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Nitrogen (N) fertilizer is essential for agricultural production as it is the main nutrient driving crop growth. However, in China, only one-third of applied N fertilizer is effectively absorbed by crops, while the rest leads to significant environmental impacts. In this study, we introduced a nitrogen threshold boundary (NTB) approach to establish different thresholds for N use efficiency (NUE) and N surplus without affecting crop yield. We also developed an integrated assessment framework to systematically evaluate the potential for improving N utilization and reducing environmental impacts in the production of grain crops (rice, wheat, maize, and soybeans) and cash crops (tea, fruits, and vegetables) at the county level in Fujian Province. Three N management strategies were evaluated: a scenario with reduced N surplus (S1), a scenario with increased NUE (S2), and a combined scenario that simultaneously reduces N surplus and increases NUE (S3). The predictions indicate that, under the aforementioned scenarios, there will be a decrease of 66%, 58%, and 71% in N application without affecting crop yields, respectively. Correspondingly, N surplus will decrease by 65%, 56%, and 67%, while greenhouse gas (GHG) emissions will decrease by 54%, 48%, and 57%. In addition, NUE will increase by 23%, 17% and 25%, respectively. It is notable that scenario S3 demonstrated the greatest potential for improvement. For cash crops, N application will decrease by 65–78%, NUE will increase by 13–21%, N surplus will decrease by 63–74%, and GHG emissions will reduce by 66–78%. In contrast, for grain crops, N application will decrease by 27–38%, NUE will increase by 9–13%, N surplus will decrease by 26–37%, and GHG emissions will reduce by 24–28%. Overall, the potential for improvement is greater for cash crops compared to grain crops. The application of the assessment framework in this study demonstrates its effectiveness as a valuable tool for promoting green and sustainable development in conventional agricultural regions. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) plays a vital role in the global carbon cycle and soil quality assessment. The Qinghai–Tibet Plateau is one of the largest plateaus in the world. Therefore, in this region, SOC density and the spatial distribution of SOC are highly sensitive to climate change and human intervention. Given the insufficient understanding of the spatial distribution of SOC density in the Qinghai–Tibet Plateau, this study utilized machine learning (ML) algorithms to estimate the density and distribution pattern of SOC density in the region. In this study, we first collected multisource data, such as optical remote sensing data, synthetic aperture radar) (SAR) data, and other environmental variables, including socioeconomic factors, topographic factors, climate factors, and soil properties. Then, we used ML algorithms, namely random forest (RF), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM), to estimate the topsoil SOC density and spatial distribution patterns of SOC density. We also aimed to investigate any driving factors. The results are as follows: (1) The average SOC density is 5.30 kg/m2. (2) Among the three ML algorithms used, LightGBM showed the highest validation accuracy (R2 = 0.7537, RMSE = 2.4928 kgC/m2, MAE = 1.7195). (3) The normalized difference vegetation index (NDVI), valley depth (VD), and temperature are crucial in predicting the spatial distribution of topsoil SOC density. Feature importance analyses conducted using the three ML models all showed these factors to be among the top three in importance, with contribution rates of 14.08%, 12.29%, and 14.06%; 17.32%, 20.73%, and 24.62%; and 16.72%, 11.96%, and 20.03%. (4) Spatially, the southeastern part of the Qinghai–Tibet Plateau has the highest topsoil SOC density, with recorded values ranging from 8.41 kg/m2 to 13.2 kg/m2, while the northwestern part has the lowest density, with recorded values ranging from 0.85 kg/m2 to 2.88 kg/m2. Different land cover types showed varying SOC density values, with forests and grasslands having higher SOC densities compared to urban and bare land areas. The findings of this study provide a scientific basis for future soil resource management and improved carbon sequestration accounting in the Qinghai–Tibet Plateau. [ABSTRACT FROM AUTHOR]

Forests are one oi the most important terrestrial ecosystems and play a crucial role in the construction oi ecological civilization. Understanding the changes in gross ecosystem product ( GEP) and its response to land use change in areas with unique advantages in iorest resources is oi great signiiicance for promoting sustainable development. We examined the GEP oi Zixi County oi Jiangxi Province. Based on the theory and method oi ecosystem value accounting, and on the basis oi the general indicator system ior regulating services ( water source conservation, soil conservation, oxygen supply, carbon iixation, climate control, flood regulation, water puriiication, air cleaning, species conservation) and the general indicator system ior cultural services ( landscape recreation, education), we added negative oxygen ions and health care indicators and introduced iorest ecosystem service correction coeiiicients to construct a GEP indicator system and method with unique advantages in forest resources. We evaluated the spatio-temporal variations of GEP in 2010, 2017 and 2020, and quantified the impact of land use/land cover change on GEP by means of elasticity index and value profit and loss analysis. The results showed that the GEP of Zixi County in 2010, 2017 and 2020 was 16.788 billion, 26.817 billion, and 38.407 billion yuan, respectively. Forests contributed the largest amount of GEP, followed by wetland, farmland, grassland, and town. During the study period, the added value of GEP mainly came from forest ecosystems. Land use change had an important impact on GEP. From 2010 to 2020, the total land use change area in the study area was 8501.88 hm2. The amplitude of land use change was grassland (-2811.17 hm2)>town (1428.06 hm2)>forest ( 1357.67 hm2 )> wetland ( 1031.05 hm2) > farmland (-1005.01 hm2). The absolute changes of various land use types were mainly occurred from 2017 to 2020. The elasticity index of each indicator on forest ecosystems was significantly higher than that of other ecosystems, indicating that GEP had the highest sensitivity to changes in forest area. The results of value profit and loss analysis showed that urban development had reduced GEP to some extent, while the protection of forest and wetland had increased GEP. From 2010 to 2020, the conversion of land use types in Zixi County led to an increase of 1.865 billion yuan in GEP, indicating that land use change in Zixi County had a positive ecological impact on the whole. Our results reflect the green development effect of Zixi County after becoming a national ecological civilization construction demonstration county, which could provide decision-making support for high-quality development, high-level protection and sustainable development and utilization of land resources in the future, and provide reference for other similar areas of GEP accounting. [ABSTRACT FROM AUTHOR]

Remote sensing indices are widely used in various fields of geoscience research. However, there are limits to how effectively the knowledge of indices can be managed or analyzed. One of the main problems is the lack of ontology models and research on indices, which makes it difficult to acquire and update knowledge in this area. Additionally, there is a lack of techniques to analyze the mathematical semantics of indices, making it difficult to directly manage and analyze their mathematical semantics. This study utilizes an ontology and mathematical semantics integration method to offer a novel knowledge graph for a remote sensing index knowledge graph (RSIKG) so as to address these issues. The proposed semantic hierarchical graph structure represents the indices of knowledge with an entity-relationship layer and a mathematical semantic layer. Specifically, ontologies in the entity-relationship layer are constructed to model concepts and relationships among indices. In the mathematical semantics layer, index formulas are represented using mathematical semantic graphs. A method for calculating similarity for index formulas is also proposed. The article describes the entire process of building RSIKG, including the extraction, storage, analysis, and inference of remote sensing index knowledge. Experiments provided in this article demonstrate the intuitive and practical nature of RSIKG for analyzing indices knowledge. Overall, the proposed methods can be useful for knowledge queries and the analysis of indices. And the present study lays the groundwork for future research on analysis techniques and knowledge processing related to remote sensing indices. [ABSTRACT FROM AUTHOR]