[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3175":3,"related-3175":80},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"primary_topic":8,"secondary_topics":31,"classification":32,"search_phrases":59,"slug":62,"view_count":63,"doi":64,"paper":65,"created_at":79},3175,"Assessing the reliability of remote sensing-based evapotranspiration models for enhanced water management under center-pivot irrigation system in arid region. Case study: East Oweinat, Egypt","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs40899-026-01401-0","Assessing the reliability of remote sensing-based evapotranspiration models for enhanced water management under center-pivot irrigation system in arid region. Case study: East Oweinat, Egypt。Sustainable Water Resources Management",null,"Sustainable Water Resources Management","2026-09-21T00:00:00Z","论文",10,false,71,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},12,20,17,13,9,1,"埃及干旱区中心支轴灌溉下遥感蒸散发模型可靠性评估，方法扎实但属区域案例研究，对国内智慧灌溉有参考价值。",[24],{"name":9,"url":6},[26,27,28,29,30],"遥感","水资源管理","精准灌溉","蒸散发","干旱区农业",[],{"status":33,"locked":13,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":13,"reason":58},[38,47,53],{"code":39,"score":40,"supported":41,"title":42,"abstract":45,"tags":46},"H3",25,true,[43,44],"irrigation","evapotranspiration",[43,44],[27,28,29],{"code":48,"score":16,"supported":41,"title":49,"abstract":51,"tags":52},"I3",[50],"remote sensing",[50],[26],{"code":54,"score":21,"supported":13,"title":55,"abstract":56,"tags":57},"H7",[],[],[30],"农业相关性需要复核",[60,61],"East Oweinat 中心支轴灌溉","遥感 蒸散发 模型 灌溉管理","EastOweinat中心支轴灌溉-3175",3,"10.1007\u002Fs40899-026-01401-0",{"doi":64,"openalex_id":66,"authors":67,"venue":9,"cited_by_count":76,"oa_url":8,"card":8,"direction":77,"ingested_from":78},"W7213900869",[68,70,72,74],{"name":69,"orcid":8},"Hadeel A. Ibrahim",{"name":71,"orcid":8},"Mohamed H. Elgamal",{"name":73,"orcid":8},"Ashraf M. Ghanem",{"name":75,"orcid":8},"Mohamed H. Nour",0,"农业遥感与作物表型","openalex","2026-09-22T23:30:23.383931Z",{"total":81,"page":21,"page_size":81,"items":82},6,[83,186,300,346,407,477],{"id":84,"title":85,"url":86,"summary":87,"summary_zh":88,"content":8,"source_name":89,"source_url":86,"published_at":90,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":91,"score_detail":92,"sources":97,"tags":99,"primary_topic":102,"secondary_topics":106,"classification":118,"search_phrases":154,"slug":157,"view_count":63,"doi":158,"paper":159,"created_at":185},2318,"Estimation of field-scale crop evapotranspiration from the mechanistic SIF-ET model using the UAV hyperspectral imagery","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.eja.2026.128332","Accurate estimation of evapotranspiration (ET) is critical for agricultural water management and understanding land-atmosphere interactions. Conventional thermal and optical remote sensing is a powerful tool for ET estimation, but existing methods remain constrained by empirical parameterization, calibration complexity, and limited applicability. As a direct indicator of vegetation photosynthesis, solar-induced chlorophyll fluorescence (SIF) enables consistent and accurate ET estimation through a mechanistically grounded framework that does not require site-specific empirical calibration of the SIF-GPP relationship. However, current SIF-based ET estimation mainly rely on coarse-resolution satellite data and its capacity to resolve high-resolution ET dynamics over farmland remains unexplored. To address this, the mechanistic light response model and water-carbon relationship was integrated to construct a SIF-ET model at field-scale using UAV narrow-band hyperspectra imagery. The principal conclusions are: (1) High-resolution crop (canola,soybean and wheat) canopy SIF 740 at 1-nm SR was retrieved from UAV hyperspectra imagery using the Photochemistry and Energy Fluxes (SCOPE) model; (2)The daily ET estimates from SIF-ET model were validated against the in-situ measurements across multiple growth stages and water-nitrogen treatments, with wheat exhibiting the highest precision (R²=0.72–0.94, RMSE = 0.04–0.11 mm\u002Fd, MAPE = 1.70–5.89%), followed by canola and soybean; (3) Cumulative ET errors across repeated UAV observations within each growth stage remained within acceptable bounds, indicating the stability of SIF-ET model in capturing long-term ET dynamics. Overall, the SIF-ET model from UAV hyperspectra imagery revealed fine-scale cropland ET spatial-temporal heterogeneity. These findings provide support for precision irrigation, rational water-nitrogen management, and stress diagnostics under changing environmental conditions.","准确估算蒸散发（ET）对农业水资源管理和理解陆气相互作用至关重要。传统的热红外与光学遥感是估算ET的有力工具，但现有方法仍受限于经验参数化、校准复杂性和适用性有限等问题。作为植被光合作用的直接指示因子，日光诱导叶绿素荧光（SIF）能够通过机理明确的框架实现一致且准确的ET估算，且无需对SIF-GPP关系进行站点特定的经验校准。然而，当前基于SIF的ET估算主要依赖粗分辨率卫星数据，其解析农田高分辨率ET动态的能力尚未得到探索。为此，本研究整合机理光响应模型与水碳关系，利用无人机窄波段高光谱影像构建了田块尺度的SIF-ET模型。主要结论如下：（1）利用光化学与能量通量（SCOPE）模型，从无人机高光谱影像中反演了1 nm光谱分辨率下油菜、大豆和小麦的高分辨率冠层SIF₇₄₀；（2）基于SIF-ET模型的日ET估算值在多个生育期和水氮处理下与原位观测进行了验证，其中小麦精度最高（R²=0.72–0.94，RMSE=0.04–0.11 mm\u002Fd，MAPE=1.70–5.89%），油菜和大豆次之；（3）各生育期内重复无人机观测的累积ET误差均在可接受范围内，表明SIF-ET模型在捕捉长期ET动态方面具有稳定性。总体而言，基于无人机高光谱影像的SIF-ET模型揭示了精细尺度的农田ET时空异质性。这些发现为精准灌溉、合理水氮管理以及变化环境条件下的胁迫诊断提供了支持。","European Journal of Agronomy","2026-09-12T00:00:00Z",81,{"impact":93,"substance":94,"depth":93,"authority":95,"freshness":20,"relevant":21,"comment":96},18,22,14,"该研究基于无人机高光谱与SIF-ET机理模型实现田块尺度作物蒸散发高精度估算，方法新颖、验证充分，对精准灌溉与水氮管理有实质参考价值，值得进入每日精选。",[98],{"name":89,"url":86},[100,101,26,28,29],"智慧农业","无人机",{"code":39,"slug":43,"name":103,"path":104,"parent_code":105},"水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[107,112],{"code":48,"slug":108,"name":109,"path":110,"parent_code":111},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"code":113,"slug":114,"name":115,"path":116,"parent_code":117},"A4","soybean-oilseeds","大豆与油料","\u002Ftopic\u002Ffield-crops\u002Fsoybean-oilseeds","A",{"status":119,"locked":13,"version":34,"method":35,"evidence":120},"classified",{"candidates":121,"agricultural_evidence":41},[122,129,135,142,149],{"code":39,"score":123,"supported":41,"title":124,"abstract":125,"tags":128},19,[44],[126,127,29,43,44],"灌溉","水资源",[28,29],{"code":48,"score":130,"supported":41,"title":131,"abstract":133,"tags":134},15,[132],"hyperspectral",[26,50],[26],{"code":113,"score":20,"supported":41,"title":136,"abstract":137,"tags":141},[],[138,139,140],"大豆","油菜","soybean",[],{"code":143,"score":81,"supported":41,"title":144,"abstract":145,"tags":148},"A2",[],[146,147],"小麦","wheat",[],{"code":150,"score":21,"supported":13,"title":151,"abstract":152,"tags":153},"I1",[],[],[100],[155,156],"智慧农业 精准灌溉 无人机 蒸散发","智慧农业 精准灌溉","智慧农业精准灌溉无人机蒸散发-2318","10.1016\u002Fj.eja.2026.128332",{"doi":158,"openalex_id":160,"authors":161,"venue":89,"cited_by_count":76,"oa_url":86,"card":180,"direction":77,"ingested_from":78},"W7212242584",[162,164,166,169,172,174,177],{"name":163,"orcid":8},"Ruiqi Du",{"name":165,"orcid":8},"Yonghong Zhang",{"name":167,"orcid":168},"Youzhen Xiang","https:\u002F\u002Forcid.org\u002F0000-0002-8268-1609",{"name":170,"orcid":171},"Fucang Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-6659-3262",{"name":173,"orcid":8},"Jian Gao",{"name":175,"orcid":176},"Qiliang Yang","https:\u002F\u002Forcid.org\u002F0000-0002-3274-2119",{"name":178,"orcid":179},"Xianghui Lu","https:\u002F\u002Forcid.org\u002F0000-0003-0638-3068",{"tldr":181,"method":182,"finding":183,"direction":77,"opportunity":184},"利用无人机高光谱影像构建田间尺度SIF-ET机理模型，实现作物蒸散发高精度估算。","无人机窄波段高光谱结合SCOPE模型反演SIF，耦合光响应与水碳关系构建SIF-","小麦估算精度最高（R²=0.72–0.94），模型能稳定捕捉多生育期蒸散发动态与空间异质性。","可探索多作物多环境下的SIF-ET普适性，并融合热红外与机器学习提升胁迫诊断能力。","2026-09-13T23:30:22.677041Z",{"id":187,"title":188,"url":189,"summary":190,"summary_zh":191,"content":8,"source_name":192,"source_url":189,"published_at":193,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":194,"score_detail":195,"sources":197,"tags":199,"primary_topic":202,"secondary_topics":203,"classification":214,"search_phrases":270,"slug":273,"view_count":76,"doi":274,"paper":275,"created_at":299},5564,"Dynamic thermal imaging for crop water stress detection across phenological stages: Enhancing irrigation scheduling precision","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.17728","Thermal remote sensing and derived indices such as the crop water stress index (CWSI) can assess crop water status and support precision irrigation. However, most operational and artificial intelligence (AI)-based approaches rely on fixed, season-long thresholds to interpret canopy–air temperature differences and often overlook the influence of crop phenology. This review synthesises evidence from grapevine, wheat, maize, potato and several other crops on how developmental stage affects the non-water-stressed baseline (NWSB), CWSI thresholds and irrigation-scheduling algorithms. It covers CWSI theory and estimation practices; crop-specific, phenology-dependent behaviour of baselines and thresholds; a comparison of threshold-based, machine learning, Internet of Things (IoT), digital twin and Food and Agriculture Organization (FAO)-56 approaches with respect to crop phenology; and a conceptual framework for phenology-aware scheduling. Across the reviewed crops, baseline behaviour generally varied systematically across the growing season, the relationship between CWSI and crop water status generally strengthened after canopy closure and CWSI measured during reproductive stages was generally most closely related to yield. A quantitative audit of 56 AI-, thermal-imaging-, or CWSI-based studies found that 78.6 % either did not explicitly incorporate crop phenology or relied on day of year as a temporal proxy, whereas only 5.4 % explicitly encoded phenological stage. The proposed framework considers phenology explicitly in baseline estimation, threshold setting and data fusion or machine learning for irrigation scheduling; the framework will require multi-year, multi-site field validation before operational use. Future research should prioritise standardised phenology definitions and long-term, multi-location studies to establish and validate stage-specific parameters.","热红外遥感及其衍生的作物水分胁迫指数（crop water stress index, CWSI）等指标可评估作物水分状况并支持精准灌溉。然而，大多数业务化方法和基于人工智能（artificial intelligence, AI）的方法依赖固定的全生育期阈值来解释冠层—空气温差，且往往忽视作物物候的影响。本文综述了葡萄、小麦、玉米、马铃薯及其他若干作物在发育阶段如何影响非水分胁迫基线（non-water-stressed baseline, NWSB）、CWSI阈值和灌溉调度算法的相关证据。综述内容涵盖CWSI理论与估算实践；基线和阈值的作物特异性、物候依赖性行为；基于阈值、机器学习、物联网（Internet of Things, IoT）、数字孪生及联合国粮农组织（Food and Agriculture Organization, FAO）-56等方法在作物物候方面的比较；以及物候感知调度的概念框架。在所综述的作物中，基线行为在整个生长季通常呈系统性变化，CWSI与作物水分状况之间的关系在冠层封垄后总体增强，且在生殖阶段测得的CWSI与产量的关系总体最为密切。对56项基于AI、热成像或CWSI的研究进行的定量审查发现，78.6%的研究要么未明确纳入作物物候，要么依赖年积日作为时间代理变量，而仅5.4%的研究明确编码了物候阶段。所提出的框架在基线估算、阈值设定以及用于灌溉调度的数据融合或机器学习中明确考虑物候；该框架在投入业务应用前尚需多年、多站点的田间验证。未来研究应优先建立标准化的物候定义，并开展长期、多地点研究以建立和验证阶段特异性参数。","Plant Science Today","2026-10-21T00:00:00Z",79,{"impact":93,"substance":94,"depth":93,"authority":16,"freshness":20,"relevant":21,"comment":196},"系统综述热成像与CWSI在物候期感知灌溉中的应用，含56项研究量化审计，方法学价值突出，但属综述类论文，影响偏学术层面。",[198],{"name":192,"url":189},[100,200,26,201,28],"农业人工智能","作物水分胁迫",{"code":39,"slug":43,"name":103,"path":104,"parent_code":105},[204,209],{"code":205,"slug":206,"name":207,"path":208,"parent_code":111},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":210,"slug":211,"name":212,"path":213,"parent_code":111},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":119,"locked":13,"version":34,"method":35,"evidence":215},{"candidates":216,"agricultural_evidence":41},[217,221,228,235,242,246,250,257,264],{"code":39,"score":130,"supported":41,"title":218,"abstract":219,"tags":220},[43],[126,43],[28],{"code":205,"score":12,"supported":41,"title":222,"abstract":223,"tags":227},[],[224,225,226],"机器学习","machine learning","artificial intelligence",[200],{"code":210,"score":20,"supported":41,"title":229,"abstract":230,"tags":234},[],[231,232,233],"物联网","internet of things","IoT",[],{"code":150,"score":236,"supported":41,"title":237,"abstract":238,"tags":241},7,[],[239,240],"数字孪生","digital twin",[100],{"code":48,"score":236,"supported":41,"title":243,"abstract":244,"tags":245},[],[26,50],[26],{"code":143,"score":81,"supported":41,"title":247,"abstract":248,"tags":249},[],[146,147],[],{"code":251,"score":81,"supported":41,"title":252,"abstract":253,"tags":256},"A3",[],[254,255],"玉米","maize",[],{"code":258,"score":81,"supported":41,"title":259,"abstract":260,"tags":263},"A5",[],[261,262],"马铃薯","potato",[],{"code":265,"score":63,"supported":41,"title":266,"abstract":267,"tags":269},"B2",[],[268],"葡萄",[],[271,272],"热成像 作物水分胁迫","CWSI 灌溉调度","热成像作物水分胁迫-5564","10.14719\u002Fpst.17728",{"doi":274,"openalex_id":276,"authors":277,"venue":192,"cited_by_count":76,"oa_url":189,"card":293,"direction":298,"ingested_from":78},"W7223088921",[278,281,284,287,290],{"name":279,"orcid":280},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":282,"orcid":283},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":285,"orcid":286},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":288,"orcid":289},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":291,"orcid":292},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":294,"method":295,"finding":296,"direction":77,"opportunity":297},"综述物候期对热成像CWSI阈值的影响，提出物候感知灌溉调度框架。","综述葡萄、小麦、玉米等作物的热遥感CWSI研究，并审计56项AI\u002F热成像研究。","78.6%研究未显式纳入物候，仅5.4%编码物候期；CWSI与产量在生殖期关联最强。","可开展多站点多年物候期特异性CWSI阈值标定与标准化物候定义研究。","智慧农业 \u002F 农业物联网","2026-10-22T11:20:24.526683Z",{"id":301,"title":302,"url":303,"summary":304,"summary_zh":8,"content":8,"source_name":305,"source_url":303,"published_at":306,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":307,"score_detail":308,"sources":312,"tags":314,"primary_topic":8,"secondary_topics":318,"classification":319,"search_phrases":330,"slug":333,"view_count":76,"doi":334,"paper":335,"created_at":345},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":309,"substance":310,"depth":95,"authority":20,"freshness":63,"relevant":21,"comment":311},8,16,"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。",[313],{"name":305,"url":303},[315,26,29,316,317],"农业水资源","埃塞俄比亚","SEBAL模型",[],{"status":33,"locked":13,"version":34,"method":35,"evidence":320},{"candidates":321,"agricultural_evidence":13,"reason":58},[322,326],{"code":39,"score":19,"supported":41,"title":323,"abstract":324,"tags":325},[44],[44],[315,29],{"code":48,"score":16,"supported":41,"title":327,"abstract":328,"tags":329},[50],[50],[26],[331,332],"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":334,"openalex_id":336,"authors":337,"venue":305,"cited_by_count":76,"oa_url":344,"card":8,"direction":77,"ingested_from":78},"W7222946042",[338,340,342],{"name":339,"orcid":8},"Mulatu Ayalew",{"name":341,"orcid":8},"Balew Yeneneh",{"name":343,"orcid":8},"Muktar Asmamaw","https:\u002F\u002Fwww.researchsquare.com\u002Farticle\u002Frs-11063250\u002Flatest.pdf","2026-10-21T23:30:25.009175Z",{"id":347,"title":348,"url":349,"summary":350,"summary_zh":351,"content":8,"source_name":352,"source_url":349,"published_at":306,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":353,"score_detail":354,"sources":356,"tags":358,"primary_topic":8,"secondary_topics":361,"classification":362,"search_phrases":386,"slug":389,"view_count":76,"doi":390,"paper":391,"created_at":406},5482,"A Conceptual Framework for Aquifer Delineation and Managed Aquifer Recharge in Deccan Trap Basaltic Terrain of Drought-Prone Area","https:\u002F\u002Fdoi.org\u002F10.71097\u002Fijaidr.v17.i2.2197","Drought-prone regions underlain by Deccan Trap basalt face persistent challenges in groundwater availability due to highly heterogeneous aquifer properties, limited natural recharge, variable fracture networks, and increasing groundwater demand. This study proposes a conceptual framework for aquifer delineation and Managed Aquifer Recharge (MAR) in basaltic terrain by integrating geological, hydrogeological, geophysical, topographical, and remote sensing information. The framework conceptualises aquifer systems through the identification of weathered zones, fractured basalt, vesicular horizons, joints, lineaments, and geological contacts that influence groundwater occurrence and movement. Spatial datasets are integrated within a Geographic Information System to delineate groundwater potential zones and identify suitable locations for MAR interventions. The proposed framework further links aquifer characteristics with recharge structures such as check dams, recharge shafts, percolation tanks, recharge trenches, and injection-based systems according to local hydrogeological conditions. Groundwater-level monitoring, aquifer response assessment, and periodic evaluation are incorporated to support adaptive recharge management. The framework provides a systematic basis for moving from aquifer characterization to site-specific recharge planning and monitoring. It can support sustainable groundwater management in drought-prone Deccan Trap landscapes by improving understanding of aquifer connectivity, recharge potential, and groundwater storage dynamics. The framework is particularly relevant for water-stressed regions where conventional groundwater development alone cannot ensure long-term water security.","以德干暗色岩玄武岩为基底的干旱易发地区，由于含水层特性高度非均质、天然补给有限、裂隙网络多变以及地下水需求不断增加，面临着地下水可获得性方面的持续挑战。本研究提出一个概念框架，通过整合地质、水文地质、地球物理、地形和遥感信息，在玄武岩地区进行含水层圈定与地下水人工补给（Managed Aquifer Recharge, MAR）。该框架通过识别影响地下水赋存与运动的风化带、裂隙玄武岩、气孔状层位、节理、线理和地质接触带，对含水层系统进行概念化。空间数据集在地理信息系统内进行整合，以圈定地下水潜力区并确定适合MAR干预的位置。所提出的框架进一步将含水层特征与补给结构（如拦水坝、补给井、渗滤池、补给沟和注入式系统）依据当地水文地质条件相联系。框架纳入了地下水位监测、含水层响应评估和定期评价，以支持适应性补给管理。该框架为从含水层表征转向特定场地的补给规划与监测提供了系统基础。通过增进对含水层连通性、补给潜力和地下水储存动态的理解，该框架可支持干旱易发德干暗色岩景观区的可持续地下水管理。该框架尤其适用于仅靠常规地下水开发无法确保长期水安全的水资源紧张地区。","Journal of Advances in Developmental Research",49,{"impact":309,"substance":93,"depth":95,"authority":81,"freshness":63,"relevant":21,"comment":355},"该论文提出玄武岩地区含水层划分与地下水回补的概念框架，方法有一定参考价值，但属概念性研究、缺乏实证数据，且来源期刊影响力有限、时效偏旧，不宜进入每日精选。",[357],{"name":352,"url":349},[26,30,359,360],"地下水管理","水资源安全",[],{"status":33,"locked":13,"version":34,"method":35,"evidence":363},{"candidates":364,"agricultural_evidence":13,"reason":58},[365,371,375,382],{"code":54,"score":130,"supported":41,"title":366,"abstract":368,"tags":370},[367],"drought",[369,367],"干旱",[30],{"code":48,"score":236,"supported":41,"title":372,"abstract":373,"tags":374},[],[26,50],[26],{"code":376,"score":81,"supported":41,"title":377,"abstract":378,"tags":381},"I4",[],[379,380],"地理信息","geographic information",[],{"code":39,"score":63,"supported":41,"title":383,"abstract":384,"tags":385},[],[127],[],[387,388],"Deccan Trap 玄武岩 含水层","managed aquifer recharge 干旱区","DeccanTrap玄武岩含水层-5482","10.71097\u002Fijaidr.v17.i2.2197",{"doi":390,"openalex_id":392,"authors":393,"venue":352,"cited_by_count":76,"oa_url":399,"card":400,"direction":298,"ingested_from":78},"W7222957903",[394,397],{"name":395,"orcid":396},"Muneeb Ur Rahman","https:\u002F\u002Forcid.org\u002F0000-0001-9713-4598",{"name":398,"orcid":8},"K.M. Wanjarwadkar -","https:\u002F\u002Fwww.ijaidr.com\u002Fpapers\u002F2026\u002F2\u002F2197.pdf",{"tldr":401,"method":402,"finding":403,"direction":404,"opportunity":405},"提出整合多源信息的玄武岩地区含水层圈定与人工补给概念框架。","融合地质、水文地质、地球物理、地形与遥感数据，在GIS中集成分析。","框架可将含水层特征与补给设施匹配，支持自适应地下水管理。","其他","可在中国玄武岩或干旱区验证该框架，并耦合遥感与AI提升补给选址精度。","2026-10-21T23:30:11.716732Z",{"id":408,"title":409,"url":410,"summary":411,"summary_zh":412,"content":8,"source_name":413,"source_url":414,"published_at":415,"category":11,"cover_url":8,"hotness":40,"is_selected":13,"score":416,"score_detail":417,"sources":420,"tags":424,"primary_topic":426,"secondary_topics":427,"classification":428,"search_phrases":461,"slug":464,"view_count":76,"doi":465,"paper":466,"created_at":476},5322,"MACHINE LEARNING BASED PREDICTION OF HYDROGEOLOGICAL PARAMETERS AND GROUNDWATER POTENTIAL INDEX USING GEOSPATIAL AND BOREHOLE DATA","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23434956","Abstract: Groundwater is a critical natural resource for domestic consumption, agricultural production, industrial development, and ecological sustainability. Accurate prediction of hydrogeological parameters and groundwater potential is essential for sustainable groundwater management, particularly in regions experiencing irregular rainfall, increasing water demand, and declining groundwater levels. This research paper presents a machine learning-based framework for predicting hydrogeological parameters and developing a Groundwater Potential Index (GWPI) by integrating geospatial datasets with borehole records. The proposed approach combines terrain characteristics, geological formations, soil properties, land use and land cover, drainage density, rainfall, slope, and remotely sensed indices with borehole-derived information, including water-table depth, aquifer thickness, lithology, and well yield. Machine learning algorithms such as Random Forest, Support Vector Regression, Gradient Boosting, and Artificial Neural Networks can be employed to estimate hydrogeological variables and identify areas with different groundwater potential levels. The predicted parameters are integrated within a geospatial framework to generate a spatially distributed groundwater potential index. Model performance can be assessed using the coefficient of determination (\\(R^2\\)), root mean square error (RMSE), and mean absolute error (MAE). The proposed framework offers a reproducible and scalable approach for groundwater exploration, borehole siting, and regional water-resource planning. Since no study-area dataset or model outputs have been provided, the paper presents a methodological framework rather than claiming experimentally validated results. Keywords: Machine Learning, Groundwater Potential Index, Hydrogeological Parameters, Geospatial Analysis, Borehole Data, Random Forest, Remote Sensing, Groundwater Management.","摘要：地下水是生活用水、农业生产、工业发展和生态可持续性的关键自然资源。准确预测水文地质参数和地下水潜力对于可持续地下水管理至关重要，尤其是在降雨不规律、用水需求不断增加和地下水位持续下降的地区。本文提出了一种基于机器学习的框架，通过将地理空间数据集与钻孔记录相结合，预测水文地质参数并构建地下水潜力指数（GWPI）。所提方法综合了地形特征、地质构造、土壤性质、土地利用与土地覆盖、河网密度、降雨、坡度以及遥感指数，并结合钻孔衍生信息，包括水位埋深、含水层厚度、岩性和井出水量。可采用随机森林、支持向量回归、梯度提升和人工神经网络等机器学习算法估算水文地质变量，并识别具有不同地下水潜力水平的区域。预测参数在地理空间框架内进行集成，以生成空间分布的地下水潜力指数。模型性能可通过决定系数（\\(R^2\\)）、均方根误差（RMSE）和平均绝对误差（MAE）进行评估。所提框架为地下水勘探、钻孔选址和区域水资源规划提供了一种可重复且可扩展的方法。由于未提供研究区数据集或模型输出，本文呈现的是方法学框架，而非声称经过实验验证的结果。关键词：机器学习，地下水潜力指数，水文地质参数，地理空间分析，钻孔数据，随机森林，遥感，地下水管理。","ACCENT JOURNAL OF  ECONOMICS ECOLOGY  & ENGINEERING","https:\u002F\u002Fajeee.co.in\u002Findex.php\u002Fajeee\u002Farticle\u002Fview\u002F6754","2026-10-17T00:00:00Z",47,{"impact":309,"substance":16,"depth":95,"authority":418,"freshness":20,"relevant":21,"comment":419},4,"方法框架类论文，无实测数据与验证结果，主题与农业水资源相关但公共价值有限，不宜进入每日精选。",[421,422],{"name":413,"url":414},{"name":413,"url":423},"https:\u002F\u002Fajeee.co.in\u002Findex.php\u002Fajeee\u002Farticle\u002Fdownload\u002F6754\u002F5489",[224,26,27,425],"地下水监测",{"code":205,"slug":206,"name":207,"path":208,"parent_code":111},[],{"status":119,"locked":13,"version":34,"method":35,"evidence":429},{"candidates":430,"agricultural_evidence":41},[431,435,439,446,450,455],{"code":205,"score":130,"supported":41,"title":432,"abstract":433,"tags":434},[225],[224,225],[224],{"code":48,"score":236,"supported":41,"title":436,"abstract":437,"tags":438},[],[26,50],[26],{"code":440,"score":81,"supported":41,"title":441,"abstract":442,"tags":445},"H2",[],[443,444],"土壤","soil",[],{"code":39,"score":418,"supported":41,"title":447,"abstract":448,"tags":449},[],[127],[27],{"code":376,"score":63,"supported":41,"title":451,"abstract":452,"tags":454},[],[453],"空间分析",[],{"code":456,"score":63,"supported":41,"title":457,"abstract":458,"tags":460},"I7",[],[459],"methodological framework",[],[462,463],"机器学习 地下水潜力指数","钻孔数据 水文地质参数","机器学习地下水潜力指数-5322","10.5281\u002Fzenodo.23434956",{"doi":465,"openalex_id":467,"authors":468,"venue":413,"cited_by_count":76,"oa_url":414,"card":471,"direction":77,"ingested_from":78},"W7222805722",[469],{"name":470,"orcid":8},"Prof. I Pandu Ranga Reddy Dr. Hari Ramavath",{"tldr":472,"method":473,"finding":474,"direction":77,"opportunity":475},"提出融合地理空间与钻孔数据的机器学习框架，预测水文地质参数与地下水潜力指数。","随机森林、SVR、梯度提升、神经网络结合遥感与钻孔数据建模。","构建了可复现的地下水潜力评估方法框架，尚未经实测数据验证。","可将该框架落地到具体农业区，用实测钻孔数据验证并耦合灌溉需水预测。","2026-10-19T23:30:22.978385Z",{"id":478,"title":479,"url":480,"summary":481,"summary_zh":482,"content":8,"source_name":483,"source_url":480,"published_at":484,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":485,"sources":487,"tags":489,"primary_topic":8,"secondary_topics":493,"classification":494,"search_phrases":520,"slug":523,"view_count":76,"doi":524,"paper":525,"created_at":554},5088,"Spatial Analysis of the Potential for Irrigation Development in the Ertis Water Management Basin Using Multi-Criteria Analysis and Remote Sensing","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fearth7050171","Sustainable expansion of irrigated agriculture requires the identification of areas with favorable biophysical conditions while also considering water-resource and infrastructure constraints. This study assessed the biophysical land suitability for potential irrigation development within the Ertis Water Management Basin (WMB), Kazakhstan, using an integrated remote sensing, geographic information system (GIS), and Analytic Hierarchy Process (AHP) framework. Four criteria—slope, normalized difference vegetation index (NDVI), land use\u002Fland cover (LULC), and soil texture—were standardized into five suitability classes and integrated using an AHP-based weighted overlay. The derived weights were 0.51 for slope, 0.26 for NDVI, 0.14 for LULC, and 0.09 for soil texture, with a consistency ratio of approximately 1%. The resulting assessment classified approximately 10% of the basin as highly suitable (S1), 43% as suitable (S2), 18% as moderately suitable (S3), 2% as marginally suitable (S4), and 27% as not suitable (NS), indicating that approximately 53% of the basin falls within the two highest suitability classes. Regional analysis showed that substantial shares of suitable land are concentrated in Pavlodar and Abay Regions, while suitable areas were identified across a wide range of distances from the river network. These findings indicate considerable biophysical potential for irrigation development across the Ertis WMB, with suitable areas occurring both near and farther from the river network. The resulting suitability map provides a spatial basis for identifying areas where more detailed assessments of water availability, irrigation infrastructure, and development feasibility can subsequently be undertaken.","灌溉农业的可持续扩展需要识别具有有利生物物理条件的区域，同时还需考虑水资源和基础设施约束。本研究采用遥感、地理信息系统（GIS）和层次分析法（AHP）集成框架，评估了哈萨克斯坦额尔齐斯水资源管理流域（WMB）内潜在灌溉开发的生物物理土地适宜性。将坡度、归一化植被指数（NDVI）、土地利用\u002F土地覆盖（LULC）和土壤质地四项指标标准化为五个适宜性等级，并利用基于AHP的加权叠加进行集成。所得权重为：坡度0.51、NDVI 0.26、LULC 0.14、土壤质地0.09，一致性比率约为1%。评估结果将流域约10%的区域划为高度适宜（S1），43%为适宜（S2），18%为中等适宜（S3），2%为勉强适宜（S4），27%为不适宜（NS），表明流域约53%的面积属于两个最高适宜性等级。区域分析显示，适宜土地中有相当比例集中在巴甫洛达尔州和阿拜州，同时在距河网不同距离范围内均识别出适宜区域。这些结果表明，额尔齐斯水资源管理流域内灌溉开发具有可观的生物物理潜力，适宜区域既分布于河网附近，也分布于距河网较远的地方。所生成的适宜性图为后续开展更详细的水资源可利用性、灌溉基础设施和开发可行性评估提供了空间依据。","Earth","2026-10-15T00:00:00Z",{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":486},"基于遥感、GIS与AHP的灌溉潜力空间评估，方法规范、数据翔实，对农业水资源规划有参考价值，但属区域案例研究，公共影响有限。",[488],{"name":483,"url":480},[26,27,490,491,492],"GIS","土地适宜性","灌溉潜力",[],{"status":33,"locked":13,"version":34,"method":35,"evidence":495},{"candidates":496,"agricultural_evidence":41,"reason":519},[497,501,506,510,514],{"code":39,"score":93,"supported":41,"title":498,"abstract":499,"tags":500},[43],[126,127,43],[27],{"code":376,"score":93,"supported":41,"title":502,"abstract":504,"tags":505},[503],"spatial analysis",[379,490,380],[490],{"code":48,"score":130,"supported":41,"title":507,"abstract":508,"tags":509},[50],[26,50],[26],{"code":440,"score":81,"supported":41,"title":511,"abstract":512,"tags":513},[],[443,444],[],{"code":515,"score":21,"supported":13,"title":516,"abstract":517,"tags":518},"H1",[],[],[491],"多个产业证据相当，需要确认主归属",[521,522],"Ertis 流域 灌溉潜力","哈萨克斯坦 灌溉 遥感","Ertis流域灌溉潜力-5088","10.3390\u002Fearth7050171",{"doi":524,"openalex_id":526,"authors":527,"venue":483,"cited_by_count":76,"oa_url":480,"card":549,"direction":77,"ingested_from":78},"W7222066993",[528,531,534,537,540,543,546],{"name":529,"orcid":530},"Nurgul Aitekeyeva","https:\u002F\u002Forcid.org\u002F0000-0003-3619-7362",{"name":532,"orcid":533},"Asset Yegizbayeva","https:\u002F\u002Forcid.org\u002F0000-0003-2114-8365",{"name":535,"orcid":536},"Nurlan Bekmukhamedov","https:\u002F\u002Forcid.org\u002F0000-0002-3157-7705",{"name":538,"orcid":539},"Kristina Konstantinova","https:\u002F\u002Forcid.org\u002F0009-0004-0212-5656",{"name":541,"orcid":542},"Kudaibergen Kyrgyzbay","https:\u002F\u002Forcid.org\u002F0000-0002-4279-6436",{"name":544,"orcid":545},"Nurdaulet B. Zhumabay","https:\u002F\u002Forcid.org\u002F0009-0004-2476-6526",{"name":547,"orcid":548},"Nurlan Balgabayev","https:\u002F\u002Forcid.org\u002F0000-0001-8127-7981",{"tldr":550,"method":551,"finding":552,"direction":77,"opportunity":553},"用遥感、GIS与AHP评估哈萨克斯坦额尔齐斯流域灌溉开发的土地适宜性。","遥感、GIS与AHP加权叠加，采用坡度、NDVI、土地利用和土壤质地四因子。","约53%流域属高或较高适宜等级，集中在巴甫洛达尔与阿拜地区。","可结合水资源可用性与灌溉基础设施约束，开展适宜区开发可行性的多尺度综合评估。","2026-10-16T23:30:26.892701Z"]