This paper focuses on the research of anti-jamming issues for Low Earth Orbit (LEO) satellite constellations. Initially, the anti-jamming problem is modeled as a Local Interaction Markov Game (LIMG) and proven to be an Exact Potential Game (EPG), with at least one pure strategy Nash Equilibrium (NE) existing. Secondly, based on the theoretical analysis and the ”offline training and online execution” architecture, a Distributed Multi-Agent Deep Reinforcement Learning-based anti-jamming (DMDRLA) scheme is proposed, and its convergence and asymptotic optimality are theoretically analyzed. Finally, simulations validate that the proposed DM-DRLA scheme can effectively balance the training costs and performance optimization of the anti-jamming model, making it suitable for anti-jamming issues in resource-constrained LEO satellite networks.

This paper proposes a game-based anti-jamming channel selection method for satellite-terrestrial networks. Firstly, to address the insufficient consideration of user demands in existing schemes, a Mean Opinion Score (MOS)-based Quality of Experience (QoE) model is established. Secondly, the interaction between jammer and users is modeled as a one-leader multi-follower Stackelberg game, while local cooperation among users is formulated as a local altruistic game. Finally, a hierarchical Q-learning with spatial altruistic play (HQ-SAP) algorithm is proposed to obtain the game equilibrium solution. Simulations validate its convergence and effectiveness, achieving a good satisfaction rate for the network.

Sea surface roughness (SSR) is a common metric used to characterize the condition of sea surfaces. Sun glitter (SG) is the reflected sunlight off the fluctuating sea surface, with its distribution and intensity markedly influenced by SSR. However, the estimation of SSR through single-angle SG images may produce ambiguity, due to the assumptions applied in the underlying statistical model describing the orientation of the sea surface facets as a function of wind speed. In this study, we investigate the causes of SSR ambiguity and its distribution characteristics via simulation experiments. Based on these findings, we propose a method for identifying critical points of ambiguity and eliminating them for SSR estimation, leveraging single-angle SG images. This approach enables pixel-scale adaptive quantitative inversion of SSR. To validate our model, publicly available global high-resolution SG images (near-infrared (NIR) and shortwave infrared (SWIR) bands) captured by the Landsat-8 Operational Land Imager (OLI) and Sentinel-2 MultiSpectral Instrument sensors are utilized for SSR estimation. Subsequently, the modal values of SSR are expressed as wind-speed values for accuracy assessment. The estimated wind speeds exhibit good agreement with reanalysis wind data from the European Centre for Medium-Range Weather Forecasting (ECMWF) and buoy wind data from the National Data Buoy Center (NDBC). Meanwhile, the comparison results of the two bands are also very consistent, indicating that the method is stable for application on NIR and SWIR bands. The model’s efficacy is demonstrated using typical oceanographic scenarios from two regions: the Timor Sea and the English Channel. The resulting SSR images depict detailed oceanographic features with minimal noise, showcasing quantitative SSR changes at the pixel level. These outcomes underscore the effectiveness of the proposed method in resolving SSR ambiguity inherent in widely used optical remote sensing images such as Landsat-8 and Sentinel-2, thereby enhancing SSR retrieval accuracy.

In the context of global climate change, detecting coral bleaching using high-resolution multispectral remote sensing images is essential. However, the difficulty in obtaining the bottom reflectance spectrum restricts the ability to capture changes in highly heterogeneous coral reefs. Furthermore, the coupling between bottom reflectance and water-column absorption affects inverting bottom reflectance. We propose a normalized bottom reflectance change index (NBRCI) based on time series of quantitatively and physically inverted bottom reflectance without ground data. We compare the results with coral bleaching temperature products to validate change processes. Experimental results from the Landsat-8 images of Kuria Island–Aranuka Atoll and French Frigate Shoals of Pacific Islands from 2013 to 2023 prove that the NBRCI can effectively characterize the degree of change in the bottom and identify the spatiotemporal distribution of coral bleaching and recovery. NBRCI is beneficial for monitoring the health of coral reef benthic habitats at the pixel level around the globe.

Due to the inconsistency between resource environment and value data types, the hybrid model of resource environment value chain cannot effectively coordinate the relationship among resources, environment, and value chain. The circulation of resource chain, ecological chain, and value chain is not completed independently; they are intertwined and promote each other. Therefore, this paper proposes a hybrid resource environmental value chain model based on a discrete time algorithm. Analyze the hybrid internal structure of resource chain, environment chain, and value chain model; integrate the natural resource information through the objective function of natural resource integration; obtain the indicators affecting the environment; and complete the analysis of the characteristics of environmental change. On this basis, the relationship between resources, environment, and economic value is analyzed, and the hybrid content of resources, environment, and value chain is determined. The discrete‐time algorithm is introduced to transform the hybrid content into the same data format, obtain the objective function and constraints of the resource environment value chain hybrid model, and complete the construction of the resource environment value chain hybrid model based on the discrete‐time algorithm. The simulation results show that the hybrid model designed in this paper can effectively improve the resource saving rate, up to 97%, and the error of resource environment value chain data fusion is the lowest, and the time is less than 1 min which was a considerable achievement.