Garlic and winter wheat are major economic and grain crops in China, and their boundaries have increased substantially in recent decades. Updated and accurate garlic and winter wheat maps are critical for assessing their impacts on society and the environment. Remote sensing imagery can be used to monitor spatial and temporal changes in croplands such as winter wheat and maize. However, to our knowledge, few studies are focusing on garlic area mapping. Here, we proposed a method for coupling active and passive satellite imagery for the identification of both garlic and winter wheat in Northern China. First, we used passive satellite imagery (Sentinel-2 and Landsat-8 images) to extract winter crops (garlic and winter wheat) with high accuracy. Second, we applied active satellite imagery (Sentinel-1 images) to distinguish garlic from winter wheat. Third, we generated a map of the garlic and winter wheat by coupling the above two classification results. For the evaluation of classification, the overall accuracy was 95.97%, with a kappa coefficient of 0.94 by eighteen validation quadrats (3 km by 3 km). The user’s and producer’s accuracies of garlic are 95.83% and 95.85%, respectively; and for the winter wheat, these two accuracies are 97.20% and 97.45%, respectively. This study provides a practical exploration of targeted crop identification in mixed planting areas using multisource remote sensing data.

Accurate and reliable land cover information is vital for ecosystem management and regional sustainable development, especially for ecologically vulnerable areas. The South China Karst, one of the largest and most concentrated karst distribution areas globally, has been undergoing large-scale afforestation projects to combat accelerating land degradation since the turn of the new millennium. Here, we assess five recent and widely used global land cover datasets (i.e., CCI-LC, MCD12Q1, GlobeLand30, GlobCover, and CGLS-LC) for their comparative performances in land dynamics monitoring in the South China Karst during 2000–2020 based on the reference China Land Use/Cover Database. The assessment proceeded from three aspects: areal comparison, spatial agreement, and accuracy metrics. Moreover, divergent responses of overall accuracy with regard to varying terrain and geomorphic conditions have also been quantified. The results reveal that obvious discrepancies exist amongst land cover maps in both area and spatial patterns. The spatial agreement remains low in the Yunnan–Guizhou Plateau and heterogeneous mountainous karst areas. Furthermore, the overall accuracy of the five datasets ranges from 40.3% to 52.0%. The CGLS-LC dataset, with the highest accuracy, is the most accurate dataset for mountainous southern China, followed by GlobeLand30 (51.4%), CCI-LC (50.0%), MCD12Q1 (41.4%), and GlobCover (40.3%). Despite the low overall accuracy, MCD12Q1 has the best accuracy in areas with an elevation above 1200 m or a slope greater than 25°. With regard to geomorphic types, accuracy in non-karst areas is evidently higher than in karst areas. Additionally, dataset accuracy declines significantly (p < 0.05) with an increase in landscape heterogeneity in the region. These findings provide useful guidelines for future land cover mapping and dataset fusion.

Surface ozone (O₃) pollution is an emerging environmental abiotic stress that poses substantial risks to crop yield losses and food security worldwide, and especially in China. However, the O₃-induced detrimental effects on double-season rice have rarely been investigated at large scales and over relatively long temporal spans. In this study, we estimated the crop production reductions and associated economic losses for double-season rice across southern China during 2013–2019, using a high spatial resolution surface ozone reanalysis dataset and rice distribution maps, and county-level production data, in combination with a locally derived exposure-response function for rice. Results show that AOT40 (cumulative hourly O₃ exposure above 40 ppb) presented generally increasing trends over growing seasons in 2013–2019, spanning from 4.0 to 7.1 ppm h and 6.1 to 10.5 ppm h for double-early rice and double-late rice, respectively. Moreover, O₃-induced relative yield losses ranged from 4.0% to 6.6% for double-early rice and 6.3% to 11.1% for double-late rice. Over the seven years, ambient O₃ exposure resulted in crop production losses of 1951.5 × 10⁴ tons and economic losses of 8,081.03 million USD in total. To combat the O₃-induced agricultural risks, measures such as stringent precursors emission reductions and breeding O₃-resistant cultivars should be continuously implemented in the future.

Leaf area index (LAI) and leaf chlorophyll content (LCC) are key indicators for monitoring rice growth dynamics. While UAV-based hyperspectral data is widely used, its high redundancy poses challenges for efficient information extraction. To address this, we propose a two-step generic framework. First, synthetic spectra generated by a field-constrained PROSAIL model are used to train a one-dimensional convolutional neural network with a self-attention mechanism that derives Spectral Composite Variables (SCVs) from redundant hyperspectral data. Then, the SCVs are combined with canopy temperature (from thermal infrared sensors) and crop height (derived from UAV-based LiDAR and RGB imagery) to develop a retrieval model, validated through both within-site and cross-site strategies. Results showed that the SCVs generated exhibited strong correlations with LAI and LCC, averaging 0.83 and 0.85, respectively. Moreover, the proposed framework achieved high retrieval accuracy across all growth stages (e.g., booting, heading, filling), with mean R² values of 0.76 for LAI and 0.71 for LCC. Specifically, both estimations reached peak performance during the heading stage, with an R² of 0.83 and RMSE of 0.47 m²/m² for LAI, and an R² of 0.77 and RMSE of 4.13 μg/cm² for LCC. Cross-site validation confirmed the model’s robustness and transferability, with the best performance consistently observed during the heading stage. Benefiting from this framework, spatial predictions of LAI and LCC at centimeter-level resolution closely aligned with observed patterns, enabling precise monitoring of rice growth. Overall, this study presents a robust and transferable solution for overcoming hyperspectral redundancy and enhancing crop growth estimation accuracy.