Cropland soil organic carbon (SOC) is undergoing substantial alterations due to both environmental and anthropogenic changes. Although numerous case studies have been conducted, there remains a lack of quantification of the consequences of such environmental and anthropogenic changes on the SOC sequestration across global agricultural systems. Here, we conducted a global meta-analysis of SOC changes under different fertilizer managements, namely unbalanced application of chemical fertilizers (UCF), balanced application of chemical fertilizers (CF), chemical fertilizers with straw application (CFS), and chemical fertilizers with manure application (CFM). We show that topsoil organic carbon (C) increased by 0.9 (0.7–1.0, 95% confidence interval (CI)) g kg⁻¹ (10.0%, relative change, hereafter the same), 1.7 (1.2–2.3) g kg⁻¹ (15.4%), 2.0 (1.9–2.2) g kg⁻¹ (19.5%) and 3.5 (3.2–3.8) g kg⁻¹ (36.2%) under UCF, CF, CFS and CFM, respectively. The C sequestration durations were estimated as 28–73 years under CFS and 26–117 years under CFM but with high variability across climatic regions. At least 2.0 Mg ha⁻¹ yr⁻¹ C input is needed to maintain the SOC in ~85% cases. We highlight a great C sequestration potential of applying CF, and adopting CFS and CFM is highly important for either improving or maintaining current SOC stocks across all agro–ecosystems.

Atmospheric methane (CH₄) is a potent greenhouse gas that is strongly influenced by several human activities. China, as one of the major agricultural and energy production countries, contributes considerably to the global anthropogenic CH₄ emissions by rice cultivation, ruminant feeding, and coal production. Understanding the characteristics of China's CH₄ emissions is necessary for interpreting source contributions and for further climate change mitigation. However, the scarcity of data from some sources or years and spatially explicit information pose great challenges to completing an analysis of CH₄ emissions. This study provides a comprehensive comparison of China's anthropogenic CH₄ emissions by synthesizing the most current and publicly available datasets (13 inventories). The results show that anthropogenic CH₄ emissions differ widely among inventories, with values ranging from 44.4–57.5 Tg CH₄ yr⁻¹ in 2010. The discrepancy primarily resulted from the energy sector (27.3 %–60.0 % of total emissions), followed by the agricultural (26.9 %–50.8 %) and waste treatment (8.1 %–21.2 %) sectors. Temporally, emissions among inventories stabilized in the 1990s but increased significantly thereafter, with annual average growth rates (AAGRs) of 2.6 %–4.0 % during 2000–2010 but slower AAGRs of 0.5 %–2.2 % during 2011–2015, and the emissions became relatively stable, with AAGRs of 0.3 %–0.8 %, during 2015–2019 because of the stable emissions from the energy sector (mainly coal production). Spatially, there are large differences in emissions hotspot identification among inventories, and incomplete information on emission patterns may mislead or bias mitigation efforts for CH₄ emission reductions. The availability of detailed activity data for sectors or subsectors and the use of region-specific emission factors play important roles in understanding source contributions and reducing the uncertainty in bottom-up inventories. Data used in this article are available at https://doi.org/10.6084/m9.figshare.12720989 (Lin et al., 2021).

Daily air temperature is a measurement that is required by many biogeochemical models. This study compared daily maximum (T _max), minimum (T _min) and mean (T _mean) air temperature observations collected at 678 standard meteorological stations of China in 2003 with estimates derived from daytime and night-time land surface temperature (LST) observed by the Moderate Resolution Imaging Spectroradiometer (MODIS) on board TERRA and AQUA satellites. Correlation analysis showed that the determination coefficients (R ² > 0.81) between models using night-time LSTs and the observed air temperatures were higher than those using daytime LSTs (R ² > 0.57), but with significant seasonal variation. Though estimates derived from coupled daytime and night-time LSTs were more accurate than using night-time or daytime LSTs alone, the available pixels were substantially reduced. Four empirical models were established for T _max, T _min and T _mean with MODIS night-time LSTs alone, or with coupled daytime and night-time LSTs, respectively. Solar declination was incorporated into the models to simulate seasonal variation of the correlations. Model validation showed that percentage of residuals within –3°C to 3°C ranged approximately from 60.2% to 74.3%, 64.4% to 69.9% and 76.8% to 85.7% for T _max, T _min and T _mean, respectively. It was concluded that night-time LST was the optimum predictor for estimating daily T _min, T _mean and even T _max when considering both the performance of the models and the availability of the LST data. Moreover, there was no significant difference between LSTs of TERRA and AQUA for estimating daily air temperatures.

Background: Quantifying CO₂ emissions from cities is of great importance because cities contribute more than 70% of the global total CO₂ emissions. As the largest urbanized megalopolis region in northern China, the Beijing-Tianjin-Hebei (Jing-Jin-Ji, JJJ) region (population: 112.7 million) is under considerable pressure to reduce carbon emissions. Despite the several emission inventories covering the JJJ region, a comprehensive evaluation of the CO₂ emissions at the prefectural city scale in JJJ is still limited, and this information is crucial to implementing mitigation strategies. Results: Here, we collected and analyzed 8 published emission inventories to assess the emissions and uncertainty at the JJJ city level. The results showed that a large discrepancy existed in the JJJ emissions among downscaled country-level emission inventories, with total emissions ranging from 657 to 1132 Mt CO₂ (or 849 ± 214 for mean ± standard deviation (SD)) in 2012, while emission estimates based on provincial-level data estimated emissions to be 1038 and 1056 Mt. Compared to the mean emissions of city-data-based inventories (989 Mt), provincial-data-based inventories were 6% higher, and national-data-based inventories were 14% lower. Emissions from national-data-based inventories were 53–75% lower in the high-emitting industrial cities of Tangshan and Handan, while they were 47–160% higher in Beijing and Tianjin than those from city-data-based inventories. Spatially, the emissions pattern was consistent with the distribution of urban areas, and urban emissions in Beijing contributed 50–70% of the total emissions. Higher emissions from Beijing and Tianjin resulted in lower estimates of prefectural cities in Hebei for some national inventories. Conclusions: National-level data-based emission inventories produce large differences in JJJ prefectural city-level emission estimates. The city-level statistics data-based inventories produced more consistent estimates. The consistent spatial distribution patterns recognized by these inventories (such as high emissions in southern Beijing, central Tianjin and Tangshan) potentially indicate areas with robust emission estimates. This result could be useful in the efficient deployment of monitoring instruments, and if proven by such measurements, it will increase our confidence in inventories and provide support for policy makers trying to reduce emissions in key regions.

Spatially and temporally resolved observations of near-surface air temperatures (Ta, 1.5–2 m above ground) are essential for understanding hydrothermal circulation at the land–atmosphere interface. However, the uneven spatial distribution of meteorological stations may not effectively capture the true nature of the overall climate pattern. Several studies have attempted to retrieve spatially continuous Ta from remotely sensed and continuously monitored Land Surface Temperature (LST). However, the topographical control of the relationship between LST and Ta in regions with complex topographies and highly variable weather station densities is poorly understood. The aim of this study is to improve the accuracy of Ta estimations from the Moderate Resolution Imaging Spectroradiometer (MODIS) LST via parameterization of the physiographic variables according to the terrain relief. The performances of both Terra and Aqua MODIS LST in estimating Ta have been explored in China. The results indicated that the best agreement was found between Terra nighttime LST (LSTmodn) and the observed Ta in China. In flat terrain areas, the LSTmodn product is significantly linearly correlated with Ta (R² > 0.80), while, in mountainous areas, the LSTmodn-Ta relationship differed significantly from simple linear correlation. By taking the physiographic features into account, including the seasonal vegetation cover (NDVI), the altitudinal gradient (RDLS), and the ambient absolute humidity (AH), the accuracy of the estimation was substantially improved. The study results indicated that the relevant environmental factors must be considered when interpreting the spatiotemporal variation of the surface energy flux over complex topography.