Active Microwave Research Articles

Microwave assisted self-repairable vitrimeric coating for anti-corrosive applications

Vitrimeric coatings are revolutionary and most widely emerging materials for metal corrosion inhibition. As harsh environmental conditions damages polymer-derived protective films and results into micro cracks and corroded metal surfaces. In this work, an active microwave responsive vitrimeric epoxy coating embedded with weathering resistant properties is introduced along with systematic comparative study for the effect of ortho and para position of amine in hardening agent. The developed microwave radiation responsive coating system felicitates self-healing due to disulfide exchange mechanism and corrosion inhibition properties in marine environments. Compared to conventional coatings, prepared graphene oxide (GO) incorporated vitrimeric network showed more promising protection against corrosion in offshore applications with 95.4 % protection efficiency and hydrophobicity with 96-degree contact angle. This study covers the self-healing properties, thermal mechanical study, hydrophobicity measurement, and thorough corrosion examination of synthesized material coated on 304 grade SS substrate. The coating reports excellent self-healing efficiency, reliable mechanical strength (storage modulus-129GPa and loss modulus-83GPa), fast stress relaxation and low activation energy (63KJ/mol) for dynamic disulfide exchange reaction, furthermore low corrosion rate of 1.20 × 10−3mm/y was observed for the coating. Thus, demonstrates its importance for self-healable anti-corrosive application for the marine environment.

Nondestructive evaluation of carbon fiber reinforced polymer (CFRP)-timber interfacial debonding using active microwave thermography (AMT)

Carbon fiber reinforced polymer (CFRP) is increasingly used to strengthen the flexural, shear, axial, and seismic performance of existing timber structures, but CFRP-timber interfacial debonding can substantially weaken the composite action and trigger catastrophic structural failure. Therefore, efficient nondestructive evaluation method is needed to inspect the interfacial debonding, flaws, and imperfections after the installation and periodically during the service of CFRP strengthened timber structures. This study is the first work to investigate nondestructive evaluation of CFRP-timber interfacial condition using active microwave thermography (AMT). AMT utilized microwave as an excitation to generate heat in CFRP and to indirectly detect the interfacial defect from the temperature anomaly on the surface of CFRP. Ten CFRP-timber specimens with different shapes and sizes of interfacial defects were tested under AMT. The experimental results showed that AMT can intuitively detect the CFRP-timber interfacial debonding at the earliest within 8 s to 11 s, which has advantages in speed and visualization. It should be noted that the blur effect can decrease the precision of detection of the shape, especially sharp corners, of the defect. The comparison between specimens with different parameters showed that larger areas of debonding can be detected easily while small ones cannot. In this study, defects with a diameter ≥ 7 mm can be detected.

Forward modelling of synthetic-aperture radar (SAR) backscatter during lake ice melt conditions using the Snow Microwave Radiative Transfer (SMRT) model

Abstract. Monitoring of lake ice is important to maintain transportation routes, but in recent decades the number of in situ observations have declined. Remote sensing has worked to fill this gap in observations, with active microwave sensors, particularly synthetic-aperture radar (SAR), being a crucial technology. However, the impact of wet conditions on radar and how interactions change under these conditions have been largely ignored. It is important to understand these interactions as warming conditions are likely to lead to an increase in the occurrence of slush layers. This study works to address this gap using the Snow Microwave Radiative Transfer (SMRT) model to conduct forward-modelling experiments of backscatter for Lake Oulujärvi in Finland. Experiments were conducted under dry conditions, under moderate wet conditions, and under saturated conditions. These experiments reflected field observations during the 2020–2021 ice season. Results of the dry-snow experiments support the dominance of surface scattering from the ice–water interface. However, conditions where layers of wet snow are introduced show that the primary scattering interface changes depending on the location of the wet layer. The addition of a saturated layer at the ice surface results in the highest backscatter values due to the larger dielectric contrast created between the overlying dry snow and the slush layer. Improving the representation of these conditions in SMRT can also aid in more accurate retrievals of lake ice properties such as roughness, which is key for inversion modelling of other properties such as ice thickness.

Snow water equivalent retrieval over Idaho – Part 1: Using Sentinel-1 repeat-pass interferometry

Abstract. Snow water equivalent (SWE) is identified as the key element of the snowpack that impacts rivers' streamflow and water cycle. Both active and passive microwave remote sensing methods have been used to retrieve SWE, but there does not currently exist a SWE product that provides useful estimates in mountainous terrain. Active sensors provide higher-resolution observations, but the suitable radar frequencies and temporal repeat intervals have not been available until recently. Interferometric synthetic aperture radar (InSAR) has been shown to have the potential to estimate SWE change. In this study, we apply this technique to a long time series of 6 d temporal repeat Sentinel-1 C-band data from the 2020–2021 winter. The retrievals show statistically significant correlations both temporally and spatially with independent in situ measurements of SWE. The SWE change measurements vary between −5.3 and 9.4 cm over the entire time series and all the in situ stations. The Pearson correlation and RMSE between retrieved SWE change observations and in situ stations measurements are 0.8 and 0.93 cm, respectively. The total retrieved SWE in the entire 2020–2021 time series shows an SWE error of less than 2 cm for the nine in situ stations in the scene. Additionally, the retrieved SWE using Sentinel-1 data is well correlated with lidar snow depth data, with correlation of more than 0.47. Low temporal coherence is identified as the main reason for degrading the performance of SWE retrieval using InSAR data. We also show that the performance of the phase unwrapping algorithm degrades in regions with low temporal coherence. A higher frequency such as L-band improves the temporal coherence and SWE ambiguity. SWE retrieval using C-band Sentinel-1 data is shown to be successful, but faster revisit is required to avoid low temporal coherence. Global SWE retrieval using radar interferometry will have a great opportunity with the upcoming L-band 12 d repeat-pass NASA-ISRO Synthetic Aperture Radar (NISAR) data and the future 6 d repeat-pass Radar Observing System for Europe in L-band (ROSE-L) data.

Marine oil spill detection using improved polarimetric feature based on polarization SAR image

ABSTRACT Monitoring marine oil pollution holds both practical and scientific significance. Synthetic Aperture Radar (SAR) is an active microwave remote sensing technique capable of all-weather and all-day with fine spatial resolution. However, under low wind conditions, rain cells and young ice are examples of look-alikes affect the accuracy of oil spill detection. Polarimetric SAR assumes a crucial role in this context, as it can extract abundant polarimetric features by polarimetric target decomposition. Drawing inspiration from this advancement, an improved polarimetric feature named relative feature based on Cloude-Pottier target decomposition was proposed. The Jeffries-Matusita distance indicates the substantial potential of the relative feature in detecting oil spills. The improved polarimetric feature within U-Net, FCN-8s, and DeepLabv3+ResNet-18 for oil spill detection using polarization SAR images. Experiment results demonstrated that the relative feature has superior performance compared to other polarimetric features and obtained the highest accuracy and dice within U-Net compared to the other two models. These findings introduce promising concepts for achieving rapid and precise detection of oil spills in future applications.

Water vapor measurements inside clouds and storms using a differential absorption radar

Abstract. NASA's Vapor In-cloud Profiling Radar (VIPR) is a tunable G-band radar designed for in-cloud and precipitation humidity remote sensing. VIPR estimates humidity using the differential absorption radar (DAR) technique. This technique exploits the difference between atmospheric attenuation at different frequencies (“on” and “off” an absorption line) and combines it with the ranging capabilities of the radar to estimate the absorbing gas concentration along the radar path. We analyze the VIPR humidity measurements during two NASA field campaigns: (1) the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign, with the objective of studying wintertime snowstorms focusing on east coast cyclones; and (2) the Synergies Of Active optical and Active microwave Remote Sensing Experiment (SOA2RSE) campaign, which studied the synergy between DAR (VIPR) and differential absorption lidar (DIAL, the High altitude Lidar Observatory – HALO) measurements. We discuss a comparison with dropsondes launched during these campaigns as well as an intercomparison against the ERA5 reanalysis fields. Thus, this study serves as an additional evaluation of ERA5 lower tropospheric humidity fields. Overall, in-cloud and in-snowstorm comparisons suggest that ERA5 and VIPR agree within 20 % or better against the dropsondes. The exception is during SOA2RSE (i.e., in fair weather), where ERA5 exhibits up to a 50 % underestimation above 4 km. We also show a smooth transition in water vapor profiles between the in-cloud and clear-sky measurements obtained from VIPR and HALO respectively, which highlights the complementary nature of these two measurement techniques for future airborne and space-based missions.

Planar Mercator Smith Chart: An Alternative Approach for Active and Passive Microwave Circuits

This letter proposes a Planar Mercator Smith chart (PMSC), which can be used to design and analyze both active and passive microwave circuits on a 2-D flat surface. The entire surface of the 3-D Smith chart comprises of whole impedance plane, which includes all possible loads is projected to the 2-D flat surface of the PMSC using the mathematical principle of Mercator projection. This eliminates the need for CAD software to be used as a design tool or visualization aid unlike a 3-D Smith chart, and it may be a useful graphical chart for solving microwave problems involving any type of load.

Active and Passive Microwave Remote Sensing of Priestley Glacier, Antarctica

Active and Passive Microwave Remote Sensing of Priestley Glacier, Antarctica

Effect of Signal Modulation on Active Microwave Thermography

Effect of Signal Modulation on Active Microwave Thermography

Modified Thermographic Signal-to-Noise Ratio for Active Microwave Thermography

Modified Thermographic Signal-to-Noise Ratio for Active Microwave Thermography

Estimation of All-Sky High-Resolution Gross Primary Production Across Different Biome Types Using Active Microwave Satellite Images and Environmental Data

Estimation of All-Sky High-Resolution Gross Primary Production Across Different Biome Types Using Active Microwave Satellite Images and Environmental Data

Automated High-Resolution Bathymetry from Sentinel-1 SAR Images in Deeper Nearshore Coastal Waters in Eastern Florida

Synthetic aperture radar (SAR) imagers are active microwave sensors that could overcome many challenges of passive optical bathymetry inversion, yet their capacity to yield accurate high-resolution bathymetric mapping is not studied sufficiently. In this study, we evaluate the feasibility of applying fast Fourier transform (FFT) to SAR data in coastal nearshore bathymetry derivation in Florida’s coastal waters. The study aims to develop a robust SAR bathymetry inversion framework across extensive spatial scales to address the dearth of bathymetric data in deeper nearshore coastal regions. By leveraging the Sentinel-1 datasets as a rich source of training data, our method yields high-resolution and accurate depth extraction up to 80 m. A comprehensive workflow to determine both the wavelength and peak wave period is associated with the proposed automated model compilation. A novel contour geometry-based spectral analysis technique for wavelength retrieval is presented that enables an efficient and scalable SAR bathymetry model. Multi-date SAR images were used to assess the robustness of the proposed depth-retrieval model. An accuracy assessment against the GMRT data demonstrated the high efficacy of the proposed approach, achieving a coefficient of determination (R2) above 0.95, a root-mean-square error (RMSE) of 1.56–10.20 m, and relative errors of 3.56–11.08% in automatically extracting the underwater terrain at every 50 m interval. A sensitivity analysis was conducted to estimate the uncertainty associated with our method. Overall, this study highlights the potential of SAR technology to produce updated, cost-effective, and accurate bathymetry maps of high resolution and to fill bathymetric data gaps worldwide. The code and datasets are made publicly available.

MIC: Microwave Imaging Curtain for Dynamic and Automatic Detection of Weapons and Explosive Belts

DEXTER (detection of explosives and firearms to counter terrorism) is a project funded by NATO’s Science for Peace and Security (SPS) program with the goal of developing an integrated system capable of remotely and accurately detecting explosives and firearms in public places without impeding the flow of pedestrians. While body scanner systems in secure areas of public places are becoming more and more efficient, the attack at Brussels airport on 22 March 2016, upstream of these systems, in the middle of the crowd of passengers, demonstrated the lack of discreet and real-time security against threats of mass terrorism. The NATO-SPS international and multi-year DEXTER project aims to provide new technical and strategic solutions to fill this gap. This project is based on multi-sensor coordination and fusion, from hyperspectral remote laser to smart glasses, artificial algorithms, and suspect identification and tracking. One of these sensors is dedicated to threat detection (large weapon or explosive belt) using the clothing of pedestrians by means of an active microwave component. This project is referred to as MIC (Microwave Imaging Curtain), also supported by the French SGDSN (General Secretariat of Defense and National Security), and utilizes a radar system capable of generating 3D images in real-time to address non-checkpoint detection of explosives and firearms. The project, led by ONERA (France), is based on a radar imaging system developed by the Fraunhofer FHR institute, using a MIMO architecture with an Ultra-Wide Band waveform. Although high-resolution 3D microwave imaging is already being used in expensive body scanners to detect firearms concealed under clothing, MIC’s innovative approach lies in utilizing a high-resolution 3D imaging device that can detect larger dangerous objects carried by moving individuals at a longer range, in addition to providing discrete detection in pedestrian flow. Automatic detection and classification of these dangerous objects is carried out on 3D radar images using a deep-learning network. This paper will outline the project’s objectives and constraints, as well as the design, architecture, and performance of the final system. Additionally, it will present real-time imaging results obtained during a live demonstration in a relevant environment.

Use of a new Tibetan Plateau network for permafrost to characterize satellite-based products errors: An application to soil moisture and freeze/thaw

Two basic properties of soil moisture, namely water content and its phase status (freeze/thaw, referred to as F/T), strongly influence the pattern and efficiency of water and heat exchanges at the land-atmosphere interface. Therefore, accurate soil moisture and F/T retrievals are crucial to explore the impact of soil moisture and temperature on multi-sphere interactions, especially in the permafrost region of the third pole (Tibetan Plateau, TP), which is highly sensitive to climate changes. However, given the difficulty of installing and maintaining ground stations in harsh environments, dense ground networks are scarce over the TP with an average altitude of 4000 m. In this study, we reported a new soil moisture and temperature wireless sensor network consisting of 10 stations, which were established over the permafrost in the WuDaoLiang region (referred to as SMN-WDL) of the TP in 2019. In situ measurements from the SMN-WDL were used to simultaneously assess satellite-based surface soil moisture (SSM) and F/T products. The reliability of categorical triple collocation (CTC) method was investigated by comparing CTC-derived and ground-based relative performance rankings. The results reveal that the low-frequency (L-band) passive microwave SSM and F/T products outperformed both high-frequency (C/X/Ka-bands) passive and active microwave products, especially SMAP (Soil Moisture Active Passive) SSM and F/T products displayed the best statistical metrics to SMN-WDL ground-based measurements. The existing satellite F/T products had varying degrees of earlier freezing in autumn and later thawing in spring (mainly in ASCAT F/T) within SMN-WDL, which resulted in unsatisfactory thaw accuracy (< 85% in most cases) and overall accuracy (33.16% to 94.92%). In SMN-WDL, using existing F/T products as a masking criterion to filter out frozen soil for SSM validation studies would incorrectly mask out too many data samples and, lead to a decrease of the performance of SSM products. The overestimation of F/T thresholds may be one of the main reasons for the above misclassification of satellite F/T products. Compared to existing F/T products, the overall accuracy of threshold-optimal F/T retrievals can be increased by >10% during nighttime after optimizing thresholds based on ground-based measurements. CTC can provide consistent performance ranking with ground-based results in most cases, but the risk of CTC performing incorrect rankings was higher within the triplet where two products strongly covariate or one of the products has a significant error. This study is the first to use dense ground-based measurements in the permafrost region (SMN-WDL) to characterize the errors of four satellite SSM and F/T products. These results are expected to provide a reference with smaller representativeness errors for the further refinement of satellite retrievals in the permafrost region of the third pole.

Characterization of the Moon’s South polar craters using optical, microwave and thermal remote sensing

The peculiar illumination and insolation conditions at the poles of the Moon lead to the formation of Permanently Shadowed Regions (PSR) that act as cold traps for volatiles and are important for In Situ Resource Utilization (ISRU). However, since these regions lie in shadows of surrounding topographic highs, the ability of optical remote sensing in imaging these regions for photo reconnaissance is limited and using other modes of remote sensing becomes imperative. In this work, reflectance from optical data, backscatter from active microwave data and temperature from thermal data from the surfaces of three craters – de Gerlache, Haworth and Shoemaker – on the South Pole of the Moon, in close proximity to the Artemis III candidate landing sites, has been studied. Images from Clementine UVVIS, LRO WAC and LRO NAC have been compared with fully polarimetric data from the L-bands sensor of Chandrayaan-2 DFSAR. Further, the temperatures at the surface of the craters have been studied using data from LRO DIVINER. Since large parts of the craters under study lie in permanent shadows, Clementine UVVIS and LRO WAC could image only some parts of the rims. LRO NAC, with its longer exposure times, could image some parts within the crater however the walls of the crater and the floor near the walls remained in shadow. DFSAR was found to be useful in viewing the permanently shadowed regions of the craters. Overall, the backscatter in HH and VV (co-pol) channels was higher than the backscatter in HV and VH (cross-pol) channels. The elevated rims of the craters were clearly visible in co-pol channels and the rough texture of the ejecta of the craters had a distinct high backscatter in cross-pol channels. This fully polarimetric data can be used to map the interiors of the craters where optical data has low albedo. Analysis of the thermal data revealed that within the craters, in the regions that were in perpetual shadows, the maximum summer temperatures were less than 150 K making these regions cold traps for volatiles and possible locations of surface water–ice. The parts of craters lying in the shadows of surrounding higher features had lower maximum temperatures in summer and the parts facing them had higher maximum temperatures due to reflected insolation. Seleno Referenced Image (SRI) product from Chandrayaan-2 DFSAR was found to be a viable alternative to optical data for viewing the PSRs and for their geologic mapping.

A review of recent advance of ship detection in single-channel SAR images

Synthetic aperture radar (SAR) is an active microwave imaging sensor for high-resolution observation, with the ability of working in all-weather and all-day. Recently, SAR images have been widely used in many fields. Among them, ship detection in single-channel SAR images is a significant part of civilian and military fields. This article first discusses the characteristic of SAR images and the detectability of ships, then summarizes the recent advance of traditional and deep learning-based methods used for ship detection in single-channel SAR images. In addition, the characteristics and existing problems of various methods are discussed and their future development trends are predicted. Aiming at the problems of the large amount of calculation, multi-scale and densely docked ship detection in single-channel SAR images, an improved deep learning-based detection algorithm is proposed, which has achieved excellent performance on the SAR ship detection dataset (SSDD).

Development of the SURface Fast Emissivity Model for Ocean (SURFEM‐Ocean) Based on the PARMIO Radiative Transfer Model

AbstractA Passive and Active Reference Microwave to Infrared Ocean (PARMIO) physical radiative transfer model has been selected by an international team of experts to be used as a community ocean surface emissivity model. A fast version of this physical community model is developed within the framework of European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Numerical Weather Prediction Satellite Application Facilities visiting scientist programme: the SURface Fast Emissivity Model for Ocean (SURFEM‐Ocean), for implementation in the Radiative Transfer code for TOVS (RTTOV) as an alternative/replacement of the well‐known FAST microwave Emissivity Model (FASTEM). First, the configuration of the PARMIO model is adapted to microwave frequencies from 500 MHz to 700 GHz, and issues observed in the preliminary studies with cold sea surface temperatures (SST) and high ocean wind speeds (OWS) are corrected. Second, an artificial neural network method is used to reproduce the results of the PARMIO model much faster. The computation of the analytical Jacobians is also implemented. Finally, SURFEM‐Ocean results are compared with satellite observations and other ocean emissivity models. SURFEM‐Ocean extends the capability of FASTEM. It computes emissivities from 500 MHz to 700 GHz, for all incidence angles, for the fourth Stokes polarizations, for OWS from 0 to 50 m/s, SST from −2 to 30°C, sea surface salinity from 0 to 40 psu, and for all wind directions.

A Differential Active SLSP-Based Microwave Sensor for Liquid Characterization

A Differential Active SLSP-Based Microwave Sensor for Liquid Characterization

A new global C-band vegetation optical depth product from ASCAT: Description, evaluation, and inter-comparison

Active microwave measurements have the potential to estimate vegetation optical depth (VOD), an indicator related to vegetation water content and biomass. The Advanced SCATterometer (ASCAT) provides long-term C-band backscatter data at vertical-vertical (VV) polarization from 2007. So far, very few studies have considered retrieving VOD from this active sensor. This study presents a new publicly released global long-term and continuous (2007–2020) C-band VOD dataset retrieved from the ASCAT observations, named the ASCAT INRAE-BORDEAUX or ASCAT IB VOD product. The retrieval algorithm is based on the Water Cloud Model (WCM) including the Ulaby bare soil model. The algorithm takes advantage of a multi-temporal (MT) retrieval method relying on a cost function where constraints to the retrieved parameters are implemented and a reanalysis soil moisture (SM) dataset from ERA5-Land is used as an input. The performance of ASCAT IB VOD was evaluated by inter-comparing it with ASCAT Technische Universität Wien (TUW), the Advanced Microwave Scanning Radiometer 2 (AMSR2), and VOD Climate Archive (VODCA) VOD products (the last two products are estimated from passive microwave observations). Results showed that ASCAT IB VOD presented the highest spatial correlation with aboveground biomass (R ∼ 0.83) and with the Global Ecosystem Dynamics Investigation (GEDI) canopy height (R ∼ 0.84–0.85). In terms of temporal performance, ASCAT IB VOD had the highest correlation R values with leaf area index (LAI) and Normalized Difference Water Index (NDWI) in most parts of the globe from 2013 to 2018. This contrasts with AMSR2 VODs which correlated better with Normalized Difference Vegetation Index (NDVI). The new ASCAT-based VOD product on a global scale highlighted the potential benefit of combining active (namely ASCAT) and passive (namely AMSR2) VOD products for vegetation studies.

Correction for the Attenuation Due to Atmospheric Gas and Stratiform Clouds in Triple-Frequency Radar Observations of the Microphysical Properties of Snowfall

For triple-frequency radar, the attenuation attributed to atmospheric gases and stratiform clouds is diverse due to different snowfall microphysical properties, particularly in regions far from the radar. When using triple-frequency ground-based radar measurements, evaluating the attenuation of the three radars at different heights is common to derive attenuation-corrected effective reflectivity. Therefore, this study proposes a novel quality-controlled approach to identify radar attenuation due to gases and stratiform clouds that can be neglected due to varying snowfall microphysical properties and assess attenuation along the radar observation path. The key issue lies in the lack of information about vertical hydrometeor and cloud distribution. Therefore, European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data are employed. The Self-Similar-Rayleigh-Gans Approximation (SSRGA) for the nonspherical scattering model in the Passive and Active Microwave TRAnsfer model 2 (PAMTRA2) is compared and analyzed against other scattering models to obtain the optimal triple-frequency radar attenuation correction strategies for stratiform cloud meteorological conditions with varying snowfall microphysical properties. This methodology paves the way for understanding differential attenuation attributed to gas and stratiform clouds with snowfall microphysical properties. Simultaneously, the bin-by-bin approximation method is used to perform the attenuation correction. The two-way attenuation correction increased up to 4.71 dB for heights above 6 km, remaining minimal for regions with heights below 6 km. These values, attributable to gases and stratiform clouds’ two-way attenuation, are nonnegligible, especially at distances far from the W-band radar at heights above 6 km. Both values are relatively small for the X- and Ka-band radars and can be neglected for the varying snowfall microphysical properties. The attenuation correction of triple-frequency radar reflectivity is validated using the cross-calibration and dual-frequency reflectivity ratios. The results show that the method is valid and feasible.