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.

Deep learning has achieved significant success in pan sharpening, but there are still two nonnegligible challenges. First, most of the existing methods rely on reduced-resolution training samples, limiting their performance when migrating from simulated to real-world scenes. Second, they pay insufficient attention to the imaging mechanism and the complexity and heterogeneity of remote sensing information, which leads to unclear representation of the relationships between images and the underutilization of the full-resolution features. In response to the abovementioned issues, this article presents an unsupervised pan-sharpening network incorporating imaging spectral prior and spatial-spectral compensation, named USCPNet. First, a structure-guided cross-attention (CA) residual (SCAR) block is constructed, deriving the desired high-resolution multispectral (HRMS) image guided by the panchromatic texture-structure features. A spectrally adaptive degradation network (SNet) coupling imaging spectral prior is then introduced, which characterizes the pixel-by-pixel spectral mapping between the HRMS image and the high-resolution panchromatic (HRPAN) image, to implement a precise spatial constraint driven by the imaging mechanism. In addition, given the difficulty of comprehensively extracting and integrating complementary features within an unsupervised framework through single-stream fusion, spatial-spectral joint progressive compensated (SSPC) stages are employed to achieve refined enhancement of the effective information in the predicted HRMS image through iterative rounds of residual fusion. Experiments conducted on Gaofen-1 (GF-1), Gaofen-2 (GF-2), and WorldView-2 (WV-2) satellite images reveal that the proposed USCPNet excels in spatial enhancement (SE) while preserving spectral fidelity. USCPNet also demonstrates advantages in specific applications, such as large-scale image fusion and vegetation index generation, compared with state-of-the-art methods.

Soil moisture (SM) downscaling aims to solve the coarse resolution problem of passive microwave SM products. On the basis of SMAP SM products and related MODIS products, this study develops a deep residual cycle generative adversarial network (GAN) based heterogeneous spatial-spectral fusion method to downscale SMAP SM from 36km to 9km. On the one hand, the proposed method creatively regards the MODIS products that can reflect the SM state as the spectral features of SM in a broad sense and performs the heterogeneous spatial-spectral fusion between the low-resolution (LR) SM product and high-resolution (HR) MODIS products. On the other hand, considering the spatial correlation of SM, the proposed method utilizes a deep residual cycle generative adversarial network (GAN) to extract and fuse features of heterogeneous images through convolutions. Both qualitative and quantitative evaluation of experimental results shows that the proposed method can generate high accuracy SM products.

In the field of remote sensing image pansharpening, deep learning-based methods have shown impressive performances recently. However, most deep learning-based pansharpening methods are based on supervised learning, which requires a large number of training images. In addition, obtaining large amounts of images with a high spatial and spectral resolution for training may be difficult in practice. In this letter, a novel self-supervised learning method based on a cycle-consistent generative adversarial network (CycleGAN) is proposed for remote sensing image pansharpening, without requiring large volumes of data for training. The framework contains two generators and two discriminators, and applies a residual neural network to the first generator. The panchromatic (PAN) image and multispectral (MS) image are input into the first generator to obtain the fused image, and then the fused image is input into the second generator to obtain a PAN image, which should be consistent with the input PAN image. The experimental results show that the proposed method performs better than the state-of-the-art unsupervised pansharpening method, and also achieves a competitive performance when compared with a supervised method.

It is a challenging task to integrate the spatial, temporal, and spectral information of multi-source remote sensing images, especially in the case of heterogeneous images. To this end, for the first time, this paper proposes a heterogeneous integrated framework based on a novel deep residual cycle generative adversarial network (GAN). The proposed network consists of a forward fusion part and a backward degeneration feedback part. The forward part generates the desired fusion result from the various observations; the backward degeneration feedback part considers the imaging degradation process and regenerates the observations inversely from the fusion result. The heterogeneous integrated fusion framework supported by the proposed network can simultaneously merge the complementary spatial, temporal, and spectral information of multi-source heterogeneous observations to achieve heterogeneous spatio-spectral fusion, spatio-temporal fusion, and heterogeneous spatio-temporal-spectral fusion. Furthermore, the proposed heterogeneous integrated fusion framework can be leveraged to relieve the two bottlenecks of land-cover change and thick cloud cover. Thus, the inapparent and unobserved variation trends of surface features, which are caused by the low-resolution imaging and cloud contamination, can be detected and reconstructed well. Images from many different remote sensing satellites, i.e., Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat 8, Sentinel-1, and Sentinel-2, were utilized in the experiments conducted in this study, and both the qualitative and quantitative evaluations confirmed the effectiveness of the proposed image fusion method.

Image fusion is a powerful means to integrate complementary spatio-temporal-spectral information among multi-source remote sensing images. The existing remote sensing image fusion is mostly limited to the fusion between optical images, and most of them are limited to the fusion between two sensors. Based on this, this paper proposes a heterogeneous spatio-temporal-spectral fusion method based on deep learning. Specifically, it combines the low-spatial-resolution (LR) cloudy image with the high-spatial-resolution (HR) SAR images and the HR cloud-free optical image to remove the clouds and improve the spatial resolution of the LR cloudy image. The SAR image is acquired at the same date as the LR cloudy image, while the HR cloud-free image is acquired at another date. Experiments are performed on the images of Landsat 8, Sentinel-1, and Sentinel-2. The experimental results show that the proposed method can effectively achieve the joint goal of spatial resolution improvement and cloud removal of the Landsat image.

In this paper, a new pansharpening method with residual convolutional neural network (RCNN)) is proposed. The proposed method utilizes a novel end-to-end CNN, which maps the differential information between the high spatial resolution panchromatic image (HR-PAN) and the low spatial resolution multispectral image (LR-MS) to the differential information between the HR-PAN image and the high spatial resolution multispectral image (HR-MS). Unlike the CNN-based pansharpening methods in other literatures, the proposed method makes full use of the spatial information in the HR-PAN image, and simultaneously preserve the spectral information of the MS image. Experimental results at both reduced resolution and full resolution demonstrate the superior performance of the proposed method comparing to state-of-the-art pansharpening methods in both quantitative and visual assessments.

In the field of spatial–spectral fusion, the variational model-based methods and the deep learning (DL)-based methods are state-of-the-art approaches. This paper presents a fusion method that combines the deep neural network with a variational model for the most common case of spatial–spectral fusion: panchromatic (PAN)/multispectral (MS) fusion. Specifically, a deep residual convolutional neural network (CNN) is first trained to learn the gradient features of the high spatial resolution multispectral image (HR-MS). The image observation variational models are then formulated to describe the relationships of the ideal fused image, the observed low spatial resolution multispectral image (LR-MS) image, and the gradient priors learned before. Then, fusion result can then be obtained by solving the fusion variational model. Both quantitative and visual assessments on high-quality images from various sources demonstrate that the proposed fusion method is superior to all the mainstream algorithms included in the comparison, in terms of overall fusion accuracy.