[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3495":3,"related-3495":138},{"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":31,"secondary_topics":37,"classification":49,"search_phrases":106,"slug":109,"view_count":110,"doi":111,"paper":112,"created_at":137},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。",null,"Journal of Geography Environment and Earth Science International","2026-09-24T00:00:00Z","论文",10,false,66,{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":20,"relevant":21,"comment":22},12,18,16,8,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","遥感","GIS","干旱监测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis","I",[38,44],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"code":45,"slug":46,"name":47,"path":48,"parent_code":36},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":50,"locked":14,"version":51,"method":52,"evidence":53},"classified","2026-10-22.2","rules",{"candidates":54,"agricultural_evidence":56},[55,64,71,76,86,94,101],{"code":32,"score":18,"supported":56,"title":57,"abstract":58,"tags":63},true,[29],[59,60,29,61,62],"地理信息","空间分析","spatial analysis","geographic information",[29],{"code":39,"score":65,"supported":56,"title":66,"abstract":68,"tags":70},15,[67],"drought",[69,67],"干旱",[30],{"code":45,"score":65,"supported":56,"title":72,"abstract":74,"tags":75},[73],"remote sensing",[28,73],[28],{"code":77,"score":13,"supported":56,"title":78,"abstract":79,"tags":85},"I2",[],[80,81,82,83,84],"深度学习","机器学习","machine learning","deep learning","artificial intelligence",[27],{"code":87,"score":88,"supported":56,"title":89,"abstract":90,"tags":93},"H2",6,[],[91,92],"土壤","soil",[],{"code":95,"score":88,"supported":56,"title":96,"abstract":97,"tags":100},"H3",[],[98,99],"水资源","evapotranspiration",[],{"code":102,"score":21,"supported":14,"title":103,"abstract":104,"tags":105},"I1",[],[],[26],[107,108],"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":111,"openalex_id":113,"authors":114,"venue":10,"cited_by_count":129,"oa_url":6,"card":130,"direction":134,"ingested_from":136},"W7214156857",[115,117,119,121,123,125,127],{"name":116,"orcid":9},"V. Dhanalakshmi",{"name":118,"orcid":9},"N. Manikandan",{"name":120,"orcid":9},"V. S. Jinsy",{"name":122,"orcid":9},"K. V. Sumesh",{"name":124,"orcid":9},"P. Nideesh",{"name":126,"orcid":9},"P. S. Manju",{"name":128,"orcid":9},"N. Gopika",0,{"tldr":131,"method":132,"finding":133,"direction":134,"opportunity":135},"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。","农业遥感与作物表型","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。","openalex","2026-09-25T23:30:30.576065Z",{"total":88,"page":21,"page_size":88,"items":139},[140,226,281,354,434,503],{"id":141,"title":142,"url":143,"summary":144,"summary_zh":145,"content":9,"source_name":146,"source_url":143,"published_at":147,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":148,"score_detail":149,"sources":155,"tags":157,"primary_topic":161,"secondary_topics":162,"classification":165,"search_phrases":203,"slug":206,"view_count":21,"doi":207,"paper":208,"created_at":225},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。","Discover Forests","2026-09-18T00:00:00Z",72,{"impact":17,"substance":150,"depth":151,"authority":152,"freshness":153,"relevant":21,"comment":154},21,17,13,9,"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。",[156],{"name":146,"url":143},[27,28,158,159,160],"气候韧性","土地适宜性","农林复合经营",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[163,164],{"code":45,"slug":46,"name":47,"path":48,"parent_code":36},{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":50,"locked":14,"version":51,"method":52,"evidence":166},{"candidates":167,"agricultural_evidence":56},[168,175,179,183,187,194,199],{"code":39,"score":18,"supported":56,"title":169,"abstract":171,"tags":174},[170],"climate resilience",[172,173,170],"气候变化","气候适应",[158],{"code":45,"score":65,"supported":56,"title":176,"abstract":177,"tags":178},[73],[28,73],[28],{"code":32,"score":153,"supported":56,"title":180,"abstract":181,"tags":182},[],[59,29,62],[],{"code":87,"score":88,"supported":56,"title":184,"abstract":185,"tags":186},[],[91,92],[],{"code":188,"score":189,"supported":56,"title":190,"abstract":191,"tags":193},"H6",3,[],[192],"农业生态",[],{"code":195,"score":21,"supported":14,"title":196,"abstract":197,"tags":198},"H1",[],[],[159],{"code":77,"score":21,"supported":14,"title":200,"abstract":201,"tags":202},[],[],[27],[204,205],"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":207,"openalex_id":209,"authors":210,"venue":146,"cited_by_count":129,"oa_url":143,"card":220,"direction":134,"ingested_from":136},"W7213553408",[211,213,215,218],{"name":212,"orcid":9},"Deepak Kumar Mishra",{"name":214,"orcid":9},"Ujjwal Kumar",{"name":216,"orcid":217},"A. Arunachalam","https:\u002F\u002Forcid.org\u002F0000-0001-6590-4113",{"name":219,"orcid":9},"Ayyanadar Arunachalam",{"tldr":221,"method":222,"finding":223,"direction":134,"opportunity":224},"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。","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。","2026-09-19T23:30:33.082917Z",{"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":129,"score_detail":234,"sources":236,"tags":238,"primary_topic":243,"secondary_topics":244,"classification":247,"search_phrases":263,"slug":266,"view_count":129,"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":129,"substance":129,"depth":129,"authority":129,"freshness":129,"relevant":129,"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":45,"slug":46,"name":47,"path":48,"parent_code":36},[245,246],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":50,"locked":14,"version":51,"method":52,"evidence":248},{"candidates":249,"agricultural_evidence":56},[250,254,259],{"code":45,"score":65,"supported":56,"title":251,"abstract":252,"tags":253},[73],[28,73],[240],{"code":32,"score":255,"supported":56,"title":256,"abstract":257,"tags":258},14,[29],[59,29],[],{"code":39,"score":20,"supported":56,"title":260,"abstract":261,"tags":262},[],[69,67],[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":129,"oa_url":274,"card":275,"direction":134,"ingested_from":136},"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":134,"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":289,"score_detail":290,"sources":292,"tags":294,"primary_topic":298,"secondary_topics":299,"classification":301,"search_phrases":330,"slug":333,"view_count":129,"doi":334,"paper":335,"created_at":353},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":20,"substance":18,"depth":65,"authority":13,"freshness":153,"relevant":21,"comment":291},"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。",[293],{"name":287,"url":284},[28,29,295,296,297],"农用地适宜性","土地覆被","孟加拉农业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[300],{"code":45,"slug":46,"name":47,"path":48,"parent_code":36},{"status":50,"locked":14,"version":51,"method":52,"evidence":302},{"candidates":303,"agricultural_evidence":56},[304,308,312,319,326],{"code":32,"score":18,"supported":56,"title":305,"abstract":306,"tags":307},[29],[59,29,62],[29],{"code":45,"score":65,"supported":56,"title":309,"abstract":310,"tags":311},[73],[28,73],[28],{"code":313,"score":88,"supported":56,"title":314,"abstract":315,"tags":318},"A1",[],[316,317],"稻米","rice",[],{"code":320,"score":88,"supported":56,"title":321,"abstract":322,"tags":325},"G",[],[323,324],"农村发展","rural development",[],{"code":87,"score":88,"supported":56,"title":327,"abstract":328,"tags":329},[],[91,92],[],[331,332],"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":334,"openalex_id":336,"authors":337,"venue":347,"cited_by_count":129,"oa_url":284,"card":348,"direction":134,"ingested_from":136},"W7222895468",[338,340,342,344],{"name":339,"orcid":9},"Raju Ahmed",{"name":341,"orcid":9},"Shaikh Mehdee Mohammad",{"name":343,"orcid":9},"Ummul Momanin Coalee",{"name":345,"orcid":346},"Khandakar Hasan Mahmud","https:\u002F\u002Forcid.org\u002F0000-0003-1222-0791","The Bangladesh Rural Development Studies",{"tldr":349,"method":350,"finding":351,"direction":134,"opportunity":352},"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":355,"title":356,"url":357,"summary":358,"summary_zh":359,"content":9,"source_name":360,"source_url":357,"published_at":361,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":362,"score_detail":363,"sources":366,"tags":368,"primary_topic":372,"secondary_topics":373,"classification":378,"search_phrases":418,"slug":421,"view_count":422,"doi":9,"paper":423,"created_at":433},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":19,"substance":364,"depth":151,"authority":152,"freshness":20,"relevant":21,"comment":365},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。",[367],{"name":360,"url":357},[26,369,28,370,371],"精准农业","盐碱地治理","土壤监测",{"code":45,"slug":46,"name":47,"path":48,"parent_code":36},[374,375],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":87,"slug":92,"name":376,"path":377,"parent_code":43},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"status":50,"locked":14,"version":51,"method":52,"evidence":379},{"candidates":380,"agricultural_evidence":56},[381,386,390,394,399,403,408,412],{"code":45,"score":18,"supported":56,"title":382,"abstract":383,"tags":385},[73],[28,73,384],"multispectral",[28],{"code":32,"score":151,"supported":56,"title":387,"abstract":388,"tags":389},[29],[59,60,29,62],[],{"code":87,"score":65,"supported":56,"title":391,"abstract":392,"tags":393},[92],[91,92],[370],{"code":102,"score":13,"supported":56,"title":395,"abstract":397,"tags":398},[396],"precision agriculture",[],[26,369],{"code":77,"score":88,"supported":56,"title":400,"abstract":401,"tags":402},[],[81,82],[],{"code":95,"score":189,"supported":56,"title":404,"abstract":405,"tags":407},[],[406],"灌溉",[],{"code":39,"score":189,"supported":56,"title":409,"abstract":410,"tags":411},[],[69],[],{"code":413,"score":189,"supported":56,"title":414,"abstract":415,"tags":417},"I7",[],[416],"methodological framework",[],[419,420],"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":424,"authors":425,"venue":360,"cited_by_count":129,"oa_url":357,"card":428,"direction":134,"ingested_from":136},"W7221843179",[426],{"name":427,"orcid":9},"Sadullayev Sardor Zokirjon o'g'li",{"tldr":429,"method":430,"finding":431,"direction":134,"opportunity":432},"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":435,"title":436,"url":437,"summary":438,"summary_zh":439,"content":9,"source_name":440,"source_url":437,"published_at":441,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":442,"score_detail":443,"sources":445,"tags":447,"primary_topic":451,"secondary_topics":452,"classification":458,"search_phrases":482,"slug":485,"view_count":129,"doi":486,"paper":487,"created_at":502},4753,"Mapping groundwater potential zones using GIS, remote sensing and analytical hierarchy process (AHP) in Pader and Kitgum Districts, Uganda","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs43621-026-04943-7","Groundwater is a crucial source of freshwater in semi-arid regions, particularly in water-stressed areas, such as northern Uganda, where the surface water is scarce and unreliable precipitation patterns changes. This province lacks sufficient clean water for human consumption and only 20% of the water for agriculture extracted from the ground due to inaccessibility and insufficient information regarding the groundwater. With regard to these difficulties in water that are faced by the local communities, it has not been yet studied the distribution of groundwater in Kitgum and Pader districts. Therefore, the result of this research contributes preliminary groundwater investigation for contacting this indispensable source of water. The study employed a geospatial approach integrated with multi-criteria analysis to identify groundwater potential zones in Kitgum and Pader districts of Acholi subregion. The Utilization of Geographic Information Systems (GIS), remote sensing, and the Analytic Hierarchy Process (AHP) and six thematic layers; rainfall, drainage density, slope, soil type, geology, and land use\u002Fland cover were selected based on their hydrogeological influence and the accessibility of the data. After standardizing, and assigning normalized weights,a weighted overlay analysis in ArcGIS Pro version 3.1.0 was used to synthesize these layers into a comprehensive groundwater potential zonation map. The result categorized the study area into five zones; very low, low, medium, high, and very high potential. The analysis revealed that Kitgum District is predominantly characterized by very low to low groundwater potential zones of 67.24% when it is combined both, constrained by factors such as higher drainage density, and steeper slopes. Conversely, Pader District showed favorable conditions, with 85.5% of its area classified as high to very high groundwater potential, attributed to gentle slopes, and higher rainfall, that enhance infiltration capacity. Validation against yield data from 18 existing boreholes demonstrated a 75.0% alignment between the model and actual well performance, confirming the model’s reliability at acceptable result. This study demonstrated the effectiveness of GIS-based and AHP as a cost-effective tool for preliminary groundwater exploration in data-scarce environments. The resulting groundwater potential map provides a vital scientific foundation for sustainable water resource management, enabling targeted borehole siting, informed aquifer development, and resilience-building strategies to enhance water security for communities and livelihoods in the Acholi sub-region.","地下水是半干旱地区重要的淡水来源，尤其是在乌干达北部等水资源紧张地区，那里地表水稀缺且降水模式变化不可靠。该地区缺乏足够的清洁饮用水供人类消费，由于地下水难以获取且信息不足，仅有20%的农业用水来自地下开采。鉴于当地社区面临这些水资源困难，基特古姆和帕德尔地区的地下水分布尚未得到研究。因此，本研究结果为接触这一不可或缺的水源提供了初步的地下水调查。本研究采用地理空间方法与多准则分析相结合，识别阿乔利次区域基特古姆和帕德尔地区的地下水潜力区。利用地理信息系统（GIS）、遥感技术和层次分析法（AHP），基于其水文地质影响和数据可获取性，选取了六个主题图层：降雨量、河网密度、坡度、土壤类型、地质和土地利用\u002F土地覆盖。经过标准化和归一化权重赋值后，使用ArcGIS Pro 3.1.0版本中的加权叠加分析将这些图层综合成一张全面的地下水潜力分区图。结果将研究区划分为五个区域：极低、低、中等、高和极高潜力。分析表明，基特古姆区主要以极低至低地下水潜力区为主，两者合计占67.24%，受较高河网密度和较陡坡度等因素制约。相反，帕德尔区表现出有利条件，其85.5%的面积被归类为高至极高地下水潜力，这归因于平缓的坡度和较高的降雨量，增强了入渗能力。利用18口现有钻孔的出水量数据进行验证，模型与实际井表现之间的一致率达到75.0%，证实了模型在可接受结果范围内的可靠性。本研究证明了基于GIS和AHP的方法作为数据稀缺环境中初步地下水勘探的成本效益工具的有效性。所生成的地下水潜力图为可持续水资源管理提供了重要的科学基础，有助于有针对性地选址钻孔，为知情决策提供依据。","Discover Sustainability","2026-10-10T00:00:00Z",67,{"impact":20,"substance":364,"depth":151,"authority":152,"freshness":153,"relevant":21,"comment":444},"基于GIS、遥感与AHP的乌干达北部地下水潜力制图研究，方法成熟、验证可靠，对缺水地区农业水资源规划有参考价值，但属区域性初步研究，公共影响有限。",[446],{"name":440,"url":437},[28,448,29,449,450],"水资源管理","地下水潜力","乌干达农业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[453,454],{"code":45,"slug":46,"name":47,"path":48,"parent_code":36},{"code":95,"slug":455,"name":456,"path":457,"parent_code":43},"irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",{"status":50,"locked":14,"version":51,"method":52,"evidence":459},{"candidates":460,"agricultural_evidence":56},[461,465,469,474,478],{"code":32,"score":18,"supported":56,"title":462,"abstract":463,"tags":464},[29],[59,29,62],[29],{"code":45,"score":65,"supported":56,"title":466,"abstract":467,"tags":468},[73],[28,73],[28],{"code":95,"score":20,"supported":56,"title":470,"abstract":471,"tags":473},[],[98,472],"water resource",[449,448],{"code":87,"score":88,"supported":56,"title":475,"abstract":476,"tags":477},[],[91,92],[],{"code":39,"score":189,"supported":56,"title":479,"abstract":480,"tags":481},[],[69],[],[483,484],"乌干达 Kitgum Pader 地下水","GIS AHP 地下水潜力","乌干达KitgumPader地下水-4753","10.1007\u002Fs43621-026-04943-7",{"doi":486,"openalex_id":488,"authors":489,"venue":440,"cited_by_count":129,"oa_url":437,"card":497,"direction":134,"ingested_from":136},"W7221339030",[490,493,495],{"name":491,"orcid":492},"Ahmed Abdirizak Dirie","https:\u002F\u002Forcid.org\u002F0009-0002-8109-0724",{"name":494,"orcid":9},"Abdulkadir Ahmed Mohamed",{"name":496,"orcid":9},"Ambiga Kannapiran",{"tldr":498,"method":499,"finding":500,"direction":134,"opportunity":501},"用GIS、遥感和AHP制图评估乌干达两区地下水潜力并验证。","GIS与遥感结合AHP，加权叠加6个主题图层，用18口井数据验证。","Kitgum区67.24%为低潜力，Pader区85.5%为高潜力，模型与井数据吻合75%。","可将该地下水潜力制图方法迁移到其他数据稀缺区，并结合农业灌溉需求优化井位。","2026-10-12T23:30:24.761508Z",{"id":504,"title":505,"url":506,"summary":507,"summary_zh":508,"content":9,"source_name":509,"source_url":506,"published_at":510,"category":12,"cover_url":9,"hotness":511,"is_selected":14,"score":148,"score_detail":512,"sources":514,"tags":518,"primary_topic":522,"secondary_topics":523,"classification":530,"search_phrases":560,"slug":563,"view_count":129,"doi":564,"paper":565,"created_at":586},4294,"GIS- and Remote Sensing-Based Identification of Suitable Agricultural Waste Landfill Sites for Climate-Smart Agriculture in Ilorin, Nigeria","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23168428","Abstract Lack of a designated landfill for agricultural waste management (AWM) in Ilorin municipalities led to indiscriminate disposal of agricultural waste within the city and the only existing Sokoto-Ayekale general solid waste dumpsite, posing environmental and public health risks while undermining climate-smart agriculture (CSA). The study identifies suitable landfill sites for AWM by integrating Geographic Information System (GIS), Remote Sensing (RS), and Multi-Criteria Decision Analysis (MCDA) to locate environmentally and climate-resilient sites. Spatial data employed for the study include Shuttle Radar Topography Mission (SRTM), Landsat 9 imagery, Digital Elevation Model (DEM), road networks, waterbodies, built-up areas, soil quality, slope, and geology, which were processed in ArcGIS 10.3 to develop constraint thematic suitability maps. The Analytical Hierarchy Process (AHP) was employed to derive criterion weights, and the Weighted Linear Combination (WLC) was used to generate the map. AHP consistency ratio (CR) was used to validate the model’s consistency (CR=0.016), which falls within the acceptable CR \u003C0.1. The AHP weighted results were 0.41, 0.2, 0.14, 0.10, 0.08, and 0.07 for waterbodies, built-up area, slope, soil quality, geology, and road network, respectively. Maps were created for each criterion, reclassified, and integrated using the weighted linear combination method to generate an overall AWM suitability map. The study identified four potential locations for AWM. Approximately 507.23 km² are unsuitable, 1204.21 km² are marginally suitable, 4899.28 km² are moderately suitable, and 2021.7 km² are highly suitable for AWM landfill development. The findings present a scientifically based decision-support framework to guide policymakers, environmental agencies, and urban planners in developing sustainable AWM strategies that minimize environmental risk while advancing climate-smart agriculture. Keywords: Agricultural waste, Climate-smart agriculture, Landfill, Suitability, Sustainability","摘要 伊洛林各市镇缺乏用于农业废弃物管理（AWM）的指定填埋场，导致农业废弃物在城市内部及唯一现有的索科托-阿耶卡莱一般固体废弃物倾倒场被随意处置，既构成环境和公共健康风险，也削弱了气候智慧型农业（CSA）。本研究通过整合地理信息系统（GIS）、遥感（RS）和多准则决策分析（MCDA），识别适用于AWM的填埋场选址，以确定具有环境韧性和气候韧性的场址。研究采用的空间数据包括航天飞机雷达地形测绘任务（SRTM）数据、Landsat 9影像、数字高程模型（DEM）、道路网络、水体、建成区、土壤质量、坡度和地质，并在ArcGIS 10.3中进行处理以生成约束性专题适宜性图。采用层次分析法（AHP）推导准则权重，并运用加权线性组合（WLC）生成地图。使用AHP一致性比率（CR）验证模型的一致性（CR=0.016），该值处于可接受的CR\u003C0.1范围内。AHP加权结果分别为：水体0.41、建成区0.2、坡度0.14、土壤质量0.10、地质0.08、道路网络0.07。针对每个准则生成地图，经重分类后采用加权线性组合方法进行整合，生成总体AWM适宜性图。研究确定了四个潜在的AWM选址。约507.23 km²不适宜，1204.21 km²勉强适宜，4899.28 km²中等适宜，2021.7 km²高度适宜建设AWM填埋场。研究结果为政策制定者、环境机构和城市规划者提供了基于科学的决策支持框架，以指导制定可持续的AWM策略，在降低环境风险的同时推进气候智慧型农业。关键词：农业废弃物，气候智慧型农业，填埋场，适宜性，可持续性","Zenodo (CERN European Organization for Nuclear Research)","2026-10-05T00:00:00Z",25,{"impact":17,"substance":150,"depth":151,"authority":152,"freshness":153,"relevant":21,"comment":513},"GIS与遥感结合MCDA为农业废弃物填埋选址提供决策框架，方法规范、数据翔实，但属区域性案例研究，公共影响有限。",[515,516],{"name":509,"url":506},{"name":509,"url":517},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23168429",[28,519,520,29,521],"农业废弃物","气候智慧农业","选址评估",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[524,529],{"code":525,"slug":526,"name":527,"path":528,"parent_code":43},"H5","waste","面源污染与废弃物利用","\u002Ftopic\u002Fenvironment\u002Fwaste",{"code":45,"slug":46,"name":47,"path":48,"parent_code":36},{"status":50,"locked":14,"version":51,"method":52,"evidence":531},{"candidates":532,"agricultural_evidence":56},[533,537,542,546,552,556],{"code":32,"score":18,"supported":56,"title":534,"abstract":535,"tags":536},[29],[59,29,62],[29],{"code":525,"score":65,"supported":56,"title":538,"abstract":540,"tags":541},[539],"agricultural waste",[519,539],[519],{"code":45,"score":65,"supported":56,"title":543,"abstract":544,"tags":545},[73],[28,73],[28],{"code":102,"score":547,"supported":56,"title":548,"abstract":550,"tags":551},11,[549],"smart agriculture",[549],[],{"code":87,"score":88,"supported":56,"title":553,"abstract":554,"tags":555},[],[91,92],[],{"code":39,"score":21,"supported":14,"title":557,"abstract":558,"tags":559},[],[],[520],[561,562],"Ilorin 农业废弃物 填埋场 选址","GIS 遥感 农业废弃物 填埋场","Ilorin农业废弃物填埋场选址-4294","10.5281\u002Fzenodo.23168428",{"doi":564,"openalex_id":566,"authors":567,"venue":509,"cited_by_count":129,"oa_url":506,"card":579,"direction":585,"ingested_from":136},"W7220389353",[568,570,572,574,577],{"name":569,"orcid":9},"Saheed Mohammed Ishaq",{"name":571,"orcid":9},"Alamin Adedeji Adenekan",{"name":573,"orcid":9},"Ibrahim Dauda",{"name":575,"orcid":576},"Afeez Oladeji Amoo","https:\u002F\u002Forcid.org\u002F0000-0002-0412-9333",{"name":578,"orcid":9},"Nureni Babatunde Amoo",{"tldr":580,"method":581,"finding":582,"direction":583,"opportunity":584},"用GIS、遥感和多准则决策为尼日利亚Ilorin筛选农业废弃物填埋场适宜选址。","GIS\u002FRS结合AHP与加权线性组合，基于DEM、Landsat 9等空间数据。","识别出4处潜在选址，约2021.7 km²高度适宜，模型一致性良好。","农业绿色发展与碳","可将该选址框架与温室气体排放、运输成本及气候情景耦合，拓展到其他区域。","智慧农业 \u002F 农业物联网","2026-10-06T23:30:07.715522Z"]