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Passive microwave remote sensing is a vital tool for acquiring valuable information regarding the Earth's surface, with significant applications in agriculture, water management, forestry, and various environmental disciplines. Precision agricultural (PA) practices necessitate the availability of field-scale, high-resolution remote sensing data products. This study focuses on the design and development of a cost-effective, portable L-band microwave radiometer capable of operating from an unmanned aircraft system platform to measure high-resolution surface brightness temperature ($T_{B}$). This radiometer consists of a dual-polarized (Horizontal polarized, H-pol and Vertical polarized, V-pol) antenna and a software-defined radio-based receiver system with a 30 MHz sampling rate. The post-processing methodology encompasses the conversion of raw in-phase and quadratic (I&Q) surface emissions to radiation $T_{B}$ through internal and external calibrations. Radiometric measurements were conducted over an experimental site covering both bare soil within an agricultural field and a large water body. The results yielded a high-resolution $T_{B}$ map that effectively delineated the boundaries between land and water, and identified land surface features. The radiometric temperature measurements of the sky and blackbody demonstrated a standard deviation of 0.95 K for H-pol and 0.57 K for V-pol in the case of the sky and 0.39 K for both H-pol and V-pol in the case of the blackbody observations. The utilization of I&Q samples acquired via the radiometer digital back-end facilitates the generation of different time–frequency (TF) analyses through short-time Fourier transform and power spectral density (PSD). The transformation of radiometer samples into TF representations aids in the identification and mitigation of radio frequency interference originating from the instrument itself and external sources.

The existence of radio frequency interference (RFI) has a great influence on the measurement of synthetic aperture interferometric radiometer (SAIR). The strong RFI signal energy picked up by the sidelobes of synthetic beam (or array factor) of SAIR easily results in false positives, deteriorating the RFI detection performance, especially when considering the representative scenes where RFIs have a large dynamic range of intensity. In this article, we propose a novel RFI source detection method by exploiting the array factor property (AFP) of SAIR. The AFP-based RFI detection method mainly consists of three steps. First, RFI sources are recovered from visibility function samples through sparse reconstruction (SR). In the recovered RFI map, the background noise is reduced and the potential RFI target regions could be extracted well. Second, the AFP of SAIR is analyzed concerning distribution characteristics of main beam and sidelobes. Based on the AFP analysis, we present a new spatial weight indicator (SWI) describing the probability of one potential RFI being a false positive. Then, an SWI-based probability map (SPM) is generated to discriminate true sources with the false positive sources. Third, the SPM and the SR-based map are combined to reconstruct the RFI image, where false positives are filtered out while true RFI sources are retained. The experiments using synthetic data and real SMOS satellite data demonstrate the effectiveness and superiority of the proposed method in RFI detection tasks for SAIR.

The accuracy of geophysical retrievals from radiometers relies on calibration quality, encompassing both absolute radiometric accuracy and spectral consistency. Radiometers have employed various calibration techniques, including external targets, vicarious sources, and internal calibrators like noise diodes or matched reference loads. Calibration techniques face challenges like frequency dependence, instrumental effects, environmental influences, drift, aging, and radio frequency interference. Recent hardware advancements enable radiometers to collect raw samples containing both temporal and spectral information. Leveraging advanced modeling techniques like deep learning (DL) enables detecting subtle correlations, non-linear dependencies, and higher-order interactions within the data extracting valuable information that may have been challenging with conventional methods. This study utilizes NASA's Soil Moisture Active Passive (SMAP) satellite's level 1A and level 1B data products to develop a DL-based radiometer calibrator to estimate antenna temperature. Spectrograms of second raw moments equivalent to power carrying the 2-D spectral features serve as primary input in a supervised convolutional neural network-based architecture. DL-based calibrator has demonstrated high correlation and low root mean square error when incorporating spectral information from both reference and noise diodes and when not considering this information. Findings suggest that the ancillary features such as internal thermistor temperature and loss elements exhibit sufficient accuracy in estimating antenna temperature to compensate for variations in receiver noise temperature and short-term gain fluctuations in the absence of the reference load and noise diode power. The proposed calibration technique with reduced reference information might enable radiometers for a higher number of antenna scene observations within a footprint.

As next-generation communication services and satellite systems expand across diverse frequency bands, there is a pressing need to use the spectrum as efficiently as possible. A possible strategy to achieve this is to facilitate the coexistence of different radio services, for example between telecommunication and microwave remote sensing systems. However, this approach could increase the risk of radio frequency interference (RFI) affecting passive microwave sensors, which are crucial for environmental and geoscience applications. Therefore, the availability of a testbed along with a dataset encompassing a diverse array of scenarios under a controlled environment is very important in order to study coexistence scenarios and reduce the risk of interference associated with them. This study presents a physical environmentally controlled testbed including a passive fully calibrated L-band radiometer with a digital back-end capable of collecting raw in-phase/quadrature (IQ) samples and an active fifth-generation (5G) wireless communication system with the capability of transmitting waveforms with advanced modulations. Various RFI situations such as in-band, transition band, and out-of-band transmission effects are quantified in terms of their effect on the measured brightness temperature. A detailed procedure and publicly accessible dataset are provided to help test the impact of wireless communication on passive sensing, enabling the scientific community to facilitate coexistence research and quantify interference effects on radiometers. This dataset includes publicly available raw radiometer and 5G communication samples along with preprocessed time-frequency representations and true brightness temperature data.

The passive and active L-band system (PALS) is an airborne microwave instrument suite that combines a separate radar and radiometer airborne instrument with a common antenna. PALS has been used for salinity, wind speed, and soil moisture (SM) measurements, which is critical for calibration and validation of the SM active passive satellite brightness temperature measurements. The combined 1.413 GHz radiometer and 1.26 GHz radar measurements can provide high-accuracy SM measurements over vegetated areas. The new upgraded version of the PALS instrument employs a novel single-chain receiver topology and combines the radar and radiometer instruments. The reduced-noise single-chain receiver PALS instrument was deployed on a DC-3 aircraft for SM measurement in the summer of 2019.

The study of brightness temperature (TB) anomalies always postulates a new understanding of the subsurface deposits of the Moon. In this study, the TB performances of the lunar farside highlands were comprehensively studied using TB data derived from the Chang'e (CE)-2 microwave radiometer, accompanied by Kaguya multiband imager data and Lunar Reconnaissance Orbiter diviner thermal infrared (TIR) data. The main results are as follows: First, we identified two anomaly belts surrounding Giordano Bruno and King Craters, covering an area of about 244 806 km2. Second, through similarity analysis and theoretical simulation, the cause of the anomaly was ascribed to the existence of surface or subsurface rocks. Finally, combining the in situ observations from the Apollo, CE-3, and CE-4 missions and the sensitivity of the electromagnetic waves to the sizes of the rocks in the TIR and microwave domains, we conclude that the rocks should be in centimeter-meter scale and they are widely distributed in the abnormal belts. These findings provide a new understanding of the surface features in and surrounding the impact craters.

Thanks to the frequent revisiting, satellite microwave radiometers have great potential for surface soil moisture (SM) monitoring. However, their spatial resolution is not sufficient for hydrological studies in small catchments as well as applications to precision farming. In this study, a disaggregation technique based on machine learning is proposed: the technique combines Sentinel-1 (S-1) SAR data with SM generated from advanced microwave scanning radiometer 2 by the IFACs HydroAlgo algorithm, with the aim of enhancing the SM spatial resolution from the original 10 km to about 30 m. To this scope, two machine-learning techniques have been considered for the implementation, namely artificial neural networks (ANNs) and random forests (RF). Training is carried out by aggregating and coregistering S-1 data with the HydroAlgo SM at 10-km resolution. After training, the ANN and RF algorithms are applied pixel-by-pixel to the S-1 images at full resolution for generating the enhanced SM maps. The method has been implemented and validated in two agricultural areas located in Central Italy, where a series of experiments has been carried out between 2019 and 2020 for collecting the main soil and vegetation parameters at the same time of satellite overpasses. To assess the actual resolution of the output SM, the validation against in situ measurements has been carried out by aggregating data at 10, 30, 50, and 70 m. The results confirmed the effectiveness of the proposed method: validation carried out at 30 m obtained R≃0.82 and RMSE≃0.05 m3/m3 that represent a noticeable improvement with respect to the results obtained by HydroAlgo at 10 km (R≃0.56 and RMSE >> 0.1 m3/m3). Validation results also pointed out the superior performances of the ANN based with respect to the RF-based disaggregation.

In recent years, due to growing number of humanitarian disasters, international attention to refugees has increased. However, many refugee settlements are located in remote areas and there is a lack of relevant surveying data, which leads to the evaluation with difficulties. In this study, we attempt to use nighttime remote sensing to analyze temporal evolution and living conditions of selected refugee camps. First, utilizing monthly nighttime data from the visible infrared imaging radiometer suite with time series analysis, six typical refugee camps in Africa and Asia were selected for a large-scale assessment and comparison, suggesting that time series nighttime light imagery can reflect evolutional trends and economic levels of refugee camps. Second, we use the Kutupalong refugee camp in Bangladesh as a case to show the capacity of the high resolution nighttime light data in retrieving living condition information of the camps, by analyzing the nighttime light data from sustainable development goals satellite-1 combined with humanitarian points of interest data. Specifically, based on spatial analysis (e.g., hotspot analysis), the distribution pattern of nighttime light in the camp was further explored, and the sanitation security situation in the camp was assessed. This study demonstrates that nighttime remote sensing imagery is potentially useful in evaluating refugee camps around the world, providing technical support to international humanitarian relief work.

Evaluating the complex surface deposits of the Schiller–Schickard cryptomare region can enhance our understanding of basaltic volcanic activity and lunar surface evolution. This study utilizes brightness temperature (TB), normalized TB (nTB), and TB difference (dTB) maps generated from the Chang'E (CE)-2 microwave radiometer data. The S–S region's surface deposits were also evaluated by combining the datasets from the Lunar Prospector Thorium, Diviner, LOLA, Clementine UV-VIS, Bouguer Gravity, and Lunar Reconnaissance Orbiter Wide Angle Camera. The main research findings are as follows. First, based on the TB distribution characteristics of craters, four types of craters are identified in the S–S region. The microwave radiation TB of each type of crater is affected by the varying degrees of influencing factors. Second, according to the nTB and dTB performances, combined with the distribution of surface compositions and high-density materials, the extent of mare-like cryptomare deposits (MCD) is considered more reasonable. It is speculated that there are at least two different periods of buried mare deposit eruptions in Region MCD. Finally, within Region MCD, two patches with low TB anomaly are discovered. These discoveries advance our knowledge of the Moon's surface evolution.

Monitoring vegetation dynamics is essential for ecological processes, environmental changes, and natural resource protection. Fine-scale representation of vegetation indices is necessary for regions with complex topography and high diversity species. However, the advanced very-high-resolution radiometer (AVHRR), which covers an extensive time range with high temporal resolution, does not provide normalized difference vegetation index (NDVI) data with sufficient spatial resolutions for a detailed analysis of vegetation changes. The moderate resolution imaging spectroradiometer (MODIS), which has a higher temporal and spatial resolution, has only been limited to the last few decades. To deal with these issues, we propose a multiscale residual convolutional neural network (MRCNN) that utilizes a multiscale structure with a residual convolutional neural network to combine MODIS NDVI and AVHRR NDVI data. The MRCNN algorithm improved mean absolute error (MAE) and root mean squared error (RMSE) by 0.026 and 0.032, respectively, resulting in a 64.38% improvement for MAE and 62.79% improvement for RMSE compared to AVHRR NDVI. It also increased the peak signal-to-noise ratio by 28.5% and the structural similarity index by 16.2%. The MRCNN method accurately captures the actual state of MODIS NDVI and consistently tracks changing trends in the vegetation index. It is exact in complex terrain and diverse vegetation areas. This method enhances the spatial resolution of AVHRR NDVI and significantly improves the accuracy of monitoring nationwide vegetation index changes over 30 years. The findings establish a solid scientific foundation for implementing ecological conservation measures and promoting sustainable vegetation growth.

We describe an RF-photonic system architecture to measure microwave spectrum over a wide bandwidth with high resolution. The approach relies on the up-conversion of the electronic signal to the optical domain using a high-speed electro-optic modulator, and an arrayed-waveguide grating (AWG) for spectral analysis. The modulator is implemented in lithium niobate whereas the AWG takes the form of a photonic integrated circuit fabricated in a silicon-on-insulator material platform. We present experimental results showing high-frequency response, in excess of 200 GHz, for the modulator, and broadband response of the AWG with the free spectral range of 81 GHz. The system is suitable for the measurement of atmospheric radiation between the oxygen and water vapor absorption lines for the monitoring of the planetary boundary layer to aid in weather prediction and the modeling of climate dynamics.

The localization of electromagnetic radiation leakage through cabin gaps is a critical and challenging aspect of electromagnetic compatibility design for spacecraft. This paper proposes a localization method based on synthetic aperture interferometric passive radiometry imaging. Electromagnetic radiation signals are measured at a certain distance from the spacecraft surface to form visibility samples. These visibility samples and the modified brightness temperature of the spacecraft surface electromagnetic radiation leakage constitute a pair for Fourier transform. The electromagnetic leakage location image of spacecraft surface is obtained through inverse Fourier transform. A sparse sampling method based on particle swarm optimization is proposed to improve testing efficiency. The impacts of various factors, including positional parameters, positioning accuracy of the test antenna, and scanning parameters on the imaging results are analyzed. Experiments are conducted on a cabin with 1 m $\times 1$ m $\times 1$ m having 51 holes on one surface. The algorithm proposed in this article is validated to effectively image and locate electromagnetic leakage points at different frequencies, with an absolute positioning error not exceeding 16mm. In addition, the effectiveness of the sparse sampling method is confirmed. Hence, it can accurately locate the position of electromagnetic leakage by saving 83.3% of the test time, with a misjudgment rate of 5.9%.

A new forward model (FM) was developed to characterize the influence of precipitation on L-band passive ocean surface measurements. The FM, which relates rain-induced brightness temperature (TB) variations to the rain rate and wind speed (WS), was established through a machine learning approach (referred to as the ML-FM). The soil moisture active passive (SMAP) data matched with integrated multisatellite retrievals for global precipitation measurement (IMERG) rain rate data and cross-calibrated multiplatform (CCMP) wind data were binned as a function of the rain rate, WS, and wind direction. The ML-FM was validated by comparing the simulated top-of-atmosphere (TOA) TB values with SMAP measurements. The results showed favorable agreement between the ML-FM outputs and SMAP data, with a root mean square error (RMSE) smaller than 0.55 K for both the horizontal and vertical polarizations. The validation results for ensuring more reasonable rainfall intensity distributions showed that the ML-FM returned stable results with a slightly reduced RMSE of ∼0.75 K for both the horizontal and vertical polarizations. Based on the ML-FM, we found that sea surface emission exhibited significant dependence on the rain rate for both polarizations. In addition, the ML-FM demonstrated signal saturation when the rain rate exceeded 45 mm/h, while precipitation slightly affected the directional characteristics of sea surface emission. These effects accounted for ∼0.3 K at a rain rate of 50 mm/h. Overall, our analyses demonstrated that the proposed ML-FM achieved superior performance in retrieving the TOA TB for both the vertical and horizontal polarizations with a higher accuracy than existing models.

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.

The Moon-based Earth Radiation budget Detector (MERD) is an instrument designed to measure the Earth-reflected Solar Radiation (ERSR) and the Earth thermal radiation from the Moon as part of future mission of China's Chang'e project. The accuracy of its calibration technology directly determines the measurement precision. For traceability to International System of Units (SI) and improve the calibration accuracy in the ERSR wave band, a vacuum shortwave radiance calibration system is built, which consists of an in-situ traceable Electrical Substitution Transfer Radiometer (ESTR) and a Shortwave Calibration Light Source (SWCLS). Where, SWCLS is designed with a electrically variable slit and a 12-hole narrow-band filter wheel to achieve continuous broad-spectrum radiance output and discrete narrow-spectrum band tuning, respectively. Additionally, wide dynamic range output is from the introduction of elliptic land spherical mirrors for bilateral highly efficient focusing structure. This paper presents the mathematical process of shortwave calibration and focuses on the design and analysis of SWCLS based on high stability, radiance and uniformity. Results show that light source stability is 0.05922%/h, radiance uniformity is 99.49% at the calibration distance of 300 mm, the range of output radiant brightness in the spectral range 0.3–2.5 μm is 0.03–1272.20 W/m2/sr.

Optimizing the spatial distribution of urban functional zones (UFZs) effectively improves the thermal environment. This study utilized an enhanced regression tree model and relied on Ecosystem Spaceborne Thermal Radiometer Experiment data to analyze the relative contributions and marginal effects of 2-D/3-D urban morphological factors on the diurnal land surface temperature (LST) in Shenyang, China. The results showed that public and residential areas dominated Shenyang's UFZs. The temperature in industrial areas was the highest during the day, and residential and commercial functional areas are high-temperature concentration areas. Furthermore, the effects of the urban spatial morphology on the LST differed between diverse time points and UFZs. The digital elevation model and the normalized difference vegetation index contributed significantly to daytime and nighttime LSTs. Construction indicators, such as the normalized difference built-up index and the proportion of construction land, significantly impacted commercial services. Residential daytime LST had a large contribution value, and the sum of its contribution rates reached approximately 30%. Population greatly contributed to the nighttime LST of the industrial and residential zones, accounting for 16.77% and 22.06%, respectively. Vegetation contributed to the cooling effect on daytime LST in summer, especially in industrial areas, contributing 29.79%. In addition, 3-D indicators, such as building height and building density, contributed to diurnal LST. Finally, when the proportion of construction land reached approximately 45%, it negatively affected LST. In this study, the main factors affecting day and night LSTs were identified, and this work acts as a relevant strategic reference for alleviating the urban heat island effect.

Global-scale remotely sensed nighttime light (NTL) data, such as the Suomi National Polar-orbiting Partnership with the Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) Day/Night Band (DNB) NTL data, has been widely applied across multiple disciplines. However, its broader application is still limited by its coarse spatial resolution. We proposed the NTL conditional multiscale downscaling model (NTL-CMDM) for downscaling NPP/VIIRS DNB. The model uses multisource scale factors as conditional constraints, progressively integrating NTL and scale factors to downscale NPP/VIIRS DNB from 500 to 130 m using data from 201 Chinese cities. The downscaled results were validated against the 130 m Loujia1-01 suggest that the NTL data quality was improved after downscaling, yielding higher the coefficient of determination (R: 0.407 versus 0.702) and lower root-mean-square error (RMSE: 7.020 versus 26.424 nWcm−2sr−1) values than those of the original NPP/VIIRS DNB. The downscaled results exhibit richer NTL feature details and show similarity to Luojia-1-01. More importantly, the downscaling enhances the accuracy of NTL statistical metrics, improving illuminated area by 10.23% and radiance estimation by 6.12%. Furthermore, the usability of the downscaled results was assessed by estimating county-level GDP. The GDP estimates based on the downscaled data were superior to those from the original NPP/VIIRS DNB data and consistent with the estimates obtained from Luojia1-01. Finally, generalization ability test using different algorithms in multiple cities demonstrate that NTL-CMDM is robust to cities with different NTL structures. The study verifies the practicability of employing deep learning methods to downscale NTL data, providing a feasible pathway for acquiring high-resolution NTL data over an expanded area.

We describe a method to estimate coefficients ${{{\bm{h}}}_{\bm{n}}}$ of a power spectral density of the form ${\bm{\Sigma}}{{{\bm{h}}}_{\bm{n}}}/{{{\bm{f}}}^{\bm{n}}}$ (${\bm{f}}\;{\bm{\ }}\text{is}$ the frequency and integer ${\bm{n}} \geq 0$) from corresponding measured time series with periodic gaps. This technique is applied to consistently estimate the amount of $1/{\bm{f}}$ noise present in weather system follow-on microwave microwave imager's (MWI) channels from the time-series data collected with different periodic gaps during prelaunch ground tests. The method assumes that the power spectrum of $1/{\bm{f}}$ noise present in MWI can be represented as a second-order frequency polynomial model of the form ${\bm{\Sigma}}{{{\bm{h}}}_{\bm{n}}}/{{{\bm{f}}}^{\bm{n}}}{\bm{\ }}$ and attempts to retrieve the true spectrum by solving for the ${{{\bm{h}}}_{\bm{n}}}$ coefficients using the power spectrum of the time-series data with periodic gaps. The method also assumes that the periodicity and duration of the data gaps are known and consistent for a given time series. The theoretical basis of the new technique is derived and tested using simulation and the new procedure is then applied to real test data to estimate the coefficients of the frequency polynomial. As a quantitative estimate for the $1/{\bm{f}}$ noise, the radiometer gain fluctuation (${\bm{\Delta}}{\bm{G}}/{\bm{G}}$) at 1 Hz is then solved from the frequency polynomial of the gain fluctuation power spectrum. The 1 Hz $( {{\bm{\Delta}}{\bm{G}}/{\bm{G}}} )$ values were compared between two sets of ground test data for the same MWI channels but with large (92%) and small (4.89%) duty cycles. The similarity of the 1 Hz $( {{\bm{\Delta}}{\bm{G}}/{\bm{G}}} )$ values extracted from these two disparate datasets establishes confidence in the method. The derived noise power spectrum is then used to simulate MWIs radiometric brightness temperature images and predict the level of unwanted striping in the flight data due to $1/{\bm{f}}$ noise content. This method may be applicable to solve for the polynomial coefficients of the power spectrum of any noise process, which can be modeled as a frequency polynomial, given the polynomial form is known a priori.

Microwave remote sensing technology has emerged to provide valuable products to monitor and assess soil moisture content at regional or global scales. However, each soil moisture product exhibits different advantages and shortcomings. Data fusion could help improve accuracy by merging information from different sources. In this research, a traditional triple collocation (TC) based method and a novel long short term memory network (LSTM) are used to merge soil moisture products from the soil moisture active passive mission, Advanced Microwave Scanning Radiometer 2 (AMSR2), and The Advanced SCATterometer for a study area located in western Europe. This research reveals that the LSTM outperforms the traditional TC based method for data fusion. The study identifies that both climate forcing and physiographic attributes significantly influence the spatial and temporal variations observed in the LSTM merging scheme. Consequently, the study highlights the considerable potential of the LSTM method for large-scale integration of remote sensing soil moisture data.

Soil moisture (SM) is a critical parameter in understanding the Earth's hydrological cycle and managing water resources. Remote sensing instruments, such as Advanced SCATterometer (ASCAT), can provide valuable long-term SM. However, compatibility issues may arise when integrating ASCAT SM retrieval with another retrieval, such as soil moisture active passive (SMAP), a high-quality microwave radiometer-based SM retrieval. In this study, we propose a novel image-to-image translation approach based on the U-Net architecture to convert ASCAT SM data into the format of SMAP (ASCAT2SMAP). The resulting SM from the ASCAT2SMAP was evaluated using temporally separated SMAP data and independent in-situ SM measurement from the International Soil Moisture Network (ISMN). In the separately divided test periods, ASCAT2SMAP showed good agreement with SMAP with R of 928, ubRMSD of 0.043 $\text{m}^{3}/\text{m}^{3}$, and bias of 0.002 $\text{m}^{3}/\text{m}^{3}$. When evaluating ASCAT2SMAP with ISMN data, it showed a better agreement than ASCAT and more similar metrics with SMAP. Moreover, we found that the ASCAT2SMAP is more robust to a problem of subsurface scattering than the original ASCAT SM. When simulating V-polarized brightness temperature from ASCAT2SMAP SM, it showed good agreement with ubRMSD of 5.602 K and bias of −0.135 K. Our results are expected to provide a valuable perspective preceding to creation of harmonized SM datasets from different sensors, contributing to improved data integration and analysis in the field of geoscience and remote sensing.

Soil moisture is a critical variable in climate forecasting, hydrology, and others. Satellite-based remote sensing techniques have been used to map soil moisture globally, including visual bands, infrared, Synthetic Aperture Radar (SAR), passive microwave remote sensing, etc. Passive microwave remote sensing techniques, especially at the L-band, such as the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP), have higher accuracy than the others due to their higher sensitivity to the dielectric constant of the soil profile. However, the unclear sensing depth at L-band for SMOS and SMAP leads to a mismatch in the calibration/validation and application of their soil moisture products. In this study, we apply soil temperature sensing depth model, i.e., the tau-z model, to the soil temperature, soil moisture, and brightness temperature (TB) data collected during the Soil Moisture Experiment in the Luan River (SMELR). The effectiveness of the tau-z model in interpreting the L-band microwave observations is validated through forward simulations with the Community Microwave Emission Modelling (CMEM). Results showed that: 1) the bias in TB simulation can be reduced from 26.22 K/12.00 K to -11.66 K/-8.839 K for H/V-polarization; 2) the RMSE is reduced from 30.2 K/20.48 K to 12.92 K/11.66 K for H/V-polarization by considering the microwave sensing depth. Daily TB signal variation partly attributed to the soil temperature sensing depth due to different bands' penetration capacity. The result is expected to improve the understanding of microwave data collected by muli-frequency synthetic platforms such as Copernicus Microwave Imaging Radiometer (CIMR).

High mountain Asia (HMA) holds the largest concentration of glaciers outside the polar regions, and the melting glaciers provide freshwater for more than one billion people. Therefore, it is crucial to estimate glacier mass changes over HMA. Though Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) can accurately monitor monthly global mass changes, the low spatial resolution of ∼300 km limits the ability to reveal detailed spatiotemporal characteristics of mountain glacier changes. In this contribution, we derived a high-resolution (0.5°×0.5°) mascon (mass concentration) solution from April 2002 to August 2023, which is innovatively constrained by multisource prior information from advanced spaceborne thermal emission and reflection radiometer, satellite altimetry, and WaterGAP global hydrology model. Therefore, the developed mascon solution shows higher spatial resolution than the three state-of-the-art global mascon solutions regarding the spatial distribution of mass change trends. Moreover, our mascon solution is highly correlated with in situ measurements at four stations compared to the three mascon solutions in capturing the interannual mass variations with an average correlation coefficient of 0.60. Then, we further investigate the spatiotemporal mass change characteristics in HMA with the developed high-spatial resolution mascon solution. Results show that the Central Himalayas, Western Himalayas, and Nyainqentanglha experienced the most substantial losses from 2002 to 2023, with trends of 5.46 ± 0.42, 3.92 ± 0.80, and 3.79 ± 1.20 Gt/yr, respectively. Additionally, seasonal variations’ magnitudes and temporal patterns vary with regions, exhibiting significant spatial heterogeneity. Our high-spatial resolution mascon solution provides valuable records, unveiling mass changes and potential glacier instability hidden in low-spatial resolution solutions. This enhancement strengthens the capabilities of GRACE/GRACE-FO in comprehending the global glacier response to climate change.

Marius Hills volcanic complex (MHVC) comprises one of the densest volcanic structures on the Moon and is of significant interest for comprehending lunar volcanism. In this article, brightness temperature (TB), normalized TB, and brightness temperature difference were first generated based on microwave radiometer (MRM) data from the ChangE-2 Lunar Orbiter and (FeO+TiO2) abundance was calculated from Clementine ultraviolet-visible data to investigate the microwave thermal emissions and evolutionary history of the MHVC. The results are as follows. First, surface composition derived from the optical data is strongly influenced by impact events, and MRM data can better reflect the thermophysical properties of surface deposits. Second, a notable concentration of high TB values at daytime suggests an enrichment of volcanic activities. Third, later volcanic activities are indicated by the TB performances at daytime and night in multiple regions. Finally, a hot anomaly was detected by MRM data, suggesting the higher substrate temperature and the existence of a potential hidden volcanic structure. The results suggest that MRM data provide new insights into the evolutionary history of the MHVC, as well as an important perspective for lunar thermal evolution.

Ice surface temperature (IST) plays a fundamental role in the Antarctic ice sheet/shelf study. However, the production of spatially and temporally continuous Antarctic IST products remains a challenge. We proposed an instantaneous IST retrieval framework that can generate the spatially and temporally continuous Antarctic IST using Advanced Microwave Scanning Radiometer 2 data. To generate a temporally continuous IST product, we developed an innovative scheme, which was based on the acquisition time difference between input and output data. We considered the impact of terrain and sensor observation state. The corresponding parameters were used as the auxiliary variables to improve the model accuracy. We trained and validated nine machine learning models using the generated sample set. The Light Gradient Boosting Machine (LightGBM) model presents the best performance, and the root-mean-square error (RMSE) of the LightGBM model is only half of that of the typical linear models. The RMSE of the LightGBM model decreased with the training sample set size and stabilized at 1.67 K. Further validation using multisource data showed that the IST retrieved using the LightGBM model has RMSEs of 1.39–2.32 K (relative to IST from Landsat-8) and 3.7–5.9 K (relative to IST from Baseline Surface Radiation Network data). Compared to the commonly used ERA5 IST data, the retrieved IST in this study has higher accuracy. We retrieved Antarctic IST from 2013 to 2020. Antarctic IST decreased continuously from 2013 to 2015. After 2015, Antarctic IST increased with large fluctuations.

Forest fires are one of the most common natural disasters and public crisis events, presenting a serious threat to the ecosystem and human security. The traditional forest fire monitoring is time-consuming and inaccurate, but nighttime light remote sensing is enhancing the efficiency and speed of forest fire detection. Thus, this study focused on forest fire detection in Southwest China that was affected by fires in 2021 and 2022 using daily remote sensing nighttime light data from the black marble product VNP46A2 of the Suomi National Polar-Orbiting Partnership-Visible Infrared Imaging Radiometer Suite. Multiple features were developed based on the temporal features of day-level nighttime light radiances, which were divided into black pixels, stable light pixels, and forest fire pixels using the random forest classification model. To verify the accuracy, a confusion matrix was constructed to calculate the accuracy of the forest fire identification results by combining multisource data, and the results were compared with the results of different machine learning models and fire products. Moreover, the spatiotemporal distribution of forest fires was analyzed using trend analysis and nearest-neighbor analysis. Results show that the overall accuracy of pixel classification was over 92% in both years, with forest fire pixels classified with user's accuracy exceeding 99%. The forest fires were mainly dispersed and concentrated around the stable light pixels 400–700 m, and the distribution in different regions showed obvious spatial heterogeneity. Compared with 2021, the distribution of forest fires decreased significantly in 2022. This study can support the future management and protection of forest resources and the prediction of fire disasters. The dataset (2021 and 2022) of forest fires in Southwest China is available free of charge.1

The area far from the Procellarum KREEP Terrane (PKT) is important for future landing exploration. Mare Crisium, located in the area, has been identified as an essential landing option for the Lunar Geophysical Network (LGN) mission. Utilizing high-resolution images, topographic data, surface compositions, and Chang'e-2 microwave radiometer (MRM) data, a systematic analysis and comparison have been done between Maria Crisium and Smythii in terms of geomorphological feature, regolith properties, lunar crust and mantle properties, and microwave thermal emission characteristics. The results indicate that, except for investigating central magnetic anomaly in situ, Mare Smythii can complete all the scientific goals in Mare Crisium proposed by predecessors. Moreover, Mare Smythii has more complex geomorphology; the regolith here has a high detection priority including the cryptomare, swirls, and the probable water ice and volatile; and there exists the MRM anomaly, probably representing a new material that has not been detected until now. Generally, Mare Smythii should have a high priority for future exploration of the lunar surface in the region far from the PKT.

Soil moisture (SM) is crucial for the Earth's ecosystem, impacting climate and vegetation health. Obtaining in situ observations of SM is labor-intensive and complex, particularly in remote and densely vegetated regions like the Amazon rainforest. NASA's soil moisture active and passive (SMAP) mission, utilizing an L-band radiometer, aims to monitor global SM. While it has been validated in areas with low vegetation water content (VWC) (< 5 ${\text{kgm}}^{ - 2}$), its efficiency in the Amazon, with dense canopies and high VWC (> 10 ${\text{kgm}}^{ - 2}$), is limitedly investigated due to scarce in situ measurements. This study assessed and analyzed the SMAP SM retrievals in the Amazon, employing the single-channel algorithm and adjusting vegetation optical depth (τ) and single scattering albedo (ω), two key vegetation parameters. It incorporated in situ SM observations from three old-growth rainforest locations: Tambopata (Southwest Amazon), Manaus (Central Amazon), and Caxiuana (Eastern Amazon). The SMAP SM deviated substantially from the in situ SM. However, calibrating τ and ω values, characterized by a lower τ, resulted in better agreement with the in situ measurements. This study emphasizes the pressing need for innovative methodologies to accurately retrieve SM in high-VWC regions like the Amazon rainforest using SMAP data.

Although satellite-based soil moisture products are useful at regional and global scales, they do not meet the needs of users who require high-resolution information for applications such as precision agriculture or catchment hydrologic modeling. The advent of uncrewed aerial systems (UASs) has opened new opportunities for bridging this need. We offer one of the very first validation studies on a dry-down event captured by a novel L-band radiometer onboard a Black Swift Technologies fixed-wing S2 UAS. A fallow cornfield at the Beltsville Agricultural Research Center was selected as a study area to validate volumetric soil moisture estimates in mid-April. By leveraging a comparable radiative transfer model to that of soil moisture active passive and soil moisture ocean salinity derived products, brightness temperature retrievals from this sensor are shown to successfully capture a week of dry-down poststorm event, as validated by HydraGO probe estimates (calibrated with gravimetric samples) and a nearby soil climate analysis network station. However, with a reported spatially averaged bias of −0.107 m3/m3 (compared to calibrated moisture values) and an ubRMSE of 0.028 m3/m3 potential concerns remain regarding the sensor calibration, vegetation and surface roughness corrections, and georeferencing. Nevertheless, given the potential of UAS for on-demand, high-resolution soil moisture retrievals, this collaborative effort provides critical feedback for informing future applications and improvements in the field of passive microwave remote sensing.

Dust deposited on the surface of snow and glaciers can significantly reduce the snow and ice albedo and accelerate melting. Manual observations of the dust mass concentration (DMC) on snow and glacier surfaces are routinely performed at many locations worldwide. However, snow and ice surface DMC monitoring methods based on remote sensing data still face challenges. This study presents a new retrieval scheme for estimating dust load on snow-covered surfaces from a moderate-resolution imaging spectroradiometer and visible infrared imaging radiometer suite in Northeast and Northwest China that utilizes a classical snow radiative transfer model. Our results indicate that the coefficient of variation of DMC retrieved from the in situ measurements of snow spectral reflectance is 4%, which is within a +4% difference compared with DMC observed in snow and ice samples. Estimating atmospheric dust pollutants content deposited on snow surfaces based on satellite remote sensing observations is feasible. In Northwest China, the root-mean-square error (RMSE) of the DMC values retrieved from VNP09GA data is 9.78 ppm, while that of the DMC values retrieved from MOD09GA data is 13.74 ppm. In Northeast China, the RMSE of the DMC values retrieved from VNP09GA data is 73.98 ppm, while that of the DMC values retrieved from MOD09GA data is 184.32 ppm. The research results can realize continuous monitoring of the atmospheric dust pollutants deposited on snow surfaces, which is of great practical significance to understanding and studying the pollution process of atmospheric dust on snow.

A new approach based on deep learning methods is presented for reconstructing $L$-band brightness temperature images from the inversion of interferometric data, namely, complex visibilities here simulated from observations of the Soil Moisture and Ocean Salinity (SMOS) interferometric radiometer. A specific deep neural network (DNN) architecture composed of a fully connected layer followed by a contracting and expansive path is proposed to learn the relationship between the simulated visibilities and the brightness temperature maps. The performances of the DNN are compared with those of algebraic inversions based on Fourier theory, which are all affected by strong aliases in the synthesized field of view (FOV), as a consequence of the spacing between the elementary antennas of SMOS, which does not satisfy the Nyquist criteria. In the alias-free FOV (AFFOV) of the algebraic reconstructions, these latter are outperformed by the DNN reconstructions: average mean absolute error (MAE) of about 0.7 K for the DNN instead of 3.7 K. Outside the AFFOV of the algebraic reconstructions, the DNN reconstructions do not show significant signs of field aliasing although the MAE increases: average MAE of about 1.5 K in the whole FOV. An analysis of the role of different neurons in the hidden layers is presented, and it is shown that some neurons are specialized in reconstructing what corresponds roughly to the AFFOV region of algebraic approaches, while other neurons are specialized in dealing with the external regions of this AFFOV.

The NASA-launched Soil Moisture Active Passive satellite mission (SMAP) had the objective to globally characterize soil moisture with an intermediate resolution (9 km), through the integration of radar (3 km) and radiometer (36 km) observations. The SMAP team has evaluated various downscaling techniques to achieve this goal. This study examined the performance of an additional downscaling technique, the Bayesian merging method, as an alternative candidate approach. This method breaks from the standard linear downscaling techniques of SMAP, opting instead for a more innovative approach based on Bayes' theorem. Here, the intermediate resolution soil moisture is achieved via the incorporation of a background estimate, which is refined through comparison between observed and predicted brightness temperatures and backscatter coefficients that link the high- and low-resolution data. However, it is crucial to assess the robustness of the Bayesian method using actual satellite observations, in addition to its prior evaluation using synthetic datasets. The fourth Soil Moisture Active Passive Experiment (SMAPEx-4), conducted in Australia, represented the sole occasion for concurrent high-resolution airborne observations during operation of the SMAP radar. As such, this study employed the Bayesian algorithm using the SMAP datasets throughout the SMAPEx-4 period. Downscaled soil moisture products from this method, as well as from the official baseline and enhancement techniques, were compared. The average root-mean-square-error and R2 of the 9 km downscaled soil moisture were found to be 0.035 cm3/cm3 and 0.55 for the Bayesian method, 0.093 cm3/cm3 and 0.35 for the baseline, and 0.069 cm3/cm3 and 0.41 for the enhancement method.

The advanced technology microwave sounder (ATMS) is an important satellite instrument that provides vital data on atmosphere temperature and moisture for weather forecasting and climate research, and helps us plan for extreme weather. However, its coarse resolution and angular dependence have long been a challenge for improving image visualization. This article proposes a method to enhance the imagery visualization for ATMS, combining limb correction (LC) with artificial intelligence (AI) resolution enhancement (RE). Measurement data from the ATMS onboard NOAA-20 were utilized to train the LC method, which were then validated using newly acquired NOAA-21 ATMS data. The AI RE was performed using enhanced super-resolution generative adversarial networks, which increased the pixel resolution by a factor of four. The high-resolution (HR) Advanced Microwave Scanning Radiometer 2 data served as a reference to initially and quantitatively evaluate the RE method. The combined method of LC and AI RE produced an angular-dependence-free and HR ATMS image, resulting in a significant improvement in image visualization, including surface and atmosphere information, and allows for clear identification of severe weather events. For the swift identification and analysis of tropical cyclones in the upcoming season, as of this writing, this proposed method has been routinely employed to produce high-quality global ATMS images, and these images are showcased and tested in the NOAA internal HR imagery visualization system—JSTAR Mapper. Moreover, concentrated efforts are being made to further enhance these images in preparation for an official release.

The correction of sea surface brightness temperature is crucial for improving the accuracy of sea surface salinity (SSS) retrieval by L-band microwave radiometer. However, the traditional method of correcting brightness temperature using only wind speed and significant wave height (SWH) is inadequate, as sea surface roughness is affected by multiple factors. The Global Navigation Satellite System Reflectometer (GNSS-R) observables, which directly respond to sea surface roughness, have been preliminarily validated in ground-based experiments for their potential to correct sea surface brightness temperature. Compared with ground-based GNSS-R, spaceborne GNSS-R has a wider coverage and can better support the brightness temperature correction of spaceborne L-band microwave radiometers. This article has preliminarily verified the correlation between cyclone GNSS (CYGNSS) observables and brightness temperature variations, and found that the incidence angle of the observable needs to be taken into account when retrieving SSS jointly with soil moisture active and passive (SMAP) and CYGNSS. A multilayer perceptron (MLP) model was established to assess the SSS retrieval performance of SMAP combined with different parameters. The results show that the retrieval performance based on the MLP model is better than that based on the geophysical model function model. Compared with joint wind speed and SWH, joint CYGNSS observables performs better in retrieving SSS. The root mean square error of retrieval salinity decreased from 0.58 to 0.46 psu, and the correlation coefficient (R) increased from 0.83 to 0.90. This provides reference for future joint retrieval of SSS using L-band microwave radiometers and spaceborne GNSS-R.

FengYun-3E (FY3E) is the world's first dust-dawn orbiting meteorological satellite for civil use, which has filled the vacancy of global early-morning-orbit satellite observation by working together with FengYun-3C and FengYun-3D satellites. The Medium Resolution Spectral Imager-Low Light (MERSI-LL) sensor carried by FY3E can detect surface temperature variation. The radiometric calibration conditions of MERSI-LL thermal infrared bands were evaluated using the collected field measurements and atmospheric transfer simulations during 5–23 December, 2022, at Lake Erhai. A thermal infrared radiometer equipped on an unmanned surface vehicle was used to continuously collect surface-emitted radiance and water temperature. Atmospheric conditions, surface emissivity, and aerosol optical depth measured near-field experiment site were adopted by atmospheric radiative transfer code to calculate the influence of atmosphere on long-wave radiation during the propagation from land surface to satellite aperture. The good in-orbit operational status could be detected according to our calibration experiments, and the calibration analysis suggested that the differences between the simulated brightness temperature and satellite-based brightness temperature are 0.71 K with an RMSE of 0.79 K and 0.11 K with an RMSE of 0.47 K for channels 6 and 7, respectively, which achieved better or similar calibration accuracy by comparing with TIR channels of other FengYun satellites.

This work explores the potential of obtaining high-resolution thermal infrared (TIR) data provided by the Sentinel-2 (S2) & Sentinel-3 (S3) constellation in a typical semiarid agricultural environment. Maps of land surface temperature (LST) with 10–20 m spatial resolution were obtained from the synergy S2–S3 in the Barrax test site in Spain, for a set of 14 different dates in the summers of 2018–2019. Ground measurements of LST transects covering a variety of croplands and surface conditions were used for a ground validation of the disaggregation approaches. A cross validation of the LST products was also conducted using Landsat-8/TIRS images. Two recent approaches exploiting the linkages between shortwave and thermal data were adapted and tested, with differences in the inputs, the physical-mathematical framework, or the treatment of the LST residuals, and two options for the original 1 km S3 LST data were considered. Despite the large range of temperatures registered (295–330 K), differences with observed values resulted in an average RMSE < 3.0 K and a negligible systematic deviation, showing good results even in small fields ∼1 ha. Results confirm the need for appropriate adjustment techniques of the LST residuals obtained to better capture the low temperature conditions. The systematic overestimations introduced by the use of the operational sea and land surface temperature radiometer L2 LST product, and the limitations associated with certain irrigation management are discussed. Results in this work offer a solution to the lack of high-resolution satellite TIR data, and provide new opportunities for LST applications in agricultural areas.

To measure the distinct interaction of the Earth's materials with solar electromagnetic radiation, field spectroradiometers are commonly utilized. These are used to validate spectroradiometers deployed on various platforms through comparison exercises. Following metrology standards, the inclusion of uncertainties is required. Thus, field spectroradiometers need to be calibrated regularly against traceable radiance sources. In this article, we present a laboratory radiometric calibration protocol for the calibration of a heliosphere integrating sphere to make it traceable to the International System of Units as well as to establish an uncertainty budget. We adopted a transfer radiometer approach including four spectroradiometers that were calibrated at the Deutsches Zentrum für Luft und Raumfahrt Radiometric Standard facility before transferring that calibration to the heliosphere. After considering various sources of uncertainty by employing an uncertainty tree diagram approach, we arrive at an overall propagated uncertainty of approximately 1.5%. In future publications, we will present how to extend the traceability to other attenuations provided by the heliosphere. Its application to the calibration of a field spectroradiometer will be the focus of a future publication.

P-band radiometry has been demonstrated to have a deeper sensing depth than L-band, making the consideration of multilayer microwave interactions necessary. In addition, the scattering and phase interference effects are different at the P-band, requiring a reconsideration of the need for coherent models. However, the impact remains to be clarified, and understanding the validity and limitations of these models at both L- and P-bands is crucial for their refinement and application. Therefore, two general categories of microwave emission models, including two stratified coherent models (Njoku and Wilhite) and four incoherent models (conventional tau-omega model and three multilayer models being zero-order, first-order, and incoherent solution), were intercompared for the first time on the same dataset. This evaluation utilized observations of L- and P-bands radiometry under different land cover conditions from a tower-based experiment in Victoria, Australia. Model estimations of brightness temperature (TB) were consistent with measurements, with the lowest root mean square error (RMSE) at P-band V-polarization under corn (2 K) and the highest RMSE at L-band H-polarization under bare soil (13 K). Coherent models performed slightly better than incoherent models under bare soil (3 K less RMSE), while the opposite was true under vegetated soil conditions (1 K less RMSE). Coherent and incoherent models showed maximum differences (3 K at P-band and 2 K at L-band), correlating strongly with soil moisture variations at 0–10 cm. Findings suggest that coherent and incoherent models performed similarly; thus, incoherent models may be preferable for estimating TB at L- and P-bands due to reduced computational complexity. [ABSTRACT FROM AUTHOR]

Based on delay-Doppler maps (DDMs) in raw counts generated by spaceborne global navigation satellite system reflectometry (GNSS-R) receivers, retrieving ocean surface wind speeds is feasible, so several spaceborne GNSS-R missions have been carried out. However, it is currently troubled by global data latency of several hours or even more due to the bottleneck in the satellite downlink. Consequently, this article, for the first time, presents an algorithm for spaceborne GNSS-R receivers to conduct the DDM calibration in orbit and then to retrieve ocean surface wind speeds in real time, which contributes to not only lightening the burden on downloading a wealth of scientific data but also broadcasting real-time ocean surface wind speeds to users. Since there is a power correlation between direct and reflected signals from the same GNSS satellite with respect to the GNSS-R receiver, this algorithm calibrates direct signal power first, and then it estimates the real-time GNSS transmitter effective isotropic radiated power at the reflected signal according to the normalized antenna pattern of the corresponding GNSS satellite. Afterward, DDMs in raw counts are calibrated. Finally, ocean surface wind speeds are computed using pretrained geophysical model functions. Exploiting the scientific data from the GNOS-II onboard China's FY-3E satellite, this algorithm is validated carefully, and final retrieved ocean surface wind speeds against collocated European Centre for Medium-Range Weather Forecasts wind speeds have an overall root-mean-square error of 1.68 m/s and 1.50 m/s for GPS-R and BDS-R, respectively. [ABSTRACT FROM AUTHOR]