[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3479":3,"related-3479":62},{"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,"search_phrases":32,"slug":35,"view_count":36,"doi":37,"paper":38,"created_at":61},3479,"Artificial Intelligence-Enabled Mangrove Ecosystem Monitoring Using Remote Sensing and Environmental Data","https:\u002F\u002Fdoi.org\u002F10.68012\u002Fair.v1i2.252","Mangrove ecosystems play a critical role in coastal protection, carbon sequestration, biodiversity conservation, and climate change mitigation; however, increasing anthropogenic pressures and environmental changes have accelerated mangrove degradation, creating an urgent need for efficient and scalable monitoring approaches. This study aims to develop an Artificial Intelligence-Enabled framework for monitoring mangrove ecosystem conditions by integrating remote sensing imagery with environmental datasets to improve the accuracy and timeliness of ecosystem assessment in tropical coastal regions. The proposed method combines multispectral satellite images, including vegetation indices derived from remote sensing data, with environmental variables such as temperature, precipitation, salinity, and tidal information, which are subsequently processed using a deep learning-based classification model to identify and categorize mangrove health conditions. Experimental evaluation demonstrates that the integration of remote sensing and environmental data significantly enhances model performance compared with approaches relying solely on satellite imagery, achieving high classification accuracy and improving the detection of early signs of ecosystem degradation. The findings further reveal that environmental parameters contribute substantially to distinguishing healthy, moderately degraded, and severely degraded mangrove areas across heterogeneous coastal environments. Consequently, the proposed framework provides an intelligent and reliable decision support tool for environmental monitoring agencies and policymakers while contributing to the development of resilient coastal ecosystem management and sustainable environmental governance in tropical archipelagic regions.","红树林生态系统在海岸防护、碳固存、生物多样性保护及气候变化减缓中发挥着关键作用；然而，日益加剧的人为压力和环境变化加速了红树林退化，亟需高效且可扩展的监测方法。本研究旨在开发一个人工智能驱动的框架，通过整合遥感影像与环境数据集来监测红树林生态系统状况，以提高热带沿海地区生态系统评估的准确性和时效性。所提出的方法将多光谱卫星影像（包括由遥感数据衍生的植被指数）与温度、降水、盐度和潮汐信息等环境变量相结合，随后使用基于深度学习的分类模型进行处理，以识别和分类红树林健康状况。实验评估表明，与仅依赖卫星影像的方法相比，遥感与环境数据的整合显著提升了模型性能，实现了较高的分类精度，并改善了对生态系统退化早期迹象的检测。研究结果进一步揭示，环境参数对于区分异质性沿海环境中健康、中度退化和严重退化的红树林区域具有重要贡献。因此，所提出的框架为环境监测机构和政策制定者提供了一种智能且可靠的决策支持工具，同时有助于热带群岛地区韧性沿海生态系统管理和可持续环境治理的发展。",null,"AI Innovation and Resilience for the Environment (AIR)","2026-09-22T00:00:00Z","论文",10,false,76,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,21,17,12,8,1,"将遥感与环境数据融合的深度学习框架用于红树林健康监测，方法有创新且结论可靠，对沿海生态治理有参考价值，但属细分领域研究，公共影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"农业人工智能","深度学习","遥感监测","生态监测","红树林",[33,34],"红树林 遥感 人工智能","红树林生态系统 监测","红树林遥感人工智能-3479",0,"10.68012\u002Fair.v1i2.252",{"doi":37,"openalex_id":39,"authors":40,"venue":10,"cited_by_count":36,"oa_url":52,"card":53,"direction":59,"ingested_from":60},"W7213895268",[41,44,47,49],{"name":42,"orcid":43},"Dirvi Surya Abbas","https:\u002F\u002Forcid.org\u002F0000-0002-7819-2837",{"name":45,"orcid":46},"Asep Sutarman","https:\u002F\u002Forcid.org\u002F0009-0002-1029-7963",{"name":48,"orcid":9},"Ryan Davis",{"name":50,"orcid":51},"Maulana Abbas","https:\u002F\u002Forcid.org\u002F0009-0009-0137-9650","https:\u002F\u002Fjournal.sundarapublishing.com\u002Findex.php\u002Fair\u002Farticle\u002Fdownload\u002F252\u002F142",{"tldr":54,"method":55,"finding":56,"direction":57,"opportunity":58},"融合遥感影像与环境数据，用深度学习构建红树林生态系统健康监测框架。","多光谱卫星影像与植被指数，结合温度、降水、盐度、潮汐等环境变量，训练深度学习分类","融合环境数据显著提升分类精度，能更早识别红树林退化迹象，有效区分不同退化程度。","农业遥感与作物表型","可探索多源时序遥感与环境数据融合的早期退化预警，并迁移至其他滨海湿地生态系统监测。","智慧农业 \u002F 农业物联网","openalex","2026-09-25T23:30:14.373467Z",{"total":63,"page":22,"page_size":63,"items":64},6,[65,107,149,185,214,257],{"id":66,"title":67,"url":68,"summary":69,"summary_zh":70,"content":9,"source_name":71,"source_url":68,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":72,"score_detail":73,"sources":79,"tags":81,"search_phrases":85,"slug":88,"view_count":36,"doi":89,"paper":90,"created_at":106},3514,"Artificial Intelligence for Climate Adaptation Decision Support in Data-Poor Developing Regions","https:\u002F\u002Fdoi.org\u002F10.22541\u002Fessoar.15009304\u002Fv1","Climate adaptation is a sequence of decisions taken under uncertainty, and the regions where climate risk is rising fastest are those with the least information to guide them. Only about 10 per cent of deaths are registered in the WHO African Region; nearly 90 per cent of required surface weather observations are missing across least developed countries and small island states; and only 40 per cent of African countries have multi-hazard early warning systems. This report examines whether artificial intelligence — machine learning, remote sensing and predictive analytics — can close these information gaps and improve adaptation decisions in data-poor developing regions. The report organises the problem as a decision chain with three information gaps — observation, prediction and decision — followed by an action gap that AI cannot close. It finds that AI has advanced fastest on prediction: AI weather models became operational at ECMWF in 2025, AI flood forecasts now cover 100 countries and about 700 million people, satellite nowcasts reach a continent with little radar, and AI monsoon-onset forecasts reached 38 million Indian farmers in 2025. On observation, satellite machine learning explains around 70 per cent of the variation in village wealth but only up to about half of the variation in changes over time. On decision, evidence from Togo, Bangladesh and Kenya shows that AI-assisted targeting, forecast-based triggers and satellite index insurance can deliver assistance faster and better, within clear limits. The report's central argument is the ground-truth paradox: AI stretches scarce observations further, but every AI product must be trained and verified against ground truth, so reliance on AI raises the value of each remaining station, survey and label. The 2025 interruption of FEWS NET and termination of the DHS Program show how fragile that foundation is. Because the value of information is the product of skill, lead time, reach, trust and the means to act, the highest returns usually lie not in more skilful models but in dissemination, institutions and prearranged finance. The report sets out a risk register, a six-principle policy framework, actions by actor and a roadmap to 2030.","气候适应是在不确定性下做出的一系列决策，而气候风险上升最快的地区恰恰是指导信息最匮乏的地区。世卫组织非洲区域仅登记了约10%的死亡病例；最不发达国家和小岛屿国家缺失了近90%所需的地面天气观测数据；仅有40%的非洲国家拥有多灾种早期预警系统。本报告考察人工智能——机器学习、遥感和预测分析——能否弥合这些信息缺口，改善数据匮乏的发展中地区的适应决策。报告将这一问题组织为一条决策链，包含三个信息缺口——观测、预测和决策——以及一个人工智能无法弥合的行动缺口。报告发现，人工智能在预测方面进展最快：人工智能天气模型于2025年在欧洲中期天气预报中心（ECMWF）投入业务运行，人工智能洪水预报现已覆盖100个国家和约7亿人口，卫星临近预报覆盖了一个几乎没有雷达的大陆，人工智能季风爆发预报于2025年惠及3800万印度农民。在观测方面，卫星机器学习可解释村庄财富约70%的变异，但对时间变化的解释力仅约一半。在决策方面，来自多哥、孟加拉国和肯尼亚的证据表明，人工智能辅助的目标定位、基于预报的触发机制和卫星指数保险能够在明确限度内更快、更好地提供援助。报告的核心论点是地面真值悖论：人工智能能够将稀缺的观测数据发挥更大效用，但每个人工智能产品都必须依据地面真值进行训练和验证，因此对人工智能的依赖提升了每一个剩余站点、调查和标注数据的价值。2025年FEWS NET的中断和DHS项目的终止表明这一基础何等脆弱。由于信息的价值是技能、提前期、覆盖面、信任和行动手段的乘积，最高回报通常不在于更精密的模型，而在于传播、制度和预先安排的融资。报告提出了风险登记册、六项原则的政策框架、各行为主体的行动以及到2030年的路线图。","OpenAlex",86,{"impact":74,"substance":75,"depth":76,"authority":77,"freshness":21,"relevant":22,"comment":78},22,24,19,13,"系统梳理AI在数据匮乏地区气候适应决策中的观测、预测与决策三类信息缺口，提出“地面真值悖论”，数据与结论扎实，对农业信息化与智慧农业有较强参考价值。",[80],{"name":71,"url":68},[82,27,83,29,84],"智慧农业","气候适应","早期预警",[86,87],"AI 气候适应 决策支持","数据匮乏地区 农业预警","AI气候适应决策支持-3514","10.22541\u002Fessoar.15009304\u002Fv1",{"doi":89,"openalex_id":91,"authors":92,"venue":9,"cited_by_count":36,"oa_url":98,"card":99,"direction":105,"ingested_from":60},"W7214097088",[93,95],{"name":94,"orcid":9},"H Heuristics",{"name":96,"orcid":97},"Hunter Hughes","https:\u002F\u002Forcid.org\u002F0009-0002-6161-9387","https:\u002F\u002Fessopenarchive.org\u002Fdoi\u002Fpdf\u002F10.22541\u002Fessoar.15009304\u002Fv1",{"tldr":100,"method":101,"finding":102,"direction":103,"opportunity":104},"评估AI能否弥补数据匮乏地区气候适应决策的信息缺口，并提出地面真值悖论。","梳理观测、预测、决策三环节，结合AI天气模型、卫星ML与多国案例证据。","AI预测进展最快，但依赖地面真值；最高回报常在传播、制度与预置资金而非模型。","农业人工智能与决策模型","可研究AI辅助农业气候适应中地面真值稀缺下的验证与信任机制，及预置资金触发设计。","数字乡村与农业信息化","2026-09-25T23:30:46.008325Z",{"id":108,"title":109,"url":110,"summary":111,"summary_zh":9,"content":9,"source_name":112,"source_url":110,"published_at":113,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":114,"score_detail":115,"sources":119,"tags":121,"search_phrases":123,"slug":126,"view_count":36,"doi":127,"paper":128,"created_at":148},3463,"Cotton leaf disease classification using deep learning models for smart agriculture","https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs40066-026-00610-2","Cotton leaf disease classification using deep learning models for smart agriculture。Agriculture & Food Security","Agriculture & Food Security","2026-09-25T00:00:00Z",66,{"impact":20,"substance":116,"depth":117,"authority":77,"freshness":13,"relevant":22,"comment":118},16,15,"核心期刊论文，方法有一定参考价值，但属细分技术研究，产业影响有限。",[120],{"name":112,"url":110},[82,27,28,122],"棉花病害",[124,125],"棉花叶部病害 深度学习 分类","农业人工智能 智慧农业 棉花病害 深度学习","棉花叶部病害深度学习分类-3463","10.1186\u002Fs40066-026-00610-2",{"doi":127,"openalex_id":129,"authors":130,"venue":112,"cited_by_count":36,"oa_url":110,"card":9,"direction":59,"ingested_from":60},"W7214238516",[131,133,136,139,142,145],{"name":132,"orcid":9},"Hina Kiran Abbas",{"name":134,"orcid":135},"Muhammad Farrukh Shahid","https:\u002F\u002Forcid.org\u002F0009-0004-8787-1868",{"name":137,"orcid":138},"Rehab Bahaaddin Ashari","https:\u002F\u002Forcid.org\u002F0000-0003-1225-7535",{"name":140,"orcid":141},"Arwa Mashat","https:\u002F\u002Forcid.org\u002F0000-0002-0612-6005",{"name":143,"orcid":144},"Tariq Jamil Saifullah Khanzada","https:\u002F\u002Forcid.org\u002F0000-0003-1617-4403",{"name":146,"orcid":147},"M. Hassan Tanveer","https:\u002F\u002Forcid.org\u002F0000-0001-9266-6368","2026-09-25T23:30:09.012501Z",{"id":150,"title":151,"url":152,"summary":153,"summary_zh":154,"content":9,"source_name":155,"source_url":152,"published_at":156,"category":12,"cover_url":9,"hotness":157,"is_selected":14,"score":158,"score_detail":159,"sources":161,"tags":165,"search_phrases":169,"slug":172,"view_count":36,"doi":173,"paper":174,"created_at":184},3357,"AI-Driven Precision Agriculture and Crop Resilience: Integrating Artificial Intelligence, IoT and Remote Sensing for Climate-Resilient Indian Agriculture: A Vision for Viksit Bharat 2047","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22914538","Abstract Agriculture is central to India's economic development, food security, rural employment, and the achievement of the Viksit Bharat@2047 vision. However, Indian agriculture faces increasingly complex challenges, including climate variability, water scarcity, soil degradation, pest and disease outbreaks, fragmented landholdings, market uncertainty, and unequal access to agricultural knowledge. These challenges require a transition from conventional, input-intensive agriculture towards data-driven, resource-efficient, climate-resilient and farmer-centric production systems. Agriculture in India is increasingly affected by climate variability, water scarcity, soil degradation, pest and disease outbreaks, and unpredictable weather conditions. These challenges threaten crop productivity and food security, particularly among small and marginal farmers. Artificial Intelligence (AI), Internet of Things (IoT), remote sensing, and machine learning offer new opportunities to transform conventional agricultural practices into data-driven precision agriculture systems. This paper presents a conceptual framework for AI-driven precision agriculture aimed at improving crop resilience under changing climatic conditions. Artificial Intelligence (AI), combined with precision agriculture, Internet of Things (IoT), remote sensing, satellite imagery, drones, machine learning, robotics and digital public infrastructure, offers significant opportunities to transform Indian agriculture. AI can support crop and yield prediction, disease and pest identification, weather-based advisories, irrigation optimisation, soil management, crop insurance, market intelligence and early-warning systems. The paper also discusses challenges related to digital inclusion, data governance, affordability, AI reliability, farmer skills, privacy and institutional coordination. It argues that India's objective should not simply be the digitisation of agriculture, but the creation of an intelligent, inclusive and resilient agricultural ecosystem in which technology augments farmer knowledge and decision-making. By 2047, India can aspire to establish globally competitive agriculture that produces more with fewer resources, withstands climate shocks, generates higher and more stable farm incomes, and ensures sustainable food and nutritional security.","摘要 农业对印度的经济发展、粮食安全、农村就业以及“发达印度@2047”愿景的实现至关重要。然而，印度农业面临日益复杂的挑战，包括气候变异性、水资源短缺、土壤退化、病虫害暴发、土地持有碎片化、市场不确定性以及农业知识获取不平等。这些挑战要求从传统的投入密集型农业向数据驱动、资源高效、气候韧性且以农民为中心的生产体系转型。印度农业日益受到气候变异性、水资源短缺、土壤退化、病虫害暴发及不可预测天气条件的影响。这些挑战威胁着作物生产力和粮食安全，尤其是对小农和边缘农民而言。人工智能（AI）、物联网（IoT）、遥感和机器学习为将传统农业实践转变为数据驱动的精准农业系统提供了新机遇。本文提出了一个AI驱动的精准农业概念框架，旨在改善气候变化条件下作物的韧性。人工智能（AI）与精准农业、物联网（IoT）、遥感、卫星影像、无人机、机器学习、机器人技术及数字公共基础设施相结合，为改造印度农业提供了重大机遇。AI可支持作物与产量预测、病虫害识别、基于天气的农事建议、灌溉优化、土壤管理、作物保险、市场情报及预警系统。本文还讨论了与数字包容、数据治理、可负担性、AI可靠性、农民技能、隐私及机构协调相关的挑战。文章认为，印度的目标不应仅仅是农业数字化，而应是创建一个智能、包容且有韧性的农业生态系统，使技术增强农民的知识与决策能力。到2047年，印度有望建立具有全球竞争力的农业，以更少资源生产更多产品，抵御气候冲击，创造更高且更稳定的农业收入，并确保可持续的粮食与营养安全。","Zenodo (CERN European Organization for Nuclear Research)","2026-09-30T00:00:00Z",25,69,{"impact":74,"substance":17,"depth":116,"authority":77,"freshness":36,"relevant":22,"comment":160},"概念性框架论文，系统梳理AI、IoT与遥感在印度气候韧性农业中的应用与挑战，有参考价值但无实证数据，且发布日期在未来、时效性缺失，暂不宜进入每日精选。",[162,163],{"name":155,"url":152},{"name":155,"url":164},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22914539",[166,82,27,167,168,29],"数字乡村","农业物联网","气候韧性",[170,171],"印度 精准农业 AI","农业人工智能 农业物联网 数字乡村 智慧农业","印度精准农业AI-3357","10.5281\u002Fzenodo.22914538",{"doi":173,"openalex_id":175,"authors":176,"venue":155,"cited_by_count":36,"oa_url":152,"card":179,"direction":59,"ingested_from":60},"W7214083098",[177],{"name":178,"orcid":9},"Twinkal Prakash Sawant",{"tldr":180,"method":181,"finding":182,"direction":59,"opportunity":183},"提出AI+物联网+遥感驱动的精准农业概念框架，提升印度气候韧性作物生产。","概念框架分析，整合AI、IoT、遥感、卫星、无人机、机器学习与数字公共基础设施。","印度农业应构建智能、包容、有韧性的生态系统，而非仅数字化，以应对气候与资源挑战。","可实证检验小农户场景下AI+IoT+遥感集成对作物韧性与收入的实际效果及数字包容机制。","2026-09-24T23:30:13.211525Z",{"id":186,"title":187,"url":188,"summary":189,"summary_zh":9,"content":9,"source_name":190,"source_url":9,"published_at":191,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":192,"score_detail":193,"sources":195,"tags":197,"search_phrases":201,"slug":204,"view_count":36,"doi":9,"paper":205,"created_at":213},3325,"MBF-HybridNet：在极端气候下仍可提前一月预测冬小麦产量的多分支AI模型——Qingdao六县R² 0.756-0.765 MAPE 4.2%","https:\u002F\u002Fbioengineer.org\u002Fnew-multi-branch-ai-model-predicts-winter-wheat-yields-weeks-before-harvest-even-under-extreme-weather\u002F","MBF-HybridNet由青岛六县研究团队开发和测试：采用多分支并行架构，包括处理日常遥感和气象数据的动态变量模块、处理年度尺度极端气候指数数据的动态ECI模块以及处理土壤属性的静态变量模块。动态模块堆叠三个二维卷积层，插入自注意力机制；静态模块独立处理土壤有机碳、阳离子交换容量、pH、砂和粘土含量。研究团队计算了九个极端气候指数（热日、热应力强度、连续热日、霜日、冷应力强度、连续冷日、强降水日、连续湿日和连续干日）用于每个生长阶段。2004至2019年留一年交叉验证，MBF-HybridNet在三个累积生长阶段的R²达到0.756-0.765，平均绝对百分比误差约为4.2%。","MBF-HybridNet 2026 (Qingdao)","2026-09-17T00:00:00Z",78,{"impact":17,"substance":74,"depth":17,"authority":20,"freshness":21,"relevant":22,"comment":194},"多分支AI融合遥感气象与极端气候指数，提前一月预测冬小麦产量且精度可靠，方法新颖、数据扎实，对农业信息化与智慧农业有较高参考价值。",[196],{"name":190,"url":188},[82,27,198,29,199,200],"产量预测","冬小麦","极端气候",[202,203],"MBF-HybridNet 冬小麦 产量预测","青岛 冬小麦 遥感 极端气候","MBF-HybridNet冬小麦产量预测-3325",{"doi":9,"openalex_id":9,"authors":206,"venue":9,"cited_by_count":36,"oa_url":9,"card":207,"direction":103,"ingested_from":212},[],{"tldr":208,"method":209,"finding":210,"direction":103,"opportunity":211},"提出多分支AI模型MBF-HybridNet，融合遥感、气象与土壤数据，提前一月预测冬小麦产量。","多分支并行架构，含2D卷积、自注意力与极端气候指数，2004-2019年留一年交","在青岛六县三个累积生长阶段R²达0.756-0.765，MAPE约4.2%，极端气候下仍可提前一月预","可探索极端气候指数与深度学习结合在其他作物或区域的泛化能力，并提升可解释性。","agent","2026-09-24T00:04:02.748442Z",{"id":215,"title":216,"url":217,"summary":218,"summary_zh":219,"content":9,"source_name":220,"source_url":217,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":221,"score_detail":222,"sources":226,"tags":228,"search_phrases":231,"slug":234,"view_count":36,"doi":235,"paper":236,"created_at":256},3277,"Downscaling of SMAP Soil Moisture Based on the Transformer Algorithm in Anhui Province","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18193272","Soil moisture (SM) is critical for climate, water, and agriculture, but Soil Moisture Active Passive (SMAP) passive microwave products have coarse resolution, limiting regional applications. This study develops an SM downscaling framework based on Transformer and its variants (PatchTST and iTransformer), integrating multi-source satellite and groundwater data to generate 1 km daily SM products (2015–2022). Compared with Random Forest (RF), Long Short-Term Memory (LSTM), and Convolutional Neural Network–LSTM (CNN-LSTM), Transformer and its variants achieve superior accuracy and generalization. Validated against in situ measurements and SMCI1.0, the Transformer-downscaled SM product achieved the best accuracy with ubRMSE = 0.0372 m3\u002Fm3 and RMSE = 0.0591 m3\u002Fm3. The downscaled SM dataset not only captured finer spatial details but also preserved the spatial patterns and seasonal dynamics of the original SMAP product and showed good responsiveness to precipitation events. Feature importance analysis revealed that, aside from precipitation, the diurnal land surface temperature difference had a greater impact on SM than individual daytime or nighttime land surface temperature, ranking just below vegetation indices and soil texture factors, while groundwater level showed higher importance than elevation and surface temperature. This study confirms the effectiveness of Transformer-based models for SM spatial downscaling, providing a novel framework integrating remote sensing and deep hydrological information to generate accurate 1 km SM products.","土壤水分（SM）对气候、水资源和农业至关重要，但土壤水分主动被动（SMAP）被动微波产品分辨率较粗，限制了区域应用。本研究构建了一个基于Transformer及其变体（PatchTST和iTransformer）的土壤水分降尺度框架，融合多源卫星和地下水数据，生成1 km日尺度土壤水分产品（2015—2022年）。与随机森林（RF）、长短期记忆网络（LSTM）和卷积神经网络—长短期记忆网络（CNN-LSTM）相比，Transformer及其变体取得了更高的精度和泛化能力。利用站点实测数据和SMCI1.0进行验证，Transformer降尺度土壤水分产品精度最优，ubRMSE = 0.0372 m³\u002Fm³，RMSE = 0.0591 m³\u002Fm³。降尺度土壤水分数据集不仅捕捉到了更精细的空间细节，还保留了原始SMAP产品的空间格局和季节动态，并对降水事件表现出良好的响应。特征重要性分析表明，除降水外，昼夜地表温差对土壤水分的影响大于单独的白天或夜间地表温度，其重要性仅次于植被指数和土壤质地因子，而地下水埋深的重要性高于高程和地表温度。本研究证实了基于Transformer的模型在土壤水分空间降尺度中的有效性，为融合遥感和深层水文信息生成准确的1 km土壤水分产品提供了一种新框架。","Remote Sensing",81,{"impact":17,"substance":74,"depth":17,"authority":223,"freshness":224,"relevant":22,"comment":225},14,9,"基于Transformer的SMAP土壤水分1km降尺度研究，方法新颖、验证充分，对区域农业旱情监测有实用价值。",[227],{"name":220,"url":217},[82,27,28,229,230],"遥感","土壤墒情",[232,233],"SMAP 土壤水分 降尺度","Transformer 土壤水分 安徽","SMAP土壤水分降尺度-3277","10.3390\u002Frs18193272",{"doi":235,"openalex_id":237,"authors":238,"venue":220,"cited_by_count":36,"oa_url":217,"card":251,"direction":57,"ingested_from":60},"W7208807695",[239,241,243,245,247,249],{"name":240,"orcid":9},"Yuyang Fan",{"name":242,"orcid":9},"Jianwei Ma",{"name":244,"orcid":9},"Mengmeng Li",{"name":246,"orcid":9},"Changqing Ke",{"name":248,"orcid":9},"Bin Cheng",{"name":250,"orcid":9},"Zheng Duan",{"tldr":252,"method":253,"finding":254,"direction":57,"opportunity":255},"基于Transformer及变体融合多源卫星与地下水数据，将SMAP土壤湿度降尺度至1km日尺度。","Transformer、PatchTST、iTransformer，融合多源卫星","Transformer降尺度产品精度最优（ubRMSE=0.0372），保留原产品时空格局并响应降水","可探索Transformer降尺度产品在区域干旱监测、灌溉决策及作物估产中的耦合应用。","2026-09-23T23:30:19.132307Z",{"id":258,"title":259,"url":260,"summary":261,"summary_zh":262,"content":9,"source_name":263,"source_url":260,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":264,"score_detail":265,"sources":268,"tags":270,"search_phrases":273,"slug":276,"view_count":36,"doi":277,"paper":278,"created_at":296},3256,"Leakage-aware, calibrated, and explainable deep learning for robust almond disease classification","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112464","Leakage-aware, calibrated, and explainable deep learning for robust almond disease classification。Computers and Electronics in Agriculture","面向稳健杏仁病害分类的泄漏感知、校准且可解释的深度学习。《农业计算机与电子》","Computers and Electronics in Agriculture",72,{"impact":20,"substance":266,"depth":19,"authority":223,"freshness":224,"relevant":22,"comment":267},20,"核心期刊论文，方法上有防泄漏、校准与可解释性创新，但作物小众、属细分技术进展，未达每日精选门槛。",[269],{"name":263,"url":260},[82,27,28,271,272],"病害识别","巴旦木",[274,275],"巴旦木 病害 深度学习","农业人工智能 智慧农业 深度学习 病害识别","巴旦木病害深度学习-3256","10.1016\u002Fj.compag.2026.112464",{"doi":277,"openalex_id":279,"authors":280,"venue":263,"cited_by_count":36,"oa_url":260,"card":291,"direction":103,"ingested_from":60},"W7213988471",[281,284,287,289],{"name":282,"orcid":283},"Abebaw Degu Workneh","https:\u002F\u002Forcid.org\u002F0000-0001-7694-1577",{"name":285,"orcid":286},"Badr Elkari","https:\u002F\u002Forcid.org\u002F0000-0002-0893-783X",{"name":288,"orcid":9},"Meryam El Mouhtadi",{"name":290,"orcid":9},"Mohammad Furqan Ali",{"tldr":292,"method":293,"finding":294,"direction":103,"opportunity":295},"提出防泄漏、校准且可解释的深度学习框架，用于稳健的杏仁病害分类。","采用防数据泄漏的深度学习训练、概率校准与可解释性分析。","该框架能提升杏仁病害分类的稳健性、可信度与可解释性。","可探索防泄漏与校准机制在其他作物病害识别中的泛化及田间部署。","2026-09-23T23:30:01.628054Z"]