Yu Qu

Dynamic monitoring of cropland via remote sensing imagery is vital for safeguarding cropland resources and ensuring food security through accurate change detection (CD). However, convolutional neural network (CNN)-based methods, reliant on extensive convolution and pooling operations to extract deep cropland features, often accumulate irrelevant features and lose critical ones, degrading performance, especially in dense, continuous cropland regions. To address this, we first modified the recurrent residual convolutional neural network (R2U-Net) to improve the ability to extract cropland boundary, and developed a novel feature-difference-enhanced network (FDE-Net) to capture pixel-based cropland change information. Furthermore, we introduced a majority voting mechanism method to generate parcel-based cropland change maps from the cropland boundary and pixel-based CD results produced by the CNN. Using Sentinel-2 imagery as our data source, experiments revealed that R2U-Net achieved precision and F1-scores of 89.93% and 85.78%, respectively, for cropland boundary extraction. The FDE-Net yielded pixel-based cropland change detection results with precision, F1-scores and IOU of 68.09%,75.08% and 84.26%, respectively. Ultimately, we produced parcel-based cropland change maps for the Huicheng District through the majority voting mechanism, resulting in precision and F1-scores of 78.91% and 81.64%. Additionally, we extracted various types of cropland changes in the Huicheng District from 2019 to 2020, including cropland to cropland, cropland to urban, and cropland to other uses. Compared to the pixel-based method, the overall accuracy for these three change types was 84.86%, 81.86%, and 82.36%, respectively. Our methodology can obtain a more comprehensive and precise depiction of land use changes. Critically, this approach has surmounted challenges posed by fragmented or ambiguous change detection regions, ultimately elevating the reliability and fidelity of our findings.

Methane is a potent greenhouse gas that significantly drives climate change and affects human health and ecosystems by promoting surface ozone formation. However, the spatial continuity of current satellite-derived column-averaged dry-air mole fraction of methane (XCH₄) data remains limited, and the sparse distribution of ground-based observations further hinders our understanding of methane sources and trends. To address this, we developed a novel spatiotemporal Transformer model to fill gaps in satellite retrievals and correct biases, thereby generating a global, daily, gapless XCH₄ dataset at 0.1° × 0.1° (∼10 × 10 km near the equator) grid resolution from 2003 to 2020. This model integrates XCH₄ retrievals from the Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) and Greenhouse Gases Observing Satellite (GOSAT), together with XCH₄ reanalysis from the Copernicus Atmosphere Monitoring Service (CAMS), meteorological variables, and other auxiliary datasets. Our fused daily XCH₄ dataset yielded a mean coefficient of determination (R²) value of 0.93, a root-mean-square error (RMSE) of 13.10 ppb, and a bias of 7.45 ppb when independently compared with measurements from the Total Carbon Column Observing Network (TCCON). Furthermore, we performed bias correction on the reconstructed XCH₄ data using the TCCON network and supplementary variables to improve consistency across satellites and enhance accuracy, achieving a mean cross-validation R² (CV-R²) of 0.97, an RMSE of 7.51 ppb, and a bias of 0.51 ppb. We observed a global annual growth rate of 6.09 ppb/yr (p < 0.001) from 2003 to 2020, with the average annual trends in developed countries being much lower than those in developing countries, at 5.86 ppb/yr and 6.16 ppb/yr (p < 0.001), respectively. Bangladesh exhibited the highest annual average concentration and trend, with average values of 1850.21 ± 41.85 ppb and 8.07 ppb/yr (p < 0.001), respectively. This unique global gapless daily XCH₄ dataset enables the precise detection of local emissions and provides valuable support for informing methane emission management policies and enhancing our understanding of global methane sources and their spatiotemporal variations.