[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3329":3,"related-3329":56},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"search_phrases":31,"slug":34,"view_count":35,"doi":36,"paper":37,"created_at":55},3329,"Nonlinear threshold effects of waterlogging stress on crop productivity across China: A remote sensing-based macro-assessment","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.104985","Nonlinear threshold effects of waterlogging stress on crop productivity across China: A remote sensing-based macro-assessment。Agricultural Systems",null,"Agricultural Systems","2026-09-23T00:00:00Z","论文",10,false,84,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},22,21,18,14,9,1,"基于遥感开展全国尺度涝渍胁迫非线性阈值评估，方法新颖、结论具宏观参考价值，值得进入每日精选。",[24],{"name":9,"url":6},[26,27,28,29,30],"粮食安全","农业气象","作物产量","遥感监测","涝渍胁迫",[32,33],"全国 涝渍胁迫 作物产量 遥感","作物产量 农业气象 涝渍胁迫 粮食安全","全国涝渍胁迫作物产量遥感-3329",0,"10.1016\u002Fj.agsy.2026.104985",{"doi":36,"openalex_id":38,"authors":39,"venue":9,"cited_by_count":35,"oa_url":8,"card":8,"direction":8,"ingested_from":54},"W7214084384",[40,42,44,46,48,50,52],{"name":41,"orcid":8},"Yi Yang",{"name":43,"orcid":8},"Shuyang Wu",{"name":45,"orcid":8},"Tianqi Ge",{"name":47,"orcid":8},"Weimin Jin",{"name":49,"orcid":8},"Hanqing Wu",{"name":51,"orcid":8},"Bofu Zheng",{"name":53,"orcid":8},"Wei Wan","openalex","2026-09-24T23:30:04.275048Z",{"total":57,"page":21,"page_size":57,"items":58},6,[59,112,159,181,224,272],{"id":60,"title":61,"url":62,"summary":63,"summary_zh":64,"content":8,"source_name":65,"source_url":62,"published_at":66,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":67,"score_detail":68,"sources":71,"tags":73,"search_phrases":77,"slug":80,"view_count":35,"doi":81,"paper":82,"created_at":111},3169,"Climate change and the rising threat of Macrophomina phaseolina: implications for food security, food safety, and sustainable crop health management","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10725-026-01525-5","Abstract Macrophomina phaseolina is a destructive soil-borne necrotrophic fungal pathogen that causes substantial yield losses in a wide range of economically important crops worldwide. Disease severity is strongly enhanced under drought, high temperature, and salinity stress, conditions that are becoming increasingly prevalent under current climate change scenarios. This review examines the interactions between climate-driven abiotic stress, host physiological regulation, and pathogen aggressiveness, highlighting how stress-induced disruptions in hormonal signalling, reactive oxygen species homeostasis, antioxidant defence systems, and plant metabolism collectively increase susceptibility to M. phaseolina . Recent advances in understanding pathogen virulence mechanisms, plant immune responses, and resistance-associated molecular pathways are synthesised together with emerging evidence from transcriptomics, proteomics, metabolomics, and comparative genomics. The review further evaluates current management strategies, including host resistance, biological control, plant growth-promoting microorganisms, stress priming, and integrated disease management, while discussing their limitations under field conditions. Emerging technologies such as precision agriculture, remote sensing, artificial intelligence-assisted disease forecasting, and multi-omics approaches are highlighted as promising tools for improving early diagnosis, risk prediction, and climate-resilient disease management. By integrating advances in plant physiology, molecular biology, and sustainable crop protection, this review provides a comprehensive framework for understanding M. phaseolina pathogenesis under changing environmental conditions. It identifies key research priorities to improve crop resilience and safeguard global food security.","摘要 菜豆壳球孢（Macrophomina phaseolina）是一种具有破坏性的土传死体营养型真菌病原菌，在全球范围内对多种具有重要经济价值的作物造成严重产量损失。在干旱、高温和盐胁迫条件下，病害严重程度显著加剧，而这些条件在当前气候变化情景下正变得越来越普遍。本文综述了气候驱动的非生物胁迫、寄主生理调控与病原菌致病力之间的相互作用，重点阐述了胁迫诱导的激素信号传导紊乱、活性氧稳态失衡、抗氧化防御系统受损以及植物代谢改变如何共同增加对菜豆壳球孢的易感性。本文综合了病原菌毒力机制、植物免疫反应及抗性相关分子通路方面的最新研究进展，并结合转录组学、蛋白质组学、代谢组学和比较基因组学的新兴证据。本文进一步评估了当前的管理策略，包括寄主抗性、生物防治、植物促生微生物、胁迫 priming 和病害综合管理，同时讨论了这些策略在田间条件下的局限性。精准农业、遥感、人工智能辅助病害预测和多组学方法等新兴技术被重点介绍为改善早期诊断、风险预测和气候韧性病害管理的有前景的工具。通过整合植物生理学、分子生物学和可持续作物保护方面的进展，本文为理解变化环境条件下菜豆壳球孢的致病机制提供了综合框架，并确定了提高作物韧性和保障全球粮食安全的关键研究优先方向。","Plant Growth Regulation","2026-09-22T00:00:00Z",80,{"impact":18,"substance":69,"depth":18,"authority":19,"freshness":12,"relevant":21,"comment":70},20,"核心期刊综述，系统梳理气候胁迫下土传病害机制与AI遥感等智慧防控手段，对农业信息化与粮食安全主题有聚合价值。",[72],{"name":65,"url":62},[74,26,75,76,29],"智慧农业","植物病害","气候变化",[78,79],"Macrophomina phaseolina 病害 防控","气候变暖 土传病害 粮食安全","Macrophominaphaseolina病害防控-3169","10.1007\u002Fs10725-026-01525-5",{"doi":81,"openalex_id":83,"authors":84,"venue":65,"cited_by_count":35,"oa_url":62,"card":104,"direction":110,"ingested_from":54},"W7213972258",[85,87,89,92,95,98,101],{"name":86,"orcid":8},"Sindiswa Khawula",{"name":88,"orcid":8},"Siyabonga Ntshalitshali",{"name":90,"orcid":91},"Arun Gokul","https:\u002F\u002Forcid.org\u002F0000-0003-1575-0632",{"name":93,"orcid":94},"Lee‐Ann Niekerk","https:\u002F\u002Forcid.org\u002F0000-0002-9788-3131",{"name":96,"orcid":97},"Ashwil Klein","https:\u002F\u002Forcid.org\u002F0000-0002-5606-886X",{"name":99,"orcid":100},"Marshall Keyster","https:\u002F\u002Forcid.org\u002F0000-0002-8718-736X",{"name":102,"orcid":103},"Mbukeni Nkomo","https:\u002F\u002Forcid.org\u002F0000-0002-7652-1588",{"tldr":105,"method":106,"finding":107,"direction":108,"opportunity":109},"综述气候变化下干旱高温盐胁迫加剧菜豆壳球孢菌病害的机制与可持续防控策略。","整合转录组、蛋白组、代谢组、比较基因组及精准农业、遥感、AI预测等技术。","气候胁迫破坏激素与ROS平衡降低作物抗性，需多组学与智能技术实现早期预警和抗性管理。","农业人工智能与决策模型","可构建融合多组学与气象遥感的AI病害预警模型，并研发胁迫 priming 与生防协同的田间方案。","农业遥感与作物表型","2026-09-22T23:30:22.790623Z",{"id":113,"title":114,"url":115,"summary":116,"summary_zh":117,"content":8,"source_name":118,"source_url":115,"published_at":119,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":120,"score_detail":121,"sources":125,"tags":127,"search_phrases":131,"slug":134,"view_count":35,"doi":135,"paper":136,"created_at":158},3069,"Impact of Urbanization on Agriculture and Wetland Management in Makurdi LGA of Benue State, Nigeria","https:\u002F\u002Fdoi.org\u002F10.65757\u002Fhjev1n22","Wetlands are among the most productive ecosystems, providing essential ecological, hydrological, and socio-economic services, including flood regulation, groundwater recharge, water purification, biodiversity conservation, and agricultural support. However, rapid urbanization has accelerated the conversion of wetlands to built-up areas and agricultural land, threatening their ecological integrity. This study assessed the impact of urbanization on agriculture and wetland management in Makurdi Local Government Area, Benue State, over a twenty-year period (2004–2024). The specific objectives were to examine the spatial distribution and extent of wetlands, evaluate the relationship between urban growth and wetland dynamics, and determine the extent of wetland encroachment resulting from urban development. The study employed Geographic Information Systems (GIS) and Remote Sensing techniques using Landsat 7 ETM+, Landsat 8 OLI\u002FTIRS, and Landsat 9 OLI-2\u002FTIRS-2 satellite imagery acquired for 2004, 2014, and 2024. Image preprocessing, supervised classification, change detection, cross-tabulation matrix analysis, and accuracy assessment were performed in ArcGIS and ERDAS Imagine, while field observations and GPS data were used for ground-truthing and validation. Seven land-use\u002Fland-cover classes were identified: wetlands, farmland, shrubland, forest, water bodies, sandbars, and settlements. The findings revealed significant land-use and land-cover changes over the study period. Wetland coverage declined from 4,478.76 ha (5.37%) in 2004 to 2,664.36 ha (2.39%) in 2024, while settlements expanded from 1,182.60 ha (1.42%) to 5,119.22 ha (4.61%) during the same period. Farmland increased from 39.23% to 41.09%, indicating continued conversion of natural ecosystems to agricultural and urban uses. Cross-tabulation analysis further demonstrated substantial transitions of wetlands and farmlands into settlement areas, confirming that urban expansion has become a major driver of wetland degradation in Makurdi. Classification accuracy assessments yielded overall accuracies ranging from 72.15% to 75.93%, with Kappa coefficients between 0.60 and 0.68, indicating that the classified maps were sufficiently reliable for land-use change analysis. The study concludes that rapid urbanization has significantly contributed to wetland loss and fragmentation and has increasingly encroached on agricultural lands, posing serious threats to ecosystem sustainability, biodiversity conservation, flood regulation, and long-term food security. To address these challenges, the study recommends strengthening wetland protection policies, implementing continuous GIS-based monitoring, promoting sustainable urban development, and establishing institutional frameworks to conserve and effectively manage wetlands and agricultural lands in Makurdi","湿地是最具生产力的生态系统之一，提供重要的生态、水文和社会经济服务，包括洪水调节、地下水补给、水质净化、生物多样性保护和农业支持。然而，快速城市化加速了湿地向建成区和农业用地的转化，威胁其生态完整性。本研究评估了贝努埃州马库尔迪地方政府区二十年期间（2004—2024年）城市化对农业和湿地管理的影响。具体目标包括：考察湿地的空间分布与范围，评估城市增长与湿地动态之间的关系，并确定城市发展导致的湿地侵占程度。研究采用地理信息系统（GIS）和遥感技术，使用2004年、2014年和2024年的Landsat 7 ETM+、Landsat 8 OLI\u002FTIRS和Landsat 9 OLI-2\u002FTIRS-2卫星影像。在ArcGIS和ERDAS Imagine中进行了影像预处理、监督分类、变化检测、交叉列联表矩阵分析和精度评估，同时利用实地观测和GPS数据进行地面验证与确认。研究识别出七类土地利用\u002F土地覆盖类型：湿地、农田、灌丛、森林、水体、沙洲和居民点。研究结果显示，研究期间土地利用和土地覆盖发生了显著变化。湿地覆盖面积从2004年的4,478.76公顷（5.37%）下降至2024年的2,664.36公顷（2.39%），而居民点面积同期从1,182.60公顷（1.42%）扩张至5,119.22公顷（4.61%）。农田面积从39.23%增至41.09%，表明自然生态系统持续向农业和城市用途转化。交叉列联表分析进一步表明，湿地和农田大量转变为居民点用地，证实城市扩张已成为马库尔迪湿地退化的主要驱动因素。分类精度评估的总体精度为72.15%至75.93%，Kappa系数在0.60至0.68之间，表明分类图对于土地利用变化分析具有足够的可靠性。研究认为，快速城市化显著加剧了湿地丧失和破碎化，并日益侵占农业用地，对生态系统可持续性、生物多样性保护","Hensard Journal of Environment","2026-09-20T00:00:00Z",56,{"impact":122,"substance":18,"depth":123,"authority":57,"freshness":20,"relevant":21,"comment":124},8,15,"基于Landsat遥感与GIS的尼日利亚湿地退化实证研究，数据扎实但属区域案例，公共影响有限。",[126],{"name":118,"url":115},[26,29,128,129,130],"土地利用","城市化","湿地保护",[132,133],"Makurdi 湿地 城市化","Benue State 遥感","Makurdi湿地城市化-3069","10.65757\u002Fhjev1n22",{"doi":135,"openalex_id":137,"authors":138,"venue":118,"cited_by_count":35,"oa_url":115,"card":153,"direction":110,"ingested_from":54},"W7213745048",[139,141,143,145,147,149,151],{"name":140,"orcid":8},"Joy Iganya Agene",{"name":142,"orcid":8},"Onoja Sunday",{"name":144,"orcid":8},"Akintunde Elijah",{"name":146,"orcid":8},"Ugese Ayangeaor Andrew",{"name":148,"orcid":8},"Kebiru Umoru",{"name":150,"orcid":8},"Aku Umaku Esther",{"name":152,"orcid":8},"Ombugu Gideon",{"tldr":154,"method":155,"finding":156,"direction":110,"opportunity":157},"用遥感与GIS评估尼日利亚马库尔迪20年间城市化对湿地和农业用地的侵占影响。","Landsat 7\u002F8\u002F9影像，监督分类、变化检测与交叉表分析，GPS实地验证。","湿地占比由5.37%降至2.39%，建设用地由1.42%升至4.61%，城市化是湿地退化主因。","可结合多源高分辨率遥感与CA-Markov模型，预测湿地-农田-城市冲突并支撑国土空间优化。","2026-09-21T23:30:24.084401Z",{"id":160,"title":161,"url":162,"summary":163,"summary_zh":8,"content":8,"source_name":164,"source_url":8,"published_at":165,"category":166,"cover_url":8,"hotness":12,"is_selected":167,"score":168,"score_detail":169,"sources":171,"tags":173,"search_phrases":176,"slug":179,"view_count":35,"doi":8,"paper":8,"created_at":180},2841,"中国农科院资划所多因子监测实现小麦晚霜冻害大范围动态预警登European Journal of Agronomy","https:\u002F\u002Fwww.caas.cn\u002Fxwzx\u002Fmtxw\u002Fbe939456a5354c4da317420bf21577db.htm","近日,中国农业科学院农业资源与农业区划研究所农业遥感团队提出了多因子综合霜冻监测指数,提升小麦晚霜冻害监测精度。黄淮海平原是我国冬小麦核心产区,每年3至4月小麦拔节至抽穗期间频发的晚霜冻害严重威胁粮食安全。该指数综合高精度气象预报和冬小麦生长进程信息,兼顾低温强度、持续时间及不同生育期敏感性,并利用遥感土壤水分数据构建非线性调节系数,精准刻画湿润土壤对低温的缓冲效应,突破了气象站点的空间局限。","中国农业科学院 \u002F 中国科学报","2026-09-17T00:00:00Z","报道",true,87,{"impact":16,"substance":16,"depth":18,"authority":123,"freshness":12,"relevant":21,"comment":170},"国家级科研团队在核心期刊提出多因子霜冻监测指数，方法新颖、结论可靠，对黄淮海冬小麦防灾减灾有实质价值，值得进入每日精选。",[172],{"name":164,"url":162},[74,174,26,175,27],"农业遥感","小麦",[177,178],"农业气象 农业遥感 智慧农业 粮食安全","农业气象 农业遥感","农业气象农业遥感智慧农业粮食安全-2841","2026-09-18T00:03:28.607700Z",{"id":182,"title":183,"url":184,"summary":185,"summary_zh":186,"content":8,"source_name":187,"source_url":184,"published_at":188,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":189,"score_detail":190,"sources":194,"tags":196,"search_phrases":199,"slug":202,"view_count":35,"doi":203,"paper":204,"created_at":223},2769,"Climate change impacts and resilience pathways in somalia through a systematic review","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs42452-026-09538-5","Somalia is among the most climate-vulnerable countries worldwide, characterized by predominantly arid and semi-arid regions increasingly affected by recurrent droughts, irregular precipitation, rising temperatures, land degradation, and water scarcity, all of which compromise livelihoods, food security, and socio-economic stability. This study addresses the limited synthesis of recent, spatially explicit evidence on climate change impacts in Somalia by bringing together current literature and institutional reports to analyze the effects of climate change and identify adaptation and resilience strategies The review is entirely desk-based and utilizes 30 secondary data articles from reputable sources such as FAO-SWALIM, the World Bank, ICPAC, FSNAU, USAID, and peer-reviewed research articles from Scopus and web of science published from 2008 to 2025. The results showed that over 60% of Somalia’s territory is categorized as arid or hyper-arid, with the southern parts seeing the most acute soil erosion and environmental deterioration. Significant drought occurrences, especially in 2010–2011 and 2016–2017, were greatly impacted by extensive climatic factors including ENSO and the Indian Ocean Dipole. Climate variability has resulted in reduced crop yields, pasture degradation, livestock mortality, persistent water shortages, and extensive food insecurity, while inadequate governance, restricted climate financing, and deficient early-warning and monitoring systems hinder effective adaptation. The review emphasizes that climate change presents a systemic threat to Somalia’s environmental and livelihood systems, highlighting the necessity for integrated, spatially informed strategies that incorporate GIS-based environmental monitoring, sustainable land and water management, climate-smart agriculture, strengthened institutions, and enhanced access to climate finance to support long-term resilience and sustainable development.","索马里是全球最易受气候变化影响的国家之一，其国土以干旱和半干旱地区为主，日益受到反复出现的干旱、降水不规律、气温上升、土地退化及水资源短缺的影响，这些问题共同威胁着生计、粮食安全和社会经济稳定。本研究针对索马里气候变化影响近期空间明确证据综合不足的问题，汇集当前文献和机构报告，分析气候变化的影响并识别适应与韧性策略。本综述完全基于案头研究，使用了来自FAO-SWALIM、世界银行、ICPAC、FSNAU、USAID等知名来源的30篇二手数据文献，以及2008年至2025年间发表于Scopus和Web of Science的同行评审研究论文。结果表明，索马里超过60%的领土被归类为干旱或极度干旱，南部地区土壤侵蚀和环境退化最为严重。重大干旱事件，尤其是2010—2011年和2016—2017年的干旱，受到包括厄尔尼诺-南方涛动（ENSO）和印度洋偶极子（IOD）在内的广泛气候因素的显著影响。气候变率导致作物减产、牧场退化、牲畜死亡、持续的水资源短缺和大范围的粮食不安全，而治理不足、气候融资受限以及预警和监测系统薄弱则阻碍了有效适应。本综述强调，气候变化对索马里的环境和生计系统构成系统性威胁，凸显了采取综合性、空间信息化策略的必要性，这些策略应包括基于GIS的环境监测、可持续土地和水资源管理、气候智慧型农业、强化制度以及增强气候融资渠道，以支持长期韧性和可持续发展。","Discover Applied Sciences","2026-09-16T00:00:00Z",76,{"impact":18,"substance":69,"depth":191,"authority":192,"freshness":122,"relevant":21,"comment":193},17,13,"系统综述整合2008-2025年30篇文献，揭示索马里干旱与粮食安全影响并提出GIS监测与气候智慧农业等韧性路径，对干旱区农业信息化有参考价值。",[195],{"name":187,"url":184},[26,76,197,29,198],"气候智慧农业","干旱监测",[200,201],"气候智慧农业 干旱监测 气候变化 粮食安全","气候智慧农业 干旱监测","气候智慧农业干旱监测气候变化粮食安全-2769","10.1007\u002Fs42452-026-09538-5",{"doi":203,"openalex_id":205,"authors":206,"venue":187,"cited_by_count":35,"oa_url":184,"card":216,"direction":222,"ingested_from":54},"W7213257664",[207,209,211,214],{"name":208,"orcid":8},"Abdiaziz Hassan Nur",{"name":210,"orcid":8},"Mohamed Osman Abdulkadir",{"name":212,"orcid":213},"Omar Ali","https:\u002F\u002Forcid.org\u002F0009-0001-3009-1850",{"name":215,"orcid":8},"Ahmed Sodal Asir",{"tldr":217,"method":218,"finding":219,"direction":220,"opportunity":221},"系统综述索马里气候变化影响与韧性路径，基于30篇文献与机构报告。","文献综述，采用FAO-SWALIM、世行等2008-2025年30篇二手数据。","超60%国土干旱，干旱致减产、缺水与粮食不安全，治理与预警不足阻碍适应。","农业绿色发展与碳","可结合GIS与遥感构建索马里干旱预警和气候智慧型农业适应决策模型。","智慧农业 \u002F 农业物联网","2026-09-17T23:30:10.557669Z",{"id":225,"title":226,"url":227,"summary":228,"summary_zh":229,"content":8,"source_name":230,"source_url":227,"published_at":231,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":232,"score_detail":233,"sources":236,"tags":238,"search_phrases":242,"slug":245,"view_count":35,"doi":246,"paper":247,"created_at":271},2674,"Bi-decadal drought assessment in Northwestern Algeria: Integrating meteorological and remote sensing indices","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00704-026-06526-y","Abstract Drought is an escalating hazard in arid and semi‑arid regions with significant implications for agriculture, ecosystems, and water resources. This study presents a 2003–2023 integrated assessment of meteorological and vegetation-based drought across northwest Algeria, using the Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset and Moderate Resolution Imaging Spectroradiometer (MODIS) remotely‑sensed products. Meteorological drought was quantified with the Standardized Precipitation Index (SPI) approximated using standardized precipitation anomalies, at 3‑, 6‑, and 12‑month timescales, whereas vegetation and thermal stress were assessed with MODIS‑derived Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and Vegetation Health Index (VHI). Temporal trends were evaluated using the Mann–Kendall test and Sen’s slope estimator, and relationships between precipitation and vegetation were examined with Pearson correlation. We identified recurrent drought episodes in 2007–2009, 2011–2012, and a pronounced dry phase from 2020–2023. Mann–Kendall results indicated widespread drying across all SPI timescales, with 56% of the study area showing significant negative trends at SPI‑3 (mean Sen’s slope = − 0.10 yr⁻ 1 ). Vegetation indices mirrored these changes, with VHI showing substantially more degraded area than improvement (4.89% vs 0.51% of the domain), while VCI and TCI responses were spatially heterogeneous. The correlation analysis showed moderate but statistically significant relationships between SPI and VHI at the short-term scale (SPI-3 vs. VHI, r = 0.567, p = 0.007), followed by SPI-6 ( r = 0.509, p = 0.019), indicating that vegetation response is more strongly associated with short- to medium-term precipitation variability. These results demonstrate the value of combining meteorological and satellite vegetation indices for regional drought monitoring and early warning, and underscore an ongoing shift toward increased aridity with implications for water management and agricultural adaptation.","摘要 干旱是干旱和半干旱地区日益加剧的灾害，对农业、生态系统和水资源具有重大影响。本研究利用气候灾害组红外降水与站点数据（CHIRPS）数据集和中分辨率成像光谱仪（MODIS）遥感产品，对阿尔及利亚西北部2003—2023年气象干旱和基于植被的干旱进行了综合评估。气象干旱采用标准化降水指数（SPI）量化，该指数通过标准化降水距平在3个月、6个月和12个月时间尺度上近似计算；植被和热胁迫则通过MODIS衍生的植被状态指数（VCI）、温度状态指数（TCI）和植被健康指数（VHI）进行评估。采用Mann–Kendall检验和Sen斜率估计器评估时间趋势，并使用Pearson相关分析检验降水与植被之间的关系。我们识别出2007—2009年、2011—2012年的反复干旱事件，以及2020—2023年显著的干旱阶段。Mann–Kendall结果表明，所有SPI时间尺度上均呈现大范围干旱化趋势，研究区56%的面积在SPI-3上表现出显著负趋势（Sen斜率均值 = −0.10 yr⁻¹）。植被指数反映了这些变化，VHI显示退化面积远大于改善面积（分别占研究区的4.89%和0.51%），而VCI和TCI的响应在空间上具有异质性。相关分析表明，SPI与VHI在短期尺度上存在中等但统计显著的关系（SPI-3与VHI，r = 0.567，p = 0.007），其次为SPI-6（r = 0.509，p = 0.019），表明植被响应与短期至中期降水变率的关系更为密切。这些结果证明了结合气象指标和卫星植被指数进行区域干旱监测与预警的价值，并凸显了干旱化持续加剧的趋势及其对水资源管理和农业适应的影响。","Theoretical and Applied Climatology","2026-09-15T00:00:00Z",71,{"impact":234,"substance":17,"depth":191,"authority":192,"freshness":122,"relevant":21,"comment":235},12,"基于CHIRPS与MODIS的阿尔及利亚西北部20年干旱综合评估，方法整合气象与遥感指标，结论对区域干旱监测与农业适应有参考价值，但属境外区域研究，国内产业影响有限。",[237],{"name":230,"url":227},[27,239,29,240,241],"水资源管理","植被指数","干旱预警",[243,244],"水资源管理 农业气象 干旱预警 植被指数","水资源管理 农业气象","水资源管理农业气象干旱预警植被指数-2674","10.1007\u002Fs00704-026-06526-y",{"doi":246,"openalex_id":248,"authors":249,"venue":230,"cited_by_count":35,"oa_url":227,"card":266,"direction":110,"ingested_from":54},"W7213346236",[250,253,256,259,261,264],{"name":251,"orcid":252},"Ramzi Benhizia","https:\u002F\u002Forcid.org\u002F0000-0002-8967-2051",{"name":254,"orcid":255},"Brahim Abdelkebir","https:\u002F\u002Forcid.org\u002F0000-0002-8761-8537",{"name":257,"orcid":258},"Behnam Ata","https:\u002F\u002Forcid.org\u002F0000-0002-9690-7269",{"name":260,"orcid":8},"Mukovhe Vele Singo",{"name":262,"orcid":263},"Kwanele Phinzi","https:\u002F\u002Forcid.org\u002F0000-0003-1865-7011",{"name":265,"orcid":8},"György Szabó",{"tldr":267,"method":268,"finding":269,"direction":110,"opportunity":270},"整合气象与遥感指数评估阿尔及利亚西北部2003–2023年干旱演变。","CHIRPS降水与MODIS植被指数，SPI、VCI、TCI、VHI，Mann-","全区普遍变干，SPI-3显著负趋势占56%，VHI退化面积远大于改善，短期降水与植被相关性最强。","可引入机器学习融合多源遥感与气象数据，构建区域干旱预警与作物适应性决策模型。","2026-09-16T23:30:30.795285Z",{"id":273,"title":274,"url":275,"summary":276,"summary_zh":277,"content":8,"source_name":278,"source_url":275,"published_at":231,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":168,"score_detail":279,"sources":282,"tags":284,"search_phrases":287,"slug":290,"view_count":35,"doi":291,"paper":292,"created_at":322},2670,"A knowledge-guided machine learning framework for cross-scale wheat harvest monitoring via sample augmentation","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.rse.2026.115671","Accurate monitoring of wheat harvest is crucial for precision agriculture and ensuring food security. However, rapid changes in land surface composition during the harvest period in intensive agricultural regions make it difficult to obtain sufficiently high-confidence ground samples, limiting the performance and generalization of data-driven remote sensing methods. Therefore, this study proposes a Knowledge-Guided Machine Learning (KGML) framework that integrates multi-satellite Earth observation data (PlanetScope, Sentinel-2, and MODIS) to monitor harvest from the field to regional scales. Ground data were collected using vehicle-mounted cameras and smartphones during the 2023 and 2024 wheat harvest periods. The results showed that combining spectral knowledge rules with a Random Forest model (regional accuracy >0.80) generated numerous high-confidence augmented samples from PlanetScope imagery. The augmented dataset was used to train a Hybrid CNN-Transformer-LSTM (HCTL) model with two pathways: Sentinel-2 classification for field-level harvest mapping (overall accuracy = 0.93) and MODIS regression for sub-pixel harvest fraction estimation, which showed high agreement with PlanetScope-derived harvest fractions (R 2 = 0.97, RMSE = 0.07, rRMSE = 0.15). The harvest dates derived from the MODIS harvest fraction time series showed high consistency with field observations (R 2 = 0.82, RMSE = 1.30 days). This framework provides an effective solution for wheat harvest monitoring by bridging the gap between limited ground-truth data and multi-scale satellite observations, thereby supporting food security assessments and informed agricultural management decisions.","准确监测小麦收获对精准农业和保障粮食安全至关重要。然而，在集约化农业区域，收获期地表组成的快速变化使得获取足够高置信度的地面样本变得困难，限制了数据驱动遥感方法的性能和泛化能力。因此，本研究提出了一种知识引导机器学习（KGML）框架，集成多卫星地球观测数据（PlanetScope、Sentinel-2和MODIS），实现从田块到区域尺度的收获监测。地面数据通过车载摄像头和智能手机在2023年和2024年小麦收获期采集。结果表明，将光谱知识规则与随机森林模型相结合（区域精度>0.80），可从PlanetScope影像中生成大量高置信度增强样本。利用该增强数据集训练了混合CNN-Transformer-LSTM（HCTL）模型，该模型包含两条路径：Sentinel-2分类用于田块尺度收获制图（总体精度=0.93），MODIS回归用于亚像元收获比例估算，其结果与PlanetScope-derived收获比例高度一致（R²=0.97，RMSE=0.07，rRMSE=0.15）。由MODIS收获比例时间序列提取的收获日期与田间观测结果高度一致（R²=0.82，RMSE=1.30天）。该框架通过弥合有限地面真值数据与多尺度卫星观测之间的差距，为小麦收获监测提供了有效解决方案，从而支持粮食安全评估和农业管理决策。","Remote Sensing of Environment",{"impact":16,"substance":280,"depth":18,"authority":123,"freshness":20,"relevant":21,"comment":281},23,"提出知识引导机器学习框架，融合多源卫星数据实现田块到区域尺度的跨尺度小麦收获监测，方法新颖、精度可靠，对精准农业与粮食安全评估有实质参考价值。",[283],{"name":278,"url":275},[74,285,26,29,286],"农业人工智能","小麦收获",[288,289],"农业人工智能 小麦收获 智慧农业 粮食安全","农业人工智能 小麦收获","农业人工智能小麦收获智慧农业粮食安全-2670","10.1016\u002Fj.rse.2026.115671",{"doi":291,"openalex_id":293,"authors":294,"venue":278,"cited_by_count":35,"oa_url":275,"card":317,"direction":110,"ingested_from":54},"W7213296259",[295,298,300,302,304,306,308,310,313,315],{"name":296,"orcid":297},"Mingchao Shao","https:\u002F\u002Forcid.org\u002F0000-0003-2619-4272",{"name":299,"orcid":8},"Chongya Jiang",{"name":301,"orcid":8},"Jingwei An",{"name":303,"orcid":8},"Haokai Zhu",{"name":305,"orcid":8},"Yue Li",{"name":307,"orcid":8},"Xia Yao",{"name":309,"orcid":8},"Tao Cheng",{"name":311,"orcid":312},"Hengbiao Zheng","https:\u002F\u002Forcid.org\u002F0009-0008-4778-0450",{"name":314,"orcid":8},"Weixing Cao",{"name":316,"orcid":8},"Yan Zhu",{"tldr":318,"method":319,"finding":320,"direction":110,"opportunity":321},"提出知识引导机器学习框架，用样本增强实现田块到区域尺度的冬小麦收获监测。","融合PlanetScope、Sentinel-2、MODIS与车载相机地面数据，","增强样本训练的HCTL模型田块分类精度0.93，区域收获比例R²=0.97，收获日期误差约1.3天。","可迁移至其他作物收获监测，并探索知识规则自动化构建与跨区域泛化能力。","2026-09-16T23:30:30.474537Z"]