The sensitivity of Cyclone Global Navigation Satellite System (CyGNSS) data to inland water bodies was well documented, however, its advantage over other sensors has seldom been reported. In this work, a semantic segmentation method is adopted for detecting inland water bodies using the CyGNSS data. The widely-used LinkNet with global attention mechanism (GAM) and atrous spatial pyramid pooling (ASPP), namely GA-LinkNet, is equipped to better extract water distributions. The performance comparison with an existing method and other deep networks proved the accuracy and effectiveness of this approach. Satisfactory agreement between the derived and referenced water masks was achieved, with the over accuracy being 0.959 and 0.976, the mean Intersection over Union being 0.785 and 0.641, and the F1 scores being 0.879 and 0.781 for the Amazon and Congo regions, respectively. Furthermore, underestimation of water by the reference data was shown during evaluation, which proves the usefulness of CyGNSS-derived water mask for improving the existing water mask products.

Three deep-blue fluorescent 9,10-bis(4-tert-butylphenyl)phenanthrenes with diphenyl, -naphthyl, and -pyrenyl moieties at C3 and C6 positions were synthesized and used as the host for doped blue fluorescent devices; one of these devices reveals excellent external quantum efficiency of 7.7% and current efficiency of 9.8 cd A⁻¹ with low efficiency roll-off, deep-blue color coordinates (0.14, 0.14) and long operational lifetime.

Convolutional neural networks (CNNs) have achieved great progress in the classification of surface objects with hyperspectral data, but due to the limitations of convolutional operations, CNNs cannot effectively interact with contextual information. Transformer succeeds in solving this problem, and thus has been widely used to classify hyperspectral surface objects in recent years. However, the huge computational load of Transformer poses a challenge in hyperspectral semantic segmentation tasks. In addition, the use of single Transformer discards the local correlation, making it ineffective for remote sensing tasks with small datasets. Therefore, we propose a new Transformer layered architecture that combines Transformer with CNN, adopts a feature dimensionality reduction module and a Transformer-style CNN module to extract shallow features and construct texture constraints, and employs the original Transformer Encoder to extract deep features. Furthermore, we also designed a simple Decoder to process shallow spatial detail information and deep semantic features separately. Experimental results based on three publicly available hyperspectral datasets show that our proposed method has significant advantages compared with other traditional CNN, Transformer-type models.

The use of remote sensing imagery has significantly enhanced the efficiency of building extraction; however, the precise estimation of building height remains a formidable challenge. In light of ongoing advancements in computer vision, numerous techniques leveraging convolutional neural networks and Transformers have been applied to remote sensing imagery, yielding promising outcomes. Nevertheless, most existing approaches directly estimate height without considering the intrinsic relationship between semantic building segmentation and building height estimation. In this study, we present a unified architectural framework that integrates the tasks of building semantic segmentation and building height estimation. We introduce a Transformer model that systematically merges multi-level features with semantic constraints and leverages shallow spatial detail feature cues in the encoder. Our approach excels in both height estimation and semantic segmentation tasks. Specifically, the coefficient of determination (${\mathrm{R}}^2$) in the height estimation task attains a remarkable 0.9671, with a root mean square error (RMSE) of 1.1733 m. The mean intersection over union (mIoU) for building semantic segmentation reaches 0.7855. These findings underscore the efficacy of multi-task learning by integrating semantic segmentation with height estimation, thereby enhancing the precision of height estimation.

This research introduces a new deep learning (DL) framework, multi‐head self‐attention‐aided vision Transformer (MSA‐ViT), for soil moisture (SM) retrieval using Cyclone Global Navigation Satellite System (CYGNSS) data. We first assess the sensitivity of CYGNSS reflectivity to SM, demonstrating a strong physical linkage through coherent scattering theory. The proposed MSA‐ViT model integrates this physical understanding with DL to capture nonlinear interactions between SM, surface roughness, and vegetation attenuation. Using data from January 2020 to December 2024, we aggregated observations over multiple temporal scales (3–60 days) to capture diverse hydrological patterns. The MSA‐ViT model was initially trained using 10‐day averaged data and subsequently tested across varying temporal scales to confirm its ability to reflect SM dynamics. Comparative experiments with conventional techniques such as linear regression and shallow neural networks, alongside other established DL models, demonstrated the outperformance of the proposed MSA‐ViT‐based approach. Following the initial validation, the training data set was expanded with a broader range of temporal patterns to enhance the model's generalization capabilities. Further evaluation was conducted through time series analysis, comparing the model's 3‐day retrievals with the Soil Moisture Active Passive data, the CYGNSS L3 SM V3.2 product, the in situ International Soil Moisture Network measurements and the Global Precipitation Measurement records, which showed consistent alignment with SMAP SMs and clear seasonal variability. Results also demonstrate improvement over the current CYGNSS L3 product on both precision and coverage. This comprehensive validation across large watersheds and diverse spatiotemporal scales attests to the model's robustness and its applicability for different ecosystem types.

Although extensive research has been conducted on retrieving sea ice variables from synthetic aperture radar (SAR) and multimodal remote sensing data, cross-scene retrieval using regional training models remains a significant challenge. Previous studies have employed multi-task learning but have not sufficiently explored the interplay between network architectures and multi-task performance. Moreover, self-supervised learning (SSL) has shown promise in improving tasks with limited training samples, though its potential in sea ice variable retrieval requires further study. To address the challenge of cross-scene retrieval of sea ice variables, we introduce a novel and effective method called Multimodal Fusion Domain Adaptive (MFDA), which combines three key strategies: 1) Employ SSL methods for multimodal data to pre-train the model, improving its noise sensitivity and promoting a hierarchical understanding of multimodality. 2) Propose a unified convolutional and Transformer-based data fusion architecture to enhance the integration of multimodal data and improve semantic understanding. 3) Incorporate a domain adaptation module between the multimodal encoder and the multi-task decoding predictor to facilitate the model’s understanding of the semantic gaps between different regional environments. The performance of the proposed MFDA has been extensively evaluated on the Ai4Arctic dataset. The experimental results demonstrate that MFDA achieves superior performance compared to other state-of-the-art sea ice classification approaches for the task of cross-scene sea ice retrieval. The code will be made available at https://github.com/yuweikong/MFDA.