China is frequently subjected to local and regional drought disasters, and thus, drought monitoring is vital. Drought assessments based on available surface soil moisture (SM) can account for soil water deficit directly. Microwave remote sensing techniques enable the estimation of global SM with a high temporal resolution. At present, the evaluation of Soil Moisture Active Passive (SMAP) SM products is inadequate, and L-band microwave data have not been applied to agricultural drought monitoring throughout China. In this study, first, we provide a pivotal evaluation of the SMAP L3 radiometer-derived SM product using in situ observation data throughout China, to assist in subsequent drought assessment, and then the SMAP-Derived Soil Water Deficit Index (SWDI-SMAP) is compared with the atmospheric water deficit (AWD) and vegetation health index (VHI). It is found that the SMAP can obtain SM with relatively high accuracy and the SWDI-SMAP has a good overall performance on drought monitoring. Relatively good performance of SWDI-SMAP is shown, except in some mountain regions; the SWDI-SMAP generally performs better in the north than in the south for less dry bias, although better performance of SMAP SM based on the R is shown in the south than in the north; differences between the SWDI-SMAP and VHI are mainly shown in areas without vegetation or those containing drought-resistant plants. In summary, the SWDI-SMAP shows great application potential in drought monitoring.

Automatically extracting water bodies is a significant task in interpreting remote sensing images (RSIs). Convolutional neural networks (CNNs) have exhibited excellent performance in processing RSIs, which have been widely used for fine-grained extraction of water bodies. However, it is difficult for the extraction accuracy of CNNs to satisfy the requirements in practice due to the limited receptive field and the gradually reduced spatial size during the encoder stage. In complicated scenarios, in particular, the existing methods perform even worse. To address this problem, a novel boundary-guided semantic context network (BGSNet) is proposed to accurately extract water bodies via leveraging boundary features to guide the integration of semantic context. Firstly, a boundary refinement (BR) module is proposed to preserve sufficient boundary distributions from shallow layer features. In addition, abstract semantic information of deep layers is also captured by a semantic context fusion (SCF) module. Based on the results obtained from the aforementioned modules, a boundary-guided semantic context (BGS) module is devised to aggregate semantic context information along the boundaries, thereby enhancing intra-class consistency of water bodies. Extensive experiments were conducted on the Qinghai–Tibet Plateau Lake (QTPL) and the Land-cOVEr Domain Adaptive semantic segmentation (LoveDA) datasets. The results demonstrate that the proposed BGSNet outperforms the mainstream approaches in terms of OA, MIoU, F1-score, and kappa. Specifically, BGSNet achieves an OA of 98.97% on the QTPL dataset and 95.70% on the LoveDA dataset. Additionally, an ablation study was conducted to validate the efficacy of the proposed modules.

Radar precipitation information with high spatial and temporal resolution is essential for flood forecasting, urban pluvial flood warning, and digital twin basins, yet radar quantitative precipitation estimation (QPE) is affected by observational errors, retrieval relationships, terrain effects, and multi-source merging. Based on 1328 Web of Science Core Collection publications (2000–2025), this study integrates bibliometric analysis, Transformer-based semantic representation, BERTopic modeling, semantic association analysis, and bibliographic coupling to map the topic structure, thematic evolution, and methodological lineages of research on radar precipitation uncertainty and hydrological applications. Nineteen micro-level topics were identified and grouped into four categories: measurement error identification and correction, uncertainty characterization and modeling, error propagation and scale effects, and operational applications and risk decision-making. The semantic structure shows that observation and correction and hydrological propagation constitute the main branches, while AI-driven forecasting is emerging. Methodological lineage analysis indicates that Statistical Fusion dominates the field, Physics provides a foundation, and AI and Hybrid methods have grown markedly in recent years, with Hybrid methods bridging physical mechanisms, statistical fusion, and data-driven approaches. Future research should integrate explainable AI, physical constraints, probabilistic modeling, and hydrological-response consistency testing to advance chain-wide uncertainty representation from observation and forecasting to hydrological propagation and risk-informed decision-making.