Peiyu Zhang

Robust, national-scale quantification of soil organic carbon (SOC) dynamics in China’s paddy fields has been hindered by widely divergent estimates and a lack of comprehensive driver attribution. To address this, we developed a new empirical model from a comprehensive database of 746 long-term field observations (125 sites) to identify predominant drivers and quantify national-scale SOC stock dynamics from 1980 to 2018. The model explained 43% of the variance in topsoil SOC change. Organic matter input was the dominant driver (21.83% variance), with livestock manure demonstrating the highest C sequestration efficiency, followed by green manure and straw. Soil pH, latitude (as a climate proxy), and initial SOC content were also critical controllers. We estimate that China’s paddy topsoils (0–20 cm) acted as a significant C sink from 1980 to 2018, accumulating 242.51 ± 85.80 Tg C (an average rate of 6.65 Tg C yr⁻¹), bringing the 2018 national stock to 1220.48 ± 85.80 Tg C. Spatially, sequestration was highest in central (e.g., Hunan) and northeastern (e.g., Heilongjiang) China, while Chongqing experienced a net SOC loss. Crucially, our study provides a new long-term benchmark that reconciles previous, higher estimates from shorter timeframes, empirically demonstrating that sequestration rates are non-linear and diminish over time. These findings confirm that the C sequestration potential of paddy soils, while substantial, is finite and requires spatially targeted management of organic inputs and soil pH to maintain.

Quantifying the temporal and spatial patterns of impervious surfaces (IS) is important for assessing the environmental and ecological impacts of urbanization. In order to better extract IS, and to explore the divergence in urbanization in different regions, research on the regional differentiation features and regional change difference features of IS are required. To extract China’s 2013 urban impervious area, we used the 2013 night light (NTL) data and the Moderate Resolution Imaging Spectroradiometer Normalized Difference Vegetation Index and enhanced vegetation index (EVI) temporal series data, and used three urban impervious surface extraction indexes—Human Settlements Index, Vegetation-Adjusted NTL Urban Index, and the EVI-adjusted NTL index (EANTLI)—which are recognized as the best and most widely used indexes for extracting urban impervious areas. We used the classification results of the Landsat-8 images as the benchmark data to visually compare and verify the results of the urban impervious area extracted by the three indexes. We determined that the EANTLI index better reflects the distribution of the impervious area. Therefore, we used the EANTLI index to extract the urban impervious area from 2003 to 2013 in the study area, and researched the spatial and temporal differentiation in urban IS. The results showed that China’s urban IS area was 70,179.06 km², accounting for 0.73% of the country’s land area in 2013, compared with 20,565.24 km² in 2003, which accounted for 0.21% of the land area, representing an increase of 0.52%. On a spatial scale, like economic development, the distribution of urban impervious surfaces was different in different regions. The overall performance of the urban IS percentage was characterized by a decreasing trend from Northwest China, Southwest China, the Middle Reaches of the Yellow River, Northeast China, the Middle Reaches of the Yangtze River, Southern Coastal China, and Northern Coastal China to Eastern Coastal China. On the provincial scale, the urban IS expansion showed considerable differences in different regions. The overall performance of the Urban IS Expansion index showed that the eastern coastal areas had higher values than the western inland areas. The cities or provinces of Beijing, Tianjin, Jiangsu, and Shanghai had the largest growth in impervious areas. Spatially and temporally quantifying the change in urban impervious areas can help to better understand the intensity of urbanization in a region. Therefore, quantifying the change in urban impervious area has an important role in the study of regional environmental and economic development, policy formulation, and the rational use of resources in both time and space.

Winter wheat is the second largest food crop in China. It is important to obtain reliable winter wheat acreage to guarantee the food security for the most populous country in the world. This paper focuses on assessing the feasibility of in-season winter wheat mapping and investigating potential classification improvement by using SAR (Synthetic Aperture Radar) images, optical images, and the integration of both types of data in urban agricultural regions with complex planting structures in Southern China. Both SAR (Sentinel-1A) and optical (Landsat-8) data were acquired, and classification using different combinations of Sentinel-1A-derived information and optical images was performed using a support vector machine (SVM) and a random forest (RF) method. The interference coherence and texture images were obtained and used to assess the effect of adding them to the backscatter intensity images on the classification accuracy. The results showed that the use of four Sentinel-1A images acquired before the jointing period of winter wheat can provide satisfactory winter wheat classification accuracy, with an F1 measure of 87.89%. The combination of SAR and optical images for winter wheat mapping achieved the best F1 measure–up to 98.06%. The SVM was superior to RF in terms of the overall accuracy and the kappa coefficient, and was faster than RF, while the RF classifier was slightly better than SVM in terms of the F1 measure. In addition, the classification accuracy can be effectively improved by adding the texture and coherence images to the backscatter intensity data.