[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1447":3,"related-1447":125},{"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":23,"tags":25,"primary_topic":9,"secondary_topics":31,"classification":32,"search_phrases":98,"slug":101,"view_count":102,"doi":103,"paper":104,"created_at":124},1447,"GIS based multi criteria assessment of surface irrigation suitability in Ameka District of Ethiopia","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs43621-026-04460-7","Evaluating land suitability for surface irrigation is critical for improving agricultural productivity and optimizing resource use. However, the potential of land and water resources for irrigation development in the Ameka District of Central Ethiopia remains undocumented. This study assessed land suitability for surface irrigation and the production of four key crops onion, potato, wheat, and teff using an integrated approach combining Geographic Information Systems (GIS), Remote Sensing (RS), Spatial Multi-Criteria Evaluation (SMCE), and the Analytical Hierarchy Process (AHP). Seven biophysical and socio-economic factors were analyzed: soil type, slope, land use\u002Fland cover (LULC), mean annual temperature, mean annual rainfall, proximity to perennial streams, and market access. Suitability criteria were standardized following FAO guidelines, and their relative importance was determined using the Analytical Hierarchy Process (AHP) based on consultations with 15 domain experts. The results indicate that of the total 17,592 ha in the district, 2107 ha (12%) are highly suitable (S1) for surface irrigation, 6613 ha (37.6%) are moderately suitable (S2), 7503 ha (42.6%) are marginally suitable (S3), and 1369 ha (7.8%) are unsuitable (N). Overall, more than 91% of the district exhibits some degree of irrigation potential. Approximately 8720 ha classified as highly or moderately suitable represent the most promising areas for immediate irrigation development. Crop-specific suitability analysis revealed distinct spatial patterns: onion (2104 ha highly suitable; 8409 ha moderately suitable), potato (2393 ha highly suitable; 4680 ha moderately suitable), wheat (2018 ha highly suitable; 3096 ha moderately suitable), and teff (2004 ha highly suitable; 10,450 ha moderately suitable). These findings provide critical guidance for agricultural planning and policy by identifying priority areas for irrigation investment. Integrating crop-specific suitability maps into land-use planning and agricultural extension programs can significantly enhance the effectiveness of irrigation development strategies. Future research should incorporate hydrological modeling, economic feasibility analysis, and climate variability assessments to strengthen evidence-based irrigation planning in the region.","评估土地对地表灌溉的适宜性对于提高农业生产力和优化资源利用至关重要。然而，埃塞俄比亚中部阿梅卡地区土地和水资源用于灌溉开发的潜力尚未有文献记载。本研究采用地理信息系统（GIS）、遥感（RS）、空间多准则评价（SMCE）和层次分析法（AHP）相结合的综合方法，评估了地表灌溉的土地适宜性以及洋葱、马铃薯、小麦和苔麸四种主要作物的生产适宜性。研究分析了七个生物物理和社会经济因素：土壤类型、坡度、土地利用\u002F土地覆盖（LULC）、年均气温、年均降水量、与常年性河流的邻近度以及市场可达性。适宜性标准按照联合国粮农组织（FAO）指南进行标准化，其相对重要性基于对15位领域专家的咨询，采用层次分析法（AHP）确定。结果表明，在该地区总计17，592公顷的土地中，2，107公顷（12%）高度适宜（S1）地表灌溉，6，613公顷（37.6%）中等适宜（S2），7，503公顷（42.6%）勉强适宜（S3），1，369公顷（7.8%）不适宜（N）。总体而言，该地区超过91%的土地具有一定程度的灌溉潜力。约8，720公顷被划分为高度或中等适宜的土地是近期灌溉开发最有前景的区域。作物-specific适宜性分析揭示了明显的空间格局：洋葱（2，104公顷高度适宜；8，409公顷中等适宜）、马铃薯（2，393公顷高度适宜；4，680公顷中等适宜）、小麦（2，018公顷高度适宜；3，096公顷中等适宜）和苔麸（2，004公顷高度适宜；10，450公顷中等适宜）。这些发现通过识别灌溉投资的优先区域，为农业规划和政策提供了关键指导。将作物-specific适宜性图纳入土地利用规划和农业推广计划，可显著提升灌溉开发战略的有效性。未来研究应纳入水文建模、经济可行性分析和气候变率评估，以加强该地区基于证据的灌溉规划。",null,"Discover Sustainability","2026-09-01T00:00:00Z","论文",10,false,71,{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":20,"relevant":21,"comment":22},12,22,18,7,1,"基于GIS与AHP的灌溉适宜性评估，为埃塞俄比亚地区农业规划提供数据支持，方法系统，结论可靠。",[24],{"name":10,"url":6},[26,27,28,29,30],"遥感","灌溉适宜性","GIS评估","埃塞俄比亚","农业规划",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":41,"reason":97},[38,48,56,62,70,77,84,91],{"code":39,"score":40,"supported":41,"title":42,"abstract":44,"tags":47},"H3",17,true,[43],"irrigation",[45,46,43],"灌溉","水资源",[],{"code":49,"score":40,"supported":41,"title":50,"abstract":52,"tags":55},"I4",[51],"GIS",[53,51,54],"地理信息","geographic information",[],{"code":57,"score":20,"supported":41,"title":58,"abstract":59,"tags":61},"I3",[],[26,60],"remote sensing",[26],{"code":63,"score":64,"supported":41,"title":65,"abstract":66,"tags":69},"A2",6,[],[67,68],"小麦","wheat",[],{"code":71,"score":64,"supported":41,"title":72,"abstract":73,"tags":76},"A5",[],[74,75],"马铃薯","potato",[],{"code":78,"score":64,"supported":41,"title":79,"abstract":80,"tags":83},"G1",[],[81,82],"农业推广","agricultural extension",[],{"code":85,"score":64,"supported":41,"title":86,"abstract":87,"tags":90},"H2",[],[88,89],"土壤","soil",[],{"code":92,"score":93,"supported":41,"title":94,"abstract":95,"tags":96},"J1",3,[],[30],[],"多个产业证据相当，需要确认主归属",[99,100],"埃塞俄比亚 灌溉适宜性 农业规划 GIS评估","埃塞俄比亚 灌溉适宜性","埃塞俄比亚灌溉适宜性农业规划GIS评估-1447",2,"10.1007\u002Fs43621-026-04460-7",{"doi":103,"openalex_id":105,"authors":106,"venue":10,"cited_by_count":116,"oa_url":6,"card":117,"direction":121,"ingested_from":123},"W7204955025",[107,109,112,114],{"name":108,"orcid":9},"Teshale Tirulo Bachore",{"name":110,"orcid":111},"Tamrat Sinore","https:\u002F\u002Forcid.org\u002F0000-0002-5403-069X",{"name":113,"orcid":9},"Mulatu Chernet Madolo",{"name":115,"orcid":9},"Yosef Aman Haji",0,{"tldr":118,"method":119,"finding":120,"direction":121,"opportunity":122},"评估埃塞俄比亚Ameka区地表灌溉适宜性，结合GIS与AHP，识别高适宜区并分析作物适宜性。","GIS、遥感、空间多准则评价与层次分析法，分析土壤、坡度等七因素。","91%土地有灌溉潜力，12%高适宜，37.6%中等适宜，作物适宜性空间格局各异。","农业遥感与作物表型","可引入水文模型、经济可行性与气候变率评估，完善灌溉规划，并探索动态适宜性预测。","openalex","2026-09-02T23:30:55.438811Z",{"total":64,"page":21,"page_size":64,"items":126},[127,178,269,335,387,450],{"id":128,"title":129,"url":130,"summary":131,"summary_zh":9,"content":9,"source_name":132,"source_url":130,"published_at":133,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":134,"score_detail":135,"sources":141,"tags":143,"primary_topic":9,"secondary_topics":147,"classification":148,"search_phrases":162,"slug":165,"view_count":116,"doi":166,"paper":167,"created_at":177},5489,"Estimating Evapotranspiration Using the SEBAL Model and Remote Sensing: Spatiotemporal Dynamics in Quara District, Northwest Ethiopia","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-11063250\u002Fv1","Estimating Evapotranspiration Using the SEBAL Model and Remote Sensing: Spatiotemporal Dynamics in Quara District, Northwest Ethiopia。Research Square","Research Square","2026-10-19T00:00:00Z",50,{"impact":136,"substance":137,"depth":138,"authority":139,"freshness":93,"relevant":21,"comment":140},8,16,14,9,"该研究用SEBAL模型与遥感估算埃塞俄比亚西北部蒸散发，方法有一定参考价值，但属区域性案例研究，公共影响有限，且发布已逾一周，时效性偏弱，不建议进入每日精选。",[142],{"name":132,"url":130},[144,26,145,29,146],"农业水资源","蒸散发","SEBAL模型",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":149},{"candidates":150,"agricultural_evidence":14,"reason":161},[151,157],{"code":39,"score":152,"supported":41,"title":153,"abstract":155,"tags":156},13,[154],"evapotranspiration",[154],[144,145],{"code":57,"score":17,"supported":41,"title":158,"abstract":159,"tags":160},[60],[60],[26],"农业相关性需要复核",[163,164],"SEBAL 模型 遥感 蒸散发","Quara 地区 蒸散发","SEBAL模型遥感蒸散发-5489","10.21203\u002Frs.3.rs-11063250\u002Fv1",{"doi":166,"openalex_id":168,"authors":169,"venue":132,"cited_by_count":116,"oa_url":176,"card":9,"direction":121,"ingested_from":123},"W7222946042",[170,172,174],{"name":171,"orcid":9},"Mulatu Ayalew",{"name":173,"orcid":9},"Balew Yeneneh",{"name":175,"orcid":9},"Muktar Asmamaw","https:\u002F\u002Fwww.researchsquare.com\u002Farticle\u002Frs-11063250\u002Flatest.pdf","2026-10-21T23:30:25.009175Z",{"id":179,"title":180,"url":181,"summary":182,"summary_zh":183,"content":9,"source_name":184,"source_url":181,"published_at":185,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":186,"score_detail":187,"sources":189,"tags":191,"primary_topic":194,"secondary_topics":198,"classification":208,"search_phrases":252,"slug":255,"view_count":21,"doi":256,"paper":257,"created_at":268},3901,"Geospatial analysis of irrigation suitability in Nigeria using remote sensing and GIS","https:\u002F\u002Fdoi.org\u002F10.53030\u002Ftjags.1937471","This research offers a comprehensive geospatial evaluation of land suitability for irrigation across Nigeria. It leverages 2020 Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data, processed within the Google Earth Engine (GEE) cloud-based computational environment. Given growing concerns about food security and climate variability, ascertaining areas amenable to irrigation is paramount for fostering sustainable agricultural development within Nigeria. This investigation integrated multi-temporal remote sensing observations, including the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), and Normalized Difference Water Index - NDWI, to construct an Irrigation Suitability Index (ISI). These datasets underwent analysis at a 500-meter spatial resolution, encompassing the entirety of the nation. Weighted overlay analysis, performed within the GEE environment, generated a nationwide map delineating irrigation potential across five distinct categories: Very Low, Low, Moderate, High, and Very High suitability. The findings reveal suitability scores ranging from a minimum of 0.11 (very low) to a maximum of 1 (very high). Specifically, northern Nigerian states such as Sokoto, Kebbi, Yobe, and Borno demonstrated high to very high suitability. In contrast, southern states, including Lagos, Rivers, Cross River, and Delta, were characterized by very low suitability. The study underscores significant geographical disparities, particularly in arid northern regions which experience greater constraints due to lower precipitation and elevated evapotranspiration. These findings offer actionable directives for agricultural planners, policymakers, and stakeholders seeking to enhance irrigation capacity and strengthen food security resilience amidst evolving climatic conditions.","本研究对尼日利亚全国灌溉土地适宜性进行了综合地理空间评估。研究利用2020年中分辨率成像光谱仪（MODIS）卫星数据，在谷歌地球引擎（GEE）云端计算环境中进行处理。鉴于粮食安全和气候变率日益受到关注，确定适宜灌溉的区域对于促进尼日利亚可持续农业发展至关重要。本研究整合了多时相遥感观测数据，包括归一化植被指数（NDVI）、地表温度（LST）和归一化水体指数（NDWI），以构建灌溉适宜性指数（ISI）。这些数据集以500米空间分辨率进行分析，覆盖全国范围。在GEE环境中进行的加权叠加分析生成了全国灌溉潜力分布图，划分为五个类别：极低、低、中等、高和极高适宜性。研究结果显示，适宜性评分从最低0.11（极低）到最高1（极高）不等。具体而言，尼日利亚北部各州如索科托、凯比、约贝和博尔诺表现出高至极高适宜性。相比之下，南部各州包括拉各斯、河流州、十字河州和三角洲州则以极低适宜性为特征。研究强调了显著的地理差异，特别是干旱的北部地区因降水量较低和蒸散量较高而面临更大限制。这些发现为农业规划者、政策制定者和利益相关者提供了可操作的指导，以在气候条件不断变化的情况下增强灌溉能力并提升粮食安全韧性。","OpenAlex","2026-09-30T00:00:00Z",78,{"impact":19,"substance":18,"depth":40,"authority":152,"freshness":136,"relevant":21,"comment":188},"基于MODIS与GEE的全国尺度灌溉适宜性制图，方法规范、结论具体，对农业规划有参考价值，但属他国案例，国内落地性有限。",[190],{"name":184,"url":181},[192,193,26,51,27],"数字农业","粮食安全",{"code":39,"slug":43,"name":195,"path":196,"parent_code":197},"水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[199,204],{"code":57,"slug":200,"name":201,"path":202,"parent_code":203},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"code":49,"slug":205,"name":206,"path":207,"parent_code":203},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"status":209,"locked":14,"version":34,"method":35,"evidence":210},"classified",{"candidates":211,"agricultural_evidence":41},[212,216,221,225,231,237,243,247],{"code":39,"score":40,"supported":41,"title":213,"abstract":214,"tags":215},[43],[45,43,154],[],{"code":57,"score":217,"supported":41,"title":218,"abstract":219,"tags":220},15,[60],[26,60],[26],{"code":49,"score":139,"supported":41,"title":222,"abstract":223,"tags":224},[51],[],[51],{"code":226,"score":20,"supported":41,"title":227,"abstract":228,"tags":230},"A8",[],[193,229],"food security",[193],{"code":232,"score":93,"supported":41,"title":233,"abstract":234,"tags":236},"H7",[],[235],"干旱",[],{"code":238,"score":93,"supported":41,"title":239,"abstract":240,"tags":242},"H8",[],[241],"可持续农业",[],{"code":92,"score":93,"supported":41,"title":244,"abstract":245,"tags":246},[],[30],[],{"code":248,"score":21,"supported":14,"title":249,"abstract":250,"tags":251},"I1",[],[],[192],[253,254],"尼日利亚 灌溉适宜性 遥感","Google Earth Engine 灌溉","尼日利亚灌溉适宜性遥感-3901","10.53030\u002Ftjags.1937471",{"doi":256,"openalex_id":258,"authors":259,"venue":9,"cited_by_count":116,"oa_url":9,"card":263,"direction":121,"ingested_from":123},"W7214955379",[260],{"name":261,"orcid":262},"Emmanuel M. Menegbo","https:\u002F\u002Forcid.org\u002F0000-0002-6694-3226",{"tldr":264,"method":265,"finding":266,"direction":121,"opportunity":267},"用遥感与GIS评估尼日利亚全国灌溉适宜性并绘制五级分区图。","2020年MODIS数据在GEE中计算NDVI、LST、NDWI并加权叠加。","北部各州适宜性高，南部低，全国适宜性得分0.11至1。","可结合土壤、地下水与作物需水数据，将静态适宜性图升级为动态灌溉决策支持系统。","2026-10-01T23:30:29.159371Z",{"id":270,"title":271,"url":272,"summary":273,"summary_zh":274,"content":9,"source_name":275,"source_url":272,"published_at":276,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":277,"score_detail":278,"sources":280,"tags":282,"primary_topic":286,"secondary_topics":291,"classification":292,"search_phrases":318,"slug":321,"view_count":116,"doi":322,"paper":323,"created_at":334},5588,"Certified Temporal Fusion of Artificial Intelligence Climate Analytics for Crop Failure Early Warning Under Sparse Agricultural Sensors: Error Bounds and Famine Relief Targeting","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545489","Sparse agricultural sensor networks across sub-Saharan Africa leave crop-failure early-warning systems with too little ground observation to support reliable famine relief targeting. The framework treats sensor sparsity as a missing-data problem with a known observation operator and propagates climate-analytics uncertainty through a temporal Kalman-Buys filter whose gain is constrained by a certified risk envelope. Using NASA POWER monthly reanalysis for Accra, Ghana (2000-2023, 312 observations), the analysis establishes a mean temperature of 26.521 C and a warming trend of 0.1622 C per decade, which calibrates the climate-analytics prior. The derived bounds show that relief-targeting error scales with the square root of sensor density and with the Lipschitz constant of the crop-response function, yielding an explicit sensor-placement rule for a target misclassification rate. The contribution is a certified, assumption-explicit bridge between climate analytics and famine relief targeting that states the conditions under which early warning is trustworthy.","撒哈拉以南非洲稀疏的农业传感器网络使得作物歉收预警系统缺乏足够的地面观测数据，难以支撑可靠的饥荒救援目标定位。该框架将传感器稀疏性视为具有已知观测算子的缺失数据问题，并通过时间Kalman-Buys滤波器传播气候分析不确定性，该滤波器的增益受经认证的风险包络约束。利用NASA POWER对加纳阿克拉的月度再分析数据（2000—2023年，312个观测值），分析确定了平均气温为26.521°C、每十年0.1622°C的增暖趋势，据此校准了气候分析先验。推导出的界限表明，救援目标定位误差随传感器密度的平方根以及作物响应函数的Lipschitz常数缩放，从而为目标误分类率给出了明确的传感器布设规则。该研究的贡献在于构建了一个经认证的、假设明确的气候分析与饥荒救援目标定位之间的桥梁，阐明了早期预警可信赖的条件。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",74,{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":13,"relevant":21,"comment":279},"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。",[281],{"name":275,"url":272},[283,284,193,26,285],"智慧农业","农业人工智能","气候分析",{"code":287,"slug":288,"name":289,"path":290,"parent_code":203},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[],{"status":209,"locked":14,"version":34,"method":35,"evidence":293},{"candidates":294,"agricultural_evidence":41},[295,300,306,310,314],{"code":287,"score":139,"supported":41,"title":296,"abstract":298,"tags":299},[297],"artificial intelligence",[],[284],{"code":301,"score":93,"supported":41,"title":302,"abstract":303,"tags":305},"I5",[],[304],"农业传感",[],{"code":226,"score":21,"supported":14,"title":307,"abstract":308,"tags":309},[],[],[193],{"code":248,"score":21,"supported":14,"title":311,"abstract":312,"tags":313},[],[],[283],{"code":57,"score":21,"supported":14,"title":315,"abstract":316,"tags":317},[],[],[26],[319,320],"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":322,"openalex_id":324,"authors":325,"venue":275,"cited_by_count":116,"oa_url":272,"card":328,"direction":333,"ingested_from":123},"W7223111766",[326],{"name":327,"orcid":9},"Fatima-Zahra El Khattabi",{"tldr":329,"method":330,"finding":331,"direction":121,"opportunity":332},"提出带误差界的时间融合框架，在稀疏传感器下用气候分析预警作物歉收。","卡尔曼-布西滤波+NASA POWER再分析数据，风险包络约束增益。","救济瞄准误差随传感器密度平方根和作物响应Lipschitz常数缩放，给出布点规则。","可将该认证误差界框架扩展到多作物多区域，结合卫星遥感弥补地面传感器稀疏。","农业人工智能与决策模型","2026-10-22T11:21:02.682497Z",{"id":336,"title":337,"url":338,"summary":339,"summary_zh":9,"content":9,"source_name":132,"source_url":338,"published_at":276,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":340,"score_detail":341,"sources":343,"tags":345,"primary_topic":348,"secondary_topics":352,"classification":353,"search_phrases":365,"slug":368,"view_count":116,"doi":369,"paper":370,"created_at":386},5579,"A Composite Field-Evaluation Framework for Digital Agriculture Maps","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-11283264\u002Fv1","A Composite Field-Evaluation Framework for Digital Agriculture Maps。Research Square",38,{"impact":136,"substance":64,"depth":136,"authority":64,"freshness":13,"relevant":21,"comment":342},"预印本提出数字农业地图的田间复合评估框架，方法有一定新意，但摘要信息极少、未经同行评审，公共影响有限，暂不宜进入每日精选。",[344],{"name":132,"url":338},[192,346,26,347],"农业信息化","地图评估",{"code":248,"slug":349,"name":350,"path":351,"parent_code":203},"smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture",[],{"status":209,"locked":14,"version":34,"method":35,"evidence":354},{"candidates":355,"agricultural_evidence":41},[356,361],{"code":248,"score":152,"supported":41,"title":357,"abstract":359,"tags":360},[358],"digital agriculture",[358],[346,192],{"code":57,"score":21,"supported":14,"title":362,"abstract":363,"tags":364},[],[],[26],[366,367],"Research Square 数字农业","农业信息化 地图评估 数字农业 遥感","ResearchSquare数字农业-5579","10.21203\u002Frs.3.rs-11283264\u002Fv1",{"doi":369,"openalex_id":371,"authors":372,"venue":132,"cited_by_count":116,"oa_url":338,"card":9,"direction":385,"ingested_from":123},"W7223099987",[373,376,379,382],{"name":374,"orcid":375},"Phillip Lanza","https:\u002F\u002Forcid.org\u002F0000-0002-5545-1744",{"name":377,"orcid":378},"David G. Rossiter","https:\u002F\u002Forcid.org\u002F0000-0003-4558-1286",{"name":380,"orcid":381},"Diana Stuart Sinton","https:\u002F\u002Forcid.org\u002F0000-0001-5828-9001",{"name":383,"orcid":384},"Louis Longchamps","https:\u002F\u002Forcid.org\u002F0000-0002-4761-6094","数字乡村与农业信息化","2026-10-22T11:20:47.463814Z",{"id":388,"title":389,"url":390,"summary":391,"summary_zh":392,"content":9,"source_name":393,"source_url":390,"published_at":394,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":395,"score_detail":396,"sources":399,"tags":401,"primary_topic":406,"secondary_topics":407,"classification":408,"search_phrases":429,"slug":432,"view_count":116,"doi":433,"paper":434,"created_at":449},5575,"Wetland vegetation dynamics and its linkage with climatic, hydrological, and land-use\u002F land cover changes in the Foket floodplain of the Fetam River Watershed, Northwestern Ethiopia","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44274-026-01136-x","Timely monitoring of wetland changes is vital for effective conservation and management of the landscape. This study, therefore, examines vegetation dynamics and their climatic, hydrological, and land-use drivers in the Foket floodplain wetland, Upper Blue Nile basin, Ethiopia. The LULC analysis was employed based on 1985, 2002, and 2020 cloud-free Landsat images. The maximum likelihood classification algorithm was applied to classify the images into eight LULC classes. The spatial and temporal dynamics of the floodplain’s surface water were further analyzed using the Global Surface Water Explorer (1984–2020), focusing on water occurrence, seasonality, transitions, and changes in surface-water dynamics. The Normalized Difference Vegetation Index (NDVI) was used to assess the temporal dynamics of wetland vegetation, while annual rainfall, temperature, and evapotranspiration data were used to examine the climatic context of vegetation change. The results reveal that the area coverage of water body and wetland decreased by 63.2% and 29%, respectively, over the study period. In contrast, cultivated land, settlement, and eucalyptus plantation had expanded by 39.8%, 280.6%, and 380%, respectively, at the expense of wetland, woodland, and shrubland. Surface-water dynamics showed a substantial decline in seasonally inundated areas and changes in the spatial distribution and persistence of surface water. This indicates the reduction of the floodplain’s hydrological connectivity. The NDVI analysis also revealed a significant increasing of wetland vegetation over 1985–2020\u002F21. Vegetation growth was very fast during 1985–2000, slowed down between 2001 and 2012, and dropped slightly from 2013 to 2020\u002F21. Analysis of climatic variables showed no major change in annual rainfall or evapotranspiration, whereas temperature exhibited a significant upward trend. The overall findings revealed the association of the floodplain vegetation dynamics with land-use land cover, hydrological, and climatic changes. This indicates that land-use land cover, hydrological, and climatic changes have contributed to the degradation of the floodplain vegetation, highlighting the urgent need for integrated management interventions that regulate agricultural expansion, grazing, and water diversion while maintaining hydrological connectivity and wetland vegetation. Clinical trial number Not applicable (NA).","及时监测湿地变化对于景观的有效保护和管理至关重要。因此，本研究考察了埃塞俄比亚上青尼罗河流域福凯特洪泛平原湿地的植被动态及其气候、水文和土地利用驱动因素。基于1985年、2002年和2020年无云Landsat影像进行土地利用\u002F土地覆盖（LULC）分析，采用最大似然分类算法将影像分为八个LULC类别。进一步利用全球地表水探索器（1984—2020年）分析洪泛平原地表水的时空动态，重点关注水的出现频率、季节性、转换及地表水动态变化。采用归一化差异植被指数（NDVI）评估湿地植被的时间动态，同时利用年降水量、气温和蒸散发数据考察植被变化的气候背景。结果表明，研究期间水体和湿地的面积覆盖率分别下降了63.2%和29%。相比之下，耕地、居民点和桉树种植园分别扩张了39.8%、280.6%和380%，侵占了湿地、林地和灌丛。地表水动态显示季节性淹没区域大幅减少，地表水的空间分布和持续性发生变化，表明洪泛平原水文连通性降低。NDVI分析还揭示1985—2020\u002F21年间湿地植被显著增加。植被生长在1985—2000年间非常迅速，2001—2012年间放缓，2013—2020\u002F21年间略有下降。气候变量分析显示年降水量或蒸散发无重大变化，而气温呈显著上升趋势。总体研究结果揭示了洪泛平原植被动态与土地利用\u002F土地覆盖、水文和气候变化的关联。这表明土地利用\u002F土地覆盖、水文和气候变化共同导致了洪泛平原植被退化，凸显了迫切需要综合管理干预措施，以调控农业扩张、放牧和引水，同时维持水文连通性和湿地植被。临床试验编号：不适用（NA）。","Discover Environment","2026-10-20T00:00:00Z",65,{"impact":136,"substance":397,"depth":137,"authority":17,"freshness":139,"relevant":21,"comment":398},20,"基于Landsat与NDVI的湿地植被动态实证研究，数据扎实但属区域案例，国际参考价值有限，不宜进入每日精选。",[400],{"name":393,"url":390},[402,29,403,404,405],"NDVI","土地利用变化","湿地遥感","水文连通性",{"code":39,"slug":43,"name":195,"path":196,"parent_code":197},[],{"status":209,"locked":14,"version":34,"method":35,"evidence":409},{"candidates":410,"agricultural_evidence":41},[411,415,420,425],{"code":39,"score":64,"supported":41,"title":412,"abstract":413,"tags":414},[],[145,154],[],{"code":232,"score":93,"supported":41,"title":416,"abstract":417,"tags":419},[],[418],"气候变化",[],{"code":421,"score":21,"supported":14,"title":422,"abstract":423,"tags":424},"H6",[],[],[403],{"code":57,"score":21,"supported":14,"title":426,"abstract":427,"tags":428},[],[],[402],[430,431],"土地利用变化 埃塞俄比亚 水文连通性 湿地遥感","土地利用变化 埃塞俄比亚","土地利用变化埃塞俄比亚水文连通性湿地遥感-5575","10.1007\u002Fs44274-026-01136-x",{"doi":433,"openalex_id":435,"authors":436,"venue":393,"cited_by_count":116,"oa_url":443,"card":444,"direction":121,"ingested_from":123},"W7223064056",[437,440],{"name":438,"orcid":439},"Workiyie Worie Assefa","https:\u002F\u002Forcid.org\u002F0000-0002-4029-9022",{"name":441,"orcid":442},"Belachew Getnet Eneyew","https:\u002F\u002Forcid.org\u002F0000-0003-1196-9004","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44274-026-01136-x.pdf",{"tldr":445,"method":446,"finding":447,"direction":121,"opportunity":448},"分析埃塞俄比亚Foket洪泛湿地植被动态及其气候、水文与土地利用驱动因素。","用Landsat影像做最大似然分类，结合全球地表水数据与NDVI及气候数据分析。","水体与湿地面积大幅减少，耕地、居民点和桉树种植扩张，温度显著上升。","可结合多源遥感与水文连通性指标，量化湿地退化对农业灌溉与乡村生态服务的影响。","2026-10-22T11:20:42.476484Z",{"id":451,"title":452,"url":453,"summary":454,"summary_zh":455,"content":9,"source_name":456,"source_url":453,"published_at":394,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":116,"score_detail":457,"sources":459,"tags":461,"primary_topic":9,"secondary_topics":464,"classification":465,"search_phrases":472,"slug":475,"view_count":116,"doi":476,"paper":477,"created_at":492},5574,"Aggregation of results of automatic classification of remote sensing data of natural objects","https:\u002F\u002Fdoi.org\u002F10.31772\u002F2712-8970-2026-27-3-390-399","Methods for aggregating the results of automatic classification of multidimensional spectral remote sensing data are proposed. This task is accomplished using a modified «Forel» algorithm and a procedure for testing the hypothesis of the homogeneity of the distribution laws of random variables characterizing the detected classes. When automatic classification algorithms are designed to detect sets of random variable values corresponding to unimodal fragments of their probability density, it is reasonable to regroup classes within the space of their mean random variable values. This is implemented using a modification of the “Forel” algorithm, the parameters of which are determined based on the results of a computational experiment. If additional information about the properties of a natural object is available, it can be used to aggregate the results of automatic classification in the spectral feature space. Under these conditions, class aggregation is based on testing the hypothesis of the homogeneity of the distribution laws of the random variables that characterize the additional information. To solve this problem, a nonparametric pattern recognition algorithm that follows the maximum likelihood criterion is used. The optimal blur coefficients of the classification algorithm's kernel functions are selected based on the requirement to minimize the pattern recognition error estimate in the “sliding exam” mode. The obtained results are summarized in the construction of an information graph, the nodes of which correspond to classes. An edge is formed between two graph nodes if the pattern recognition error for a given pair of classes does not differ significantly from 0,5. A modification of the Kolmogorov–Smirnov test is used to test the hypotheses. A macroclass is formed by classes corresponding to a complete subgraph that do not reliably differ in the distribution patterns of features in the additional information. The sequential procedure for forming macroclasses begins with finding the complete subgraph with the maximum number of classes. The results obtained form the basis for developing a decision rule for assessing the membership of spectral feature values in the resulting macroclasses. The proposed method for aggregating the results of automatic classification is relevant for the system analysis of remote sensing data.","针对多维光谱遥感数据自动分类结果的聚合方法被提出。该任务通过改进的“Forel”算法以及用于检验表征所检测类别的随机变量分布律同质性假设的程序来完成。当自动分类算法旨在检测对应于其概率密度单峰片段的随机变量值集合时，在其均值随机变量值空间内重新分组类别是合理的。这通过“Forel”算法的改进版本实现，其参数基于计算实验结果确定。如果存在关于自然对象属性的附加信息，则可将其用于聚合光谱特征空间中的自动分类结果。在这些条件下，类别聚合基于检验表征附加信息的随机变量分布律同质性假设。为解决该问题，采用遵循最大似然准则的非参数模式识别算法。分类算法核函数的最优模糊系数基于在“滑动检验”模式下最小化模式识别误差估计的要求进行选择。所得结果汇总于信息图的构建中，其节点对应于类别。如果给定类别对的模式识别误差与0.5无显著差异，则在两个图节点之间形成边。使用Kolmogorov–Smirnov检验的改进版本来检验假设。宏类由对应于完全子图的类别形成，这些类别在附加信息中的特征分布模式上没有可靠差异。形成宏类的顺序过程从寻找具有最大类别数的完全子图开始。所得结果构成了开发决策规则的基础，该规则用于评估光谱特征值在所得宏类中的隶属关系。所提出的用于聚合自动分类结果的方法对于遥感数据的系统分析具有相关性。","Siberian Aerospace Journal",{"impact":116,"substance":116,"depth":116,"authority":116,"freshness":116,"relevant":116,"comment":458},"该文为遥感数据自动分类结果聚合的算法论文，属通用遥感方法研究，与三农、农业信息化、智慧农业无直接关联，不建议进入每日精选。",[460],{"name":456,"url":453},[26,462,463],"光谱数据","自动分类",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":466},{"candidates":467,"agricultural_evidence":14,"reason":161},[468],{"code":57,"score":217,"supported":41,"title":469,"abstract":470,"tags":471},[60],[26,60],[26],[473,474],"光谱数据 自动分类 遥感","光谱数据 自动分类","光谱数据自动分类遥感-5574","10.31772\u002F2712-8970-2026-27-3-390-399",{"doi":476,"openalex_id":478,"authors":479,"venue":456,"cited_by_count":116,"oa_url":486,"card":487,"direction":121,"ingested_from":123},"W7223058937",[480,483],{"name":481,"orcid":482},"Aleksandr V. Lapko","https:\u002F\u002Forcid.org\u002F0000-0002-0664-3870",{"name":484,"orcid":485},"Vasiliy A. Lapko","https:\u002F\u002Forcid.org\u002F0000-0001-6938-9323","https:\u002F\u002Fjournals.eco-vector.com\u002F2712-8970\u002Farticle\u002Fdownload\u002F719886\u002F228882",{"tldr":488,"method":489,"finding":490,"direction":121,"opportunity":491},"提出聚合多维光谱遥感自动分类结果的方法，合并相似类别为宏类。","改进Forel算法、非参数模式识别、Kolmogorov-Smirnov检验与信","基于分布同质性检验可有效聚合自动分类类别，形成稳定宏类。","可将该聚合框架用于作物遥感分类后处理，提升类别一致性与可解释性。","2026-10-22T11:20:42.307504Z"]