P-band Synthetic Aperture Radar Research Articles

An improved ionospheric scintillation correction method for spaceborne P-band polarimetric SAR

ABSTRACT Spaceborne P-band synthetic aperture radar (SAR) will suffer from ionospheric scintillation, which introduces a random phase into the azimuth signal and leads to image defocusing. For full-polarimetric SAR, it can be corrected by deriving the scintillation phase from Faraday rotation (FR) estimation. When estimating the FR angle, how to preserve changes in scintillation phase while smoothing noise is a key issue. The current method uses a rectangle averaging window. An improved correction method is proposed. Based on the correlation of scintillation phase, a new averaging window for noise suppression is designed to reduce scintillation estimation error. The method does not need any prior information of scintillation. The effectiveness of the proposed method is tested by simulations based on PALSAR2 data. For a non-homogeneous scene, when turbulence strength C k L = 1034 and C k L = 1035, the proposed method has obvious improvement compared with the current method.

InSAR-DEM Block Adjustment Model for Upcoming BIOMASS Mission: Considering Atmospheric Effects

The unique P-band synthetic aperture radar (SAR) instrument, BIOMASS, is scheduled for launch in 2024. This satellite will enhance the estimation of subcanopy topography, owing to its strong penetration and fully polarimetric observation capability. In order to conduct global-scale mapping of the subcanopy topography, it is crucial to calibrate systematic errors of different strips through interferometric SAR (InSAR) DEM (digital elevation model) block adjustment. Furthermore, the BIOMASS mission will operate in repeat-pass interferometric mode, facing the atmospheric delay errors introduced by changes in atmospheric conditions. However, the existing block adjustment methods aim to calibrate systematic errors in bistatic mode, which can avoid possible errors from atmospheric effects through interferometry. Therefore, there is still a lack of systematic error calibration methods under the interference of atmospheric effects. To address this issue, we propose a block adjustment model considering atmospheric effects. Our model begins by employing the sub-aperture decomposition technique to form forward-looking and backward-looking interferograms, then multi-resolution weighted correlation analysis based on sub-aperture interferograms (SA-MRWCA) is utilized to detect atmospheric delay errors. Subsequently, the block adjustment model considering atmospheric effects can be established based on the SA-MRWCA. Finally, we use robust Helmert variance component estimation (RHVCE) to build the posterior stochastic model to improve parameter estimation accuracy. Due to the lack of spaceborne P-band data, this paper utilized L-band Advanced Land Observing Satellite (ALOS)-1 PALSAR data, which is also long-wavelength, to emulate systematic error calibration of the BIOMASS mission. We chose climatically diverse inland regions of Asia and the coastal regions of South America to assess the model’s effectiveness. The results show that the proposed block adjustment model considering atmospheric effects improved accuracy by 72.2% in the inland test site, with root mean square error (RMSE) decreasing from 10.85 m to 3.02 m. Moreover, the accuracy in the coastal test site improved by 80.2%, with RMSE decreasing from 16.19 m to 3.22 m.

Maps of active layer thickness in northern Alaska by upscaling P-band polarimetric synthetic aperture radar retrievals

Extensive, detailed information on the spatial distribution of active layer thickness (ALT) in northern Alaska and how it evolves over time could greatly aid efforts to assess the effects of climate change on the region and also help to quantify greenhouse gas emissions generated due to permafrost thaw. For this reason, we have been developing high-resolution maps of ALT throughout northern Alaska. The maps are produced by upscaling from high-resolution swaths of estimated ALT retrieved from airborne P-band synthetic aperture radar (SAR) images collected for three different years. The upscaling was accomplished by using hundreds of thousands of randomly selected samples from the SAR-derived swaths of ALT to train a machine learning regression algorithm supported by numerous spatial data layers. In order to validate the maps, thousands of randomly selected samples of SAR-derived ALT were excluded from the training in order to serve as validation pixels; error performance calculations relative to these samples yielded root-mean-square errors (RMSEs) of 7.5–9.1 cm, with bias errors of magnitude under 0.1 cm. The maps were also compared to ALT measurements collected at a number of in situ test sites; error performance relative to the site measurements yielded RMSEs of approximately 11–12 cm and bias of 2.7–6.5 cm. These data are being used to investigate regional patterns and underlying physical controls affecting permafrost degradation in the tundra biome.

UAV-Based P-Band SAR Tomography With Long Baseline: A Multimaster Approach

Due to the advantage of flexible and rapid deployment, unmanned aerial vehicle (UAV)-based synthetic aperture radar (SAR) tomography (TomoSAR) is a promising technology in 3-D urban mapping. The long baseline is indispensable for P-band SAR systems to achieve high elevation resolution. It will introduce two problems. On the one hand, the unavoidable spatial decorrelation brings serious phase noise and sidelobes in 3-D imaging. On the other hand, the noticeable image distortion fails the image registration and the TomoSAR data stack (TDS) construction. Aiming at the above problems, this article proposes a multimaster (MM) TomoSAR approach via three main contributions. First, the traditional TomoSAR signal model is extended to the MM case to improve the number of baselines and the average image coherence of the TDS and suppress the sidelobes. Second, a short-baseline-recursion image registration method is proposed to achieve high-precision image registration. Third, a TDS optimization processing consisting of interferometric SAR (InSAR) phase screening and baseline sign reassignment is introduced. Moreover, a clustering-based outliers’ elimination method is also adopted to ensure the 3-D imaging quality. Computer simulation and long-baseline P-band UAV-SAR experiment validate the proposed approach.

Underlying topography and forest height estimation from SAR tomography based on a nonparametric spectrum estimation method with low sidelobes

ABSTRACT The underlying topography and forest height play an indispensable role in many fields, including geomorphology, civil engineering construction, forest investigation, and the modeling of natural disasters. As a new microwave remote sensing technology with three-dimensional imaging capability, synthetic aperture radar (SAR) tomography (TomoSAR) has already been proven to be an important tool for underlying topography and forest height estimation. Many spectrum estimation methods have now been proposed for TomoSAR. However, most of the commonly used methods are susceptible to noise and inevitably produce sidelobes, resulting in a reduced accuracy for the inversion of forest structural parameters. In this paper, to solve this problem, a nonparametric spectrum estimation method with low sidelobes – the G-Pisarenko method – is introduced. This method performs a logarithmic operation on the covariance matrix to obtain the main scattering characteristics of the objects of interest while suppressing the noise as much as possible. The effectiveness of the proposed method is demonstrated by the use of both simulated data and P-band airborne SAR data from a tropical forest region in Gabon, Africa. The results show that the proposed method can reduce the sidelobes and improve the estimation accuracy for the underlying topography and forest height.

Ionospheric Phase Scintillation Correction Based on Multi-Aperture Faraday Rotation Estimation in Spaceborne P-Band Full-Polarimetric SAR Data

The spaceborne P-band fully polarimetric synthetic aperture radar (SAR) working system is highly susceptible to the scintillation effects induced by ionospheric irregularities due to its low carrier frequency. The scintillation phase error (SPE) is a dominant factor that leads to azimuth decorrelation. The aperture-dependent and spatial-varying characteristics of the SPE promote the complexity of the SPE estimation and compensation. In this paper, a methodology is described that compensates the SPE by estimating the Faraday rotation (FR) angle from fully polarimetric SAR data. The multi-aperture scheme is adopted, including the sub-aperture FR estimation, multi-aperture splicing, and overall compensation, to take the complicated characteristics of the aperture-dependent and spatial-varying SPE into account. The methodology is finally validated on simulated data derived from the airborne P-band SAR real data, and compared with an existing method. The new method does not need prior knowledge of the ionospheric height. Furthermore, its performance is investigated in relation to several key factors in different simulation conditions.

Factors Controlling a Synthetic Aperture Radar (SAR) Derived Root-Zone Soil Moisture Product over The Seward Peninsula of Alaska

Root-zone soil moisture exerts a fundamental control on vegetation, energy balance, and the carbon cycle in Arctic ecosystems, but it is still not well understood in vast, remote, and understudied regions of discontinuous permafrost. The root-zone soil moisture product (30 m resolution) used in this analysis was retrieved from a time-series P-Band (420–440 MHz) synthetic aperture radar (SAR) backscatter observations (August 2017 & October 2017). While similar approaches have been taken to retrieve surface (0 cm to 5 cm) soil moisture from L-Band (1.2 GHz) SAR backscatter, this is one of the first known attempts at reaching the root-zone in permafrost regions. Here, we analyze secondary factors (excluding primary factors, such as precipitation) controlling summer (August) soil moisture at depths of 6 cm, 12 cm, and 20 cm over a 4500 km2 area on the Seward Peninsula of Alaska. Using a random forest model, we quantify the impact of topography, vegetation, and meteorological factors on soil moisture distributions. In developing the random forest model, we explore a variety of feature scales (30 m, 60 m, 90 m, 120 m, 180 m, and 240 m), tune hyperparameters (the structure of individual decision trees making up the ensemble including the number and depth of trees), and perform the final feature selection using cross-validated recursive feature elimination. Results suggest that root-zone soil moisture on the Seward Peninsula is primarily controlled by vegetation at 6 cm, but deeper in the soil column topography and meteorological factors, such as predominant winter wind direction and summer insolation, play a larger role. The random forest model accounts for 40% to 60% of the variation observed (R2 = 0.44 at 6 cm, R2 = 0.52 at 12 cm, R2 = 0.58 at 20 cm). These results indicate that vegetation is the dominant control on soil moisture shallow in the soil column, but the impact of vegetation does not extend to deeper layers retrieved from P-Band SAR backscatter.

Compensation for the ionospheric dispersion effect on spaceborne P-band full-polarimetric wideband SAR

ABSTRACT Spaceborne P-band full-polarimetric wideband synthetic aperture radar (SAR) will be affected by ionospheric phase dispersion and polarimetric dispersion. An improved contrast optimization autofocus (COA) method using four polarization channel data is proposed to compensate for both phase dispersion and polarimetric dispersion. The basis and steps of the method are given. Its performance is analysed by typical target simulation under clutter background and compared with traditional COA method for single channel. The residual quadratic phase error (QPE) of single channel compensated by traditional method may exceed 45° focusing threshold, while the proposed method can reduce the error to less than 10°. Due to the lack of spaceborne P-band SAR data, we simulate the effect of ionosphere on P-band SAR based on ALOS2 PALSAR2 data and verify the proposed method. The simulation result shows the proposed method is effective and superior to traditional method.

Assessment of underlying topography and forest height inversion based on TomoSAR methods

ABSTRACT Due to the strong penetrability, long-wavelength synthetic aperture radar (SAR) can provide an opportunity to reconstruct the three-dimensional structure of the penetrable media. SAR tomography (TomoSAR) technology can resynthesize aperture perpendicular to the slant-range direction and then obtain the tomographic profile consisting of power distribution of different heights, providing a powerful technical tool for reconstructing the three-dimensional structure of the penetrable ground objects. As an emerging technology, it is different from the traditional interferometric SAR (InSAR) technology and has advantages in reconstructing the three-dimensional structure of the illuminated media. Over the past two decades, many TomoSAR methods have been proposed to improve the vertical resolution, aiming to distinguish the locations of different scatters in the unit pixel. In order to cope with the forest mission of European Space Agency (ESA) that is designed to provide P-band SAR measurements to determine the amount of biomass and carbon stored in forests, it is necessary to systematically evaluate the performance of forest height and underlying topography inversion using TomoSAR technology. In this paper, we adopt three typical algorithms, namely, Capon, Multiple Signal Classification (MUSIC), and Compressed Sensing (CS), to evaluate the performance in forest height and underlying topography inversion. The P-band airborne full-polarization (FP) SAR data of Lopè National Park in the AfriSAR campaign implemented by ESA in 2016 is adopted to verify the experiment. Furthermore, we explore the effects of different baseline designs and filter methods on the reconstruction of the tomographic profile. The results show that a better tomographic profile can be obtained by using Hamming window filter and Capon algorithm in uniform baseline distribution and a certain number of acquisitions. Compared with LiDAR results, the root-mean-square error (RMSE) of forest height and underlying topography obtained by Capon algorithm is 2.17 m and 1.58 m, which performs the best among the three algorithms.

Forest Height Estimation Approach Combining P-Band and X-Band Interferometric SAR Data

Forest height is an essential parameter used to derive important information about forest ecosystems, such as forest above-ground biomass. In this article, a forest height estimation approach combining P-band and X-band interferometric synthetic aperture radar (InSAR) was introduced. The forest height was estimated using the difference in the penetration of long- and short-wavelength radars to the forest. That is, the P-band and X-band InSAR data were used to extract the digital terrain model (DTM) and digital surface model (DSM), respectively. For the DTM, an improved time-frequency (TF) analysis method was used to reduce the effect of forest scatterers on the extraction of a pure understory terrain phase based on P-band InSAR. For the DSM, a novel compensation algorithm based on a multi-layer model (MLM) was proposed to remove the penetration bias of the X-band. Compared to the existing method based on the infinitely deep uniform volumes (IDUV) model, the MLM-based method is more in line with the characteristics of forest structure and the scattering mechanism for X-band InSAR. The airborne P-band repeat-pass InSAR and spaceborne X-band (TanDEM-X) single-pass InSAR data were used to verify the proposed method over the study area in the Saihanba Forest Farm in Hebei, China. The results demonstrated that the improved TF method can achieve high-precision DTM extraction based on P-band InSAR data, and the root mean square error (RMSE) was 0.94 m. The proposed MLM-based compensation method of the DSM achieved a smaller error (RMSE: 1.67 m) compared to the IDUV-based method (RMSE: 3.01 m). Under the same DTM extracted by P-band InSAR, the estimation accuracy of forest height based on the MLM method was 86.58% (RMSE: 1.81 m), which was 8.49% higher than that of the IDUV-based method (RMSE: 2.98 m).

SAR Imaging Algorithm of Ocean Waves Based on Optimum Subaperture.

Synthetic Aperture Radar (SAR) is widely applied to the field of ocean remote sensing. Clear SAR images are the basis for ocean information acquisitions, such as parameter retrieval of ocean waves and wind field inversion of the ocean surface. However, the SAR ocean images are usually blurred, which seriously affects the acquisition of ocean information. The reasons for the wave blurring in SAR images mainly include the following two aspects. One is that when SAR observes the ocean, the motion of ocean waves will have a greater impact on imaging quality. The other is that the ocean’s surface is seriously decorrelated within the integration time. In order to obtain clear SAR images of ocean waves, a SAR imaging algorithm of ocean waves based on the optimum subaperture is proposed, aiming at the above two aspects. The optimum focus setting of the ocean waves is calculated, drawing support from the azimuth phase velocity of the dominant wave. The optimum subaperture is further calculated according to the proposed new evaluation, namely, F. Finally, according to the optimum focus setting and the optimum subaperture, the dominant wave is refocused, and a clear SAR image of the dominant wave can be obtained. The proposed algorithm was applied to airborne L-band and P-band SAR data. Furthermore, the proposed algorithm was compared with present methods, and the results sufficiently demonstrated the effectiveness and superiority of the proposed algorithm.

Sensitivity of Multifrequency Polarimetric SAR Data to Postfire Permafrost Changes and Recovery Processes in Arctic Tundra

We used full-polarimetric L-band and P-band synthetic aperture radar (SAR) data collected from the recent NASA Arctic Boreal Vulnerability Experiment (ABoVE) airborne campaign and Sentinel-1 C-band dual-polarization data to understand the sensitivity of radar backscatter intensity and phase to fire-induced changes in the surface and subsurface soil processes in Arctic tundra underlain by permafrost. The 2007 Anaktuvuk River fire on the Alaska North Slope was used as a case study. At ~10-year postfire, we observed a strong increase (>~3–4 dB) in the low-frequency radar backscatter in severely burned areas during the thaw season, in contrast to limited (< ~0.5 dB) C-band backscatter differences (VV and VH) between burned and unburned areas. However, C-band winter backscatter is generally higher (>1 dB) in burned areas than the adjacent unburned areas. Polarimetric decomposition analysis indicated a general trend toward more random surface scattering, and strong increases in double-bounce scattering and volume scattering power at both P- and L-band in the burned areas. The ice-rich yedoma region shows the largest backscatter increases in burned areas and the highest correlation with burn severity and microtopography changes. The above backscatter changes are attributed to increasing surface roughness and microtopography due to ice-wedge degradation and thermokarst development and increasing subsurface scattering due to an overall drier and deeper active layer in burned areas. Among all frequencies, P-band shows consistently larger contrast in backscatter power and phase between burned and unburned areas, which makes it potentially more useful to study fire–permafrost interactions in the Arctic over decadal time scales.

A Novel Iterative Reweighted Method for Forest Height Inversion Using Multibaseline PolInSAR Data

Multibaseline polarimetric synthetic aperture radar interferometry (PolInSAR) is one of the advanced technologies of forest height inversion, as it provides rich observation information. In this letter, we propose a new iterative reweighted method for multibaseline PolInSAR joint inversion of forest height. First, we establish a better stochastic model and weight function to obtain more accurate parameter estimation considering the relationship between vertical wavenumber and forest height. Second, according to the proposed inversion criterion, the baseline observations with unsuitable interferometric geometry are regarded as gross errors and eliminated through reweighted iteration. Finally, we select airborne P-band synthetic aperture radar (SAR) data collected by the F-SAR system during the AfriSAR 2016 campaign for experimental validation. The experimental results show that using initial iteration values obtained by three optimal baseline selection methods, the accuracy of the proposed method achieves 4.47, 4.46, and 4.24 m, which is about 34.74%, 32.32%, and 33.23% higher than those of the existing multibaseline joint inversion method [root mean square error (RMSE) = 6.85, 6.59, and 6.35 m], respectively.

Autofocus-Based Estimation of Penetration Depth and Permittivity of Ice Volumes and Snow Using Single SAR Images

An intrinsic challenge in the geophysical interpretation of low-frequency synthetic aperture radar (SAR) imagery of semitransparent media, such as ice sheets, is the position ambiguity of the scattering structures within the glacial volume. Commonly tackled by applying interferometric and tomographic techniques, their spaceborne implementation exhibits by orders higher complexity compared to missions relying on single SAR images, making them cost expensive or, in the context of planetary missions, even impossible due to limited navigation capability. Besides, even these sophisticated techniques are commonly biased due to inaccurate permittivity estimates, leading to geometric distortions up to several meters. We present a novel inversion procedure to estimate volume parameters of ice sheets, namely, the depth of the scattering layer within the glacial volume and the dielectric permittivity of the ice, based on single-image single-polarization SAR acquisitions. The information is inherent in the processed SAR data as phase errors on the azimuth signals resulting from uncompensated nonlinear propagation of the radar echoes through ice. We suggest a local map-drift autofocus approach to quantify and spatially resolve the phase errors and an inversion model to relate them to the penetration depth and permittivity. Testing the proposed technique using P-band SAR data acquired using DLR’s airborne sensor F-SAR during the ARCTIC15 campaign in Greenland shows promising results and good agreement with tomographic products of the analyzed test site.

Tropical Forest Vertical Structure Characterization: From GEDI to P-Band SAR Tomography

Estimating tropical forests vertical structure using remote sensing is a challenge. Active sensors such as low-frequency Synthetic Aperture Radar (SAR) operating at P-band, with a wavelength of ~ 69 cm wavelength, and Light Detection and Ranging (LiDAR) are able to penetrate thick vegetation layers. While NASA’s Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne liDAR data, the ESA’s next Earth Explorer BIOMASS mission will acquire multiple acquisitions over the same areas to form three-dimensional images through SAR tomography (TomoSAR) technique. Our study shows the potential value of GEDI and TomoSAR acquisitions in producing accurate estimates of forests vertical structure. By analyzing airborne P-band TomoSAR, airborne LiDAR, and spaceborne GEDI LiDAR at a tropical forest site in Paracou, French Guiana, South America, we show that both GEDI and P-band TomoSAR can directly measure surface, vegetation heights, and vertical profiles with high resolution and precision. Airborne TomoSAR is of higher quality than GEDI due to better penetration properties and precision. However, the GEDI vegetation height root-mean-square error is less than 5 m, for an average forest height value around 30 m at the Paracou site, which is similar to the expected performance of the future spaceborne BIOMASS mission. These results suggest GEDI measurements, i.e. shots with sensitivity greater than 98%, will provide a good reference of forest structure to calibrate the BIOMASS mission algorithms.

Spiral SAR Imaging with Fast Factorized Back-Projection: A Phase Error Analysis.

This paper presents a fast factorized back-projection (FFBP) algorithm that can satisfactorily process real P-band synthetic aperture radar (SAR) data collected from a spiral flight pattern performed by a drone-borne SAR system. Choosing the best setup when processing SAR data with an FFBP algorithm is not so straightforward, so predicting how this choice will affect the quality of the output image is valuable information. This paper provides a statistical phase error analysis to validate the hypothesis that the phase error standard deviation can be predicted by geometric parameters specified at the start of processing. In particular, for a phase error standard deviation of ~12°, the FFBP is up to 21 times faster than the direct back-projection algorithm for 3D images and up to 13 times faster for 2D images.

Forest height retrieval using P-band airborne multi-baseline SAR data: A novel phase compensation method

Synthetic aperture radar (SAR) tomography (TomoSAR) has been well-established for three-dimensional (3-D) information extraction of forests using the multi-baseline SAR data stacks. The multi-baseline SAR data stacks can be acquired by spaceborne and airborne SAR systems, but for forest scenarios, the data stacks acquired by the airborne SAR system are mostly used. Such a data stack has the advantages of short revisiting time and weak temporal decorrelation. However, due to the baseline errors (caused by the residual platform motion and the measurement errors of the navigation instruments), phase errors (PEs) will occur. PEs are independent of one track to the other, resulting in spreading and defocusing in tomographic imaging. In this paper, we proposed a novel phase compensation method named NC-PGA, which combines the methods of network construction (NC) and phase gradient autofocus (PGA) to estimate and compensate the PEs. The NC method uses the Delaunay triangulation network and beamforming to obtain an accurate elevation estimate of the selected permanent scatterers, which can be used as the prior information for subsequent processing to overcome the shortcomings of the PGA method in PEs estimation. The PGA method uses the spatial invariance of PEs in a limited area to compensate for the PE of each track. The applicability of the NC-PGA method is demonstrated using simulated data and real data. The real data contains two data stacks. The one is acquired by a full-polarization P-band airborne SAR system (developed independently by our project research team) over the study area in Saihanba Forest Farm in Hebei, China. The other one is acquired by ONERA SETHI airborne system over Paracou, French Guiana, in the frame of the European Space Agency’s campaign TropiSAR. We select a test area in the study area and successfully retrieve the height of the forest, and use LiDAR data for results validation and evaluation.

Potential of P-Band SAR Tomography in Forest Type Classification

Forest type classification using spaceborne remote sensing is a challenge. Low-frequency Synthetic Aperture Radar (SAR) signals (i.e., P-band, ∼0.69 m wavelength) are needed to penetrate a thick vegetation layer. However, this measurement alone does not guarantee a good performance in forest classification tasks. SAR tomography, a technique employing multiple acquisitions over the same areas to form a three-dimensional image, has been demonstrated to improve SAR’s capability in many applications. Our study shows the potential value of SAR tomography acquisitions to improve forest classification. By using P-band tomographic SAR data from the German Aerospace Center F-SAR sensor during the AfriSAR campaign in February 2016, the vertical profiles of five different forest types at a tropical forest site in Mondah, Gabon (South Africa) were analyzed and exploited for the classification task. We demonstrated that the high sensitivity of SAR tomography to forest vertical structure enables the improvement of classification performance by up to 33%. Interestingly, by using the standard Random Forest technique, we found that the ground (i.e., at 5–10 m) and volume layers (i.e., 20–40 m) play an important role in identifying the forest type. Together, these results suggested the promise of the TomoSAR technique for mapping forest types with high accuracy in tropical areas and could provide strong support for the next Earth Explorer BIOMASS spaceborne mission which will collect P-band tomographic SAR data.

Translation between High- and Low-frequency SAR Images using Cycle-Consistent Conditional Adversarial Network

The bi-frequency (high- and low) synthetic aperture radar (SAR) images cannot be directly compared due to their distinct statistical properties. To diminish their statistical difference, we manage to translate the bi-frequency SAR images into one another. Therefore, we propose a cycle-consistent conditional adversarial network to achieve the goal. The cycle-consistency criteria in the Cycle GAN and the conditional generation adversarial networks in the Pix2Pix are integrated to construct the cycle-consistent conditional adversarial network. Experiments on Ku-band and P-band SAR images validate that our method outperforms Cycle GAN and Pix2Pix.

Deep convolutional neural network for P-band spaceborne synthetic aperture radar imaging through the ionosphere

The dispersion characteristics of the background ionosphere and the random fluctuations of the ionospheric irregularities are an important source of phase error that seriously damages the quality of radar images. To mitigate the ionospheric distortions of P-band spaceborne synthetic aperture radar (SAR) images, a super-resolution deep learning method is proposed in this paper. Different from the traditional imaging method based on the prior knowledge of imaging, the method proposed in this paper directly trains the ionospheric implicit imaging model of spaceborne SAR without complicated iterative processes. First, the phase errors, which are caused by the dispersion and the scintillation in the range and azimuth directions, respectively, are analyzed. Second, an improved u-net structure that embeds the residual network between the encoder and decoder is briefly proposed. Finally, the range doppler algorithm is used to preprocess radar image as the input of the convolutional neural network (CNN) and is compare with the predicted output of the CNN. Experimental results prove the effectiveness of the proposed method in radar image focusing.