Brightness Temperature Images Research Articles

Radio Frequency Interference Mitigation in Data and Image Bi-Domains for an Aperture Synthesis Radiometer

For synthetic aperture microwave radiometers, the problem of Radio Frequency Interference (RFI) is becoming more and more serious, which affects both the scientific retrieval of remote sensing data and the imaging quality of brightness temperature (BT) images. In the visibility data domain, the array factor synthesis algorithm is commonly employed to mitigate RFI sources and their Gibbs trailing. In the BT image domain, the CLEAN algorithm is typical applied to mitigate RFI sources and their Gibbs trailing. However, the array factor synthesis algorithm can result in anomalous BT points near the “zero trap” region, and the CLEAN algorithm will miss some BT points below a certain threshold. In this paper, a Bi-domain combined mitigation algorithm is proposed to mitigate RFI sources and their Gibbs trailing. Following initial mitigation in the visibility data domain, dual thresholds are applied to normalize anomalous BT points near the “zero trap” region, thereby enhancing imaging quality. The effectiveness of the Bi-domain combined mitigation algorithm is verified by using both measured data from SMOS L1A and simulated data. The experimental results demonstrate that the Bi-domain combined mitigation algorithm is superior to the array factor synthesis algorithm and the CLEAN algorithm in mitigating RFI sources and their Gibbs trailing.

Focal-TSMP: deep learning for vegetation health prediction and agricultural drought assessment from a regional climate simulation

Abstract. Satellite-derived agricultural drought indices can provide a complementary perspective of terrestrial vegetation trends. In addition, their integration for drought assessments under future climates is beneficial for providing more comprehensive assessments. However, satellite-derived drought indices are only available for the Earth observation era. In this study, we aim to improve the agricultural drought assessments under future climate change by applying deep learning (DL) to predict satellite-derived vegetation indices from a regional climate simulation. The simulation is produced by the Terrestrial Systems Modeling Platform (TSMP) and performed in a free evolution mode over Europe. TSMP simulations incorporate variables from underground to the top of the atmosphere (ground-to-atmosphere; G2A) and are widely used for research studies related to water cycle and climate change. We leverage these simulations for long-term forecasting and DL to map the forecast variables into normalized difference vegetation index (NDVI) and brightness temperature (BT) images that are not part of the simulation model. These predicted images are then used to derive different vegetation and agricultural drought indices, namely NDVI anomaly, BT anomaly, vegetation condition index (VCI), thermal condition index (TCI), and vegetation health index (VHI). The developed DL model could be integrated with data assimilation and used for downstream tasks, i.e., for estimating the NDVI and BT for periods where no satellite data are available and for modeling the impact of extreme events on vegetation responses with different climate change scenarios. Moreover, our study could be used as a complementary evaluation framework for TSMP-based climate change simulations. To ensure reliability and to assess the model’s applicability to different seasons and regions, we provide an analysis of model biases and uncertainties across different regions over the pan-European domain. We further provide an analysis about the contribution of the input variables from the TSMP model components to ensure a better understanding of the model prediction. A comprehensive evaluation of the long-term TSMP simulation using reference remote sensing data showed sufficiently good agreements between the model predictions and observations. While model performance varies on the test set between different climate regions, it achieves a mean absolute error (MAE) of 0.027 and 1.90 K with coefficient of determination (R2) scores of 0.88 and 0.92 for the NDVI and BT, respectively, at 0.11° resolution for sub-seasonal predictions. In summary, we demonstrate the feasibility of using DL on a TSMP simulation to synthesize NDVI and BT satellite images, which can be used for agricultural drought forecasting. Our implementation is publicly available at the project page (https://hakamshams.github.io/Focal-TSMP, last access: 4 April 2024).

Applications of Ground-Based Infrared Cameras for Remote Sensing of Volcanic Plumes

Ground-based infrared cameras can be used effectively and safely to provide quantitative information about small to moderate-sized volcanic eruptions. This study describes an infrared camera that has been used to measure emissions from the Mt. Etna and Stromboli (Sicily, Italy) volcanoes. The camera provides calibrated brightness temperature images in a broadband (8–14 µm) channel that is used to determine height, plume ascent rate and volcanic cloud/plume temperature and emissivity at temporal sampling rates of up to 1 Hz. The camera can be operated in the field using a portable battery and includes a microprocessor, data storage and WiFi. The processing and analyses of the data are described with examples from the field experiments. The updraft speeds of the small eruptions at Stromboli are found to decay with a timescale of ∼10 min and the volcanic plumes reach thermal equilibrium within ∼2 min. A strong eruption of Mt. Etna on 1 April 2021 was found to reach ∼9 km, with ascent speeds of 10–20 ms−1. The plume, mostly composed of the gases CO2, water vapour and SO2, became bent over by the prevailing winds at high levels, demonstrating the need for multiple cameras to accurately infer plume heights.

One‐Minute Resolution GOES‐R Observations of Lamb and Gravity Waves Triggered by the Hunga Tonga‐Hunga Ha'apai Eruptions on 15 January 2022

AbstractWe use high temporal‐resolution mesoscale imagery from the Geostationary Operational Environmental Satellite‐R (GOES‐R) series to track the Lamb and gravity waves generated by the 15 January 2022 Hunga Tonga‐Hunga Ha'apai eruption. The 1‐min cadence of these limited area (∼1,000×1,000 km2) brightness temperatures ensures an order of magnitude better temporal sampling than full‐disk imagery available at 10‐min or 15‐min cadence. The wave patterns are visualized in brightness temperature image differences, which represent the time derivative of the full waveform with the level of temporal aliasing being determined by the imaging cadence. Consequently, the mesoscale data highlight short‐period variations, while the full‐disk data capture the long‐period wave packet envelope. The full temperature anomaly waveform, however, can be reconstructed reasonably well from the mesoscale waveform derivatives. The reconstructed temperature anomaly waveform essentially traces the surface pressure anomaly waveform. The 1‐min imagery reveals waves with ∼40–80 km wavelengths, which trail the primary Lamb pulse emitted at ∼04:29 UTC. Their estimated propagation speed is ∼315 ± 15 m s−1, resulting in typical periods of 2.1–4.2 min. Weaker Lamb waves were also generated by the last major eruption at ∼08:40–08:45 UTC, which were, however, only identified in the near field but not in the far field. We also noted wind effects such as mean flow advection in the propagation of concentric gravity wave rings and observed gravity waves traveling near their theoretical maximum speed.

Brightness Temperature Image Reconstruction of the Tilted Mirrored Aperture Synthesis Using CNN and Transformer

Brightness Temperature Image Reconstruction of the Tilted Mirrored Aperture Synthesis Using CNN and Transformer

A Space-Time Variational Method for Retrieving Upper-Level Vortex Winds from GOES-16 Rapid Scans over Hurricanes

A space-time variational method is developed for retrieving upper-level vortex winds from geostationary satellite rapid infrared scans over hurricanes. In this method, new vortex-flow-dependent correlation functions are formulated for the radial and tangential components of the vortex wind. These correlation functions are used to construct the background error covariance matrix and its square root matrix. The resulting square root matrix is then employed to precondition the cost function, constrained by an advection equation formulated for rapidly scanned infrared image movements. This newly formulated and preconditioned cost function is more suitable for deriving upper-level vortex winds from GOES-16 rapid infrared scans over hurricanes than the cost function in the recently adopted optical flow technique. The new method was applied to band-13 (10.3 µm) brightness temperature images scanned every min from GOES-16 over Hurricanes Laura on 27 August 2020 and Hurricanes Ida on 29 August 2021. The retrieved vortex winds were shown to not only be much denser than operationally produced atmospheric motion vectors (AMVs) but also more rotational and better organized around the eyewall than the super-high-resolution AMVs derived from optical-flow technique. By comparing their component velocities (projected along radar beams) with limited radar velocity observations available near the cloud top, the vortex winds retrieved using the new method were also shown to be more accurate than the super-high-resolution AMVs derived from the optical-flow technique. The new method is computationally efficient for real-time applications and potentially useful for hurricane wind nowcasts. Furthermore, the combined use of VF-dependent covariance functions and imagery advection equation is not only novel but was also found to be critically important for the improved performance of the method. This finding implies that similar combined approaches can be developed with improved performance for retrieving vortex flows rapidly scanned using other types of remote sensing on different scales, such as tornadic mesocyclones rapidly scanned by phased-array radars.

Deep convective cloud system size and structure across the global tropics and subtropics

Abstract. A new database is constructed comprising millions of deep convective clouds that spans the global tropics and subtropics for the entire record of the MODIS instruments on the Terra and Aqua satellites. The database is a collection of individual cloud objects ranging from isolated convective cells to mesoscale convective cloud systems spanning hundreds of thousands of square kilometers in cloud area. By matching clouds in the database with the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis dataset and microwave imager brightness temperatures from the Advanced Microwave Scanning Radiometer – EOS (AMSR-E) instrument, the database is designed to explore the relationships among the horizontal scale of cloud systems, the thermodynamic environment within which the cloud resides, the amount of aerosol in the environment, and indicators of the microphysical structure of the clouds. We find that the maximum values of convective available potential energy and vertical shear of the horizontal wind associated with a cloud impose a strong constraint on the size attained by a convective cloud system, although the relationship varies geographically. The cloud database provides a means of empirically studying the factors that determine the spatial structure and coverage of convective cloud systems, which are strongly related to the overall radiative forcing by cloud systems. The observed relationships between cloud system size and structure from this database can be compared with similar relationships derived from simulated clouds in atmospheric models to evaluate the representation of clouds and convection in weather forecast and climate projection simulations, including whether models exhibit the same relationships between the atmospheric environment and cloud system size and structure. Furthermore, the dataset is designed to probe the impacts of aerosols on the size and structure of deep convective cloud systems.

Regional-Based Object Detection Using Polarization and Fisher Vectors in Passive Millimeter-Wave Imaging

Passive millimeter-wave (PMMW) imaging is a powerful approach for detecting hidden objects underneath clothing. The theoretical basis of object detection methods is the contrast of brightness temperature (TB) image. TB differences may be caused by the diversity of material, physical temperature, surface structure, and so on. Existing methods are mainly based on single-polarization and single-pixel processing, which usually generate many discrete pixels of false alarms or missing detections. In this article, we present a regional-based method for hidden object detection using polarization and Fisher vector (FV) features. The necessity of polarization averaging for detection is revealed by theoretical simulation and experimental analyses. Based on the superpixel segmentation of polarization mean image, a modified FV, regional mean FV (RMFV), is created to extract concealed object features. Various imaging experimental data of typical security inspection scenarios are applied to verify the proposed method. The robustness and effectiveness are proved by comparing with several state-of-the-art methods.

Evaluating the effective resolution of enhanced resolution SMAP brightness temperature image products

The MEaSUREs Calibrated Enhanced-Resolution Passive Microwave Daily Equal-Area Scalable Earth Grid 2.0 Brightness Temperature (CETB) Earth System Data Record (ESDR) includes conventional- and enhanced-resolution radiometer brightness temperature (TB) images on standard, compatible grids from calibrated satellite radiometer measurements collected over a multi-decade period. Recently, the CETB team processed the first 4 years of enhanced resolution Soil Moisture Active Passive (SMAP) L-band (1.41 GHz) radiometer TB images. The CETB processing employs the radiometer form of the Scatterometer Image Reconstruction (rSIR) algorithm to create enhanced resolution images, which are posted on fine resolution grids. In this paper, we evaluate the effective resolution of the SMAP TB image products using coastline and island crossings. We similarly evaluate the effective resolution of the SMAP L1C_TB_E enhanced resolution product that is based on Backus-Gilbert processing. We present a comparison of the spatial resolution of the rSIR and L1C_TB_E enhanced resolution products with conventionally-processed (gridded) SMAP data. We find that the effective resolution of daily CETB rSIR SMAP TB images is slightly finer than that of L1C_TB_E and about 30% finer than conventionally processed data.

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

In order to improve the spatial resolution of a one-dimensional aperture synthesis (1-D AS) radiometer without increasing the size of the antenna array, the method of visibility extension (VE) is proposed in this article. In the VE method, prior information about the visibility distribution of various scenes is learnt by a residual convolutional neural network (ResCNN). Specifically, the relationship between the distribution of low-frequency visibility and that of high-frequency visibility is learnt. Then, the ResCNN is used to estimate the high-frequency visibility samples from the low-frequency visibility samples obtained by the AS system. Furthermore, the low- and high-frequency visibility samples are combined to reconstruct the brightness temperature image of the scene, to enhance the spatial resolution of AS. The simulation and experiment both demonstrate that the VE method can enhance the spatial resolution of 1-D AS.

High-Resolution Imaging of Radiation Brightness Temperature Obtained by Drone-Borne Microwave Radiometer

A digital automatic gain compensation (AGC) drone-borne K-band microwave radiometer with continuous high-speed acquisition and fast storage functions is designed and applied to obtain high-resolution radiation brightness temperature (TB) images. In this paper, the composition of the drone-borne passive microwave observation system is introduced, a data processing method considering the topography and angle correction is proposed, the error analysis of the projection process is carried out, and finally, a high-resolution microwave radiation TB image is obtained by a demonstration area experiment. The characteristics of the radiometer are tested by experiments, and the standard deviation of the TB is 1K. The data processing method proposed is verified using a demonstration case. The corrected data have a good correlation with the theoretical values, of which the R2 is 0.87. A high-resolution radiation TB image is obtained, and the results show the TB characteristics of different objects well. The boundary of the ground object is closer to the real value after correction.

A Simplified Coastline Inflection Method for Correcting Geolocation Errors in FengYun-3D Microwave Radiation Imager Images

Passive microwave (PMW) sensors are popularly applied to Earth observations. However, the satellite PMW radiometer data sometimes have non-negligible errors in geolocation. Coastline inflection methods (CIMs) are widely used to improve geolocation errors of PMW images. However, they commonly require accuracy satellite flight parameters, which are difficult to obtain by users. In this study, a simplified coastline inflection method (SCIM) is proposed to correct the geolocation errors without demanding for the satellite flight parameters. SCIM is applied to improve geolocation errors of FengYun-3D (FY-3D) Microwave Radiation Imager (MWRI) brightness temperature images from 2018 and 2019. It reduces the geolocation errors of MWRI images to 0.15 pixels in the along-track and cross-track direction. This means reductions of 75% and 86% of the geolocation errors, respectively. The mean brightness temperature differences between the ascending and descending MWRI images are reduced by 34%, demonstrating the improved geolocation accuracy of SCIM. The corrected images are also used to estimate Arctic sea ice concentration (SIC). By comparing with SICs retrieved from the un-corrected images, the root mean square error (RMSE) and mean absolute error (MAE) of the SICs from the corrected images are reduced from 13.7% to 10.2% and 8.9% to 6.9%, respectively. The mean correlation coefficient (R) increases from 0.91 to 0.95. All these results indicate that SCIM can reduce geolocation errors of satellite-based PMW images significantly. As SCIM is very simple and easy to be applied, it could be a useful method for users of PMW images.

A Combined RFI Localization Algorithm of BT Image and Subspace Decomposition for Synthetic Aperture Interferometric Radiometer

Radio frequency interferences (RFIs) in the L-band heavily contaminate remote sensing data and bring many challenges to the product quality of synthesis aperture interferometric radiometers. Detecting and localizing RFIs is the urgent need for switching off these RFIs and conducting mitigation algorithms. The localization algorithm based on the brightness temperature (BT) image is limited by the resolution to separate closely spaced RFIs and achieve high localization accuracy in some cases. To achieve super-resolution, the MUSIC method using subspace decomposition technique is suggested. Nevertheless, when the BT image SNR is low, the power of RFI is insufficient to overwhelm the background scene, and the performance of the MUSIC algorithm is unsatisfactory. In this paper, a further improvement is proposed by combining BT image and subspace decomposition. In the BT image domain, background scene cancellation and RFI target enhancement are carried out for enhancing the SNR. In the frequency domain, the MUSIC algorithm is applied utilizing subspace-decomposition to achieve super-resolution localization. Experiments and simulations based on SMOS data validate that the method presented in this paper performs better than both the classical MUSIC algorithm and BT-based localization algorithm in low SNR cases.

Image Reconstruction of Synthetic Aperture Radiometer by Transformer

In passive microwave remote sensing of the earth, compared with real aperture radiometer, synthetic aperture radiometer is a very powerful instrument with many advantages. However, the system design is more complex than the real aperture radiometer, and the hardware ideality is often not guaranteed. The non-ideal characteristics of the system hardware will bring a variety of errors to the system, which will cause the Fourier transform relationship between the visibility function and the brightness temperature image to no longer be established, thus reducing the quality of microwave brightness temperature image reconstruction by traditional methods. In this paper, a new microwave brightness temperature image reconstruction method for synthetic aperture radiometer by Transformer is proposed. This method uses a specially designed Transformer structure to extract the spectrum features in the visibility function. This method learns the mapping relationship between the visibility function and the original scene brightness temperature image through the supervised learning method, and learns as much as possible the spectrum information contained in the original scene brightness temperature image. Moreover, when there are missing baselines, this method will supplement the missing observation frequency information, so as to obtain better reconstructed image quality. With the above advantages, this method can suppress the Gibbs oscillation, and greatly reduce the side lobe. Compared with the existing reconstruction methods, whether missing baselines or not, the proposed image reconstruction method by Transformer has advantages in image quality. We verify the performance of this brightness temperature image reconstruction method through simulation and experiment.

A Coastal RFI Mitigation Method for Synthetic Aperture Interferometric Radiometer

This letter pays attention to the mitigation of the Radio Frequency Interference (RFI) sources located at coastal regions for Synthetic Aperture Interferometric Radiometer (SAIR). To remove an RFI at the visibility level, it will be necessary to estimate the Brightness Temperature (BT) of the point source, which is currently done by computing the median of the surrounded pixels to get the background BT, and subtracting it from the original contaminated BT. However, this strategy is based on the assumption that the background BT distribution around the RFI is flat, and this is not reasonable for those RFIs from coastal cities, and could result in estimation deviation. A robust RFI mitigation method is proposed without introducing complex computing. Instead of focusing only on the local area, the basic idea of our method is to step into the wide area, and minimize the overall fluctuation of the pixels affected by the RFI in the BT image. The method tests based on SMOS visibility samples validate the correctness of the developed method.

Deep Learning Imaging for 1-D Aperture Synthesis Radiometers

For one-dimensional aperture synthesis (1-D AS) radiometers, truncated sampling occurs in the frequency domain due to the system baseline limitation. Therefore, there is an obvious Gibbs oscillation in the reconstructed image. To solve this problem, an imaging method based on a one-dimensional convolutional neural network (1-D CNN) is proposed in this paper. Compared with deep learning methods based on two-dimensional convolutions, the 1-D convolution not only reduces the amount of computation but also produces further performance improvements. The input data of the network are the 1-D visibility function samples, and the output data are the 1-D brightness temperature (BT) samples. The network learns the mapping relationship from the training of the 1-D visibility function samples and 1-D BT samples to complete 1-D AS imaging without any prior knowledge. To verify the performance of this imaging method, simulations and experiments based on the airborne C-band one-dimensional microwave interferometric radiometer (ACMIR) system are implemented. The simulation and experimental results demonstrate that the proposed AS-CNN method achieves higher performance than the IFFT method in terms of image quality and Gibbs phenomenon suppression. In the case of an antenna failure and missing baseline, the AS-CNN method proposed in this paper can still obtain a BT image with high imaging quality, which shows that the robustness of the network is better than that of the IFFT method.

Array Configuration Design for Mirrored Aperture Synthesis Radiometers Based on Dual-Polarization Measurements

In mirrored aperture synthesis (MAS), the antenna array determines the rank of the transformation matrix connecting the cross-correlations to the cosine visibilities. However, the transformation matrix is rank-deficient, resulting in errors in the reconstructed brightness temperature (BT) image. In this paper, the signal propagations for the vertically polarized wave and horizontally polarized wave are analyzed. Then, the optimization model of the antenna array based on dual-polarization is established. The optimal array configurations are presented, with the corresponding transformation matrices being almost column full ranks. Simulation results demonstrate the validity of the proposed optimization model.

A New Objective Typhoon Location Algorithm Considering a Perturbation Factor Based on FY-4A Brightness Temperature Data

Abstract In this paper, a new objective typhoon positioning algorithm was proposed. The algorithm uses L1 12-channel far-infrared data of the FY-4A geostationary meteorological satellite for objective positioning, verified against best path data provided by the Tropical Cyclone Data Center of the China Meteorological Administration (CMA). By calculating the tangential and radial perturbation values of infrared brightness temperature images, the perturbation factor can be obtained. By adopting the position of the maximum perturbation factor as the center of a circle and considering a radius of no more than 20 km, the position of the minimum perturbation factor was determined; this value represents the central position of the typhoon. Tropical cyclones in 2019 and 2020 were selected for objective positioning, and the objective positioning results were verified against the corresponding time in the best path dataset. The results included centralized verification results for 29 typhoons and optimal path data in 2019. The maximum average error reached 54.67 km, with an annual average typhoon positioning error of 16.15 km. The centralization verification results for 23 typhoons and optimal path data in 2020 indicated a minimum average error of 12.71 km, a maximum average error of 18.56 km, and an annual average typhoon positioning error of 14.82 km. The positioning results for these two years suggest that the longitude obtained with the perturbation factor positioning method is satisfactory, exhibiting an error of less than 20 km. Significance Statement The purpose of this study is to help researchers make scientific discoveries and help the development of typhoon center location technology in the future. This is important because accurate positioning of typhoon center can provide effective help for typhoon path prediction and typhoon intensity determination.

A Near-Field Imaging Algorithm Based on Angular Spectrum Theory for Synthetic Aperture Interferometric Radiometer

For the near-field synthetic aperture interferometric radiometer (SAIR), the Fourier transform relationship between the visibility function and the near-field brightness temperature (BT) distribution is not valid. It is a challenging task for near-field SAIR imaging to realize very close-range accurate imaging with a large field of view (FOV). This article presents a new SAIR near-field imaging algorithm based on the angular spectrum theory to realize the passive millimeter-wave (PMMW) imaging, called synthetic-angular-spectrum imaging (SASI) algorithm. This SASI algorithm mainly addresses data samples of a 4-D visibility function acquired from planar arrays. First, we invert 4-D visibility samples into the angular spectrum domain via the Fourier transformation. The obtained angular spectrum data cannot be directly used to estimate the near-field BT distribution. Second, a dedicated phase factor compensation is adopted for handling this problem. The dimension-reducing accumulation is employed to generate the synthetic angular spectrum (SAS) of near-field BT distribution. Finally, we reconstruct the BT image by using the generated SAS data. Simulation and experiment results show that the presented SASI algorithm has the superiority on image reconstruction quality, especially for the off-axis regions, compared with the existing near-field SAIR imaging methods.

Non-Uniform Synthetic Aperture Radiometer Image Reconstruction Based on Deep Convolutional Neural Network

When observing the Earth from space, the synthetic aperture radiometer antenna array is sometimes set as a non-uniform array. In non-uniform synthetic aperture radiometer image reconstruction, the existing brightness temperature image reconstruction methods include the grid method and array factor forming (AFF) method. However, when using traditional methods for imaging, errors are usually introduced or some prior information is required. In this article, we propose a new IASR imaging method with deep convolution neural network (CNN). The frequency domain information is extracted through multiple convolutional layers, global pooling layers, and fully connected layers to achieve non-uniform synthetic aperture radiometer imaging. Through extensive numerical experiments, we demonstrate the performance of the proposed imaging method. Compared to traditional imaging methods such as the grid method and AFF method, the proposed method has advantages in image quality, computational efficiency, and noise suppression.