[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4515":3,"related-4515":107},{"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":9,"secondary_topics":32,"classification":33,"search_phrases":71,"slug":74,"view_count":22,"doi":75,"paper":76,"created_at":106},4515,"A hybrid DeepGR4J and regionalization framework for lake water balance assessment in data-scarce regions: Application to Lake Tana, Ethiopia","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.ejrh.2026.104046","Study region Lake Tana, the largest freshwater lake in Ethiopia and the headwater of the Blue Nile River, is located in the northwestern Ethiopian Highlands and is vital for water supply, hydropower, irrigation, and ecosystem services. The basin has strong rainfall seasonality, complex topography, and many ungauged catchments, making hydrological assessment difficult due to limited observations. Study focus This study develops a hybrid hydrological framework integrating the DeepGR4J rainfall-runoff model with regression-based regionalization to improve lake water balance estimation under data-scarce conditions. DeepGR4J combines the conceptual GR4J model with LSTM-based error correction to enhance runoff simulation, while regionalization transfers parameters from gauged to ungauged watersheds using physiographic and remote sensing descriptors. The framework is applied for 2000–2020 using CHIRPS precipitation, ERA5 temperature, and Hargreaves evapotranspiration estimates. New hydrological insights for the region Results show a mean annual inflow of 12.61 km³ yr⁻¹ and a combined water-balance outflow term of 12.67 km³ yr⁻¹, resulting in a net storage decline of −0.20 km³ yr⁻¹ and a residual imbalance of 0.06 km³ yr⁻¹. The model reproduces observed lake levels with high accuracy (R² = 0.94, NSE = 0.94, RMSE = 0.18 m). Findings highlight the strong contribution of ungauged catchments and demonstrate that hybrid conceptual-deep learning models with regionalization improve water balance estimation in data-scarce lake basins.","研究区域：塔纳湖（Lake Tana）是埃塞俄比亚最大的淡水湖，也是青尼罗河（Blue Nile River）的发源地，位于埃塞俄比亚高原西北部，对供水、水电、灌溉和生态系统服务至关重要。该流域降雨季节性显著、地形复杂，且存在大量无测站集水区，加之观测资料有限，使得水文评估十分困难。研究重点：本研究构建了一个混合水文框架，将DeepGR4J降雨-径流模型与基于回归的区域化方法相结合，以改进资料稀缺条件下的湖泊水量平衡估算。DeepGR4J将概念性GR4J模型与基于LSTM的误差校正相结合，以增强径流模拟；区域化方法则利用地貌和遥感描述因子将参数从有测站流域迁移至无测站流域。该框架应用于2000—2020年，采用CHIRPS降水、ERA5气温和Hargreaves蒸散发估算数据。该区域的新水文认识：结果表明，年均入湖水量为12.61 km³ yr⁻¹，水量平衡出流项合计为12.67 km³ yr⁻¹，导致净蓄水量下降−0.20 km³ yr⁻¹，残差失衡为0.06 km³ yr⁻¹。该模型以较高精度再现了观测湖水位（R² = 0.94，NSE = 0.94，RMSE = 0.18 m）。研究结果凸显了无测站集水区的重要贡献，并表明结合区域化的混合概念性-深度学习模型能够改进资料稀缺湖泊流域的水量平衡估算。",null,"Journal of Hydrology Regional Studies","2026-10-07T00:00:00Z","论文",10,false,73,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,22,18,13,8,1,"将概念水文模型与LSTM误差校正及区域化方法结合，在缺资料流域实现高精度水量平衡评估，方法新颖且结论可靠，对农业水资源管理有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"深度学习","遥感","水资源管理","水文模型","数据稀缺",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":14,"reason":70},[39,50,57,63],{"code":40,"score":41,"supported":42,"title":43,"abstract":44,"tags":49},"H3",11,true,[],[45,46,47,48],"灌溉","蒸散发","irrigation","evapotranspiration",[30,29],{"code":51,"score":52,"supported":42,"title":53,"abstract":54,"tags":56},"I2",7,[],[27,55],"deep learning",[27],{"code":58,"score":52,"supported":42,"title":59,"abstract":60,"tags":62},"I3",[],[28,61],"remote sensing",[28],{"code":64,"score":65,"supported":42,"title":66,"abstract":67,"tags":69},"H6",3,[],[68],"生态系统服务",[],"农业相关性需要复核",[72,73],"Lake Tana 水量平衡","DeepGR4J 径流模拟","LakeTana水量平衡-4515","10.1016\u002Fj.ejrh.2026.104046",{"doi":75,"openalex_id":77,"authors":78,"venue":10,"cited_by_count":97,"oa_url":98,"card":99,"direction":103,"ingested_from":105},"W7220864762",[79,82,84,87,90,92,94],{"name":80,"orcid":81},"Temesgen T. Mihret","https:\u002F\u002Forcid.org\u002F0000-0001-9793-9070",{"name":83,"orcid":9},"Fasikaw A. Zemale",{"name":85,"orcid":86},"Abeyou Wale Worqlul","https:\u002F\u002Forcid.org\u002F0000-0002-7990-8446",{"name":88,"orcid":89},"Ayenew Desalegn Ayalew","https:\u002F\u002Forcid.org\u002F0000-0002-7331-8170",{"name":91,"orcid":9},"Margaret Chen",{"name":93,"orcid":9},"Seifu A. Tilahun",{"name":95,"orcid":96},"Nicola Fohrer","https:\u002F\u002Forcid.org\u002F0000-0002-7456-6301",0,"https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2214581826009444\u002Fpdf",{"tldr":100,"method":101,"finding":102,"direction":103,"opportunity":104},"提出DeepGR4J与区域化混合框架，评估数据稀缺的埃塞俄比亚塔纳湖水量平衡。","GR4J概念模型结合LSTM误差校正，回归区域化，CHIRPS与ERA5数据。","年均入流12.61 km³，储量净减0.20 km³，湖水位模拟R²=0.94。","农业遥感与作物表型","可将该混合框架迁移至数据稀缺农业流域，结合遥感蒸散与作物需水评估灌溉可持续性。","openalex","2026-10-09T23:30:28.784309Z",{"total":108,"page":22,"page_size":108,"items":109},6,[110,188,267,342,400,479],{"id":111,"title":112,"url":113,"summary":114,"summary_zh":115,"content":9,"source_name":116,"source_url":113,"published_at":117,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":118,"score_detail":119,"sources":121,"tags":123,"primary_topic":126,"secondary_topics":131,"classification":132,"search_phrases":157,"slug":160,"view_count":22,"doi":161,"paper":162,"created_at":187},4503,"FUSING DEEP LEARNING AND REMOTE SENSING DATA TO ESTIMATE NON-OPTICALLY ACTIVE WATER QUALITY VARIABLES","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23243307","The efficient monitoring of inland water bodies such as rivers and lakes is crucial not only for ensuring human health and ecosystem balance, but also for the effective management of water resources. Two water quality variables of high importance and interest are Dissolved Oxygen (DO) and pH. The former variable is an ecosystem health index as it is connected to hypoxia events, while the latter one is linked to biochemical processes and the acidity of water. Currently, conventional water quality monitoring methods (e.g., via in-situ point sampling) are accurate for the particular point, but costly and time-consuming. On the other hand, satellite remote sensing provides a remedy, as it offers high spatial and temporal coverage. Most of the water quality applications of remote sensing data have focused on optically active parameters like Chlorophyll-a and Colored Dissolved Organic Matter (CDOM), but the estimation of non-optically active variables such as pH and DO poses a significant challenge. This study introduces a novel approach, that fuses Earth Observation (EO) and in-situ data, in a deep learning context, to provide estimations of non-optically active variables, characterized by high accuracy, high spatial coverage and low latency. The approach also considers the data imbalance challenge, which is common in environmental datasets. The approach is applied to Lake Yliki in central Greece. The Boeotikos Kifissos river basin, which lies upstream of Lake Yliki, is dominated by intense agricultural activity. The lake is also part of Athens’ water distribution system, making its efficient monitoring even more important.","对河流、湖泊等内陆水体的高效监测，不仅对保障人类健康和生态系统平衡至关重要，也对水资源的有效管理不可或缺。溶解氧（Dissolved Oxygen, DO）和pH值是两项备受关注且极为重要的水质变量。前者作为生态系统健康指标，与缺氧事件密切相关；后者则与生物化学过程及水体酸度相关。目前，传统的水质监测方法（如原位定点采样）对特定点位而言较为准确，但成本高昂且耗时费力。相比之下，卫星遥感提供了一种解决方案，因其具备较高的时空覆盖能力。遥感数据在水质方面的应用大多集中于光学活性参数，如叶绿素a和有色溶解有机物（Colored Dissolved Organic Matter, CDOM），而对pH值和溶解氧等非光学活性变量的估算则构成重大挑战。本研究提出了一种新方法，在深度学习框架下融合地球观测（Earth Observation, EO）数据与原位数据，以实现对非光学活性变量的高精度、高空间覆盖度和低延迟估算。该方法还考虑了环境数据集中常见的数据不平衡问题。该方法已应用于希腊中部的伊利基湖（Lake Yliki）。位于伊利基湖上游的维奥蒂亚基菲索斯河流域（Boeotikos Kifissos river basin）以密集的农业活动为主。该湖也是雅典供水系统的组成部分，因此对其开展高效监测尤为重要。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-08T00:00:00Z",81,{"impact":19,"substance":18,"depth":19,"authority":20,"freshness":13,"relevant":22,"comment":120},"将深度学习与遥感、地面实测数据融合估算内陆水体非光学活性水质参数，方法新颖且面向农业流域与饮用水源监测，具备较高专业参考价值。",[122],{"name":116,"url":113},[27,124,28,29,125],"农业面源污染","水质监测",{"code":127,"slug":128,"name":129,"path":130,"parent_code":9},"I","digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":133,"locked":14,"version":35,"method":36,"evidence":134},"partial",{"candidates":135,"agricultural_evidence":42,"reason":156},[136,141,145,151],{"code":51,"score":137,"supported":42,"title":138,"abstract":139,"tags":140},15,[55],[27,55],[27],{"code":58,"score":137,"supported":42,"title":142,"abstract":143,"tags":144},[61],[28,61],[28],{"code":40,"score":146,"supported":42,"title":147,"abstract":148,"tags":150},4,[],[149],"水资源",[29],{"code":152,"score":22,"supported":14,"title":153,"abstract":154,"tags":155},"H5",[],[],[124],"一级领域明确，二级方向需要细分",[158,159],"Yliki湖 水质 遥感","溶解氧 pH 遥感 估算","Yliki湖水质遥感-4503","10.5281\u002Fzenodo.23243307",{"doi":161,"openalex_id":163,"authors":164,"venue":116,"cited_by_count":97,"oa_url":113,"card":182,"direction":103,"ingested_from":105},"W7220943541",[165,168,171,174,176,179],{"name":166,"orcid":167},"Vasiliki Thomopoulou","https:\u002F\u002Forcid.org\u002F0009-0000-8361-6879",{"name":169,"orcid":170},"Panagiotis Kossieris","https:\u002F\u002Forcid.org\u002F0000-0002-5201-3079",{"name":172,"orcid":173},"Georgios Bariamis","https:\u002F\u002Forcid.org\u002F0000-0002-9548-3054",{"name":175,"orcid":9},"Konstantinos Peroulis",{"name":177,"orcid":178},"G. Katsouras","https:\u002F\u002Forcid.org\u002F0000-0003-3249-3113",{"name":180,"orcid":181},"Christos Makropoulos","https:\u002F\u002Forcid.org\u002F0000-0003-0308-4265",{"tldr":183,"method":184,"finding":185,"direction":103,"opportunity":186},"融合深度学习与遥感及实地数据，估算湖泊非光学活性水质变量（溶解氧和pH）。","深度学习融合地球观测与实地数据，处理数据不平衡，应用于希腊Yliki湖。","该方法能高精度、高覆盖、低延迟地估算非光学活性水质变量。","可探索将此类方法迁移至农业流域水质监测，结合农业活动数据提升预警能力。","2026-10-09T23:30:25.773729Z",{"id":189,"title":190,"url":191,"summary":192,"summary_zh":193,"content":9,"source_name":194,"source_url":195,"published_at":196,"category":12,"cover_url":9,"hotness":197,"is_selected":14,"score":198,"score_detail":199,"sources":203,"tags":207,"primary_topic":210,"secondary_topics":214,"classification":215,"search_phrases":251,"slug":254,"view_count":97,"doi":255,"paper":256,"created_at":266},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",25,47,{"impact":21,"substance":17,"depth":200,"authority":146,"freshness":201,"relevant":22,"comment":202},14,9,"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。",[204,205],{"name":194,"url":195},{"name":194,"url":206},"https:\u002F\u002Fajeee.co.in\u002Findex.php\u002Fajeee\u002Farticle\u002Fdownload\u002F6754\u002F5489",[208,28,29,209],"机器学习","地下水监测",{"code":51,"slug":211,"name":212,"path":213,"parent_code":127},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[],{"status":216,"locked":14,"version":35,"method":36,"evidence":217},"classified",{"candidates":218,"agricultural_evidence":42},[219,224,228,235,239,245],{"code":51,"score":137,"supported":42,"title":220,"abstract":222,"tags":223},[221],"machine learning",[208,221],[208],{"code":58,"score":52,"supported":42,"title":225,"abstract":226,"tags":227},[],[28,61],[28],{"code":229,"score":108,"supported":42,"title":230,"abstract":231,"tags":234},"H2",[],[232,233],"土壤","soil",[],{"code":40,"score":146,"supported":42,"title":236,"abstract":237,"tags":238},[],[149],[29],{"code":240,"score":65,"supported":42,"title":241,"abstract":242,"tags":244},"I4",[],[243],"空间分析",[],{"code":246,"score":65,"supported":42,"title":247,"abstract":248,"tags":250},"I7",[],[249],"methodological framework",[],[252,253],"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":255,"openalex_id":257,"authors":258,"venue":194,"cited_by_count":97,"oa_url":195,"card":261,"direction":103,"ingested_from":105},"W7222805722",[259],{"name":260,"orcid":9},"Prof. I Pandu Ranga Reddy Dr. Hari Ramavath",{"tldr":262,"method":263,"finding":264,"direction":103,"opportunity":265},"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":268,"title":269,"url":270,"summary":271,"summary_zh":272,"content":9,"source_name":273,"source_url":270,"published_at":274,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":275,"score_detail":276,"sources":279,"tags":281,"primary_topic":285,"secondary_topics":286,"classification":287,"search_phrases":316,"slug":319,"view_count":97,"doi":320,"paper":321,"created_at":341},5166,"Application of the SCS–CN Method Integrated with GIS and Remote Sensing for Watershed Runoff Estimation: A Review","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fijecc\u002F2026\u002Fv16i105735","Accurate estimation of runoff is crucial for watershed planning, soil and water conservation, flood mitigation, and the management of ungauged watersheds. The Soil Conservation Service Curve Number (SCS-CN) method has been extensively used because of its minimal input requirements and its ability to model the spatial influence of land use, soil properties, and hydrologic soil groups. This review compiles applications of the GIS-based SCS-CN method reported for watersheds in semi-arid and monsoon-affected countries such as India, Saudi Arabia, Afghanistan, China, Iraq, and Ethiopia. The reviewed literature employed rainfall data, land-use\u002Fland-cover data, soil maps, hydrologic soil groups, digital elevation models, and watershed morphometry to calculate curve numbers and direct runoff. The reported CN values varied substantially, from about 40 under dry conditions to more than 90 under urban, rocky, agricultural, or poorly infiltrating conditions. Runoff estimates ranged from relatively low values in arid catchments to more than 1,300 mm per year in high-rainfall watersheds. Several studies reported high R2 correlations between observed and estimated runoff, typically ranging from 0.73 to 0.98. Important limitations identified in the review include the sensitivity of results to the choice of curve number, antecedent moisture condition, initial abstraction assumption, land-use classification, soil-group assignment, rainfall representation, and the lack of observed discharge data. The overall design of the SCS-CN method and its integration with RS and GIS offer a practical approach to assessing spatial runoff and prioritising watersheds, but the method requires local calibration, uncertainty analysis, and validation against observed rainfall–runoff data.","径流的准确估算对于流域规划、水土保持、洪水缓解以及无资料流域的管理至关重要。土壤保持服务曲线数（SCS-CN）方法因其输入需求少、能够模拟土地利用、土壤性质和水文土壤组的空间影响而被广泛使用。本综述汇编了基于GIS的SCS-CN方法在半干旱和受季风影响国家（如印度、沙特阿拉伯、阿富汗、中国、伊拉克和埃塞俄比亚）流域的应用报道。所综述的文献利用降雨数据、土地利用\u002F土地覆盖数据、土壤图、水文土壤组、数字高程模型和流域形态计量来计算曲线数和直接径流。报道的CN值差异很大，从干旱条件下的约40到城市、岩石、农业或入渗不良条件下的90以上不等。径流估算值从干旱集水区的相对较低值到高降雨流域的年径流量超过1300 mm不等。多项研究报告了观测径流与估算径流之间的高R²相关性，通常在0.73至0.98之间。本综述识别出的重要局限性包括：结果对曲线数选择、前期土壤湿润条件、初始损失假设、土地利用分类、土壤组赋值、降雨代表性以及缺乏实测流量数据的敏感性。SCS-CN方法的总体设计及其与遥感和GIS的集成，为评估空间径流和流域优先排序提供了一种实用方法，但该方法需要本地校准、不确定性分析，并依据实测降雨—径流数据进行验证。","International Journal of Environment and Climate Change","2026-10-16T00:00:00Z",63,{"impact":21,"substance":19,"depth":277,"authority":17,"freshness":201,"relevant":22,"comment":278},16,"综述系统梳理了GIS与遥感集成的SCS-CN径流估算方法及其局限，对农业信息化中的流域水资源与水土保持管理有参考价值，但属方法综述、影响面有限，未达每日精选门槛。",[280],{"name":273,"url":270},[28,282,283,284,30],"GIS","水土保持","流域管理",{"code":127,"slug":128,"name":129,"path":130,"parent_code":9},[],{"status":133,"locked":14,"version":35,"method":36,"evidence":288},{"candidates":289,"agricultural_evidence":42,"reason":156},[290,294,298,302,306,312],{"code":58,"score":17,"supported":42,"title":291,"abstract":292,"tags":293},[61],[28],[28],{"code":240,"score":17,"supported":42,"title":295,"abstract":296,"tags":297},[282],[282],[282],{"code":229,"score":108,"supported":42,"title":299,"abstract":300,"tags":301},[],[232,233],[],{"code":64,"score":146,"supported":42,"title":303,"abstract":304,"tags":305},[],[283],[283],{"code":307,"score":65,"supported":42,"title":308,"abstract":309,"tags":311},"H7",[],[310],"干旱",[],{"code":40,"score":22,"supported":14,"title":313,"abstract":314,"tags":315},[],[],[30],[317,318],"SCS-CN GIS 遥感 径流估算","水土保持 水文模型 流域管理 遥感","SCS-CNGIS遥感径流估算-5166","10.9734\u002Fijecc\u002F2026\u002Fv16i105735",{"doi":320,"openalex_id":322,"authors":323,"venue":273,"cited_by_count":97,"oa_url":270,"card":336,"direction":103,"ingested_from":105},"W7222632567",[324,326,328,330,332,334],{"name":325,"orcid":9},"Sudhir Lad",{"name":327,"orcid":9},"R. D. Bansod",{"name":329,"orcid":9},"Bhaupatil Gavit",{"name":331,"orcid":9},"Sachin Nandgude",{"name":333,"orcid":9},"Shailendra Gadge",{"name":335,"orcid":9},"Mahesh Patil",{"tldr":337,"method":338,"finding":339,"direction":103,"opportunity":340},"综述GIS与遥感集成的SCS-CN法在半干旱与季风区流域径流估算的应用。","汇总印度、中国等国文献，用降雨、土地利用、土壤图、DEM与流域形态计算CN和径流","CN约40至90以上，年径流差异大，R²多在0.73至0.98，但结果对CN与前期湿度等敏感。","可研究CN本地化校准与不确定性量化，并结合实测径流验证，提升无资料流域径流估算可靠性。","2026-10-17T23:30:28.373754Z",{"id":343,"title":344,"url":345,"summary":346,"summary_zh":347,"content":9,"source_name":348,"source_url":345,"published_at":349,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":350,"score_detail":351,"sources":354,"tags":356,"primary_topic":359,"secondary_topics":363,"classification":364,"search_phrases":381,"slug":384,"view_count":97,"doi":385,"paper":386,"created_at":399},5156,"BGDNet: a boundary-guided deformable network for accurate farmland parcel segmentation in high-resolution remote sensing imagery","https:\u002F\u002Fdoi.org\u002F10.1080\u002F19479832.2026.2748607","To address the challenges of complex farmland parcel shapes, blurred boundaries, and frequent adhesion between adjacent parcels in high-resolution remote sensing imagery, this study proposes BGDNet, a boundary-guided and deformable convolution collaboratively driven network for farmland segmentation. First, DCN is introduced into the encoder to enhance the model’s ability to adaptively represent irregular farmland geometries. Second, a MSAM and a FEM are constructed for multi-scale contextual aggregation and cross-layer feature calibration, respectively, thereby improving semantic representation capability in complex scenes. On this basis, a BGDF is proposed, which generates spatial gating weights through collaborative learning between boundary and semantic branches, enabling dynamic constraint of decoder features by boundary information. This alleviates parcel adhesion and improves boundary localisation accuracy. Experimental results on the FGFD demonstrate that the BGDNet achieves an IoU of 85.57% and an F1-score of 92.22%, outperforming several mainstream semantic segmentation models in terms of segmentation accuracy, boundary integrity, and visual consistency. Qualitative visualisation and ablation studies validate the effectiveness of each module and their synergistic contributions. The proposed method provides an effective solution for high-precision farmland extraction under complex terrain conditions and offers technical support for agricultural resource investigation, cultivated land monitoring, and smart agriculture management.","针对高分辨率遥感影像中农田地块形状复杂、边界模糊以及相邻地块间频繁粘连等问题，本研究提出了一种边界引导与可变形卷积协同驱动的农田分割网络BGDNet。首先，在编码器中引入可变形卷积（DCN），增强模型对不规则农田几何形状的自适应表征能力。其次，构建多尺度注意力模块（MSAM）和特征增强模块（FEM），分别用于多尺度上下文聚合和跨层特征校准，从而提升复杂场景下的语义表征能力。在此基础上，提出边界引导动态融合模块（BGDF），通过边界分支与语义分支的协同学习生成空间门控权重，使边界信息能够动态约束解码器特征，从而缓解地块粘连问题并提高边界定位精度。在FGFD数据集上的实验结果表明，BGDNet的IoU达到85.57%，F1分数达到92.22%，在分割精度、边界完整性和视觉一致性方面优于多种主流语义分割模型。定性可视化与消融实验验证了各模块的有效性及其协同贡献。所提方法为复杂地形条件下的高精度农田提取提供了有效解决方案，并为农业资源调查、耕地监测和智慧农业管理提供了技术支撑。","International Journal of Image and Data Fusion","2026-10-15T00:00:00Z",71,{"impact":17,"substance":352,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":353},20,"提出边界引导可变形卷积网络用于高分辨率遥感农田地块分割，方法新颖、指标可靠，属细分领域技术进展，值得关注但影响面有限。",[355],{"name":348,"url":345},[357,27,28,358],"智慧农业","农田地块分割",{"code":58,"slug":360,"name":361,"path":362,"parent_code":127},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[],{"status":216,"locked":14,"version":35,"method":36,"evidence":365},{"candidates":366,"agricultural_evidence":42},[367,371,377],{"code":58,"score":137,"supported":42,"title":368,"abstract":369,"tags":370},[61],[28,61],[28],{"code":372,"score":52,"supported":42,"title":373,"abstract":374,"tags":376},"I1",[],[357,375],"smart agriculture",[357],{"code":51,"score":22,"supported":14,"title":378,"abstract":379,"tags":380},[],[],[27],[382,383],"BGDNet 农田分割","高分辨率遥感 地块提取","BGDNet农田分割-5156","10.1080\u002F19479832.2026.2748607",{"doi":385,"openalex_id":387,"authors":388,"venue":348,"cited_by_count":97,"oa_url":9,"card":393,"direction":398,"ingested_from":105},"W7221931728",[389,391],{"name":390,"orcid":9},"Xianjun Du",{"name":392,"orcid":9},"Binda Bai",{"tldr":394,"method":395,"finding":396,"direction":103,"opportunity":397},"提出边界引导可变形网络BGDNet，实现高分辨率遥感耕地地块精准分割。","可变形卷积编码器+多尺度注意力+边界语义门控融合，在FGFD数据集验证。","IoU达85.57%、F1达92.22%，边界完整性与抗粘连优于主流分割模型。","可探索边界引导机制向地块实例化、跨区域迁移及多时相耕地变化监测延伸。","智慧农业 \u002F 农业物联网","2026-10-17T23:30:10.405075Z",{"id":401,"title":402,"url":403,"summary":404,"summary_zh":405,"content":9,"source_name":406,"source_url":403,"published_at":349,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":350,"score_detail":407,"sources":410,"tags":412,"primary_topic":9,"secondary_topics":415,"classification":416,"search_phrases":444,"slug":447,"view_count":97,"doi":448,"paper":449,"created_at":478},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",{"impact":17,"substance":352,"depth":408,"authority":20,"freshness":201,"relevant":22,"comment":409},17,"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。",[411],{"name":406,"url":403},[28,29,282,413,414],"土地适宜性","灌溉潜力",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":417},{"candidates":418,"agricultural_evidence":42,"reason":443},[419,423,430,434,438],{"code":40,"score":19,"supported":42,"title":420,"abstract":421,"tags":422},[47],[45,149,47],[29],{"code":240,"score":19,"supported":42,"title":424,"abstract":426,"tags":429},[425],"spatial analysis",[427,282,428],"地理信息","geographic information",[282],{"code":58,"score":137,"supported":42,"title":431,"abstract":432,"tags":433},[61],[28,61],[28],{"code":229,"score":108,"supported":42,"title":435,"abstract":436,"tags":437},[],[232,233],[],{"code":439,"score":22,"supported":14,"title":440,"abstract":441,"tags":442},"H1",[],[],[413],"多个产业证据相当，需要确认主归属",[445,446],"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":448,"openalex_id":450,"authors":451,"venue":406,"cited_by_count":97,"oa_url":403,"card":473,"direction":103,"ingested_from":105},"W7222066993",[452,455,458,461,464,467,470],{"name":453,"orcid":454},"Nurgul Aitekeyeva","https:\u002F\u002Forcid.org\u002F0000-0003-3619-7362",{"name":456,"orcid":457},"Asset Yegizbayeva","https:\u002F\u002Forcid.org\u002F0000-0003-2114-8365",{"name":459,"orcid":460},"Nurlan Bekmukhamedov","https:\u002F\u002Forcid.org\u002F0000-0002-3157-7705",{"name":462,"orcid":463},"Kristina Konstantinova","https:\u002F\u002Forcid.org\u002F0009-0004-0212-5656",{"name":465,"orcid":466},"Kudaibergen Kyrgyzbay","https:\u002F\u002Forcid.org\u002F0000-0002-4279-6436",{"name":468,"orcid":469},"Nurdaulet B. Zhumabay","https:\u002F\u002Forcid.org\u002F0009-0004-2476-6526",{"name":471,"orcid":472},"Nurlan Balgabayev","https:\u002F\u002Forcid.org\u002F0000-0001-8127-7981",{"tldr":474,"method":475,"finding":476,"direction":103,"opportunity":477},"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",{"id":480,"title":481,"url":482,"summary":483,"summary_zh":484,"content":9,"source_name":116,"source_url":482,"published_at":485,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":486,"score_detail":487,"sources":489,"tags":491,"primary_topic":495,"secondary_topics":496,"classification":501,"search_phrases":525,"slug":528,"view_count":97,"doi":529,"paper":530,"created_at":540},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。","2026-10-14T00:00:00Z",74,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":201,"relevant":22,"comment":488},"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。",[490],{"name":116,"url":482},[492,28,29,493,494],"农业遥感","灌溉农业","盐渍化",{"code":58,"slug":360,"name":361,"path":362,"parent_code":127},[497],{"code":240,"slug":498,"name":499,"path":500,"parent_code":127},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"status":216,"locked":14,"version":35,"method":36,"evidence":502},{"candidates":503,"agricultural_evidence":42},[504,508,512,516,520],{"code":58,"score":277,"supported":42,"title":505,"abstract":506,"tags":507},[61],[28,61],[492,28],{"code":240,"score":41,"supported":42,"title":509,"abstract":510,"tags":511},[282],[282],[],{"code":229,"score":52,"supported":42,"title":513,"abstract":514,"tags":515},[],[232,233],[494],{"code":307,"score":65,"supported":42,"title":517,"abstract":518,"tags":519},[],[310],[],{"code":40,"score":521,"supported":14,"title":522,"abstract":523,"tags":524},2,[],[],[29,493],[526,527],"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":529,"openalex_id":531,"authors":532,"venue":116,"cited_by_count":97,"oa_url":482,"card":535,"direction":103,"ingested_from":105},"W7221819099",[533],{"name":534,"orcid":9},"Akbarov Sarvarbek",{"tldr":536,"method":537,"finding":538,"direction":103,"opportunity":539},"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"]