[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4510":3,"related-4510":113},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":78,"slug":81,"view_count":82,"doi":83,"paper":84,"created_at":112},4510,"Organic carbon sequestration potential in alluvial agricultural terraces: A case study of a lama-bordo system in Oaxaca, Mexico","https:\u002F\u002Fdoi.org\u002F10.4067\u002Fs0718-58392026000600930","Soil is the primary C reservoir in terrestrial ecosystems and provides a variety of ecosystem services.Therefore, quantifying soil organic C (SOC) storage is essential for conservation technologies that ensure the sustainability of this non-renewable resource.In this context, the objective of this study was to estimate the SOC content within a pre-Hispanic agricultural terrace system, known locally as lama-bordo, in the Mixteca Alta region of Oaxaca, Mexico, using soil sampling, remote sensing, and geographic information systems.Based on the organic matter content, the SOC was determined in each soil sample from the surface layer (0-30 cm).The total SOC content in the lama-bordo system (LBS) was estimated using the average depth of each section and a highresolution digital elevation model.The results indicate that the LBS is capable of storing between 254.88 and 449.68 Mg ha -1 SOC, which can prevent the emission of C into the atmosphere through its conservation.These findings highlight the need for further research to generate more comprehensive knowledge of the functions of alluvial agricultural terraces.","土壤是陆地生态系统中主要的碳库，并提供多种生态系统服务。因此，量化土壤有机碳（SOC）储量对于确保这一不可再生资源可持续性的保护技术至关重要。在此背景下，本研究旨在利用土壤采样、遥感（remote sensing）和地理信息系统（geographic information systems），估算墨西哥瓦哈卡州米斯特卡阿尔塔地区一处前西班牙时期的农业梯田系统（当地称为lama-bordo）中的SOC含量。基于有机质含量，测定了每个表层（0-30 cm）土壤样品的SOC。利用每个断面的平均深度和高分辨率数字高程模型（digital elevation model），估算了lama-bordo系统（LBS）的SOC总含量。结果表明，LBS能够储存254.88至449.68 Mg ha⁻¹的SOC，通过对其进行保护可防止碳释放到大气中。这些发现凸显了进一步研究的必要性，以更全面地认识冲积农业梯田的功能。",null,"Chilean journal of agricultural research","2026-10-07T00:00:00Z","论文",10,false,66,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":17,"relevant":21,"comment":22},8,20,17,13,1,"基于遥感与GIS量化前西班牙时期梯田系统固碳潜力，方法新颖数据扎实，但属区域案例研究，公共影响有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"遥感","土壤固碳","地理信息系统","墨西哥","农业梯田",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"H4","carbon","农业碳与绿色低碳","\u002Ftopic\u002Fenvironment\u002Fcarbon","H",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":45},[44,50,58,64,71],{"code":32,"score":17,"supported":45,"title":46,"abstract":48,"tags":49},true,[47],"carbon sequestration",[],[],{"code":51,"score":52,"supported":45,"title":53,"abstract":54,"tags":57},"H2",7,[],[55,56],"土壤","soil",[27],{"code":59,"score":52,"supported":45,"title":60,"abstract":61,"tags":63},"I3",[],[26,62],"remote sensing",[26],{"code":65,"score":52,"supported":45,"title":66,"abstract":67,"tags":70},"I4",[],[68,69],"地理信息","geographic information",[28],{"code":72,"score":73,"supported":45,"title":74,"abstract":75,"tags":77},"H6",3,[],[76],"生态系统服务",[],[79,80],"Oaxaca lama-bordo 梯田 固碳","墨西哥 梯田 土壤有机碳","Oaxacalama-bordo梯田固碳-4510",0,"10.4067\u002Fs0718-58392026000600930",{"doi":83,"openalex_id":85,"authors":86,"venue":10,"cited_by_count":82,"oa_url":103,"card":104,"direction":110,"ingested_from":111},"W7220837836",[87,89,92,95,97,100],{"name":88,"orcid":9},"B. Estela Santiago-Mejía",{"name":90,"orcid":91},"Demetrio S. Fernández-Reynoso","https:\u002F\u002Forcid.org\u002F0000-0002-1734-3152",{"name":93,"orcid":94},"Gerardo Colín-García","https:\u002F\u002Forcid.org\u002F0000-0002-3155-6699",{"name":96,"orcid":9},"M. Magdalena Nevárez-Favela",{"name":98,"orcid":99},"Moisés Márquez-Velázquez","https:\u002F\u002Forcid.org\u002F0009-0004-9341-628X",{"name":101,"orcid":102},"Madai Sánchez-Galindo","https:\u002F\u002Forcid.org\u002F0000-0002-4371-0817","http:\u002F\u002Fwww.scielo.cl\u002Fpdf\u002Fchiljar\u002Fv86n6\u002F0718-5839-chiljar-86-06-930.pdf",{"tldr":105,"method":106,"finding":107,"direction":108,"opportunity":109},"估算墨西哥瓦哈卡前西班牙时期梯田系统的土壤有机碳储量。","土壤采样、遥感与GIS结合，基于DEM估算0-30cm SOC。","梯田系统可储存254.88-449.68 Mg\u002Fha SOC，保护可防止碳释放。","农业绿色发展与碳","可延伸研究不同梯田类型或区域的碳汇潜力及保护措施的固碳效益。","农业遥感与作物表型","openalex","2026-10-09T23:30:26.302393Z",{"total":114,"page":21,"page_size":114,"items":115},6,[116,210,269,353,421,516],{"id":117,"title":118,"url":119,"summary":120,"summary_zh":121,"content":9,"source_name":122,"source_url":119,"published_at":123,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":124,"score_detail":125,"sources":131,"tags":133,"primary_topic":138,"secondary_topics":139,"classification":143,"search_phrases":161,"slug":164,"view_count":82,"doi":165,"paper":166,"created_at":209},5467,"Temperature-directed valorisation of coconut frond through pyrolysis for carbon sequestration and sustainable agricultural residue management","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1966170","Coconut frond, an abundantly generated yet underutilized agricultural residue, was pyrolysed at 350 °C, 450 °C, and 550 °C alongside traditionally produced biochar (BC Trad) to evaluate the effects of pyrolysis temperature on physicochemical characteristics, fuel properties, carbon stability, and residue-valorisation potential. Comprehensive characterization using proximate and ultimate analyses, BET\u002FBJH, FTIR, XRD, Raman spectroscopy, SEM-EDS, total pyrogenic carbon (TPC), estimated CO 2 -equivalent stable-carbon retention potential, fuel and energy indices, and 90-day carbon mineralization incubation revealed pronounced temperature-dependent transformations. Biochar carbon recovery was highest at 350 °C (78.91%), demonstrating greater retention of feedstock carbon at lower pyrolysis temperature, whereas increasing temperature progressively enhanced carbon concentration, fixed carbon, thermostable carbon fraction, porosity, and surface area. BET surface area increased from 4.45 m 2 g −1 in BC Trad to 16.67 m 2 g −1 in BC550, accompanied by progressive pore refinement and depletion of oxygen-containing functional groups. Raman spectroscopy showed a decline in ID\u002FIG from 1.28 to 0.80, consistent with increasing structural ordering and aromatic condensation at higher temperatures. Fuel quality also improved with increasing temperature, with higher heating value increasing from 20.72 to 25.56 MJ kg −1 and fuel ratio from 1.49 to 4.26. TPC and estimated CO 2 -equivalent stable-carbon retention potential were highest at BC350 (0.29 kg C kg −1 feedstock and 0.86 kg CO 2 kg −1 feedstock, respectively), whereas BC550 exhibited the lowest apparent net soil-system carbon mineralization among the biochars during the 90-day incubation. Separately, its lower H\u002FC and O\u002FC ratios corresponded to a modelled BC +100 stability index of 0.720 and an estimated mean residence time of 347.61 years, providing model-derived indications of longer-term carbon persistence. Overall, the findings demonstrate a clear temperature-dependent trade-off between carbon recovery and carbon stability, with BC350 providing greater structural recalcitrance and apparent short-term mineralization resistance; the optimal pyrolysis temperature therefore depends on the intended end use rather than a single treatment being universally superior. Together, the short-term mineralization results and model-derived stability estimates indicate distinct dimensions of carbon persistence that should be considered when optimizing coconut-frond pyrolysis for sustainable agricultural-residue management, durable soil-carbon sequestration, and circular bioeconomy applications.","椰子叶是一种大量产生但未充分利用的农业废弃物，其在350 °C、450 °C和550 °C下热解，并与传统生产的生物炭（BC Trad）进行对比，以评估热解温度对理化特性、燃料性质、碳稳定性及残余物增值潜力的影响。采用工业分析和元素分析、BET\u002FBJH、FTIR、XRD、拉曼光谱、SEM-EDS、总热解碳（TPC）、估算CO₂当量稳定碳保留潜力、燃料与能量指标以及90天碳矿化培养进行综合表征，揭示了显著的温度依赖性变化。生物炭碳回收率在350 °C时最高（78.91%），表明较低热解温度下原料碳的保留更多，而升高温度则逐步提高碳浓度、固定碳、热稳定碳组分、孔隙率和比表面积。BET比表面积从BC Trad的4.45 m² g⁻¹增至BC550的16.67 m² g⁻¹，同时伴随孔隙细化和含氧官能团的消耗。拉曼光谱显示ID\u002FIG从1.28降至0.80，与较高温度下结构有序性和芳香缩合度增加相一致。燃料品质亦随温度升高而改善，高位热值从20.72增至25.56 MJ kg⁻¹，燃料比从1.49增至4.26。TPC和估算CO₂当量稳定碳保留潜力在BC350时最高（分别为0.29 kg C kg⁻¹原料和0.86 kg CO₂ kg⁻¹原料），而BC550在90天培养期间表现出生物炭中最低的表观净土壤系统碳矿化。此外，其较低的H\u002FC和O\u002FC比对应模拟BC +100稳定性指数为0.720，估算平均停留时间为347.61年，提供了模型推导的较长时期碳持久性指示。总体而言，研究结果揭示了碳回收与碳稳定性之间明显的温度依赖性权衡，BC350表现出更高的结构惰性和表观短期抗矿化性；因此，最佳热解温度取决于预期最终用途，而非单一处理普遍优越。短期矿化结果与模型推导的稳定性估算共同表明，碳持久性具有不同维度，应予考虑。","Frontiers in Sustainable Food Systems","2026-10-20T00:00:00Z",74,{"impact":126,"substance":127,"depth":128,"authority":20,"freshness":129,"relevant":21,"comment":130},12,22,18,9,"该研究系统考察了热解温度对椰叶生物炭碳回收与碳稳定性的权衡关系，数据翔实、结论可靠，对农业废弃物资源化与土壤固碳具有参考价值，但属于细分领域基础研究，公共影响有限。",[132],{"name":122,"url":119},[134,135,136,27,137],"生物炭","农业废弃物","秸秆利用","热解技术",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[140],{"code":51,"slug":56,"name":141,"path":142,"parent_code":36},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"status":39,"locked":14,"version":40,"method":41,"evidence":144},{"candidates":145,"agricultural_evidence":45},[146,151,155],{"code":32,"score":147,"supported":45,"title":148,"abstract":149,"tags":150},11,[47],[47],[],{"code":51,"score":17,"supported":45,"title":152,"abstract":153,"tags":154},[],[55,56],[27,134],{"code":156,"score":157,"supported":45,"title":158,"abstract":159,"tags":160},"H5",5,[],[135],[135,136],[162,163],"椰叶 热解 生物炭","生物炭 碳封存 农业废弃物","椰叶热解生物炭-5467","10.3389\u002Ffsufs.2026.1966170",{"doi":165,"openalex_id":167,"authors":168,"venue":122,"cited_by_count":82,"oa_url":203,"card":204,"direction":108,"ingested_from":111},"W7223016223",[169,172,174,177,180,182,185,188,190,193,195,198,201],{"name":170,"orcid":171},"Morasanahalli Lokesha Sachin","https:\u002F\u002Forcid.org\u002F0009-0008-8615-0822",{"name":173,"orcid":9},"Yogananda Shivalli Boregowda",{"name":175,"orcid":176},"Matadadoddi Nanjundegowda Thimmegowda","https:\u002F\u002Forcid.org\u002F0000-0001-7632-1036",{"name":178,"orcid":179},"M. A. Ananthakumar","https:\u002F\u002Forcid.org\u002F0000-0003-4376-9541",{"name":181,"orcid":9},"Pudiyanda Shadali Fathima",{"name":183,"orcid":184},"C. M. Sunil","https:\u002F\u002Forcid.org\u002F0000-0002-9625-5686",{"name":186,"orcid":187},"K. M. Pravalika","https:\u002F\u002Forcid.org\u002F0009-0004-5258-392X",{"name":189,"orcid":9},"M.C. Anjali",{"name":191,"orcid":192},"Sathisha Govinakere Sambegowda","https:\u002F\u002Forcid.org\u002F0009-0000-8620-6461",{"name":194,"orcid":9},"B. Divya",{"name":196,"orcid":197},"M. K. Shruthi","https:\u002F\u002Forcid.org\u002F0000-0003-3703-9817",{"name":199,"orcid":200},"Santosh Onte","https:\u002F\u002Forcid.org\u002F0009-0002-8367-1303",{"name":202,"orcid":9},"Sanjeev Kumar","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1966170\u002Fpdf",{"tldr":205,"method":206,"finding":207,"direction":108,"opportunity":208},"研究不同热解温度对椰叶生物炭碳固存与稳定性的影响。","350\u002F450\u002F550°C热解椰叶，多手段表征并做90天矿化培养。","碳回收率与碳稳定性随温度呈权衡，最佳温度取决于用途。","可探索热解温度与还田情景耦合的长期碳固存模型及经济性评估。","2026-10-21T23:30:07.083785Z",{"id":211,"title":212,"url":213,"summary":214,"summary_zh":215,"content":9,"source_name":122,"source_url":213,"published_at":216,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":217,"score_detail":218,"sources":221,"tags":223,"primary_topic":228,"secondary_topics":229,"classification":230,"search_phrases":251,"slug":254,"view_count":21,"doi":255,"paper":256,"created_at":268},3991,"Long-term straw incorporation in farmland promotes carbon sequestration and mitigates emissions: a global meta-analysis","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1965841","Background Straw incorporation is an important agronomic practice for soil carbon seques-tration. However, the underlying processes of it remain unclear. Objective This study compared global carbon cycle processes under straw incorporation durations of ≤5 years and >5 years. Methods This study was based on a global meta-analysis. Results Straw incorporation improved soil physicochemical properties and increased soil organic carbon storage by 23.57% relative to no return. Long-term incorporation (>5 years) further increased SOC storage compared with ≤5 years. Microbial diversity and community structure were altered, with greater changes under longer durations. Straw incorporation enhanced genes involved in carbon degradation, fixation, and methane metabolism, and increased enzyme activities, soil respiration, and mineralization rate. Straw incorporation increased CH 4 , CO 2 , and N 2 O emissions, but the emission increment was lower in the >5 years group than in the ≤5 years group. Global warming potential showed a similar trend. Random forest analysis indicated that soil carbon storage was mainly driven by mineralization rate and microbial necromass carbon, while GWP was primarily regulated by available nitrogen and microbial necromass carbon. Conclusion These findings demonstrate that long-term straw incorporation can effectively enhance carbon sequestration and reduce the global warming potential in farmland.","背景 秸秆还田是促进土壤碳固存的重要农艺措施，但其内在过程仍不明确。目的 本研究比较了秸秆还田年限≤5年和>5年条件下的全球碳循环过程。方法 本研究基于全球荟萃分析。结果 与不还田相比，秸秆还田改善了土壤理化性质，使土壤有机碳储量提高了23.57%。与≤5年相比，长期还田（>5年）进一步提高了土壤有机碳储量。微生物多样性和群落结构发生改变，且还田年限越长变化越大。秸秆还田增强了参与碳降解、碳固定和甲烷代谢的基因，并提高了酶活性、土壤呼吸和矿化速率。秸秆还田增加了CH₄、CO₂和N₂O排放，但>5年组的排放增量低于≤5年组。全球增温潜势也表现出相似趋势。随机森林分析表明，土壤碳储量主要受矿化速率和微生物残体碳驱动，而全球增温潜势主要受有效氮和微生物残体碳调控。结论 这些发现表明，长期秸秆还田能够有效增强农田碳固存并降低全球增温潜势。","2026-10-01T00:00:00Z",80,{"impact":128,"substance":219,"depth":128,"authority":20,"freshness":17,"relevant":21,"comment":220},23,"全球尺度meta分析，量化长期秸秆还田的固碳与减排效应，数据扎实、结论明确，对农田碳汇与绿色农业有参考价值。",[222],{"name":122,"url":213},[224,225,226,27,227],"秸秆还田","碳循环","农业减排","农田碳汇",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":231},{"candidates":232,"agricultural_evidence":45},[233,237,241,246],{"code":32,"score":126,"supported":45,"title":234,"abstract":235,"tags":236},[47],[47],[226],{"code":51,"score":52,"supported":45,"title":238,"abstract":239,"tags":240},[],[55,56],[27],{"code":156,"score":242,"supported":45,"title":243,"abstract":244,"tags":245},4,[],[224],[224],{"code":72,"score":73,"supported":45,"title":247,"abstract":248,"tags":250},[],[249],"生物多样性",[],[252,253],"秸秆还田 土壤固碳 全球meta分析","长期秸秆还田 温室气体排放","秸秆还田土壤固碳全球meta分析-3991","10.3389\u002Ffsufs.2026.1965841",{"doi":255,"openalex_id":257,"authors":258,"venue":122,"cited_by_count":82,"oa_url":213,"card":263,"direction":108,"ingested_from":111},"W7215031184",[259,261],{"name":260,"orcid":9},"Xiaoyan Wang",{"name":262,"orcid":9},"Feng Luo",{"tldr":264,"method":265,"finding":266,"direction":108,"opportunity":267},"全球荟萃分析表明长期秸秆还田（>5年）比短期更能固碳并降低全球增温潜势。","全球荟萃分析，比较≤5年与>5年秸秆还田下碳循环过程与微生物机制。","长期还田进一步提升有机碳储量，温室气体排放增量更小，GWP更低。","可探究不同气候与土壤条件下长期还田的固碳减排阈值及微生物调控机制。","2026-10-02T23:30:09.544882Z",{"id":270,"title":271,"url":272,"summary":273,"summary_zh":274,"content":9,"source_name":275,"source_url":272,"published_at":276,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":277,"score_detail":278,"sources":281,"tags":283,"primary_topic":288,"secondary_topics":293,"classification":295,"search_phrases":330,"slug":333,"view_count":242,"doi":334,"paper":335,"created_at":352},3554,"Spatial Estimation of Carbon Sequestration Using Remote Sensing and Its Application in Carbon-credit Assessment: A Comprehensive Review","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fjgeesi\u002F2026\u002Fv30i91125","Carbon sequestration is fundamental to climate-change mitigation because terrestrial and aquatic ecosystems store carbon in vegetation, biomass, soils, and the Earth’s crust. Reliable estimation of carbon stocks and their temporal and spatial changes is essential for the formulation of climate policy, ecosystem management, carbon accounting, and carbon-credit assessment. Conventional field measurements provide detailed information but are constrained by sampling requirements, cost, labour, spatial heterogeneity, and limitations in continuous monitoring. Remote sensing provides a complementary approach by enabling spatially continuous observations of vegetation characteristics, land-cover change, biomass, and other carbon-related variables across different spatial and temporal scales. Advances in multispectral, hyperspectral, synthetic aperture radar, LiDAR, thermal, and high-resolution satellite observations have extended the capability for carbon estimation. Their integration with geographic information systems, field observations, statistical techniques, and machine-learning algorithms enables precise spatial assessment of above-ground biomass, below-ground biomass, soil organic carbon, forest carbon, agricultural carbon, mangrove carbon, and blue-carbon resources. However, uncertainties associated with sensor characteristics, field-data quality, model selection, spatial variability, temporal dynamics, and scaling remain acknowledged limitations. Converting spatial carbon estimates into carbon credits requires consideration of baseline conditions, additionality, permanence, leakage, uncertainty, verification, and measurement, reporting, and verification requirements. This review critically examines remote sensing technologies, carbon-estimation models, spatial mapping approaches, machine-learning techniques, carbon-accounting frameworks, and their integration into carbon-credit assessment. It places particular emphasis on multisource data fusion, emerging Earth-observation technologies, artificial intelligence, uncertainty assessment, and digital MRV systems. Major methodological, technological, and policy gaps are identified, along with opportunities for developing transparent, spatially explicit, scientifically robust, and scalable carbon-credit assessment frameworks.","碳固存是减缓气候变化的基础，因为陆地和水生生态系统将碳储存在植被、生物量、土壤和地壳中。可靠估算碳储量及其时空变化，对于制定气候政策、生态系统管理、碳核算和碳信用评估至关重要。传统野外测量可提供详细信息，但受采样要求、成本、劳动力、空间异质性以及连续监测局限性等因素制约。遥感提供了一种互补方法，能够在不同时空尺度上对植被特征、土地覆盖变化、生物量及其他碳相关变量进行空间连续观测。多光谱、高光谱、合成孔径雷达、激光雷达、热红外和高分辨率卫星观测的进步，拓展了碳估算能力。这些技术与地理信息系统、野外观测、统计技术和机器学习算法的集成，使得对地上生物量、地下生物量、土壤有机碳、森林碳、农业碳、红树林碳和蓝碳资源进行精确空间评估成为可能。然而，与传感器特性、野外数据质量、模型选择、空间变异性、时间动态和尺度转换相关的不确定性仍是公认的局限。将空间碳估算转化为碳信用，需要考虑基线条件、额外性、永久性、泄漏、不确定性、核查以及测量、报告和核查要求。本综述批判性地审视了遥感技术、碳估算模型、空间制图方法、机器学习技术、碳核算框架及其在碳信用评估中的集成。特别强调了多源数据融合、新兴地球观测技术、人工智能、不确定性评估和数字化MRV系统。识别了主要的方法学、技术和政策差距，以及开发透明、空间明确、科学稳健且可扩展的碳信用评估框架的机遇。","Journal of Geography Environment and Earth Science International","2026-09-25T00:00:00Z",67,{"impact":126,"substance":128,"depth":279,"authority":126,"freshness":129,"relevant":21,"comment":280},16,"系统综述遥感碳汇估算与碳信用评估的方法、不确定性与数字MRV进展，对农业碳汇与碳交易信息化有参考价值，但属综述类论文，公共影响有限。",[282],{"name":275,"url":272},[284,26,285,286,287],"机器学习","碳汇","碳信用","数字MRV",{"code":59,"slug":289,"name":290,"path":291,"parent_code":292},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[294],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":296},{"candidates":297,"agricultural_evidence":45},[298,304,310,316,320,324],{"code":59,"score":128,"supported":45,"title":299,"abstract":300,"tags":303},[62],[26,62,301,302],"multispectral","hyperspectral",[26],{"code":32,"score":305,"supported":45,"title":306,"abstract":307,"tags":309},15,[47],[308,47],"农业碳",[285],{"code":311,"score":52,"supported":45,"title":312,"abstract":313,"tags":315},"I2",[],[284,314],"artificial intelligence",[284],{"code":51,"score":114,"supported":45,"title":317,"abstract":318,"tags":319},[],[55,56],[],{"code":65,"score":114,"supported":45,"title":321,"abstract":322,"tags":323},[],[68,69],[],{"code":325,"score":73,"supported":45,"title":326,"abstract":327,"tags":329},"H7",[],[328],"气候变化",[],[331,332],"遥感 碳汇 估算","碳信用 评估 遥感","遥感碳汇估算-3554","10.9734\u002Fjgeesi\u002F2026\u002Fv30i91125",{"doi":334,"openalex_id":336,"authors":337,"venue":275,"cited_by_count":82,"oa_url":272,"card":347,"direction":110,"ingested_from":111},"W7214392294",[338,340,343,345],{"name":339,"orcid":9},"Aishwarya Desai",{"name":341,"orcid":342},"Himalaya Ganachari","https:\u002F\u002Forcid.org\u002F0000-0002-6157-9242",{"name":344,"orcid":9},"Sangita Shinde",{"name":346,"orcid":9},"Sachinkumar Nandgude",{"tldr":348,"method":349,"finding":350,"direction":108,"opportunity":351},"综述遥感碳储量估算技术及其在碳信用评估中的应用与挑战。","综述多光谱、高光谱、SAR、LiDAR等遥感与GIS、机器学习融合方法。","遥感可空间连续估算多类碳库，但不确定性及MRV要求制约碳信用转化。","可研究多源数据融合与AI驱动的数字MRV，降低碳信用评估不确定性。","2026-09-26T23:30:30.002197Z",{"id":354,"title":355,"url":356,"summary":357,"summary_zh":358,"content":9,"source_name":359,"source_url":356,"published_at":360,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":361,"score_detail":362,"sources":366,"tags":368,"primary_topic":372,"secondary_topics":373,"classification":374,"search_phrases":400,"slug":403,"view_count":73,"doi":404,"paper":405,"created_at":420},2423,"Spatio-temporal evolution and driving force analysis of carbon storage coupled with PLUS-InVEST-GeoDetector in the Fen River Basin of China","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-71784-9","Abstract Against the backdrop of China’s “Dual Carbon” (carbon peak and carbon neutrality) strategic initiative, terrestrial ecosystem carbon sequestration has become a core pillar of climate mitigation and ecological governance. This study systematically quantifies the spatiotemporal distribution of carbon storage and and projects long-term carbon sink trajectories under divergent socioecological development pathways across the Fen River Basin—a representative ecologically fragile tributary catchment of the Yellow River. Four scenarios based on the basin’s ecological landscape planning were established: Natural Development, Economic Priority, Cropland Protection, and Ecological Protection. By coupling the PLUS and InVEST models, an analytical framework linking land use change with ecosystem carbon storage was constructed to simulate and predict the spatial distribution and dynamics of carbon storage under different scenarios. The Geodetector was used to identify the key driving factors. The results indicate that: (1) From 2005 to 2020, cropland remained the dominant land use type but showed a continuous decreasing trend alongside grassland degradation. Forest land area initially decreased before recovering slightly, while built-land expanded consistently. (2) The total carbon storage in the basin showed a declining trend, decreasing from 700.32 Tg in 2005 to 683.48 Tg in 2020, with a cumulative loss of 16.84 Tg. Soil organic carbon was the major carbon pool. Spatially, areas with high carbon storage were primarily distributed in the eastern and western parts of the basin, while the central-southern regions generally had lower carbon storage. Carbon storage exhibited significant positive spatial autocorrelation, with High-High clusters (hot spots) being more extensive than Low-Low clusters (cold spots). The area experiencing carbon loss was substantially larger than the area with carbon gain. (3) Multi-scenario simulations revealed that different development pathways significantly impact carbon storage. The Ecological Protection scenario yielded the highest carbon storage (683.57 Tg), highlighting the effectiveness of ecological protection in enhancing regional carbon sink capacity. (4) NDVI was the dominant factor influencing the spatial heterogeneity of carbon storage, possessing strong independent explanatory power. Its interactive effects with other factors further enhanced the ability to explain variations in carbon storage. This study elucidates the coupling mechanism between land use change and carbon storage, providing a scientific reference for ecological protection, land structure optimization, and carbon sink enhancement in the Fen River Basin.","在中国“双碳”（碳达峰与碳中和）战略倡议背景下，陆地生态系统碳汇已成为气候减缓与生态治理的核心支柱。本研究系统量化了汾河流域——黄河具有代表性的生态脆弱支流流域——碳储量的时空分布，并预测了不同社会生态发展路径下的长期碳汇轨迹。基于流域生态景观规划，设立了四种情景：自然发展、经济优先、耕地保护和生态保护。通过耦合PLUS与InVEST模型，构建了土地利用变化与生态系统碳储量关联的分析框架，以模拟和预测不同情景下碳储量的空间分布与动态变化。运用地理探测器识别关键驱动因子。结果表明：（1）2005年至2020年，耕地仍为主要土地利用类型，但呈持续下降趋势，草地亦退化。林地面积先减少后略有恢复，建设用地持续扩张。（2）流域碳储量总量呈下降趋势，从2005年的700.32 Tg降至2020年的683.48 Tg，累计损失16.84 Tg。土壤有机碳为主要碳库。空间上，高碳储量区主要分布于流域东西部，中南部地区碳储量普遍较低。碳储量表现出显著的正空间自相关，高-高集聚区（热点）范围大于低-低集聚区（冷点）。碳损失面积显著大于碳增益面积。（3）多情景模拟揭示，不同发展路径对碳储量影响显著。生态保护情景碳储量最高（683.57 Tg），凸显了生态保护在提升区域碳汇能力方面的有效性。（4）NDVI是影响碳储量空间分异的主导因子，具有较强独立解释力。其与其他因子的交互作用进一步增强了对碳储量变化的解释能力。本研究阐明了","Scientific Reports","2026-09-12T00:00:00Z",77,{"impact":279,"substance":363,"depth":128,"authority":364,"freshness":17,"relevant":21,"comment":365},21,14,"耦合PLUS-InVEST-GeoDetector量化汾河流域碳储时空演变并模拟多情景碳汇路径，方法新颖、数据扎实，对黄河流域生态保护与土地结构优化有参考价值。",[367],{"name":359,"url":356},[26,369,370,371,285],"生态保护","黄河流域","土地利用",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":375},{"candidates":376,"agricultural_evidence":45},[377,381,385,391,396],{"code":32,"score":52,"supported":45,"title":378,"abstract":379,"tags":380},[],[285,47],[285],{"code":51,"score":114,"supported":45,"title":382,"abstract":383,"tags":384},[],[55,56],[],{"code":386,"score":73,"supported":45,"title":387,"abstract":388,"tags":390},"H1",[],[389],"耕地保护",[],{"code":72,"score":392,"supported":14,"title":393,"abstract":394,"tags":395},2,[],[],[371,369],{"code":59,"score":21,"supported":14,"title":397,"abstract":398,"tags":399},[],[],[26],[401,402],"土地利用 生态保护 黄河流域 碳汇","土地利用 生态保护","土地利用生态保护黄河流域碳汇-2423","10.1038\u002Fs41598-026-71784-9",{"doi":404,"openalex_id":406,"authors":407,"venue":359,"cited_by_count":82,"oa_url":414,"card":415,"direction":110,"ingested_from":111},"W7212371101",[408,410,412],{"name":409,"orcid":9},"Jie Chen",{"name":411,"orcid":9},"Pengxiang Gao",{"name":413,"orcid":9},"Yi Hou","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-71784-9_reference.pdf",{"tldr":416,"method":417,"finding":418,"direction":108,"opportunity":419},"耦合PLUS-InVEST-GeoDetector模拟汾河流域多情景碳储量演变并识别驱动因子。","PLUS与InVEST耦合模拟四情景土地利用，GeoDetector识别驱动因子","2005-2020年碳储量下降16.84 Tg，生态保护情景碳储量最高，NDVI为主导驱动因子。","可引入农业管理措施与政策情景，细化耕地保护对碳汇的贡献机制研究。","2026-09-14T23:30:24.574426Z",{"id":422,"title":423,"url":424,"summary":425,"summary_zh":426,"content":9,"source_name":427,"source_url":424,"published_at":428,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":429,"score_detail":430,"sources":432,"tags":434,"primary_topic":439,"secondary_topics":440,"classification":442,"search_phrases":485,"slug":488,"view_count":82,"doi":489,"paper":490,"created_at":515},2325,"Carbon Farming in the Digital Era: Soil Carbon Sequestration, Permanence, Measurement, Reporting and Verification","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fjabb\u002F2026\u002Fv29i104418","Carbon farming is increasingly promoted as a way to rebuild soil organic carbon while creating verifiable climate benefits and new farm income streams. Its credibility, however, depends on a chain of conditions that extends well beyond whether a practice can increase soil carbon at an experimental site. This critical narrative review evaluates the biophysical basis of agricultural soil carbon sequestration, the distinction between molecular persistence and project-level permanence, the measurement of stock change, and the rapidly developing role of digital technologies in measurement, reporting and verification (MRV). Literature published from 2000 to 6 July 2026 was examined, with earlier conceptual material considered only where necessary. Evidence was prioritised from peer-reviewed field studies, meta-analyses, methodological studies and authoritative technical frameworks. The evidence supports the capacity of cover crops, organic amendments, diversified rotations and some biochar applications to raise soil carbon under appropriate conditions, but effect sizes are strongly conditioned by baseline stocks, climate, texture, depth, carbon inputs, management history and system boundaries. Apparent gains can also arise from altered depth distribution or transferred organic matter rather than additional atmospheric carbon removal. Permanence is therefore a management and accounting property, not simply a property of chemically resistant carbon. Direct remeasurement remains the evidentiary anchor for stock-change assessment, yet high spatial variability and slow accumulation make short project periods statistically difficult. Fixed-depth accounting can bias comparisons when bulk density changes; equivalent-soil-mass approaches and explicit uncertainty analysis are more defensible. Remote sensing, digital soil mapping, spectroscopy, machine learning and process-based models can reduce transaction costs and improve stratification, activity monitoring and extrapolation, but they do not remove the need for field calibration, independent validation and periodic soil remeasurement. High-integrity carbon farming consequently requires hybrid MRV, conservative uncertainty treatment, transparent counterfactual baselines, reversal provisions, leakage and non-carbon greenhouse-gas accounting, auditable data lineage and safeguards for equitable participation. Digitalisation can make carbon farming more scalable, but only if it strengthens rather than substitutes for biophysical and governance integrity.","碳农业日益被推广为一种在重建土壤有机碳的同时创造可验证气候效益和新型农业收入来源的途径。然而，其可信度取决于一系列条件，这些条件远不止于某项实践能否在试验点增加土壤碳。本批判性叙事综述评估了农业土壤碳固存的生物物理基础、分子持久性与项目层面永久性之间的区别、碳储量变化的测量，以及数字技术在测量、报告与核查（MRV）中迅速发展的作用。本文考察了2000年至2026年7月6日发表的文献，仅在必要时参考更早的概念性材料。证据优先取自同行评议的田间研究、荟萃分析、方法学研究及权威技术框架。证据支持覆盖作物、有机改良剂、多样化轮作及部分生物炭应用在适当条件下提高土壤碳的能力，但效应量强烈受基线碳储量、气候、质地、深度、碳输入、管理历史和系统边界的影响。表观增益也可能源于深度分布的改变或有机物的转移，而非额外的大气碳去除。因此，永久性是一种管理与核算属性，而不仅仅是化学抗性碳的属性。直接再测量仍是碳储量变化评估的证据锚点，但高空间变异性和缓慢积累使短项目周期在统计上难以奏效。当容重变化时，固定深度核算可能使比较产生偏差；等效土壤质量方法和明确的不确定性分析更具辩护力。遥感、数字土壤制图、光谱学、机器学习和过程模型可以降低交易成本，改善分层、活动监测和外推，但它们并不能消除田间校准、独立验证和定期土壤再测量的需要。因此，高完整性碳农业需要混合MRV、保守的不确定性处理、透明的反事实基线、逆转条款、泄漏和非碳温室气体核算、可审计的数据溯源以及公平参与的保障措施。数字化可以使碳农业更具可扩展性，但前提是它加强而非替代生物物理","Journal of Advances in Biology & Biotechnology","2026-09-11T00:00:00Z",79,{"impact":128,"substance":127,"depth":128,"authority":20,"freshness":17,"relevant":21,"comment":431},"系统评述数字技术支撑土壤碳汇MRV的方法与治理条件，对农业碳汇数字化与碳交易机制建设有实质参考价值。",[433],{"name":427,"url":424},[435,436,437,438,27],"数字乡村","智慧农业","遥感监测","碳农业",{"code":51,"slug":56,"name":141,"path":142,"parent_code":36},[441],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":443},{"candidates":444,"agricultural_evidence":45},[445,449,453,457,464,469,475,480],{"code":51,"score":305,"supported":45,"title":446,"abstract":447,"tags":448},[56],[55,56],[27],{"code":32,"score":147,"supported":45,"title":450,"abstract":451,"tags":452},[47],[47],[],{"code":59,"score":52,"supported":45,"title":454,"abstract":455,"tags":456},[],[26,62],[437],{"code":458,"score":114,"supported":45,"title":459,"abstract":460,"tags":463},"B7",[],[461,462],"温室","greenhouse",[],{"code":311,"score":114,"supported":45,"title":465,"abstract":466,"tags":468},[],[284,467],"machine learning",[],{"code":470,"score":73,"supported":45,"title":471,"abstract":472,"tags":474},"A7",[],[473],"轮作",[],{"code":476,"score":21,"supported":14,"title":477,"abstract":478,"tags":479},"I1",[],[],[436],{"code":481,"score":21,"supported":14,"title":482,"abstract":483,"tags":484},"I6",[],[],[435],[486,487],"土壤固碳 数字乡村 智慧农业 遥感监测","土壤固碳 数字乡村","土壤固碳数字乡村智慧农业遥感监测-2325","10.9734\u002Fjabb\u002F2026\u002Fv29i104418",{"doi":489,"openalex_id":491,"authors":492,"venue":427,"cited_by_count":82,"oa_url":424,"card":510,"direction":110,"ingested_from":111},"W7212265915",[493,496,498,500,502,504,506,508],{"name":494,"orcid":495},"Samarpan Chakraborty","https:\u002F\u002Forcid.org\u002F0000-0002-2740-8722",{"name":497,"orcid":9},"Anusmita Goswami",{"name":499,"orcid":9},"Ritam Dhar",{"name":501,"orcid":9},"Kasturi Mandal",{"name":503,"orcid":9},"Tamalika Mondal",{"name":505,"orcid":9},"Sancharee Paul",{"name":507,"orcid":9},"Priya Sarawgi",{"name":509,"orcid":9},"Sujan Biswas",{"tldr":511,"method":512,"finding":513,"direction":108,"opportunity":514},"综述数字时代碳农业的土壤固碳、持久性与MRV，强调混合验证与保守核算。","2000-2026年文献批判性综述，聚焦田间研究、荟萃分析与技术框架。","数字技术可降本增效，但无法替代田间校准与定期土壤复测，需混合MRV。","可研究遥感与模型融合的混合MRV框架，量化不确定性并保障小农公平参与。","2026-09-13T23:30:23.245745Z",{"id":517,"title":518,"url":519,"summary":520,"summary_zh":9,"content":521,"source_name":522,"source_url":9,"published_at":523,"category":524,"cover_url":9,"hotness":13,"is_selected":14,"score":217,"score_detail":525,"sources":527,"tags":529,"primary_topic":534,"secondary_topics":535,"classification":536,"search_phrases":578,"slug":581,"view_count":82,"doi":9,"paper":9,"created_at":582},5498,"江西省「十五五」农业农村现代化规划印发 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