[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1999":3,"related-1999":94},{"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":18,"tags":20,"primary_topic":22,"secondary_topics":28,"classification":29,"search_phrases":57,"slug":58,"view_count":15,"doi":59,"paper":60,"created_at":93},1999,"Effects of multi human disturbance on soil organic carbon: mediation by landscape patterns and forest community attributes in forest villages of the Henan section, Yellow River Basin","https:\u002F\u002Fdoi.org\u002F10.3389\u002Fffgc.2026.1923061","Introduction National Forest Villages (NFVs) represent a policy-relevant rural greening initiative with potential contributions to soil organic carbon (SOC) maintenance, yet how multi human disturbances are associated with forest SOC remains poorly understood. Methods We investigated all 99 NFVs in the Henan section of the Yellow River Basin by integrating field surveys, remote sensing data, and statistical approaches. Forest SOC concentration (SOCc), SOC density (SOCs), and forest-to-cropland enrichment ratios (RSOCc and RSOCs) were quantified using paired forest–cropland plots. Human disturbance was characterized by land use human disturbance (LUHD) and multiscale socioeconomic human disturbance (SEHD), while landscape patterns and forest community attributes were quantified independently. After controlling for geoclimatic variation, partial correlations, partial redundancy analysis, variation partitioning, and piecewise structural equation modeling were used to identify their associations with SOC. Results Weak LUHD NFVs generally maintained 25%–35% higher SOCc and relative SOC enrichment than severely disturbed NFVs, whereas SOCs and landscape diversity were highest under moderate LUHD. Increasing LUHD was also associated with pronounced changes in forest landscape configuration, including reduced forest dominance and greater forest isolation under severe disturbance. Human disturbance and landscape patterns independently explained 13.0% and 7.9% of SOC variation, respectively, whereas the unique contribution of forest community attributes was smaller and non-significant (3.8%). Piecewise SEM further showed that landscape configuration had the largest total association with overall SOC variation. LUHD and urban socioeconomic activity exhibited opposing direct and landscape-mediated associations with SOC. Discussion These findings highlight landscape configuration as a key linkage between human disturbance and forest SOC and suggest that maintaining forest dominance, limiting excessive impervious surface expansion, and conserving tree diversity are important for coordinating rural development with SOC management in NFVs.","国家森林乡村（NFVs）代表了一项具有政策相关性的乡村绿化举措，可能对土壤有机碳（SOC）维持有所贡献，然而多种人为干扰如何与森林SOC相关联仍知之甚少。方法：我们通过整合实地调查、遥感数据和统计方法，调查了黄河流域河南段全部99个国家森林乡村。利用成对的森林—农田样地，量化了森林SOC浓度（SOCc）、SOC密度（SOCs）以及森林与农田的富集比（RSOCc和RSOCs）。人为干扰通过土地利用人为干扰（LUHD）和多尺度社会经济人为干扰（SEHD）来表征，同时独立量化了景观格局和森林群落属性。在控制地理气候变异后，采用偏相关分析、偏冗余分析、变异分区和分段结构方程模型来识别其与SOC的关联。结果：与严重干扰的NFVs相比，弱LUHD的NFVs通常维持高出25%–35%的SOCc和相对SOC富集，而SOCs和景观多样性在中度LUHD下最高。LUHD的增加还与森林景观构型的显著变化相关，包括严重干扰下森林优势度降低和森林隔离度增大。人为干扰和景观格局分别独立解释了SOC变异的13.0%和7.9%，而森林群落属性的独特贡献较小且不显著（3.8%）。分段SEM进一步表明，景观构型对整体SOC变异具有最大的总关联。LUHD和城市社会经济活动对SOC表现出相反的直接影响和景观中介关联。讨论：这些发现凸显了景观构型是人为干扰与森林SOC之间的关键纽带，并表明维持森林优势度、限制不透水面过度扩张以及保护树木多样性，对于协调NFVs中的乡村发展与SOC管理具有重要意义。",null,"Frontiers in Forests and Global Change","2026-09-08T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"研究聚焦森林土壤碳与人类干扰，非三农信息化核心主题。",[19],{"name":10,"url":6},[21],"土壤有机碳",{"code":23,"slug":24,"name":25,"path":26,"parent_code":27},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":30,"locked":14,"version":31,"method":32,"evidence":33},"classified","2026-10-22.2","rules",{"candidates":34,"agricultural_evidence":37},[35,42,50],{"code":23,"score":36,"supported":37,"title":38,"abstract":39,"tags":41},15,true,[24],[40,24],"土壤",[21],{"code":43,"score":44,"supported":37,"title":45,"abstract":46,"tags":49},"I3",6,[],[47,48],"遥感","remote sensing",[],{"code":51,"score":52,"supported":37,"title":53,"abstract":54,"tags":56},"G",3,[],[55],"rural development",[],[],"Effectsofmultihumandisturbanceonsoilorga-1999","10.3389\u002Fffgc.2026.1923061",{"doi":59,"openalex_id":61,"authors":62,"venue":10,"cited_by_count":15,"oa_url":84,"card":85,"direction":91,"ingested_from":92},"W7211964788",[63,66,68,70,72,75,77,79,81],{"name":64,"orcid":65},"Chenhui Wei","https:\u002F\u002Forcid.org\u002F0000-0001-7189-3123",{"name":67,"orcid":9},"Xule Ning",{"name":69,"orcid":9},"Yao Siqi",{"name":71,"orcid":9},"Manqi Miao",{"name":73,"orcid":74},"Zhendong Hong","https:\u002F\u002Forcid.org\u002F0000-0001-6190-1011",{"name":76,"orcid":9},"Dongying Wu",{"name":78,"orcid":9},"Chen He",{"name":80,"orcid":9},"Lin Zhang",{"name":82,"orcid":83},"Zi Wang","https:\u002F\u002Forcid.org\u002F0000-0001-8005-7733","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fforests-and-global-change\u002Farticles\u002F10.3389\u002Fffgc.2026.1923061\u002Fpdf",{"tldr":86,"method":87,"finding":88,"direction":89,"opportunity":90},"研究黄河流域河南段国家森林村庄中多种人为干扰对土壤有机碳的影响及其景观和群落中介机制。","整合野外调查、遥感数据和统计方法，使用偏相关、偏冗余分析、变异分区和分段结构方程","轻度干扰维持较高碳浓度，中度干扰碳密度和景观多样性最高；景观格局是主要中介。","农业绿色发展与碳","可探索不同干扰强度下景观配置与土壤碳的因果机制，以及如何优化乡村规划以协同碳管理。","农业遥感与作物表型","openalex","2026-09-09T23:30:22.619470Z",{"total":44,"page":95,"page_size":44,"items":96},1,[97,215,310,383,485,517],{"id":98,"title":99,"url":100,"summary":101,"summary_zh":102,"content":9,"source_name":103,"source_url":100,"published_at":104,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":105,"score_detail":106,"sources":113,"tags":115,"primary_topic":119,"secondary_topics":120,"classification":126,"search_phrases":167,"slug":170,"view_count":15,"doi":171,"paper":172,"created_at":214},5102,"Integrating Proximal and Remote Sensing to Improve Characterization and Mapping of Soil Organic Carbon Stocks under Contrasting Land Uses","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs42729-026-03629-3","Abstract Soils are fundamental to support ecosystem functions. Therefore, understanding soil organic carbon stock (SCS) as a strategic environmental component for climate regulation, sustainable agricultural practices, and maximizing fertilizer application is essential. This study aimed to predict and map the SCS at Palmital Farm, Minas Gerais state, using land use, parent materials, soil classes, proximal and remote sensing data as predictor variables. A total of 76 soil samples were collected in the surface (A) and subsurface (B) horizons using a regular grid design (200 × 200 m). Carbon content was determined using the Walkley–Black method. Prediction models were created via Random Forest algorithm and validated through leave-one-out cross-validation. Environmental and anthropogenic influences on SCS were analyzed statistically and spatially. The highest mean SCS values were found at the surface horizon under native vegetation and pasture areas (~ 33 Mg ha⁻¹), due to continuous organic matter deposition and lack of disturbance. Charnockite-derived soils showed higher SCS (31.24 Mg ha⁻¹, surface). Among soil classes, Argissolos stood out reaching the highest SCS (35.86 Mg ha⁻¹). Remote sensing data and terrain attributes yielded the best SCS prediction (R²=0.43). The main factors influencing SCS were land use\u002Fmanagement and soil texture. Also, strong correlations were observed between vegetation indices, some terrain attributes and SCS. This approach, combining multiple variables, indicated a moderate potential to achieve accurate SCS predictions using these data, demonstrating the high variability and complexity of SCS spatial variation in detailed-scale mapping.","摘要 土壤是支撑生态系统功能的基础。因此，理解土壤有机碳储量（SCS）作为气候调节、可持续农业实践和最大化肥料施用的战略性环境组成部分至关重要。本研究旨在利用土地利用、成土母质、土壤类别、近端和遥感数据作为预测变量，对米纳斯吉拉斯州Palmital农场的SCS进行预测和制图。采用规则网格设计（200 × 200 m），在表层（A）和亚表层（B）共采集76个土壤样品。碳含量采用Walkley–Black法测定。通过随机森林算法建立预测模型，并采用留一交叉验证进行验证。对环境和人为因素对SCS的影响进行了统计和空间分析。表层中原生植被和牧草地利用方式下的SCS均值最高（约33 Mg ha⁻¹），这归因于有机质的持续沉积和缺乏扰动。源自紫苏花岗岩的土壤表现出较高的SCS（31.24 Mg ha⁻¹，表层）。在土壤类别中，Argissolos表现突出，达到最高SCS（35.86 Mg ha⁻¹）。遥感数据和地形属性获得了最佳的SCS预测效果（R²=0.43）。影响SCS的主要因素为土地利用\u002F管理和土壤质地。此外，植被指数、部分地形属性与SCS之间观察到较强的相关性。这种结合多变量的方法表明，利用这些数据实现准确SCS预测具有中等潜力，同时也揭示了详细尺度制图中SCS空间变异的高变异性和复杂性。","Journal of soil science and plant nutrition","2026-10-14T00:00:00Z",67,{"impact":107,"substance":108,"depth":109,"authority":110,"freshness":111,"relevant":95,"comment":112},8,20,17,13,9,"该研究结合近地与遥感数据及随机森林算法预测土壤有机碳储量，方法新颖、数据扎实，对农业信息化与精准农业有参考价值，但属区域性案例研究，影响范围有限。",[114],{"name":103,"url":100},[116,117,21,47,118],"数字农业","随机森林","土壤制图",{"code":23,"slug":24,"name":25,"path":26,"parent_code":27},[121],{"code":43,"slug":122,"name":123,"path":124,"parent_code":125},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":30,"locked":14,"version":31,"method":32,"evidence":127},{"candidates":128,"agricultural_evidence":37},[129,134,138,145,151,157,162],{"code":23,"score":130,"supported":37,"title":131,"abstract":132,"tags":133},16,[24],[40,24],[118,21],{"code":43,"score":36,"supported":37,"title":135,"abstract":136,"tags":137},[48],[47,48],[47],{"code":139,"score":44,"supported":37,"title":140,"abstract":141,"tags":144},"F3",[],[142,143],"肥料","fertilizer",[],{"code":146,"score":52,"supported":37,"title":147,"abstract":148,"tags":150},"H8",[],[149],"可持续农业",[],{"code":152,"score":52,"supported":37,"title":153,"abstract":154,"tags":156},"I4",[],[155],"空间分析",[],{"code":158,"score":95,"supported":14,"title":159,"abstract":160,"tags":161},"I1",[],[],[116],{"code":163,"score":95,"supported":14,"title":164,"abstract":165,"tags":166},"I2",[],[],[117],[168,169],"Palmital Farm 土壤有机碳","Minas Gerais 遥感 土壤碳","PalmitalFarm土壤有机碳-5102","10.1007\u002Fs42729-026-03629-3",{"doi":171,"openalex_id":173,"authors":174,"venue":103,"cited_by_count":15,"oa_url":208,"card":209,"direction":91,"ingested_from":92},"W7222335269",[175,178,181,184,187,190,193,196,199,202,205],{"name":176,"orcid":177},"Leonardo Augusto de Sousa","https:\u002F\u002Forcid.org\u002F0009-0003-1560-5600",{"name":179,"orcid":180},"Fernanda Almeida Bócoli","https:\u002F\u002Forcid.org\u002F0000-0001-5476-6886",{"name":182,"orcid":183},"Eduane José de Pádua","https:\u002F\u002Forcid.org\u002F0009-0003-1782-2756",{"name":185,"orcid":186},"Luiza Maria Pereira Pierangeli","https:\u002F\u002Forcid.org\u002F0000-0002-9218-3718",{"name":188,"orcid":189},"Junior César Avanzi","https:\u002F\u002Forcid.org\u002F0000-0003-2455-0325",{"name":191,"orcid":192},"Bruno Montoani Silva","https:\u002F\u002Forcid.org\u002F0000-0002-8240-8987",{"name":194,"orcid":195},"Carlos Victor Lima Albuquerque","https:\u002F\u002Forcid.org\u002F0009-0009-4597-4443",{"name":197,"orcid":198},"Luiz Roberto Guimarães Guilherme","https:\u002F\u002Forcid.org\u002F0000-0002-5387-6028",{"name":200,"orcid":201},"Marco Aurélio Carbone Carneiro","https:\u002F\u002Forcid.org\u002F0000-0003-4349-3071",{"name":203,"orcid":204},"Nilton Curi","https:\u002F\u002Forcid.org\u002F0000-0002-2604-0866",{"name":206,"orcid":207},"Sérgio Henrique Godinho Silva","https:\u002F\u002Forcid.org\u002F0000-0003-2750-5976","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs42729-026-03629-3.pdf",{"tldr":210,"method":211,"finding":212,"direction":91,"opportunity":213},"结合近地与遥感数据及随机森林预测并制图巴西农场不同土地利用下的土壤有机碳储量。","76个样点、Walkley-Black法测碳，随机森林建模，留一交叉验证。","遥感与地形因子预测最佳（R²=0.43），土地利用和土壤质地主导碳储量空间变异。","可探索多源遥感与深度学习方法提升小尺度土壤碳制图精度，并耦合管理措施情景。","2026-10-16T23:30:31.433840Z",{"id":216,"title":217,"url":218,"summary":219,"summary_zh":220,"content":9,"source_name":221,"source_url":218,"published_at":222,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":223,"score_detail":224,"sources":229,"tags":231,"primary_topic":236,"secondary_topics":237,"classification":247,"search_phrases":276,"slug":279,"view_count":280,"doi":281,"paper":282,"created_at":309},4172,"Machine learning reveals distinct drivers of soil organic carbon across the mountain-oasis-desert system of northwest China","https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs13021-026-00522-5","The mountain-oasis-desert system (MODS) is a distinctive landscape pattern shaped by interactions between geomorphological configurations and hydrological processes in arid regions. However, most soil organic carbon (SOC) mapping studies treat arid landscapes as homogeneous units, leaving limited quantitative evidence on how SOC patterns and environmental associations differ among MODS subsystems. As a critical ecosystem highly sensitive to climate change and human activities, understanding these responses is essential for regional soil carbon management and ecological restoration. In this study, we developed an ecosystem-based modeling framework in the Hexi Region (HXR) to construct separate SOC prediction models for different subsystems, aiming to investigate the spatiotemporal dynamics of SOC stocks and identify their key driving factors across MODS. The results show that incorporating remote sensing (RS) variables significantly improved model performance, increasing the mean R 2 from 0.51 to 0.58. Among the tested models, XGBoost achieved the best performance, providing an effective tool for SOC mapping in arid regions. Shapley additive explanations (SHAP) analysis of the best-performing XGBoost models identified distinct leading predictors across the MODS, including surface shortwave radiation (SRAD) in mountain and desert ecosystems and the difference vegetation index (DVI) in oasis ecosystems. Furthermore, the partial least squares path model (PLS-PM) revealed contrasting influences of climate change and human activities across ecological subsystems. Climatic factors exerted the strongest influence in the mountainous subsystem, whereas in the oasis subsystem intensive human management became the dominant driver of soil carbon cycling. From 2001 to 2023, SOC stocks in oasis regions increased significantly at a rate of 0.17 t C ha⁻ 1 year⁻ 1 , potentially associated with land-use change and agricultural management. Approximately 19.3% of the study area experienced significant SOC stocks declines. By resolving subsystem-specific SOC patterns and environmental associations, this study advances ecosystem-aware SOC assessment in arid regions. The resulting framework also provides a basis for differentiated carbon management across MODS.","山地-绿洲-荒漠系统（MODS）是干旱区地貌格局与水文过程相互作用形成的独特景观格局。然而，大多数土壤有机碳（SOC）制图研究将干旱区景观视为均质单元，关于MODS各子系统间SOC格局及其环境关联差异的定量证据仍然有限。作为对气候变化和人类活动高度敏感的关键生态系统，理解这些响应对于区域土壤碳管理和生态恢复至关重要。本研究在河西地区（HXR）构建了基于生态系统的建模框架，针对不同子系统分别建立SOC预测模型，旨在探究MODS中SOC储量的时空动态并识别其关键驱动因子。结果表明，引入遥感（RS）变量显著提升了模型性能，平均R²从0.51提高至0.58。在测试的模型中，XGBoost表现最佳，为干旱区SOC制图提供了有效工具。对最优XGBoost模型的SHAP（Shapley加法解释）分析识别出MODS各子系统的不同主导预测因子，其中山地和荒漠生态系统为地表短波辐射（SRAD），绿洲生态系统为差值植被指数（DVI）。此外，偏最小二乘路径模型（PLS-PM）揭示了气候变化和人类活动在各生态子系统中的差异化影响。气候因子在山地子系统中影响最强，而在绿洲子系统中，集约化人类管理成为土壤碳循环的主导驱动因素。2001年至2023年，绿洲区域SOC储量以0.17 t C ha⁻¹ year⁻¹的速率显著增加，这可能与土地利用变化和农业管理有关。约19.3%的研究区域经历了SOC储量的显著下降。通过解析子系统特定的SOC格局及其环境关联，本研究推进了干旱区生态系统感知型SOC评估，所构建的框架也为MODS差异化碳管理提供了依据。","Carbon Balance and Management","2026-10-03T00:00:00Z",79,{"impact":130,"substance":225,"depth":226,"authority":227,"freshness":111,"relevant":95,"comment":228},22,18,14,"基于XGBoost与SHAP的干旱区SOC分异驱动研究，方法新颖、数据翔实，对区域碳管理与生态修复有参考价值。",[230],{"name":221,"url":218},[232,21,233,234,235],"机器学习","生态修复","遥感监测","干旱区农业",{"code":23,"slug":24,"name":25,"path":26,"parent_code":27},[238,243],{"code":239,"slug":240,"name":241,"path":242,"parent_code":27},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"code":163,"slug":244,"name":245,"path":246,"parent_code":125},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":30,"locked":14,"version":31,"method":32,"evidence":248},{"candidates":249,"agricultural_evidence":37},[250,254,261,266,271],{"code":23,"score":36,"supported":37,"title":251,"abstract":252,"tags":253},[24],[40,24],[21],{"code":239,"score":13,"supported":37,"title":255,"abstract":256,"tags":260},[],[257,258,259],"气候变化","干旱","climate change",[235],{"code":163,"score":111,"supported":37,"title":262,"abstract":264,"tags":265},[263],"machine learning",[],[232],{"code":43,"score":267,"supported":37,"title":268,"abstract":269,"tags":270},7,[],[47,48],[234],{"code":272,"score":95,"supported":14,"title":273,"abstract":274,"tags":275},"H6",[],[],[233],[277,278],"河西地区 土壤有机碳 机器学习","山地-绿洲-荒漠 遥感 碳储量","河西地区土壤有机碳机器学习-4172",2,"10.1186\u002Fs13021-026-00522-5",{"doi":281,"openalex_id":283,"authors":284,"venue":221,"cited_by_count":15,"oa_url":218,"card":304,"direction":91,"ingested_from":92},"W7219075005",[285,287,290,292,295,297,300,302],{"name":286,"orcid":9},"Y. Chen",{"name":288,"orcid":289},"Haiyang Xi","https:\u002F\u002Forcid.org\u002F0000-0003-3158-8161",{"name":291,"orcid":9},"Meng Zhu",{"name":293,"orcid":294},"Wenju Cheng","https:\u002F\u002Forcid.org\u002F0000-0002-1822-5668",{"name":296,"orcid":9},"Yulian Hao",{"name":298,"orcid":299},"Linbo Qu","https:\u002F\u002Forcid.org\u002F0009-0007-4669-8407",{"name":301,"orcid":9},"Zhehui Wang",{"name":303,"orcid":9},"Bin Wang",{"tldr":305,"method":306,"finding":307,"direction":91,"opportunity":308},"构建基于生态系统的机器学习框架，预测西北荒漠-绿洲-山地系统土壤有机碳并识别各子系统驱动因子。","XGBoost、SHAP、PLS-PM，结合遥感变量与2001-2023年多源数","遥感变量提升模型R²至0.58；山地和沙漠由辐射主导，绿洲由人类管理主导，绿洲碳储量年增0.17 t","可延伸研究绿洲农业管理措施对土壤碳的长期影响，并开发子系统自适应的碳监测与决策模型。","2026-10-04T23:30:24.631460Z",{"id":311,"title":312,"url":313,"summary":314,"summary_zh":315,"content":9,"source_name":316,"source_url":313,"published_at":317,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":318,"score_detail":319,"sources":322,"tags":324,"primary_topic":327,"secondary_topics":333,"classification":336,"search_phrases":355,"slug":358,"view_count":280,"doi":359,"paper":360,"created_at":382},3792,"Estimation of soil organic carbon under rice-fallow system using Sentinel-2 derivatives","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.11182","The evaluation of soil properties under a century-old traditional rice-fallow practice in highly fragile topography (i.e., hill slopes and lowlands) of North-east (NE) India in the Eastern Himalaya faces multiple challenges in conventional studies of soil properties. Alternatively, remote sensing offers a viable solution. An attempt was made to study the soil organic carbon (SOC) under century old traditional rice-fallow practice in highly fragile topography (i.e., hill slopes and lowlands) of Bhoirymbong, Meghalaya, NE India in Eastern Himalaya using remote sensing indices derived from Sentinel-2. The objective was to develop a suitable regression model for rice-fallow systems across different hill slopes in northeastern India in the Eastern Himalaya. A total of 100 composite soil samples from the 0–15 cm depth were collected from rice-fallow areas on various slopes (nearly level: 0–3 %, gentle: 3–8 %, moderate: 8–15 %, steep: 15–30 % and very steep: > 30 %) during November 2020. Each composite sample was prepared from 10 randomly collected soil samples within a 10 × 10 m area and analysed for SOC using standard protocols in the analytical laboratory of School of Natural Resource Management, College of Post Graduate Studies in Agricultural Sciences, Central Agricultural University, Imphal. Nearly synchronised, cloud-free (\u003C 10 % cloud cover) Sentinel-2 data consistent with soil sampling period were accessed from the US Geological Survey (USGS) official website to derive 15 indices. Results showed that brightness index (BI), BI2 and the soil bareness index (SBI) were the most influential indices for SOC. The highest and lowest SOC values were found in gently undulating topography (2–5 % slope) and rolling topography (10–15 % slope), at 2.86 % and 1.17 %, respectively. After rice crop harvesting, the red-NIR-based indices did not show a significant difference, whereas the green-NIR-based indices were significantly different at p \u003C 0.05. Hence, it is concluded that green-NIR-based indices are the most suitable for SOC prediction in NE India in the Eastern Himalaya.","在印度东北部东喜马拉雅地区高度脆弱的地形（即山坡和低地）下，对百年传统水稻休耕实践下的土壤性质进行评估，在常规土壤性质研究中面临多重挑战。遥感则提供了一种可行的替代方案。本研究尝试利用Sentinel-2衍生的遥感指数，研究印度东北部东喜马拉雅地区梅加拉亚邦Bhoirymbong在高度脆弱地形（即山坡和低地）下百年传统水稻休耕实践中的土壤有机碳（SOC）。目标是建立一个适用于东喜马拉雅地区印度东北部不同山坡水稻休耕系统的合适回归模型。2020年11月，在不同坡度（近水平：0–3%、缓坡：3–8%、中坡：8–15%、陡坡：15–30%和极陡坡：>30%）的水稻休耕区采集了100个0–15 cm深度的复合土壤样品。每个复合样品由10 × 10 m区域内随机采集的10个土壤样品制备而成，并在Central Agricultural University, Imphal农业科学研究生院自然资源管理学院分析实验室采用标准方法分析SOC。从美国地质调查局（USGS）官方网站获取与土壤采样期基本同步、无云（云量\u003C10%）的Sentinel-2数据，以衍生15个指数。结果表明，亮度指数（BI）、BI2和土壤裸露指数（SBI）是对SOC影响最大的指数。SOC最高值和最低值分别出现在缓起伏地形（2–5%坡度）和波状地形（10–15%坡度），分别为2.86%和1.17%。水稻收获后，基于红-近红外的指数未显示出显著差异，而基于绿-近红外的指数在p \u003C 0.05水平上差异显著。因此，得出结论：基于绿-近红外的指数最适合东喜马拉雅地区印度东北部的SOC预测。","Plant Science Today","2026-09-28T00:00:00Z",69,{"impact":320,"substance":108,"depth":130,"authority":110,"freshness":107,"relevant":95,"comment":321},12,"基于Sentinel-2遥感指数估算稻作休耕系统土壤有机碳，方法有新意但属区域案例研究，对国内智慧农业遥感应用有参考价值。",[323],{"name":316,"url":313},[116,325,21,234,326],"Sentinel-2","水稻休耕",{"code":328,"slug":329,"name":330,"path":331,"parent_code":332},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice","A",[334,335],{"code":23,"slug":24,"name":25,"path":26,"parent_code":27},{"code":43,"slug":122,"name":123,"path":124,"parent_code":125},{"status":30,"locked":14,"version":31,"method":32,"evidence":337},{"candidates":338,"agricultural_evidence":37},[339,343,347,351],{"code":23,"score":36,"supported":37,"title":340,"abstract":341,"tags":342},[24],[40,24],[21],{"code":328,"score":227,"supported":37,"title":344,"abstract":345,"tags":346},[329],[330,329],[],{"code":43,"score":107,"supported":37,"title":348,"abstract":349,"tags":350},[],[47,48],[325,234],{"code":158,"score":95,"supported":14,"title":352,"abstract":353,"tags":354},[],[],[116],[356,357],"印度东北部 水稻休耕 土壤有机碳","Sentinel-2 遥感 土壤有机碳","印度东北部水稻休耕土壤有机碳-3792","10.14719\u002Fpst.11182",{"doi":359,"openalex_id":361,"authors":362,"venue":316,"cited_by_count":15,"oa_url":313,"card":377,"direction":91,"ingested_from":92},"W7214617000",[363,366,368,371,374],{"name":364,"orcid":365},"Janaki Singh Naorem","https:\u002F\u002Forcid.org\u002F0000-0001-9534-0339",{"name":367,"orcid":9},"D Priya",{"name":369,"orcid":370},"Deepak Kumar Dwivedi","https:\u002F\u002Forcid.org\u002F0009-0003-5408-887X",{"name":372,"orcid":373},"J S Rajkumar","https:\u002F\u002Forcid.org\u002F0009-0003-7863-277X",{"name":375,"orcid":376},"Tamang Basant","https:\u002F\u002Forcid.org\u002F0000-0001-6856-7298",{"tldr":378,"method":379,"finding":380,"direction":91,"opportunity":381},"用Sentinel-2指数估算印度东北部稻作休耕系统不同坡度的土壤有机碳。","采集100个0-15cm土样，结合Sentinel-2的15个遥感指数建立回归模","绿光-近红外指数最适合预测SOC，缓坡SOC最高（2.86%），陡坡最低（1.17%）。","可探索多时相Sentinel-2与地形因子耦合，提升复杂地形区SOC制图精度与普适性。","2026-09-29T23:30:28.690009Z",{"id":384,"title":385,"url":386,"summary":387,"summary_zh":388,"content":9,"source_name":389,"source_url":386,"published_at":390,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":391,"score_detail":392,"sources":394,"tags":396,"primary_topic":400,"secondary_topics":406,"classification":408,"search_phrases":435,"slug":438,"view_count":280,"doi":439,"paper":440,"created_at":484},2937,"Modeling the effects of livestock rotation frequency on forage production and soil carbon using a spatially explicit grazing distribution","https:\u002F\u002Fdoi.org\u002F10.1080\u002F17583004.2026.2732593","Livestock grazing strongly influences terrestrial ecosystems and carbon (C) dynamics, but outcomes depend on grazing management. High-frequency rotation (HFR) grazing may reduce the uneven grazing distribution commonly associated with low-frequency rotation (LFR), which can alter forage production and soil organic carbon (SOC). However, most ecosystem models do not explicitly represent uneven grazing distribution, limiting their ability to evaluate management effects. We enhanced the MEMS ecosystem model by incorporating discrete spatial units to simulate grazing distribution driven by key environmental drivers, including forage availability and quality, and distance to water. Using remote sensing-derived enhanced vegetation index (EVI), we verified the simulated grazing distribution at an experimental rangeland site in Oklahoma. We tested the model’s sensitivity to rotation frequency under different management (stocking rate, timing, and duration) and weather (typical, dry, and wet) scenarios. Simulated light and moderate grazing generally sustained forage production and SOC regardless of rotation frequency. However, under heavy grazing, the 30-year projection showed HFR reduced localized overgrazing and sustained higher pasture average forage productivity and SOC stocks than LFR. Long-term experimental data are needed to validate these results. Our study offers guidance for designing grazing management experiments to address this critical knowledge gap and advance C management strategies.","牲畜放牧对陆地生态系统和碳（C）动态具有强烈影响，但其结果取决于放牧管理方式。高频轮牧（HFR）可能减少通常与低频轮牧（LFR）相关的不均匀放牧分布，从而改变饲草产量和土壤有机碳（SOC）。然而，大多数生态系统模型并未明确表征不均匀放牧分布，限制了其评估管理效果的能力。我们通过引入离散空间单元增强了MEMS生态系统模型，以模拟由关键环境驱动因素（包括饲草可获得性和质量，以及与水源的距离）驱动的放牧分布。利用遥感反演的增强植被指数（EVI），我们在俄克拉荷马州一个实验性牧场验证了模拟的放牧分布。我们测试了模型在不同管理（载畜率、时间和持续时间）和天气（典型、干旱和湿润）情景下对轮牧频率的敏感性。模拟的轻度与中度放牧无论轮牧频率如何，通常均能维持饲草生产和SOC。然而，在重度放牧下，30年预测显示HFR减少了局部过度放牧，并比LFR维持了更高的牧场平均饲草生产力和SOC储量。需要长期实验数据来验证这些结果。我们的研究为设计放牧管理实验提供了指导，以弥补这一关键知识空白并推进碳管理策略。","Carbon Management","2026-09-17T00:00:00Z",75,{"impact":320,"substance":225,"depth":226,"authority":227,"freshness":111,"relevant":95,"comment":393},"改进MEMS模型显式模拟放牧空间分布，揭示高频轮牧在重度放牧下维持牧草生产与土壤碳的优势，方法新颖但尚缺长期实测验证，属细分领域有价值研究。",[395],{"name":389,"url":386},[21,234,397,398,399],"草地碳汇","轮牧管理","生态模型",{"code":401,"slug":402,"name":403,"path":404,"parent_code":405},"C4","feed","饲料与饲草","\u002Ftopic\u002Flivestock\u002Ffeed","C",[407],{"code":23,"slug":24,"name":25,"path":26,"parent_code":27},{"status":30,"locked":14,"version":31,"method":32,"evidence":409},{"candidates":410,"agricultural_evidence":37},[411,415,421,427,431],{"code":23,"score":36,"supported":37,"title":412,"abstract":413,"tags":414},[24],[40,24],[21],{"code":401,"score":227,"supported":37,"title":416,"abstract":418,"tags":420},[417],"forage",[419,417],"饲草",[],{"code":405,"score":422,"supported":37,"title":423,"abstract":425,"tags":426},11,[424],"livestock",[424],[],{"code":43,"score":267,"supported":37,"title":428,"abstract":429,"tags":430},[],[47,48],[234],{"code":239,"score":52,"supported":37,"title":432,"abstract":433,"tags":434},[],[258],[],[436,437],"MEMS模型 放牧分布","俄克拉荷马 轮牧 土壤碳","MEMS模型放牧分布-2937","10.1080\u002F17583004.2026.2732593",{"doi":439,"openalex_id":441,"authors":442,"venue":389,"cited_by_count":15,"oa_url":477,"card":478,"direction":483,"ingested_from":92},"W7213466883",[443,445,448,450,453,455,458,460,463,465,467,469,471,473,475],{"name":444,"orcid":9},"Yao Zhang",{"name":446,"orcid":447},"Rafael S. Santos","https:\u002F\u002Forcid.org\u002F0000-0003-4829-0644",{"name":449,"orcid":9},"Emma Hamilton",{"name":451,"orcid":452},"Paige Stanley","https:\u002F\u002Forcid.org\u002F0000-0002-4444-3351",{"name":454,"orcid":9},"Hao Yang",{"name":456,"orcid":457},"Keith Paustian","https:\u002F\u002Forcid.org\u002F0000-0001-5262-5596",{"name":459,"orcid":9},"Erica L. Patterson",{"name":461,"orcid":462},"Stephen M. Ogle","https:\u002F\u002Forcid.org\u002F0000-0003-1899-7446",{"name":464,"orcid":9},"Isabella C.F. Maciel",{"name":466,"orcid":9},"Guilhermo F. S. Congio",{"name":468,"orcid":9},"Hugh Aljoe",{"name":470,"orcid":9},"Alejandro Romero-Ruiz",{"name":472,"orcid":9},"Jeff J. Goodwin",{"name":474,"orcid":9},"Jason Rowntree",{"name":476,"orcid":9},"M. Francesca Cotrufo","https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F17583004.2026.2732593?needAccess=true",{"tldr":479,"method":480,"finding":481,"direction":89,"opportunity":482},"改进MEMS模型模拟放牧分布，评估轮牧频率对牧草生产和土壤碳的影响。","增强MEMS模型空间离散单元，用EVI遥感验证，模拟不同放牧和天气情景。","重度放牧下高频轮牧减少局部过牧，维持更高牧草生产力和土壤有机碳。","需长期实验验证模型结果，并探索不同气候和放牧管理下土壤碳动态的普适性机制。","智慧农业 \u002F 农业物联网","2026-09-19T23:30:13.860699Z",{"id":486,"title":487,"url":488,"summary":489,"summary_zh":9,"content":490,"source_name":491,"source_url":9,"published_at":492,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":493,"score_detail":494,"sources":497,"tags":499,"primary_topic":503,"secondary_topics":504,"classification":505,"search_phrases":512,"slug":515,"view_count":280,"doi":9,"paper":9,"created_at":516},471,"Soil Organic Carbon Estimation Using Dual-Interval Synergistic Selection and Overlap-Constrained Ridge Regression","https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1700","本研究开发了一种协同区间约束Ridge回归框架sicRidge,用于土壤有机碳(SOC)预测。SOC是土壤质量、农田生产力和陆地碳循环的关键指标,可见-近红外(Vis-NIR)光谱提供快速预测方法,但波长点选择可能破坏连续光谱结构,传统波长区间选择可能无法充分利用光谱区域间的互补信息。使用滑动窗口策略生成连续候选区间,采用双区间协同搜索与重叠约束识别互补且低冗余的区间组合。使用168个土壤样本的Vis-NIR光谱,sicRidge与全光谱Ridge回归、5种波长点选择Ridge模型及多种波长区间选择基准模型比较,sicRidge在140个选定波段下实现最佳预测性能(R2P=0.834,RMSEP=2.010 g\u002Fkg,RPD=2.483,RPIQ=3.777),最优区间为570~649 nm和1880~1939 nm。原文第一单位为浙江大学,发表于MDPI Agriculture 2026年第16卷第16期。","## 1. Introduction\n\nSoil organic carbon (SOC) is a key indicator in soil quality assessment, the maintenance of farmland productivity, and research on carbon cycling in terrestrial ecosystems. Its content directly affects soil fertility, aggregate stability, and water and nutrient retention capacity, and is closely associated with soil degradation monitoring, cultivated land quality assessment, and sustainable agricultural management [[1](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1700#B1-agriculture-16-01700),[2](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1700#B2-agriculture-16-01700)]. Although conventional chemical analysis methods are reliable, they are labor-intensive, costly, and time-consuming, making them unsuitable for the rapid analysis of large numbers of samples [[3](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1700#B3-agriculture-16-01700)]. Visible and near-infrared (Vis-NIR) spectroscopy has been widely used for soil property prediction because it is rapid, non-destructive, and suitable for multi-attribute estimation [[4](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1700#B4-agriculture-16-01700),[5](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1700#B5-agriculture-16-01700)]. In SOC studies, Vis-NIR spectra capture information related to organic matter, moisture, mineral composition, and soil color. However, SOC-related spectral responses are typically distributed across multiple interrelated wavelength regions and are further influenced by moisture, particle composition, iron oxides, and background reflectance [[6](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1700#B6-agriculture-16-01700),[7](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1700#B7-agriculture-16-01700),[8](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1700#B8-agriculture-16-01700),[9](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1700#B9-agriculture-16-01700)]. Consequently, spectral modeling remains challenging because of high dimensionality, strong collinearity, and noise [[10](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1700#B10-agriculture-16-01700)].\n\nTo address the modeling problems caused by high-dimensional spectral variables, various variable selection and feature compression methods have been proposed. According to the selection unit, Vis-NIR variable selection methods can be broadly divided into wavelength point selection (WPS) and wavelength interval selection (WIS) [[11](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1700#B11-agriculture-16-01700)]. WPS selects discrete wavelengths, whereas WIS operates on continuous spectral intervals. In practical prediction tasks, different feature selection strategies usually need to be combined with regression models to form a complete spectral modeling workflow. Typical WPS methods include competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), uninformative variable elimination (UVE), variable importance in projection (VIP), and iteratively retains informative variables (IRIV) [[12](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1700#B12-agriculture-16-01700),[13](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1700#B13-agriculture-16-01700),[14](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1700#B14-agriculture-16-01700),[15](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1700#B15-agriculture-16-01700),[16](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1700#B16-agriculture-16-01700)]. These methods can reduce the input dimensionality, remove redundant variables, and improve modeling efficiency to some extent. However, for continuous spectral signals such as Vis-NIR spectra, wavelength point selection may weaken the structural information of continuous absorption regions, and the selected variables may be susceptible to local noise and sample fluctuations [[17](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1700#B17-agriculture-16-01700),[18](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1700#B18-agriculture-16-01700)]. In addition, WPS methods generally use the contribution of individual wavelength points as the main selection criterion, providing relatively limited utilization of the continuous structure of adjacent wavelengths and the potential complementary information among different spectral regions. Therefore, relying solely on discrete wavelength selection may be insufficient to fully characterize the complex and continuous spectral response patterns of SOC.\n\nWIS methods use continuous wavelength intervals as the basic selection unit, which is more consistent with the broad, overlapping, and continuously distributed absorption characteristics of near-infrared spectra. Representative WIS-related benchmark models include interval partial least squares regression (iPLSR), synergy interval partial least squares regression (siPLSR), and moving window partial least squares regression (MWPLS) [[19](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1700#B19-agriculture-16-01700),[20](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1700#B20-agriculture-16-01700)]. These models divide the full spectrum into several continuous intervals and select effective spectral regions for modeling, thereby preserving local absorption structures while achieving variable compression. This helps improve model interpretability. Nevertheless, existing WIS modeling strategies still have room for improvement. On the one hand, because iPLSR, siPLSR, and MWPLS mostly use partial least squares regression (PLSR) as the core regression model, their interval selection results and prediction performance may be affected, to some extent, by the choice of regression model. On the other hand, existing interval-based methods generally emphasize local optima obtained from a single interval or a limited combination of intervals, and the synergistic relationships among intervals across different spectral regions remain insufficiently explored [[21](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1700#B21-agriculture-16-01700),[22](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1700#B22-agriculture-16-01700),[23](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1700#B23-agriculture-16-01700)]. In addition, during interval combination, how to control redundant information caused by interval overlap and how to balance search efficiency with model performance improvement remain issues worthy of further investigation.\n\nThe overall goal of this study was to develop an interval-based spectral modeling framework for soil organic carbon prediction that can preserve continuous spectral structures while more effectively exploiting complementary information across different spectral regions. The novelty of this study is reflected in three aspects. First, unlike conventional wavelength point selection methods that operate on discrete bands, the proposed framework uses continuous sliding-window intervals as the basic selection unit, thereby preserving local spectral structure. Second, unlike existing interval-based methods that mainly focus on single intervals or limited local combinations, the proposed sicRidge framework introduces a cross-region dual-interval synergistic search strategy to identify interval combinations with complementary predictive contributions. Third, an overlap constraint is incorporated into the interval-combination process to reduce redundant interval pairs and improve the efficiency of interval screening under a unified Ridge regression framework. Based on this design, this study systematically compares sicRidge with full-spectrum Ridge regression, WPS-Ridge benchmark models, and WIS-related benchmark models to evaluate whether cross-region interval synergy can improve SOC prediction under the current dataset.\n\n## 2. Materials and Methods\n\n### 2.1. Soil Samples and Spectral Data Acquisition\n\nA total of 168 soil samples were used in this study, including 118 samples collected from Xiangshan County, Ningbo City, Zhejiang Province, China, and 50 samples collected from Yongxiu County, Jiujiang City, Jiangxi Province, China. The Xiangshan samples were collected on 27 December 2025, and the Yongxiu samples were collected on 28 December 2025, all from citrus orchards. The sampling depth was 0–20 cm, and each sample represented a single-point sample. In the subsequent modeling analysis, samples from the two counties were pooled into a combined dataset and then partitioned into the calibration set and prediction set.\n\nAfter collection, all samples from the two sampling batches were processed using the same laboratory protocol. Specifically, the samples were oven-dried at 105 °C, ground, passed through a 2 mm sieve, and then stored in sealed bags before laboratory determination and spectral acquisition. The soil samples were sent to the Qiuyin Soil Testing Laboratory on 30 December 2025 for the determination of soil organic carbon content. Soil organic carbon was measured using the potassium dichromate external heating method. For analytical quality control, each sample was determined in triplicate, and the mean value of the three measurements was used as the final SOC reference value for subsequent spectral modeling. The allowable relative error among repeated measurements was set at 2%; when the error exceeded 2%, the sample was remeasured. The measured SOC values were used directly as the reference values for subsequent spectral modeling. The descriptive statistics of SOC content are shown in [Table 1](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1700#table_body_display_agriculture-16-01700-t001).\n\n**Table 1.** Descriptive statistics of soil organic carbon content.\n\nIn this study, a portable spectrometer (OFS2500, Oceanhood, Shanghai, China) was used to acquire Vis-NIR spectral information from the soil samples in December 2025. The instrument operates over a wavelength range of 342–2504 nm, covering the visible to near-infrared regions. Its built-in interpolation algorithm outputs spectral wavelengths at 1 nm intervals, generating a total of 2163 spectral variables. Considering that the spectral regions at both ends are generally affected by weakened instrumental edge response, relatively low signal-to-noise ratio, and strong noise interference, their stability and interpretability are relatively poor. Therefore, before subsequent modeling, the low-quality bands at both ends were removed, and only spectral variables within the range of 400~2500 nm were retained for analysis. In terms of instrument performance, the OFS2500 has a spectral resolution of 2.5 nm and a signal-to-noise ratio of 500:1 in the range of 342~900 nm, and a spectral resolution of 8 nm with a signal-to-noise ratio of up to 2000:1 in the range of 900~2504 nm.\n\nFor spectral measurement, each soil sample was placed in a 10 cm diameter Petri dish and spread evenly to form a sample layer of approximately 5 mm thickness. Spectral acquisition was conducted in a dark box to avoid interference from external light sources. During measurement, the spectrometer probe was positioned 2 cm above the sample surface, and the integration time was set to 2500 us. To maintain measurement stability, white and dark reference corrections were performed every 15 min throughout the experiment. The laboratory temperature was maintained at 27 °C during spectral acquisition. Before each scan, the soil sample was re-mixed to reduce random variation. Each sample was scanned three times, and the averaged spectrum was used as the representative spectrum of that sample. The raw spectral curves of the soil samples are shown in [Figure 1](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1700#fig_body_display_agriculture-16-01700-f001)a.\n\n**Figure 1.** Raw and preprocessed spectra of soil samples at different preprocessing stages: (**a**) raw spectra; (**b**) spectra after standard normal variate transformation (SNV); (**c**) spectra after standard normal variate transformation and first derivative preprocessing (SNV + FD); and (**d**) spectra after standard normal variate transformation, first derivative preprocessing and Savitzky–Golay smoothing (SNV + FD + SG).\n\n### 2.2. Spectral Preprocessing and Dataset Partitioning\n\nBecause the raw spectra were affected by scattering effects, baseline drift, and noise from low-quality bands at both ends of the spectral range, the effective spectral range was first selected from the raw spectra. Multiple preprocessing methods were then applied to extract stable spectral features and provide reliable inputs for subsequent interval modeling. Specifically, the raw spectra were first truncated from 342~2504 nm to 400~2500 nm, retaining 2101 bands. Standard normal variate (SNV) transformation [[24](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1700#B24-agriculture-16-01700)], first derivative (FD) preprocessing [[25](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1700#B25-agriculture-16-01700)], and Savitzky–Golay (SG) smoothing [[26](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1700#B26-agriculture-16-01700)] were then sequentially applied.\n\nAmong these preprocessing steps, SNV was used to reduce the effects caused by particle size, sample surface conditions, and scattering differences. FD preprocessing was applied to enhance local variation features in the spectral curves and reduce baseline drift. In the present study, the first derivative was calculated along the wavelength axis using the first-order finite difference, and the last derivative value was repeated once to retain the original spectral dimensionality. Because FD preprocessing may amplify high-frequency noise, SG smoothing was further applied after FD preprocessing to reduce noise effects. The SG smoothing procedure used a window length of 11 bands and a polynomial order of 2. The spectral curves at different preprocessing stages are shown in [Figure 1](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1700#fig_body_display_agriculture-16-01700-f001)b–d. In the present study, the SNV + FD + SG sequence was adopted as the preprocessing scheme under the current dataset based on preliminary comparative screening within the calibration set.\n\nIn the subsequent modeling process, based on the spectral matrix after SNV + FD + SG preprocessing, the variables were further standardized using the mean and standard deviation of the calibration set to ensure that the inputs of the calibration set and prediction set were on the same scale.\n\nTo ensure the objectivity and reproducibility of model evaluation, the calibration set and prediction set were divided using a fixed stratified hold-out strategy based on the sorted SOC reference values. First, all samples were sorted in ascending order according to the SOC values. Then, every four consecutive samples in the sorted sequence were grouped together, and the first sample in each group was assigned to the prediction set, whereas the remaining three samples were assigned to the calibration set. Using this strategy, 126 samples were assigned to the calibration set and 42 samples to the prediction set. This partitioning strategy ensured that the calibration set covered the full distribution range of the target variable, while the prediction set was used as an independent hold-out set under the current data distribution.\n\n### 2.3. Selection of the Full-Spectrum Reference Model and Construction of Feature Selection Benchmark Models\n\nTo determine a full-spectrum reference model suitable for the current dataset, this study first compared several spectral regression models under the full-spectrum condition, including Ridge regression [[27](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1700#B27-agriculture-16-01700)], partial least squares regression (PLSR) [[28](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1700#B28-agriculture-16-01700)], Random Forest [[29](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1700#B29-agriculture-16-01700)], support vector regression (SVR) [[30](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1700#B30-agriculture-16-01700)], and least squares boosting (LSBoost) [[31](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1700#B31-agriculture-16-01700)]. Model selection was performed exclusively within the calibration set by cross-validation, and the regressor with the best cross-validation performance was adopted for the subsequent wavelength-selection and interval-selection procedures. The prediction set was kept completely independent during model selection and was used only for the final hold-out evaluation of the selected model.\n\nTo compare the modeling effects of different feature selection strategies, several WPS methods were further introduced as benchmark methods, including CARS [[15](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1700#B15-agriculture-16-01700)], SPA [[12](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1700#B12-agriculture-16-01700)], UVE [[13](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1700#B13-agriculture-16-01700)], VIP [[14](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1700#B14-agriculture-16-01700)], and IRIV [[16](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1700#B16-agriculture-16-01700)]. The feature subsets obtained by each WPS method were combined with Ridge regression to construct the CARS-Ridge, SPA-Ridge, UVE-Ridge, VIP-Ridge, and IRIV-Ridge benchmark models, respectively. These models were used to compare the effects of discrete wavelength point selection strategies under a unified regression framework.\n\nThis study also compared several WIS-related benchmark models to evaluate the effectiveness of continuous interval selection and combination search strategies in sicRidge. The existing WIS-PLS-related methods used in this study included iPLSR [[20](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1700#B20-agriculture-16-01700)], siPLSR [[19](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1700#B19-agriculture-16-01700)], and MWPLS [[32](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1700#B32-agriculture-16-01700)]. Considering that different regression models may affect the results, Ridge regression-based WIS benchmark models, namely iRidge, siRidge, and MWRidge, were further constructed based on the existing WIS partitioning strategies. It should be noted that iRidge, siRidge, and MWRidge are benchmark model names constructed in this study rather than existing standard algorithm names. Their purpose was to minimize the influence of differences between PLSR and Ridge regression when evaluating interval selection effects.\n\n### 2.4. Construction of the Synergistic Interval-Constrained Ridge Regression Spectral Modeling Framework\n\nTo exploit complementary information across spectral regions while preserving continuous spectral structures, we constructed a synergistic interval-constrained Ridge regression framework (sicRidge). The framework uses the preprocessed Vis-NIR spectral matrix as input and continuous wavelength intervals as the basic selection unit. Its main steps include sliding-window candidate interval generation, dual-interval synergistic search, overlap constraint filtering, and final Ridge regression modeling. Unlike WPS methods, which focus on the selection of discrete wavelength points, and existing WIS methods, which mostly emphasize a single interval or limited interval combinations, sicRidge aims to identify cross-region interval combinations with complementary contributions and low redundancy within the calibration set. Modeling and comparison are then performed under a unified Ridge regression framework. The prediction set was used only for the final hold-out validation of the model and was not involved in candidate interval generation, interval combination selection, determination of the number of intervals, parameter optimization, or variable standardization, thereby ensuring the relative objectivity of model evaluation. The overall workflow of the proposed sicRidge framework is illustrated in [Figure 2](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1700#fig_body_display_agriculture-16-01700-f002).\n\n**Figure 2.** Conceptual workflow of the proposed sicRidge framework for SOC prediction from Vis-NIR spectra.\n\nThe detailed interval search procedure, parameter optimization strategy, and comparison of results under different numbers of intervals in sicRidge are further presented in [Section 3](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1700#sec3-agriculture-16-01700).\n\n### 2.5. Model Evaluation Metrics\n\nTo comprehensively evaluate the prediction per","MDPI Agriculture 2026-08-08","2026-08-08T00:00:00Z",62,{"impact":320,"substance":226,"depth":130,"authority":320,"freshness":495,"relevant":95,"comment":496},4,"论文提出新的SOC光谱估算方法，对农业信息化有技术参考价值，但影响范围有限。",[498],{"name":491,"url":488},[21,500,501,502],"光谱分析","Ridge回归","变量选择",{"code":23,"slug":24,"name":25,"path":26,"parent_code":27},[],{"status":30,"locked":14,"version":31,"method":32,"evidence":506},{"candidates":507,"agricultural_evidence":37},[508],{"code":23,"score":320,"supported":37,"title":509,"abstract":510,"tags":511},[24],[40],[21],[513,514],"土壤有机碳 光谱分析 变量选择 Ridge回归","土壤有机碳 光谱分析","土壤有机碳光谱分析变量选择Ridge回归-471","2026-08-12T23:57:24.375712Z",{"id":518,"title":519,"url":520,"summary":521,"summary_zh":522,"content":9,"source_name":523,"source_url":520,"published_at":524,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":525,"score_detail":526,"sources":528,"tags":530,"primary_topic":536,"secondary_topics":537,"classification":539,"search_phrases":557,"slug":560,"view_count":15,"doi":561,"paper":562,"created_at":577},5583,"Active-Labeling Evaluation for Computer Agriculture Nematode-Cyst Detection Under Sparse Soil-Sample Ground Truth: An Artificial Intelligence Annotation-Efficiency Bound","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23544145","Active learning promises to reduce the annotation burden of training nematode-cyst detection models for precision agriculture, but the size of that reduction is not known when ground truth comes from sparse soil samples rather than exhaustive visual inspection. the present analysis derives an annotation-efficiency bound for pool-based active learning under sparse soil-sample ground truth. The detection task is formalised as a two-stage process in which a classifier selects candidate cysts from soil imagery and a laboratory assay confirms a small labelled subset. Under a declared variance structure with between-cluster standard deviation 6.0, residual standard deviation 4.5 and intraclass correlation 0.64, the bound is evaluated across a design grid of 8, 16, 24 and 32 clusters and effect sizes of 3, 5 and 8 percentage points, with 2000 replications per cell. The annotation-efficiency gain is bounded by the ratio of label noise to cluster-level variance, and that 52 clusters are required to detect a 5-percentage-point improvement at 80% power. The bound provides a design rule for allocating annotation effort between active selection and confirmatory soil sampling.","主动学习有望减轻精准农业中训练线虫囊检测模型所需的标注负担，但当真实标签来自稀疏土壤样本而非详尽的视觉检查时，这种负担减轻的幅度尚不清楚。本分析推导了在稀疏土壤样本真实标签下基于池的主动学习的标注效率界限。检测任务被形式化为两阶段过程：分类器从土壤图像中筛选候选线虫囊，实验室检测确认一个小的已标注子集。在设定的方差结构下——组间标准差为6.0、残差标准差为4.5、组内相关系数为0.64——该界限在8、16、24和32个簇的设计网格以及3、5和8个百分点的效应量下进行评估，每个单元格重复2000次。标注效率增益受标签噪声与簇级方差之比的约束，并且在80%检验功效下检测5个百分点的改进需要52个簇。该界限为在主动选择与验证性土壤采样之间分配标注工作量提供了设计规则。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",64,{"impact":107,"substance":108,"depth":109,"authority":111,"freshness":13,"relevant":95,"comment":527},"该研究给出稀疏土壤样本下主动学习标注效率的理论边界与聚类设计规则，方法新颖、结论具体，但属细分方法学研究，产业影响有限，适合作为专业精选而非头条。",[529],{"name":523,"url":520},[531,532,533,534,535],"智慧农业","农业人工智能","主动学习","线虫检测","土壤采样",{"code":23,"slug":24,"name":25,"path":26,"parent_code":27},[538],{"code":163,"slug":244,"name":245,"path":246,"parent_code":125},{"status":30,"locked":14,"version":31,"method":32,"evidence":540},{"candidates":541,"agricultural_evidence":37},[542,546,551],{"code":23,"score":227,"supported":37,"title":543,"abstract":544,"tags":545},[24],[40,24],[],{"code":163,"score":111,"supported":37,"title":547,"abstract":549,"tags":550},[548],"artificial intelligence",[],[532],{"code":158,"score":267,"supported":37,"title":552,"abstract":553,"tags":556},[],[554,555],"精准农业","precision agriculture",[531],[558,559],"线虫孢囊 检测 主动学习","稀疏土壤样本 标注效率","线虫孢囊检测主动学习-5583","10.5281\u002Fzenodo.23544145",{"doi":561,"openalex_id":563,"authors":564,"venue":523,"cited_by_count":15,"oa_url":520,"card":571,"direction":575,"ingested_from":92},"W7223082195",[565,567,569],{"name":566,"orcid":9},"Amine Ben Salah",{"name":568,"orcid":9},"Ines Trabelsi",{"name":570,"orcid":9},"Mohamed Aziz Khelifi",{"tldr":572,"method":573,"finding":574,"direction":575,"opportunity":576},"推导稀疏土壤样本真值下主动学习标注效率的理论上界。","基于方差结构与设计网格的统计模拟，2000次重复。","标注效率增益受标签噪声与簇间方差之比限制，检测5个百分点提升需52簇。","农业人工智能与决策模型","可将该上界推广到其他稀疏真值农业检测任务，并探索主动选择与确认采样的最优分配策略。","2026-10-22T11:20:58.901747Z"]