Using a neural network to retrieve ice water path (IWP) from spaceborne microwave radiometer brightness temperatures (BTs) is a new technical method for remote sensing ice cloud microphysical parameters, but constructing a high-quality retrieval database remains a key limitation to its application. To this end, this study proposes a database construction method driven by the "All-Day Radiance Matching IWP (ARM-IWP) Construction" algorithm. This algorithm is used to extend the spatial coverage of CloudSat-derived IWP along the satellite tracks to both sides of the orbit. Designed for day and night, this algorithm aims to cover the swath of the global precipitation measurement (GPM) microwave imager (GMI). Based on this database, the IWP retrieval algorithm built with the neural network has the following performance metrics: a root mean square error (RMSE) of 610.41 g/m(2) , a mean absolute percentage error (MAPE) of 48.7%, a coefficient of determination of 0.729, and a Pearson correlation coefficient of 0.855. The results indicate that the neural network model established using the constructed database can effectively and stably retrieve the IWP from the GMI data.

The microwave imager combined active and passive (MICAP) serves as a primary payload for the Chinese Ocean Salinity Mission (COSM). Its passive component employs 1-D synthetic aperture radiometry, while the active component utilizes digital beamforming scatterometry. This integrated design, which marks itself as the first spaceborne application, enables multifrequency active-passive observation on multisea surface parameters within a unified payload, yet simultaneously poses challenges for high-accuracy brightness temperature (TB) reconstruction. The conventional G-matrix method's accuracy is fundamentally constrained by underdetermination and disparities among element antenna patterns-a limitation notably observed in the SMOS mission. While deep learning approaches demonstrate efficacy in simulations, they encounter a "Sim-to-Real" gap arising from deviations between idealized training data and actual observations, primarily due to the imperfect characterization of on-orbit antenna patterns. To address these, this article proposes a high-accuracy physics-guided residual U-Net, named inverse fast Fourier transform (IFFT)-ResUNet, combined with a fine-tuning strategy. The proposed architecture embeds the IFFT as a physical prior to guide the data-driven reconstruction. To bridge the visibility functions (VFs) inconsistency, a transfer learning strategy is adopted, adapting the model from general imaging laws learned on simulations to real measurements. End-to-end simulation results demonstrate that the proposed method achieves high-precision reconstruction with a root-mean-square error (RMSE) of 0.14 K. Furthermore, the physics-guided fine-tuning strategy exhibits exceptional data efficiency, achieving performance comparable to or better than training from scratch using only 10% of the training data, thereby drastically reducing the dependency on extensive on-orbit calibration samples. Validation using the MICAP airborne flight campaign data over Laizhou Bay confirms the method's practical feasibility and lays a foundation for its subsequent application to spaceborne observations.

The measured antenna temperature of microwave radiometers differs from the true brightness temperature due to antenna pattern effects. Corrections for the antenna pattern effects constitutes an essential component of microwave radiometer calibration. The Compact Atmospheric Microwave Sounder (CAMS) is a cross-track scanning microwave designed for small satellites. It adopts full-circle sampling on the scan plane. Leveraging its special scan geometry, a new method of antenna pattern correction (APC) is developed. This method utilizes adjacent samplings from consecutive scans to obtain APC coefficients, and correct antenna temperature to the pixel level brightness temperature. For the first time, real samplings from beyond the Earth swath are introduced to assist APC near the swath edges. The performance of the method are analyzed through scenarios of coastlines and Earth swath edges. Analysis in the coastline scenarios demonstrates that the proposed method is more effective in correcting antenna pattern effects and detecting brightness temperature variations than traditional APC approaches in heterogeneous Earth scenarios. Comparative analysis of the method at Earth swath edges demonstrates that the introduction of samplings outside the swath effectively enhances the precision of corrected brightness temperature at swath edges. This method provides a reference for antenna pattern correction and sampling strategy in other microwave radiometers.

The remote sensing of the geomagnetic field via microwave radiometry represents an innovative approach to rapidly acquire global geomagnetic field distribution data, which is pivotal for both research and practical applications of the geomagnetic field. This paper delves into the underlying principles of atmospheric remote sensing to elucidate the mechanisms of geomagnetic remote sensing. We analyze the Zeeman effect or atmospheric oxygen spectral lines, the resultant spectral line splitting, and polarization induced by the geomagnetic field. Utilizing a vector radiative transfer model expressed in Stokes parameters, we simulate the Zeeman effect of atmospheric oxygen molecules and the consequent impact on the brightness temperature on microwave radiation. Simulations, based on International Geomagnetic Reference Field (IGRF) data, demonstrate that brightness temperature variations can reach tens of Kelvin within the global geomagnetic field intensity range of approximately 40 mu T, with these variations being significantly correlated to the global geomagnetic field intensity distribution. We further simulate the effects of observation angle, frequency band, and magnetic field intensity on the brightness temperature of fully polarized radiation. The findings indicate that the sensitivity of brightness temperature to magnetic field information is most pronounced within the MHz range near the oxygen spectral line center of THz bands. Due to the distinct characteristics of Zeeman splitting spectral lines, the brightness temperature of different polarizations and their sensitivity to magnetic field intensity vary across different oxygen absorption bands. At the oxygen absorption peak of 61 similar to 773 GHz, the sensitivity to magnetic field intensity changes is approximately 2 K/mu T. Additionally, the angle between the radiation vector and the magnetic field vector significantly influences the brightness temperature characteristics of different polarization states. Notably, the T-3 and T-4 polarization brightness temperatures exhibit the strongest signal when the radiation vector is parallel to the magnetic field vector, with T-4 polarization showing greater sensitivity to magnetic field changes. These insights underscore the necessity for a comprehensive analysis of various parameter configurations in the future design of spaceborne microwave radiometer-based geomagnetic field remote sensing detection schemes to identify the optimal detection combination.

Hyperspectral microwave radiometer is a new type of passive microwave remote sensor for observing middle and upper atmospheric temperature, humidity, trace gas, and other parameters such as winds. The digital spectrometer, which allows the fine sampling of the spectral lines, is the core component of the radiometer. In this article, we propose the design and implementation of a new type of wideband, real-time channelized digital spectrometer, which realizes the core base-64 real-time complex fast Fourier transform (FFT) algorithm and channelization algorithm by improving the filter bank, 128-channel parallel processing of FFT and complex number processing. The digital spectrometer has a sampling rate of 20 Gsps, a quantization bit number of 8 bits, and an input bandwidth of 10 GHz, which realizes the spectrum analysis of 4096 channels. Then, an observation test was carried out using a V-band ground-based microwave radiometer equipped with the 10-GHz spectrometer, and the atmospheric temperature profile was successfully measured from the surface to the stratosphere. The retrieval results were compared with the ERA5 reanalysis data and the L2 temperature products of the FY-3-D/MWTS-MWHS and Aura/microwave limb sounder (MLS), with a better consistency, which proved the application and potential of the new wideband digital spectrometer in the atmospheric sounding.

In relatively dry areas, due to lower soil moisture and less vegetation coverage, land surface microwave radiation comes from a certain depth of soil, while infrared skin temperature is only sensitive to the thin layer of the land surface. The inconsistencies in the detection depth of the microwave and infrared can lead to significant differences in retrieved emissivity between day and night. To improve the instantaneous microwave emissivity retrieval over barren areas, the land surface effective temperature calculation method was proposed based on AMSR2, which constructed the relationship between effective temperature and microwave brightness temperatures (TBs) in hours, approximating that monthly mean skin temperature equals monthly mean effective temperature and ignoring the change in emissivity over a month. The results showed that the effective temperature from 10.65 to 89 GHz had significantly smaller diurnal amplitude than skin temperature, and the lower the frequency, the smaller the amplitude. The effective temperature was then applied to the instantaneous emissivity inversion, and it was shown that this method remarkably reduced the emissivity difference between day and night, with the mean difference at the magnitude of 10<^>-3.

The geomagnetic field is a crucial physical field of the Earth,playing an essential role in various domains such as earth and space physics research,applied research in geology,and national strategic security.To facilitate profound scientific and applied investigations in geomagnetics and relat-ed fields,it is important to develop magnetic field measurement methods that are highly convenient,cost-effective,cover a wide spatial range with exceptional precision,and provide multi-dimensional geo-magnetic data.In order to overcome the limitations of on-site magnetic field measurement methods based on high-precision magnetometers regarding efficiency and detection range,remote sensing detec-tion has emerged as a promising new area for magnetic field detection.This paper comprehensively ana-lyzes the fundamental principles,research progressions,and application status of microwave radiometer remote sensing technology for studying the geomagnetic field.Based on this analysis,discusses the re-quirements and technical challenges faced by this method along with future development trends aimed at enhancing its detection capabilities.Furthermore,it proposes new prospects for future research in mag-netic field remote sensing detection including planetary magnetic field remote sensing.

The scanning microwave radiometer (SMR) onboard the Haiyang-2B satellite (HY-2B) can provide valuable observation data for various research fields. Resampling enables consistent spatial characteristics of SMR brightness temperature (BT) data from different channels, which is essential for the multichannel collaborative retrieval of geophysical parameters. However, as the satellite moves, the relative position between the fields of view (FOVs) of different channels changes, and the error resulting from this factor is ignored by the traditional resampling method. Therefore, in this letter, the traditional resampling effect of SMR data at different orbital positions is analyzed in detail. In addition, an improved resampling method based on multiple scan rows is proposed to reduce resampling errors while maintaining real-time resampling. In the long-term data experiment, compared with the traditional method, the mean fit error and fit error variation range obtained by the proposed method can be reduced by 57.92% and 54.07%, respectively. In the BT error analysis experiment, the differences in spatial resolution and geolocation between different channels can be eliminated more accurately by the proposed method, and the maximum and average BT errors on the selected long-term data are reduced by 3.80 and 1.82 K, respectively.

The thermal infrared (TIR) emissivity and physical temperature of snow together determine the thermal radiation of snow. The modeling of snow and ice TIR emissivity is important for climate models and remote sensing. Previous snow and ice TIR emissivity models fail in predicting the sensitivity of emissivity to snow type and snow microstructure, which was measured in experiments. Empirical models were proposed to simulate such sensitivity but not in a unified theoretical framework. In this study, we propose a snow and ice TIR emissivity model based on photon tracking by assuming that the geometric optics approximation is still valid in TIR spectral region. It is proved that the proposed model can predict both the TIR emissivity's sensitivity to grain size for small grain sizes and the TIR emissivity's sensitivity to snow density. These features can fully explain the experiment observed features. Moreover, the proposed model simulates snow and ice TIR emissivity in a unified theoretical framework. We also explain that the observed emissivity's sensitivity to snow type is actually caused by the sensitivity to snow density, not grain size. This proposed model can be further used in climate models and remote sensing.

The microwave radiation imager (MWRI) onboard the Fengyun-3D satellite can provide valuable observation data in many fields such as meteorological research and weather forecasting. However, its coarse spatial resolution limits data application. Recently, image super-resolution (SR) methods based on deep learning have been introduced into the spatial resolution enhancement of radiometers and achieved better results than traditional methods. Most of them use the degradation model to generate dataset and build model based on convolutional neural network (CNN). However, the dataset generation method based on the degradation model may impair the information in the original brightness temperature (BT) data. Moreover, CNN-based SR methods often struggle to model long-distance dependencies, which may impact the spatial resolution enhancement of BT data. To address these issues, we propose a dataset generation method based on BT data matching and introduce a SR network model based on the Transformer structure. We refer to it as the SR transformer BT data matching method. Results indicate that the method significantly improves the spatial resolution of MWRI data over current methods and exhibits strong generalization for long-term data outside the training time range.

中国风云三号(FY3)卫星是国际气象组织组网卫星之一,其搭载的微波成像仪(MWRI)可为气象研究、天气预报等多个领域提供有效数据。在应用MWRI数据进行多频率探测通道的协同反演时,必须考虑通过重采样来使不同探测频点的数据具有一致的空间分辨率。Backus-Gilbert(BG)方法是一种被广泛用于星载微波辐射计亮温数据重采样的方法。传统重采样方式应用BG方法计算辐射计单次旋转扫描得到的采样点的权重系数(预计算权重系数),并将其直接应用到所有旋转扫描获取的采样点上,进而完成全部数据的重采样。然而,在卫星仪器的采样过程中,地球椭球面等因素的影响会导致观测视场之间的相对位置发生改变,将预计算权重系数直接应用到全部数据会给重采样带来误差。因此,本文提出一种结合天线方向图投影定位方法的重采样方式,详细分析了MWRI数据在不同轨道位置的重采样效果。实验结果表明,本文采用的重采样方式能够在MWRI的不同的通道组合上修正传统重采样方式造成的平均1.32 K的亮温误差。本文指出并修正了重采样亮温的一种误差来源。未来在使用多频率探测通道数据协同反演地球物理参数时,根据反演参数对亮温变化的敏感程度,应用本文方式获取的重采样数据可以获得更准确的反演结果。

The derivation of ice water path (IWP) from microwave radiometer measurements is challenging. This study presents a deep learning framework for global retrieval of IWP using observations from the Microwave Humidity Sounder-II (MWHS-II) aboard the FengYun-3D (FY-3D) satellites. Two deep learning models, Deep Forest (DF21) and Quantile Regression Neural Network (QRNN) are constructed to detect ice cloud flags and retrieve IWP. By collocating MWHS-II observations with 2C-ICE, a joint product of CloudSat and CALIPSO, deep learning models learn the characteristics of IWP from MWHS-II brightness temperatures. The test results show that the MWHS-II channels provide more information on IWP than the MWHS channels, particularly the 89 GHz channel and the 118 GHz channels with an offset of ≥ 0.8 GHz. Combining the QRNN and DF21 models, the IWP retrieval results in an RMSE of 707.346 g/m 2 , MAPE of 65.122%, MBE of -104 g/m 2 , determination coefficient (R 2 ) of 0.683, and Pearson correlation coefficient (PCC) of 0.831. Application of the models to MWHS-II observations of Tropical Cyclone CILIDA shows better agreement with 2C-ICE. All datasets exhibit a similar feature on the monthly mean scale, but the magnitudes of IWP differ. Compared to GMI-GPROF, MODIS, and ERA5 IWP products, MWHS-II results are closest to 2C-ICE. Similar results are also shown for the zonal mean data. These results show that deep learning methods efficiently and probabilistically retrieve IWP from long-term observation data of MWHS/MWHS-II.

This paper describes the Altay 2024 airborne field campaign in support of snow observation retrieved from spaceborne InSAR measurements from the Chinese LuTan-1 (a spaceborne L-band SAR constellation launched in 2022). The airborne and field measurements that are synchronized with LuTan-1 InSAR acquisitions will be conducted in January-February 2024 (snow on) and May-July 2024 (snow off). The remote sensing and in-situ measurements include various in-situ observations and drone-based lidar measurements. We first provide the overview of the Altay 2024 campaign including the choice of the in-situ measurement locations and flight tracks of the drone-based lidar. Then, historical InSAR dataset from all the available L/C-band SAR’s (e.g. JAXA’s ALOS, ESA’s Sentinel-1, China’s LuTan-1) over the study area are used to generate SWE change products, which are further compared against the in-situ measurements when available. This synergetic spaceborne airborne field campaign will directly validate the LuTan-1 derived snow products using the acquired airborne and field dataset, which can also support the design of future spaceborne mission concepts for snow retrieval.

This paper presents a terahertz high gain beam steering transmitarray antenna (BSTA) working at 340 GHz. Substrateless double hexagon ring slots unit-cells which present low loss characteristics at THz band are used to constitute the layout of THz BSTA. To improve the beam steering performance, bifocal technique is used to design the layout of BSTA. Because the fabrication risk of the THz BSTA prototype increases a lot as the aperture dimension is enlarged, four inch silicon wafer is chosen after weighting the risk and gain of the BSTA. Micromachining process is used to fabricate the large aperture THz BSTA to ensure the machining accuracy of the unit-cells. The measured results of the prototype show that the THz BSTA could realize. - 15 degrees similar to 15 degrees range beam scanning with gain > 38:3 dB, scanning loss < 1:2 dB, and side lobe level < -17.8 dB, by moving the feed along the focal plane of the BSTA.

This paper presents a WR-2.2 band waveguide orthogonal mode transducer (OMT) for an ice cloud remote sensing radiometer. A holistic approach is proposed to the design and manufacturing. Given the difficulties of using computer numerically controlled (CNC) milling techniques to fabricate the OMT and its assembly, both the design and fabrication processes are optimized to reduce the possibility of error, which could affect OMT performance. In the design, the OMT utilizes a side-arm structure, with a compact 90° waveguide twist designed in the side-arm path to reduce insertion loss and achieve two symmetric and opposing output ports in the radiometer. Regarding the challenges in CNC milling technology for such high frequencies, a comprehensive sensitivity analysis is performed following the OMT design to identify the design parameters with high sensitivity. Then, an acceptable tolerance range for CNC milling is obtained. To control fabrication uncertainty and minimize tolerance, an effective fabrication refinement strategy is proposed for accessible CNC machining, reducing the range of fabrication errors to − 3 to 2 µm within 3 iterations. Comprehensive measurements and analyses are performed on the fabricated OMT as well as the one with an antenna and a receiver. The measured insertion losses of the H and V copolarization of the OMT are better than 1.4 dB and 2 dB, respectively. The isolation is better than 40 dB. The measured H and V cross-polarizations are better than 25 dB. They demonstrate the OMT with a good performance for radiometer system.

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As of now, direct measurements of middle and upper atmospheric wind are still scarce, and the observation method is limited, especially for the upper stratosphere and lower mesosphere. This paper presents a study of band selection to derive line-of-sight wind from 30 km to more than 120 km using a high-spectral-resolution terahertz (THz) radiometer, which can fill the measurement gap between lidar and interferometer data. Simulations from 0.1-5 THz for evaluating the feasibility of the spaceborne THz limb sounder are described in this study. The results show that high-precision wind (better than 5 ms-1) can be obtained from 40 to 70 km by covering a cluster of strong O3 lines. By choosing strong O2 or H2O lines, the high-quality measurement can be extended to 105 km. The O atom (OI) lines can provide wind signals in the higher atmosphere. In addition, performance of different instrument parameters, including spectral resolution, bandwidth, and measurement noise, was analyzed, and, lastly, four different band combinations are suggested.

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Higher spatial resolution can improve the application ability of spaceborne microwave radiometer data. To investigate the capability to enhance the spatial resolution of the radiometer measurements, we analyzed the effect of enhanced field of view (FOV) and sampling strategy on improving spatial resolution using the Backus–Gilbert (BG) method, with the scanning parameters of FengYun-3D Microwave Radiation Imager (MWRI) as an example. The results suggest that selecting an appropriate enhanced FOV and increasing the sampling overlap rate can yield better spatial resolution and effectively reduce the brightness temperature (BT) error that arises from resolution enhancement. As an example, the MWRI 18.7 GHz channel with a spatial resolution of 30 × 50 km is taken. When the spatial resolution of the enhanced FOV is set to 25 × 35 km, compared with other options, the average BT error caused by resolution enhancement is reduced to 0.38 K, and the spatial resolution is improved to 27.3 × 40.5 km. In addition, when the sampling overlap rate of cross-track and along-track directions is set to 91.4% × 92.4%, the highest in the experimental conditions, compared with the original sampling setting, the average BT error caused by resolution enhancement is reduced from 0.72 to 0.4 K, and the spatial resolution is improved to 22.8 × 35.7 km. These experimental findings may provide insights into designing future radiometers and applying the BG method.