As a fundamental and important task in remote sensing, remote sensing image scene understanding (RSISU) has attracted tremendous research interest in recent years. RSISU includes the following sub-tasks: remote sensing image scene classification, remote sensing image scene retrieval, and scene-driven remote sensing image object detection. Although these sub-tasks have different goals, they share some communal hints. Hence, this paper tries to discuss them as a whole. Similar to other domains (e.g., speech recognition and natural image recognition), deep learning has also become the state-of-the-art technique in RSISU. To facilitate the sustainable progress of RSISU, this paper presents a comprehensive review of deep-learning-based RSISU methods, and points out some future research directions and potential applications of RSISU.

Summary: The spring phenology has advanced significantly over recent decades with climate change, impacting large‐scale biogeochemical cycles, climate feedback, and other essential ecosystem processes. Although numerous prognostic models have been developed for spring phenology, regional analyses of the optimality (OPT) strategy model that incorporate environmental variables beyond temperature and photoperiod remain lacking. We investigated the roles of solar radiation (SR) and three water stress factors (precipitation (P), soil moisture, and vapor pressure deficit (VPD)) on spring phenology from 1982 to 2015 using the OPT model with Global Inventory Modeling and Mapping Studies NDVI3g dataset and environmental data from TerraClimate, CRU_TS, and Global Land Data Assimilation System across the Northern Hemisphere (> 30°N). Our results show that SR and water stress factors significantly impacted intrasite decadal spring phenology variability, with water stress factors dominant in grassland ecosystems while SR dominated in the rest of the ecosystem types. Enhanced models incorporating SR (OPT‐S) and VPD (OPT‐VPD) outperformed the original OPT model, likely due to improved representation of the adaptive strategy of spring phenology to optimize photosynthetic carbon gain while minimizing frost risk. Our research enhances the understanding of the key environmental drivers influencing decadal spring phenology variation in the Northern Hemisphere and contributes to more accurate forecasts of ecological responses to global environmental change.

Remote sensing-based urban change detection has seen substantial progress with the development of deep learning. Nevertheless, its performance under low-light imaging conditions, such as nighttime, twilight, and cloudy or rainy weather, remains limited due to image degradation, reduced contract, and weak spatial features. To address these challenges, we propose MambaLCD, a novel change detection method that integrates a Siamese visual state space encoder, a bi-temporal feature fusion module, and a mask decoder. The encoder captures spatial dependencies via multi-directional state-space scanning, while the fusion module enhances semantic consistency through illumination-aware recalibration. The decoder integrates multi-scale features using attention-inspired pooling for precise pixel-level change masks. Comprehensive experiments on three benchmark datasets including S2Looking, WHU-CD and LEVIR-CD demonstrate the effectiveness of Mamba-LCD. Specifically, on the low-lightdominated S2Looking dataset, Mamba-LCD outperforms 10 state-of-the-art CNN-, Transformer-, and Mamba-based methods, surpassing the previous best results by 0.73 and 1.58, respectively. Moreover, it maintains competitive performance on LEVIR-CD and WHU-CD under normal lighting. We further conducted ablation studies to evaluate the impact of different activation functions and fusion strategies. The results suggest the robustness and adaptability of Mamba-LCD, indicating its potential for urban monitoring across diverse illumination scenarios.

Spring leaf phenology in temperate tree species is highly sensitive to climate change and significantly affects plant photosynthetic performance, resource utilization, competition and trophic interactions, thereby impacting various ecosystem functions. Although optimality‐based (OPT) approaches for modelling spring phenology are increasingly recognized, the optimal representation of the underlying principle (balancing photosynthesis gains with chilling risks) remains controversial. Here, we integrated a coupled Farquhar–Medlyn photosynthesis model into an existing OPT model, and termed the resulting model R‐OPT, and evaluated its performance using the PEP725 dataset, which includes 409,144 site‐species‐year records from across Europe. Our results show that R‐OPT outperforms both the default OPT and non‐optimality‐based models (e.g. the chilling‐forcing trade‐off and growing degree day models). This improved performance is consistent within and across five focal tree species but varies by region: R‐OPT excels in lowland, moist environments but is less effective in high‐altitude, cold, and dry areas, possibly due to an incomplete representation of environmental constraints on photosynthetic carbon gain in these regions. Our research advances leaf spring phenology modelling by emphasizing an optimality principle that balances photosynthetic carbon gain with chilling risk, improving the representation of plant photosynthesis processes and enhancing understanding of environmental factors influencing phenology in the context of climate change.

The precision of remote sensing methodologies in estimating live aboveground biomass (AGB) exhibits significant variability across ecosystems. Dryland systems, owing to their critical contributions to global carbon cycling, necessitate highly resolved spatiotemporal AGB assessments to elucidate their role in climate feedback mechanisms. However, the structural characteristics of arid and semiarid vegetation introduce substantial methodological constraints in biomass quantification via remote sensing platforms. This paper put forward a new method to retrieve AGB using simulated ICESat-2 LiDAR data. By using simulated photon-counting LiDAR data of spaceborne ICESat-2 and using multiple machine learning algorithms (random forest, extreme gradient boosting tree, support vector machine) to train simulated photon-counting LiDAR data, we created a novel model to retrieve AGB based on simulated ICESat2 data. The results showed that the RF algorithm was the best (R² = 0.61, RMSE = 17.45, RE = 49.24%) in AGB inversion. The result maps could provide timely guidance and suggestions for ecological protection and economic construction in the study area, and also provide reliable basis for large-scale vegetation AGB remote sensing monitoring, carbon cycle research.

Accurate prediction of spring phenology is critical for understanding ecosystem carbon and water dynamics under changing climates. In this study, we applied a revised optimality-based model (R-OPT) that integrates a mechanistic photosynthesis framework into the existing OPT model to simulate leaf unfolding date. We evaluated R-OPT alongside three widely used models—Growing Degree Days (GDD), Chilling–Forcing Trade-off (CFT), and Optimality-based (OPT) models—across multiple Plant Functional Types (PFTs) and sites using repeated 5-fold cross-validation. Findings reveal that R-OPT consistently outperforms the other models, achieving the lowest median RMSE (13.11 days), indicating enhanced predictive accuracy and explanatory power. Although the model incurs slightly higher complexity (median AIC = 13.44), the improvement in prediction justifies the trade-off. Our results highlight the importance of incorporating plant functional traits and environmental heterogeneity in phenological modeling. PFT-specific differences, such as the lower RMSEs for evergreen forbs and deciduous broadleaf PFTs versus larger uncertainties for drought-deciduous and semi-evergreen PFTs, underscore that current models may insufficiently capture key environmental drivers, including precipitation and partial leaf retention. Latitudinal and elevational variations in trade-off parameter a, and the prominence of leaf-level carbon assimilation traits (Aleaf) as drivers of phenology, demonstrate the critical role of physiological traits in shaping PFT-specific phenological timing. These findings have significant implications for large-scale ecosystem modeling. By linking phenology directly to photosynthetic processes, R-OPT enhances predictive skill and biological interpretability, supporting improved simulations of carbon and water fluxes. Overall, R-OPT offers a mechanistically grounded and robust framework for advancing predictive understanding of spring phenology and its ecological and climate-relevant consequences.