[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3847":3,"related-3847":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":24,"tags":26,"primary_topic":9,"secondary_topics":32,"classification":33,"search_phrases":69,"slug":72,"view_count":73,"doi":74,"paper":75,"created_at":93},3847,"Estimation of Soil Organic Carbon Using Artificial Neural Networks and Regression Models","https:\u002F\u002Fdoi.org\u002F10.17475\u002Fkastorman.2047250","Aim of study: This study aimed to estimate the soil organic carbon (SOC) content in red pine (Pinus brutia Ten.) plantation areas under semiarid climatic conditions using multiple linear regression (MLR) and artificial neural network (ANN) models. Area of study: The research was carried out within the boundaries of the Tokat-Niksar Ayvaz Forest Management Directorate, Türkiye. Material and method: The study material consisted of 92 disturbed soil samples collected from the topsoil (0-10 cm and 10-30 cm) at 46 sampling plots, along with vegetation and topographic indices. Various physical and chemical analyses were performed on the soil samples. In addition to soil properties, topographic and remote sensing indices were used as predictor variables. SOC estimations were carried out using MLR and ANN models. Main results: According to the findings, SOC content increased with higher wilting point (WP), elevation, and normalized difference vegetation index (NDVI), while it decreased with increasing lime content (CaCO₃), pH, and bulk density (BD). In the MLR model, WP, BD, pH, CaCO₃, soil moisture index (MSI), easterly exposure (EExp.), and valley depth (VDep.) were identified as significant variables, resulting in an R² value of 0.822. The ANN model showed higher predictive performance with the same variables, yielding R² = 0.937 and a low relative prediction error (4.3%). Research highlights: Both models were found suitable for SOC estimation; however, the ANN model represented complex and nonlinear environmental interactions more effectively.","研究目的：本研究旨在利用多元线性回归（MLR）和人工神经网络（ANN）模型估算半干旱气候条件下红松（Pinus brutia Ten.）人工林区域的土壤有机碳（SOC）含量。研究区域：研究在土耳其托卡特-尼克萨尔阿伊瓦兹森林管理区范围内开展。材料与方法：研究材料包括在46个采样样地采集的表层土壤（0~10 cm和10~30 cm）的92个扰动土壤样品，以及植被和地形指数。对土壤样品进行了多种物理和化学分析。除土壤性质外，还将地形和遥感指数用作预测变量。采用MLR和ANN模型进行SOC估算。主要结果：研究结果表明，SOC含量随凋萎点（WP）、海拔和归一化植被指数（NDVI）的升高而增加，随石灰含量（CaCO₃）、pH和容重（BD）的升高而降低。在MLR模型中，WP、BD、pH、CaCO₃、土壤水分指数（MSI）、东坡向（EExp.）和谷深（VDep.）被确定为显著变量，R²值为0.822。ANN模型在相同变量下表现出更高的预测性能，R² = 0.937，相对预测误差较低（4.3%）。研究亮点：两种模型均适用于SOC估算；然而，ANN模型能更有效地表征复杂且非线性的环境交互作用。",null,"Kastamonu University Journal of Forestry Faculty","2026-09-28T00:00:00Z","论文",10,false,65,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,20,16,12,9,1,"方法对比扎实、样本与指标明确，但属区域性小样本研究，应用范围有限，可作为技术参考而非重大资讯。",[25],{"name":10,"url":6},[27,28,29,30,31],"土壤有机碳","智慧林业","遥感反演","土壤碳汇","人工神经网络",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":14,"reason":68},[39,48,56,63],{"code":40,"score":41,"supported":42,"title":43,"abstract":45,"tags":47},"H2",15,true,[44],"soil",[46,44],"土壤",[27],{"code":49,"score":50,"supported":42,"title":51,"abstract":52,"tags":55},"I3",6,[],[53,54],"遥感","remote sensing",[],{"code":57,"score":58,"supported":42,"title":59,"abstract":60,"tags":62},"H7",3,[],[61],"干旱",[],{"code":64,"score":22,"supported":14,"title":65,"abstract":66,"tags":67},"H4",[],[],[30],"农业相关性需要复核",[70,71],"土耳其 红松 土壤有机碳 神经网络","NDVI 土壤有机碳 估算","土耳其红松土壤有机碳神经网络-3847",2,"10.17475\u002Fkastorman.2047250",{"doi":74,"openalex_id":76,"authors":77,"venue":10,"cited_by_count":84,"oa_url":85,"card":86,"direction":90,"ingested_from":92},"W7214593796",[78,81],{"name":79,"orcid":80},"Ergün Kahveci","https:\u002F\u002Forcid.org\u002F0000-0003-2178-4302",{"name":82,"orcid":83},"Salih Malkoçoğlu","https:\u002F\u002Forcid.org\u002F0000-0002-9599-628X",0,"https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F6480291",{"tldr":87,"method":88,"finding":89,"direction":90,"opportunity":91},"用人工神经网络与多元线性回归估算半干旱区红松林土壤有机碳含量。","92个扰动土样，结合地形与遥感指数，构建MLR和ANN模型。","ANN预测性能更高（R²=0.937），优于MLR（R²=0.822），能更好处理非线性关系。","农业遥感与作物表型","可探索多源遥感与深度学习结合，提升不同气候区SOC估算精度与泛化能力。","openalex","2026-09-30T23:30:29.605904Z",{"total":50,"page":22,"page_size":50,"items":95},[96,215,327,390,468,533],{"id":97,"title":98,"url":99,"summary":100,"summary_zh":101,"content":9,"source_name":102,"source_url":99,"published_at":103,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":104,"score_detail":105,"sources":108,"tags":110,"primary_topic":115,"secondary_topics":119,"classification":128,"search_phrases":187,"slug":190,"view_count":84,"doi":191,"paper":192,"created_at":214},5472,"Turning dirt into gold: the business case of soil carbon for agrarian pathways in arid climate","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1966265","Introduction Agricultural carbon sequestration offers considerable potential for mitigating anthropogenic climate change, particularly in the low-organic-carbon soils of the Middle East. This study presents a foundational scenario-based assessment of soil carbon sequestration and economic feasibility. Materials and methods Crop production and market datasets from Jordanian statistics (1998–2023) were used to estimate potential soil carbon sequestration and economic performance. Using benchmarks from literature in comparable agroecological regions, four soil carbon management practices were evaluated across multiple crops for their carbon storage potential using Monte Carlo uncertainty and sensitivity analysis. Sensitivity of economic performance was assessed under optimistic\u002Fpessimistic scenarios. Results Among the evaluated practices, the highest carbon sequestration rate was achieved through increased Soil Organic Carbon (SOC), reaching a maximum of 27.44 metric tons of CO₂ per hectare per year (tCO₂\u002Fha\u002Fyear) under the high scenario for cucumbers. In contrast, barley showed the highest sequestration rate at 0.45 tCO₂\u002Fha\u002Fyear under increased SOC in the high scenario. Sensitivity analysis showed that ±20% uncertainty in the management coefficients resulted in practice-specific changes of 1.11 tCO₂\u002Fha\u002Fyear for increased SOC and 0.46 tCO₂\u002Fha\u002Fyear for improved cropland management. Horticultural crops, particularly cucumbers and lemons, exhibited the highest sequestration rates and strongest responses to management interventions. Field crops such as wheat, barley, and olives demonstrated lower, more stable sequestration rates. Economic analysis revealed that cucumbers, grapes, potatoes, and Open-field farming (OFF) tomatoes achieved the highest tri-pillar performance (net and adjusted profits and break-even cost) alongside effective carbon sequestration. These findings highlight that crop choice is a primary determinant of both crop profitability and sequestrated carbon potential and feasibility, with significant implications for climate-smart agriculture, carbon credit mechanisms, and intensification strategies in data-scarce regions. Study limitations While empirical field validation remains a future priority, this model-based study provides a screening-level theoretical baseline to guide targeted research and investment in arid agricultural systems.","引言 农业碳封存为缓解人为气候变化提供了可观潜力，尤其是在中东低有机碳土壤中。本研究提出了一项基于情景的土壤碳封存与经济学可行性基础评估。材料与方法 利用约旦统计数据（1998—2023年）中的作物生产和市场数据集，估算潜在的土壤碳封存及经济表现。参考可比农业生态区域文献中的基准值，采用蒙特卡洛不确定性分析和敏感性分析，评估了四种土壤碳管理措施在多种作物上的碳储存潜力。在乐观\u002F悲观情景下评估了经济表现的敏感性。结果 在评估的各项措施中，通过增加土壤有机碳（SOC）实现的碳封存速率最高，在黄瓜的高情景下达到最高27.44公吨二氧化碳\u002F公顷\u002F年（tCO₂\u002Fha\u002Fyear）。相比之下，大麦在增加SOC的高情景下表现出最高的封存速率，为0.45 tCO₂\u002Fha\u002Fyear。敏感性分析表明，管理系数±20%的不确定性导致增加SOC和改善农田管理分别产生1.11 tCO₂\u002Fha\u002Fyear和0.46 tCO₂\u002Fha\u002Fyear的措施特异性变化。园艺作物，尤其是黄瓜和柠檬，表现出最高的封存速率和对管理干预的最强响应。小麦、大麦和橄榄等大田作物表现出较低但更稳定的封存速率。经济分析显示，黄瓜、葡萄、马铃薯和露地栽培（OFF）番茄在有效碳封存的同时实现了最高的三支柱表现（净利润、调整后利润和盈亏平衡成本）。这些发现突出表明，作物选择是作物盈利能力和封存碳潜力及可行性的主要决定因素，对气候智慧型农业、碳信用机制和数据稀缺地区的集约化策略具有重要意义。研究局限性 尽管田间实证验证仍是未来的优先事项，但这项基于模型的研究提供了筛选层面的理论基线，以指导干旱农业系统中的针对性研究和投资。","Frontiers in Sustainable Food Systems","2026-10-20T00:00:00Z",70,{"impact":20,"substance":18,"depth":19,"authority":106,"freshness":21,"relevant":22,"comment":107},13,"基于约旦1998–2023年统计数据的模型情景研究，量化了不同作物固碳速率与经济收益，对干旱区农业碳汇与碳信用机制有参考价值，但属区域性模拟研究，尚未经田间验证。",[109],{"name":102,"url":99},[111,112,113,30,114],"气候智慧农业","碳交易","干旱农业","作物选择",{"code":40,"slug":44,"name":116,"path":117,"parent_code":118},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[120,124],{"code":57,"slug":121,"name":122,"path":123,"parent_code":118},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"code":64,"slug":125,"name":126,"path":127,"parent_code":118},"carbon","农业碳与绿色低碳","\u002Ftopic\u002Fenvironment\u002Fcarbon",{"status":129,"locked":14,"version":35,"method":36,"evidence":130},"classified",{"candidates":131,"agricultural_evidence":42},[132,137,143,149,156,163,169,175,181],{"code":40,"score":133,"supported":42,"title":134,"abstract":135,"tags":136},14,[44],[46,44],[],{"code":57,"score":13,"supported":42,"title":138,"abstract":139,"tags":142},[],[140,61,141],"气候变化","climate change",[111],{"code":64,"score":17,"supported":42,"title":144,"abstract":145,"tags":148},[],[146,147],"农业碳","carbon sequestration",[30,112],{"code":150,"score":50,"supported":42,"title":151,"abstract":152,"tags":155},"A2",[],[153,154],"小麦","wheat",[],{"code":157,"score":50,"supported":42,"title":158,"abstract":159,"tags":162},"B1",[],[160,161],"番茄","黄瓜",[],{"code":164,"score":58,"supported":42,"title":165,"abstract":166,"tags":168},"A5",[],[167],"马铃薯",[],{"code":170,"score":58,"supported":42,"title":171,"abstract":172,"tags":174},"B2",[],[173],"葡萄",[],{"code":176,"score":58,"supported":42,"title":177,"abstract":178,"tags":180},"H6",[],[179],"农业生态",[],{"code":182,"score":58,"supported":42,"title":183,"abstract":184,"tags":186},"I1",[],[185],"smart agriculture",[],[188,189],"约旦 土壤碳汇 黄瓜","土壤固碳 经济可行性","约旦土壤碳汇黄瓜-5472","10.3389\u002Ffsufs.2026.1966265",{"doi":191,"openalex_id":193,"authors":194,"venue":102,"cited_by_count":84,"oa_url":99,"card":207,"direction":213,"ingested_from":92},"W7223064172",[195,197,199,202,204],{"name":196,"orcid":9},"Tasnim Althalaj",{"name":198,"orcid":9},"Ross. M. Thompson",{"name":200,"orcid":201},"Tala H. Qtaishat","https:\u002F\u002Forcid.org\u002F0000-0002-2700-6686",{"name":203,"orcid":9},"Dan Bumblauskas",{"name":205,"orcid":206},"Fayha Al-Shibli","https:\u002F\u002Forcid.org\u002F0000-0002-5628-0975",{"tldr":208,"method":209,"finding":210,"direction":211,"opportunity":212},"基于约旦1998–2023年数据，评估干旱区土壤固碳潜力与经济可行性。","用蒙特卡洛不确定性与敏感性分析，评估四种土壤碳管理措施在多作物下的表现。","黄瓜等园艺作物固碳率最高，作物选择是固碳与盈利的关键决定因素。","农业绿色发展与碳","数据稀缺干旱区可结合田间验证与碳信用机制，研究作物级固碳经济激励设计。","智慧农业 \u002F 农业物联网","2026-10-21T23:30:07.383006Z",{"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":227,"tags":229,"primary_topic":233,"secondary_topics":234,"classification":240,"search_phrases":279,"slug":282,"view_count":84,"doi":283,"paper":284,"created_at":326},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":17,"substance":18,"depth":225,"authority":106,"freshness":21,"relevant":22,"comment":226},17,"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。",[228],{"name":221,"url":218},[230,231,27,53,232],"数字农业","随机森林","土壤制图",{"code":40,"slug":44,"name":116,"path":117,"parent_code":118},[235],{"code":49,"slug":236,"name":237,"path":238,"parent_code":239},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":129,"locked":14,"version":35,"method":36,"evidence":241},{"candidates":242,"agricultural_evidence":42},[243,247,251,258,264,270,274],{"code":40,"score":19,"supported":42,"title":244,"abstract":245,"tags":246},[44],[46,44],[232,27],{"code":49,"score":41,"supported":42,"title":248,"abstract":249,"tags":250},[54],[53,54],[53],{"code":252,"score":50,"supported":42,"title":253,"abstract":254,"tags":257},"F3",[],[255,256],"肥料","fertilizer",[],{"code":259,"score":58,"supported":42,"title":260,"abstract":261,"tags":263},"H8",[],[262],"可持续农业",[],{"code":265,"score":58,"supported":42,"title":266,"abstract":267,"tags":269},"I4",[],[268],"空间分析",[],{"code":182,"score":22,"supported":14,"title":271,"abstract":272,"tags":273},[],[],[230],{"code":275,"score":22,"supported":14,"title":276,"abstract":277,"tags":278},"I2",[],[],[231],[280,281],"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":283,"openalex_id":285,"authors":286,"venue":221,"cited_by_count":84,"oa_url":320,"card":321,"direction":90,"ingested_from":92},"W7222335269",[287,290,293,296,299,302,305,308,311,314,317],{"name":288,"orcid":289},"Leonardo Augusto de Sousa","https:\u002F\u002Forcid.org\u002F0009-0003-1560-5600",{"name":291,"orcid":292},"Fernanda Almeida Bócoli","https:\u002F\u002Forcid.org\u002F0000-0001-5476-6886",{"name":294,"orcid":295},"Eduane José de Pádua","https:\u002F\u002Forcid.org\u002F0009-0003-1782-2756",{"name":297,"orcid":298},"Luiza Maria Pereira Pierangeli","https:\u002F\u002Forcid.org\u002F0000-0002-9218-3718",{"name":300,"orcid":301},"Junior César Avanzi","https:\u002F\u002Forcid.org\u002F0000-0003-2455-0325",{"name":303,"orcid":304},"Bruno Montoani Silva","https:\u002F\u002Forcid.org\u002F0000-0002-8240-8987",{"name":306,"orcid":307},"Carlos Victor Lima Albuquerque","https:\u002F\u002Forcid.org\u002F0009-0009-4597-4443",{"name":309,"orcid":310},"Luiz Roberto Guimarães Guilherme","https:\u002F\u002Forcid.org\u002F0000-0002-5387-6028",{"name":312,"orcid":313},"Marco Aurélio Carbone Carneiro","https:\u002F\u002Forcid.org\u002F0000-0003-4349-3071",{"name":315,"orcid":316},"Nilton Curi","https:\u002F\u002Forcid.org\u002F0000-0002-2604-0866",{"name":318,"orcid":319},"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":322,"method":323,"finding":324,"direction":90,"opportunity":325},"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":328,"title":329,"url":330,"summary":331,"summary_zh":332,"content":9,"source_name":333,"source_url":330,"published_at":334,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":335,"score_detail":336,"sources":340,"tags":342,"primary_topic":9,"secondary_topics":346,"classification":347,"search_phrases":364,"slug":367,"view_count":84,"doi":368,"paper":369,"created_at":389},4610,"UAV-based estimation of aboveground biomass carbon in the Sundarbans mangrove forest of Bangladesh","https:\u002F\u002Fdoi.org\u002F10.1088\u002F2515-7620\u002Faeb18f","Abstract Reliable biomass and carbon assessments are essential for understanding mangrove forest dynamics and their role in global carbon cycling. However, field-based biomass estimation is time-consuming, labour-intensive, and costly. Remote sensing provides a practical and cost-effective alternative, particularly with unmanned aerial vehicles (UAVs) that can capture high-resolution imagery. The assessment of biomass carbon in the Sundarbans Mangrove Forest (SMF) using high-resolution remote sensing remains understudied. This study develops an integrated carbon assessment framework for estimating aboveground biomass carbon in the SMF using UAV-derived canopy metrics and field measurements. Error propagation and feature importance analyses were performed to quantify uncertainty and assess predictor influence, providing a robust and uncertainty-aware approach for mangrove biomass carbon assessment. CPA was used to estimate proxy DBH using linear, polynomial, logarithmic, power, and exponential regression models. All CPA–DBH models showed comparable performance (R2 = 0.73-0.75). The linear and polynomial models achieved the lowest RMSE (1.77 cm), while the polynomial model produced the lowest MAE (1.22 cm) and was therefore selected for estimating Proxy DBH. Field-based AGBC was calculated from measured tree height and DBH using species-specific and generic allometric equations applicable to the Sundarbans, whereas UAV-based AGBC was estimated using UAV-derived Mean DSM Height and CPA-derived Proxy DBH. Simple linear regression (SLR), multiple linear regression (MLR), and machine-learning (ML) models (Random Forest, Support Vector Machine, Artificial Neural Network, and Extreme Gradient Boosting) were evaluated for AGBC prediction. The MLR model showed the best performance on the validation\u002Fmodel-selection subset (R² = 0.86, RMSE = 14.70 Mg C ha⁻¹, MedAE = 4.58 Mg C ha⁻¹). UAV-derived AGBC estimates closely matched field measurements and previously reported values for the Sundarbans, demonstrating the potential of high-resolution UAV metrics for reliable mangrove AGBC estimation and supporting carbon monitoring, REDD+ initiatives, and sustainable mangrove management in Bangladesh.","可靠的生物量和碳评估对于理解红树林动态及其在全球碳循环中的作用至关重要。然而，基于实地调查的生物量估算耗时、费力且成本高昂。遥感提供了一种实用且具有成本效益的替代方案，尤其是利用能够获取高分辨率影像的无人机（UAV）。利用高分辨率遥感对孙德尔本斯红树林（SMF）进行生物量碳评估的研究仍较为缺乏。本研究开发了一个综合碳评估框架，利用无人机获取的冠层指标和实地测量数据估算SMF的地上生物量碳。通过误差传播和特征重要性分析量化不确定性并评估预测因子的影响，为红树林生物量碳评估提供了一种稳健且考虑不确定性的方法。采用CPA（冠层投影面积）通过线性、多项式、对数、幂函数和指数回归模型估算代理DBH（胸径）。所有CPA–DBH模型均表现出相近的性能（R² = 0.73–0.75）。线性模型和多项式模型取得了最低的RMSE（1.77 cm），而多项式模型产生了最低的MAE（1.22 cm），因此被选用于估算代理DBH。基于实地的AGBC（地上生物量碳）通过实测树高和DBH，采用适用于孙德尔本斯的物种特异性异速生长方程和通用异速生长方程计算，而基于无人机的AGBC则利用无人机获取的平均DSM高度和CPA衍生的代理DBH进行估算。采用简单线性回归（SLR）、多元线性回归（MLR）和机器学习（ML）模型（随机森林、支持向量机、人工神经网络和极端梯度提升）评估AGBC预测性能。MLR模型在验证\u002F模型选择子集上表现最佳（R² = 0.86，RMSE = 14.70 Mg C ha⁻¹，MedAE = 4.58 Mg C ha⁻¹）。无人机获取的AGBC估算值与实地测量结果及先前报道的孙德尔本斯数值高度吻合，表明高分辨率无人机指标在可靠估算红树林AGBC方面具有潜力，可支持孟加拉国的碳监测、REDD+倡议和可持续红树林管理。","Environmental Research Communications","2026-10-07T00:00:00Z",80,{"impact":337,"substance":338,"depth":337,"authority":133,"freshness":17,"relevant":22,"comment":339},18,22,"该研究利用无人机高分辨率影像与机器学习模型估算孟加拉国孙德尔本斯红树林地上生物量碳，方法新颖、数据可靠，对林业碳汇监测与REDD+具有参考价值，但属境外案例、应用范围有限，可作为专业资讯收录。",[341],{"name":333,"url":330},[343,53,28,344,345],"无人机","红树林","碳汇计量",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":348},{"candidates":349,"agricultural_evidence":14,"reason":68},[350,355,360],{"code":49,"score":351,"supported":42,"title":352,"abstract":353,"tags":354},7,[],[53,54],[53],{"code":275,"score":58,"supported":42,"title":356,"abstract":357,"tags":359},[],[358],"机器学习",[],{"code":176,"score":22,"supported":14,"title":361,"abstract":362,"tags":363},[],[],[344],[365,366],"孟加拉国 孙德尔本斯 红树林 无人机","无人机 生物量碳 遥感估算","孟加拉国孙德尔本斯红树林无人机-4610","10.1088\u002F2515-7620\u002Faeb18f",{"doi":368,"openalex_id":370,"authors":371,"venue":333,"cited_by_count":84,"oa_url":383,"card":384,"direction":211,"ingested_from":92},"W7220935167",[372,375,378,380],{"name":373,"orcid":374},"Md Abul Hashem","https:\u002F\u002Forcid.org\u002F0009-0006-2712-3168",{"name":376,"orcid":377},"Md Sarwar Hossain","https:\u002F\u002Forcid.org\u002F0000-0001-6101-659X",{"name":379,"orcid":9},"Md. Tauhidor Rahaman",{"name":381,"orcid":382},"Brian Barrett","https:\u002F\u002Forcid.org\u002F0000-0002-4380-4020","https:\u002F\u002Fiopscience.iop.org\u002Farticle\u002F10.1088\u002F2515-7620\u002Faeb18f\u002Fpdf",{"tldr":385,"method":386,"finding":387,"direction":90,"opportunity":388},"利用无人机冠层指标与地面数据估算孟加拉孙德尔本斯红树林地上生物量碳。","无人机高分辨率影像提取冠层指标，结合CPA估算DBH，用MLR与机器学习模型预测","多元线性回归表现最佳（R²=0.86），无人机估算值与实测及历史值高度吻合。","可探索多时相无人机与卫星融合，提升红树林碳储量动态监测及不确定性量化能力。","2026-10-10T23:31:07.399747Z",{"id":391,"title":392,"url":393,"summary":394,"summary_zh":395,"content":9,"source_name":396,"source_url":393,"published_at":334,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":397,"score_detail":398,"sources":401,"tags":403,"primary_topic":9,"secondary_topics":409,"classification":410,"search_phrases":447,"slug":450,"view_count":22,"doi":451,"paper":452,"created_at":467},4495,"A review of forest innovation: Technologies and sustainable practices for environmental conservation","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.nxsust.2026.100546","Forests play a crucial role in climate regulation, biodiversity conservation, ecosystem service provision, and sustainable socio-economic development. However, increasing deforestation, climate change, habitat fragmentation, and unsustainable resource exploitation continue to threaten forest ecosystems worldwide. The objective of this review is to determine the ecological, economic, and governance conditions under which specific forest innovations translate into measurable sustainability outcomes, and to synthesize these conditions into coherent, evidence-based pathways applicable across different regional contexts. Framed through a social-ecological systems (SES) and resilience lens, this review critically examines recent advances in forest innovation by integrating sustainable forest management practices with emerging technological developments, including precision forestry, remote sensing, geographic information systems (GIS), LiDAR, unmanned aerial vehicles (UAVs), biotechnology, artificial intelligence (AI), digital forestry, biomass utilization, and forest-based bioeconomy approaches. Unlike conventional narrative reviews, this study applies a common five criterion evaluation framework comprising ecological effectiveness, technological maturity, economic feasibility, governance requirements, and regional applicability to comparatively evaluates the strengths, limitations, implementation challenges of these innovations across diverse ecological and socio-economic contexts, thereby moving beyond the largely descriptive, single-domain coverage of prior forest technology and forest governance reviews to an explicitly cross-domain, comparative synthesis. The review further explores the contributions of community-based forest management, climate-smart forestry, carbon management strategies, ecosystem services, green infrastructure, and innovative forest-derived products toward achieving long-term environmental sustainability. The analysis demonstrates that technological innovations alone cannot ensure sustainable forest management unless supported by effective governance, equitable community participation, adequate financial investment, and adaptive policy frameworks. The review identifies critical knowledge gaps related to technology adoption, institutional capacity, climate resilience, and long-term monitoring, and derives four specific, evidence-anchored integrated pathways that combine ecological, technological, economic, and governance innovations. This review provides a comprehensive, deliberately broad-scope synthesis of contemporary forest innovation and offers practical recommendations for researchers, policymakers, and forest managers working toward resilient forest ecosystems and the achievement of the United Nations Sustainable Development Goals, particularly SDG 15 (Life on Land).","森林在气候调节、生物多样性保护、生态系统服务供给以及可持续社会经济发展中发挥着至关重要的作用。然而，日益加剧的森林砍伐、气候变化、栖息地破碎化以及不可持续的资源开发持续威胁着全球森林生态系统。本综述旨在明确特定森林创新在何种生态、经济与治理条件下能够转化为可衡量的可持续性成果，并将这些条件综合为适用于不同区域背景的连贯且基于证据的路径。本综述以社会-生态系统（SES）和韧性为理论框架，通过将可持续森林管理实践与新兴技术发展相结合，批判性地审视了森林创新的最新进展，这些技术包括精准林业、遥感、地理信息系统（GIS）、激光雷达（LiDAR）、无人机（UAV）、生物技术、人工智能（AI）、数字林业、生物质利用以及基于森林的生物经济方法。与传统叙述性综述不同，本研究采用统一的五标准评价框架，包括生态有效性、技术成熟度、经济可行性、治理要求和区域适用性，对上述创新在不同生态和社会经济背景下的优势、局限及实施挑战进行比较评估，从而超越以往森林技术和森林治理综述 largely 描述性、单一领域的覆盖范围，实现明确的跨领域比较综合。本综述进一步探讨了社区森林管理、气候智慧型林业、碳管理策略、生态系统服务、绿色基础设施以及创新性林产品对实现长期环境可持续性的贡献。分析表明，除非得到有效治理、公平的社区参与、充足的财政投资和适应性政策框架的支持，否则仅靠技术创新无法确保可持续森林管理。本综述识别了与技术采用、机构能力、气候韧性和长期监测相关的关键知识空白，并推导出四条具体的、以证据为锚点的综合路径，这些路径融合了生态、技术、经济和治理创新。Th","Next Sustainability",77,{"impact":337,"substance":399,"depth":337,"authority":106,"freshness":351,"relevant":22,"comment":400},21,"该综述系统整合精准林业、遥感、AI等技术与治理条件，提出四条证据支撑路径，对智慧林业与数字乡村建设有较高参考价值，但属综述类论文、时效性一般，适合进入主题聚合而非头条精选。",[402],{"name":396,"url":393},[404,28,405,406,407,408],"人工智能","遥感监测","森林碳汇","数字林业","可持续经营",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":411},{"candidates":412,"agricultural_evidence":14,"reason":68},[413,421,426,433,437,443],{"code":176,"score":21,"supported":42,"title":414,"abstract":415,"tags":420},[],[416,417,418,419],"生物多样性","生态系统服务","biodiversity","ecosystem service",[],{"code":57,"score":21,"supported":42,"title":422,"abstract":423,"tags":425},[],[140,141,424],"climate resilience",[],{"code":265,"score":21,"supported":42,"title":427,"abstract":428,"tags":432},[],[429,430,431],"地理信息","GIS","geographic information",[],{"code":49,"score":351,"supported":42,"title":434,"abstract":435,"tags":436},[],[53,54],[405],{"code":275,"score":438,"supported":42,"title":439,"abstract":440,"tags":442},4,[],[441],"artificial intelligence",[404],{"code":64,"score":22,"supported":14,"title":444,"abstract":445,"tags":446},[],[],[406],[448,449],"森林创新 遥感 LiDAR 无人机","数字林业 森林碳汇 可持续经营","森林创新遥感LiDAR无人机-4495","10.1016\u002Fj.nxsust.2026.100546",{"doi":451,"openalex_id":453,"authors":454,"venue":396,"cited_by_count":84,"oa_url":461,"card":462,"direction":213,"ingested_from":92},"W7220854619",[455,458],{"name":456,"orcid":457},"Shivani Garg","https:\u002F\u002Forcid.org\u002F0000-0001-7879-7177",{"name":459,"orcid":460},"Pooja Arora","https:\u002F\u002Forcid.org\u002F0000-0002-4860-4714","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2949823626003016\u002Fpdf",{"tldr":463,"method":464,"finding":465,"direction":90,"opportunity":466},"综述森林创新技术，用五标准框架比较其可持续性成效与实施条件。","社会生态系统与韧性视角，五标准评估框架，综合遥感、AI、数字林业等。","技术创新需治理、社区参与、投资和适应性政策配合才能实现可持续森林管理。","可研究遥感\u002FAI在森林碳汇监测中的技术采纳障碍与制度适配路径。","2026-10-09T23:30:10.090218Z",{"id":469,"title":470,"url":471,"summary":472,"summary_zh":473,"content":9,"source_name":474,"source_url":471,"published_at":475,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":476,"score_detail":477,"sources":479,"tags":481,"primary_topic":9,"secondary_topics":486,"classification":487,"search_phrases":513,"slug":516,"view_count":84,"doi":517,"paper":518,"created_at":532},4348,"Soil and Water Conservation Mechanism in Oil Palm Plantations: A Review of Best Management Practices and Decision Support Strategies","https:\u002F\u002Fdoi.org\u002F10.37394\u002F232015.2026.22.93","Soil and water conservation on oil palm plantation is increasingly considered as a glimmer of hope for the future sustainability of land functionality and environment. Appropriate management practices and informed decision-making frameworks thus have a strong influence on conservation outcomes. This study aims to systematically identify, categorize, synthesize and quantitatively aggregate peer-reviewed evidence regarding soil and water conservation mechanisms in oil palm plantations, focusing on best management practice strategies and decision support. This study uses Systematic Litigation Review (SLR) the Scopus database is used as a reference in searching scientific publication. A keyword-based structured search was used to identify peer-reviewed articles published from 2020 to 2025 and subsequent screening in sequence based on relevance and language. Thirty-eight articles met all the selection criteria for analysis. Then we extracted data and analyzed it using thematic synthesis with descriptive quantitative aggregation of reported metrics (soil loss, SOC change, runoff reduction, or yield response). The results show six main interrelated themes: control of soil erosion; soil fertility and organic matter management; conservation of water and hydrological regulation; integrated land use and landscape management; agronomic best methods practices; and support for decision-making processes. Quantitative synthesis show that cover-based and structural BMPs decrease soil loss by 50–80%, organic amendments increase SOC by 10–35% and integrated water management decreases runoff by 20–60% across regions reported.","油棕种植园的水土保持日益被视为实现土地功能与环境未来可持续性的一线希望。因此，适当的管理实践和基于信息的决策框架对保护成效具有重要影响。本研究旨在系统识别、分类、综合并定量汇总关于油棕种植园水土保持机制的同行评审证据，重点关注最佳管理实践策略与决策支持。本研究采用系统性文献综述（SLR），以Scopus数据库作为科学出版物检索的参考依据。通过基于关键词的结构化检索，识别2020年至2025年发表的同行评审文章，并依据相关性和语言依次进行筛选。共有38篇文章符合所有分析筛选标准。随后，我们提取数据并采用主题综合法进行分析，同时对所报告指标（土壤流失量、土壤有机碳变化、径流减少量或产量响应）进行描述性定量汇总。结果显示六个主要且相互关联的主题：土壤侵蚀控制；土壤肥力与有机质管理；水资源保护与水文调节；综合土地利用与景观管理；农艺最佳方法实践；以及决策过程支持。定量综合表明，在所报告的各区域中，基于覆盖和工程性最佳管理实践（BMPs）使土壤流失减少50%–80%，有机改良剂使土壤有机碳（SOC）增加10%–35%，综合水资源管理使径流减少20%–60%。","WSEAS TRANSACTIONS ON ENVIRONMENT AND DEVELOPMENT","2026-10-05T00:00:00Z",63,{"impact":17,"substance":18,"depth":225,"authority":20,"freshness":50,"relevant":22,"comment":478},"系统综述量化了油棕园水土保持措施效果，对热带农业可持续管理有参考价值，但属细分领域研究，公共影响有限。",[480],{"name":474,"url":471},[482,27,483,484,485],"保护性耕作","决策支持","水土保持","油棕种植",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":488},{"candidates":489,"agricultural_evidence":14,"reason":68},[490,494,499,503,509],{"code":40,"score":41,"supported":42,"title":491,"abstract":492,"tags":493},[44],[46,44],[27],{"code":275,"score":20,"supported":42,"title":495,"abstract":497,"tags":498},[496],"decision support",[496],[483],{"code":176,"score":438,"supported":42,"title":500,"abstract":501,"tags":502},[],[484],[484],{"code":504,"score":58,"supported":42,"title":505,"abstract":506,"tags":508},"H3",[],[507],"水资源",[],{"code":259,"score":22,"supported":14,"title":510,"abstract":511,"tags":512},[],[],[482],[514,515],"油棕种植园 水土保持","油棕 最佳管理实践","油棕种植园水土保持-4348","10.37394\u002F232015.2026.22.93",{"doi":517,"openalex_id":519,"authors":520,"venue":474,"cited_by_count":84,"oa_url":526,"card":527,"direction":213,"ingested_from":92},"W7220407703",[521,524],{"name":522,"orcid":523},"Loso Judijanto","https:\u002F\u002Forcid.org\u002F0009-0007-7766-0647",{"name":525,"orcid":9},"Sri Nurhayati","https:\u002F\u002Fwseas.com\u002Fjournals\u002Fead\u002F2026\u002Fb885115-041(2026).pdf",{"tldr":528,"method":529,"finding":530,"direction":211,"opportunity":531},"系统综述油棕园水土保持机制，归纳最佳管理实践与决策支持策略。","系统文献综述，检索Scopus 2020-2025年38篇文献，主题综合与定量汇","覆盖与工程措施减蚀50-80%，有机改良增碳10-35%，综合水管理减径流20-60%。","可构建面向油棕园的水土保持决策支持系统，融合多源数据与模型，填补热带种植园智能管理空白。","2026-10-07T23:30:11.425576Z",{"id":534,"title":535,"url":536,"summary":537,"summary_zh":9,"content":9,"source_name":538,"source_url":9,"published_at":539,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":540,"score_detail":541,"sources":544,"tags":546,"primary_topic":552,"secondary_topics":556,"classification":557,"search_phrases":576,"slug":579,"view_count":58,"doi":9,"paper":580,"created_at":589},4196,"中国农业大学学报2026年第10期：人工智能赋能质量农业发展等6篇论文集中刊发","https:\u002F\u002Fcjournal.hep.com.cn\u002F1007-4333\u002FCN\u002F2026\u002F31\u002F10","中国农业大学学报2026年第31卷第10期集中刊发多篇论文：(1)徐雷《人工智能赋能质量农业发展：农产品智能检测与质量保障研究进展》（1-13）；(2)马勇等《基于\"AI+半导体光催化\"的特种鱼养殖水体绿色原位治理与智能调控方案研究》（14-26）；(3)丁晓颖等《基于机器学习与多维度气象特征的湖北省稻瘟病预测研究》（27-42）；(4)刘宁等《低空技术赋能智慧林业的关键经济学问题与展望》（43-58）；(5)王珏等《智能经济对绿色全要素生产率的影响》（59-75）；(6)蔡雪玲《中国农业农村数字化绿色化协同发展的时空格局及其增收效应》（76-93）。","中国农业大学学报2026, 31(10)","2026-10-01T00:00:00Z",81,{"impact":337,"substance":338,"depth":542,"authority":133,"freshness":17,"relevant":22,"comment":543},19,"核心期刊集中刊发6篇AI赋能农业论文，方法新颖、信息增量足，但属学术成果汇编，产业级影响有限。",[545],{"name":538,"url":536},[547,548,549,28,550,551],"智慧农业","农业人工智能","农业数字化","绿色低碳","稻瘟病预测",{"code":275,"slug":553,"name":554,"path":555,"parent_code":239},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[],{"status":129,"locked":14,"version":35,"method":36,"evidence":558},{"candidates":559,"agricultural_evidence":42},[560,564,568,572],{"code":275,"score":438,"supported":42,"title":561,"abstract":562,"tags":563},[],[358],[548],{"code":182,"score":73,"supported":14,"title":565,"abstract":566,"tags":567},[],[],[549,547],{"code":64,"score":22,"supported":14,"title":569,"abstract":570,"tags":571},[],[],[550],{"code":239,"score":22,"supported":14,"title":573,"abstract":574,"tags":575},[],[],[549],[577,578],"中国农业大学学报 人工智能 质量农业","机器学习 稻瘟病预测 湖北","中国农业大学学报人工智能质量农业-4196",{"doi":9,"openalex_id":9,"authors":581,"venue":9,"cited_by_count":84,"oa_url":9,"card":582,"direction":586,"ingested_from":588},[],{"tldr":583,"method":584,"finding":585,"direction":586,"opportunity":587},"中国农业大学学报2026年第10期集中刊发6篇论文，主题涵盖人工智能赋能质量农业、智慧林业、绿色发展","论文合集，涉及AI检测、机器学习、半导体光催化、低空技术、智能经济与数字化绿色化","AI、低空技术、机器学习等正赋能农业质量检测、病害预测、水体治理及林业经济，并影响绿色全要素生产率与","农业人工智能与决策模型","可探索AI与半导体光催化、低空技术在种养业中的集成应用，以及数字化绿色化协同的农户增收机制。","agent","2026-10-05T00:04:56.013263Z"]