Polarimetric synthetic aperture radar (SAR) can capture rich polarization information of targets, but it is inherently affected by speckle. Learning-based methods have demonstrated superior speckle suppression potential. Most existing methods use optical images to simulate SAR noise for model training. Because of the significant differences in the imaging mechanisms between optical and SAR images, the data characteristics of these two types differ significantly, resulting in poor generalization performance. To this end, an Information Flow Switching-based Despeckling Network (IFSDN) is proposed for dual-polarization SAR image. By using the long time series data, the first dual-polarization SAR real dataset is constructed. The hybrid feature extraction module (HFEM) is constructed to independently extract and integrate features from both the diagonal and nondiagonal elements of the covariance matrix. Additionally, the multihierarchical residual attention despeckling (MRAD) module performs despeckling on feature maps from low to high levels. On this basis, the information flow switching mechanism facilitates the interaction of dominant features before and after despeckling, injecting spatial details into the despeckled results, reducing speckle noise, and preserving polarization information. By considering temporal changes, an adaptive joint loss function is, furthermore, constructed to guide the network training process, achieving high-fidelity despeckling while maintaining spatial-polarization information. Experiments show that IFSDN outperforms existing state-of-the-art methods in the speckle removal task for real dual-polarization SAR images, which can effectively preserve spatial and polarization information while suppressing speckles. Besides, generalization experiments demonstrate that the proposed model can be effectively applied to diverse datasets across various climate zones, showcasing its strong robustness.

Surface soil moisture plays a pivotal role in various hydrological processes. Precisely assessing soil moisture with high resolution is crucial for effective water resource management, informed agricultural decision-making, and in-depth climate change research. While passive microwave remote sensing is a primary technology for regional soil moisture monitoring, its practical application is hindered by data discontinuity and low resolution. To address these challenges, we propose an integrated learning framework to enhance the continuity and resolution of soil moisture data across China. Leveraging low-resolution passive microwave soil moisture data, moderate-resolution assimilated soil moisture data, and multiple high-resolution ancillary inputs, the network effectively captures the spatiotemporal dynamics of soil moisture through integrating gap-filling, multisource fusion, and spatial downscaling processes. Validation against in situ data demonstrates the significant enhancements achieved by the proposed method, with an average R value of 0.706 and an average unbiased root mean square error of 0.055 m3/m3. Comparative analysis further confirms its superior accuracy and robustness across diverse regions. These findings highlight the potential of this integrated learning framework to advance hydrological applications, enhance agricultural production, and support climate research.

Supervised deep learning despeckling methods usually use optical images to simulate multiplicative noise for training. However, due to the different imaging mechanisms of optical images and SAR images, the data characteristics of the two are significantly different, resulting in poor generalization performance of the model trained through the above form. Besides, the existing deep learning models do not fully consider the physical scattering mechanism, which causes the loss of polarization information. To solve those problems, an unsupervised deep learning method is proposed for dual-polarization SAR image despeckling. Under this framework, we combine the dual-polarization SAR covariance matrix and polarization decomposition information to construct a Dual-branch SAR image Despeckling Network (DSDN). The residual channel and the spatial attention mechanism are embedded to calibrate the polarization and spatial feature maps. The cross-attention mechanism is designed to mine the association of feature maps before and after denoising. Besides, the dual-branch joint loss function is proposed to constrain the training process. Spatial information experiments and polarization information experiments indicate that, compared with the existing state-of-the-art SAR despeckling methods, the proposed method can effectively remove the coherent speckle noise of dual-polarization SAR images, and can better preserve the polarization information. Codes are available at https://github.com/LiupengLin/DSDN.

Soil moisture (SM) is a key state variable in hydrology, climatology and water resource management. Microwave remote sensing can retrieve soil moisture at regional or global scales, but limited by spatial resolution, it is difficult to accurately reflect the details of soil moisture. To overcome this limitation, in this paper, we propose a Soil Moisture Downscaling residual dense Network (SMDN) based on spatiotemporal information. We model the relationship between low-resolution geoscience parameters and soil moisture, and then downscale soil moisture products by applying the model to high-resolution geoscience parameters. On this basis, the residual correction is performed on the soil moisture downscaling results to generate high-precision soil moisture products. Visual evaluation and quantitative experiments show that the proposed network can effectively improve the spatial detail information, maintain the spatial distribution pattern, and have strong stability.

Dense medium-resolution images play an important role in time-series geoscience applications. However, due to technical limitations, remote sensing imaging systems inevitably trade off temporal frequency and spatial swaths, resulting in difficulties to acquire images simultaneously with high spatial and temporal resolution. To overcome this limitation, under the framework of residual learning, we propose a Cross-attention based Adaptive weighting Fusion nEtwork (CAFE) for MODIS-Landsat spatiotemporal fusion to generate dense medium-resolution images. Based on the cross-attention mechanism, we propose multi-channel separated cross-attention and full-feature joint cross-attention blocks to enhance spatial resolution and retain spectral signatures from the perspectives of band-wise processing and full-feature joint processing, respectively. The adaptive temporal difference weighting mechanism is proposed to improve the ability to capture dynamic land surface changes. Besides, we employ an adaptive fusion loss function to constrain the network training. Experimental results indicate that the developed method is superior to several existing algorithms in terms of visual evaluation and quantitative evaluation and it can generate high-quality fusion results in scenarios of both subtle and dramatic temporal changes. Codes will be available at https://github.com/LiupengLin/CAFE.

Precipitation is a critical process in the terrestrial hydrological circulation, affecting climate change, water resource management, and agricultural production. Satellite-borne observations have prominent advantages in macro and mesoscopic quantitative precipitation estimation. Nevertheless, they are subject to low spatial resolution and inherent biases. Therefore, this study utilizes the surface-surface downscaling network and point-surface fusion network for fine-resolution and high-precision precipitation mapping over China. To deeply explore the complicated relationships between various ancillary factors, ground measurements and satellite precipitation, an attention mechanism based convolutional network (AMCN) is used for spatial downscaling and a geo-intelligent deep belief network (Geoi-DBN) is used for ground-satellite fusion. Experimental results indicate that cascaded networks toward two different objectives are superior to baseline methods, achieving R2 and RMSE of about 0.84 and 27.23 mm/month, respectively. Besides, the assistance of geo-intelligent items and ancillary factors contributes to fusion accuracy. This study provides an effective way for precipitation estimation over China.

Deep learning shows potential superiority in the image fusion field. To solve the problem of the spatial resolution degradation of polarimetric synthetic aperture radar (PolSAR) images caused by system limitation, we propose a fully PolSAR images and DualSAR images fusion network (FDFNet). We use low resolution (LR)-PolSAR super-resolution (LPSR) and modified cross attention mechanism (MCroAM) to perform data fusion on LR-PolSAR and high resolution (HR)-dual-polarization synthetic aperture radar (DualSAR) and design a polarimetric decomposition attention module to introduce the polarimetric parameters of LR-PolSAR images to maintain polarimetric information. Besides, we use the differential information between LR-PolSAR and HR-DualSAR to guide spatial resolution reconstruction. The loss function based on the$L_{1} $norm is used to constrain the network training process. The experimental results show the superiority of the proposed method over the existing methods in visual and quantitative evaluation. In addition, polarimetric decomposition experiments verify the effectiveness of the proposed method to maintain polarimetric information.

The data fusion technology aims to aggregate the characteristics of different data and to obtain products with multiple data advantages. To solve the problem of reduced resolution of polarimetric synthetic aperture radar (PolSAR) images due to system limitations, we propose a fully PolSAR images and single-polarization synthetic aperture radar (SinSAR) images fusion network to generate high-resolution PolSAR (HR-PolSAR) images. To take advantage of the polarimetric information of the low-resolution PolSAR (LR-PolSAR) images and the spatial information of the high-resolution single-polarization SAR (HR-SinSAR) images, we propose a fusion framework for joint LR-PolSAR images and HR-SinSAR images and design a cross-attention mechanism to extract features from the joint input data. Besides, based on the physical imaging mechanism, we designed the PolSAR polarimetric loss functions for constrained network training. The experimental results confirm the superiority of the fusion network over traditional algorithms. The average peak signal-to-noise ratio (PSNR) is increased by more than 3.6 dB, and the average mean absolute error (MAE) is reduced to less than 0.07. Experiments on polarimetric decomposition and polarimetric signature show that it maintains polarimetric information well.

In order to solve the problem of full-polarimetric SAR image degradation, this paper proposes a full-polarimetric SAR image super-resolution reconstruction method combined with a convolutional neural network and residual compensation. Through the advantages of the deep convolutional neural network for nonlinear model fitting, this paper performs super-resolution reconstruction on low-resolution full-polarimetric SAR images, and then applies residual compensation to network reconstruction results, using low-resolution image information to the network. The super-resolution reconstruction results are corrected to obtain a high-resolution full-polarimetric SAR image. Compared with the traditional full-polarimetric SAR image super-resolution reconstruction method, the proposed method shows excellent results in both visual and quantitative evaluation indicators, especially the reconstruction of detailed information.