[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4895":3,"related-4895":98},{"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":22,"doi":73,"paper":74,"created_at":97},4895,"Advancing Micro-Ecotone Mapping Using UAV Photogrammetry and GIS","https:\u002F\u002Fdoi.org\u002F10.64229\u002Fzkxf5z57","Ecotones are transitional boundaries between ecosystems and essential to regulating biodiversity and ecological processes. Although ecotones are often wider transition zones like aquatic-terrestrial boundaries, they can happen at fine spatial scales (micro-ecotones) and occur over only a few meters or even centimeters. In the past, ecotones have been examined using one-dimensional transects or coarsely resolved remote sensing data for analysis. However, such methods have been too coarse to detect the fine-scale spatial heterogeneity of micro-ecotones. This work develops a geospatial framework to map fine-scale aquatic-terrestrial micro-ecotones at Jahangirnagar University, Bangladesh, utilizing high-resolution unmanned aerial vehicle (UAV) photogrammetry integrated with geographic information systems. High-resolution RGB imagery was obtained using a DJI Mavic Pro and at a height of 200 ft, producing a 3 cm vertical accuracy. Object-based image classification was applied in ArcGIS Pro using support vector machine classifiers, spectral indices, textural measures, and structural features derived from digital elevation model and canopy models to delineate micro-ecotones. The accuracy assessment confirmed that a high degree of accuracy (96.20%, Kappa coefficient- 0.9584) exists between the orthomosaic and classified image. The data analysis revealed a specific spatial pattern in micro-ecotone distribution, with a significant decrease in micro-ecotone density with increasing elevation. In addition, Spearman correlation analyses clearly revealed a strong negative relationship (–0.971) between elevation and micro-ecotone occurrence, confirming that micro-relief features and water proximity are key factors that regulate micro-ecotone occurrence. The combination of UAV imagery, OBIC, and field verification can be used for micro-ecotone configuration and vegetation distribution.","生态交错带是生态系统之间的过渡边界，对调节生物多样性和生态过程至关重要。尽管生态交错带通常是较宽的过渡区域，如水陆交界带，但它们也可以出现在精细的空间尺度上（微观生态交错带），仅跨越几米甚至几厘米。过去，生态交错带通常采用一维样带或粗分辨率遥感数据进行研究分析。然而，这些方法过于粗糙，难以探测微观生态交错带的精细空间异质性。本研究开发了一个地理空间框架，利用高分辨率无人机（UAV）摄影测量技术并结合地理信息系统，对孟加拉国贾汉吉尔纳加尔大学的水陆微观生态交错带进行精细尺度制图。使用DJI Mavic Pro在200英尺高度获取高分辨率RGB影像，垂直精度达到3厘米。在ArcGIS Pro中应用基于对象的图像分类方法，采用支持向量机分类器，结合光谱指数、纹理特征以及由数字高程模型和冠层模型衍生的结构特征来 delineate 微观生态交错带。精度评估证实，正射影像与分类图像之间具有高度一致性（总体精度96.20%，Kappa系数0.9584）。数据分析揭示了微观生态交错带分布的特定空间格局，即微观生态交错带密度随海拔升高而显著降低。此外，Spearman相关分析清晰地揭示了海拔与微观生态交错带出现之间存在强负相关关系（–0.971），证实微地形特征和水体邻近性是调控微观生态交错带出现的关键因素。无人机影像、基于对象的图像分类与野外验证相结合的方法可用于微观生态交错带构型与植被分布研究。",null,"Journal of Environmental Ecology","2026-10-13T00:00:00Z","论文",10,false,72,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,21,17,13,9,1,"该研究提出无人机摄影测量与GIS结合的微生态交错带精细制图框架，方法新颖、精度高，对农业生态监测有参考价值，但属国际案例、影响范围有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","遥感","无人机遥感","GIS","生态制图",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":14,"reason":68},[39,48,55,63],{"code":40,"score":41,"supported":42,"title":43,"abstract":44,"tags":47},"I4",15,true,[30],[45,46],"地理信息","geographic information",[30],{"code":49,"score":50,"supported":42,"title":51,"abstract":52,"tags":54},"I3",8,[],[28,53],"remote sensing",[29,28],{"code":56,"score":57,"supported":42,"title":58,"abstract":59,"tags":62},"H6",6,[],[60,61],"生物多样性","biodiversity",[],{"code":64,"score":22,"supported":14,"title":65,"abstract":66,"tags":67},"I1",[],[],[27],"农业相关性需要复核",[70,71],"无人机遥感 智慧农业 生态制图 遥感","无人机遥感 智慧农业","无人机遥感智慧农业生态制图遥感-4895","10.64229\u002Fzkxf5z57",{"doi":73,"openalex_id":75,"authors":76,"venue":10,"cited_by_count":88,"oa_url":89,"card":90,"direction":94,"ingested_from":96},"W7221666940",[77,79,81,83,85],{"name":78,"orcid":9},"Bibi Hafsa",{"name":80,"orcid":9},"Sifatul Hoque Siam",{"name":82,"orcid":9},"Mahib Reza",{"name":84,"orcid":9},"Ripa Akter",{"name":86,"orcid":87},"Khandakar Hasan Mahmud","https:\u002F\u002Forcid.org\u002F0000-0003-1222-0791",0,"https:\u002F\u002Fjee.cultechpub.com\u002Findex.php\u002Fjee\u002Farticle\u002Fdownload\u002F60\u002F21",{"tldr":91,"method":92,"finding":93,"direction":94,"opportunity":95},"用无人机摄影测量与GIS绘制高精度水陆微型生态过渡带分布图。","DJI Mavic Pro 200英尺RGB影像，面向对象分类+SVM，结合DE","分类精度96.20%，微型生态过渡带密度随海拔升高显著下降，与海拔呈强负相关（-0.971）。","农业遥感与作物表型","可将该高分辨率微型生态过渡带制图框架迁移至农田-水体交错带，研究其与作物长势及生物多样性的关系。","openalex","2026-10-14T23:30:26.895895Z",{"total":57,"page":22,"page_size":57,"items":99},[100,211,277,342,402,452],{"id":101,"title":102,"url":103,"summary":104,"summary_zh":105,"content":9,"source_name":106,"source_url":103,"published_at":107,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":108,"score_detail":109,"sources":113,"tags":115,"primary_topic":118,"secondary_topics":123,"classification":134,"search_phrases":182,"slug":185,"view_count":186,"doi":187,"paper":188,"created_at":210},3495,"Remote Sensing and GIS in Modern Drought Assessment: Bridging Conventional Methods and Emerging Technologies","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fjgeesi\u002F2026\u002Fv30i91123","Drought is a complex and recurring hydroclimatic hazard that affects agricultural production, water resources, ecosystems and socioeconomic development. Effective drought assessment requires approaches capable of capturing its spatial and temporal variability and its multiple dimensions. This review examines the evolution of drought assessment from conventional drought indices to integrated approaches based on remote sensing and Geographic Information Systems (GIS), with an emphasis on their applications, strengths, limitations and emerging developments. Conventional indices, including the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Palmer Drought Severity Index (PDSI), Reconnaissance Drought Index (RDI) and Percent of Normal Precipitation Index (PNPI), remain widely used because of their established methodologies and long-term applicability. However, their dependence on meteorological observations can limit spatial characterisation and the representation of vegetation, soil moisture and other land-surface responses. Remote sensing provides spatially extensive and repeated observations of vegetation condition, land surface temperature, soil moisture, evapotranspiration and water-related conditions, enabling the development of satellite-derived drought indicators and indices. GIS further facilitates the integration, spatial analysis, visualisation, and mapping of drought-related information from multiple sources. The review also discusses hybrid approaches that combine climate-based indices with satellite-derived indicators, as well as drought monitoring platforms and multi-source assessment frameworks. Despite substantial advances, challenges remain regarding cloud contamination, differences in spatial and temporal resolution, data continuity, ground-based validation and uncertainty associated with multi-source datasets. Emerging machine learning, deep learning and artificial intelligence approaches offer opportunities for integrating heterogeneous datasets and improving drought characterisation and early warning. Overall, the integration of conventional observations, remote sensing, GIS and advanced analytical approaches provides a promising framework for more comprehensive drought monitoring and risk assessment under increasing climate variability and change.","干旱是一种复杂且反复出现的水文气候灾害，影响农业生产、水资源、生态系统和社会经济发展。有效的干旱评估需要能够捕捉其时空变异性和多维特征的方法。本文综述了干旱评估从传统干旱指数到基于遥感与地理信息系统（GIS）的综合方法的演变，重点探讨其应用、优势、局限性和新兴发展。传统指数，包括标准化降水指数（SPI）、标准化降水蒸散指数（SPEI）、帕尔默干旱强度指数（PDSI）、侦察干旱指数（RDI）和降水距平百分率指数（PNPI），因其方法成熟且具有长期适用性而仍被广泛使用。然而，这些指数对气象观测的依赖可能限制其空间表征能力以及对植被、土壤水分和其他陆面响应的刻画。遥感提供了对植被状况、地表温度、土壤水分、蒸散量及与水相关状况的大范围重复观测，使得卫星衍生的干旱指标和指数得以发展。GIS进一步促进了多来源干旱相关信息的整合、空间分析、可视化和制图。本文还讨论了将基于气候的指数与卫星衍生指标相结合的混合方法，以及干旱监测平台和多源评估框架。尽管取得了实质性进展，但在云污染、时空分辨率差异、数据连续性、地面验证以及多源数据集相关的不确定性方面仍存在挑战。新兴的机器学习、深度学习和人工智能方法为整合异质数据集、改进干旱表征和预警提供了机遇。总体而言，在气候变异性和变化日益加剧的背景下，传统观测、遥感、GIS和先进分析方法的整合为更全面的干旱监测和风险评估提供了一个有前景的框架。","Journal of Geography Environment and Earth Science International","2026-09-24T00:00:00Z",66,{"impact":17,"substance":110,"depth":111,"authority":17,"freshness":50,"relevant":22,"comment":112},18,16,"综述系统梳理遥感与GIS在干旱评估中的应用演进，方法学价值明确，但属综述类论文、非国内落地事件，影响力有限。",[114],{"name":106,"url":103},[27,116,28,30,117],"农业人工智能","干旱监测",{"code":40,"slug":119,"name":120,"path":121,"parent_code":122},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis","I",[124,130],{"code":125,"slug":126,"name":127,"path":128,"parent_code":129},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"code":49,"slug":131,"name":132,"path":133,"parent_code":122},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":135,"locked":14,"version":35,"method":36,"evidence":136},"classified",{"candidates":137,"agricultural_evidence":42},[138,144,150,154,164,171,178],{"code":40,"score":110,"supported":42,"title":139,"abstract":140,"tags":143},[30],[45,141,30,142,46],"空间分析","spatial analysis",[30],{"code":125,"score":41,"supported":42,"title":145,"abstract":147,"tags":149},[146],"drought",[148,146],"干旱",[117],{"code":49,"score":41,"supported":42,"title":151,"abstract":152,"tags":153},[53],[28,53],[28],{"code":155,"score":13,"supported":42,"title":156,"abstract":157,"tags":163},"I2",[],[158,159,160,161,162],"深度学习","机器学习","machine learning","deep learning","artificial intelligence",[116],{"code":165,"score":57,"supported":42,"title":166,"abstract":167,"tags":170},"H2",[],[168,169],"土壤","soil",[],{"code":172,"score":57,"supported":42,"title":173,"abstract":174,"tags":177},"H3",[],[175,176],"水资源","evapotranspiration",[],{"code":64,"score":22,"supported":14,"title":179,"abstract":180,"tags":181},[],[],[27],[183,184],"遥感 GIS 干旱评估","卫星遥感 干旱指数","遥感GIS干旱评估-3495",5,"10.9734\u002Fjgeesi\u002F2026\u002Fv30i91123",{"doi":187,"openalex_id":189,"authors":190,"venue":106,"cited_by_count":88,"oa_url":103,"card":205,"direction":94,"ingested_from":96},"W7214156857",[191,193,195,197,199,201,203],{"name":192,"orcid":9},"V. Dhanalakshmi",{"name":194,"orcid":9},"N. Manikandan",{"name":196,"orcid":9},"V. S. Jinsy",{"name":198,"orcid":9},"K. V. Sumesh",{"name":200,"orcid":9},"P. Nideesh",{"name":202,"orcid":9},"P. S. Manju",{"name":204,"orcid":9},"N. Gopika",{"tldr":206,"method":207,"finding":208,"direction":94,"opportunity":209},"综述了从传统干旱指数到遥感、GIS及AI集成的现代干旱评估方法演进。","文献综述，对比SPI、SPEI等传统指数与遥感、GIS及混合方法。","遥感与GIS弥补传统指数空间局限，但云污染、分辨率差异和验证仍是挑战。","可探索多源遥感与机器学习融合的干旱早期预警，重点解决数据不确定性与地面验证。","2026-09-25T23:30:30.576065Z",{"id":212,"title":213,"url":214,"summary":215,"summary_zh":216,"content":9,"source_name":217,"source_url":214,"published_at":218,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":219,"score_detail":220,"sources":223,"tags":225,"primary_topic":228,"secondary_topics":232,"classification":233,"search_phrases":260,"slug":263,"view_count":88,"doi":264,"paper":265,"created_at":276},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。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",74,{"impact":17,"substance":221,"depth":110,"authority":17,"freshness":13,"relevant":22,"comment":222},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。",[224],{"name":217,"url":214},[27,116,226,28,227],"粮食安全","气候分析",{"code":155,"slug":229,"name":230,"path":231,"parent_code":122},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[],{"status":135,"locked":14,"version":35,"method":36,"evidence":234},{"candidates":235,"agricultural_evidence":42},[236,240,247,252,256],{"code":155,"score":21,"supported":42,"title":237,"abstract":238,"tags":239},[162],[],[116],{"code":241,"score":242,"supported":42,"title":243,"abstract":244,"tags":246},"I5",3,[],[245],"农业传感",[],{"code":248,"score":22,"supported":14,"title":249,"abstract":250,"tags":251},"A8",[],[],[226],{"code":64,"score":22,"supported":14,"title":253,"abstract":254,"tags":255},[],[],[27],{"code":49,"score":22,"supported":14,"title":257,"abstract":258,"tags":259},[],[],[28],[261,262],"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":264,"openalex_id":266,"authors":267,"venue":217,"cited_by_count":88,"oa_url":214,"card":270,"direction":275,"ingested_from":96},"W7223111766",[268],{"name":269,"orcid":9},"Fatima-Zahra El Khattabi",{"tldr":271,"method":272,"finding":273,"direction":94,"opportunity":274},"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。","农业人工智能与决策模型","2026-10-22T11:21:02.682497Z",{"id":278,"title":279,"url":280,"summary":281,"summary_zh":282,"content":9,"source_name":283,"source_url":280,"published_at":218,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":284,"score_detail":285,"sources":288,"tags":290,"primary_topic":294,"secondary_topics":295,"classification":296,"search_phrases":322,"slug":325,"view_count":88,"doi":326,"paper":327,"created_at":341},5572,"Potential erosion risk assessment of Kanak stream basin (Sivas, Sarkisla) with the ICONA model","https:\u002F\u002Fdoi.org\u002F10.51753\u002Fflsrt.1828829","The rapid development of technology following the Industrial Revolution has increased human domination over nature. Erosion, which plays a critical role in the process of natural shaping of the earth, can accelerate over time because of improper utilization of the natural environment by humans. The main objective of this study is to determine the erosion susceptibility of the Kanak Creek Basin, one of the sub-basins in the upper reaches of the Kizilirmak River, using the ICONA model, whose main parameters are slope, lithology, land use and vegetation cover, and Geographic Information Systems (GIS) and Remote Sensing techniques, and to list the measures that can be taken against erosion. According to the results of the ICONA model potential erosion level map, 48% of the study area has low erosion susceptibility. These areas largely correspond to the vicinity of Sama Mountain, which has volcanic rocks resistant to erosion, and the base of Sarkisla Plain where the slope is very low. The plain floor is largely covered with agricultural lands. The expansion of irrigated agriculture in this region is one of the main measures that can be taken against erosion. Erosion susceptibility increases around Turna Mountain on the northern border of the basin and Cal Mountain in the south due to both the increase in slope and the lack of resistance of the rocks in the region to erosion, and corresponds to 30% of the study area. In 22% of the basin, susceptibility to erosion is at a medium level. Most of these areas consist of natural meadows and pastures. It is essential to take measures against overgrazing in these areas to prevent the complete loss of the already sparse vegetation cover. Planning land use in the basin in a sustainable manner, arranging fields and layers perpendicular to the slope, expanding irrigated agriculture, and preventing overgrazing are of vital importance in terms of combating soil erosion.","工业革命以来技术的迅猛发展增强了人类对自然的支配能力。侵蚀在地球自然塑造过程中起着关键作用，而人类对自然环境的不当利用会加速侵蚀进程。本研究的主要目标是利用ICONA模型（其主要参数为坡度、岩性、土地利用和植被覆盖）以及地理信息系统（GIS）和遥感技术，确定克孜勒河上游子流域之一卡纳克溪流域的侵蚀敏感性，并列出可采取的侵蚀防治措施。根据ICONA模型潜在侵蚀等级图的结果，研究区48%的区域侵蚀敏感性较低。这些区域主要对应于具有抗侵蚀火山岩的萨马山附近以及坡度极低的萨尔基什拉平原底部。平原底部大部分为农业用地覆盖。在该地区扩大灌溉农业是防治侵蚀的主要措施之一。在流域北部边界的图尔纳山和南部的恰尔山周围，由于坡度增大以及该地区岩石抗侵蚀能力不足，侵蚀敏感性增加，占研究区面积的30%。流域22%的区域侵蚀敏感性为中等水平。这些区域大部分为天然草甸和牧场。在这些地区采取措施防止过度放牧至关重要，以避免本已稀疏的植被覆盖完全丧失。以可持续方式规划流域土地利用、沿垂直于坡向布置田块和土层、扩大灌溉农业以及防止过度放牧，对防治土壤侵蚀具有至关重要的意义。","Frontiers in Life Sciences and Related Technologies",61,{"impact":50,"substance":110,"depth":286,"authority":17,"freshness":21,"relevant":22,"comment":287},14,"该研究以ICONA模型结合GIS与遥感评估土耳其小流域侵蚀风险，方法规范、结论具体，但属区域性案例，公共影响有限，可作为专业参考而非每日精选。",[289],{"name":283,"url":280},[28,291,292,30,293],"土地利用","土壤侵蚀","水土保持",{"code":40,"slug":119,"name":120,"path":121,"parent_code":122},[],{"status":135,"locked":14,"version":35,"method":36,"evidence":297},{"candidates":298,"agricultural_evidence":42},[299,303,308,312,317],{"code":40,"score":13,"supported":42,"title":300,"abstract":301,"tags":302},[],[45,30,46],[30],{"code":49,"score":304,"supported":42,"title":305,"abstract":306,"tags":307},7,[],[28,53],[28],{"code":165,"score":57,"supported":42,"title":309,"abstract":310,"tags":311},[],[168,169],[],{"code":172,"score":242,"supported":42,"title":313,"abstract":314,"tags":316},[],[315],"灌溉",[],{"code":56,"score":318,"supported":14,"title":319,"abstract":320,"tags":321},2,[],[],[291,293],[323,324],"ICONA 模型 侵蚀风险","Kanak 流域 侵蚀","ICONA模型侵蚀风险-5572","10.51753\u002Fflsrt.1828829",{"doi":326,"openalex_id":328,"authors":329,"venue":283,"cited_by_count":88,"oa_url":9,"card":335,"direction":94,"ingested_from":96},"W7223117733",[330,332],{"name":331,"orcid":9},"MERİÇ KURT",{"name":333,"orcid":334},"Celal ŞENOL","https:\u002F\u002Forcid.org\u002F0000-0003-0857-866X",{"tldr":336,"method":337,"finding":338,"direction":339,"opportunity":340},"用ICONA模型评估土耳其Kanak流域潜在侵蚀风险并列出防治措施。","ICONA模型结合GIS与遥感，输入坡度、岩性、土地利用与植被覆盖。","48%区域低侵蚀风险，30%高敏感，22%中等，主因坡度与岩性。","农业绿色发展与碳","可将ICONA与遥感时序数据结合，动态监测流域侵蚀并评估农业管理措施效果。","2026-10-22T11:20:39.475037Z",{"id":343,"title":344,"url":345,"summary":346,"summary_zh":347,"content":9,"source_name":348,"source_url":345,"published_at":218,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":349,"score_detail":350,"sources":353,"tags":355,"primary_topic":358,"secondary_topics":359,"classification":360,"search_phrases":377,"slug":380,"view_count":88,"doi":381,"paper":382,"created_at":401},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":351,"depth":19,"authority":286,"freshness":13,"relevant":22,"comment":352},20,"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。",[354],{"name":348,"url":345},[27,356,28,357],"作物制图","SAR",{"code":49,"slug":131,"name":132,"path":133,"parent_code":122},[],{"status":135,"locked":14,"version":35,"method":36,"evidence":361},{"candidates":362,"agricultural_evidence":42},[363,368,373],{"code":49,"score":13,"supported":42,"title":364,"abstract":365,"tags":367},[],[28,53,366],"multispectral",[28],{"code":248,"score":57,"supported":42,"title":369,"abstract":370,"tags":372},[],[226,371],"food security",[],{"code":64,"score":22,"supported":14,"title":374,"abstract":375,"tags":376},[],[],[27],[378,379],"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":381,"openalex_id":383,"authors":384,"venue":348,"cited_by_count":88,"oa_url":345,"card":396,"direction":94,"ingested_from":96},"W7223117203",[385,388,390,393],{"name":386,"orcid":387},"Xi Kan","https:\u002F\u002Forcid.org\u002F0000-0002-4629-9357",{"name":389,"orcid":9},"Wentao Lv",{"name":391,"orcid":392},"Lei Gong","https:\u002F\u002Forcid.org\u002F0000-0001-6429-267X",{"name":394,"orcid":395},"Linglong Zhu","https:\u002F\u002Forcid.org\u002F0000-0002-8863-9911",{"tldr":397,"method":398,"finding":399,"direction":94,"opportunity":400},"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",{"id":403,"title":404,"url":405,"summary":406,"summary_zh":407,"content":9,"source_name":217,"source_url":405,"published_at":218,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":408,"score_detail":409,"sources":411,"tags":413,"primary_topic":416,"secondary_topics":417,"classification":419,"search_phrases":436,"slug":439,"view_count":88,"doi":440,"paper":441,"created_at":451},5570,"Evaluation of Counterfactual Explanation Robustness for Computer Vision Agriculture Yield-Forecast Models Under Satellite Cloud-Mask Gaps: An Interpretability Reliability Bound for Cooperative Storage Decisions","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545079","Counterfactual explanations are increasingly proposed as the interface through which cooperative storage managers interrogate computer-vision yield-forecast models, yet their reliability under the satellite cloud-mask gaps that routinely afflict agricultural remote sensing has not been bounded. The bound is derived from a Lipschitz continuity assumption on the forecast model and a masking-operator perturbation analysis, and it is expressed as a function of the cloud-mask gap fraction, the model's local Lipschitz constant, and the counterfactual search tolerance. A replication-based evaluation of the before-after-control-impact estimator was conducted to characterise the operating characteristics of the design under a declared variance structure (between-cluster SD = 6.0, residual SD = 4.5, intraclass correlation = 0.64), with power estimated across 2000 design cells. The reliability bound degrades non-linearly as the gap fraction rises, and that the intraclass correlation of 0.64 implies that cooperative-level aggregation of counterfactual recourse is necessary to preserve explanation stability. The contribution is a decision-relevant reliability bound that storage cooperatives can use to set a maximum tolerable cloud-mask gap before counterfactual recourse is suspended.","反事实解释日益被提出作为合作式仓储管理者质询计算机视觉产量预测模型的接口，然而在农业遥感中经常出现的卫星云掩膜缺口条件下，其可靠性尚未得到界定。该界基于预测模型上的Lipschitz连续性假设和掩膜算子扰动分析推导得出，并表示为云掩膜缺口比例、模型局部Lipschitz常数和反事实搜索容差的函数。对前后对照影响估计量进行了基于重复的评估，以表征该设计在给定方差结构（组间标准差=6.0，残差标准差=4.5，组内相关系数=0.64）下的运行特征，并在2000个设计单元中估计了检验效能。可靠性界随缺口比例上升呈非线性退化，且组内相关系数0.64意味着需要对反事实救济进行合作社层面的聚合，以保持解释的稳定性。本文的贡献在于提供了一个与决策相关的可靠性界，仓储合作社可据此设定在暂停反事实救济之前可容忍的最大云掩膜缺口。",67,{"impact":50,"substance":351,"depth":19,"authority":17,"freshness":13,"relevant":22,"comment":410},"该论文提出反事实解释在卫星云掩膜缺口下的可靠性边界，方法新颖且结论可用于合作社决策，但属细分方法学研究，产业影响有限。",[412],{"name":217,"url":405},[27,116,414,28,415],"产量预测","可解释性",{"code":155,"slug":229,"name":230,"path":231,"parent_code":122},[418],{"code":49,"slug":131,"name":132,"path":133,"parent_code":122},{"status":135,"locked":14,"version":35,"method":36,"evidence":420},{"candidates":421,"agricultural_evidence":42},[422,428,432],{"code":155,"score":17,"supported":42,"title":423,"abstract":425,"tags":427},[424],"computer vision",[426],"计算机视觉",[116],{"code":49,"score":50,"supported":42,"title":429,"abstract":430,"tags":431},[],[28,53],[414,28],{"code":64,"score":22,"supported":14,"title":433,"abstract":434,"tags":435},[],[],[27],[437,438],"农业人工智能 产量预测 可解释性 智慧农业","农业人工智能 产量预测","农业人工智能产量预测可解释性智慧农业-5570","10.5281\u002Fzenodo.23545079",{"doi":440,"openalex_id":442,"authors":443,"venue":217,"cited_by_count":88,"oa_url":405,"card":446,"direction":94,"ingested_from":96},"W7223114002",[444],{"name":445,"orcid":9},"Kadiatou Barry",{"tldr":447,"method":448,"finding":449,"direction":94,"opportunity":450},"推导卫星云掩膜缺口下反事实解释可靠性的理论界，并评估其对合作社储粮决策的影响。","基于Lipschitz连续性假设与掩膜扰动分析推导可靠性界，并用2000个设计单","缺口比例上升使可靠性非线性下降；ICC=0.64表明需合作社层级聚合以保持解释稳定。","可研究不同作物区云掩膜统计特性下的自适应解释暂停阈值与聚合策略。","2026-10-22T11:20:39.240791Z",{"id":453,"title":454,"url":455,"summary":456,"summary_zh":457,"content":9,"source_name":458,"source_url":455,"published_at":459,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":460,"score_detail":461,"sources":463,"tags":465,"primary_topic":469,"secondary_topics":475,"classification":477,"search_phrases":502,"slug":505,"view_count":88,"doi":506,"paper":507,"created_at":549},5567,"Capturing Spatial Heterogeneity Using UAV-Derived Multispectral Phenotypes and Random Regression Models Improves Genetic Evaluation in Alfalfa Breeding Trials","https:\u002F\u002Fdoi.org\u002F10.64898\u002F2026.10.14.759249","Spatial heterogeneity is a major source of environmental noise in field trials, reducing genetic evaluation accuracy. Conventional spatial correction methods use field layout and plot proximity but often fail to capture in-season dynamics or leverage temporal data from high-throughput phenotyping. We hypothesized that longitudinal UAV-derived multispectral measurements could characterize field-scale environmental heterogeneity and improve genetic evaluation in breeding trials. Multispectral imagery was collected throughout the growing season across eight alfalfa trials in New York and New Mexico (20222023). Vegetation indices were evaluated for heritability and association with biomass yield using univariate and multi-trait models. Longitudinal measurements were modeled with random regression (RR) to estimate plot-specific permanent environmental effects (PEE), incorporated into genomic mixed models within a two-stage framework. Model performance was compared with conventional autoregressive (AR) and baseline (no correction) models using metrics of consistency and accuracy of estimated genetic effects across replicates. Vegetation indices showed moderate to high heritability, with GNDVI consistently outperforming NDVI and NDRE. Both univariate and multi-trait analyses revealed substantial spatial variability, particularly in New Mexico trials. RR-derived PEE captured field-scale spatial patterns and improved genetic signal recovery relative to baseline and AR models, with the largest gains under strong spatial heterogeneity and comparable performance where variability was limited. The RR framework improved reproducibility of genetic effect estimates across independent field subsets without compromising agreement with full-trial evaluations. These findings show that integrating UAV data via random regression enhances genetic signal recovery and selection accuracy, with broader potential for precision agriculture and biosensor-driven crop management.","空间异质性是田间试验中环境噪声的主要来源，会降低遗传评估的准确性。传统空间校正方法利用田间布局和小区邻近性，但往往无法捕捉生育期内的动态变化，也未能充分利用高通量表型分析产生的时间序列数据。我们假设，基于无人机（UAV）的纵向多光谱测量能够表征田块尺度的环境异质性，并改善育种试验中的遗传评估。在2022—2023年纽约州和新墨西哥州的8个紫花苜蓿试验中，于整个生长季收集了多光谱影像。采用单性状和多性状模型评估了植被指数的遗传力及其与生物量产量的关联。纵向测量数据采用随机回归（RR）建模，以估计小区特异性永久环境效应（PEE），并在两阶段框架中将其纳入基因组混合模型。利用重复间估计遗传效应的一致性和准确性指标，将模型性能与传统的自回归（AR）模型和基线（无校正）模型进行比较。植被指数表现出中到高的遗传力，其中GNDVI始终优于NDVI和NDRE。单性状和多性状分析均揭示了显著的空间变异，尤其在新墨西哥州的试验中更为明显。RR衍生的PEE捕捉到了田块尺度的空间格局，相对于基线模型和AR模型改善了遗传信号的恢复，在强空间异质性条件下增益最大，而在变异有限时性能相当。RR框架提高了遗传效应估计在独立田间子集间的可重复性，同时未损害与全试验评估的一致性。这些结果表明，通过随机回归整合UAV数据可增强遗传信号恢复和选择准确性，在精准农业和生物传感器驱动的作物管理方面具有更广泛的潜力。","bioRxiv (Cold Spring Harbor Laboratory)","2026-10-20T00:00:00Z",79,{"impact":110,"substance":221,"depth":110,"authority":17,"freshness":21,"relevant":22,"comment":462},"该研究利用无人机多光谱表型与随机回归模型校正田间空间异质性，显著提升苜蓿育种遗传评估精度，方法新颖、数据覆盖两州八组试验，对智慧育种有实质参考价值。",[464],{"name":458,"url":455},[27,466,467,28,468],"无人机","育种","紫花苜蓿",{"code":470,"slug":471,"name":472,"path":473,"parent_code":474},"A7","forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping","A",[476],{"code":49,"slug":131,"name":132,"path":133,"parent_code":122},{"status":135,"locked":14,"version":35,"method":36,"evidence":478},{"candidates":479,"agricultural_evidence":42},[480,486,490,496],{"code":470,"score":41,"supported":42,"title":481,"abstract":483,"tags":485},[482],"alfalfa",[484,482],"苜蓿",[468],{"code":49,"score":17,"supported":42,"title":487,"abstract":488,"tags":489},[366],[366],[28],{"code":64,"score":304,"supported":42,"title":491,"abstract":492,"tags":495},[],[493,494],"精准农业","precision agriculture",[27],{"code":497,"score":498,"supported":42,"title":499,"abstract":500,"tags":501},"C4",4,[],[484],[468],[503,504],"无人机 多光谱 苜蓿 育种","随机回归 空间异质性 遗传评估","无人机多光谱苜蓿育种-5567","10.64898\u002F2026.10.14.759249",{"doi":506,"openalex_id":508,"authors":509,"venue":458,"cited_by_count":88,"oa_url":455,"card":544,"direction":94,"ingested_from":96},"W7223083966",[510,513,516,519,522,524,526,529,532,535,538,541],{"name":511,"orcid":512},"Dinesh Ghimire","https:\u002F\u002Forcid.org\u002F0009-0008-2281-1396",{"name":514,"orcid":515},"Ranjita Thapa","https:\u002F\u002Forcid.org\u002F0000-0001-6116-7611",{"name":517,"orcid":518},"Karl H. Kunze","https:\u002F\u002Forcid.org\u002F0000-0003-4548-1808",{"name":520,"orcid":521},"Felipe Sabadin","https:\u002F\u002Forcid.org\u002F0000-0003-2937-1465",{"name":523,"orcid":9},"Julie Hansen",{"name":525,"orcid":9},"Christopher Pierce",{"name":527,"orcid":528},"Liam Wickes-Do","https:\u002F\u002Forcid.org\u002F0009-0003-2927-4590",{"name":530,"orcid":531},"Nicholas Santantonio","https:\u002F\u002Forcid.org\u002F0000-0002-4351-4023",{"name":533,"orcid":534},"Ian M. Ray","https:\u002F\u002Forcid.org\u002F0000-0003-2056-536X",{"name":536,"orcid":537},"Michael A. Gore","https:\u002F\u002Forcid.org\u002F0000-0001-6896-8024",{"name":539,"orcid":540},"Virginia Mae Moore","https:\u002F\u002Forcid.org\u002F0000-0001-7888-3366",{"name":542,"orcid":543},"Kelly R. Robbins","https:\u002F\u002Forcid.org\u002F0000-0001-9522-9585",{"tldr":545,"method":546,"finding":547,"direction":94,"opportunity":548},"用无人机多光谱时序表型和随机回归模型校正田间空间异质性，提升苜蓿育种遗传评估精度。","八项苜蓿试验的无人机多光谱植被指数，随机回归估计小区永久环境效应并嵌入基因组混合","随机回归提取的环境效应能捕捉田块尺度空间格局，在强异质性下显著提升遗传信号恢复与选择准确性。","可将无人机时序表型与随机回归框架推广到其他作物育种试验，并探索多源遥感与基因组选择的联合建模。","2026-10-22T11:20:32.655394Z"]