Optical remote-sensing images have a wide range of applications, but they are often obscured by clouds, which affects subsequent analysis. Therefore, cloud removal becomes a necessary preprocessing step. In this paper, a novel and superior transformer-based network is proposed, named Cloudformer. The proposed method novelly combines the advantages of convolution and a self-attention mechanism: it uses convolution layers to extract simple features over a small range in the shallow layer, and exerts the advantage of a self-attention mechanism in extracting correlation in a large range in the deep layer. This method also introduces Locally-enhanced Positional Encoding (LePE) to flexibly generate suitable positional encodings for different inputs and to utilize local information to enhance encoding capabilities. Exhaustive experiments on public datasets demonstrate the superior ability of the method to remove both thin and thick clouds, and the effectiveness of the proposed modules is validated by ablation studies.

Clouds often contaminate remote sensing images, which leads to missing land feature information and subsequent application degradation. Low-rank tensor completion has shown great potential in the reconstruction of multi-temporal remote sensing images. However, existing methods ignore different low-rank properties in the spatial and temporal dimensions, such that they cannot utilize spatial and temporal information adequately. In this paper, we propose a new frequency spectrum-modulated tensor completion method (FMTC). First, remote sensing images are rearranged as third-order spatial–temporal tensors for each band. Then, Fourier transform (FT) is introduced in the temporal dimension of the rearranged tensor to generate a spatial–frequential tensor. In view of the fact that land features represent low-frequency components and fickle clouds represent high-frequency components in the time domain, we chose adaptive weights for the completion of different low-rank spatial matrixes, according to the frequency spectrum. Then, Invert Fourier Transform (IFT) was implemented. Through this method, the joint low-rank spatial–temporal constraint was achieved. The simulated data experiments demonstrate that FMTC is applicable on different land-cover types and different missing sizes. With real data experiments, we have validated the effectiveness and stability of FMTC for time-series remote sensing image reconstruction. Compared with other algorithms, the performance of FMTC is better in quantitative and qualitative terms, especially when considering the spectral accuracy and temporal continuity.

As the most important city in China, Beijing has experienced an economic soar, large-scale population growth and eco-environment changes in the last 20 years. Evaluating climate- and human-induced vegetation changes could reveal the relationship of vegetation-climate-human activities and provide important insights for the coordination of economic growth and environmental protection. Based on a long-term MODIS vegetation index dataset, meteorological data (temperature, precipitation) and impervious surface data, the Theil-Sen regression and the Mann-Kendall method are used to estimate vegetation change trends in this study and the residual analysis is utilized to distinguish the impacts of climate factors and human activities on vegetation restoration and degradation from 2000 to 2019 in Beijing. Our results show that the increasing vegetation areas account for 80.2% of Beijing. The restoration of vegetation is concentrated in the urban core area and mountainous area, while the degradation of vegetation is mainly concentrated in the suburbs. In recent years, the vegetation in most mountainous areas has changed from restoration to significant restoration, indicating that the growth of mountain vegetation has continued to restore. We also found that in the process of urban expansion, vegetation browning occurred in 53.1% of the urban built-up area, while vegetation greening occurred in the remaining area. We concluded that precipitation is the main climatic factor affecting the growth of vegetation in Beijing’s mountainous areas through correlation analysis. Human activities have significantly promoted the vegetation growth in the northern mountainous area thanks to the establishment of environmental protection areas. The negative correlation between vegetation and the impervious surface tends to gradually expand outwards, which is consistent with the trend of urban expansion. The positive correlation region remains stable, but the positive correlation is gradually enhanced. The response of vegetation to urbanization demonstrated a high degree of spatial heterogeneity. These findings indicated that human activities played an increasingly important role in influencing vegetation changes in Beijing.

Thick cloud and shadows have a significant impact on the availability of optical remote sensing data. Although various methods have been proposed to address this issue, they still have some limitations. First, most approaches rely on a single clear reference image as complementary information, which becomes challenging when the target image has large missing areas. Secondly, the existing methods that can utilize multiple reference images require the complementary data to have high temporal correlation, which is not suitable for situations where the difference between the reference image and the target image is large. To overcome these limitations, a flexible spatiotemporal deep learning framework based on generative adversarial networks is proposed for thick cloud removal, which allows for the use of three arbitrary temporal images as references. The framework incorporates a three-step encoder that can leverage the uncontaminated information from the target image to assimilate the reference images, enhancing the model’s ability to handle reference images with diverse temporal differences. A series of simulated and real experiments on Landsat 8 and Sentinel 2 data is performed to demonstrate the effectiveness of the proposed method. The proposed method is especially applicable to small/large-scale regions with reference images that are significantly different from the target image.

Optical satellite imagery has become indispensable for Earth observation, yet thick cloud contamination severely impedes continuous monitoring capabilities. Current temporal-based methodologies present dual limitations: conventional approaches suffer from inefficient processing speeds and poor robustness, while existing deep learning-based solutions exhibit constrained temporal adaptability by relying on limited temporal information from single or fixed-period images. These inherent shortcomings consequently hinder their scalability in large-scale real-world applications. To address this challenge, we propose a Multi-temporal Adaptive Cloud Removal Transformer (MACRT), which uses a temporal aggregation strategy to integrate information from multiple reference images, reduce the constraint of reference image selection, and provide a more practical cloud removal solution. Since neighboring pixels of remote sensing images have strong correlations, the proposed network architecture introduces a novel spatio-spectral multi-head attention (SSMHA) module. This module decomposes the attention computation into two branches: coarse-resolution spatial and spectral, which work together to capture long-range dependencies across both dimensions while maintaining a compact parameter count. Extensive experiments on multispectral datasets (Landsat and Sentinel-2) demonstrate the superior performance of MACRT. Quantitative evaluations on Landsat imagery reveal improvements of 1.7 % in SSIM and 2.1° in SAM over state-of-the-art methods, achieving enhanced structural preservation and spectral accuracy. The source code, pretrained models, and all experimental results will be publicly available at https://github.com/Zhangyu-student/ACRT upon the paper’s acceptance.

Cloud contamination in multi-temporal remote sensing images, particularly due to thick clouds, significantly impacts the accuracy of environmental monitoring tasks. While existing Low-Rank Tensor Completion (LRTC) methods address cloud removal, they struggle with strict multi-temporal correlation requirements and poor generalization due to parameter sensitivity. To overcome these limitations, we propose WLRT-DR, a novel two-stage framework combining Wavelet-domain Low-Rank Tensor Completion with Deep Regularization. First, we introduce discrete wavelet transform (DWT) to decompose multi-temporal imagery into frequency-specific subbands (LL, LH, HL, HH). Empirical analysis reveals distinct low-rank properties in each subband, enabling targeted low-rank processing that better preserves spatial textures and structural continuity compared to unified spatial-domain LRTC. Second, we develop a dual-phase architecture where wavelet-domain LRTC (Stage 1) is synergistically combined with deep network regularization (Stage 2). This hybrid strategy leverages the complementary strengths: wavelet-domain low-rank recovery efficiently reconstructs fundamental spatial features, while pretrained deep networks impose non-linear constraints to enhance spectral fidelity. Comprehensive validation on simulated (Landsat data covering diverse Beijing landscapes) and real-world data demonstrates that WLRT-DR delivers enhanced reconstruction accuracy, robustness to complex spatio-temporal patterns, and improved processing efficiency. The framework provides an effective alternative for cloud reconstruction by integrating traditional tensor completion and deep learning in a novel manner.