[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3554":3,"related-3554":122},{"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":44,"search_phrases":94,"slug":97,"view_count":98,"doi":99,"paper":100,"created_at":121},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。",null,"Journal of Geography Environment and Earth Science International","2026-09-25T00:00:00Z","论文",10,false,67,{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":20,"relevant":21,"comment":22},12,18,16,9,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"机器学习","遥感","碳汇","碳信用","数字MRV",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[38],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"H4","carbon","农业碳与绿色低碳","\u002Ftopic\u002Fenvironment\u002Fcarbon","H",{"status":45,"locked":14,"version":46,"method":47,"evidence":48},"classified","2026-10-22.2","rules",{"candidates":49,"agricultural_evidence":51},[50,58,65,72,80,87],{"code":32,"score":18,"supported":51,"title":52,"abstract":54,"tags":57},true,[53],"remote sensing",[27,53,55,56],"multispectral","hyperspectral",[27],{"code":39,"score":59,"supported":51,"title":60,"abstract":62,"tags":64},15,[61],"carbon sequestration",[63,61],"农业碳",[28],{"code":66,"score":67,"supported":51,"title":68,"abstract":69,"tags":71},"I2",7,[],[26,70],"artificial intelligence",[26],{"code":73,"score":74,"supported":51,"title":75,"abstract":76,"tags":79},"H2",6,[],[77,78],"土壤","soil",[],{"code":81,"score":74,"supported":51,"title":82,"abstract":83,"tags":86},"I4",[],[84,85],"地理信息","geographic information",[],{"code":88,"score":89,"supported":51,"title":90,"abstract":91,"tags":93},"H7",3,[],[92],"气候变化",[],[95,96],"遥感 碳汇 估算","碳信用 评估 遥感","遥感碳汇估算-3554",4,"10.9734\u002Fjgeesi\u002F2026\u002Fv30i91125",{"doi":99,"openalex_id":101,"authors":102,"venue":10,"cited_by_count":112,"oa_url":6,"card":113,"direction":119,"ingested_from":120},"W7214392294",[103,105,108,110],{"name":104,"orcid":9},"Aishwarya Desai",{"name":106,"orcid":107},"Himalaya Ganachari","https:\u002F\u002Forcid.org\u002F0000-0002-6157-9242",{"name":109,"orcid":9},"Sangita Shinde",{"name":111,"orcid":9},"Sachinkumar Nandgude",0,{"tldr":114,"method":115,"finding":116,"direction":117,"opportunity":118},"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。","农业绿色发展与碳","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。","农业遥感与作物表型","openalex","2026-09-26T23:30:30.002197Z",{"total":74,"page":21,"page_size":74,"items":123},[124,216,292,377,485,549],{"id":125,"title":126,"url":127,"summary":128,"summary_zh":129,"content":9,"source_name":130,"source_url":127,"published_at":131,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":132,"score_detail":133,"sources":138,"tags":140,"primary_topic":144,"secondary_topics":147,"classification":153,"search_phrases":170,"slug":173,"view_count":112,"doi":174,"paper":175,"created_at":215},5178,"Spatial Modeling of Topsoil Total Carbon and Texture from Spectral Characteristics of Bare Soil Surface Images of Landsat 5\u002F8 Using Machine Learning","https:\u002F\u002Fdoi.org\u002F10.1134\u002Fs1064229326601228","— This study examines the possibility for detecting bare soil surface (BSS), using remote sensing data and for applying this approach to model texture and total soil carbon in the topsoil. The study area comprises agricultural fields within the North Caucasus Federal Agricultural Research Center in the northeastern spurs of the Stavropol Upland. Landsat 5\u002F8 imagery and spectral indices (NDVI, NBR2, and BSI) were used to classify BSS. Optimal discrimination of BSS from green and non-photosynthetic vegetation was achieved, using a BSI threshold of >0.18 (F1-score = 0.98). Spatial modeling of topsoil properties was conducted by comparing five regression models (Linear Regression, K-Nearest Neighbors, Random Forest, Extreme Gradient Boosting, and Cubist). A linear regression model demonstrated the highest performance; key predictors included the mean and standard deviation of SWIR2. The model exhibits strong predictive capability for sand, silt, clay, and total carbon (adjusted R 2 = 0.82, 0.75, 0.78, and 0.74, respectively). The topsoil is characterized by the range of clay content from 5 to 30%, of silt content from 15 to 60%, of sand content from 20 to 80%, and of total carbon from 0.5 to 3%. Predictive maps of texture and total carbon derived from BSS reveal considerable local heterogeneity, with soils consisting of Chernozems and Phaeozems, influenced by parent rock variability. The modeling results highlight the high predictive power of the BSS spectral characteristics, even when used as a single predictor for digital soil mapping.","本研究探讨了利用遥感数据检测裸露土壤表面（BSS）的可能性，并将该方法应用于表层土壤质地和总碳的建模。研究区包括位于斯塔夫罗波尔高地东北支脉的北高加索联邦农业研究中心内的农田。利用Landsat 5\u002F8影像和光谱指数（NDVI、NBR2和BSI）对BSS进行分类。当BSI阈值>0.18时，实现了BSS与绿色植被及非光合植被的最优区分（F1分数=0.98）。通过比较五种回归模型（线性回归、K近邻、随机森林、极端梯度提升和Cubist）对表层土壤属性进行了空间建模。线性回归模型表现出最高的性能；关键预测变量包括SWIR2的均值和标准差。该模型对砂粒、粉粒、黏粒和总碳表现出较强的预测能力（调整R²分别为0.82、0.75、0.78和0.74）。表层土壤的黏粒含量范围为5%至30%，粉粒含量为15%至60%，砂粒含量为20%至80%，总碳含量为0.5%至3%。基于BSS生成的质地和总碳预测图揭示了显著的局部异质性，土壤由黑钙土和暗色始成土组成，受母岩变异性的影响。建模结果凸显了BSS光谱特征的高预测能力，即使仅作为数字土壤制图的单一预测变量使用亦然。","Eurasian Soil Science","2026-10-15T00:00:00Z",69,{"impact":17,"substance":134,"depth":19,"authority":135,"freshness":136,"relevant":21,"comment":137},20,13,8,"方法新颖、结论可靠，但属区域尺度研究，公共影响有限，适合作为技术类精选。",[139],{"name":130,"url":127},[26,27,141,142,143],"土壤碳","Landsat","数字土壤制图",{"code":73,"slug":78,"name":145,"path":146,"parent_code":43},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",[148,152],{"code":66,"slug":149,"name":150,"path":151,"parent_code":36},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":14,"version":46,"method":47,"evidence":154},{"candidates":155,"agricultural_evidence":51},[156,161,166],{"code":73,"score":157,"supported":51,"title":158,"abstract":159,"tags":160},14,[78],[77,78],[],{"code":66,"score":20,"supported":51,"title":162,"abstract":164,"tags":165},[163],"machine learning",[],[26],{"code":32,"score":136,"supported":51,"title":167,"abstract":168,"tags":169},[],[27,53],[142,27],[171,172],"Landsat 裸土 土壤碳","北高加索 土壤质地 遥感","Landsat裸土土壤碳-5178","10.1134\u002Fs1064229326601228",{"doi":174,"openalex_id":176,"authors":177,"venue":130,"cited_by_count":112,"oa_url":9,"card":210,"direction":119,"ingested_from":120},"W7222107492",[178,181,184,187,190,193,196,199,201,204,207],{"name":179,"orcid":180},"P. A. Braulov","https:\u002F\u002Forcid.org\u002F0000-0001-9919-7402",{"name":182,"orcid":183},"Pavel M. Shilov","https:\u002F\u002Forcid.org\u002F0000-0003-4113-6396",{"name":185,"orcid":186},"R. G. Gadzhiumarov","https:\u002F\u002Forcid.org\u002F0000-0002-4220-623X",{"name":188,"orcid":189},"V. K. Dridiger","https:\u002F\u002Forcid.org\u002F0000-0002-0510-2220",{"name":191,"orcid":192},"E. V. Bogdan","https:\u002F\u002Forcid.org\u002F0009-0006-4781-5786",{"name":194,"orcid":195},"V. O. Megmerova","https:\u002F\u002Forcid.org\u002F0000-0001-8655-6437",{"name":197,"orcid":198},"Petr Tsymbarovich","https:\u002F\u002Forcid.org\u002F0000-0001-8591-2612",{"name":200,"orcid":9},"A. E. Shatunov",{"name":202,"orcid":203},"Pavel P. Fil","https:\u002F\u002Forcid.org\u002F0000-0002-9851-5381",{"name":205,"orcid":206},"D. E. Mitichkin","https:\u002F\u002Forcid.org\u002F0009-0006-2397-060X",{"name":208,"orcid":209},"Dmitriy S. Fomin","https:\u002F\u002Forcid.org\u002F0000-0003-3733-0284",{"tldr":211,"method":212,"finding":213,"direction":119,"opportunity":214},"用Landsat 5\u002F8裸土光谱指数识别裸土并机器学习建模表层土壤质地与全碳。","Landsat 5\u002F8影像、NDVI\u002FNBR2\u002FBSI指数、五种回归模型对比。","BSI阈值0.18识别裸土F1达0.98，线性回归最优，SWIR2均值与标准差为关键预测因子。","可探索多时相裸土合成与深度学习模型，提升数字土壤制图精度与跨区域迁移能力。","2026-10-17T23:30:31.485949Z",{"id":217,"title":218,"url":219,"summary":220,"summary_zh":221,"content":9,"source_name":222,"source_url":219,"published_at":223,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":224,"score_detail":225,"sources":228,"tags":230,"primary_topic":234,"secondary_topics":235,"classification":238,"search_phrases":266,"slug":269,"view_count":270,"doi":271,"paper":272,"created_at":291},3916,"Hyperspectral inversion of soil total nitrogen content using spectral and machine learning","https:\u002F\u002Fdoi.org\u002F10.3329\u002Fbjb.v55i3.93850","The Agro-pastoral Transitional Ecotone of Northern Shaanxi is an ecologically fragile region. Soil nitrogen is a critical factor influencing crop productivity and regional ecological balance. Rapid and accurate monitoring of farmland soil total nitrogen (TN) content is therefore essential for implementing precision agriculture and ensuring sustainable land use in this area. This study focused on the cultivated soils of Dingbian County, a representative area within this ecotone. Field-collected soil samples underwent TN content determination and laboratory-based hyper spectral reflectance measurement. The original spectral reflectance (R) was first transformed using mathematical methods, including first-derivative (FDR), second-derivative (SDR), and logarithm of the reciprocal's first-derivative (LRFD) analysis, to enhance spectral information and suppress background noise. Subsequently, the successive projections algorithm (SPA) and correlation analysis (CA) were integrated to identify feature wavelengths highly correlated with soil TN from the transformed spectral data. Partial Least Squares Regression (PLSR), Decision Tree (DT), and Random Forest (RF) were developed to estimate soil TN content. Model performance was evaluated and compared using the coefficient of determination (R²), root mean square error (RMSE), and ratio of performance to deviation (RPD). The results indicated that: (1) Spectral derivative transformations effectively highlighted subtle information, with LRFD showing the greatest sensitivity to TN. Models built using LRFD-derived features consistently outperformed those based on original reflectance. (2) Among the machine learning algorithms, the Random Forest (RF) model demonstrated superior predictive performance on the validation set (R² = 0.85, RMSE = 0.11 g\u002Fkg, RPD = 2.15), indicating the highest stability and generalization capability. The PLSR model performed moderately well, while the DT model was prone to over fitting and yielded the lowest accuracy. (3) This study confirms that the integration of hyper spectral technology and machine learning algorithms enables efficient and precise estimation of soil TN content in farmland within the Northern Shaanxi agro-pastoral ecotone, providing a reliable technical framework for soil fertility assessment and precision nutrient management in the region. Bangladesh J. Bot. 55(3): 605-614, 2026 (September)","陕北农牧交错带是生态脆弱区，土壤氮素是影响作物生产力和区域生态平衡的关键因子。快速、准确地监测农田土壤全氮（TN）含量，对于该区实施精准农业和保障土地可持续利用至关重要。本研究以该交错带典型区域定边县的耕作土壤为研究对象，对野外采集的土壤样品进行TN含量测定和实验室高光谱反射率测量。首先采用数学方法对原始光谱反射率（R）进行变换，包括一阶微分（FDR）、二阶微分（SDR）和倒数对数一阶微分（LRFD）分析，以增强光谱信息并抑制背景噪声。随后，将连续投影算法（SPA）与相关分析（CA）相结合，从变换后的光谱数据中筛选出与土壤TN高度相关的特征波长。构建偏最小二乘回归（PLSR）、决策树（DT）和随机森林（RF）模型估算土壤TN含量。采用决定系数（R²）、均方根误差（RMSE）和相对分析误差（RPD）对模型性能进行评价与比较。结果表明：（1）光谱微分变换有效凸显了细微信息，其中LRFD对TN的敏感性最强，基于LRFD衍生特征构建的模型始终优于基于原始反射率的模型。（2）在机器学习算法中，随机森林（RF）模型在验证集上表现出优越的预测性能（R² = 0.85，RMSE = 0.11 g\u002Fkg，RPD = 2.15），具有最高的稳定性和泛化能力。PLSR模型表现中等，而DT模型易于过拟合，精度最低。（3）本研究证实，高光谱技术与机器学习算法相结合能够高效、精确地估算陕北农牧交错带农田土壤TN含量，为该区域土壤肥力评估和精准养分管理提供了可靠的技术框架。Bangladesh J. Bot. 55(3): 605-614, 2026 (September)","Bangladesh Journal of Botany","2026-09-30T00:00:00Z",70,{"impact":17,"substance":134,"depth":226,"authority":135,"freshness":136,"relevant":21,"comment":227},17,"高光谱结合机器学习反演土壤全氮，方法对比扎实、结论可靠，对精准农业养分管理有参考价值，但属区域性田间研究，影响范围有限。",[229],{"name":222,"url":219},[231,232,26,27,233],"智慧农业","精准施肥","土壤氮素",{"code":66,"slug":149,"name":150,"path":151,"parent_code":36},[236,237],{"code":73,"slug":78,"name":145,"path":146,"parent_code":43},{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":14,"version":46,"method":47,"evidence":239},{"candidates":240,"agricultural_evidence":51},[241,245,249,253,260],{"code":66,"score":59,"supported":51,"title":242,"abstract":243,"tags":244},[163],[26,163],[26],{"code":73,"score":157,"supported":51,"title":246,"abstract":247,"tags":248},[78],[77,78],[],{"code":32,"score":20,"supported":51,"title":250,"abstract":251,"tags":252},[56],[],[27],{"code":254,"score":67,"supported":51,"title":255,"abstract":256,"tags":259},"I1",[],[257,258],"精准农业","precision agriculture",[231],{"code":261,"score":98,"supported":51,"title":262,"abstract":263,"tags":265},"F3",[],[264],"养分管理",[232],[267,268],"定边县 土壤全氮 高光谱","陕北农牧交错带 土壤氮素 反演","定边县土壤全氮高光谱-3916",2,"10.3329\u002Fbjb.v55i3.93850",{"doi":271,"openalex_id":273,"authors":274,"venue":222,"cited_by_count":112,"oa_url":219,"card":285,"direction":290,"ingested_from":120},"W7214885868",[275,278,280,282],{"name":276,"orcid":277},"Zhenyu Dong","https:\u002F\u002Forcid.org\u002F0000-0003-0028-3293",{"name":279,"orcid":9},"Ni Wang",{"name":281,"orcid":9},"Jiancang Xie",{"name":283,"orcid":284},"Jungang Luo","https:\u002F\u002Forcid.org\u002F0009-0009-0285-4839",{"tldr":286,"method":287,"finding":288,"direction":119,"opportunity":289},"用高光谱与机器学习反演陕北农牧交错带农田土壤全氮含量。","野外采样测光谱，经FDR\u002FSDR\u002FLRFD变换，SPA+CA选特征波长，建PLS","LRFD变换最敏感，RF模型验证集R²=0.85、RPD=2.15，精度与泛化最优。","可探索多源遥感与深度学习融合，提升生态脆弱区土壤氮素时空反演与制图能力。","农业人工智能与决策模型","2026-10-01T23:30:53.960449Z",{"id":293,"title":294,"url":295,"summary":296,"summary_zh":297,"content":9,"source_name":298,"source_url":295,"published_at":299,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":224,"score_detail":300,"sources":302,"tags":304,"primary_topic":307,"secondary_topics":312,"classification":315,"search_phrases":345,"slug":348,"view_count":21,"doi":349,"paper":350,"created_at":376},3863,"Species identification of crops and weeds in wheat fields using hyperspectral data and machine learning algorithms","https:\u002F\u002Fdoi.org\u002F10.31489\u002F2026feb3\u002F137-151","Based on establishing the relationship between the morphological and anatomical properties of plant tissues and the recognition efficiency of the machine learning algorithm, a comprehensive assessment of the limitations of ML algorithms (using Random Forest as an example) in the multiclass identification of crops and weeds in the agroecosystems of northeastern Kazakhstan was carried out for the first time. The study substantiates the principal biological causes manifested in the optical overlap of classes in the hyperspectral feature space. Data collection was performed with a hyperspectral sensor in the VIS-NIR range. The analysis of spectral profiles was carried out based on variance distribution assessment using principal component analysis and the construction of reflectance curves. Classification of the extracted data was performed using the Random Forest algorithm. Spring wheat (Triticum aestivum) crops at different phenological phases and five dominant weed species (Euphorbia virgata, Descurainia sophia, Vicia hirsuta, Fallopia convolvulus, Echinochloa crus-galli) were selected as research objects. The algorithm demonstrated \"crop-weed\" segregation efficiency with an overall classification accuracy on test data of 72.94%. High identification accuracy was revealed for wheat (MCC=0.994) due to its spectral stability and pronounced red edge features (680-730 nm). Reduced accuracy in intra-class weed separation is driven by species-specific anatomy: thick cuticles in Euphorbia virgata increase reflectance, complex structure in Vicia hirsuta causes spectral dispersion, and mesophyll similarity between Echinochloa crus-galli and grasses leads to optical overlap with crop features. Thus, the model enables reliable crop-weed differentiation, a key requirement for precision agriculture and targeted agrochemical application.","在建立植物组织形态解剖特性与机器学习算法识别效率之间关系的基础上，首次对机器学习算法（以随机森林为例）在哈萨克斯坦东北部农业生态系统中作物与杂草多类识别方面的局限性进行了综合评估。研究证实了在高光谱特征空间中表现为类别光谱重叠的主要生物学成因。数据采集使用VIS-NIR波段的高光谱传感器完成。光谱剖面分析基于主成分分析的方差分布评估及反射率曲线构建进行。提取数据的分类采用随机森林算法完成。选取不同物候期的春小麦（Triticum aestivum）作物及五种优势杂草物种（Euphorbia virgata、Descurainia sophia、Vicia hirsuta、Fallopia convolvulus、Echinochloa crus-galli）作为研究对象。该算法在测试数据上实现了“作物-杂草”分离，总体分类精度为72.94%。小麦因其光谱稳定性及显著的红边特征（680-730 nm）而表现出较高的识别精度（MCC=0.994）。杂草类内分离精度降低由物种特异性解剖结构所致：Euphorbia virgata的厚角质层增加了反射率，Vicia hirsuta的复杂结构导致光谱离散，而Echinochloa crus-galli与禾本科植物之间的叶肉相似性导致其与作物特征发生光谱重叠。因此，该模型能够实现可靠的作物-杂草区分，这是精准农业和靶向农化施用的关键要求。","Fundamental and Experimental Biology","2026-09-29T00:00:00Z",{"impact":17,"substance":134,"depth":226,"authority":17,"freshness":20,"relevant":21,"comment":301},"高光谱结合机器学习实现麦田作物与杂草识别，方法新颖、结论具体，对精准农业有参考价值，但属细分领域研究，影响范围有限。",[303],{"name":298,"url":295},[231,26,305,306,27],"小麦","杂草识别",{"code":308,"slug":309,"name":305,"path":310,"parent_code":311},"A2","wheat","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[313,314],{"code":66,"slug":149,"name":150,"path":151,"parent_code":36},{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":14,"version":46,"method":47,"evidence":316},{"candidates":317,"agricultural_evidence":51},[318,322,326,330,334,340],{"code":308,"score":59,"supported":51,"title":319,"abstract":320,"tags":321},[309],[305,309],[305],{"code":66,"score":59,"supported":51,"title":323,"abstract":324,"tags":325},[163],[26,163],[26],{"code":32,"score":17,"supported":51,"title":327,"abstract":328,"tags":329},[56],[56],[27],{"code":254,"score":67,"supported":51,"title":331,"abstract":332,"tags":333},[],[257,258],[231],{"code":335,"score":89,"supported":51,"title":336,"abstract":337,"tags":339},"H6",[],[338],"农业生态",[],{"code":341,"score":21,"supported":14,"title":342,"abstract":343,"tags":344},"F4",[],[],[306],[346,347],"高光谱 小麦 杂草 识别","随机森林 作物 杂草 分类","高光谱小麦杂草识别-3863","10.31489\u002F2026feb3\u002F137-151",{"doi":349,"openalex_id":351,"authors":352,"venue":298,"cited_by_count":112,"oa_url":9,"card":371,"direction":290,"ingested_from":120},"W7214771122",[353,356,359,362,365,368],{"name":354,"orcid":355},"Rimma Meyramovna Ualiyeva","https:\u002F\u002Forcid.org\u002F0000-0003-3551-5007",{"name":357,"orcid":358},"Mariya M. Kaverina","https:\u002F\u002Forcid.org\u002F0000-0002-3838-7656",{"name":360,"orcid":361},"Александра Викторовна Осипова","https:\u002F\u002Forcid.org\u002F0000-0002-3947-5252",{"name":363,"orcid":364},"E. Koppayev","https:\u002F\u002Forcid.org\u002F0009-0004-0476-9017",{"name":366,"orcid":367},"М. А. Чайзабекова","https:\u002F\u002Forcid.org\u002F0009-0004-0468-956X",{"name":369,"orcid":370},"Sayan Berikovich Zhangazin","https:\u002F\u002Forcid.org\u002F0000-0002-5813-8331",{"tldr":372,"method":373,"finding":374,"direction":119,"opportunity":375},"利用高光谱数据与随机森林识别哈萨克斯坦东北部麦田作物与杂草，并分析误判的生物学原因。","VIS-NIR高光谱传感器采集数据，PCA分析光谱，随机森林分类春小麦与五种杂草","作物-杂草总体分类精度72.94%，小麦识别精度高（MCC=0.994），杂草种间分离受解剖结构影响","可探索融合形态解剖特征与深度学习，提升杂草种间分类精度，服务精准施药。","2026-09-30T23:30:54.952093Z",{"id":378,"title":379,"url":380,"summary":381,"summary_zh":382,"content":9,"source_name":383,"source_url":380,"published_at":384,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":385,"score_detail":386,"sources":389,"tags":391,"primary_topic":394,"secondary_topics":398,"classification":401,"search_phrases":450,"slug":453,"view_count":454,"doi":455,"paper":456,"created_at":484},3494,"Rice Cropping Pattern Mapping Using GEE-Based Sentinel-1\u002F2 Time-Series Imagery and Machine Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18193302","Accurate mapping of rice cropping patterns is fundamental to sustainable agricultural management and regional food security assessment. In this study, we developed a high-precision mapping framework for major rice cropping patterns in the Yangtze River Delta region, China, using the Google Earth Engine (GEE) cloud platform. A multi-temporal and multi-source feature set was constructed by integrating Sentinel-1 radar backscatter (VV\u002FVH polarization), Sentinel-2 optical indices, including the normalized difference vegetation index (NDVI) and land surface water index (LSWI), and topographic factors (DEM and slope), to classify three predominant cropping systems, namely, wheat–rice rotation, double rice, and rapeseed–rice rotation. A total of 548 field-surveyed sample points collected during the 2024–2025 growing season were used for model training and validation. Three classical classifiers—Random Forest (RF), A Gradient Boosting Tree (GBTREE), and a Support Vector Machine (SVM)—were systematically compared. The ablation experiment demonstrated that the fusion of Sentinel-1 and Sentinel-2 outperformed both the Sentinel-1-only and Sentinel-2-only configurations across all three classifiers. Among them, the GBTREE achieved the highest overall accuracy (93.8%), Kappa coefficient (0.87), and macro-average F1 score (88.3%) in this specific experiment. Notably, it also performed best on the more challenging double-rice class. The SHapley Additive exPlanations (SHAP)-based feature importance analysis revealed that multi-temporal NDVI phenological features were the primary drivers of classification accuracy, while radar backscatter and water indices provided essential complementary information, and topographic factors served as spatial constraints at the regional scale. The spatial distribution derived from the GBTREE classification exhibited clear patterns: wheat–rice rotation dominated the northern plains (northern Jiangsu, northern Anhui, and the Hangjiahu Plain); double rice was concentrated in the southern Zhejiang hills and scattered valley plains; and rapeseed–rice rotation showed a scattered, mosaic distribution. Overall, this study demonstrates that integrating multi-source remote sensing data on the GEE platform with the GBTREE classifier enables effective and scalable high-precision mapping of rice cropping patterns in complex agricultural landscapes. This approach provides a reliable technical foundation for regional agricultural structure analysis, crop rotation assessment, and sustainable agricultural monitoring.","准确绘制水稻种植模式图是实现可持续农业管理和区域粮食安全评估的基础。本研究基于Google Earth Engine（GEE）云平台，构建了长江三角洲地区主要水稻种植模式的高精度制图框架。通过整合Sentinel-1雷达后向散射（VV\u002FVH极化）、Sentinel-2光学指数（包括归一化差异植被指数NDVI和地表水体指数LSWI）以及地形因子（DEM和坡度），构建了多时相、多源特征集，用于分类三种主要种植制度，即麦–稻轮作、双季稻和油–稻轮作。利用2024—2025年生长季采集的548个实地调查样点进行模型训练与验证。系统比较了三种经典分类器——随机森林（RF）、梯度提升树（GBTREE）和支持向量机（SVM）。消融实验表明，Sentinel-1与Sentinel-2的融合在三种分类器中均优于仅使用Sentinel-1或仅使用Sentinel-2的配置。其中，GBTREE在本实验中取得了最高的总体精度（93.8%）、Kappa系数（0.87）和宏平均F1分数（88.3%）。值得注意的是，其在更具挑战性的双季稻类别上同样表现最佳。基于SHapley加法解释（SHAP）的特征重要性分析表明，多时相NDVI物候特征是分类精度的主要驱动因素，雷达后向散射和水体指数提供了必要的补充信息，而地形因子则在区域尺度上起到空间约束作用。基于GBTREE分类得到的空间分布呈现出清晰的格局：麦–稻轮作主导北部平原（苏北、皖北和杭嘉湖平原）；双季稻集中于浙南丘陵和零散的河谷平原；油–稻轮作则呈零散镶嵌状分布。总体而言，本研究表明，在GEE平台上整合多源遥感数据与GBTREE分类器，能够对复杂农业景观中的水稻种植模式进行有效且可扩展的高精度制图。该方法为区域农业结构分析和作物","Remote Sensing","2026-09-24T00:00:00Z",79,{"impact":19,"substance":387,"depth":18,"authority":157,"freshness":20,"relevant":21,"comment":388},22,"基于GEE与Sentinel-1\u002F2时序影像结合机器学习实现长三角水稻种植模式高精度制图，方法扎实、结论可靠，对农业遥感监测有参考价值。",[390],{"name":383,"url":380},[231,392,26,27,393],"水稻","作物分类",{"code":395,"slug":396,"name":392,"path":397,"parent_code":311},"A1","rice","\u002Ftopic\u002Ffield-crops\u002Frice",[399,400],{"code":66,"slug":149,"name":150,"path":151,"parent_code":36},{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":14,"version":46,"method":47,"evidence":402},{"candidates":403,"agricultural_evidence":51},[404,408,412,416,423,430,434,440,446],{"code":395,"score":59,"supported":51,"title":405,"abstract":406,"tags":407},[396],[392,396],[392],{"code":66,"score":20,"supported":51,"title":409,"abstract":410,"tags":411},[163],[],[26],{"code":32,"score":136,"supported":51,"title":413,"abstract":414,"tags":415},[],[27,53],[393,27],{"code":417,"score":74,"supported":51,"title":418,"abstract":419,"tags":422},"A7",[],[420,421],"轮作","crop rotation",[],{"code":424,"score":74,"supported":51,"title":425,"abstract":426,"tags":429},"A8",[],[427,428],"粮食安全","food security",[],{"code":308,"score":89,"supported":51,"title":431,"abstract":432,"tags":433},[],[309],[],{"code":435,"score":89,"supported":51,"title":436,"abstract":437,"tags":439},"A4",[],[438],"rapeseed",[],{"code":441,"score":89,"supported":51,"title":442,"abstract":443,"tags":445},"H8",[],[444],"可持续农业",[],{"code":254,"score":21,"supported":14,"title":447,"abstract":448,"tags":449},[],[],[231],[451,452],"长三角 水稻 种植模式 遥感","Sentinel-1 Sentinel-2 水稻制图","长三角水稻种植模式遥感-3494",5,"10.3390\u002Frs18193302",{"doi":455,"openalex_id":457,"authors":458,"venue":383,"cited_by_count":112,"oa_url":380,"card":479,"direction":119,"ingested_from":120},"W7214147197",[459,462,465,468,470,473,476],{"name":460,"orcid":461},"Xuan Li","https:\u002F\u002Forcid.org\u002F0000-0001-5509-2385",{"name":463,"orcid":464},"Lintao Chen","https:\u002F\u002Forcid.org\u002F0009-0000-6558-1289",{"name":466,"orcid":467},"Lin Chen","https:\u002F\u002Forcid.org\u002F0000-0002-9270-1626",{"name":469,"orcid":9},"Chao Su",{"name":471,"orcid":472},"Hoi Leong Lee","https:\u002F\u002Forcid.org\u002F0000-0002-4984-2183",{"name":474,"orcid":475},"Ruci Wang","https:\u002F\u002Forcid.org\u002F0000-0001-7049-7006",{"name":477,"orcid":478},"Xuguang Tang","https:\u002F\u002Forcid.org\u002F0009-0008-3494-8867",{"tldr":480,"method":481,"finding":482,"direction":119,"opportunity":483},"基于GEE融合Sentinel-1\u002F2时序与地形特征，用机器学习高精度制图长三角水稻种植模式。","GEE平台、Sentinel-1\u002F2时序特征、DEM、548个实地样本、RF\u002FG","GBTREE精度最高（总体93.8%、Kappa 0.87），双季稻识别最好；NDVI物候特征贡献最","可探索样本稀缺区迁移学习与多作物轮作模式泛化制图，并耦合产量与碳核算。","2026-09-25T23:30:30.511522Z",{"id":486,"title":487,"url":488,"summary":489,"summary_zh":490,"content":9,"source_name":491,"source_url":488,"published_at":492,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":493,"score_detail":494,"sources":496,"tags":498,"primary_topic":501,"secondary_topics":502,"classification":504,"search_phrases":524,"slug":527,"view_count":98,"doi":528,"paper":529,"created_at":548},3328,"Yield ranking of spring wheat breeding lines absent from model training within two contrasting seasons in northern Kazakhstan","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102588","Reliable assessment of yield models in breeding trials requires evaluation on lines that have not contributed observations to model development. We examined this problem in an advanced spring wheat yield nursery in northern Kazakhstan during the 2024 and 2025 seasons. Of 300 plots, 176 from 22 entries formed the model development set, while 124 plots representing 31 breeding lines were completely excluded from training. Eleven variables were retained from 82 candidates using the training data alone; the candidates were derived from UAV multispectral imagery, ERA5-Land reanalysis, LiDAR-derived static plot microtopography, phenology and maturity group. LightGBM yielded a pooled R² of 0.587 for the excluded lines; resampling breeding lines within season gave a mean R² of 0.564 with a 95 % confidence interval from 0.252 to 0.776. The pooled value partly reflected the strong contrast between seasons rather than discrimination among lines within a season. Random division of plots produced an R² of 0.890, indicating that apparent predictive accuracy depended strongly on validation design. Agreement between observed and predicted line rankings was moderate, with Spearman correlations of 0.612 in 2024 and 0.650 in 2025, although uncertainty was wide because only 14 and 17 independent lines were available. Six modelling approaches produced overlapping uncertainty intervals, with no clear evidence of superiority, and the complete combination of data sources was not separable from simpler configurations retaining the multispectral block. A separate analysis in which screening, selection and fitting used 2024 data alone and evaluation used 2025 data showed a severe loss of performance, with R² = −8.81 and a Spearman correlation of −0.082 that was indistinguishable from zero. Poor transfer persisted among entries grown in both seasons, so genotype novelty alone was insufficient to explain the failure. Breeding lines absent from training could therefore be ranked with moderate consistency when both observed seasonal regimes were represented during model development, whereas prediction in an unobserved season remained unreliable. The study covers one nursery complex and two seasons, so transfer across locations and broader conditions remains to be established.","在育种试验中，要对产量模型进行可靠评估，必须在未参与模型开发的品系上进行评价。我们在2024年和2025年生长季于哈萨克斯坦北部的一个高级春小麦产量圃中考察了这一问题。在300个小区中，来自22个品系的176个小区构成模型开发集，而代表31个育种品系的124个小区则完全排除在训练之外。仅使用训练数据，从82个候选变量中保留了11个变量；这些候选变量来源于无人机多光谱影像、ERA5-Land再分析数据、LiDAR衍生的静态小区微地形、物候和成熟期组。LightGBM对被排除品系给出的合并R²为0.587；在生长季内对育种品系进行重采样得到的平均R²为0.564，95%置信区间为0.252至0.776。合并值部分反映了生长季之间的强烈差异，而非生长季内品系之间的区分能力。对小区进行随机划分得到的R²为0.890，表明表观预测精度在很大程度上取决于验证设计。观测品系排名与预测品系排名之间的一致性为中等，2024年和2025年的Spearman相关系数分别为0.612和0.650，但由于仅有14个和17个独立品系可用，不确定性范围较宽。六种建模方法产生了相互重叠的不确定性区间，没有明确证据表明哪一种更优，并且完整的数据源组合与保留多光谱模块的较简单配置无法区分。另一项分析中，筛选、选择和拟合仅使用2024年数据，而评估使用2025年数据，结果显示性能严重下降，R² = −8.81，Spearman相关系数为−0.082，与零无法区分。在两个生长季均种植的品系之间，较差的迁移性依然存在，因此仅用品系新颖性不足以解释这种失败。因此，当模型开发过程中涵盖了所观测到的两种生长季情形时，未参与训练的育种品系可以以中等一致性进行排名，而在未观测生长季中的预测仍然不可靠。本研究仅涵盖一个圃系复合体和两个生长季，因此跨地点和更广泛条件下的迁移性仍有待确立。","Smart Agricultural Technology","2026-09-22T00:00:00Z",74,{"impact":17,"substance":387,"depth":18,"authority":135,"freshness":20,"relevant":21,"comment":495},"基于无人机多光谱与气象数据的春小麦育种品系产量预测研究，验证设计严谨、结论审慎，对智慧育种与遥感估产有参考价值，但属单点试验、地域性强，未达重大突破层级。",[497],{"name":491,"url":488},[231,499,26,500,27],"产量预测","小麦育种",{"code":308,"slug":309,"name":305,"path":310,"parent_code":311},[503],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":14,"version":46,"method":47,"evidence":505},{"candidates":506,"agricultural_evidence":51},[507,511,516,520],{"code":308,"score":59,"supported":51,"title":508,"abstract":509,"tags":510},[309],[305,309],[500],{"code":32,"score":136,"supported":51,"title":512,"abstract":513,"tags":515},[],[514,55],"无人机多光谱",[499,27],{"code":254,"score":21,"supported":14,"title":517,"abstract":518,"tags":519},[],[],[231],{"code":66,"score":21,"supported":14,"title":521,"abstract":522,"tags":523},[],[],[26],[525,526],"哈萨克斯坦 春小麦 产量预测","UAV 多光谱 育种试验","哈萨克斯坦春小麦产量预测-3328","10.1016\u002Fj.atech.2026.102588",{"doi":528,"openalex_id":530,"authors":531,"venue":491,"cited_by_count":112,"oa_url":488,"card":543,"direction":119,"ingested_from":120},"W7214044328",[532,535,538,541],{"name":533,"orcid":534},"Dastan Yelubayev","https:\u002F\u002Forcid.org\u002F0000-0001-5358-7982",{"name":536,"orcid":537},"TIMUR SAVIN","https:\u002F\u002Forcid.org\u002F0000-0002-3550-647X",{"name":539,"orcid":540},"Ismail Tokbergenov","https:\u002F\u002Forcid.org\u002F0000-0002-0656-9914",{"name":542,"orcid":9},"Bakhtiyar Zhanzakov",{"tldr":544,"method":545,"finding":546,"direction":119,"opportunity":547},"评估春小麦育种品系产量模型在未参与训练品系上的跨季预测能力。","无人机多光谱、ERA5-Land、LiDAR与物候数据，LightGBM等六种模","两季均参与训练时品系排名中等一致，但预测未观测季节完全失效。","需研究跨地点、跨年份可迁移的表型预测模型与验证设计，避免随机划分高估精度。","2026-09-24T23:30:03.201775Z",{"id":550,"title":551,"url":552,"summary":553,"summary_zh":554,"content":9,"source_name":555,"source_url":552,"published_at":556,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":557,"score_detail":558,"sources":560,"tags":562,"primary_topic":564,"secondary_topics":565,"classification":571,"search_phrases":607,"slug":610,"view_count":98,"doi":611,"paper":612,"created_at":632},2659,"Application of Remote Sensing and Machine Learning in Sustainable Agriculture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu18189472","Global agriculture is undergoing a period of profound transformation, driven by the need to increase food production in a context characterized by climate change, the degradation of natural resources and increasing pressure on agricultural ecosystems [...]","全球农业正经历一场深刻变革，其驱动力是在气候变化、自然资源退化以及农业生态系统压力日益增大的背景下提高粮食产量的需求。[...]","Sustainability","2026-09-16T00:00:00Z",77,{"impact":18,"substance":134,"depth":226,"authority":135,"freshness":20,"relevant":21,"comment":559},"发表于核心期刊的遥感与机器学习综述，方法视角新颖、时效性强，对智慧农业技术路线有参考价值，值得进入每日精选。",[561],{"name":555,"url":552},[231,563,26,444,27],"农业人工智能",{"code":66,"slug":149,"name":150,"path":151,"parent_code":36},[566,570],{"code":441,"slug":567,"name":568,"path":569,"parent_code":43},"sustainable-agriculture","可持续与保护性农业","\u002Ftopic\u002Fenvironment\u002Fsustainable-agriculture",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":14,"version":46,"method":47,"evidence":572},{"candidates":573,"agricultural_evidence":51},[574,578,583,587,592,599,603],{"code":66,"score":13,"supported":51,"title":575,"abstract":576,"tags":577},[163],[],[563,26],{"code":441,"score":20,"supported":51,"title":579,"abstract":581,"tags":582},[580],"sustainable 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Badea","https:\u002F\u002Forcid.org\u002F0000-0003-4521-5403",{"name":622,"orcid":623},"Aleksandar Ristić","https:\u002F\u002Forcid.org\u002F0000-0003-0979-3345",{"name":625,"orcid":626},"Paul Sestraș","https:\u002F\u002Forcid.org\u002F0000-0002-8554-0924",{"tldr":628,"method":629,"finding":630,"direction":119,"opportunity":631},"综述遥感与机器学习在可持续农业中的应用现状与前景。","综述遥感数据与机器学习方法在农业中的应用。","遥感结合机器学习可提升农业监测与可持续管理能力。","可探索多源遥感与可解释机器学习融合，用于小农户精准决策与碳核算。","2026-09-16T23:30:28.640661Z"]