Timely and accurate large-scale mapping of the spread of winter wheat ( $\emph {Triticum aestivum}$ ) is crucial to guarantee food security, study climate change, and monitor operational agriculture. Traditional winter wheat mapping frameworks are constrained by insufficient spatial resolution and heavy dependence on field surveys, while traditional machine learning models excessively rely on subjective judgment. Furthermore, collecting sufficient field samples covering a large area is expensive and time consuming. In this context, an automatic label update deep learning solution is developed to produce 10 m resolution winter wheat maps using Sentinel-2 data and existing coarse-resolution (30 m) winter wheat mapping products. In particular, a label update module considering the unique phenological (seasonal) characteristics of winter wheat is designed to update labels in the training phase. The results indicate that our method yields a satisfactory classification result with an overall accuracy exceeding 92 $\%$ and an ${F}_{1}$ score greater than 0.85 for all validation samples, even when no field survey data were used for training. Additionally, a 10 m spatial resolution winter wheat map for the entire Shandong province is generated, showing a significant correlation between the computed winter wheat map and the agricultural statistical land, with correlation coefficients of 0.95 and 0.78 at the municipal and county levels, respectively. The proposed methodology can serve as a viable and promising method for high-resolution, operational agricultural monitoring over large areas.

Although Bayesian methods have been very effective for spatial–spectral analysis of hyperspectral imagery (HSI), they had not been fully explored for enhanced subpixel mapping (SPM) by simultaneously considering several key issues, i.e., endmember variability, the discrete nature of subpixel class labels, and the spatial information in HSI. Therefore, we propose a new Bayesian SPM method based on the discrete endmember variability mixture model (DEMM) and Markov random field (MRF), which has three main characteristics. First, DEMM allows the advanced SPM by completely accounting for the endmember–abundance patterns of each pixel to accommodate the endmember variability, the discrete hidden class label field of subpixels, while taking into account the noise heterogeneity effect. Second, the discrete class label fields modeled by MRF together with the DEMM, which can be integrated into a novel Bayesian model to better exploit the spatial contextual and spectral information. Third, the resulting Bayesian model can be efficiently solved by a designed expectation–maximization iteration, where E-step estimates the subpixel class label field using a simulated annealing algorithm and M-step estimates the endmembers for each pixel in HSI using the alternating non-negative least squares approach. The experimental results on three HSI datasets demonstrate that the proposed approach outperforms previously available SPM techniques.

High-resolution land cover mapping (LCM) is pivotal in numerous disciplines but still challenging to be acquired because traditional supervised methods require a substantial number of high-resolution labels that is labouring and expensive. To this issue, abundantly available low-resolution land-cover maps are regarded as alternative label sources, but the mismatch of spatial resolution and the misidentification of different land-cover categories introduced an amount of noisy labeled samples. This study introduces a novel cross-resolution deep learning (DL) framework, termed CRNN, to generate high-resolution LCM by leveraging low-resolution mapping products. Firstly, a high-resolution backbone is proposed to safeguard the preservation of output resolution while simultaneously retaining the deep feature extraction capability of the network. Furthermore, an attention module is incorporated into the CRNN framework to alleviate the adverse impact of imbalanced samples. More importantly, to address the label noise issue, a weakly supervised loss based on feature similarity is proposed and calculated for obtaining dependable supervision information from low-resolution LCM products. The qualitative and quantitative results demonstrate that the CRNN framework surpasses several state-of-the-art methods. Moreover, based on the CRNN, the 10-m resolution land-cover maps of Beijing and Shanghai for 2020 are produced using 30-m resolution LCM products as reference data. As a further application, CRNN provides a viable and promising approach for reusing existing data products, contributing to some extent toward achieving sustainability goals.

Lutan-1 is the first L-band SAR satellite launched by China with the core mission of geohazard monitoring, but few studies have been conducted to apply it in the field of earthquakes. In this paper, the capability of Lutan-1 data in coseismic deformation analysis and seismogenic fault parameter inversion was discussed by taking the 2023 Mw6.0 Jishishan earthquake as an example. Firstly, we utilized Lutan-1 data to acquire the coseismic deformation field of the Jishishan earthquake. Subsequently, the seismogenic fault parameter and slip distribution were inverted using both uniform slip and distributed slip models. Finally, a comprehensive comparison was conducted with Sentinel-1 data in terms of the coseismic deformation field, seismic source parameters, and coherence. The comparative results demonstrate that the coseismic deformation and seismogenic fault parameter inversion derived from Lutan-1 data are consistent with those obtained from Sentinel-1 data. Moreover, Lutan-1 data exhibit superior image quality and better coherence, confirming the effectiveness and superiority of Lutan-1 data for coseismic deformation and seismogenic fault analysis. This study provides a theoretical foundation for the application of Lutan-1 in the field of earthquake disaster monitoring.

The structural stability of lava tubes is a critical factor for their potential use in lunar base construction. Previous studies could not reflect the details of lava tube boundaries and perform accurate mechanical analysis. To this end, this study proposes a robust method to construct a high-precision, real-scene 3D model based on ground lava tube point cloud data. By employing finite element analysis, this study investigated the impact of real-world cross-sectional geometry, particularly the aspect ratio, on structural stability under surface pressure simulating meteorite impacts. A high-precision 3D reconstruction was achieved using UAV-mounted LiDAR and SLAM-based positioning systems, enabling accurate geometric capture of lava tube profiles. The original point cloud data were processed to extract cross-sections, which were then classified by their aspect ratios for analysis. Experimental results confirmed that the aspect ratio is a significant factor in determining stability. Crucially, unlike the monotonic trends often suggested by idealized models, analysis of real-world geometries revealed that the greatest deformation and structural vulnerability occur in sections with an aspect ratio between 0.5 and 0.6. For small lava tubes buried 3 m deep, the ground pressure they can withstand does not exceed 6 GPa. This process helps identify areas with weaker load-bearing capacity. The analysis demonstrated that a realistic 3D modeling approach provides a more accurate and reliable assessment of lava tube stability. This framework is vital for future evaluations of lunar lava tubes as safe habitats and highlights that complex, real-world geometry can lead to non-intuitive structural weaknesses not predicted by simplified models.