Global vegetation growth is dynamically influenced and regulated by hydrological processes. Understanding vegetation responses to terrestrial water storage (TWS) dynamics is crucial for predicting ecosystem resilience and guiding water resource management under climate change. This study investigated global vegetation responses to a terrestrial water storage anomaly (TWSA) using NDVI and TWSA datasets from January 2004 to December 2023. We proposed a Pearson-ACF time lag analysis method that combined dynamic windowing and enhanced accuracy to capture spatial correlations and temporal lag effects in vegetation responses to TWS changes. The results showed the following: (1) Positive NDVI-TWSA correlations were prominent in low-latitude tropical regions, whereas negative responses occurred mainly north of 30°N and in South American rainforest, covering 38.96% of the global vegetated land. (2) Response patterns varied by vegetation type: shrubland, grassland, and cropland exhibited short lags (1–4 months), while tree cover, herbaceous wetland, mangroves, and moss and lichen typically presented delayed responses (8–9 months). (3) Significant bidirectional Granger causality was identified in 16.39% of vegetated regions, mainly in eastern Asia, central North America, and central South America. These findings underscored the vital role of vegetation in the global water cycle, providing support for vegetation prediction, water resource planning, and adaptive water management in water-scarce regions.

Accurate identification of urban functional zones is critically important for urban planning and sustainable development. However, existing deep learning methods exhibit limited cross-city generalization capabilities, rendering them inadequate for large-scale automated mapping applications. To address this challenge, this study proposes a cross-city identification framework based on SFA-Mask R-CNN (Scale-Aware Feature-enhanced Attention Mask R-CNN), which achieves precise segmentation and classification of functional zones by integrating three complementary feature enhancement mechanisms: a Convolutional Block Attention Module (CBAM) for discriminative feature recalibration, a novel Scale-Aware FPN (SA-FPN) that dynamically adjusts feature pyramid layer weights according to the scale distribution of input imagery to improve cross-city transferability, and an ASPP (Atrous Spatial Pyramid Pooling) module for multi-scale contextual feature extraction, combined with multi-source data fusion. Using three cities along the Yangtze River Economic Belt—Chengdu, Wuhan, and Shanghai—as study areas, we systematically evaluated the model’s cross-city generalization performance. The results show that the Wuhan model exhibits the strongest cross-city transferability, achieving 85.37% accuracy in Chengdu, suggesting that models trained on cities at transitional development stages possess superior generalizability. In contrast, the gradient reversal layer-based domain adaptation approach tested in this study failed to effectively enhance model performance. This study provides a practical technical pathway for “train once, apply to multiple cities” large-scale functional zone mapping and offers new perspectives for research on regional disparities in urban development.

Compound temperature and soil moisture extremes threaten terrestrial carbon uptake, but their contrasting ecological impacts remain poorly understood. We assessed dry–hot (DH), wet–hot (WH), dry–cold (DC), and wet–cold (WC) events across China from 2001 to 2025. The analyses combined daily air temperature, root-zone soil moisture, and MODIS-derived gross primary productivity (GPP). We quantified event occurrence, negative-response probability, cumulative GPP loss, and recovery time, and examined environmental predictors using explainable machine learning. WC events were most frequent, whereas DH events lasted longest. WH frequency increased over the study period, while DC frequency declined. DH and DC produced negative GPP responses in 63.5% and 67.1% of classifiable events, respectively. Among negative-response events, DH had the largest national mean cumulative event-period GPP loss below the −0.5 SD threshold (2.85 g C m⁻²) and the longest conditional recovery time (3.17 days). WC losses exceeded DC losses despite a lower negative-response probability. Losses varied with elevation, season, and event severity. Vapor pressure deficit dominated predictions of DH negative responses and losses, whereas other event types showed greater contributions from radiation, precipitation, and soil texture. These findings highlight the need to distinguish event occurrence, response probability, productivity loss, and recovery when assessing ecological risks from compound extremes.

Diameter at breast height (DBH) is a fundamental structural parameter for forest inventory and ecological analysis. However, field-based measurements (e.g., diameter tape surveys) are labor-intensive and inefficient for large-scale applications. Airborne light detection and ranging (LiDAR) provides an efficient alternative for individual-tree DBH estimation. Nevertheless, LiDAR-derived features—defined as statistical descriptors of point cloud structure and radiometric properties—are typically high-dimensional and redundant, which may degrade model performance. To address this issue, this study proposes an integrated framework combining Multi-Stage Feature Selection (MSFS) and Extreme Gradient Boosting (XGBoost) for DBH estimation. A total of 104 variables, including LiDAR-derived features (height, density, intensity, and canopy structure metrics) and structural parameters (tree height, crown diameter, and crown area), were used as predictors. The MSFS framework was applied to progressively reduce feature redundancy and identify an optimal subset, which was then used to train the XGBoost model. The results demonstrate that the MSFS–XGBoost model achieved the best performance, with a coefficient of determination (R²) of 0.901 and a root mean square error (RMSE) of 1.647 cm. Compared with models using the original feature set, R² increased by 0.384 and RMSE decreased by 1.146 cm. These findings indicate that the proposed framework effectively improves DBH estimation accuracy and provides a reliable approach for individual-tree parameter estimation and large-scale forest resource monitoring using airborne LiDAR data.

Accurate mapping of rubber plantations is essential for sustainable land management and forest conservation in tropical regions. However, existing methods face two major challenges: persistent cloud cover limits the effectiveness of optical remote sensing in tropical areas, and regional phenological heterogeneity hinders the transferability of fixed-parameter approaches. This study proposes a Phenology-Adaptive Rubber Plantation Mapping (PARM) framework that integrates Sentinel-1 SAR time-series data with optimally selected spectral indices through a cascading constraint architecture. The framework operates as a structurally coherent system wherein SAR-derived phenological anchors explicitly govern downstream optical analysis across three internally dependent modules. First, three key phenological nodes—leaf-off start (LOS), fastest greening point (FGP), and full canopy point (FCP)—are extracted directly from SAR VH-polarization backscatter time series, enabling cloud-independent extraction of phenological temporal anchors. Second, the Jeffries–Matusita (JM) distance, evaluated within SAR-constrained phenological windows, is employed to identify the optimal vegetation and water indices for each region from six candidate spectral indices. Third, a time-weighted Rubber Plantation Discrimination Index (RPDI) is constructed using the selected indices and locally extracted phenological nodes, thereby amplifying the coupled signals of canopy greenness and moisture dynamics during critical phenological transitions. The framework was validated in Hainan Island and Vietnam, two regions with contrasting phenological regimes, using a spatial-block partitioning protocol (leave-one-subregion-out combined with DBSCAN-based clustering) designed to prevent samples from the same plantation from occurring in both training and test subsets. Within the Dynamic World forest mask, PARM achieved overall accuracies of 92.04% and 91.24%, respectively (93.57% and 91.00% on the fully held-out Qionghai City and Gia Lai province subregions), with Kappa coefficients exceeding 0.81 in both regions, consistently outperforming schemes based on raw spectral bands, individual spectral indices or direct multi-source time-series stacking. Error structure analysis revealed that residual classification failures are primarily associated with landscape fragmentation, stand immaturity, and residual cloud contamination, delineating the generalizability boundaries of the framework. These results demonstrate that tightly coupling SAR-based phenological characterization with adaptive optical index selection through a cascading constraint architecture provides a reliable foundation for rubber plantation mapping in cloud-prone tropical regions.