[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2945":3,"related-2945":129},{"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":32,"secondary_topics":38,"classification":50,"search_phrases":103,"slug":106,"view_count":22,"doi":107,"paper":108,"created_at":128},2945,"Assessing spatial suitability and climate resilience of agroforestry systems in the Indian Himalaya of Uttarakhand using remote sensing and artificial neural networks","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44415-026-00113-9","Agroforestry systems are widely recognized for their capacity to enhance ecological sustainability, livelihood security, and climate resilience in mountain environments. However, their spatial suitability and long-term resilience under changing climatic conditions remain poorly quantified in the Indian Himalaya. This study assesses the spatial suitability and climate resilience of agroforestry systems in Uttarakhand using an integrated framework of remote sensing, geographic information systems (GIS), and artificial neural networks (ANNs). Landsat 8 OLI imagery (30 m resolution) was classified using a supervised Gaussian Maximum Likelihood Classifier (MLC), while land-use categories were defined based on a modified Anderson Level I\u002FII classification scheme to represent the heterogeneous Himalayan landscape. Field surveys conducted across representative agroecological zones documented the composition of tree and crop species within prevailing agroforestry systems. A multi-criteria land suitability analysis, guided by FAO (Food and Agriculture Organization of the United Nations) principles, was implemented using key biophysical variables including altitude, slope, aspect, Normalized Difference Vegetation Index (NDVI), soil properties, temperature, and precipitation. Future agroforestry patterns were simulated using an ANN model under RCP 4.5 climate scenarios. The model demonstrated strong predictive performance, indicating reliable simulation of agroforestry distribution. Results show that mid-altitudinal zones offer the highest suitability for agroforestry expansion, whereas small and fragmented systems exhibit reduced climate resilience under future projections. The findings provide a spatially explicit framework to support climate-resilient agroforestry planning and policy formulation in the Himalayan region.","农林复合系统因其在山区环境中提升生态可持续性、生计安全及气候适应能力方面的作用而受到广泛认可。然而，在印度喜马拉雅地区，其空间适宜性及气候变化条件下的长期适应能力仍缺乏充分的量化评估。本研究采用遥感、地理信息系统（GIS）与人工神经网络（ANN）相结合的综合框架，评估了北阿坎德邦农林复合系统的空间适宜性与气候适应能力。利用监督式高斯最大似然分类器（MLC）对Landsat 8 OLI影像（30 m分辨率）进行分类，同时基于改进的Anderson一级\u002F二级分类方案界定土地利用类别，以表征喜马拉雅地区异质性景观。在代表性农业生态区开展实地调查，记录了现有农林复合系统中树种和作物物种的组成。依据联合国粮食及农业组织（FAO）原则，采用多准则土地适宜性分析，纳入海拔、坡度、坡向、归一化植被指数（NDVI）、土壤属性、温度和降水等关键生物物理变量。利用ANN模型在RCP 4.5气候情景下模拟未来农林复合系统格局。模型表现出较强的预测性能，表明对农林复合系统分布的模拟可靠。结果表明，中海拔区域为农林复合系统扩展提供了最高的适宜性，而小型和破碎化系统在未来预测情景下表现出较低的气候适应能力。研究结果为喜马拉雅地区气候适应型农林复合系统规划与政策制定提供了空间明确的框架支持。",null,"Discover Forests","2026-09-18T00:00:00Z","论文",10,false,72,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,21,17,13,9,1,"遥感与人工神经网络结合评估喜马拉雅地区农林复合系统空间适宜性与气候韧性，方法新颖、结论有区域政策参考价值，但属境外区域研究，公共影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"农业人工智能","遥感","气候韧性","土地适宜性","农林复合经营",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",[39,45],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"code":46,"slug":47,"name":48,"path":49,"parent_code":44},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"status":51,"locked":14,"version":52,"method":53,"evidence":54},"classified","2026-10-22.2","rules",{"candidates":55,"agricultural_evidence":58},[56,65,71,78,86,93,98],{"code":33,"score":57,"supported":58,"title":59,"abstract":61,"tags":64},18,true,[60],"climate resilience",[62,63,60],"气候变化","气候适应",[29],{"code":40,"score":66,"supported":58,"title":67,"abstract":69,"tags":70},15,[68],"remote sensing",[28,68],[28],{"code":46,"score":21,"supported":58,"title":72,"abstract":73,"tags":77},[],[74,75,76],"地理信息","GIS","geographic information",[],{"code":79,"score":80,"supported":58,"title":81,"abstract":82,"tags":85},"H2",6,[],[83,84],"土壤","soil",[],{"code":87,"score":88,"supported":58,"title":89,"abstract":90,"tags":92},"H6",3,[],[91],"农业生态",[],{"code":94,"score":22,"supported":14,"title":95,"abstract":96,"tags":97},"H1",[],[],[30],{"code":99,"score":22,"supported":14,"title":100,"abstract":101,"tags":102},"I2",[],[],[27],[104,105],"Uttarakhand 农林复合系统 遥感","印度喜马拉雅 人工神经网络 土地适宜性","Uttarakhand农林复合系统遥感-2945","10.1007\u002Fs44415-026-00113-9",{"doi":107,"openalex_id":109,"authors":110,"venue":10,"cited_by_count":120,"oa_url":6,"card":121,"direction":125,"ingested_from":127},"W7213553408",[111,113,115,118],{"name":112,"orcid":9},"Deepak Kumar Mishra",{"name":114,"orcid":9},"Ujjwal Kumar",{"name":116,"orcid":117},"A. Arunachalam","https:\u002F\u002Forcid.org\u002F0000-0001-6590-4113",{"name":119,"orcid":9},"Ayyanadar Arunachalam",0,{"tldr":122,"method":123,"finding":124,"direction":125,"opportunity":126},"结合遥感、GIS与人工神经网络评估印度喜马拉雅地区农林复合系统的空间适宜性与气候韧性。","Landsat 8影像监督分类、FAO多准则适宜性分析、ANN在RCP4.5情景","中海拔区最适宜农林复合扩展，小而破碎系统在未来气候下韧性较低。","农业遥感与作物表型","可将该遥感-ANN框架迁移至中国山区，耦合多情景气候与农户数据优化农林复合布局。","openalex","2026-09-19T23:30:33.082917Z",{"total":80,"page":22,"page_size":80,"items":130},[131,226,281,356,429,508],{"id":132,"title":133,"url":134,"summary":135,"summary_zh":136,"content":9,"source_name":137,"source_url":134,"published_at":138,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":139,"score_detail":140,"sources":144,"tags":146,"primary_topic":149,"secondary_topics":150,"classification":153,"search_phrases":197,"slug":200,"view_count":201,"doi":202,"paper":203,"created_at":225},3495,"Remote Sensing and GIS in Modern Drought Assessment: Bridging Conventional Methods and Emerging Technologies","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fjgeesi\u002F2026\u002Fv30i91123","Drought is a complex and recurring hydroclimatic hazard that affects agricultural production, water resources, ecosystems and socioeconomic development. Effective drought assessment requires approaches capable of capturing its spatial and temporal variability and its multiple dimensions. This review examines the evolution of drought assessment from conventional drought indices to integrated approaches based on remote sensing and Geographic Information Systems (GIS), with an emphasis on their applications, strengths, limitations and emerging developments. Conventional indices, including the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Palmer Drought Severity Index (PDSI), Reconnaissance Drought Index (RDI) and Percent of Normal Precipitation Index (PNPI), remain widely used because of their established methodologies and long-term applicability. However, their dependence on meteorological observations can limit spatial characterisation and the representation of vegetation, soil moisture and other land-surface responses. Remote sensing provides spatially extensive and repeated observations of vegetation condition, land surface temperature, soil moisture, evapotranspiration and water-related conditions, enabling the development of satellite-derived drought indicators and indices. GIS further facilitates the integration, spatial analysis, visualisation, and mapping of drought-related information from multiple sources. The review also discusses hybrid approaches that combine climate-based indices with satellite-derived indicators, as well as drought monitoring platforms and multi-source assessment frameworks. Despite substantial advances, challenges remain regarding cloud contamination, differences in spatial and temporal resolution, data continuity, ground-based validation and uncertainty associated with multi-source datasets. Emerging machine learning, deep learning and artificial intelligence approaches offer opportunities for integrating heterogeneous datasets and improving drought characterisation and early warning. Overall, the integration of conventional observations, remote sensing, GIS and advanced analytical approaches provides a promising framework for more comprehensive drought monitoring and risk assessment under increasing climate variability and change.","干旱是一种复杂且反复出现的水文气候灾害，影响农业生产、水资源、生态系统和社会经济发展。有效的干旱评估需要能够捕捉其时空变异性和多维特征的方法。本文综述了干旱评估从传统干旱指数到基于遥感与地理信息系统（GIS）的综合方法的演变，重点探讨其应用、优势、局限性和新兴发展。传统指数，包括标准化降水指数（SPI）、标准化降水蒸散指数（SPEI）、帕尔默干旱强度指数（PDSI）、侦察干旱指数（RDI）和降水距平百分率指数（PNPI），因其方法成熟且具有长期适用性而仍被广泛使用。然而，这些指数对气象观测的依赖可能限制其空间表征能力以及对植被、土壤水分和其他陆面响应的刻画。遥感提供了对植被状况、地表温度、土壤水分、蒸散量及与水相关状况的大范围重复观测，使得卫星衍生的干旱指标和指数得以发展。GIS进一步促进了多来源干旱相关信息的整合、空间分析、可视化和制图。本文还讨论了将基于气候的指数与卫星衍生指标相结合的混合方法，以及干旱监测平台和多源评估框架。尽管取得了实质性进展，但在云污染、时空分辨率差异、数据连续性、地面验证以及多源数据集相关的不确定性方面仍存在挑战。新兴的机器学习、深度学习和人工智能方法为整合异质数据集、改进干旱表征和预警提供了机遇。总体而言，在气候变异性和变化日益加剧的背景下，传统观测、遥感、GIS和先进分析方法的整合为更全面的干旱监测和风险评估提供了一个有前景的框架。","Journal of Geography Environment and Earth Science International","2026-09-24T00:00:00Z",66,{"impact":17,"substance":57,"depth":141,"authority":17,"freshness":142,"relevant":22,"comment":143},16,8,"综述系统梳理遥感与GIS在干旱评估中的应用演进，方法学价值明确，但属综述类论文、非国内落地事件，影响力有限。",[145],{"name":137,"url":134},[147,27,28,75,148],"智慧农业","干旱监测",{"code":46,"slug":47,"name":48,"path":49,"parent_code":44},[151,152],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":51,"locked":14,"version":52,"method":53,"evidence":154},{"candidates":155,"agricultural_evidence":58},[156,162,168,172,181,185,192],{"code":46,"score":57,"supported":58,"title":157,"abstract":158,"tags":161},[75],[74,159,75,160,76],"空间分析","spatial analysis",[75],{"code":33,"score":66,"supported":58,"title":163,"abstract":165,"tags":167},[164],"drought",[166,164],"干旱",[148],{"code":40,"score":66,"supported":58,"title":169,"abstract":170,"tags":171},[68],[28,68],[28],{"code":99,"score":13,"supported":58,"title":173,"abstract":174,"tags":180},[],[175,176,177,178,179],"深度学习","机器学习","machine learning","deep learning","artificial intelligence",[27],{"code":79,"score":80,"supported":58,"title":182,"abstract":183,"tags":184},[],[83,84],[],{"code":186,"score":80,"supported":58,"title":187,"abstract":188,"tags":191},"H3",[],[189,190],"水资源","evapotranspiration",[],{"code":193,"score":22,"supported":14,"title":194,"abstract":195,"tags":196},"I1",[],[],[147],[198,199],"遥感 GIS 干旱评估","卫星遥感 干旱指数","遥感GIS干旱评估-3495",5,"10.9734\u002Fjgeesi\u002F2026\u002Fv30i91123",{"doi":202,"openalex_id":204,"authors":205,"venue":137,"cited_by_count":120,"oa_url":134,"card":220,"direction":125,"ingested_from":127},"W7214156857",[206,208,210,212,214,216,218],{"name":207,"orcid":9},"V. Dhanalakshmi",{"name":209,"orcid":9},"N. Manikandan",{"name":211,"orcid":9},"V. S. Jinsy",{"name":213,"orcid":9},"K. V. Sumesh",{"name":215,"orcid":9},"P. Nideesh",{"name":217,"orcid":9},"P. S. Manju",{"name":219,"orcid":9},"N. Gopika",{"tldr":221,"method":222,"finding":223,"direction":125,"opportunity":224},"综述了从传统干旱指数到遥感、GIS及AI集成的现代干旱评估方法演进。","文献综述，对比SPI、SPEI等传统指数与遥感、GIS及混合方法。","遥感与GIS弥补传统指数空间局限，但云污染、分辨率差异和验证仍是挑战。","可探索多源遥感与机器学习融合的干旱早期预警，重点解决数据不确定性与地面验证。","2026-09-25T23:30:30.576065Z",{"id":227,"title":228,"url":229,"summary":230,"summary_zh":231,"content":9,"source_name":232,"source_url":229,"published_at":233,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":120,"score_detail":234,"sources":236,"tags":238,"primary_topic":243,"secondary_topics":244,"classification":247,"search_phrases":263,"slug":266,"view_count":120,"doi":267,"paper":268,"created_at":280},5326,"Flood Hazard and Vulnerability Assessment of Chitrakoot District, Uttar Pradesh: An Integrated Geographical, GIS and Remote Sensing Approach","https:\u002F\u002Fdoi.org\u002F10.36948\u002Fijfmr.2026.v08i05.89708","Flood is one of the most important hydro-meteorological hazards affecting human settlements, agriculture, infrastructure and regional economies. Chitrakoot district of Uttar Pradesh presents a distinctive case of hydro-climatic vulnerability because it belongs to the drought-prone Bundelkhand region but simultaneously experiences recurrent riverine and flash-flood conditions during intense monsoon rainfall. The present study assesses the spatial characteristics and determinants of flood hazard and vulnerability in Chitrakoot district using an integrated geographical, GIS and remote sensing approach. The analysis incorporates the Digital Elevation Model (DEM), drainage network, physiography, river proximity, historical flood information, satellite-based flood observations and socio-economic exposure. The district's DEM indicates a marked elevation gradient, approximately from 66 m to 451 m, with relatively higher terrain in the southern part and lower terrain towards the northern Yamuna valley. This topographic configuration facilitates rapid surface runoff from elevated southern terrain towards comparatively low-lying riverine areas. The drainage system is dominated by the Yamuna and Mandakini\u002FPaisuni systems along with Baghain, Gunta, Bardaha and smaller tributaries. The Uttar Pradesh Flood Hazard Atlas identifies 5,114 ha of flood-hazard area in Chitrakoot for 1998–2020, of which 4,129 ha (81%) falls in the very-low hazard category and 985 ha (19%) in the low-hazard category. However, the historical classification does not fully represent the potential consequences of extreme rainfall events. The August 2026 flood provides compelling evidence: the Mandakini at Chitrakoot reached a reported new high flood level of 145.60 m on 29 August 2026, approximately 2.95 m above the previous reported high level, while the water level rose by about 7.5 m within six hours. The study concludes that flood risk in Chitrakoot is spatially concentrated around river corridors, low-elevation areas, floodplains, agricultural land, settlements and tourism infrastructure. A shift from conventional relief-oriented flood management towards integrated floodplain regulation, watershed management, GIS-based risk zonation, real-time monitoring and community-based preparedness is therefore essential.","洪水是影响人类住区、农业、基础设施和区域经济的最重要的水文气象灾害之一。北方邦吉德古德（Chitrakoot）县在水文气候脆弱性方面呈现出独特案例，因为它属于易受干旱影响的本德尔坎德（Bundelkhand）地区，但同时在强季风降雨期间反复遭遇河流洪水和山洪状况。本研究采用地理学、地理信息系统（GIS）和遥感相结合的综合方法，评估吉德古德县洪水灾害与脆弱性的空间特征及其决定因素。分析纳入了数字高程模型（DEM）、河网、地貌、距河流远近、历史洪水信息、基于卫星的洪水观测以及社会经济暴露度。该县的DEM显示出明显的高程梯度，大约从66米到451米，南部地形相对较高，向北部的亚穆纳（Yamuna）河谷地势降低。这种地形格局促使地表径流从较高的南部地形快速流向相对低洼的河流区域。排水系统以亚穆纳河和曼达基尼\u002F派苏尼（Mandakini\u002FPaisuni）水系为主，另有巴盖恩（Baghain）、贡塔（Gunta）、巴尔达哈（Bardaha）及较小支流。北方邦洪水灾害图集确认，1998—2020年吉德古德县洪水灾害面积为5，114公顷，其中4，129公顷（81%）属于极低灾害类别，985公顷（19%）属于低灾害类别。然而，历史分类并不能充分反映极端降雨事件的潜在后果。2026年8月的洪水提供了有力证据：据报道，2026年8月29日吉德古德的曼达基尼河达到145.60米的新高洪水位，比此前报告的高水位高出约2.95米，而水位在六小时内上升了约7.5米。研究得出结论，吉德古德的洪水风险在空间上集中于河流廊道、低海拔地区、洪泛平原、农业用地、住区和旅游基础设施周围。因此，必须从传统的以救济为导向的洪水管理转向洪泛平原综合调控、流域管理、基于GIS的风险区划、实时监测和社区为基础的备灾。","International Journal For Multidisciplinary Research","2026-10-17T00:00:00Z",{"impact":120,"substance":120,"depth":120,"authority":120,"freshness":120,"relevant":120,"comment":235},"该研究聚焦印度北方邦的洪水灾害评估，虽涉及GIS与遥感方法，但与三农、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。",[237],{"name":232,"url":229},[239,240,241,242],"防灾减灾","遥感监测","洪水风险评估","GIS应用",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[245,246],{"code":46,"slug":47,"name":48,"path":49,"parent_code":44},{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":51,"locked":14,"version":52,"method":53,"evidence":248},{"candidates":249,"agricultural_evidence":58},[250,254,259],{"code":40,"score":66,"supported":58,"title":251,"abstract":252,"tags":253},[68],[28,68],[240],{"code":46,"score":255,"supported":58,"title":256,"abstract":257,"tags":258},14,[75],[74,75],[],{"code":33,"score":142,"supported":58,"title":260,"abstract":261,"tags":262},[],[166,164],[241,239],[264,265],"Chitrakoot 洪水风险 GIS","Yamuna 洪水脆弱性 遥感","Chitrakoot洪水风险GIS-5326","10.36948\u002Fijfmr.2026.v08i05.89708",{"doi":267,"openalex_id":269,"authors":270,"venue":232,"cited_by_count":120,"oa_url":274,"card":275,"direction":125,"ingested_from":127},"W7222885988",[271],{"name":272,"orcid":273},"Birendra Yadav","https:\u002F\u002Forcid.org\u002F0009-0007-0864-474X","https:\u002F\u002Fwww.ijfmr.com\u002Fpapers\u002F2026\u002F5\u002F89708.pdf",{"tldr":276,"method":277,"finding":278,"direction":125,"opportunity":279},"用GIS与遥感综合评估Chitrakoot区洪水灾害与脆弱性空间分布。","DEM、河网、历史洪水与卫星观测及社会经济数据叠加分析。","洪水风险集中于河谷低地、农田与居民点，历史分级低估极端降雨风险。","可结合时序遥感与水文模型，构建极端降雨情景下的农田洪涝风险动态制图与预警。","2026-10-19T23:30:24.356743Z",{"id":282,"title":283,"url":284,"summary":285,"summary_zh":286,"content":9,"source_name":287,"source_url":284,"published_at":288,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":289,"sources":291,"tags":293,"primary_topic":296,"secondary_topics":297,"classification":299,"search_phrases":329,"slug":332,"view_count":22,"doi":333,"paper":334,"created_at":355},2056,"Land suitability assessment for irrigated lentil cultivation in Southwest Shewa Zone, Ethiopia using remote sensing, GIS, and the analytic hierarchy process","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-70025-3","Sustainable expansion of irrigated agriculture requires accurate identification of suitable land areas based on biophysical conditions. This study evaluates land suitability for irrigated lentils cultivation in Southwest Shewa Zone, Ethiopia, using an integrated Remote Sensing (RS), Geographic Information System (GIS), and Analytical Hierarchy Process (AHP) approach. Nine suitability criteria, including temperature, land use\u002Fland cover, slope, elevation, soil type, soil texture, soil pH, soil drainage, and distance to water sources, were analyzed to develop a spatial suitability map. The AHP approach was applied to determine the relative importance of each criterion, with distance to water (32.5%) and slope (28.2%) identified as the most influential factors. The results classified the study area into four suitability categories: highly suitable (10.47%), moderately suitable (30.47%), marginally suitable (38.88%), and not suitable (20.18%). Model performance was evaluated using Receiver Operating Characteristic (ROC) analysis, producing an Area Under the Curve (AUC) value of 0.766, indicating good predictive performance of the suitability model. The findings reveal that 40.94% of the area has high to moderate potential for irrigated lentils production, while limitations are mainly associated with soil conditions, slope, and water accessibility. The study provides a spatial decision-support framework for identifying suitable areas, prioritizing irrigation development, and promoting sustainable land resource management and climate-resilient lentils production in Ethiopia.","灌溉农业的可持续扩展需要基于生物物理条件准确识别适宜土地区域。本研究采用遥感（RS）、地理信息系统（GIS）与层次分析法（AHP）相结合的方法，评估埃塞俄比亚西南绍阿区灌溉小扁豆种植的土地适宜性。研究分析了九个适宜性指标，包括温度、土地利用\u002F土地覆盖、坡度、海拔、土壤类型、土壤质地、土壤pH值、土壤排水和距水源距离，以绘制空间适宜性图。采用层次分析法确定各指标的相对重要性，其中距水源距离（32.5%）和坡度（28.2%）被识别为影响最大的因素。结果将研究区划分为四个适宜性等级：高度适宜（10.47%）、中度适宜（30.47%）、勉强适宜（38.88%）和不适宜（20.18%）。采用受试者工作特征（ROC）分析评估模型性能，得到曲线下面积（AUC）值为0.766，表明适宜性模型具有良好的预测性能。研究结果表明，40.94%的区域具有灌溉小扁豆生产的高度至中度潜力，而限制因素主要与土壤条件、坡度和水源可及性有关。本研究提供了一个空间决策支持框架，用于识别适宜区域、优先安排灌溉开发，并促进埃塞俄比亚土地资源的可持续管理和气候适应型小扁豆生产。","Scientific Reports","2026-09-09T00:00:00Z",{"impact":17,"substance":18,"depth":19,"authority":255,"freshness":142,"relevant":22,"comment":290},"方法集成规范、结论有数据支撑，但属埃塞俄比亚区域案例，对国内三农实践的直接参考价值有限，适合作为农业遥感方法类资讯收录。",[292],{"name":287,"url":284},[28,75,294,30,295],"智慧灌溉","小扁豆",{"code":46,"slug":47,"name":48,"path":49,"parent_code":44},[298],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":51,"locked":14,"version":52,"method":53,"evidence":300},{"candidates":301,"agricultural_evidence":58},[302,306,310,317,321,325],{"code":46,"score":57,"supported":58,"title":303,"abstract":304,"tags":305},[75],[74,75,76],[75],{"code":40,"score":66,"supported":58,"title":307,"abstract":308,"tags":309},[68],[28,68],[28],{"code":186,"score":311,"supported":58,"title":312,"abstract":313,"tags":316},7,[],[314,315],"灌溉","irrigation",[294],{"code":79,"score":80,"supported":58,"title":318,"abstract":319,"tags":320},[],[83,84],[],{"code":33,"score":88,"supported":58,"title":322,"abstract":323,"tags":324},[],[63],[],{"code":94,"score":22,"supported":14,"title":326,"abstract":327,"tags":328},[],[],[30],[330,331],"土地适宜性 智慧灌溉 小扁豆 遥感","土地适宜性 智慧灌溉","土地适宜性智慧灌溉小扁豆遥感-2056","10.1038\u002Fs41598-026-70025-3",{"doi":333,"openalex_id":335,"authors":336,"venue":287,"cited_by_count":120,"oa_url":284,"card":350,"direction":125,"ingested_from":127},"W7211993031",[337,340,342,344,347],{"name":338,"orcid":339},"Kanenus Fufa Dararo","https:\u002F\u002Forcid.org\u002F0009-0007-5747-1558",{"name":341,"orcid":9},"Diro Lenjisa Geleto",{"name":343,"orcid":9},"Aster Chalchisa Eggi",{"name":345,"orcid":346},"Firdissa Sadeta Tiye","https:\u002F\u002Forcid.org\u002F0000-0002-9233-1741",{"name":348,"orcid":349},"Bekele Bedada Damtie","https:\u002F\u002Forcid.org\u002F0000-0003-3673-8560",{"tldr":351,"method":352,"finding":353,"direction":125,"opportunity":354},"用遥感、GIS与AHP评估埃塞俄比亚西南绍阿区灌溉扁豆种植土地适宜性并制图。","遥感、GIS与层次分析法（AHP），分析9项生物物理与水源距离指标，ROC验证。","高至中度适宜区占40.94%，距水源与坡度影响最大，模型AUC为0.766。","可引入多时相遥感与气候情景，动态评估灌溉适宜性并耦合作物模型优化布局。","2026-09-10T23:30:27.675890Z",{"id":357,"title":358,"url":359,"summary":360,"summary_zh":361,"content":9,"source_name":362,"source_url":359,"published_at":363,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":364,"score_detail":365,"sources":367,"tags":369,"primary_topic":373,"secondary_topics":374,"classification":376,"search_phrases":405,"slug":408,"view_count":120,"doi":409,"paper":410,"created_at":428},5321,"GIS and remote sensing based multicriteria analysis for identifying potential agricultural land: A case study on Savar Upazila, Bangladesh","https:\u002F\u002Fdoi.org\u002F10.3329\u002Fbrds.v26i1.94265","Despite of gradually decreasing its contribution to gross domestic product (GDP), agriculture, however, is the backbone of the Bangladeshi economy. Farming is the primary mean of rural livelihoods in Bangladesh but agricultural land is slowly declining day by day. Although, at present, Bangladesh is self-sufficient in terms of producing food, especially rice and some other major agricultural products, it is predicted that, in near future, the country is going to face immense challenges due to climate related threats. The research aims to identify the potential lands for suitable farming in Savar Upazila (sub-district) under Dhaka district applying a geographic information system (GIS) and remote sensing (RS) based multicriteria analysis. Relevant biophysical characteristics of soil, terrain, and landuse and land cover (LULC) were considered. Soil and evelvation data was collected from Soil Resource Development Institute (SRDI)and Shuttle Radar Topography Mission (SRTM), respectively, whereas satellite images were collected from Landsat-8. The maximum likelihood supervised classification technique was used for LULC classification, and the multicriteria evaluation (MCE) and analytical hierarchy process (AHP) approach was employed to identify suitable areas for farming. The research finds the LULC classes are agricultural land, built-up area, fallow land, vegetation cover, waterbodies, and wetland are 12.42%, 31.01%, 25.69%, 25.98%, 4.81%, and 0.09%, respectively. This research also finds that agricultural land suitability was classified as highly suitable, moderately suitable, marginally suitable, and not suitable are 24.28%, 29.53%, 24.66%, and 21.53%, respectively. In contrast, the current agricultural land usage accounts for just 12.42% of the total land area in this research area. The Kappa accuracy has been measured at a value of 0.88 and an overall accuracy value of 0.90 which indicates that the LULC classification is done almost perfectly. This study offered information at the local level that farmers may utilize to decide on cropping patterns and appropriateness. Bangladesh Rural Development Studies, 26(1), 2023 (13-26)","尽管农业在国内生产总值（GDP）中的贡献逐渐下降，但它仍是孟加拉国经济的支柱。农耕是孟加拉国农村生计的主要方式，但农业用地正日益减少。虽然目前孟加拉国在粮食生产方面，尤其是稻米和其他一些主要农产品方面已实现自给自足，但据预测，在不久的将来，该国将因气候相关威胁而面临巨大挑战。本研究旨在运用地理信息系统（GIS）和遥感（RS）基础上的多准则分析，确定达卡县萨瓦尔乡（分区）适宜耕作的土地潜力。研究考虑了土壤、地形以及土地利用与土地覆盖（LULC）等相关生物物理特征。土壤数据来自土壤资源开发研究所（SRDI），高程数据来自航天飞机雷达地形测绘任务（SRTM），卫星影像则来自Landsat-8。土地利用与土地覆盖分类采用最大似然监督分类技术，适宜耕作区的识别则运用多准则评价（MCE）和层次分析法（AHP）。研究发现，土地利用与土地覆盖类别中，农业用地、建成区、休耕地、植被覆盖、水体及湿地分别占12.42%、31.01%、25.69%、25.98%、4.81%和0.09%。研究还发现，农业用地适宜性分为高度适宜、中度适宜、勉强适宜和不适宜，分别占24.28%、29.53%、24.66%和21.53%。相比之下，当前农业用地仅占本研究区土地总面积的12.42%。Kappa精度值为0.88，总体精度值为0.90，表明土地利用与土地覆盖分类近乎完美。本研究提供了地方层面的信息，可供农民在决定种植模式和适宜性时参考。《孟加拉国农村发展研究》，第26卷第1期，2023年（第13-26页）","OpenAlex","2026-10-18T00:00:00Z",60,{"impact":142,"substance":57,"depth":66,"authority":13,"freshness":21,"relevant":22,"comment":366},"方法规范、数据可靠的区域农用地适宜性案例研究，但属孟加拉地方尺度，对国内三农信息化仅有方法借鉴价值，不宜进入每日精选。",[368],{"name":362,"url":359},[28,75,370,371,372],"农用地适宜性","土地覆被","孟加拉农业",{"code":46,"slug":47,"name":48,"path":49,"parent_code":44},[375],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":51,"locked":14,"version":52,"method":53,"evidence":377},{"candidates":378,"agricultural_evidence":58},[379,383,387,394,401],{"code":46,"score":57,"supported":58,"title":380,"abstract":381,"tags":382},[75],[74,75,76],[75],{"code":40,"score":66,"supported":58,"title":384,"abstract":385,"tags":386},[68],[28,68],[28],{"code":388,"score":80,"supported":58,"title":389,"abstract":390,"tags":393},"A1",[],[391,392],"稻米","rice",[],{"code":395,"score":80,"supported":58,"title":396,"abstract":397,"tags":400},"G",[],[398,399],"农村发展","rural development",[],{"code":79,"score":80,"supported":58,"title":402,"abstract":403,"tags":404},[],[83,84],[],[406,407],"Savar Upazila 农用地适宜性","Landsat-8 土地覆被分类","SavarUpazila农用地适宜性-5321","10.3329\u002Fbrds.v26i1.94265",{"doi":409,"openalex_id":411,"authors":412,"venue":422,"cited_by_count":120,"oa_url":359,"card":423,"direction":125,"ingested_from":127},"W7222895468",[413,415,417,419],{"name":414,"orcid":9},"Raju Ahmed",{"name":416,"orcid":9},"Shaikh Mehdee Mohammad",{"name":418,"orcid":9},"Ummul Momanin Coalee",{"name":420,"orcid":421},"Khandakar Hasan Mahmud","https:\u002F\u002Forcid.org\u002F0000-0003-1222-0791","The Bangladesh Rural Development Studies",{"tldr":424,"method":425,"finding":426,"direction":125,"opportunity":427},"用GIS与遥感多准则分析评估孟加拉Savar地区潜在农业用地适宜性。","Landsat-8监督分类提取土地利用，结合土壤、地形数据用AHP与MCE做适宜","仅12.42%为农地，高、中、边缘、不适宜分别占24.28%、29.53%、24.66%、21.53","可引入时序遥感与气候情景，动态评估适宜性变化并耦合作物种植决策支持。","2026-10-19T23:30:22.906704Z",{"id":430,"title":431,"url":432,"summary":433,"summary_zh":434,"content":9,"source_name":435,"source_url":432,"published_at":436,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":437,"score_detail":438,"sources":441,"tags":443,"primary_topic":447,"secondary_topics":448,"classification":453,"search_phrases":492,"slug":495,"view_count":496,"doi":9,"paper":497,"created_at":507},5013,"REMOTE SENSING AND GIS-BASED ASSESSMENT OF SOIL SALINITY IN IRRIGATED LANDS: METHODS, CHALLENGES AND PROSPECTS FOR PRECISION AGRICULTURE","http:\u002F\u002Fwww.arabianjournalofscience.com\u002Findex.php\u002Findex\u002Farticle\u002Fview\u002F1223","Soil salinization is a major land degradation process affecting the sustainability of irrigated agriculture, particularly in arid and semi-arid regions. Conventional soil sampling provides essential information about salinity levels but may be insufficient for continuous monitoring of large agricultural territories because of spatial variability, sampling costs, and temporal limitations. Remote sensing (RS) and geographic information systems (GIS) offer complementary approaches for identifying spatial patterns of salinity, monitoring land conditions, and supporting agricultural management. This article examines the scientific and methodological foundations of integrating multispectral satellite imagery, spectral indices, geospatial analysis, and machine learning for soil salinity assessment in irrigated lands. Particular attention is given to Landsat and Sentinel-2 imagery, the interpretation of vegetation and soil reflectance indicators, the role of electrical conductivity measurements in model calibration, and the limitations associated with vegetation cover, soil moisture, and seasonal variability. The analysis indicates that reliable salinity assessment requires the integration of satellite observations with field measurements and environmental variables rather than the isolated application of spectral indices. The article proposes a methodological framework for developing spatially explicit salinity assessment systems applicable to irrigated agricultural landscapes, including those of Central Asia. The framework provides a basis for improving monitoring strategies and supporting evidence-based land management without assuming unverified local measurement results.","土壤盐渍化是影响灌溉农业可持续性的主要土地退化过程，在干旱和半干旱地区尤为突出。传统土壤采样可提供盐分水平的基本信息，但由于空间变异性、采样成本和时间限制，可能不足以对大面积农业区域进行持续监测。遥感（RS）和地理信息系统（GIS）为识别盐渍化空间格局、监测土地状况和支持农业管理提供了互补性方法。本文探讨了整合多光谱卫星影像、光谱指数、地理空间分析和机器学习进行灌溉土地土壤盐渍化评估的科学与方法学基础。文章特别关注Landsat和Sentinel-2影像、植被与土壤反射率指标的解译、电导率测量在模型校准中的作用，以及与植被覆盖、土壤湿度和季节变异性相关的局限性。分析表明，可靠的盐渍化评估需要将卫星观测与野外测量和环境变量相结合，而非孤立地应用光谱指数。本文提出了一个方法学框架，用于开发适用于灌溉农业景观（包括中亚地区）的空间显式盐渍化评估系统。该框架为改进监测策略和支持基于证据的土地管理提供了基础，且不以未经核实的当地测量结果为假设前提。","Arabian journal of science.","2026-10-13T00:00:00Z",74,{"impact":141,"substance":439,"depth":19,"authority":20,"freshness":142,"relevant":22,"comment":440},20,"系统综述遥感与GIS在灌溉农田盐渍化评估中的方法框架与挑战，对盐碱地治理和精准农业有参考价值，但属综述性论文，非突破性成果。",[442],{"name":435,"url":432},[147,444,28,445,446],"精准农业","盐碱地治理","土壤监测",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[449,450],{"code":46,"slug":47,"name":48,"path":49,"parent_code":44},{"code":79,"slug":84,"name":451,"path":452,"parent_code":37},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"status":51,"locked":14,"version":52,"method":53,"evidence":454},{"candidates":455,"agricultural_evidence":58},[456,461,465,469,474,478,482,486],{"code":40,"score":57,"supported":58,"title":457,"abstract":458,"tags":460},[68],[28,68,459],"multispectral",[28],{"code":46,"score":19,"supported":58,"title":462,"abstract":463,"tags":464},[75],[74,159,75,76],[],{"code":79,"score":66,"supported":58,"title":466,"abstract":467,"tags":468},[84],[83,84],[445],{"code":193,"score":13,"supported":58,"title":470,"abstract":472,"tags":473},[471],"precision agriculture",[],[147,444],{"code":99,"score":80,"supported":58,"title":475,"abstract":476,"tags":477},[],[176,177],[],{"code":186,"score":88,"supported":58,"title":479,"abstract":480,"tags":481},[],[314],[],{"code":33,"score":88,"supported":58,"title":483,"abstract":484,"tags":485},[],[166],[],{"code":487,"score":88,"supported":58,"title":488,"abstract":489,"tags":491},"I7",[],[490],"methodological framework",[],[493,494],"遥感 GIS 土壤盐渍化","Landsat Sentinel-2 盐分监测","遥感GIS土壤盐渍化-5013",2,{"doi":9,"openalex_id":498,"authors":499,"venue":435,"cited_by_count":120,"oa_url":432,"card":502,"direction":125,"ingested_from":127},"W7221843179",[500],{"name":501,"orcid":9},"Sadullayev Sardor Zokirjon o'g'li",{"tldr":503,"method":504,"finding":505,"direction":125,"opportunity":506},"综述遥感与GIS评估灌溉地土壤盐渍化的方法、挑战与前景，并提出空间显式评估框架。","整合多光谱卫星影像、光谱指数、GIS空间分析与机器学习，结合电导率校准。","可靠盐渍化评估需卫星观测与实地测量及环境变量融合，而非孤立使用光谱指数。","可探索多源遥感与机器学习融合的盐渍化动态监测，并验证于中亚等干旱灌区。","2026-10-15T23:30:32.401626Z",{"id":509,"title":510,"url":511,"summary":512,"summary_zh":513,"content":9,"source_name":514,"source_url":511,"published_at":515,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":437,"score_detail":516,"sources":519,"tags":521,"primary_topic":526,"secondary_topics":527,"classification":529,"search_phrases":553,"slug":556,"view_count":120,"doi":557,"paper":558,"created_at":568},5008,"ASSESSING THE GEOECOLOGICAL IMPACT OF LARGE HYDRAULIC STRUCTURES IN ARID REGIONS USING REMOTE SENSING AND GIS TECHNOLOGIES (A CASE STUDY OF THE CENTRAL FERGANA RESERVOIR)","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23359723","This article presents a comprehensive study of the geoecological and ameliorative impacts of large hydraulic structures built under arid subtropical conditions on adjacent agricultural landscapes, using the Central Fergana Reservoir (CFR) as a case study. The main objective of the study is to assess landscape changes, the spatial dynamics of vegetation cover, and secondary soil salinization processes across 29,330 hectares within the reservoir’s impact zone between 2010 and 2025 using remote sensing and GIS technologies. The study calculates the Normalized Difference Vegetation Index (NDVI) based on Landsat satellite imagery (Landsat 5 TM, Landsat 8 OLI\u002FTIRS, and Landsat 9 OLI-2) and analyzes long-term changes. The results show that the water surface area expanded from 1% (293.3 ha) in 2010 to 15% (4,399.5 ha) by 2025. Furthermore, analyses of 27 soil profiles and laboratory measurements of electrical conductivity (\\(EC_e\\)) and moisture content confirmed the formation of localized waterlogged areas and highly saline solonchaks in the 500-meter coastal buffer zone as a result of a rise in the groundwater table to 1.0–1.5 m. Environmental stress factors identified through comparative analyses with a control (background) site were scientifically substantiated, and practical recommendations for sustainable regional management were developed.","本文以中央费尔干纳水库（Central Fergana Reservoir, CFR）为例，对干旱亚热带条件下大型水利设施对邻近农业景观的地质生态与土壤改良影响进行了综合研究。研究的主要目标在于利用遥感与GIS技术，评估2010年至2025年间水库影响区内29330公顷土地上的景观变化、植被覆盖空间动态以及次生土壤盐渍化过程。研究基于Landsat卫星影像（Landsat 5 TM、Landsat 8 OLI\u002FTIRS和Landsat 9 OLI-2）计算归一化植被指数（Normalized Difference Vegetation Index, NDVI），并分析其长期变化。结果表明，水域面积从2010年的1%（293.3公顷）扩大至2025年的15%（4399.5公顷）。此外，对27个土壤剖面的分析以及电导率（\\(EC_e\\)）和含水量的实验室测定证实，由于地下水位上升至1.0–1.5米，在500米沿岸缓冲带内形成了局部渍水区和重度盐化盐土。通过与对照（背景）样地的比较分析，科学地证实了所识别的环境胁迫因子，并提出了区域可持续管理的实践建议。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-14T00:00:00Z",{"impact":17,"substance":517,"depth":57,"authority":20,"freshness":21,"relevant":22,"comment":518},22,"基于Landsat长时序遥感与GIS评估干旱区大型水利工程对灌区植被与次生盐渍化的影响，方法规范、数据翔实，对农业遥感与灌区生态监测有参考价值，但属区域案例研究，公共影响有限。",[520],{"name":514,"url":511},[522,28,523,524,525],"农业遥感","水资源管理","灌溉农业","盐渍化",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[528],{"code":46,"slug":47,"name":48,"path":49,"parent_code":44},{"status":51,"locked":14,"version":52,"method":53,"evidence":530},{"candidates":531,"agricultural_evidence":58},[532,536,541,545,549],{"code":40,"score":141,"supported":58,"title":533,"abstract":534,"tags":535},[68],[28,68],[522,28],{"code":46,"score":537,"supported":58,"title":538,"abstract":539,"tags":540},11,[75],[75],[],{"code":79,"score":311,"supported":58,"title":542,"abstract":543,"tags":544},[],[83,84],[525],{"code":33,"score":88,"supported":58,"title":546,"abstract":547,"tags":548},[],[166],[],{"code":186,"score":496,"supported":14,"title":550,"abstract":551,"tags":552},[],[],[523,524],[554,555],"费尔干纳水库 遥感 盐渍化","Landsat NDVI 灌区 土壤盐渍化","费尔干纳水库遥感盐渍化-5008","10.5281\u002Fzenodo.23359723",{"doi":557,"openalex_id":559,"authors":560,"venue":514,"cited_by_count":120,"oa_url":511,"card":563,"direction":125,"ingested_from":127},"W7221819099",[561],{"name":562,"orcid":9},"Akbarov Sarvarbek",{"tldr":564,"method":565,"finding":566,"direction":125,"opportunity":567},"用遥感和GIS评估费尔干纳中部水库对周边农业景观的生态影响。","Landsat影像NDVI分析、27个土壤剖面及ECe与含水量测定。","水库水面从1%扩至15%，500米缓冲带地下水位升至1.0–1.5米，形成盐渍化。","可结合多源遥感与土壤盐分模型，构建干旱区水库周边盐渍化预警与灌溉调控方案。","2026-10-15T23:30:29.814507Z"]