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Urban greenspaces typically refer to urban wetland, urban forest and urban turfgrass. They play a critical role in carbon sequestration by absorbing carbon from the atmosphere; however, their capacity to retain and store carbon in the form of soil organic carbon (SOC) varies significantly. This study provides a systematic analysis and review on the capacity of different urban greenspace types in retaining and storing SOC in 30 cm soil depth on a global scale. Data came from 78 publications on the subject of SOC stocks, covering different countries and climate zones. Overall, urban greenspace types exerted significant influences on the spatial pattern of SOC stocks, with the highest value of 18.86 ± 11.57 kg m -2 (mean ± standard deviation) in urban wetland, followed by urban forest (6.50 ± 3.65 kg m -2 ), while the lowest mean value of 4.24 ± 3.28 kg m -2 was recorded in urban turfgrass soil. Soil organic carbon stocks in each urban greenspace type were significantly affected by climate zones, management/environmental settings, and selected soil properties (i.e. soil bulk density, pH and clay content). Furthermore, our analysis showed a significantly negative correlation between SOC stocks and human footprint in urban wetland, but a significantly positive relationship in urban forest and urban turfgrass. A positive correlation between SOC stocks and human footprint indicates that increased human activity and development can enhance SOC stocks through effective management and green infrastructure. Conversely, a negative correlation suggests that improper management of human activities can degrade SOC stocks. This highlights the need for sustainable practices to maintain or enhance SOC accumulation in urban greenspaces., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The carbon-nutrient-water cycles of farmland ecosystem not only provides support for crop production, but also has an impact on the environment. Comprehensively quantifying the impact of crop production on the environment can provide a basis for crop sustainable production. A series of environmental footprint approaches, including carbon footprint (CF), nitrogen footprint (NF) and water footprint (WF), were optimized to evaluate greenhouse gas (GHG) emissions, reactive nitrogen (Nr) loss and water resource consumption in crop production, and a comprehensive footprint method based on Endpoint modeling was proposed to evaluate the overall environmental impact of crop production in China. The CF, NF and WF of 28 forms of crop production varied from 1206.29 kg CO 2 equivalent (CO 2 -eq) ha -1 of oil crops to 7326.37 kg CO 2 -eq ha -1 of fiber crops, 16.07 kg Nr-eq ha -1 of oil crops to 60.70 kg Nr-eq ha -1 of sugar crops, and 4032.04 m 3 ha -1 oil crops to 12,476.28 m 3 ha -1 of sugar crops, respectively. The contribution of each component to footprints varied greatly among different crops, and fertilizer manufacture, NH 3 volatilization and green WF were generally the main contributors of CF, NF and WF, respectively. The total GHG emissions, Nr loss and water consumption were estimated to be 670.11 Tg CO 2 -eq, 5.50 Tg Nr-eq and 837.06 G m 3 for all crop production of China. The greenhouse vegetable with the highest area-scaled comprehensive footprint was 4.5 times that of the oil crops which had the lowest one. The contribution of crop production to the corresponding environmental impact in China was as low as 3.7%, of which NH 3 volatilization contributed 48% and grain production contributed 72%. Mineral N fertilization was the main driver of the variation of comprehensive footprint between provinces, with reduction of N fertilizer application as an important way to reduce the environmental impact of crop production., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Soil organic carbon (SOC) and soil carbon-to-nitrogen ratio (C:N) are the main indicators of soil quality and health and play an important role in maintaining soil quality. Together with Landsat, the improved spatial and temporal resolution Sentinel sensors provide the potential to investigate soil information on various scales. We analyzed and compared the potential of satellite sensors (Landsat-8, Sentinel-2 and Sentinel-3) with various spatial and temporal resolutions to predict SOC content and C:N ratio in Switzerland. Modeling was carried out at four spatial resolutions (800 m, 400 m, 100 m and 20 m) using three machine learning techniques: support vector machine (SVM), boosted regression tree (BRT) and random forest (RF). Soil prediction models were generated in these three machine learners in which 150 soil samples and different combinations of environmental data (topography, climate and satellite imagery) were used as inputs. The prediction results were evaluated by cross-validation. Our results revealed that the model type, modeling resolution and sensor selection greatly influenced outputs. By comparing satellite-based SOC models, the models built by Landsat-8 and Sentinel-2 performed the best and the worst, respectively. C:N ratio prediction models based on Landsat-8 and Sentinel-2 showed better results than Sentinel-3. However, the prediction models built by Sentinel-3 had competitive or better accuracy at coarse resolutions. The BRT models constructed by all available predictors at a resolution of 100 m obtained the best prediction accuracy of SOC content and C:N ratio; their relative improvements (in terms of R 2 ) compared to models without remote sensing data input were 29.1% and 58.4%, respectively. The results of variable importance revealed that remote sensing variables were the best predictors for our soil prediction models. The predicted maps indicated that the higher SOC content was mainly distributed in the Alps, while the C:N ratio shared a similar distribution pattern with land use and had higher values in forest areas. This study provides useful indicators for a more effective modeling of soil properties on various scales based on satellite imagery., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)