Passive Microwave Radiometry Research Articles

All weather land surface temperature estimation by combining thermal infrared and passive microwave radiometry: a study over India

ABSTRACT Land Surface Temperature (LST) plays a crucial role in water and energy cycle studies. However, clouds pose a significant challenge in obtaining continuous LST time series from Thermal Infrared (TIR) sensors. To overcome this challenge, this study leverages the potential of Passive Microwave Radiometry (PMR), which offers all-weather observation capabilities, albeit at a coarser spatial resolution to estimate all-weather LST over India. In this study, we trained a Random Forest (RF) model using clear sky LST from either Moderate Resolution Spectro Radiometer (MODIS) or Visible Infrared Imaging Radiometer Suite (VIIRS) and passive microwave observations from Advanced Microwave Scanning Radiometer-2 (AMSR2), enabling LST estimation at a 1 km spatial resolution under clear and cloudy conditions. The performance of the models was evaluated by comparing the RF simulated LST with observed LST data from MODIS and VIIRS satellites. The Root Mean Square Error (RMSE) for the RF models was found to be 2.17 K and 2.29 K respectively, with coefficient of determination (R2) values of 0.97 for both the models. Furthermore, comparisons with in-situ observations resulted in RMSE values of 2.24–3.70 K and 2.40–4.07 K for clear sky and cloudy sky LST, respectively, for MODIS. Similarly, for the VIIRS LST prediction model, the RMSE values were 2.61–3.51 K and 2.60–3.97 K for clear and cloudy sky conditions. These findings demonstrate the potential of using passive microwave radiometers to estimate LST and highlight the applicability of the RF model for LST estimation under overcast conditions.

From Anatolian Plateau to American Plains: A transcontinental assessment of the EUMETSAT H SAF’s new generation snow water equivalent product over Türkiye and the conterminous U.S.

The main frame of this paper is to present the first validation results of the new generation hemi-spherical daily snow water equivalent (SWE) product of the EUMETSAT H SAF, namely, H65. It utilizes data from passive microwave radiometry sensors to estimate SWE. Operating at a suitable spatial scale, it offers insights into snow accumulation and melting dynamics, advancing satellite-based snow monitoring across diverse regions. The validation period covers the 2021 snow year, from January to March 2021, during which the dry snow conditions prevail. The validation is conducted in two distinct geographic regions, Türkiye, and the conterminous U.S. For Türkiye, the in-situ snow depth measurements provided by the Turkish State Meteorological Service are employed. On the other hand, the 1-km gridded SWE dataset of NOAA National Ice and Snow Data Center is used in the validation over the U.S. The validation results over Türkiye yields an overall RMSE of 39.27 mm, whereas it reads 15.19 mm for the U.S. These results indicate that the H65 SWE product complies with the product requirement thresholds for both flat (40 mm) and mountainous (45 mm) areas.

A Simulation of Snow on Antarctic Sea Ice Based on Satellite Data and Climate Reanalyses

AbstractAlthough snow plays an important role in the energy and mass balance of sea ice, it is little studied in the Southern Ocean. We present a Lagrangian model of snow on sea ice, CASSIS, that simulates the daily creation and drift of floes. Drifting floes accumulate snow from the atmosphere and the Antarctic ice sheet, and lose snow to the ocean and snow‐ice formation. The depth of snow on Southern Ocean sea ice increases in all sectors between autumn and spring 1981–2021, reaching 40 cm in much of the Weddell Sea, coastal Amundsen Sea and south east Indian Ocean. The root mean square difference between seasonally‐averaged model and ship‐based snow depths is 13.1 cm, and between modeled and airborne snow depths from Operation IceBridge is 13.5 cm. Our model offers an alternative long‐term snow depth record to that from passive microwave (PM) radiometry, which does not capture the seasonal growth of the snow cover. We find that although the average circumpolar snow layer thickness has increased by 16 mm between 1981 and 2021 (P = 0.004), there has been a decrease of 13 mm in the Southern Pacific Ocean (P = 0.133, but significant in spring and autumn), driven by a reduction of summer sea ice extent in this region. Our model paves the way for improved satellite‐based estimates of Antarctic sea ice thickness.

Helios and Juliette: Two falsely acclaimed medicanes?

The present work analyzes the synoptic, thermodynamic, and microphysics characteristics of two Mediterranean cyclones that occurred in February–March 2023. The analysis is mainly carried out through the use of passive microwave (PMW) satellite measurements, which allow us to follow the cyclones' evolution and state whether Helios and Juliette can be considered as Mediterranean tropical-like cyclones (i.e., medicanes). Both cyclones show a very similar evolution, with a low-stratospheric warm air anomaly during the development phase, followed by the development of a warm anomaly in the low−/mid-troposphere. This feature is often observed in medicanes (e.g., Qendresa, Zorbas), except for few cases (i.e., Medicane Ianos, which shows a warm core (WC) development clearly driven by diabatic processes without a preliminary warming signal in the lower stratosphere and upper troposphere). The analysis carried out highlights that, while Helios maintains this setting through its whole lifetime, Juliette undergoes tropical transition in the final stage of its evolution. As opposed to most medicane cases, the PMW precipitation microphysics diagnostics shows the predominance of shallow clouds, with almost total absence of ice hydrometeors and deep convection in the proximity of the WC center (i.e., within 100 km radius) for Helios and during the initial stage of Juliette. PMW radiometry provides strong indication that diabatic heating plays a role in the WC development when the onset of deep convection features is identified in the proximity of the Juliette cyclone center. Moreover, the PMW cloud-top height product does not show a closed cloud-free eye for Helios, while it is observed for the final stage of Juliette as often happens during medicanes' mature phase. Therefore, we deem that while Helios can be labeled as a warm seclusion, Juliette can be included in the tropical-like cyclone category.

Age-Related Changes in the Temperature of the Lumbar Spine Measured by Passive Microwave Radiometry (MWR)

A study was conducted to determine the age dependence of the temperature of the low back in the region of the five lumbar vertebrae by using passive microwave radiometry (MWR). The rationale for the study is that the infrared brightness on which the temperature measurement is based will be dependent upon blood circulation and thus on metabolic, vascular, and other regulatory factors. The brightness and infrared temperatures were determined in five zones above each of the medial, left, and right lateral projections of the vertebrae. A total of 115 healthy subjects were recruited, aged between 18 and 84 years. No significant differences in infrared temperature were detected. As predicted, brightness temperature increased until 25 years old and then gradually decreased. In subjects over 70 years of age, compared with those aged 60–70 years, there is a significant increase in brightness temperature at the level of 3–5 lumbar vertebrae by 0.3–0.7 °C. This is interpreted as indicating that individuals who have lived to an advanced age successfully maintain metabolic and regenerative processes. The benchmark data that has been obtained can be usefully employed in future studies of the aetiology of low back pain. In particular, the prospect exists for the technology to be used to provide a non-invasive biomarker to evaluate the effectiveness of antiaging therapies.

Passive Microwave Radiometry at Different Frequency Bands for River Discharge Retrievals

AbstractThe present era of climate change and expanding population requires major improvements in sustained observation of global river discharge. Floods and droughts are affecting food supplies, and suspected long‐term trends require appropriate data for evaluation. Orbital remote sensing can address this observational need. Here we use satellite Ka‐ (36.5 GHz) and L‐band (1–2 GHz) passive microwave radiometry (PMR) to monitor river discharge changes and determine what size rivers can be measured and the frequencies and polarization configurations that yield the most robust results. Selected satellite gauging reaches (SGRs) can be measured at near‐daily intervals from 1998 to present (Ka‐band) and 2010 to present (L‐band). The SGRs are 10–36 km in length; the dynamic proportion of water surface area within each varies with river discharge. Due to contrasting dielectric properties, water and land emit different intensities of microwave radiation; thus emission from a mixed water/land pixel decreases as the proportion of water within the pixel increases. Depending on the river and floodplain morphology, water flow area can be a robust indicator of discharge and the microwave sensors can retrieve daily discharge to ±20%. Instead of spatial resolution, it is the sensor measurement precision, geolocation accuracy, and channel and floodplain morphology that most strongly affect accuracy. Calibration of flow area signals to discharge can be performed using nearby ground stations (even if now discontinued) or by comparison to hydrologic modeling.

Remote detection of a lunar granitic batholith at Compton-Belkovich.

Granites are nearly absent in the Solar System outside of Earth. Achieving granitic compositions in magmatic systems requires multi-stage melting and fractionation, which also increases the concentration of radiogenic elements1. Abundant water and plate tectonics facilitate these processes on Earth, aiding in remelting. Although these drivers are absent on the Moon, small granite samples have been found, but details of their origin and the scale of systems they represent are unknown2. Here we report microwave-wavelength measurements of an anomalously hot geothermal source that is best explained by the presence of an approximately 50-kilometre-diameter granitic system below the thorium-rich farside feature known as Compton-Belkovich. Passive microwave radiometry is sensitive to the integrated thermal gradient to several wavelengths depth. The 3-37-gigahertz antenna temperatures of the Chang'e-1 and Chang'e-2 microwave instruments allow us to measure a peak heat flux of about 180 milliwatts per square metre, which is about 20 times higher than that of the average lunar highlands3,4. The surprising magnitude and geographic extent of this feature imply an Earth-like, evolved granitic system larger than believed possible on the Moon, especially outside of the Procellarum region5. Furthermore, these methods are generalizable: similar uses of passive radiometric data could vastly expand our knowledge of geothermal processes on the Moon and other planetary bodies.

Field of the atmospheric water vapor as a characteristic of heat and dynamic processes at the ocean surface observed by the microwave radiometric means from space

An approach to indication and analysis of heat and dynamic processes at the ocean surface and in the atmosphere with the methods of satellite passive microwave radiometry is considered. It bases on a responsiveness of the oceanic and atmospheric up-going microwave radiation to these processes in the spectral band of its attenuation in the atmosphere water vapor, which seems to be as kind of window of the "radio visibility" from satellites. The effectiveness of that approach is caused by the fact that atmospheric water vapor is an active participant (agent) in its heat interaction with the ocean surface and, at the same time, serves as its reliable quantitative indicator. Measured from satellites, natural microwave radiation of the atmospheric water vapor gives distinct signals of changes occurring in the frontal, storm and cyclonic zones in the ocean; they are manifested in the form of pics or jumps of the brightness temperature. The paper provides various examples of the study of such processes as the ocean-atmosphere heat interaction at the middle latitudes of the North Atlantic, origination and propagation of the tropical hurricanes in the Gulf of Mexico and Caribbean Sea, atmospheric water vapor transport in the tropical Atlantic and its influence on cyclogenesis in the Gulf of Mexico, etc. The data of satellite, ship and buoy measurements are widely used to attain and verify results of our study.

A comparison of river streamflow measurement from optical and passive microwave radiometry

Climate change has a crucial impact on the global energy and water cycle. The hydrological cycle can be studied both from ground and satellite measurements on a global scale. Yet a comprehensive overview is challenging to establish given the spatial and temporal limitations related to various Earth Observation satellite sensors or maintenance of in-situ gauges. Optical remote sensing of visible light can not overcome the substantial obstacle from cloud cover that vastly limits its capability in daily global monitoring. Active satellite sensors like SAR or altimetry are not capable to provide global coverage on a daily basis, therefore, they can be geographically limited. Passive microwave radiometry (PMR) can acquire both daily and global scales that enables the temporally frequent and spatially extensive observations of continental river gauge. Previous studies demonstrated the use of PMR measurements for global daily river gauge benefiting from its high sensitivity of microwave radiation to water presence. This study aims at comparing the methodology of PMR to optical river gauge measurements based on the assumption that at selected locations along the river channel, increase in streamflow is related to increase in the floodplain water surface inundation. Comparison showed a significant obstacle of cloud cover over tropical regions, where PMR has the potential to measure river streamflow. Yet over regions with less clouds both optical and PMR can be good alternative to in-situ streamflow ground measurements.

Cosine Visibility Extension of 1-D Mirrored Aperture Synthesis by a CNN for Spatial Resolution Enhancement

To increase the spatial resolution of passive microwave radiometry, mirrored aperture synthesis (MAS) was presented. In this letter, the method of cosine visibility extension (CVE) is proposed to further enhance the spatial resolution of 1-D MAS. In the CVE method, a convolutional neural network (CNN) is used to learn the distribution of the cosine visibility (CV), specifically the relationship between the low- and high-frequency CV distributions of various scenes. Then, the high-frequency CVs are estimated by the CNN according to the low-frequency CVs obtained by MAS. The high- and low-frequency CVs are combined in MAS image reconstruction to enhance the spatial resolution of MAS. The simulation and experiment indicate that the CVE method can effectively enhance the spatial resolution of MAS.

Passive Microwave Radiometry and microRNA Detection for Breast Cancer Diagnostics.

Breast cancer prevention is an important health issue for women worldwide. In this study, we compared the conventional breast cancer screening exams of mammography and ultrasound with the novel approaches of passive microwave radiometry (MWR) and microRNA (miRNA) analysis. While mammography screening dynamics could be completed in 3-6 months, MWR provided a prediction in a matter of weeks or even days. Moreover, MWR has the potential of being complemented with miRNA diagnostics to further improve its predictive quality. These novel techniques can be used alone or in conjunction with more established techniques to improve early breast cancer diagnosis.

Benchmarking downscaled satellite-based soil moisture products using sparse, point-scale ground observations

While strides have been made in their accuracy and availability, the overall utility of satellite-derived surface soil moisture (SM) datasets derived from passive microwave radiometry is still reduced by their relatively coarse spatial resolution (typically >30 km). In response to this shortcoming, many independent satellite-based SM downscaling approaches have been introduced recently. However, owing to limitations in the spatial sampling characteristics of existing SM ground-monitoring networks, it has proven difficult to obtain reliable reference SM observations at the target downscaling resolution for these approaches (typically 1 to 10 km). As a result, the objective evaluation of SM downscaling approaches is often challenging and/or limited to very localized conditions. Here, we introduce and evaluate a point-scale downscaling (PSD) benchmarking strategy whereby spatially sparse, long-term, point-scale SM observations available from existing ground-based SM networks are utilized for the objective benchmarking of downscaled satellite-based SM products. First, we define criteria that must be met for a given SM downscaling strategy to add either temporal accuracy or spatial skill relative to its coarse-resolution SM baseline. Next, we illustrate, both analytically and numerically, that such criteria can be accurately evaluated using sparse, point-scale SM observations available from existing ground-based SM networks. Finally, we apply our new PSD benchmarking approach to evaluate existing fine-scale SM products. Results demonstrate that the PSD approach, in concert with existing ground-based network data, can be leveraged to robustly evaluate SM downscaling approaches.

Passive Microwave Radiometry as a Component of Imaging Diagnostics in Juvenile Idiopathic Arthritis

Juvenile idiopathic arthritis (JIA) is a disease with unknown causes in all forms of arthritis in children under 16 years of age. It is diagnosed when other joint pathologies are excluded. Difficulties in early and differential diagnoses lead to rapid disability and an unfavorable life prognosis. Therefore, a timely diagnosis is necessary to prevent irreversible damage to joints and preserve their function. Due to the widespread use of new technologies, modern multimodal imaging has gained recognition, including radiography, ultrasound, and MRI. The combination of methods plays a key role in confirming the diagnosis, monitoring the disease activity, the prognosis during the disease course, and the outcome in children with JIA. Each method has its advantages and disadvantages. The introduction of passive microwave radiometry (MWR), in combination with other imaging methods, makes it possible to expand the possibilities of screening the disease in the preclinical and early clinical phases.

Monitoring Protein Denaturation of Egg White Using Passive Microwave Radiometry (MWR).

Passive microwave radiometry (MWR) is a measurement technique based on the detection of passive radiation in the microwave spectrum of different objects. When in equilibrium, this radiation is known to be proportional to the thermodynamic temperature of an emitting body. We hypothesize that living systems feature other mechanisms of emission that are based on protein unfolding and water rotational transitions. To understand the nature of these emissions, microwave radiometry was used in several in vitro experiments. In our study, we performed pilot measurements of microwave emissions from egg whites during denaturation induced by ethanol. Egg whites comprise 10% proteins, such as albumins, mucoproteins, and globulins. We observed a novel phenomenon: microwave emissions changed without a corresponding change in the water’s thermodynamic temperature. We also found striking differences between microwave emissions and thermodynamic temperature kinetics. Therefore, we hypothesize that these two processes are unrelated, contrary to what was thought before. It is known that some pathologies such as stroke or brain trauma feature increased microwave emissions. We hypothesize that this phenomenon originates from protein denaturation and is not related to the thermodynamic temperature. As such, our findings could explain the reason for the increase in microwave emissions after trauma and post mortem for the first time. These findings could be used for the development of novel diagnostics methods. The MWR method is inexpensive and does not require fluorescent or radioactive labels. It can be used in different areas of basic and applied pharmaceutical research, including in kinetics studies in biomedicine.

Treatment and Companion Diagnostics of Lower Back Pain Using Self-Controlled Energo-Neuroadaptive Regulator (SCENAR) and Passive Microwave Radiometry (MWR).

Evaluation of the effectiveness of treatment of nonspecific lower back pain (LBP) is currently largely based on the patient’s subjective feelings. The purpose of this study was to use passive microwave radiometry (MWR) as a tool for assessing the effectiveness of various treatment methods in patients with acute and subacute nonspecific LBP. Patients with a pain assessment on a visual analogue scale (VAS) of 6 to 10 points were divided into two groups: Group I included patients with pharmacological, syndrome-oriented treatment (n = 30, age 54.9 ± 2.3 years); Group II included a combination of pharmacotherapy with self-controlled energy-neuroadaptive regulation (SCENAR) (n = 25, age 52.8 ± 2.5 years). The analysis showed that the addition of SCENAR therapy (Group II) significantly potentiated the analgesic effect at the stages of treatment, and after 3 weeks, this had increased by more than two times, by 1.3 points on the VAS. There was also a significant decrease in the maximum internal temperature and normalization of the gradient of internal and skin temperatures, and a decrease in thermo-asymmetry, as assessed by temperature fields. Thermal asymmetry visualization allows the identification of the area of pathological muscle spasm and/or inflammation in the projection of the vertebral-motor segment for the possible targeted use of treatment methods such as percutaneous electro neurostimulation, massage, manual therapy, diagnostic and treatment blocks, etc. The MWR method also avoids unnecessary radiation exposure.

A Machine Learning Algorithm for Retrieving Cloud Top Height With Passive Microwave Radiometry

This study aims to retrieve cloud top height (CTH)—excluding cirrus—using passive microwave radiometer observations combined with humidity and temperature profiles. A machine-learning-based approach, combining neural network and gradient boosting methods, is used with Cloud Profiling Radar observations as input. The subsequently derived microwave CTH predictions show a mean average error of 2.1 km and a correlation index of 0.8. The algorithm is used to retrieve the CTH during Hurricane Maria and during a mid-latitude autumn storm. This new algorithm will allow to provide estimates of CTH, at world scale, for a 20-year period.

Using AI and passive medical radiometry for diagnostics (MWR) of venous diseases

We studied the possibility of using artificial intelligence (AI) passive microwave radiometry (MWR) for the diagnostics of venous diseases. MWR measures non-invasive microwave emission (internal temperature) from human body 4 cm deep. The method has been used for early diagnostics in cancer, back pain, brain, COVID-19 pneumonia, and other diseases. In this paper, an AI model based on MWR data is proposed. The model was used to predict the disease state of phlebology patients. We have used MWR and infrared (skin temperature) data of the lower extremities to design a feature space and construct a classification algorithm. Our method has a sensitivity above 0.8 and a specificity above 0.7. At the same time, our method provides an advisory outcome in terms which are understandable for clinicians.

P1‐34: Passive microwave radiometry as an early screening diagnostic of pneumonia due to coronavirus infection 2019 lung complications in Kyrgyzstan

P1‐34: Passive microwave radiometry as an early screening diagnostic of pneumonia due to coronavirus infection 2019 lung complications in Kyrgyzstan

Passive Microwave Radiometry for the Diagnosis of Coronavirus Disease 2019 Lung Complications in Kyrgyzstan.

The global spread of severe acute respiratory syndrome coronavirus 2, which causes coronavirus disease 2019 (COVID-19), could be due to limited access to diagnostic tests and equipment. Currently, most diagnoses use the reverse transcription polymerase chain reaction (RT-PCR) and chest computed tomography (CT). However, challenges exist with CT use due to infection control, lack of CT availability in low- and middle-income countries, and low RT-PCR sensitivity. Passive microwave radiometry (MWR), a cheap, non-radioactive, and portable technology, has been used for cancer and other diseases’ diagnoses. Here, we tested MWR use first time for the early diagnosis of pulmonary COVID-19 complications in a cross-sectional controlled trial in order to evaluate MWR use in hospitalized patients with COVID-19 pneumonia and healthy individuals. We measured the skin and internal temperature using 30 points identified on the body, for both lungs. Pneumonia and lung damage were diagnosed by both CT scan and doctors’ diagnoses (pneumonia+/pneumonia−). COVID-19 was determined by RT-PCR (covid+/covid−). The best MWR results were obtained for the pneumonia−/covid− and pneumonia+/covid+ groups. The study suggests that MWR could be used for diagnosing pneumonia in COVID-19 patients. Since MWR is inexpensive, its use will ease the financial burden for both patients and countries. Clinical Trial Number: NCT04568525.

Long-Term Observation of the Arctic Sea Ice Melt Onset from Microwave Radiometry

An exercise is carried out to analyse the variability in the Arctic snowmelt onset dates (SMOD) using 34 years of passive microwave radiometry data. For this study, the sea ice prevailing regions above 40° N have been clustered into thirteen sectors. Trend analysis of the SMOD shows that 10 sectors have negative trends meaning earlier melt onsets. Negative trends in the East Siberian Sea, Kara Sea, Laptev Sea, Chukchi Sea, Central Arctic Ocean, Canadian Archipelago, Beaufort Bay and Barents Sea are all statistically significant. Although the Greenland Sea shows a statistically significant positive trend is obtained. Inter-sectorial correlation shows strong positive correlations in many sectors such as between the Canadian Archipelago and the Central Arctic, Chukchi Sea—Central Arctic, East Siberian-Central Arctic. Moreover, correlation with the Arctic Oscillation (January to March average) shows negative correlations with the SMOD.