[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1733":3,"related-1733":99},{"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":18,"tags":20,"primary_topic":24,"secondary_topics":30,"classification":31,"search_phrases":58,"slug":61,"view_count":62,"doi":63,"paper":64,"created_at":98},1733,"Machine Learning-Based Assessment of Land-Use Change, Forest Recovery, and Landscape Connectivity in Islamabad","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fland15091641","LULCC is a major driver of habitat fragmentation, biodiversity loss, and declining landscape connectivity, particularly in rapidly urbanizing regions. Although Islamabad has experienced substantial urban expansion and forest-cover change, long-term city-scale assessments linking land-cover dynamics with landscape connectivity remain limited. This study examined spatiotemporal LULCC in Islamabad from 1991 to 2021 and assessed whether recent forest recovery improved landscape structural connectivity. Landsat images acquired in 1991, 2001, 2011, and 2021 were classified into five land-cover categories: water, forest, built-up area, bare land, and agricultural land. Classification was performed using the Random Forest (RF) algorithm in Google Earth Engine (GEE). Landscape composition and spatial configuration were quantified using FRAGSTATS 4.3, while forest fragmentation was evaluated using the Landscape Fragmentation Tool v2.0 (LFT) with a 100 m edge threshold. The classifications achieved overall accuracies above 90%, with Kappa coefficients (K) greater than 0.85. Built-up area increased from 76.31 km2, representing 7.55% of the study area, in 1991, to 258.62 km2, or 25.60%, in 2021, demonstrating rapid urban expansion and associated habitat conversion. Forest cover increased to 340.86 km2 in 2001, declined to 271.96 km2 in 2011, and subsequently recovered to 409.22 km2 in 2021. Despite this increase in forest extent, fragmentation metrics indicated persistent spatial subdivision and limited structural connectivity. High patch density (PD), reduced landscape aggregation, and changes in the largest patch index (LPI) indicated persistent spatial subdivision and limited habitat continuity. These findings highlight the value of integrating RF-based land-cover classification, multitemporal remote sensing, and landscape metrics for urban environmental monitoring. The findings suggest that future land-use planning should consider landscape connectivity, protection of existing forest patches, and spatially coordinated restoration alongside continued reforestation.","土地利用\u002F土地覆盖变化（LULCC）是栖息地破碎化、生物多样性丧失及景观连通性下降的主要驱动因素，尤其在快速城市化地区表现突出。尽管伊斯兰堡经历了显著的城市扩张和森林覆盖变化，但将土地覆盖动态与景观连通性相结合的城市尺度长期评估仍较为有限。本研究分析了1991年至2021年间伊斯兰堡土地利用\u002F土地覆盖变化的时空特征，并评估了近期森林恢复是否改善了景观结构连通性。将1991年、2001年、2011年和2021年获取的Landsat影像划分为五类土地覆盖类型：水体、森林、建设用地、裸地和农业用地。分类采用谷歌地球引擎（GEE）中的随机森林（RF）算法完成。景观组成和空间配置通过FRAGSTATS 4.3进行量化，森林破碎化则利用景观破碎化工具v2.0（LFT）以100米边缘阈值进行评估。分类结果的总体精度超过90%，Kappa系数（K）大于0.85。建设用地面积从1991年的76.31平方公里（占研究区面积的7.55%）增加到2021年的258.62平方公里（占25.60%），表明城市快速扩张及随之而来的栖息地转化。森林覆盖面积在2001年增至340.86平方公里，2011年降至271.96平方公里，随后于2021年恢复至409.22平方公里。尽管森林面积有所增加，破碎化指标仍表明存在持续的空间分割和有限的结构连通性。高斑块密度（PD）、景观聚集度降低以及最大斑块指数（LPI）的变化均反映出持续的空间分割和有限的栖息地连续性。这些发现凸显了将基于随机森林的土地覆盖分类、多时相遥感与景观指标相结合用于城市环境监测的价值。研究结果表明，未来的土地利用规划应综合考虑景观连通性、现有森林斑块的保护以及空间协调的恢复措施，同时持续推进植树造林。",null,"Land","2026-09-04T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"研究伊斯兰堡城市扩张与森林恢复，非中国三农或农业信息化主题，不入选。",[19],{"name":10,"url":6},[21,22,23],"遥感","土地利用","生态",{"code":25,"slug":26,"name":27,"path":28,"parent_code":29},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":32,"locked":14,"version":33,"method":34,"evidence":35},"classified","2026-10-22.2","rules",{"candidates":36,"agricultural_evidence":39},[37,44,52],{"code":25,"score":38,"supported":39,"title":40,"abstract":42,"tags":43},8,true,[41],"machine learning",[],[],{"code":45,"score":46,"supported":39,"title":47,"abstract":48,"tags":51},"H6",7,[],[49,50],"生物多样性","biodiversity",[22],{"code":53,"score":46,"supported":39,"title":54,"abstract":55,"tags":57},"I3",[],[21,56],"remote sensing",[21],[59,60],"土地利用 生态 遥感","土地利用 生态","土地利用生态遥感-1733",2,"10.3390\u002Fland15091641",{"doi":63,"openalex_id":65,"authors":66,"venue":10,"cited_by_count":15,"oa_url":6,"card":91,"direction":95,"ingested_from":97},"W7208716166",[67,70,72,74,77,80,83,86,89],{"name":68,"orcid":69},"Muhammad Tariq Badshah","https:\u002F\u002Forcid.org\u002F0000-0003-4447-4091",{"name":71,"orcid":9},"Hakim Ullah Khan",{"name":73,"orcid":9},"Muhammad Shabir",{"name":75,"orcid":76},"Shahid Rahman","https:\u002F\u002Forcid.org\u002F0000-0002-4343-7187",{"name":78,"orcid":79},"Khadim Hussain","https:\u002F\u002Forcid.org\u002F0000-0003-3351-3438",{"name":81,"orcid":82},"Farhan Amin","https:\u002F\u002Forcid.org\u002F0000-0002-6385-5511",{"name":84,"orcid":85},"Isabel de la Torre Díez","https:\u002F\u002Forcid.org\u002F0000-0003-3134-7720",{"name":87,"orcid":88},"Mirtha Silvana Garat de Marín","https:\u002F\u002Forcid.org\u002F0000-0003-3044-8087",{"name":90,"orcid":9},"Eduardo Silva Alvarado",{"tldr":92,"method":93,"finding":94,"direction":95,"opportunity":96},"利用机器学习评估伊斯兰堡1991-2021年土地利用变化、森林恢复及景观连通性。","随机森林分类Landsat影像，FRAGSTATS和LFT分析景观格局。","城市扩张显著，森林面积先降后升，但景观破碎化持续，连通性有限。","农业遥感与作物表型","可延伸研究森林恢复与景观连通性的空间协调机制，结合多尺度数据优化城市生态规划。","openalex","2026-09-05T23:30:27.381903Z",{"total":100,"page":101,"page_size":100,"items":102},6,1,[103,183,235,301,353,466],{"id":104,"title":105,"url":106,"summary":107,"summary_zh":108,"content":9,"source_name":109,"source_url":106,"published_at":110,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":111,"score_detail":112,"sources":118,"tags":120,"primary_topic":124,"secondary_topics":125,"classification":126,"search_phrases":142,"slug":145,"view_count":146,"doi":147,"paper":148,"created_at":182},3369,"Understanding Land Use\u002FLand Cover Dynamics and Land Surface Temperature Variations Through Machine Learning and Geospatial Approach: A Case Study of Kullu District, Himachal Pradesh, India","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fatmos17100916","Machine learning (ML)-based classification provides an effective approach for extracting land use\u002Fland cover (LULC) information from multi-temporal satellite observations. This study evaluates Maximum Likelihood Classification (MLC), Support Vector Machine (SVM), and Random Forest (RF) for mapping LULC changes in Kullu district, Himachal Pradesh, India, using Landsat data from 1991 to 2022. SVM achieved the highest classification accuracy and was therefore used for subsequent LULC mapping. Six LULC classes were identified and their temporal changes were examined in relation to Landsat-derived land surface temperature (LST). The built-up area increased from 4.37% in 1991 to 20.40% in 2022, whereas vegetation declined from 34.41% to 16.23% and snow cover from 15.20% to 3.02%. The area-weighted mean LST was 4.10 °C in 1991, 10.03 °C in 2001, 8.43 °C in 2011, and 4.18 °C in 2022, representing a marginal net increase of 0.08 °C over the study period. Class-wise analysis showed that barren and agricultural lands generally recorded higher mean LST, while vegetation and waterbodies exhibited comparatively lower values. The relatively high snow-cover LST in 2022 may reflect reduced and fragmented seasonal snow cover and snowmelt conditions during image acquisition. Correlation analysis revealed an inverse relationship between LST and vegetation and snow-cover indicators, with R2 values reaching 0.66 for NDVI and generally exceeding 0.62 for NDSI. Overall, the findings demonstrate substantial LULC transformation and pronounced spatial differences in surface temperature, emphasizing the role of vegetation and snow cover in regulating thermal conditions and providing useful information for sustainable land management and spatial planning in the ecologically sensitive Himalayan environment.","基于机器学习的分类为从多时相卫星观测中提取土地利用\u002F土地覆盖（LULC）信息提供了一种有效途径。本研究利用1991年至2022年的Landsat数据，评估了最大似然分类（MLC）、支持向量机（SVM）和随机森林（RF）在印度喜马偕尔邦库鲁地区LULC变化制图中的表现。SVM取得了最高的分类精度，因此被用于后续的LULC制图。研究识别出六种LULC类别，并结合Landsat反演的地表温度（LST）分析了其时间变化。建设用地面积从1991年的4.37%增至2022年的20.40%，而植被从34.41%降至16.23%，积雪覆盖从15.20%降至3.02%。面积加权平均LST在1991年为4.10 °C，2001年为10.03 °C，2011年为8.43 °C，2022年为4.18 °C，研究期间净增幅度仅为0.08 °C。分类别分析表明，裸地和农用地通常具有较高的平均LST，而植被和水体的LST相对较低。2022年积雪覆盖区LST相对较高，可能反映了影像获取时期季节性积雪覆盖减少、破碎化及融雪条件的影响。相关分析揭示了LST与植被和积雪覆盖指标之间的反比关系，NDVI的R²达到0.66，NDSI的R²普遍超过0.62。总体而言，研究结果揭示了显著的LULC转变和地表温度的明显空间差异，强调了植被和积雪覆盖在调节热力条件中的作用，并为生态敏感的喜马拉雅地区的可持续土地管理和空间规划提供了有用信息。","Atmosphere","2026-09-23T00:00:00Z",67,{"impact":38,"substance":113,"depth":114,"authority":115,"freshness":116,"relevant":101,"comment":117},20,17,13,9,"基于Landsat与机器学习揭示喜马拉雅地区土地利用与地表温度长期演变，数据翔实但属区域案例研究，公共影响有限。",[119],{"name":109,"url":106},[121,21,22,122,123],"机器学习","地表温度","喜马拉雅",{"code":25,"slug":26,"name":27,"path":28,"parent_code":29},[],{"status":32,"locked":14,"version":33,"method":34,"evidence":127},{"candidates":128,"agricultural_evidence":39},[129,134,138],{"code":25,"score":130,"supported":39,"title":131,"abstract":132,"tags":133},15,[41],[121,41],[121],{"code":53,"score":62,"supported":14,"title":135,"abstract":136,"tags":137},[],[],[122,21],{"code":45,"score":101,"supported":14,"title":139,"abstract":140,"tags":141},[],[],[22],[143,144],"Kullu 土地利用 地表温度","Landsat 机器学习 土地覆盖","Kullu土地利用地表温度-3369",3,"10.3390\u002Fatmos17100916",{"doi":147,"openalex_id":149,"authors":150,"venue":109,"cited_by_count":15,"oa_url":106,"card":176,"direction":181,"ingested_from":97},"W7214105064",[151,154,156,159,162,165,168,170,173],{"name":152,"orcid":153},"Sanjeev Jha","https:\u002F\u002Forcid.org\u002F0000-0002-6851-7722",{"name":155,"orcid":9},"Nirbhav",{"name":157,"orcid":158},"Atul Saini","https:\u002F\u002Forcid.org\u002F0000-0001-9840-2486",{"name":160,"orcid":161},"Toushif Jaman","https:\u002F\u002Forcid.org\u002F0000-0001-6225-5648",{"name":163,"orcid":164},"Harish Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-4738-1248",{"name":166,"orcid":167},"Pradeep Kumar Jha","https:\u002F\u002Forcid.org\u002F0000-0002-5938-1747",{"name":169,"orcid":9},"Fahdah Falah Ben Hasher",{"name":171,"orcid":172},"Abinash SİLWAL","https:\u002F\u002Forcid.org\u002F0000-0002-2285-4643",{"name":174,"orcid":175},"Mohamed Zhran","https:\u002F\u002Forcid.org\u002F0000-0002-1112-387X",{"tldr":177,"method":178,"finding":179,"direction":95,"opportunity":180},"用机器学习与遥感分析印度库鲁区1991-2022年土地利用变化及其对地表温度的影响。","Landsat时序数据，对比MLC、SVM、随机森林分类，结合NDVI\u002FNDSI","建设用地从4.37%增至20.40%，植被与积雪大幅减少，LST净增0.08°C，植被和积雪与LST","可引入深度学习与多源遥感，在喜马拉雅生态敏感区开展LULC-LST耦合模拟与情景预测。","农业人工智能与决策模型","2026-09-24T23:30:34.225644Z",{"id":184,"title":185,"url":186,"summary":187,"summary_zh":188,"content":9,"source_name":189,"source_url":186,"published_at":190,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":191,"score_detail":192,"sources":195,"tags":197,"primary_topic":200,"secondary_topics":201,"classification":202,"search_phrases":217,"slug":220,"view_count":62,"doi":221,"paper":222,"created_at":234},3196,"Geospatial and machine learning techniques for spatiotemporal analyses of urban dynamics in the Coimbatore city, South India","https:\u002F\u002Fdoi.org\u002F10.3846\u002Fjeelm.2026.28264","Rapid urbanization has drastically changed the land use and environmental conditions in Indian cities and need to be monitored continuously for sustainable urban planning. The study used Landsat satellite images of the years 2001, 2013 and 2023 to examine the spatiotemporal urban dynamics of the Coimbatore city of South India. The impacts of urban sprawl on the environment were evaluated from Land Use\u002F Land Cover (LULC), Land Surface Temperature (LST) and spectral indices (NDVI, NDWI and NDBI). The built-up land increased by 1.21% (2001–2013) and 1.67% (2013–2023) and agricultural land decreased by 1.62% (2013–2023). The LULC classification had an Overall Accuracy of 95.76% with a Kappa coefficient of 0.95. The Kappa coefficient of 0.96 indicates that the ANN-CA model has high predictive reliability in predicting the LULC scenario in 2031. The results show that the continued expansion of cities leads to an increase in land surface temperature and a decrease in vegetation cover. This has major implications for sustainable urban development and contributes to SDG 11 and SDG 13.","快速城市化极大地改变了印度城市的土地利用和环境状况，需要持续监测以支持可持续城市规划。本研究利用2001年、2013年和2023年的Landsat卫星影像，考察了印度南部哥印拜陀市的时空城市动态。研究从土地利用\u002F土地覆盖（LULC）、地表温度（LST）和光谱指数（NDVI、NDWI和NDBI）方面评估了城市蔓延对环境的影响。建设用地在2001—2013年间增加了1.21%，在2013—2023年间增加了1.67%；农业用地在2013—2023年间减少了1.62%。LULC分类的总体精度为95.76%，Kappa系数为0.95。Kappa系数0.96表明，ANN-CA模型在预测2031年LULC情景方面具有较高的预测可靠性。结果表明，城市持续扩张导致地表温度升高和植被覆盖减少。这对可持续城市发展具有重要影响，并有助于实现可持续发展目标11和可持续发展目标13。","Journal of Environmental Engineering and Landscape Management","2026-09-21T00:00:00Z",62,{"impact":38,"substance":193,"depth":130,"authority":115,"freshness":38,"relevant":101,"comment":194},18,"基于Landsat多时相遥感与ANN-CA模型的印度城市扩张研究，方法规范、数据翔实，对农业用地变化与遥感监测有参考价值，但属境外区域案例，公共影响有限。",[196],{"name":189,"url":186},[198,21,22,122,199],"可持续发展","城市扩张",{"code":25,"slug":26,"name":27,"path":28,"parent_code":29},[],{"status":32,"locked":14,"version":33,"method":34,"evidence":203},{"candidates":204,"agricultural_evidence":39},[205,209,213],{"code":25,"score":38,"supported":39,"title":206,"abstract":207,"tags":208},[41],[],[],{"code":53,"score":62,"supported":14,"title":210,"abstract":211,"tags":212},[],[],[122,21],{"code":45,"score":101,"supported":14,"title":214,"abstract":215,"tags":216},[],[],[22],[218,219],"Coimbatore 城市扩张 遥感","LULC LST NDVI 印度城市","Coimbatore城市扩张遥感-3196","10.3846\u002Fjeelm.2026.28264",{"doi":221,"openalex_id":223,"authors":224,"venue":189,"cited_by_count":15,"oa_url":186,"card":229,"direction":181,"ingested_from":97},"W7213862636",[225,227],{"name":226,"orcid":9},"Nagamani Singamuthu",{"name":228,"orcid":9},"Elangovan Krishnan",{"tldr":230,"method":231,"finding":232,"direction":95,"opportunity":233},"基于Landsat影像与机器学习分析印度哥印拜陀市2001-2023年城市扩张及其环境效应。","Landsat影像、LULC分类、NDVI\u002FNDWI\u002FNDBI指数、ANN-CA","建设用地增加、农地减少，城市扩张导致地表温度上升、植被覆盖下降。","可结合多源遥感与深度学习提升城市扩张预测精度，并探究其对周边农业用地的长期影响。","2026-09-22T23:30:43.496849Z",{"id":236,"title":237,"url":238,"summary":239,"summary_zh":240,"content":9,"source_name":241,"source_url":238,"published_at":242,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":243,"score_detail":244,"sources":248,"tags":250,"primary_topic":255,"secondary_topics":256,"classification":257,"search_phrases":285,"slug":288,"view_count":15,"doi":289,"paper":290,"created_at":300},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":245,"substance":246,"depth":193,"authority":245,"freshness":13,"relevant":101,"comment":247},12,22,"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。",[249],{"name":241,"url":238},[251,252,253,21,254],"智慧农业","农业人工智能","粮食安全","气候分析",{"code":25,"slug":26,"name":27,"path":28,"parent_code":29},[],{"status":32,"locked":14,"version":33,"method":34,"evidence":258},{"candidates":259,"agricultural_evidence":39},[260,265,271,276,281],{"code":25,"score":116,"supported":39,"title":261,"abstract":263,"tags":264},[262],"artificial intelligence",[],[252],{"code":266,"score":146,"supported":39,"title":267,"abstract":268,"tags":270},"I5",[],[269],"农业传感",[],{"code":272,"score":101,"supported":14,"title":273,"abstract":274,"tags":275},"A8",[],[],[253],{"code":277,"score":101,"supported":14,"title":278,"abstract":279,"tags":280},"I1",[],[],[251],{"code":53,"score":101,"supported":14,"title":282,"abstract":283,"tags":284},[],[],[21],[286,287],"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":289,"openalex_id":291,"authors":292,"venue":241,"cited_by_count":15,"oa_url":238,"card":295,"direction":181,"ingested_from":97},"W7223111766",[293],{"name":294,"orcid":9},"Fatima-Zahra El Khattabi",{"tldr":296,"method":297,"finding":298,"direction":95,"opportunity":299},"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":302,"title":303,"url":304,"summary":305,"summary_zh":306,"content":9,"source_name":241,"source_url":304,"published_at":242,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":111,"score_detail":307,"sources":309,"tags":311,"primary_topic":314,"secondary_topics":315,"classification":320,"search_phrases":337,"slug":340,"view_count":15,"doi":341,"paper":342,"created_at":352},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。",{"impact":38,"substance":113,"depth":114,"authority":245,"freshness":13,"relevant":101,"comment":308},"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。",[310],{"name":241,"url":304},[251,252,312,21,313],"产量预测","可解释性",{"code":25,"slug":26,"name":27,"path":28,"parent_code":29},[316],{"code":53,"slug":317,"name":318,"path":319,"parent_code":29},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":32,"locked":14,"version":33,"method":34,"evidence":321},{"candidates":322,"agricultural_evidence":39},[323,329,333],{"code":25,"score":245,"supported":39,"title":324,"abstract":326,"tags":328},[325],"computer vision",[327],"计算机视觉",[252],{"code":53,"score":38,"supported":39,"title":330,"abstract":331,"tags":332},[],[21,56],[312,21],{"code":277,"score":101,"supported":14,"title":334,"abstract":335,"tags":336},[],[],[251],[338,339],"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":341,"openalex_id":343,"authors":344,"venue":241,"cited_by_count":15,"oa_url":304,"card":347,"direction":95,"ingested_from":97},"W7223114002",[345],{"name":346,"orcid":9},"Kadiatou Barry",{"tldr":348,"method":349,"finding":350,"direction":95,"opportunity":351},"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":354,"title":355,"url":356,"summary":357,"summary_zh":358,"content":9,"source_name":359,"source_url":356,"published_at":242,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":360,"score_detail":361,"sources":363,"tags":365,"primary_topic":368,"secondary_topics":374,"classification":380,"search_phrases":436,"slug":439,"view_count":15,"doi":440,"paper":441,"created_at":465},5564,"Dynamic thermal imaging for crop water stress detection across phenological stages: Enhancing irrigation scheduling precision","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.17728","Thermal remote sensing and derived indices such as the crop water stress index (CWSI) can assess crop water status and support precision irrigation. However, most operational and artificial intelligence (AI)-based approaches rely on fixed, season-long thresholds to interpret canopy–air temperature differences and often overlook the influence of crop phenology. This review synthesises evidence from grapevine, wheat, maize, potato and several other crops on how developmental stage affects the non-water-stressed baseline (NWSB), CWSI thresholds and irrigation-scheduling algorithms. It covers CWSI theory and estimation practices; crop-specific, phenology-dependent behaviour of baselines and thresholds; a comparison of threshold-based, machine learning, Internet of Things (IoT), digital twin and Food and Agriculture Organization (FAO)-56 approaches with respect to crop phenology; and a conceptual framework for phenology-aware scheduling. Across the reviewed crops, baseline behaviour generally varied systematically across the growing season, the relationship between CWSI and crop water status generally strengthened after canopy closure and CWSI measured during reproductive stages was generally most closely related to yield. A quantitative audit of 56 AI-, thermal-imaging-, or CWSI-based studies found that 78.6 % either did not explicitly incorporate crop phenology or relied on day of year as a temporal proxy, whereas only 5.4 % explicitly encoded phenological stage. The proposed framework considers phenology explicitly in baseline estimation, threshold setting and data fusion or machine learning for irrigation scheduling; the framework will require multi-year, multi-site field validation before operational use. Future research should prioritise standardised phenology definitions and long-term, multi-location studies to establish and validate stage-specific parameters.","热红外遥感及其衍生的作物水分胁迫指数（crop water stress index, CWSI）等指标可评估作物水分状况并支持精准灌溉。然而，大多数业务化方法和基于人工智能（artificial intelligence, AI）的方法依赖固定的全生育期阈值来解释冠层—空气温差，且往往忽视作物物候的影响。本文综述了葡萄、小麦、玉米、马铃薯及其他若干作物在发育阶段如何影响非水分胁迫基线（non-water-stressed baseline, NWSB）、CWSI阈值和灌溉调度算法的相关证据。综述内容涵盖CWSI理论与估算实践；基线和阈值的作物特异性、物候依赖性行为；基于阈值、机器学习、物联网（Internet of Things, IoT）、数字孪生及联合国粮农组织（Food and Agriculture Organization, FAO）-56等方法在作物物候方面的比较；以及物候感知调度的概念框架。在所综述的作物中，基线行为在整个生长季通常呈系统性变化，CWSI与作物水分状况之间的关系在冠层封垄后总体增强，且在生殖阶段测得的CWSI与产量的关系总体最为密切。对56项基于AI、热成像或CWSI的研究进行的定量审查发现，78.6%的研究要么未明确纳入作物物候，要么依赖年积日作为时间代理变量，而仅5.4%的研究明确编码了物候阶段。所提出的框架在基线估算、阈值设定以及用于灌溉调度的数据融合或机器学习中明确考虑物候；该框架在投入业务应用前尚需多年、多站点的田间验证。未来研究应优先建立标准化的物候定义，并开展长期、多地点研究以建立和验证阶段特异性参数。","Plant Science Today",79,{"impact":193,"substance":246,"depth":193,"authority":245,"freshness":116,"relevant":101,"comment":362},"系统综述热成像与CWSI在物候期感知灌溉中的应用，含56项研究量化审计，方法学价值突出，但属综述类论文，影响偏学术层面。",[364],{"name":359,"url":356},[251,252,21,366,367],"作物水分胁迫","精准灌溉",{"code":369,"slug":370,"name":371,"path":372,"parent_code":373},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[375,376],{"code":25,"slug":26,"name":27,"path":28,"parent_code":29},{"code":266,"slug":377,"name":378,"path":379,"parent_code":29},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":32,"locked":14,"version":33,"method":34,"evidence":381},{"candidates":382,"agricultural_evidence":39},[383,388,392,399,405,409,416,423,430],{"code":369,"score":130,"supported":39,"title":384,"abstract":385,"tags":387},[370],[386,370],"灌溉",[367],{"code":25,"score":13,"supported":39,"title":389,"abstract":390,"tags":391},[],[121,41,262],[252],{"code":266,"score":116,"supported":39,"title":393,"abstract":394,"tags":398},[],[395,396,397],"物联网","internet of things","IoT",[],{"code":277,"score":46,"supported":39,"title":400,"abstract":401,"tags":404},[],[402,403],"数字孪生","digital twin",[251],{"code":53,"score":46,"supported":39,"title":406,"abstract":407,"tags":408},[],[21,56],[21],{"code":410,"score":100,"supported":39,"title":411,"abstract":412,"tags":415},"A2",[],[413,414],"小麦","wheat",[],{"code":417,"score":100,"supported":39,"title":418,"abstract":419,"tags":422},"A3",[],[420,421],"玉米","maize",[],{"code":424,"score":100,"supported":39,"title":425,"abstract":426,"tags":429},"A5",[],[427,428],"马铃薯","potato",[],{"code":431,"score":146,"supported":39,"title":432,"abstract":433,"tags":435},"B2",[],[434],"葡萄",[],[437,438],"热成像 作物水分胁迫","CWSI 灌溉调度","热成像作物水分胁迫-5564","10.14719\u002Fpst.17728",{"doi":440,"openalex_id":442,"authors":443,"venue":359,"cited_by_count":15,"oa_url":356,"card":459,"direction":464,"ingested_from":97},"W7223088921",[444,447,450,453,456],{"name":445,"orcid":446},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":448,"orcid":449},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":451,"orcid":452},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":454,"orcid":455},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":457,"orcid":458},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":460,"method":461,"finding":462,"direction":95,"opportunity":463},"综述物候期对热成像CWSI阈值的影响，提出物候感知灌溉调度框架。","综述葡萄、小麦、玉米等作物的热遥感CWSI研究，并审计56项AI\u002F热成像研究。","78.6%研究未显式纳入物候，仅5.4%编码物候期；CWSI与产量在生殖期关联最强。","可开展多站点多年物候期特异性CWSI阈值标定与标准化物候定义研究。","智慧农业 \u002F 农业物联网","2026-10-22T11:20:24.526683Z",{"id":467,"title":468,"url":469,"summary":470,"summary_zh":471,"content":9,"source_name":472,"source_url":469,"published_at":473,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":474,"sources":476,"tags":478,"primary_topic":480,"secondary_topics":481,"classification":487,"search_phrases":509,"slug":512,"view_count":15,"doi":513,"paper":514,"created_at":531},5485,"Integrating machine learning and geospatial technologies for spatio-temporal analysis of urban expansion and land use transformation in Gambella Town, Ethiopia","https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pone.0360419","Rapid urbanization and the associated expansion of built-up areas are transforming land resources, ecosystem functions, and the spatial structure of cities. Distinguishing urban growth, urban expansion, and urban sprawl is essential for understanding the processes and patterns of urban transformation and supporting sustainable urban planning. This study integrates machine learning and geospatial technologies to analyze spatio-temporal land use and land cover (LULC) dynamics and urban expansion in Gambella Town, Ethiopia. Multi-temporal Landsat satellite images from 2005, 2015, and 2025 were processed using remote sensing techniques and a supervised machine learning approach based on the Random Forest (RF) classifier. Six major LULC classes were identified: Agricultural Land, Bush with Grass, Built-Up Area, Bare Land, Forest Land, and Water Bodies. The classification achieved overall accuracies of 90%, 90%, and 89% for 2005, 2015, and 2025, respectively. The results revealed substantial land transformation and rapid physical urban expansion, with the built-up area increasing from 93 ha (2.0%) in 2005–1,956 ha (41.3%) in 2025. This expansion was accompanied by major transformations of surrounding land resources, particularly the conversion of Bush with Grass and other open areas to built-up land. Agricultural land increased from 634 ha (13.4%) to 1,750 ha (36.9%), whereas Bush with Grass declined from 3,375 ha (71.1%) to 652 ha (13.7%). Socio-economic information collected from 277 respondents indicated that population growth, rural–urban migration, housing demand, employment opportunities, and infrastructure development were major drivers of urban expansion. The study distinguishes urban expansion, represented by the increase in built-up land, from urban sprawl, which refers to the spatial pattern and form of outward urban development. The integration of Random Forest classification and GIS-based spatial analysis provides valuable evidence of land transformation, vegetation loss, and increasing pressure on urban services, supporting more sustainable urban planning and land management in Gambella Town.","快速城市化及与之相关的建成区扩张正在改变土地资源、生态系统功能和城市空间结构。区分城市增长（urban growth）、城市扩张（urban expansion）和城市蔓延（urban sprawl），对于理解城市转型的过程与格局、支撑可持续城市规划至关重要。本研究融合机器学习与地理空间技术，分析埃塞俄比亚甘贝拉镇（Gambella Town）土地利用与土地覆盖（LULC）的时空动态及城市扩张。采用遥感技术和基于随机森林（RF）分类器的监督机器学习方法，处理了2005年、2015年和2025年的多时相Landsat卫星影像。研究识别出六类主要LULC类型：农业用地、灌草地、建成区、裸地、林地和水体。2005年、2015年和2025年的分类总体精度分别为90%、90%和89%。结果表明，土地发生了显著转变，城市实体空间快速扩张，建成区面积从2005年的93公顷（2.0%）增至2025年的1,956公顷（41.3%）。这一扩张伴随着周边土地资源的重大转变，尤其是灌草地及其他开阔地被转化为建成用地。农业用地从634公顷（13.4%）增至1,750公顷（36.9%），而灌草地则从3,375公顷（71.1%）降至652公顷（13.7%）。从277名受访者收集的社会经济信息表明，人口增长、城乡迁移、住房需求、就业机会和基础设施建设是城市扩张的主要驱动因素。本研究将城市扩张（以建成用地增加为表征）与城市蔓延（指城市向外发展的空间格局与形态）加以区分。随机森林分类与基于GIS的空间分析的融合，为土地转变、植被丧失及城市服务压力加剧提供了有价值的证据，有助于甘贝拉镇实现更可持续的城市规划与土地管理。","PLoS ONE","2026-10-20T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":475},"研究聚焦埃塞俄比亚城市扩张与土地利用变化，属城市地理与遥感应用，与三农、农业信息化主题无直接关联，不建议进入每日精选。",[477],{"name":472,"url":469},[121,479,22,199],"遥感监测",{"code":25,"slug":26,"name":27,"path":28,"parent_code":29},[482],{"code":483,"slug":484,"name":485,"path":486,"parent_code":29},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"status":32,"locked":14,"version":33,"method":34,"evidence":488},{"candidates":489,"agricultural_evidence":39},[490,494,501,505],{"code":25,"score":130,"supported":39,"title":491,"abstract":492,"tags":493},[41],[121,41],[121],{"code":483,"score":116,"supported":39,"title":495,"abstract":496,"tags":500},[],[497,498,499],"空间分析","GIS","spatial analysis",[],{"code":53,"score":46,"supported":39,"title":502,"abstract":503,"tags":504},[],[21,56],[479],{"code":45,"score":101,"supported":14,"title":506,"abstract":507,"tags":508},[],[],[22],[510,511],"Gambella Town 城市扩张","Random Forest 土地利用分类","GambellaTown城市扩张-5485","10.1371\u002Fjournal.pone.0360419",{"doi":513,"openalex_id":515,"authors":516,"venue":472,"cited_by_count":15,"oa_url":469,"card":526,"direction":95,"ingested_from":97},"W7223022155",[517,520,522,524],{"name":518,"orcid":519},"Kanenus Fufa Dararo","https:\u002F\u002Forcid.org\u002F0009-0007-5747-1558",{"name":521,"orcid":9},"Goanar Jal Dup",{"name":523,"orcid":9},"Weyesa Merga Chemeda",{"name":525,"orcid":9},"Shankar Karuppannan",{"tldr":527,"method":528,"finding":529,"direction":95,"opportunity":530},"用机器学习与遥感分析埃塞俄比亚甘贝拉镇2005-2025年城市扩张与土地利用变化。","Landsat多时相影像、随机森林分类与GIS空间分析，结合277份问卷。","建成区从93公顷增至1956公顷，灌草地大幅转为建成区和农地。","可将随机森林时序分类与驱动力调查结合，扩展到更多非洲小城镇并耦合城市增长模拟。","2026-10-21T23:30:23.271293Z"]