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Accurate individual tree crown identification is essential for urban forestry, yet existing datasets often lack exhaustive annotations and multi-temporal diversity. To address this limitation, an exhaustively annotated dataset was curated for crown instance segmentation, comprising 47,754 labeled individual crowns from approximately 110 species across three temporal phases. Anchored in a “crown geometry” labeling criterion focusing on upper-canopy individuals visible in the imagery, and the high-resolution imagery captured seasonal variations in shape, color, and texture, providing an empirical basis for within-site robustness. Utilizing this dataset, this study (1) compared five instance segmentation models; (2) evaluated their generalization capabilities across different temporal phases; and (3) tested a multi-temporal joint training strategy and a non-maximum suppression (NMS)-based fusion. The experiments revealed significant overfitting in single-temporal models. While ConvNeXt-V2 achieved a high segmentation mean Average Precision (Segm_mAP) of 0.852 within the same temporal phase, its performance dropped sharply to 0.361 across phases. Bi-temporal joint training significantly mitigated this issue, improving cross-temporal performance to 0.665 and further increasing within-phase accuracy to 0.874. In contrast, tri-temporal training reduced accuracy (0.748), demonstrating that effective generalizability depends on the strategic selection of complementary temporal phases rather than the mere accumulation of data. The multi-temporal training framework provided in this study could serve as a practical reference and a foundational benchmark for further urban forest structural monitoring research.

South China is dominated by mountainous agriculture and croplands that are at risk of flood disasters, posing a great threat to food security. Synthetic aperture radar (SAR) has the advantage of being all-weather, with the ability to penetrate clouds and monitor cropland inundation information. However, SAR data may be interfered with by noise, i.e., radar shadows and permanent water bodies. Existing cropland data derived from open-access landcover data are not accurate enough to mask out these noises mainly due to insufficient spatial resolution. This study proposed a method that extracted cropland inundation with a high spatial resolution cropland mask. First, the Proportional–Integral–Derivative Network (PIDNet) was applied to the sub-meter-level imagery to identify cropland areas. Then, Sentinel-1 dual-polarized water index (SDWI) and change detection (CD) were used to identify flood area from open water bodies. A case study was conducted in Fujian province, China, which endured several heavy rainfalls in summer 2022. The result of the Intersection over Union (IoU) of the extracted cropland data reached 89.38%, and the F1-score of cropland inundation achieved 82.35%. The proposed method provides support for agricultural disaster assessment and disaster emergency monitoring.

Accurate and detailed cropland maps are essential for food security, yet existing products for China exhibit substantial discrepancies. This study presents CropLayer, a 2 m resolution cropland map of China for 2020, developed from Mapbox and Google satellite imagery. The framework comprises three key stages: (1) image quality assessment (IQA) using a ResNet model to compensate for missing acquisition metadata; (2) cropland extraction via an active learning strategy guided by a Mask2Former segmentation model and XGBoost-based semantic correctness evaluation; and (3) integration of Mapbox and Google results through an XGBoost model informed by four feature groups: Geography, IQA, Regional Property, and Consistency. A three-level validation scheme (pixel, block, and region) ensures robust and interpretable accuracy across spatial scales. CropLayer achieves a pixel-level accuracy of 88.73 %, a block-level semantic correctness of 96.5 %, and provincial-level consistency, with 30 out of 32 provinces showing area estimates within ±10 % of official statistics. In comparison, only 1–9 provinces meet this criterion across eight existing datasets. CropLayer provides a reliable, high-resolution baseline for agricultural structure analysis, yield estimation, and land use planning in China. The CropLayer dataset is available at 10.5281/zenodo.14726428 (Jiang et al., 2025).

Electrochemical chlorine evolution reaction is of central importance in the chlor-alkali industry, but the chlorine evolution anode is largely limited by water oxidation side reaction and corrosion-induced performance decay in strong acids. Here we present an amorphous CoO x Cl y catalyst that has been deposited in situ in an acidic saline electrolyte containing Co 2+ and Cl - ions to adapt to the given electrochemical condition and exhibits ~100% chlorine evolution selectivity with an overpotential of ~0.1 V at 10 mA cm −2 and high stability over 500 h. In situ spectroscopic studies and theoretical calculations reveal that the electrochemical introduction of Cl - prevents the Co sites from charging to a higher oxidation state thus suppressing the O-O bond formation for oxygen evolution. Consequently, the chlorine evolution selectivity has been enhanced on the Cl-constrained Co-O * sites via the Volmer-Heyrovsky pathway. This study provides fundamental insights into how the reactant Cl - itself can work as a promoter toward enhancing chlorine evolution in acidic brine. Achieving stable and selective chlorine production is of high interest yet challenging. Here the authors report an amorphous CoOxCly catalyst prepared by in situ electrodeposition in acidic saline electrolyte which shows ~100% chlorine evolution selectivity with low overpotential and high stability over 500 h.