[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4435":3,"related-4435":110},{"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":33,"secondary_topics":38,"classification":39,"search_phrases":81,"slug":84,"view_count":85,"doi":86,"paper":87,"created_at":109},4435,"Land Use Change, Flood Exposure, and Vegetation Stress in the Chi River Basin, Northeast Thailand: A Multi-Temporal Remote Sensing and Machine Learning Assessment","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu181910188","Climate change has intensified hydrological extremes in Southeast Asia, yet the relationships between land use change, flood exposure, and vegetation stress remain poorly understood in tropical floodplain environments. This study assessed land use dynamics, flood exposure, and vegetation stress in the Upper Chi River Basin, Northeast Thailand, using multi-temporal remote sensing datasets spanning 2015–2024, official land use data from the Land Development Department (LDD), and machine learning approaches, including Random Forest classification of binary flood occurrence, SHAP analysis, and spatial statistics. Agricultural land dominated the landscape (84.2% in 2023), with 11.0% land use change occurring between 2015 and 2023, primarily agricultural conversion from forest (38.6% of Forest Area converted) and water body (30.0% of Water Body converted). Flood frequency was highest in areas classified as Water Bodies (0.38 events) and Agricultural Areas (0.55 events), while Forest Areas showed the greatest resilience (0.05 events). Random Forest classification of flood occurrence achieved an overall accuracy of 0.920 and AUC-ROC of 0.905, with DEM emerging as the dominant predictor in both Random Forest importance (0.435) and mean absolute SHAP (0.308). Vegetation stress affected 21.1% of the basin, with 59.2% of points showing an NDVI decline greater than 0.02 and 33.3% falling below the VCI \u003C 0.4 drought threshold. Four sensitivity classes were identified: Low (66.4%), Moderate (19.2%), High (12.4%), and Very High (2.0%), with Very High Sensitivity concentrated along the Chi River corridor in low-lying areas (mean DEM = 146.0 m). Moran’s I confirmed strong spatial clustering of flood frequency (I = 0.787) and sensitivity class (I = 0.534), highlighting the need for spatial statistical approaches. The findings support elevation-based land use zoning, forest conservation, and climate-resilient agricultural practices to enhance flood resilience and contribute to Sustainable Development Goals 2 (Zero Hunger), 11 (Sustainable Cities), 13 (Climate Action), and 15 (Life on Land).","气候变化加剧了东南亚的水文极端事件，但在热带洪泛平原环境中，土地利用变化、洪水暴露与植被胁迫之间的关系仍缺乏深入认识。本研究利用2015—2024年多时相遥感数据集、泰国土地发展厅（LDD）的官方土地利用数据以及机器学习方法（包括对二元洪水发生事件的随机森林分类、SHAP分析和空间统计），评估了泰国东北部奇河上游流域的土地利用动态、洪水暴露和植被胁迫。农业用地在景观中占主导地位（2023年为84.2%），2015—2023年间土地利用变化率为11.0%，主要表现为农业用地扩张导致的林地转换（38.6%的林地面积被转换）和水体转换（30.0%的水体面积被转换）。洪水频率在水体分类区域（0.38次）和农业用地分类区域（0.55次）最高，而林地表现出最强的恢复力（0.05次）。洪水发生事件的随机森林分类总体精度达到0.920，AUC-ROC为0.905，其中DEM在随机森林重要性（0.435）和平均绝对SHAP值（0.308）中均为最主要的预测因子。植被胁迫影响了流域21.1%的区域，59.2%的样点NDVI下降幅度超过0.02，33.3%的样点低于VCI \u003C 0.4的干旱阈值。研究识别出四个敏感性等级：低敏感（66.4%）、中等敏感（19.2%）、高敏感（12.4%）和极高敏感（2.0%），其中极高敏感区集中在奇河沿岸低洼地带（平均DEM = 146.0 m）。Moran's I证实了洪水频率（I = 0.787）和敏感性等级（I = 0.534）具有强烈的空间聚集性，凸显了空间统计方法的必要性。研究结果支持基于海拔的土地利用分区、森林保护以及气候适应性农业实践，以增强洪水恢复力，并助力实现可持续发展目标2（零饥饿）、11（可持续城市）、13（气候行动）和15（陆地生物）。",null,"Sustainability","2026-10-07T00:00:00Z","论文",10,false,70,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,22,18,13,9,1,"基于多时相遥感与机器学习的流域土地利用—洪涝—植被胁迫评估，方法扎实、数据翔实，对农业防灾与气候韧性有参考价值，但属区域性研究，公共影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31,32],"数字乡村","机器学习","遥感监测","土地利用","洪涝风险","植被胁迫",{"code":34,"slug":35,"name":36,"path":37,"parent_code":9},"I","digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"partial","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":48,"reason":80},[45,53,60,70,75],{"code":46,"score":47,"supported":48,"title":49,"abstract":51,"tags":52},"I2",15,true,[50],"machine learning",[28,50],[28],{"code":54,"score":47,"supported":48,"title":55,"abstract":57,"tags":59},"I3",[56],"remote sensing",[58,56],"遥感",[29],{"code":61,"score":21,"supported":48,"title":62,"abstract":63,"tags":69},"H7",[],[64,65,66,67,68],"气候变化","气候适应","干旱","climate change","drought",[],{"code":71,"score":22,"supported":14,"title":72,"abstract":73,"tags":74},"H6",[],[],[30],{"code":76,"score":22,"supported":14,"title":77,"abstract":78,"tags":79},"I6",[],[],[27],"一级领域明确，二级方向需要细分",[82,83],"泰国 Chi River 流域 遥感","土地利用变化 洪涝暴露 植被胁迫","泰国ChiRiver流域遥感-4435",2,"10.3390\u002Fsu181910188",{"doi":86,"openalex_id":88,"authors":89,"venue":10,"cited_by_count":101,"oa_url":6,"card":102,"direction":106,"ingested_from":108},"W7220850940",[90,92,95,98],{"name":91,"orcid":9},"Jiradech Majandang",{"name":93,"orcid":94},"Patiwat Littidej","https:\u002F\u002Forcid.org\u002F0000-0002-1024-547X",{"name":96,"orcid":97},"Benjamabhorn Pumhirunroj","https:\u002F\u002Forcid.org\u002F0009-0009-6607-5594",{"name":99,"orcid":100},"D. C. Slack","https:\u002F\u002Forcid.org\u002F0000-0003-0324-2163",0,{"tldr":103,"method":104,"finding":105,"direction":106,"opportunity":107},"用多时相遥感和机器学习评估泰国栖河流域土地利用变化、洪水暴露与植被胁迫的关系。","2015–2024年多时相遥感、LDD土地利用数据、随机森林、SHAP与空间统计","农地占84.2%，洪水频发于农地和水体，21.1%流域植被受胁迫，DEM是洪水主导因子。","农业遥感与作物表型","可结合高分辨率时序遥感与作物模型，量化洪水胁迫对热带平原农业产量的级联影响。","openalex","2026-10-08T23:30:29.519607Z",{"total":111,"page":22,"page_size":111,"items":112},6,[113,177,252,321,418,496],{"id":114,"title":115,"url":116,"summary":117,"summary_zh":118,"content":9,"source_name":119,"source_url":116,"published_at":120,"category":12,"cover_url":9,"hotness":121,"is_selected":14,"score":122,"score_detail":123,"sources":127,"tags":131,"primary_topic":134,"secondary_topics":135,"classification":136,"search_phrases":161,"slug":164,"view_count":101,"doi":165,"paper":166,"created_at":176},4305,"A Comparative Geographical Analysis of Land Use Patterns in Akrani and Nandurbar Tehsils of Nandurbar District","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23155213","Abstract \"A Comparative Geographical Analysis of Land Use Patterns in Akrani and Nandurbar Tehsils in Nandurbar District,\" aims to analyze and contrast the features and spatial distribution of different land-use categories in the two tehsils. An key measure of how human activity and the physical environment interact is land use. Major land-use categories include agricultural land, forest land, fallow land, barren and uncultivable land, grazing land, built-up land, and water bodies are the focus of the study. Akrani Tehsil is distinguished by hilly and rugged terrain, a substantial amount of forest cover, and primarily rain-fed agriculture, whereas Nandurbar Tehsil has relatively favorable terrain, agricultural conditions, irrigation facilities, and a higher concentration of settlements and other non-agricultural activities. In addition to geographical methods like GIS and remote sensing when appropriate, the study makes use of secondary data from government reports, Census publications, statistical records, maps, and other pertinent sources. In order to determine the regional variances, similarities, and differences in land-use patterns, as well as to investigate the impact of socioeconomic and physical factors on land usage, a comparative study is conducted. By improving knowledge of regional differences in land use, the study is expected to support sustainable resource management, land-use planning, and balanced regional development in the Nandurbar District.","《阿克兰尼与南杜尔巴尔塔卢卡土地利用模式的比较地理分析——以楠杜尔巴尔县为例》旨在分析和对比这两个塔卢卡不同土地利用类别的特征与空间分布。土地利用是衡量人类活动与自然环境相互作用的重要指标。研究重点关注的主要土地利用类别包括农业用地、林地、休耕地、荒地和不可耕种地、放牧地、建设用地及水体。阿克兰尼塔卢卡以丘陵崎岖地形、大面积森林覆盖和以雨养农业为主为特征，而南杜尔巴尔塔卢卡则拥有相对有利的地形、农业条件、灌溉设施，以及更集中的居民点和其他非农业活动。本研究在适当情况下运用地理信息系统（GIS）和遥感等地理学方法，同时利用来自政府报告、人口普查出版物、统计记录、地图及其他相关来源的二手数据。为确定土地利用模式的区域差异、相似性与不同之处，并探究社会经济和自然因素对土地利用的影响，本研究进行了比较分析。通过增进对土地利用区域差异的认识，该研究有望为楠杜尔巴尔县的可持续资源管理、土地利用规划和区域均衡发展提供支持。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-31T00:00:00Z",25,45,{"impact":111,"substance":124,"depth":20,"authority":125,"freshness":101,"relevant":22,"comment":126},14,12,"印度地方性土地利用对比研究，方法常规、无全国性价值，且发布日期晚于今日，时效性为零，不建议进入每日精选。",[128,129],{"name":119,"url":116},{"name":119,"url":130},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23155212",[29,30,132,133],"区域发展","GIS",{"code":34,"slug":35,"name":36,"path":37,"parent_code":9},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":137},{"candidates":138,"agricultural_evidence":48,"reason":80},[139,144,150,157],{"code":54,"score":140,"supported":48,"title":141,"abstract":142,"tags":143},7,[],[58,56],[29],{"code":145,"score":140,"supported":48,"title":146,"abstract":147,"tags":149},"I4",[],[148,133],"地理信息",[133],{"code":151,"score":111,"supported":48,"title":152,"abstract":153,"tags":156},"H3",[],[154,155],"灌溉","irrigation",[],{"code":71,"score":22,"supported":14,"title":158,"abstract":159,"tags":160},[],[],[30],[162,163],"Nandurbar 土地利用 对比","Akrani Nandurbar 遥感","Nandurbar土地利用对比-4305","10.5281\u002Fzenodo.23155213",{"doi":165,"openalex_id":167,"authors":168,"venue":119,"cited_by_count":101,"oa_url":116,"card":171,"direction":106,"ingested_from":108},"W7220403169",[169],{"name":170,"orcid":9},"Amol Rameshwarrao Bhuyar",{"tldr":172,"method":173,"finding":174,"direction":106,"opportunity":175},"对比分析印度Nandurbar县Akrani与Nandurbar两个乡的土地利用格局差异。","基于政府报告、普查等二手数据，结合GIS与遥感进行对比地理分析。","Akrani以林地、雨养农业为主，Nandurbar地形与灌溉条件更优、建设用地更集中。","可引入多时相遥感影像与农户调查，量化地形—灌溉差异对土地利用变化的驱动机制。","2026-10-06T23:30:19.626838Z",{"id":178,"title":179,"url":180,"summary":181,"summary_zh":182,"content":9,"source_name":183,"source_url":180,"published_at":184,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":185,"score_detail":186,"sources":189,"tags":191,"primary_topic":195,"secondary_topics":196,"classification":197,"search_phrases":224,"slug":227,"view_count":228,"doi":229,"paper":230,"created_at":251},3553,"Advances and gaps in mitigation and management practices across critical landscapes and scales","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jenvman.2026.131013","Environmental systems are increasingly stressed by the interacting effects of climate change, land-use change, agricultural intensification, urbanization, and emerging contaminants. This paper synthesizes contributions to the Journal of Environmental Management special collection, “Advances and gaps in mitigation and management practices across critical landscapes and scales,” which examines current progress and remaining challenges in environmental mitigation and best management practices. The reviewed studies span diverse landscapes, scales, and management contexts, with emphasis on water quality, water quantity, pollutant transport, ecological resilience, and sustainable agricultural systems. Four major themes emerge: the combined pressures of climate and human activity on environmental systems; innovations in modeling, monitoring, remote sensing, and statistical forecasting; evaluation of management interventions and mitigation strategies; and cross-cutting insights related to spatial variability, extreme events, long-term dynamics, and implementation gaps. Collectively, the studies demonstrate that mitigation effectiveness is highly context-dependent and influenced by hydrologic connectivity, land use, climate variability, management history, and social factors. Advances in process-based models, machine learning, geospatial technologies, and monitoring, reporting, and verification frameworks are improving the ability to predict environmental responses and evaluate intervention outcomes. However, persistent gaps remain in long-term monitoring, maintenance of management practices, adaptation to extreme events, and translation of scientific findings into policy and practice. This synthesis highlights the need for integrated, adaptive, and stakeholder-informed approaches to strengthen environmental resilience across critical landscapes.","环境系统日益受到气候变化、土地利用变化、农业集约化、城市化以及新兴污染物相互作用的影响。本文综合了《环境管理杂志》特刊“关键景观与尺度下减缓与管理实践的进展与差距”的贡献，该特刊审视了环境减缓与最佳管理实践的当前进展及剩余挑战。所综述的研究涵盖多样化的景观、尺度和管理情境，重点关注水质、水量、污染物迁移、生态韧性和可持续农业系统。由此浮现出四大主题：气候与人类活动对环境系统的复合压力；建模、监测、遥感与统计预测方面的创新；管理干预与减缓策略的评估；以及关于空间变异性、极端事件、长期动态与实施差距的交叉性见解。总体而言，这些研究表明，减缓效果高度依赖于具体情境，并受水文连通性、土地利用、气候变异性、管理历史和社会因素的影响。基于过程的模型、机器学习、地理空间技术以及监测、报告与核查框架的进步，正在提升预测环境响应和评估干预结果的能力。然而，在长期监测、管理实践的维持、极端事件的适应以及科学发现向政策与实践的转化方面，仍存在持续性的差距。本综述强调，需要采取综合性、适应性且利益相关者知情的方法，以增强关键景观的环境韧性。","Journal of Environmental Management","2026-09-25T00:00:00Z",78,{"impact":19,"substance":187,"depth":19,"authority":124,"freshness":17,"relevant":22,"comment":188},20,"核心期刊综述，系统梳理环境缓解与管理实践进展与缺口，对农业面源污染与水质管理有参考价值，但非农业信息化直接落地成果。",[190],{"name":183,"url":180},[28,192,29,193,194],"农业面源污染","农业可持续发展","水质管理",{"code":34,"slug":35,"name":36,"path":37,"parent_code":9},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":198},{"candidates":199,"agricultural_evidence":48,"reason":80},[200,204,208,212,219],{"code":46,"score":140,"supported":48,"title":201,"abstract":202,"tags":203},[],[28,50],[28],{"code":54,"score":140,"supported":48,"title":205,"abstract":206,"tags":207},[],[58,56],[29],{"code":61,"score":111,"supported":48,"title":209,"abstract":210,"tags":211},[],[64,67],[],{"code":213,"score":214,"supported":48,"title":215,"abstract":216,"tags":218},"H8",4,[],[217],"可持续农业",[193],{"code":220,"score":22,"supported":14,"title":221,"abstract":222,"tags":223},"H5",[],[],[192],[225,226],"Journal of Environmental Management 缓解措施 管理实践","农业可持续发展 农业面源污染 机器学习 水质管理","JournalofEnvironmentalManagement缓解措施管理实践-3553",3,"10.1016\u002Fj.jenvman.2026.131013",{"doi":229,"openalex_id":231,"authors":232,"venue":183,"cited_by_count":101,"oa_url":180,"card":245,"direction":106,"ingested_from":108},"W7214310594",[233,236,239,242],{"name":234,"orcid":235},"Fouad H. Jaber","https:\u002F\u002Forcid.org\u002F0000-0001-8643-8668",{"name":237,"orcid":238},"Latif Kalin","https:\u002F\u002Forcid.org\u002F0000-0001-9562-8834",{"name":240,"orcid":241},"Soni  Mulmi Pradhanang","https:\u002F\u002Forcid.org\u002F0000-0002-1142-9457",{"name":243,"orcid":244},"Aleksey Y. Sheshukov","https:\u002F\u002Forcid.org\u002F0000-0002-4842-908X",{"tldr":246,"method":247,"finding":248,"direction":249,"opportunity":250},"综述环境减缓与管理实践进展，指出成效高度依赖情境且存在实施缺口。","综合多景观尺度研究，涵盖过程模型、机器学习、遥感与MRV框架。","减缓效果受水文连通、土地利用、气候与历史管理影响，长期监测与政策转化仍不足。","农业绿色发展与碳","可研究农业景观中管理实践的长期维持机制与极端事件下的适应性策略。","2026-09-26T23:30:29.702279Z",{"id":253,"title":254,"url":255,"summary":256,"summary_zh":257,"content":9,"source_name":258,"source_url":255,"published_at":259,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":101,"score_detail":260,"sources":262,"tags":264,"primary_topic":266,"secondary_topics":267,"classification":268,"search_phrases":291,"slug":294,"view_count":214,"doi":295,"paper":296,"created_at":320},3493,"Urbanization and Land Use\u002FLand Cover Change in Jalingo Local Government Area, Taraba State, Nigeria: A Multi-Temporal Remote Sensing and Socio-Economic Assessment","https:\u002F\u002Fdoi.org\u002F10.54117\u002Fcanhm718","Rapid urbanization is a major driver of land use and land cover (LULC) transformation, particularly in rapidly developing cities of sub-Saharan Africa. This study examined the impact of urbanization on LULC in Jalingo Local Government Area of Taraba State, Nigeria, with particular emphasis on the period 1994–2024. The study integrated multi-temporal Landsat satellite imagery, Geographic Information Systems (GIS), post-classification change detection and questionnaire data to assess the spatial and temporal dynamics of urban growth and its environmental and socio-economic implications. Landsat 5 TM, Landsat 7 ETM+ and Landsat 8 OLI\u002FTIRS imagery for 1994, 2004, 2014 and 2024 were classified into four major LULC categories: built-up area, vegetation, bare surface and water body. A questionnaire survey involving 384 respondents was also used to assess perceived drivers and consequences of urbanization. The results revealed substantial transformation of the Jalingo landscape. Vegetation declined from 170.56 km² (87.47%) in 1994 to 50.39 km² (25.84%) in 2024, representing a net loss of 120.17 km². In contrast, built-up land increased from 4.30 km² (2.20%) to 18.06 km² (9.26%), while bare surfaces expanded from 18.60 km² (9.54%) to 124.68 km² (63.94%). The most rapid transformation occurred between 2004 and 2014, when vegetation declined by 81.57 km² while bare surfaces increased by 71.79 km² and built-up land by 10.46 km². Survey results indicated that government policies and initiatives, economic opportunities, population growth and infrastructure development were important perceived drivers of urbanization. Most respondents also perceived significant negative effects on agricultural land, livelihoods, food security and access to essential natural resources. The study concludes that urban growth in Jalingo has generated important economic and infrastructural benefits but has also produced substantial environmental degradation and socio-economic pressures. Strengthened land-use planning, protection of agricultural and ecological areas, urban greening, sustainable livelihood programmes, community participation and continuous GIS-based monitoring are recommended.","快速城市化是土地利用与土地覆盖(LULC)变化的主要驱动力，在撒哈拉以南非洲快速发展的城市中尤为突出。本研究考察了尼日利亚塔拉巴州贾林戈地方政府辖区城市化对土地利用与土地覆盖的影响，重点关注1994—2024年这一时期。研究综合运用多时相Landsat卫星影像、地理信息系统(GIS)、分类后变化检测和问卷调查数据，评估城市增长的空间与时间动态及其环境和社会经济影响。将1994年、2004年、2014年和2024年的Landsat 5 TM、Landsat 7 ETM+和Landsat 8 OLI\u002FTIRS影像分为四个主要土地利用与土地覆盖类别：建设用地、植被、裸地表和水体。同时开展了一项涉及384名受访者的问卷调查，以评估城市化的感知驱动因素及其后果。结果表明，贾林戈景观发生了显著转变。植被从1994年的170.56 km²(87.47%)下降至2024年的50.39 km²(25.84%)，净损失120.17 km²。相比之下，建设用地从4.30 km²(2.20%)增至18.06 km²(9.26%)，而裸地表从18.60 km²(9.54%)扩展至124.68 km²(63.94%)。变化最快的时期为2004—2014年，期间植被减少81.57 km²，裸地表增加71.79 km²，建设用地增加10.46 km²。调查结果表明，政府政策和举措、经济机会、人口增长和基础设施建设是城市化的重要感知驱动因素。大多数受访者还认为，城市化对农业用地、生计、粮食安全和基本自然资源的获取产生了显著的负面影响。研究认为，贾林戈的城市增长带来了重要的经济和基础设施效益，但也造成了严重的环境退化和社会经济压力。建议加强土地利用规划、保护农业和生态区域、推进城市绿化、实施可持续生计项目、促进社区参与以及持续开展基于GIS的监测。","OpenAlex","2026-09-24T00:00:00Z",{"impact":101,"substance":101,"depth":101,"authority":101,"freshness":101,"relevant":101,"comment":261},"研究对象为尼日利亚Jalingo地区城市化与土地利用变化，属国际区域地理研究，与三农、农业信息化、智慧农业等主题无直接关联，不建议进入每日精选。",[263],{"name":258,"url":255},[29,30,265],"城市化",{"code":34,"slug":35,"name":36,"path":37,"parent_code":9},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":269},{"candidates":270,"agricultural_evidence":48,"reason":80},[271,275,280,287],{"code":54,"score":21,"supported":48,"title":272,"abstract":273,"tags":274},[56],[],[29],{"code":145,"score":21,"supported":48,"title":276,"abstract":277,"tags":279},[],[148,133,278],"geographic information",[],{"code":281,"score":111,"supported":48,"title":282,"abstract":283,"tags":286},"A8",[],[284,285],"粮食安全","food security",[],{"code":71,"score":22,"supported":14,"title":288,"abstract":289,"tags":290},[],[],[30],[292,293],"尼日利亚 Jalingo 土地利用","Landsat 遥感 城市化","尼日利亚Jalingo土地利用-3493","10.54117\u002Fcanhm718",{"doi":295,"openalex_id":297,"authors":298,"venue":9,"cited_by_count":101,"oa_url":255,"card":315,"direction":106,"ingested_from":108},"W7214145360",[299,302,305,307,309,311,313],{"name":300,"orcid":301},"Mohammed Bakoji Yusuf","https:\u002F\u002Forcid.org\u002F0000-0002-8719-2396",{"name":303,"orcid":304},"Ambrose Audu Zemba","https:\u002F\u002Forcid.org\u002F0000-0002-7692-9911",{"name":306,"orcid":9},"Huzaima Abubakar Bakoji",{"name":308,"orcid":9},"AUWAL ADAMU BAKAKU",{"name":310,"orcid":9},"UMAR ABBA JAURO",{"name":312,"orcid":9},"Aliyu Mohammed Jingudo",{"name":314,"orcid":9},"Abubakar Isa",{"tldr":316,"method":317,"finding":318,"direction":106,"opportunity":319},"用多时相遥感和问卷评估尼日利亚Jalingo 1994–2024年城市化驱动的土地利用变化。","Landsat 5\u002F7\u002F8影像分类、GIS变化检测与384份问卷。","植被减少120.17 km²，建设用地和裸地大幅扩张，2004–2014变化最快。","可结合多源遥感与农户调查，研究西非城市扩张对农地流失和粮食安全的驱动机制。","2026-09-25T23:30:30.441601Z",{"id":322,"title":323,"url":324,"summary":325,"summary_zh":326,"content":9,"source_name":119,"source_url":324,"published_at":327,"category":12,"cover_url":9,"hotness":121,"is_selected":14,"score":328,"score_detail":329,"sources":332,"tags":336,"primary_topic":341,"secondary_topics":342,"classification":343,"search_phrases":400,"slug":403,"view_count":404,"doi":405,"paper":406,"created_at":417},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。","2026-09-30T00:00:00Z",69,{"impact":18,"substance":19,"depth":330,"authority":20,"freshness":101,"relevant":22,"comment":331},16,"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。",[333,334],{"name":119,"url":324},{"name":119,"url":335},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22914539",[27,337,338,339,340,29],"智慧农业","农业人工智能","农业物联网","气候韧性",{"code":34,"slug":35,"name":36,"path":37,"parent_code":9},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":344},{"candidates":345,"agricultural_evidence":48,"reason":80},[346,352,360,367,371,376,380,387,391,396],{"code":46,"score":19,"supported":48,"title":347,"abstract":349,"tags":351},[348],"artificial intelligence",[28,350,50,348],"病虫害识别",[338],{"code":353,"score":19,"supported":48,"title":354,"abstract":356,"tags":359},"I5",[355],"IoT",[357,358,355],"物联网","internet of things",[339],{"code":361,"score":47,"supported":48,"title":362,"abstract":364,"tags":366},"I1",[363],"precision agriculture",[365,363],"精准农业",[337],{"code":54,"score":47,"supported":48,"title":368,"abstract":369,"tags":370},[56],[58,56],[29],{"code":151,"score":21,"supported":48,"title":372,"abstract":373,"tags":375},[],[154,374,155],"水资源",[],{"code":281,"score":111,"supported":48,"title":377,"abstract":378,"tags":379},[],[284,285],[],{"code":381,"score":111,"supported":48,"title":382,"abstract":383,"tags":386},"H2",[],[384,385],"土壤","soil",[],{"code":61,"score":214,"supported":48,"title":388,"abstract":389,"tags":390},[],[64],[340],{"code":71,"score":228,"supported":48,"title":392,"abstract":393,"tags":395},[],[394],"农业生态",[],{"code":76,"score":22,"supported":14,"title":397,"abstract":398,"tags":399},[],[],[27],[401,402],"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",5,"10.5281\u002Fzenodo.22914538",{"doi":405,"openalex_id":407,"authors":408,"venue":119,"cited_by_count":101,"oa_url":324,"card":411,"direction":415,"ingested_from":108},"W7214083098",[409],{"name":410,"orcid":9},"Twinkal Prakash Sawant",{"tldr":412,"method":413,"finding":414,"direction":415,"opportunity":416},"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。","智慧农业 \u002F 农业物联网","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":419,"title":420,"url":421,"summary":422,"summary_zh":423,"content":9,"source_name":424,"source_url":421,"published_at":425,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":426,"score_detail":427,"sources":429,"tags":431,"primary_topic":434,"secondary_topics":435,"classification":436,"search_phrases":456,"slug":459,"view_count":85,"doi":460,"paper":461,"created_at":495},2657,"Retrieval of Optically Active and Inactive Water Quality Parameters Using Remote Sensing and Machine Learning: Evidence from Water Hyacinth-Infested Lake Tana, Ethiopia","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183185","Monitoring water quality is critical for protecting freshwater ecosystems and supporting sustainable water resource management. Lake Tana, Ethiopia’s largest freshwater lake, faces increasing agricultural and urban pressures, while conventional monitoring remains costly and spatially constrained. This study developed an integrated Sentinel-2 remote sensing and machine learning framework to estimate chlorophyll-a (Chl-a), turbidity (TU), total nitrogen (TN), and total phosphorus (TP) using 858 in situ observations and Google Earth Engine. Random Forest (RF), Extreme Gradient Boosting (XGB), Artificial Neural Networks (ANN), and Support Vector Regression (SVR) were evaluated using spectral bands, band combinations, and indices. RF provided the best predictions for Chl-a (R2 = 0.94 ± 0.01; RMSE = 2.11 ± 0.18 µg L−1; MARE = 5%) and TP (R2 = 0.91 ± 0.01; RMSE = 0.26 ± 0.01 mg L−1; MARE = 8.7%), whereas XGB performed best for TU (R2 = 0.93 ± 0.01; RMSE = 5.17 ± 0.43 NTU; MARE = 7%) and TN (R2 = 0.94 ± 0.02; RMSE = 0.18 ± 0.02 mg L−1; MARE = 9.9%). The strong predictive performance of RF and XGB across both optically active and inactive parameters demonstrates the capability of the framework to capture complex spectral water quality relationships and support spatially continuous assessment. Significant seasonal differences (p \u003C 0.001) showed higher dry season Chl-a (137.1%) and higher wet season TP (21.7%), TU (7.5%), and TN (3.9%). Long-term paired observations further indicated increases in Chl-a (73.7%), TN (30%), and TP (14.3%) from December 2016 to December 2025 (p \u003C 0.001). Spatial hotspot analysis revealed strong clustering of TU, TN, and TP, particularly around tributary mouths and nearshore areas, highlighting priority zones for monitoring and intervention. Overall, integrating field observations, Sentinel-2 imagery, and machine learning provides an accurate, scalable, and cost-effective approach for monitoring diverse water quality parameters. The framework offers a transferable solution for strengthening freshwater monitoring in data-scarce regions and supporting sustainable management of lakes under increasing water quality pressures.","监测水质对于保护淡水生态系统和支撑可持续水资源管理至关重要。埃塞俄比亚最大的淡水湖——塔纳湖（Lake Tana）面临着日益加剧的农业和城市压力，而传统监测手段仍然成本高昂且受空间限制。本研究开发了一套集成Sentinel-2遥感与机器学习的框架，利用858个原位观测数据和Google Earth Engine估算叶绿素a（Chl-a）、浊度（TU）、总氮（TN）和总磷（TP）。采用光谱波段、波段组合和指数，评估了随机森林（RF）、极端梯度提升（XGB）、人工神经网络（ANN）和支持向量回归（SVR）的性能。RF对Chl-a（R2 = 0.94 ± 0.01；RMSE = 2.11 ± 0.18 µg L−1；MARE = 5%）和TP（R2 = 0.91 ± 0.01；RMSE = 0.26 ± 0.01 mg L−1；MARE = 8.7%）的预测效果最佳，而XGB对TU（R2 = 0.93 ± 0.01；RMSE = 5.17 ± 0.43 NTU；MARE = 7%）和TN（R2 = 0.94 ± 0.02；RMSE = 0.18 ± 0.02 mg L−1；MARE = 9.9%）的预测效果最佳。RF和XGB在光学活性和非光学活性参数上均表现出强大的预测性能，表明该框架能够捕捉复杂的光谱水质关系并支持空间连续评估。显著的季节性差异（p \u003C 0.001）显示旱季Chl-a较高（137.1%），雨季TP（21.7%）、TU（7.5%）和TN（3.9%）较高。长期配对观测进一步表明，从2016年12月至2025年12月，Chl-a（73.7%）、TN（30%）和TP（14.3%）均有所增加（p \u003C 0.001）。空间热点分析揭示了TU、TN和TP的强烈聚集性，尤其是在支流河口和近岸区域，凸显了优先监测和干预区域。总体而言，整合实地观测、Sentinel-2影像和机器学习为监测多种水质参数提供了一种准确、可扩展且具有成本效益的方法。该框架为加强数据稀缺地区的淡水监测以及支持在水质压力日益增大背景下湖泊的可持续管理提供了一种可迁移的解决方案。","Remote Sensing","2026-09-16T00:00:00Z",82,{"impact":19,"substance":18,"depth":19,"authority":124,"freshness":13,"relevant":22,"comment":428},"基于Sentinel-2与机器学习实现湖泊多参数水质反演，方法可迁移至国内农业面源污染与渔业水域监测，数据规模与精度均具参考价值。",[430],{"name":424,"url":421},[28,192,432,29,433],"智慧渔业","水质监测",{"code":34,"slug":35,"name":36,"path":37,"parent_code":9},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":437},{"candidates":438,"agricultural_evidence":48,"reason":80},[439,443,447,452],{"code":46,"score":47,"supported":48,"title":440,"abstract":441,"tags":442},[50],[28,50],[28],{"code":54,"score":47,"supported":48,"title":444,"abstract":445,"tags":446},[56],[58,56],[29],{"code":151,"score":111,"supported":48,"title":448,"abstract":449,"tags":451},[],[374,450],"water resource",[],{"code":220,"score":22,"supported":14,"title":453,"abstract":454,"tags":455},[],[],[192],[457,458],"农业面源污染 智慧渔业 机器学习 水质监测","农业面源污染 智慧渔业","农业面源污染智慧渔业机器学习水质监测-2657","10.3390\u002Frs18183185",{"doi":460,"openalex_id":462,"authors":463,"venue":424,"cited_by_count":101,"oa_url":421,"card":490,"direction":106,"ingested_from":108},"W7213230686",[464,467,469,472,474,477,479,481,483,485,487],{"name":465,"orcid":466},"Lakachew Y. Alemneh","https:\u002F\u002Forcid.org\u002F0009-0004-3471-3778",{"name":468,"orcid":9},"Daganchew Aklog",{"name":470,"orcid":471},"Ann van Griensven","https:\u002F\u002Forcid.org\u002F0000-0002-2105-6287",{"name":473,"orcid":9},"Minychl G. Dersseh",{"name":475,"orcid":476},"Goraw Goshu","https:\u002F\u002Forcid.org\u002F0000-0001-9629-0126",{"name":478,"orcid":9},"Seleshi Yalew",{"name":480,"orcid":9},"Demesew A. Mhiret",{"name":482,"orcid":9},"Sisay B. Asress",{"name":484,"orcid":9},"Tigistu Wassie Agegnehu",{"name":486,"orcid":9},"Shawl Abebe Desta",{"name":488,"orcid":489},"Samuel Berihun Kassa","https:\u002F\u002Forcid.org\u002F0009-0004-5618-9743",{"tldr":491,"method":492,"finding":493,"direction":106,"opportunity":494},"用Sentinel-2与机器学习反演埃塞俄比亚塔纳湖四类水质参数并分析时空变化。","Sentinel-2影像、Google Earth Engine、858个实测点","RF与XGB精度最高（R²达0.91-0.94），水质参数呈显著季节与年际上升趋势。","可迁移至其他数据稀缺湖泊，探索水葫芦覆盖下水体光谱混合与多源遥感协同反演。","2026-09-16T23:30:28.415573Z",{"id":497,"title":498,"url":499,"summary":500,"summary_zh":9,"content":9,"source_name":501,"source_url":9,"published_at":502,"category":503,"cover_url":9,"hotness":13,"is_selected":14,"score":504,"score_detail":505,"sources":509,"tags":511,"primary_topic":513,"secondary_topics":514,"classification":515,"search_phrases":536,"slug":539,"view_count":85,"doi":9,"paper":9,"created_at":540},2261,"北大荒集团智慧农业:4800万亩耕地打造\"地块链\"式数字孪生管理","https:\u002F\u002Fszb.farmer.com.cn\u002Fnmrb\u002Fhtml\u002F2026\u002F20260907\u002F20260907_8\u002Fnmrb_20260907_13398_8_2096696093043691617.html","北大荒信息有限公司智慧农业大数据中心将4800万亩耕地切分成27万个地块,每块都有编号和二维码。遥感平台接入48颗卫星每5天做一次体检,5000台田间采集设备、600个气象站、500台虫情测报灯昼夜值守,3.3万台物联网设备统一接入\"地块链\"管理。5年累计为农户线上放贷400亿元。","农民日报","2026-09-06T16:00:00Z","报道",85,{"impact":506,"substance":507,"depth":19,"authority":125,"freshness":111,"relevant":22,"comment":508},26,23,"北大荒4800万亩耕地实现地块级数字孪生管理，卫星遥感、物联网与地块链数据规模具体，属产业级智慧农业标杆案例，值得入选每日精选。",[510],{"name":501,"url":499},[27,337,339,512,29],"数字孪生",{"code":34,"slug":35,"name":36,"path":37,"parent_code":9},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":516},{"candidates":517,"agricultural_evidence":48,"reason":80},[518,523,527,531],{"code":361,"score":519,"supported":48,"title":520,"abstract":521,"tags":522},21,[337,512],[337],[512,337],{"code":54,"score":214,"supported":48,"title":524,"abstract":525,"tags":526},[],[58],[29],{"code":353,"score":214,"supported":48,"title":528,"abstract":529,"tags":530},[],[357],[339],{"code":76,"score":214,"supported":48,"title":532,"abstract":533,"tags":535},[],[534],"农业大数据",[27],[537,538],"农业物联网 数字乡村 数字孪生 智慧农业","农业物联网 数字乡村","农业物联网数字乡村数字孪生智慧农业-2261","2026-09-13T00:04:03.310614Z"]