[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4517":3,"related-4517":111},{"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":90,"slug":93,"view_count":94,"doi":95,"paper":96,"created_at":110},4517,"Development of an Integrated Geoinformation System for Digital Agrochemical Services in the Aral Sea Region","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23180477","The agricultural lands of the Aral Sea region are affected by soil salinity, water scarcity, nutrient imbalance, low organic matter, and considerable spatial variability of soil properties. These conditions require a modern system for collecting, analyzing, and managing agrochemical information. An integrated geoinformation system can combine field sampling, laboratory analysis, geographic information systems, remote sensing, and digital soil mapping. Such integration provides spatially differentiated information on soil fertility and degradation, supports site-specific fertilizer management, and strengthens monitoring of agroecological conditions. The development of a unified digital agrochemical database creates a technological basis for precision agriculture and sustainable land management in the region.","咸海地区农田受到土壤盐渍化、水资源短缺、养分失衡、有机质含量低以及土壤属性空间变异性显著的影响。这些条件要求建立一套现代化的农化信息采集、分析和管理系统。综合地理信息系统可以整合野外采样、实验室分析、地理信息系统、遥感和数字土壤制图。这种整合能够提供关于土壤肥力和退化的空间分异信息，支持因地制宜的施肥管理，并加强对农业生态状况的监测。统一数字农化数据库的建立，为该地区精准农业和可持续土地管理奠定了技术基础。",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-10-07T00:00:00Z","论文",10,false,70,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,20,17,13,8,1,"面向咸海地区盐渍化农田的集成地理信息与数字农化服务系统研究，方法整合遥感与数字土壤制图，对盐碱地精准施肥与可持续土地管理有参考价值，但属区域性学术成果，公共影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","精准施肥","遥感","盐碱地治理","数字土壤",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":42,"reason":89},[39,48,55,62,68,76,83],{"code":40,"score":41,"supported":42,"title":43,"abstract":44,"tags":47},"F3",7,true,[],[45,46],"施肥","fertilizer",[28],{"code":49,"score":41,"supported":42,"title":50,"abstract":51,"tags":54},"H2",[],[52,53],"土壤","soil",[30],{"code":56,"score":41,"supported":42,"title":57,"abstract":58,"tags":61},"I1",[],[59,60],"精准农业","precision agriculture",[27],{"code":63,"score":41,"supported":42,"title":64,"abstract":65,"tags":67},"I3",[],[29,66],"remote sensing",[29],{"code":69,"score":70,"supported":42,"title":71,"abstract":72,"tags":75},"I4",6,[],[73,74],"地理信息","geographic information",[],{"code":77,"score":78,"supported":42,"title":79,"abstract":80,"tags":82},"H3",3,[],[81],"水资源",[],{"code":84,"score":78,"supported":42,"title":85,"abstract":86,"tags":88},"H6",[],[87],"农业生态",[],"多个产业证据相当，需要确认主归属",[91,92],"咸海地区 数字农业 地理信息系统","咸海 土壤盐渍化 精准施肥","咸海地区数字农业地理信息系统-4517",2,"10.5281\u002Fzenodo.23180477",{"doi":95,"openalex_id":97,"authors":98,"venue":10,"cited_by_count":101,"oa_url":6,"card":102,"direction":108,"ingested_from":109},"W7220967525",[99],{"name":100,"orcid":9},"Aytjanova Juldiz Jusupovna",0,{"tldr":103,"method":104,"finding":105,"direction":106,"opportunity":107},"为咸海地区开发集成地理信息系统，实现数字农化服务与精准施肥管理。","整合野外采样、实验室分析、GIS、遥感和数字土壤制图。","统一数字农化数据库可支持精准农业与可持续土地管理。","智慧农业 \u002F 农业物联网","可探索多源数据融合的实时土壤盐分监测与变量施肥决策模型。","农业遥感与作物表型","openalex","2026-10-09T23:30:28.911938Z",{"total":70,"page":22,"page_size":70,"items":112},[113,207,281,349,412,471],{"id":114,"title":115,"url":116,"summary":117,"summary_zh":118,"content":9,"source_name":119,"source_url":116,"published_at":120,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":121,"score_detail":122,"sources":125,"tags":127,"primary_topic":129,"secondary_topics":134,"classification":143,"search_phrases":192,"slug":195,"view_count":94,"doi":9,"paper":196,"created_at":206},5013,"REMOTE SENSING AND GIS-BASED ASSESSMENT OF SOIL SALINITY IN IRRIGATED LANDS: METHODS, CHALLENGES AND PROSPECTS FOR PRECISION AGRICULTURE","http:\u002F\u002Fwww.arabianjournalofscience.com\u002Findex.php\u002Findex\u002Farticle\u002Fview\u002F1223","Soil salinization is a major land degradation process affecting the sustainability of irrigated agriculture, particularly in arid and semi-arid regions. Conventional soil sampling provides essential information about salinity levels but may be insufficient for continuous monitoring of large agricultural territories because of spatial variability, sampling costs, and temporal limitations. Remote sensing (RS) and geographic information systems (GIS) offer complementary approaches for identifying spatial patterns of salinity, monitoring land conditions, and supporting agricultural management. This article examines the scientific and methodological foundations of integrating multispectral satellite imagery, spectral indices, geospatial analysis, and machine learning for soil salinity assessment in irrigated lands. Particular attention is given to Landsat and Sentinel-2 imagery, the interpretation of vegetation and soil reflectance indicators, the role of electrical conductivity measurements in model calibration, and the limitations associated with vegetation cover, soil moisture, and seasonal variability. The analysis indicates that reliable salinity assessment requires the integration of satellite observations with field measurements and environmental variables rather than the isolated application of spectral indices. The article proposes a methodological framework for developing spatially explicit salinity assessment systems applicable to irrigated agricultural landscapes, including those of Central Asia. The framework provides a basis for improving monitoring strategies and supporting evidence-based land management without assuming unverified local measurement results.","土壤盐渍化是影响灌溉农业可持续性的主要土地退化过程，在干旱和半干旱地区尤为突出。传统土壤采样可提供盐分水平的基本信息，但由于空间变异性、采样成本和时间限制，可能不足以对大面积农业区域进行持续监测。遥感（RS）和地理信息系统（GIS）为识别盐渍化空间格局、监测土地状况和支持农业管理提供了互补性方法。本文探讨了整合多光谱卫星影像、光谱指数、地理空间分析和机器学习进行灌溉土地土壤盐渍化评估的科学与方法学基础。文章特别关注Landsat和Sentinel-2影像、植被与土壤反射率指标的解译、电导率测量在模型校准中的作用，以及与植被覆盖、土壤湿度和季节变异性相关的局限性。分析表明，可靠的盐渍化评估需要将卫星观测与野外测量和环境变量相结合，而非孤立地应用光谱指数。本文提出了一个方法学框架，用于开发适用于灌溉农业景观（包括中亚地区）的空间显式盐渍化评估系统。该框架为改进监测策略和支持基于证据的土地管理提供了基础，且不以未经核实的当地测量结果为假设前提。","Arabian journal of science.","2026-10-13T00:00:00Z",74,{"impact":123,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":124},16,"系统综述遥感与GIS在灌溉农田盐渍化评估中的方法框架与挑战，对盐碱地治理和精准农业有参考价值，但属综述性论文，非突破性成果。",[126],{"name":119,"url":116},[27,59,29,30,128],"土壤监测",{"code":63,"slug":130,"name":131,"path":132,"parent_code":133},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[135,139],{"code":69,"slug":136,"name":137,"path":138,"parent_code":133},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"code":49,"slug":53,"name":140,"path":141,"parent_code":142},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"status":144,"locked":14,"version":35,"method":36,"evidence":145},"classified",{"candidates":146,"agricultural_evidence":42},[147,153,159,164,168,175,180,186],{"code":63,"score":148,"supported":42,"title":149,"abstract":150,"tags":152},18,[66],[29,66,151],"multispectral",[29],{"code":69,"score":19,"supported":42,"title":154,"abstract":156,"tags":158},[155],"GIS",[73,157,155,74],"空间分析",[],{"code":49,"score":160,"supported":42,"title":161,"abstract":162,"tags":163},15,[53],[52,53],[30],{"code":56,"score":13,"supported":42,"title":165,"abstract":166,"tags":167},[60],[],[27,59],{"code":169,"score":70,"supported":42,"title":170,"abstract":171,"tags":174},"I2",[],[172,173],"机器学习","machine learning",[],{"code":77,"score":78,"supported":42,"title":176,"abstract":177,"tags":179},[],[178],"灌溉",[],{"code":181,"score":78,"supported":42,"title":182,"abstract":183,"tags":185},"H7",[],[184],"干旱",[],{"code":187,"score":78,"supported":42,"title":188,"abstract":189,"tags":191},"I7",[],[190],"methodological framework",[],[193,194],"遥感 GIS 土壤盐渍化","Landsat Sentinel-2 盐分监测","遥感GIS土壤盐渍化-5013",{"doi":9,"openalex_id":197,"authors":198,"venue":119,"cited_by_count":101,"oa_url":116,"card":201,"direction":108,"ingested_from":109},"W7221843179",[199],{"name":200,"orcid":9},"Sadullayev Sardor Zokirjon o'g'li",{"tldr":202,"method":203,"finding":204,"direction":108,"opportunity":205},"综述遥感与GIS评估灌溉地土壤盐渍化的方法、挑战与前景，并提出空间显式评估框架。","整合多光谱卫星影像、光谱指数、GIS空间分析与机器学习，结合电导率校准。","可靠盐渍化评估需卫星观测与实地测量及环境变量融合，而非孤立使用光谱指数。","可探索多源遥感与机器学习融合的盐渍化动态监测，并验证于中亚等干旱灌区。","2026-10-15T23:30:32.401626Z",{"id":208,"title":209,"url":210,"summary":211,"summary_zh":212,"content":9,"source_name":213,"source_url":210,"published_at":214,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":215,"sources":217,"tags":219,"primary_topic":221,"secondary_topics":225,"classification":228,"search_phrases":256,"slug":259,"view_count":94,"doi":260,"paper":261,"created_at":280},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",{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":216},"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。",[218],{"name":213,"url":210},[27,28,172,29,220],"土壤氮素",{"code":169,"slug":222,"name":223,"path":224,"parent_code":133},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[226,227],{"code":49,"slug":53,"name":140,"path":141,"parent_code":142},{"code":63,"slug":130,"name":131,"path":132,"parent_code":133},{"status":144,"locked":14,"version":35,"method":36,"evidence":229},{"candidates":230,"agricultural_evidence":42},[231,235,240,246,250],{"code":169,"score":160,"supported":42,"title":232,"abstract":233,"tags":234},[173],[172,173],[172],{"code":49,"score":236,"supported":42,"title":237,"abstract":238,"tags":239},14,[53],[52,53],[],{"code":63,"score":241,"supported":42,"title":242,"abstract":244,"tags":245},9,[243],"hyperspectral",[],[29],{"code":56,"score":41,"supported":42,"title":247,"abstract":248,"tags":249},[],[59,60],[27],{"code":40,"score":251,"supported":42,"title":252,"abstract":253,"tags":255},4,[],[254],"养分管理",[28],[257,258],"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","10.3329\u002Fbjb.v55i3.93850",{"doi":260,"openalex_id":262,"authors":263,"venue":213,"cited_by_count":101,"oa_url":210,"card":274,"direction":279,"ingested_from":109},"W7214885868",[264,267,269,271],{"name":265,"orcid":266},"Zhenyu Dong","https:\u002F\u002Forcid.org\u002F0000-0003-0028-3293",{"name":268,"orcid":9},"Ni Wang",{"name":270,"orcid":9},"Jiancang Xie",{"name":272,"orcid":273},"Jungang Luo","https:\u002F\u002Forcid.org\u002F0009-0009-0285-4839",{"tldr":275,"method":276,"finding":277,"direction":108,"opportunity":278},"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":282,"title":283,"url":284,"summary":285,"summary_zh":286,"content":9,"source_name":287,"source_url":284,"published_at":288,"category":12,"cover_url":9,"hotness":289,"is_selected":14,"score":290,"score_detail":291,"sources":293,"tags":298,"primary_topic":9,"secondary_topics":301,"classification":302,"search_phrases":327,"slug":330,"view_count":78,"doi":331,"paper":332,"created_at":348},3515,"Soil mapping and fertilizer optimization for precision agriculture using artificial intelligence","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs13198-026-03435-1","Soil mapping and fertilizer optimization for precision agriculture using artificial intelligence。International Journal of Systems Assurance Engineering and Management","基于人工智能的精准农业土壤制图与肥料优化。《国际系统保障工程与管理杂志》","International Journal of Systems Assurance Engineering and Management","2026-09-24T00:00:00Z",25,62,{"impact":17,"substance":236,"depth":160,"authority":20,"freshness":21,"relevant":22,"comment":292},"论文探讨AI用于土壤制图与施肥优化，属智慧农业细分方向，但摘要信息有限、影响面偏窄，暂不建议进入每日精选。",[294,295],{"name":287,"url":284},{"name":296,"url":297},"Meteorology","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fmeteorology5040038",[27,299,28,29,300],"农业人工智能","土壤制图",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":303},{"candidates":304,"agricultural_evidence":42,"reason":89},[305,310,314,318,323],{"code":40,"score":160,"supported":42,"title":306,"abstract":307,"tags":309},[46],[308,46],"肥料",[28],{"code":49,"score":160,"supported":42,"title":311,"abstract":312,"tags":313},[53],[52,53],[300],{"code":56,"score":160,"supported":42,"title":315,"abstract":316,"tags":317},[60],[59,60],[27],{"code":169,"score":17,"supported":42,"title":319,"abstract":321,"tags":322},[320],"artificial intelligence",[320],[299],{"code":63,"score":22,"supported":14,"title":324,"abstract":325,"tags":326},[],[],[29],[328,329],"土壤制图 人工智能 精准施肥","精准农业 肥料优化 AI","土壤制图人工智能精准施肥-3515","10.1007\u002Fs13198-026-03435-1",{"doi":331,"openalex_id":333,"authors":334,"venue":287,"cited_by_count":101,"oa_url":297,"card":343,"direction":279,"ingested_from":109},"W7214144821",[335,338,340],{"name":336,"orcid":337},"Neetu Mittal","https:\u002F\u002Forcid.org\u002F0000-0002-2012-0523",{"name":336,"orcid":339},"https:\u002F\u002Forcid.org\u002F0000-0001-6923-0013",{"name":341,"orcid":342},"Pradeepta Kumar Sarangi","https:\u002F\u002Forcid.org\u002F0000-0003-3827-6208",{"tldr":344,"method":345,"finding":346,"direction":279,"opportunity":347},"利用人工智能进行土壤制图和肥料优化，以支持精准农业。","人工智能方法，用于土壤制图与肥料优化。","AI可提升土壤制图与肥料优化的精准性，促进精准农业。","可探索多源数据融合与实时决策模型，提升肥料推荐的自适应性和可解释性。","2026-09-25T23:30:49.869002Z",{"id":350,"title":351,"url":352,"summary":353,"summary_zh":354,"content":9,"source_name":355,"source_url":352,"published_at":356,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":357,"score_detail":358,"sources":360,"tags":362,"primary_topic":365,"secondary_topics":370,"classification":377,"search_phrases":396,"slug":399,"view_count":94,"doi":400,"paper":401,"created_at":411},2332,"Wheat Nitrogen Fertilizer Management Using GreenSeeker Handheld Crop Canopy Sensor","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fijaes.vol.11.no3.2025.pg12.22","One of the essential factors in increasing agricultural yields of cereal crops is increasing the grain yield without increasing production costs. Wheat (Triticum aestivum L.) is the main crop in the food basket of the entire world. Therefore, it is necessary to determine the appropriate nitrogen (N) requirements to obtain the optimal production of wheat crops by investigating the impact of different N levels on the wheat crop yield in the Al-Muthanna region, as well as determining the possibility of predicting grain yield using the GreenSeeker handheld proximal crop canopy sensorbased differences vegetative difference index (NDVI). Thus, there is a need to re-evaluate the previous recommendations using remote sensing techniques. The experimental treatments were five levels of N fertilizer including (0 Kg N ha-1 , 50 kg N ha-1 , 100 kg N ha-1 ,150 kg N ha-1, and 200 kg N ha-1, and each N level was divided into 100%, 70%, 50% percentages, respectively. This study was conducted at the Experiment Station and Agriculture Research of the College of AgricultureAl-Muthanna University. The NDVI measurements were obtained at FK5, FK7, and FK9 according to the Feekes scale growth stage. The results indicated significant differences in grain yield between different levels of N fertilizer and a 70% percentage outperformed on the 100% and 50% treatments for each of the two N levels of 200 and 150 kg ha-1 . The results also show significant differences between NDVI values for different N fertilizer levels. The NDVI readings and wheat yield values increased and followed a similar pattern with increasing N fertilizer levels. This suggests that NDVI can predict wheat grain yield when the NDVI is not saturated. This study showed the potential of using GreenSeeker proximal crop canopy sensor-based NDVI readings as a useful tool to predict wheat grain yield.","提高谷类作物农业产量的关键因素之一是在不增加生产成本的前提下提高谷物产量。小麦（Triticum aestivum L.）是全球粮食结构中的主要作物。因此，有必要通过研究不同氮水平对Al-Muthanna地区小麦作物产量的影响，确定获得小麦作物最佳产量所需的适宜氮（N）需求量，并确定利用基于GreenSeeker手持式近地作物冠层传感器的归一化植被差异指数（NDVI）预测谷物产量的可能性。因此，需要利用遥感技术重新评估以往的推荐方案。试验处理包括五个氮肥水平（0 kg N ha⁻¹、50 kg N ha⁻¹、100 kg N ha⁻¹、150 kg N ha⁻¹和200 kg N ha⁻¹），每个氮水平分别按100%、70%、50%的比例施用。本研究在Al-Muthanna大学农学院实验站与农业研究中心进行。NDVI测定根据Feekes尺度生育阶段在FK5、FK7和FK9时期进行。结果表明，不同氮肥水平间谷物产量存在显著差异，在200和150 kg ha⁻¹两个氮水平下，70%施用量处理均优于100%和50%处理。结果还显示，不同氮肥水平间NDVI值存在显著差异。随着氮肥水平的提高，NDVI读数和小麦产量值均增加并呈现相似的变化趋势。这表明在NDVI未饱和时，NDVI可以预测小麦谷物产量。本研究表明，利用基于GreenSeeker近地作物冠层传感器的NDVI读数作为预测小麦谷物产量的有用工具具有潜力。","INTERNATIONAL JOURNAL OF AGRICULTURE AND EARTH SCIENCE","2026-09-10T00:00:00Z",61,{"impact":17,"substance":148,"depth":160,"authority":13,"freshness":70,"relevant":22,"comment":359},"基于GreenSeeker手持冠层传感器NDVI预测小麦产量并优化氮肥用量，方法实用但属区域性田间试验，产业影响有限。",[361],{"name":355,"url":352},[27,28,363,29,364],"小麦","NDVI",{"code":366,"slug":367,"name":363,"path":368,"parent_code":369},"A2","wheat","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[371,376],{"code":40,"slug":372,"name":373,"path":374,"parent_code":375},"fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",{"code":63,"slug":130,"name":131,"path":132,"parent_code":133},{"status":144,"locked":14,"version":35,"method":36,"evidence":378},{"candidates":379,"agricultural_evidence":42},[380,384,388,392],{"code":366,"score":160,"supported":42,"title":381,"abstract":382,"tags":383},[367],[363,367],[363],{"code":40,"score":17,"supported":42,"title":385,"abstract":386,"tags":387},[46],[46],[28],{"code":63,"score":21,"supported":42,"title":389,"abstract":390,"tags":391},[],[29,66],[364,29],{"code":56,"score":22,"supported":14,"title":393,"abstract":394,"tags":395},[],[],[27],[397,398],"智慧农业 精准施肥 小麦 遥感","智慧农业 精准施肥","智慧农业精准施肥小麦遥感-2332","10.56201\u002Fijaes.vol.11.no3.2025.pg12.22",{"doi":400,"openalex_id":402,"authors":403,"venue":355,"cited_by_count":101,"oa_url":9,"card":406,"direction":108,"ingested_from":109},"W7212289620",[404],{"name":405,"orcid":9},"Mohammed A. Naser",{"tldr":407,"method":408,"finding":409,"direction":108,"opportunity":410},"用GreenSeeker手持冠层传感器NDVI预测小麦产量并优化氮肥管理。","伊拉克田间试验，5个氮水平，Feekes 5\u002F7\u002F9期测NDVI。","NDVI与产量随氮肥增加同步上升，未饱和时可预测产量，70%氮量表现更优。","可探索NDVI饱和条件下的替代植被指数及不同品种\u002F气候区的氮肥推荐模型迁移。","2026-09-13T23:30:26.260534Z",{"id":413,"title":414,"url":415,"summary":416,"summary_zh":417,"content":9,"source_name":10,"source_url":415,"published_at":418,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":121,"score_detail":419,"sources":422,"tags":424,"primary_topic":427,"secondary_topics":428,"classification":429,"search_phrases":455,"slug":458,"view_count":101,"doi":459,"paper":460,"created_at":470},5588,"Certified Temporal Fusion of Artificial Intelligence Climate Analytics for Crop Failure Early Warning Under Sparse Agricultural Sensors: Error Bounds and Famine Relief Targeting","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545489","Sparse agricultural sensor networks across sub-Saharan Africa leave crop-failure early-warning systems with too little ground observation to support reliable famine relief targeting. The framework treats sensor sparsity as a missing-data problem with a known observation operator and propagates climate-analytics uncertainty through a temporal Kalman-Buys filter whose gain is constrained by a certified risk envelope. Using NASA POWER monthly reanalysis for Accra, Ghana (2000-2023, 312 observations), the analysis establishes a mean temperature of 26.521 C and a warming trend of 0.1622 C per decade, which calibrates the climate-analytics prior. The derived bounds show that relief-targeting error scales with the square root of sensor density and with the Lipschitz constant of the crop-response function, yielding an explicit sensor-placement rule for a target misclassification rate. The contribution is a certified, assumption-explicit bridge between climate analytics and famine relief targeting that states the conditions under which early warning is trustworthy.","撒哈拉以南非洲稀疏的农业传感器网络使得作物歉收预警系统缺乏足够的地面观测数据，难以支撑可靠的饥荒救援目标定位。该框架将传感器稀疏性视为具有已知观测算子的缺失数据问题，并通过时间Kalman-Buys滤波器传播气候分析不确定性，该滤波器的增益受经认证的风险包络约束。利用NASA POWER对加纳阿克拉的月度再分析数据（2000—2023年，312个观测值），分析确定了平均气温为26.521°C、每十年0.1622°C的增暖趋势，据此校准了气候分析先验。推导出的界限表明，救援目标定位误差随传感器密度的平方根以及作物响应函数的Lipschitz常数缩放，从而为目标误分类率给出了明确的传感器布设规则。该研究的贡献在于构建了一个经认证的、假设明确的气候分析与饥荒救援目标定位之间的桥梁，阐明了早期预警可信赖的条件。","2026-10-21T00:00:00Z",{"impact":17,"substance":420,"depth":148,"authority":17,"freshness":13,"relevant":22,"comment":421},22,"该研究提出一种带误差界认证的时序融合方法，用于稀疏传感器条件下的作物歉收预警与饥荒救援瞄准，方法新颖、结论明确，但案例局限于加纳阿克拉，产业影响有限，适合作为技术前沿条目收录。",[423],{"name":10,"url":415},[27,299,425,29,426],"粮食安全","气候分析",{"code":169,"slug":222,"name":223,"path":224,"parent_code":133},[],{"status":144,"locked":14,"version":35,"method":36,"evidence":430},{"candidates":431,"agricultural_evidence":42},[432,436,442,447,451],{"code":169,"score":241,"supported":42,"title":433,"abstract":434,"tags":435},[320],[],[299],{"code":437,"score":78,"supported":42,"title":438,"abstract":439,"tags":441},"I5",[],[440],"农业传感",[],{"code":443,"score":22,"supported":14,"title":444,"abstract":445,"tags":446},"A8",[],[],[425],{"code":56,"score":22,"supported":14,"title":448,"abstract":449,"tags":450},[],[],[27],{"code":63,"score":22,"supported":14,"title":452,"abstract":453,"tags":454},[],[],[29],[456,457],"NASA POWER 加纳 气候分析","作物歉收 早期预警 传感器","NASAPOWER加纳气候分析-5588","10.5281\u002Fzenodo.23545489",{"doi":459,"openalex_id":461,"authors":462,"venue":10,"cited_by_count":101,"oa_url":415,"card":465,"direction":279,"ingested_from":109},"W7223111766",[463],{"name":464,"orcid":9},"Fatima-Zahra El Khattabi",{"tldr":466,"method":467,"finding":468,"direction":108,"opportunity":469},"提出带误差界的时间融合框架，在稀疏传感器下用气候分析预警作物歉收。","卡尔曼-布西滤波+NASA POWER再分析数据，风险包络约束增益。","救济瞄准误差随传感器密度平方根和作物响应Lipschitz常数缩放，给出布点规则。","可将该认证误差界框架扩展到多作物多区域，结合卫星遥感弥补地面传感器稀疏。","2026-10-22T11:21:02.682497Z",{"id":472,"title":473,"url":474,"summary":475,"summary_zh":476,"content":9,"source_name":477,"source_url":474,"published_at":418,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":478,"score_detail":479,"sources":481,"tags":483,"primary_topic":486,"secondary_topics":487,"classification":488,"search_phrases":504,"slug":507,"view_count":101,"doi":508,"paper":509,"created_at":528},5571,"Temporal Multi-Scale Spatio-Temporal Network (TMST): Polarization–Temporal Feature Selection and Multi-Scale Spatiotemporal Fusion for Crop Mapping","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16202068","Accurate and timely crop mapping supports agricultural resource management, yield prediction, and food security decision-making. In this study, we propose the Temporal Multi-scale Spatio-Temporal Network (TMST), a crop mapping method that integrates multi-source remote sensing time series through polarization–temporal feature selection and multi-scale spatiotemporal fusion. TMST combines Sentinel-1 dual-polarization synthetic aperture radar (SAR), Sentinel-2 multispectral data, and vegetation indices. Its Temporal Feature Importance Explainer (TFIExplainer) selects informative polarization–time features, the Multi-Scale Attention Aggregation (MSAA) module enhances spatial representations, and the Spatio-Temporal Feature Encoder (STFE) models long-range spatial dependencies and phenological dynamics; a composite loss improves minority-class and difficult-pixel recognition. On the Xinxiang dataset, TMST achieved an overall accuracy (OA) of 0.8625 and a mean F1-score (mF1) of 0.8329. Module and data-source ablations suggested benefits of the combined modules and multi-source fusion, while a cropland mask reduced non-cropland misclassification. In direct cross-dataset evaluation, models trained on a self-constructed Austrian dataset and tested on the radar-extended Panoptic Agricultural Satellite Time Series (PASTIS-R) dataset showed that TMST outperformed the evaluated baselines under the specified source–target setting. Annual mapping of Xinxiang for 2019–2023, using year-specific samples and independently trained models, provided map-based estimates of regional cropping structures and spatial patterns. Under the evaluated settings, TMST provides an effective approach to crop classification and annually updated crop mapping.","准确的作物制图有助于农业资源管理、产量预测和粮食安全决策。本研究提出了一种时间多尺度时空网络（Temporal Multi-scale Spatio-Temporal Network，TMST），这是一种通过极化—时间特征选择和多尺度时空融合来整合多源遥感时间序列的作物制图方法。TMST结合了Sentinel-1双极化合成孔径雷达（SAR）、Sentinel-2多光谱数据和植被指数。其时间特征重要性解释器（Temporal Feature Importance Explainer，TFIExplainer）用于选择信息量丰富的极化—时间特征，多尺度注意力聚合（Multi-Scale Attention Aggregation，MSAA）模块增强空间表征，时空特征编码器（Spatio-Temporal Feature Encoder，STFE）建模长程空间依赖关系和物候动态；复合损失函数改善了少数类和困难像元的识别。在新乡数据集上，TMST取得了0.8625的总体精度（OA）和0.8329的平均F1分数（mF1）。模块和数据源消融实验表明，组合模块和多源融合具有优势，而耕地掩膜减少了非耕地误分类。在直接的跨数据集评估中，在自行构建的奥地利数据集上训练并在雷达扩展的Panoptic农业卫星时间序列（PASTIS-R）数据集上测试的模型表明，在指定的源—目标设置下，TMST优于所评估的基线方法。使用逐年样本和独立训练的模型对新乡2019—2023年进行年度制图，提供了基于制图的区域种植结构和空间格局估计。在所评估的设置下，TMST为作物分类和年度更新的作物制图提供了一种有效方法。","Agronomy",73,{"impact":17,"substance":18,"depth":19,"authority":236,"freshness":13,"relevant":22,"comment":480},"提出多源遥感融合的作物制图新方法，方法新颖、实验充分，但属细分技术进展，公共影响有限。",[482],{"name":477,"url":474},[27,484,29,485],"作物制图","SAR",{"code":63,"slug":130,"name":131,"path":132,"parent_code":133},[],{"status":144,"locked":14,"version":35,"method":36,"evidence":489},{"candidates":490,"agricultural_evidence":42},[491,495,500],{"code":63,"score":13,"supported":42,"title":492,"abstract":493,"tags":494},[],[29,66,151],[29],{"code":443,"score":70,"supported":42,"title":496,"abstract":497,"tags":499},[],[425,498],"food security",[],{"code":56,"score":22,"supported":14,"title":501,"abstract":502,"tags":503},[],[],[27],[505,506],"Sentinel-1 Sentinel-2 作物分类","TMST 多尺度时空网络","Sentinel-1Sentinel-2作物分类-5571","10.3390\u002Fagronomy16202068",{"doi":508,"openalex_id":510,"authors":511,"venue":477,"cited_by_count":101,"oa_url":474,"card":523,"direction":108,"ingested_from":109},"W7223117203",[512,515,517,520],{"name":513,"orcid":514},"Xi Kan","https:\u002F\u002Forcid.org\u002F0000-0002-4629-9357",{"name":516,"orcid":9},"Wentao Lv",{"name":518,"orcid":519},"Lei Gong","https:\u002F\u002Forcid.org\u002F0000-0001-6429-267X",{"name":521,"orcid":522},"Linglong Zhu","https:\u002F\u002Forcid.org\u002F0000-0002-8863-9911",{"tldr":524,"method":525,"finding":526,"direction":108,"opportunity":527},"提出TMST网络，融合SAR与多光谱时序数据实现作物制图。","Sentinel-1\u002F2时序、极化-时序特征选择、多尺度注意力与时空编码器。","新乡OA达0.8625，多源融合与模块组合有效，跨数据集优于基线。","可探索极化-时序特征选择在跨区域迁移与少样本作物制图中的泛化能力。","2026-10-22T11:20:39.359881Z"]