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This paper presents a high-gain slotted ridge waveguide array antenna (SRWAA) with inductive diaphragms, which can realize a wide-sector beam with a low sidelobe simultaneously. The proposed antenna can cover a wide detection range and avoid interference from other directions. The expected excitation distribution for the antenna array is extracted through a beamforming method. To reduce the influence of the dispersion phenomenon on signal quality, inductive diaphragms are inserted into the sidewall of the ridge waveguide, which is fully analyzed from the point of the equivalent circuit. A cut-off-mode power divider is utilized, which can control the power ratio flexibly. A SRWAA working at 24.125 GHz, including a six-way feeding network, and a 6×24 slot array with the size of 330 mm × 66.8 mm is designed and fabricated. The measured sidelobe level (SLL) and half-power beamwidth (HPBW) in the elevation plane are -19.6 dB and 54.41°, with the counterparts in the azimuth plane -29.8 dB and 3.15°, respectively. The measured peak gain is 22.3 dBi at 24.125 GHz. The measured results are in good agreement with the simulated ones. This work has the potential to be applied in air detection, anti-unmanned aerial vehicles (UAVs), meteorological radar, and imaging radar.

Terahertz (THz) imaging has an outstanding advantage of high resolution due to the high frequency and has promising potential in VideoSAR. However, limited to the THz source power and the air absorption, the THz image usually has a low SNR and is susceptible to noise in remote sensing and imaging. In order to improve the quality of THz images, a THz image enhancement method is proposed based on the noise2noise idea. The THz images are reconstructed with the AFBP algorithm. They are organized as noisy image pairs and filtered with a mask to remove the influence of moving targets. Then, the Noise2Noise network is constructed based on the CNN network and takes the noisy image pair as input and reference. In the training stage, 1000 noisy image pairs are used as the training set and 100 noisy images are used as the test set to verify the performance of the proposed method. The experimental results based on real VideoSAR data demonstrate that the proposed method is capable of suppressing noise and enhancing the THz image.

Video synthetic aperture radar (SAR) has been found very useful in ground moving target indication (GMTI) and tracking. The dynamic shadows in video SAR imagery sequences indicate the real positions of moving targets, which can be utilized in target detection and tracking. Unfortunately, the shadow-based method often fails when the shadows are not sufficiently developed. On the other hand, the traditional energy-based GMTI methods exhibit performance degradation when SAR images of a moving target are distorted or smeared. Neither of these two methods can stand alone to provide robust detection and tracking of moving targets. This article presents a joint processing framework for video SAR GMTI and tracking by combining the target shadow and echo energy information, which effectively lowers the false alarm and miss-detection rate. This approach is very suitable for real-time surveillance of ground moving targets in a single-channel video SAR system. The proposed approach has been verified by using the simulated and the real video SAR data.

Sea-Surface small target detection is challenging for maritime radar. Unfortunately, conventional detection methods are often limited to complex marine environment and low signal-to-clutter ratio (SCR). This article presents a multi-frame detection approach for sea-surface small target by using deep convolutional neural network. The moving targets can be reconstructed and detected from the sequential Range-Doppler (RD) spectra. A two-step detection framework is proposed, where the intra-frame and the inter-frame detection is achieved by using the differences of features and inter-frame correlations between moving target and sea clutter, respectively. The proposed approach has been verified on both the simulated and real sea-surface small targets, which shows better detection performance than the conventional multi-frame detection algorithms. Additionally, this approach exhibits acceptable generalization ability.

An imaging radar based on an electronically displaced-phase-center antenna (eDPCA) has been reported recently, which provides great application potentials in many areas. To overcome the degradation of the SAR image quality caused by the inherent pattern fluctuations of the eDPCA, we propose a modified virtual array method to correct the irregularities in the eDPCA-based system. The effectiveness of the proposed approach is validated through experimental examples on a simulated eDPCA imaging system at 157 GHz.

It has long been recognized that synthetic aperture radar (SAR) images suffer in many applications from the speckle noise. Video SAR has a high frame rate of imaging and contains redundant information among frames. The temporal redundancy in video SAR images has been found useful for suppressing the speckle noise. However, the motion and the local differences between frames make it difficult to employ the temporal redundancy to suppress speckle noise. This article presents a video SAR image despeckling framework based on a new unsupervised training strategy referred to as DualNoise2Noise. This developed framework consists of a registration network and a denoising network. The registration network first compensates for the motion between two adjacent frames in video SAR in real time. After image registration, two adjacent frames with random speckle noise can be considered as the observations of the same region with local differences. The denoising network adopts the DualNoise2Noise training strategy to suppress speckle noise by using the temporal redundancy and to remove the negative impact caused by the local differences. The proposed approach has been used to process the real video SAR data, and the experimental results are convincing.

THz time-domain spectroscopy system (THz-TDS) can provide spectral information about materials, but the data acquisition takes a long time in THz imaging applications. The compressed sensing algorithm is a computational imaging technique. In this paper, we present a THz compressed sensing imaging method based on an improved linearized-ADMM algorithm. Linearized ADMM methods linearize access to the optimal solution through their gradient evaluations and can effectively solve the sparse recovery problem. The proposed algorithm needs much fewer THz spectra and reduces acquisition time. First, the Gaussian filter in the image domain and the Nesterov acceleration method are adopted in the iterative process to facilitate the convergence speed. Secondly, the MAD (Median absolute deviation) is chosen to calculate the threshold in the soft-shrink step. MAD is a robust measure of sample differences in univariate data sets. It is more flexible than the standard deviation in handling outliers in data sets, and can greatly reduce the impact of outliers on data sets. Thirdly, the L1-reweighted algorithm and averaging sparsity technique are used to enhance the performance and stability of the proposed approach. Finally, the THz beam pattern is taken into consideration to improve image resolution. The experimental results demonstrate that the proposed method has a good performance in THz imaging.

Accurate global terrestrial evapotranspiration (ET) estimation is essential to better understand Earth’s energy and water cycles. Although several global ET products exist, recent studies indicate that ET estimates exhibit high uncertainty. With the increasing trend of extreme climate hazards (e.g., droughts and heat waves), accurate ET estimation under extreme conditions remains challenging. To overcome these challenges, we used 3-hour and 0.25° Global Land Data Assimilation System (GLDAS) datasets (net radiation, land surface temperature (LST), and air temperature) and three-temperature (3T) model, without resistance and parameter calibration, in global terrestrial ET product development. The results demonstrated that the 3T model-based ET product agreed well with both global eddy covariance (EC) tower (root mean square error (RMSE) = 24.9 mm month-1) and basin-scale water balance observations (RMSE =116.0 mm yr-1). The 3T model-based global terrestrial ET product was comparable to other common ET products, i.e., MOD16, P-LSH, PML, GLEAM, GLDAS, and Fluxcom, retrieved from various models, but the 3T model performed better under extreme weather conditions in croplands than did the GLDAS, attaining 9.0–20 % RMSE reduction. The proposed ET product could provide periodic and large-scale information to support water cycle-related studies. The dataset is freely available at the Science Data Bank (http://doi.org/10.57760/sciencedb.o00014.00001, Xiong et al., 2022).

Urban evapotranspiration (ET) is an effective way to mitigate ecological challenges resulting from rapid urbanization. However, the characteristics of urban vegetation ET, especially how they respond to meteorological factors and soil water, remains unclear, which is crucial for urban ET regulation. Therefore, this study measured the actual ET rate of an urban lawn (ETlawn) using the Bowen ratio system and an urban tree (Ttree) by a sap flow system in the hot summer of a subtropical megacity, Shenzhen. The results showed that the daily ETlawn was more restricted by energy (Rs) and diffusion conditions (vapor pressure deficit, VPD), while the daily Ttree was more restricted by VPD and relative extractable water (REW) in the urban area. The daily Ttree decreased when the REW was lower than 0.18, while the daily ETlawn started to decrease when it was lower than 0.14. When REW was lower than 0.11, the Ttree stayed at a relatively low level. The impacts of VPD was more evident on the diurnal Ttree than on the diurnal ETlawn. Wind speed had a scarce impact as it was relatively low in urban areas. This study clarifies the different responses of ETlawn and Ttree to meteorological factors and soil water based on actual ET. The results are of great significance for the knowledge of urban forestry and urban hydrology.

A polarization-insensitive diffusion metasurface using a period-changed unit cell is presented for reducing the radar cross-section (RCS) of metallic objects in ultrawideband. Two metallic Minkowski loops are proposed as coding elements, different from traditional designs. The “0” element is constructed by period-changed unit cells to achieve a 180 ± 30° phase difference with the same reflection amplitude of nearly −0.9 dB in an ultrawideband from 7.1 to 29.2 GHz. Multilayer geometry with a thickness of 4.5 mm (about 0.105λ0 at the lowest operating frequency) and rotational symmetry loops are used to realize the ultrawideband characteristic and polarization-insensitive behavior. For verification, a polarization-insensitive diffusion metasurface is designed, fabricated, and measured. The simulated and measured results of the diffusion metasurface are in good consistency and the results both show that the metasurface enables a 10 dB backscattering reduction over an amazing ultrawideband ranging from 7.1 to 29.2 GHz (BW of 122%).