (36 and 89 GHz) L1R swath data, and a method to combine thin ice swath charts to a more reliable daily thin ice chart. Moderate Resolution Imaging Spectroradiometer (MODIS) ice thickness swath charts were used as reference data for the algorithm development. The algorithm is based on the classification of 36-GHz polarization ratio (PR36) and H-polarization 89–36-GHz gradient ratio (GR3689H) signatures with linear discriminant analysis. We applied an atmospheric correction to the AMSR2 L1R data following established sea ice concentration (SIC) retrieval algorithms. The PR36 and GR8936H signatures were adjusted to a constant air temperature ( $T_{a}$ ) before the thin ice detection using an empirical relationship between them and $T_{a}$ . The maximum thickness of detected thin ice was estimated to be 20 cm. The thin ice detection with the L1R data is conducted only when SIC $\ge70$ % and $T_{a}\le -5\,\,^\circ \text{C}$ to limit conditions where thick ice may be erroneously detected as thin ice. The thin ice detection algorithm was developed for the Barents and Kara Seas, but it should also be applicable for other Arctic marginal ice zones (MIZs). The daily thin ice chart was validated using an independent set of MODIS daily ice thickness charts. The average probability for misclassification of thick ice as thin ice was 10% and 32% for vice versa. We demonstrate the use of the daily thin ice chart for monitoring the thin ice fraction in the Barents and Kara Seas.

The land parameter retrieval model (LPRM) has been widely used for the retrieval of microwave-based global surface soil moisture (SSM). In the original LPRM algorithm, soil surface effective temperature (SSET) was retrieved from the Ka-band (36.5 GHz) brightness temperature (BT) and then used as input data for the estimation of SSM. In this study, SSET retrieved from the Advanced Microwave Scanning Radiometer of the Earth Observing System Radiometer (AMSR-E) Ka-band BT was validated against MODIS land surface temperature (LST) data, and the results show that when the fraction of water surface (FWS) within an AMSR-E pixel increases in the range from 0–0.01 to 0.15–0.4, the root-mean-square error (RMSE) increases drastically from 1.98 to 11.42 Kelvin (K). When validation was conducted on SSET data retrieved with the K-band (18.7 and 23.8 GHz) BT, the RMSEs were maintained below 2.0 K for FWS ranging from 0 to 0.4. Therefore, in the original LPRM, we substituted the Ka-band BT with the K-band BT to produce more accurate estimations of SSET and hence improved SSM retrievals. Our results show that for FWS ranges of 0–0.01, 0.01–0.05, 0.05–0.15, and 0.15–0.4, the correlation coefficients ( $R$ -values) between SSM retrieved with the improved LPRM and Global Land Data Assimilation System (GLDAS) measurements are 0.78, 0.75, 0.77, and 0.46, respectively, compared with the corresponding $R$ -values of 0.78, 0.65, 0.56, and 0.34 for SSM retrieved with the original LPRM. This demonstrates that against the original LPRM, our improved LPRM can produce more accurate SSM retrievals under water–land mixed pixels.

Land surface temperature (LST) is an important parameter in various fields, including hydrological, meteorological, and agricultural studies. Passive microwave techniques provide a practicable method to retrieve LST under both clear and cloudy conditions. In this study, LST derived from Advanced Microwave Scanning Radiometer 2 (AMSR2) brightness temperature data during nighttime in the period 2015–2016 using a physically-based algorithm was compared with Moderate Resolution Imaging Spectroradiometer (MODIS) LST product MYD11A1 over 16 study sites that represent four different land cover types, i.e., barren/sparsely vegetated, grasslands, croplands, and evergreen broadleaf forest. Compared to MODIS-derived LST, the root-mean-square error (RMSE) of AMSR2-derived LST is 6.0 K and the bias is 4.4 K over all study sites. For barren/sparsely vegetated sites, LST was overestimated by 6.7 K. To eliminate the systematic bias induced by the penetration depth effect of microwave radiation over barren/sparsely vegetated sites, a linear regression between AMSR2- and MODIS-derived LST was applied and the RMSE decreases from approximately 7.8 to 3.5 K. For the other three land cover types, the bias ranges from approximately 1.4 to 4.2 K and the RMSE ranges from approximately 2.1 to 5.9 K. The bias between AMSR2- and MODIS-derived LST is related to vegetation coverage. The value of bias increases with the decrease of normalized difference vegetation index. Furthermore, the RMSE has a strong dependency on precipitable water vapor (PWV). It presents a descending pattern of RMSE with the increase of PWV.

The airborne Conical Scanning Millimeter-wave Imaging Radiometer (CoSMIR) has participated in the Global Precipitation Measurement (GPM) Olympic Mountains Experiment (OLYMPEX) from November to December, 2015, with great success. With similar channels as that of the GPM Microwave Imager (GMI) at 89–183 GHz, CoSMIR served as a proxy for GMI by flying onboard the DC-8 Aircraft for a total of 17 science flights, collecting over 72 h of observations. The high-quality, calibrated brightness temperature data set is the result of several improvements made to CoSMIR prior to OLYMPEX to make the instrument more reliable. This paper describes these improvements and gives a detailed summary of the CoSMIR measurements obtained from OLYMPEX. CoSMIR experienced minor performance issues during the campaign, most of them were not excessive and only resulted in a loss of approximately 4 h of data for the entire campaign. Performance issues are discussed and shown how they were mitigated to achieve a quality data set. Comparisons of CoSMIR and GMI observations are presented to show that the CoSMIR measurements agree well with GMI. The CoSMIR data set is publicly available as a part of the OLYMPEX data suite and can reliably be used in the GPM algorithm development and related studies.

The water cycle observation mission (WCOM) will build upon previous L and C band passive microwave soil moisture satellite missions. WCOM will consist of a passive microwave synthetic aperture radiometer operating at L , S , and C bands. The WCOM requirements for passive soil moisture are to estimate soil moisture in the top 5 cm of soil layer with an error less than 0.04 m3/m3, at 15-km resolution and with a 3-day revisit. A new set of algorithms for these multi-frequency platforms will need to be developed for estimating the data products at the desired resolution. To accomplish this, a brightness temperature (TB) downscaling methodology is developed that uses passive S -band TB (30 km) to downscale L -band TB (50 km) and to estimate soil moisture at a 30-km resolution, based on the linear relationships between the passive signals of L band and S band. To test this downscaling method, analysis was performed using PALS data from the Soil Moisture Experiments in 2002 (SMEX02). For this study, 4-km L -band observations were downscaled to 800 m. The root mean square errors between the downscaled TBL at 800 m with the observed TBL at 800 m are 2.63 and 1.60 K for H and V polarizations, respectively. The results also showed that it was possible to use these disaggregated TB to estimate soil moisture to meet the mission requirement of 0.04 m3/m3. These results showed that we can obtain higher resolution soil moisture from the L -band passive TB with a high accuracy ( S -band information.

In this paper, we present the design and implementation tests of a water vapor radiometer (WVR) suitable for very long baseline interferometry (VLBI) observation. We describe the calibration method with an analysis of the sources of measurement errors. The experimental results show that the long-term measurement accuracy of the brightness temperature of the water vapor radiometer can reach 0.2 K under arbitrary ambient conditions by absolute calibration, receiver gain error calibration, and antenna feeder system temperature noise error calibration. Furthermore, we present a method for measurements of the calibration error of the oblique path measurement. This results in an oblique path wet delay measurement accuracy of the water vapor radiometer reaching 20 mm (within one month).

International audience; Vegetation optical depth (VOD) retrieved from microwave remote sensing techniques has been used as an important proxy for monitoring the vegetation dynamics at large scales. In a first study we developed a new X-VOD indice from inversion of the X-MEB model from the Microwave Scanning Radiometer 2 (AMSR2, 2012-present) observations. The Advanced Microwave Scanning Radiometer for EOS (AMSR-E, 2002–2011) and AMSR2 share many characteristics, such as local crossing time, incidence angle, frequency at X-band, etc, which makes it possible to produce a consistent long-term X-VOD product over a long period spanning from 2002 to present. The objective of this study was to merge the AMSR-E and AMSR2 observations in order, in a second step, to produce a consistent long-term X-VOD product spanning over the combined periods of AMSR-E and AMSR2.The main challenges in retrieving a consistent X-VOD data set from AMSR-E and AMSR2 are that (i) there is a bias between observations from the two sensors; and (ii) the lack of overlapping observations between the two sensors makes it impossible to achieve a direct inter-calibration. Here, to overcome this problem, we used AMSR-E slow rotation data (AMSR-E L1S), which has similar characteristics as AMSR-E and provided observations concurrently with AMSR2, as a bridge to calibrate the AMSR-E brightness temperature (TB) observations with AMSR2 TB.As our main objective in this TB calibration study was to produce a consistent long-term AMSR-E/AMSR2 X-VOD product, we evaluated the retrieved X-VOD product based on different inter-calibration, either global-based or pixel-based, methods. Both AMSR-E and AMSR2 X-VOD were evaluated against the Aboveground Biomass (AGB), Leaf Area Index (LAI) and the Normalized Difference Vegetation Index (NDVI). The results suggest that global-based inter-calibration methods using homogeneous and temporally stable land covers (Evergreen Broadleaf Forests and Snow and Ice) as reference calibration data sets provided the best results. For instance, the spatial relationships between X-VOD and AGB/LAI/NDVI are highly consistent over the AMSR-E and AMSR2 periods after the calibration work. This study laid a solid foundation for monitoring the dynamics of X-VOD, as a proxy of AGB, over the combined periods of AMSR-E and AMSR2 sensors (almost 20 years).