Jinshi Jian

Soil methane uptake (SMU), an important biological sink for atmospheric methane (CH ₄ ) in terrestrial ecosystems, represents an important component of the global CH ₄ budget. Although field SMU observations have been conducted since the 1980s, they have not yet been systematically compiled into a unified and openly available database. Here, we present the global Soil Methane Uptake Database (SMUD), which includes 2427 site-level records from 920 peer-reviewed publications spanning 1986 to 2025. SMUD contains SMU measurements at multiple temporal scales, including daily ( n =1425), monthly ( n =2001), seasonal ( n =1720), and annual ( n =1098), together with associated soil moisture and soil temperature data at daily and monthly scales where available. The collected datasets cover all major climatic zones and ecosystem types and reveal pronounced spatial and temporal variability in SMU. Our analyses show that seasonal- and biome-specific patterns are important for understanding intra-annual variability in SMU. In particular, quantitative classification showed that the relationships of SMU with soil temperature and moisture differed among biomes, resulting in four response types: temperature-dominated, moisture-dominated, jointly regulated by temperature and moisture, and no clear dominant control. Overall, SMUD provides a comprehensive basis for characterizing global SMU patterns, investigating underlying mechanisms, and improving estimates of the global SMU budget. The database, associated data, and code for reproducing the results are available at https://doi.org/10.5281/zenodo.20735332 (Jiang et al., 2026). We aim to maintain SMUD as a collaborative, community-supported, and regularly updated resource for the research community.

Extreme rainstorms increasingly threaten soil and water conservation on the Loess Plateau, yet the spatial distribution, dominant erosion processes and hotspot locations of rainstorm‐induced erosion in small catchments remain insufficiently quantified. Here, we combined post‐event field surveys with UAV‐derived orthophotos and digital terrain models to investigate 30 rainstorm events in 98 small catchments across five comprehensive management zones from 2021 to 2024. The results indicate that rainstorm‐induced erosion disasters were most severe in the Loess Plateau Gully Region (LPGR) and the Loess Hilly Gully Region (LHGR), where slope failures were dominated by landslides in gully valleys, collapses of terrace risers and gully erosion along production roads. The most common natural catastrophes in the River Valley Plain Region (RVPR) were landslides and debris flows. In the eastern Earth‐Rocky Mountainous Region (ERMR), erosion was mainly characterized by a limited number of landslides and terrace riser collapses, while floods constituted the dominant disaster type. In the northern ERMR, gully‐bank collapses, limited hillslope gully erosion and seasonal floods were the principal disasters. In the Agricultural Irrigation Region (AIR), erosion was dominated by sheet erosion on slopes and localized gully erosion associated with production and construction projects. The primary forms of erosion in small catchments under rainstorm conditions were rill, gully, landslide and collapse occurrences. The mean erosion intensities of rill, gully, landslide and collapse erosion across the different management zones ranged from 7792 to 34 847, 2731 to 152 511, 20 786 to 56 331 and 7987 to 26 645 t km ⁻² , respectively. Mean erosion‐event densities were 196, 252, 19 and 32 events km ⁻² in the LPGR, LHGR, ERMR and AIR, respectively. Landslides were the dominant sediment‐contributing process in 73.1% of the investigated rainstorm events, while gully erosion and collapse erosion were secondary hotspot processes and rill erosion was no longer a dominant regional hotspot. Landslide hotspots were concentrated on steep gully‐valley slopes below the gully shoulder line, gully erosion was closely associated with unpaved production roads, and collapses mainly occurred on terrace risers. These findings indicate that future soil‐conservation strategies should shift from simply increasing vegetation cover to integrated catchment‐scale storage–drainage design and targeted reinforcement of gully valleys, roads and terrace risers.

Soil methane uptake (SMU), the second-largest and represents a biologically mediated sink for atmospheric methane (CH₄) in terrestrial ecosystems, plays a non-negligible role in regulating the global CH₄ budget. Field SMU observations have been conducted since the 1980s, but have not yet been systematically compiled within any unified and openly available framework. Here, we present the global Soil Methane Uptake Database (SMUD), which includes 2427 site-level records from 920 peer-reviewed publications spanning 1986 to 2025. SMUD contains SMU measurements at multiple temporal scales, including daily (n = 1425), monthly (n = 2001), seasonal (n = 1720), and annual (n = 1098), along with soil moisture and temperature data at daily and monthly scales. The collected datasets cover all major climatic zones and ecosystem types, across which SMU exhibits pronounced spatial and temporal variability. We found that seasonal- and biome-specific variations were key to understanding intra-annual changes in SMU. In particular, the effects of soil temperature and moisture on SMU are biome-specific: observations from Temperate Grassland are primarily associated with temperature changes, whereas those from Tropical Rainforest and Desert are mainly controlled by moisture, and other biomes show a mix of controlling factors. Overall, SMUD provides a comprehensive basis for understanding the global patterns of SMU, unraveling the underlying mechanisms, and estimating the global SMU budget. The database, associated data, and code to reproduce the results can be found at https://doi.org/10.5281/zenodo.18299391 (Jiang et al., 2026). We aim to establish SMUD maintenance as a collaborative, community-driven, shared, and user- managed database within the research community.

Field-based measurements of soil health under various conservation management practices have been compiled into the global Soil Health Database (SoilHealthDB), supporting comprehensive analyses of the effects of systems such as cover cropping, no-tillage, agroforestry systems, and organic fertilization on soil health. To support more effective soil health assessments and related analyses, it is essential to maintain updated and standardized datasets. In response to this need, the existing database of SoilHealthDB has been updated and standardized as SoilHealthDB-V2. All soil health indicators were harmonized to improve consistency, traceability, and analytical accuracy. The number of observations increased from 5,907 to 8,874, expanding global spatiotemporal coverage and improving representation across diverse climate regions. Several new variables related to cover crop management have been introduced, including cover crop termination date, cash crop planting date, cover crop termination method and residue return method, cover crop irrigation and fertilization practices. These additions enable more detailed assessments of best management practices. Furthermore, the inclusion of experiment duration-related data allows for more robust investigations of temporal trends in soil health outcomes. New indicators related soil carbon pools further enhance the database’s utility for evaluating the effects of conservation practices on carbon cycling. Overall, SoilHealthDB-V2 offers a robust, globally relevant foundation for soil health assessment and provides critical support for developing evidence-based conservation management strategies.

With the increasing impacts of global climate change and the continuous expansion of the population, the scarcity of food and water resources, along with the protection of agricultural land, have become significant constraints to sustainable agricultural development. Terraces plays a vital role in controlling water loss and promoting sustainable agriculture, and they have been widely adopted across the globe. Using CiteSpace, this study conducted a bibliometric review of the literature on the application of remote sensing and GISs in terrace studies under global climate change. The dataset included publications from the Web of Science spanning the years 1992 to 2024. Based on a systematical analysis of 508 publications, we investigated major institutions, cross-author collaborations, keyword co-occurrences, and the evolution of the research focus areas regarding the applications of remote sensing and GISs in terrace studies. The results show that the prominent research themes in this domain include remote sensing, erosion, and climate change. China (132, 26%) and the United States (108, 21%) are the top contributors in terms of publication numbers, while European countries and institutions are more active in collaborative efforts. The research emphasis has transitioned from analyzing the environmental characteristics of terraces to a broader consideration of ecological factors and multi-scenario applications. Moreover, analyses of the keyword co-occurrence and temporal trends indicate a rising interest in the application of machine learning, deep learning, and luminescence dating in terrace studies. Moving forward, it is essential to advance the deployment of automated monitoring systems, obtain long-term continuous monitoring data, encourage the adoption of conservation agriculture technology, and strengthen early warning networks for extreme climate events in terrace research. Overall, this study underscores the importance of interdisciplinary approaches and collaborative efforts to address the myriad challenges faced by terraced agriculture in an era of rapid environmental change.

Understanding the dynamics of soil respiration (R_s) and its environmental drivers is crucial for accurately modeling terrestrial carbon fluxes. However, current methodologies often lead to divergent estimates and rely on annual predictions that may overlook critical interactions occurring at seasonal scales. A critical knowledge gap lies in understanding how temporal resolution affects both R_s predictions and their environmental drivers. Here, we employ deep learning models to predict global R_s at monthly (MRM) and annual (ARM) scales from 1982 to 2018. We then consider three main drivers potentially affecting R_s, including temperature, precipitation, and a vegetation proxy (leaf area index; LAI). Our models demonstrate strong predictive capabilities with global R_s estimation of 79.4 ± 5.7 Pg C year⁻¹ for the MRM and 78.3 ± 7.5 Pg C year⁻¹ for ARM (mean ± SD). While the difference in global estimations between both models is small, there are notable disparities in the spatial contribution of dominant drivers. The MRM highlights an influence of both temperature and LAI, while the ARM emphasizes a dominant role of precipitation. These findings underscore the critical role of temporal resolution in capturing seasonal variations and identifying key R_s‐environment relationships that annual models may obscure. High temporal resolution R_s predictions, such as those provided by the MRM, are essential for capturing nuanced seasonal interactions between R_s and its drivers, refining carbon flux models, detecting critical seasonal thresholds, and enhancing the reliability of future Earth system predictions. This work highlights the need for further research into monthly and seasonal R_s variations, as well as higher timescale resolutions, to advance our understanding of ecosystem carbon dynamics in a rapidly changing climate.

The practice of cover crops has gained popularity as a strategy to improve agricultural sustainability, but its full potential is often limited by environmental trade-offs. Using meta-analytic and data-driven quantifications of 2302 observations, we optimized cover crop practices and evaluated their benefits for global agroecosystems. Cover crops have historically boosted crop yields, soil carbon storage, and stability, but also stimulated greenhouse gas emissions. However, combining them with long-term implementation (five years or more) and climate-smart practices (such as no-tillage) can enhance these services synergistically. A biculture of legume and non-legume cover crops, terminated 25 days before planting the next crop and followed by residue mulching, is the optimal portfolio. Such optimized practices are projected to increase agroecosystem multiservices by 1.25%, equivalent to annual gains of 97.7 million metric tons in crop production, 21.7 billion metric tons in carbon dioxide sequestration, and 2.41 billion metric tons in soil erosion reduction. By 2100, the continued implementation of optimized practices could mitigate climate-related yield losses and contribute to climate neutrality and soil stabilization, especially in harsh and underdeveloped areas. These findings underscore the promising potential of optimized cover crop practices to achieve the synergy in food security and environmental protection.

As global climate change intensifies and population growth continues, water scarcity has emerged as a critical constraint to sustainable agricultural development. Conservation management, an effective water-saving technique, plays a crucial role in enhancing soil water content (SWC) and promoting sustainable agriculture. This study utilizes CiteSpace to perform a bibliometric analysis of research literature on the effects of conservation management on SWC, encompassing publications indexed in the Web of Science database from 1992 to 2024. By systematically examining 599 papers, we analyzed key research institutions, authors’ collaborative contributions, keyword co-occurrences, and shifts in research hotspots related to conservation management and its impact on SWC. The results reveal that significant topics in this field include “conservation agriculture”, “water use efficiency”, and “conservation tillage”. China (225, 38%) and the United States (129, 22%) lead in publication volume, whereas European countries and institutions show a higher degree of collaboration. The research focus has transitioned from examining the impacts and mechanisms of conservation tillage on crop yield and soil physical and chemical properties to long-term monitoring, water use efficiency, and mitigation. Furthermore, keyword co-occurrence and temporal analysis highlight a growing emphasis on soil quality and greenhouse gas emissions. In the future, it remains imperative to enhance the implementation of automated monitoring systems, secure long-term continuous monitoring data, promote conservation agriculture technology, and bolster the early warning network for extreme climate events. These measures are crucial for preserving soil nutrient levels and ensuring the sustainable development of agriculture.

Check dams on the Chinese Loess Plateau (CLP) have captured billions of tons of eroded sediment, substantially reducing sediment load in the Yellow River. However, uncertainties persist regarding the precise sediment capture and the role of these dams in Yellow River flow and sediment dynamics due to the lack of available spatial distribution datasets. We produced the first vectorized dataset of silted land formed by check dams on the CLP, combining high-resolution and easily accessible Google Earth images with object-based classification methods. The accuracy of the dataset was verified by 1947 collected test samples, and the producer’s accuracy and user’s accuracy of the dam lands were 88.9% and 99.5%, respectively. Our dataset not only provides fundamental information for accurately assessing the ecosystem service functions of check dams, but also helps to interpret current changes in sediment delivery of the Yellow River and plan future soil and water conservation projects.