[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2657":3,"related-2657":115},{"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":22,"tags":24,"primary_topic":30,"secondary_topics":35,"classification":36,"search_phrases":71,"slug":74,"view_count":75,"doi":76,"paper":77,"created_at":114},2657,"Retrieval of Optically Active and Inactive Water Quality Parameters Using Remote Sensing and Machine Learning: Evidence from Water Hyacinth-Infested Lake Tana, Ethiopia","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183185","Monitoring water quality is critical for protecting freshwater ecosystems and supporting sustainable water resource management. Lake Tana, Ethiopia’s largest freshwater lake, faces increasing agricultural and urban pressures, while conventional monitoring remains costly and spatially constrained. This study developed an integrated Sentinel-2 remote sensing and machine learning framework to estimate chlorophyll-a (Chl-a), turbidity (TU), total nitrogen (TN), and total phosphorus (TP) using 858 in situ observations and Google Earth Engine. Random Forest (RF), Extreme Gradient Boosting (XGB), Artificial Neural Networks (ANN), and Support Vector Regression (SVR) were evaluated using spectral bands, band combinations, and indices. RF provided the best predictions for Chl-a (R2 = 0.94 ± 0.01; RMSE = 2.11 ± 0.18 µg L−1; MARE = 5%) and TP (R2 = 0.91 ± 0.01; RMSE = 0.26 ± 0.01 mg L−1; MARE = 8.7%), whereas XGB performed best for TU (R2 = 0.93 ± 0.01; RMSE = 5.17 ± 0.43 NTU; MARE = 7%) and TN (R2 = 0.94 ± 0.02; RMSE = 0.18 ± 0.02 mg L−1; MARE = 9.9%). The strong predictive performance of RF and XGB across both optically active and inactive parameters demonstrates the capability of the framework to capture complex spectral water quality relationships and support spatially continuous assessment. Significant seasonal differences (p \u003C 0.001) showed higher dry season Chl-a (137.1%) and higher wet season TP (21.7%), TU (7.5%), and TN (3.9%). Long-term paired observations further indicated increases in Chl-a (73.7%), TN (30%), and TP (14.3%) from December 2016 to December 2025 (p \u003C 0.001). Spatial hotspot analysis revealed strong clustering of TU, TN, and TP, particularly around tributary mouths and nearshore areas, highlighting priority zones for monitoring and intervention. Overall, integrating field observations, Sentinel-2 imagery, and machine learning provides an accurate, scalable, and cost-effective approach for monitoring diverse water quality parameters. The framework offers a transferable solution for strengthening freshwater monitoring in data-scarce regions and supporting sustainable management of lakes under increasing water quality pressures.","监测水质对于保护淡水生态系统和支撑可持续水资源管理至关重要。埃塞俄比亚最大的淡水湖——塔纳湖（Lake Tana）面临着日益加剧的农业和城市压力，而传统监测手段仍然成本高昂且受空间限制。本研究开发了一套集成Sentinel-2遥感与机器学习的框架，利用858个原位观测数据和Google Earth Engine估算叶绿素a（Chl-a）、浊度（TU）、总氮（TN）和总磷（TP）。采用光谱波段、波段组合和指数，评估了随机森林（RF）、极端梯度提升（XGB）、人工神经网络（ANN）和支持向量回归（SVR）的性能。RF对Chl-a（R2 = 0.94 ± 0.01；RMSE = 2.11 ± 0.18 µg L−1；MARE = 5%）和TP（R2 = 0.91 ± 0.01；RMSE = 0.26 ± 0.01 mg L−1；MARE = 8.7%）的预测效果最佳，而XGB对TU（R2 = 0.93 ± 0.01；RMSE = 5.17 ± 0.43 NTU；MARE = 7%）和TN（R2 = 0.94 ± 0.02；RMSE = 0.18 ± 0.02 mg L−1；MARE = 9.9%）的预测效果最佳。RF和XGB在光学活性和非光学活性参数上均表现出强大的预测性能，表明该框架能够捕捉复杂的光谱水质关系并支持空间连续评估。显著的季节性差异（p \u003C 0.001）显示旱季Chl-a较高（137.1%），雨季TP（21.7%）、TU（7.5%）和TN（3.9%）较高。长期配对观测进一步表明，从2016年12月至2025年12月，Chl-a（73.7%）、TN（30%）和TP（14.3%）均有所增加（p \u003C 0.001）。空间热点分析揭示了TU、TN和TP的强烈聚集性，尤其是在支流河口和近岸区域，凸显了优先监测和干预区域。总体而言，整合实地观测、Sentinel-2影像和机器学习为监测多种水质参数提供了一种准确、可扩展且具有成本效益的方法。该框架为加强数据稀缺地区的淡水监测以及支持在水质压力日益增大背景下湖泊的可持续管理提供了一种可迁移的解决方案。",null,"Remote Sensing","2026-09-16T00:00:00Z","论文",10,false,82,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":13,"relevant":20,"comment":21},18,22,14,1,"基于Sentinel-2与机器学习实现湖泊多参数水质反演，方法可迁移至国内农业面源污染与渔业水域监测，数据规模与精度均具参考价值。",[23],{"name":10,"url":6},[25,26,27,28,29],"机器学习","农业面源污染","智慧渔业","遥感监测","水质监测",{"code":31,"slug":32,"name":33,"path":34,"parent_code":9},"I","digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":37,"locked":14,"version":38,"method":39,"evidence":40},"partial","2026-10-22.2","rules",{"candidates":41,"agricultural_evidence":45,"reason":70},[42,50,57,65],{"code":43,"score":44,"supported":45,"title":46,"abstract":48,"tags":49},"I2",15,true,[47],"machine learning",[25,47],[25],{"code":51,"score":44,"supported":45,"title":52,"abstract":54,"tags":56},"I3",[53],"remote sensing",[55,53],"遥感",[28],{"code":58,"score":59,"supported":45,"title":60,"abstract":61,"tags":64},"H3",6,[],[62,63],"水资源","water resource",[],{"code":66,"score":20,"supported":14,"title":67,"abstract":68,"tags":69},"H5",[],[],[26],"一级领域明确，二级方向需要细分",[72,73],"农业面源污染 智慧渔业 机器学习 水质监测","农业面源污染 智慧渔业","农业面源污染智慧渔业机器学习水质监测-2657",2,"10.3390\u002Frs18183185",{"doi":76,"openalex_id":78,"authors":79,"venue":10,"cited_by_count":106,"oa_url":6,"card":107,"direction":111,"ingested_from":113},"W7213230686",[80,83,85,88,90,93,95,97,99,101,103],{"name":81,"orcid":82},"Lakachew Y. Alemneh","https:\u002F\u002Forcid.org\u002F0009-0004-3471-3778",{"name":84,"orcid":9},"Daganchew Aklog",{"name":86,"orcid":87},"Ann van Griensven","https:\u002F\u002Forcid.org\u002F0000-0002-2105-6287",{"name":89,"orcid":9},"Minychl G. Dersseh",{"name":91,"orcid":92},"Goraw Goshu","https:\u002F\u002Forcid.org\u002F0000-0001-9629-0126",{"name":94,"orcid":9},"Seleshi Yalew",{"name":96,"orcid":9},"Demesew A. Mhiret",{"name":98,"orcid":9},"Sisay B. Asress",{"name":100,"orcid":9},"Tigistu Wassie Agegnehu",{"name":102,"orcid":9},"Shawl Abebe Desta",{"name":104,"orcid":105},"Samuel Berihun Kassa","https:\u002F\u002Forcid.org\u002F0009-0004-5618-9743",0,{"tldr":108,"method":109,"finding":110,"direction":111,"opportunity":112},"用Sentinel-2与机器学习反演埃塞俄比亚塔纳湖四类水质参数并分析时空变化。","Sentinel-2影像、Google Earth Engine、858个实测点","RF与XGB精度最高（R²达0.91-0.94），水质参数呈显著季节与年际上升趋势。","农业遥感与作物表型","可迁移至其他数据稀缺湖泊，探索水葫芦覆盖下水体光谱混合与多源遥感协同反演。","openalex","2026-09-16T23:30:28.415573Z",{"total":59,"page":20,"page_size":59,"items":116},[117,195,265,339,412,474],{"id":118,"title":119,"url":120,"summary":121,"summary_zh":122,"content":9,"source_name":123,"source_url":120,"published_at":124,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":125,"score_detail":126,"sources":130,"tags":132,"primary_topic":135,"secondary_topics":136,"classification":137,"search_phrases":167,"slug":170,"view_count":171,"doi":172,"paper":173,"created_at":194},3553,"Advances and gaps in mitigation and management practices across critical landscapes and scales","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jenvman.2026.131013","Environmental systems are increasingly stressed by the interacting effects of climate change, land-use change, agricultural intensification, urbanization, and emerging contaminants. This paper synthesizes contributions to the Journal of Environmental Management special collection, “Advances and gaps in mitigation and management practices across critical landscapes and scales,” which examines current progress and remaining challenges in environmental mitigation and best management practices. The reviewed studies span diverse landscapes, scales, and management contexts, with emphasis on water quality, water quantity, pollutant transport, ecological resilience, and sustainable agricultural systems. Four major themes emerge: the combined pressures of climate and human activity on environmental systems; innovations in modeling, monitoring, remote sensing, and statistical forecasting; evaluation of management interventions and mitigation strategies; and cross-cutting insights related to spatial variability, extreme events, long-term dynamics, and implementation gaps. Collectively, the studies demonstrate that mitigation effectiveness is highly context-dependent and influenced by hydrologic connectivity, land use, climate variability, management history, and social factors. Advances in process-based models, machine learning, geospatial technologies, and monitoring, reporting, and verification frameworks are improving the ability to predict environmental responses and evaluate intervention outcomes. However, persistent gaps remain in long-term monitoring, maintenance of management practices, adaptation to extreme events, and translation of scientific findings into policy and practice. This synthesis highlights the need for integrated, adaptive, and stakeholder-informed approaches to strengthen environmental resilience across critical landscapes.","环境系统日益受到气候变化、土地利用变化、农业集约化、城市化以及新兴污染物相互作用的影响。本文综合了《环境管理杂志》特刊“关键景观与尺度下减缓与管理实践的进展与差距”的贡献，该特刊审视了环境减缓与最佳管理实践的当前进展及剩余挑战。所综述的研究涵盖多样化的景观、尺度和管理情境，重点关注水质、水量、污染物迁移、生态韧性和可持续农业系统。由此浮现出四大主题：气候与人类活动对环境系统的复合压力；建模、监测、遥感与统计预测方面的创新；管理干预与减缓策略的评估；以及关于空间变异性、极端事件、长期动态与实施差距的交叉性见解。总体而言，这些研究表明，减缓效果高度依赖于具体情境，并受水文连通性、土地利用、气候变异性、管理历史和社会因素的影响。基于过程的模型、机器学习、地理空间技术以及监测、报告与核查框架的进步，正在提升预测环境响应和评估干预结果的能力。然而，在长期监测、管理实践的维持、极端事件的适应以及科学发现向政策与实践的转化方面，仍存在持续性的差距。本综述强调，需要采取综合性、适应性且利益相关者知情的方法，以增强关键景观的环境韧性。","Journal of Environmental Management","2026-09-25T00:00:00Z",78,{"impact":17,"substance":127,"depth":17,"authority":19,"freshness":128,"relevant":20,"comment":129},20,8,"核心期刊综述，系统梳理环境缓解与管理实践进展与缺口，对农业面源污染与水质管理有参考价值，但非农业信息化直接落地成果。",[131],{"name":123,"url":120},[25,26,28,133,134],"农业可持续发展","水质管理",{"code":31,"slug":32,"name":33,"path":34,"parent_code":9},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":138},{"candidates":139,"agricultural_evidence":45,"reason":70},[140,145,149,156,163],{"code":43,"score":141,"supported":45,"title":142,"abstract":143,"tags":144},7,[],[25,47],[25],{"code":51,"score":141,"supported":45,"title":146,"abstract":147,"tags":148},[],[55,53],[28],{"code":150,"score":59,"supported":45,"title":151,"abstract":152,"tags":155},"H7",[],[153,154],"气候变化","climate change",[],{"code":157,"score":158,"supported":45,"title":159,"abstract":160,"tags":162},"H8",4,[],[161],"可持续农业",[133],{"code":66,"score":20,"supported":14,"title":164,"abstract":165,"tags":166},[],[],[26],[168,169],"Journal of Environmental Management 缓解措施 管理实践","农业可持续发展 农业面源污染 机器学习 水质管理","JournalofEnvironmentalManagement缓解措施管理实践-3553",3,"10.1016\u002Fj.jenvman.2026.131013",{"doi":172,"openalex_id":174,"authors":175,"venue":123,"cited_by_count":106,"oa_url":120,"card":188,"direction":111,"ingested_from":113},"W7214310594",[176,179,182,185],{"name":177,"orcid":178},"Fouad H. Jaber","https:\u002F\u002Forcid.org\u002F0000-0001-8643-8668",{"name":180,"orcid":181},"Latif Kalin","https:\u002F\u002Forcid.org\u002F0000-0001-9562-8834",{"name":183,"orcid":184},"Soni  Mulmi Pradhanang","https:\u002F\u002Forcid.org\u002F0000-0002-1142-9457",{"name":186,"orcid":187},"Aleksey Y. Sheshukov","https:\u002F\u002Forcid.org\u002F0000-0002-4842-908X",{"tldr":189,"method":190,"finding":191,"direction":192,"opportunity":193},"综述环境减缓与管理实践进展，指出成效高度依赖情境且存在实施缺口。","综合多景观尺度研究，涵盖过程模型、机器学习、遥感与MRV框架。","减缓效果受水文连通、土地利用、气候与历史管理影响，长期监测与政策转化仍不足。","农业绿色发展与碳","可研究农业景观中管理实践的长期维持机制与极端事件下的适应性策略。","2026-09-26T23:30:29.702279Z",{"id":196,"title":197,"url":198,"summary":199,"summary_zh":200,"content":9,"source_name":201,"source_url":198,"published_at":202,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":203,"score_detail":204,"sources":207,"tags":209,"primary_topic":212,"secondary_topics":213,"classification":214,"search_phrases":234,"slug":237,"view_count":20,"doi":238,"paper":239,"created_at":264},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":17,"substance":18,"depth":17,"authority":205,"freshness":13,"relevant":20,"comment":206},13,"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。",[208],{"name":201,"url":198},[210,26,55,211,29],"深度学习","水资源管理",{"code":31,"slug":32,"name":33,"path":34,"parent_code":9},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":215},{"candidates":216,"agricultural_evidence":45,"reason":70},[217,222,226,230],{"code":43,"score":44,"supported":45,"title":218,"abstract":220,"tags":221},[219],"deep learning",[210,219],[210],{"code":51,"score":44,"supported":45,"title":223,"abstract":224,"tags":225},[53],[55,53],[55],{"code":58,"score":158,"supported":45,"title":227,"abstract":228,"tags":229},[],[62],[211],{"code":66,"score":20,"supported":14,"title":231,"abstract":232,"tags":233},[],[],[26],[235,236],"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":238,"openalex_id":240,"authors":241,"venue":201,"cited_by_count":106,"oa_url":198,"card":259,"direction":111,"ingested_from":113},"W7220943541",[242,245,248,251,253,256],{"name":243,"orcid":244},"Vasiliki Thomopoulou","https:\u002F\u002Forcid.org\u002F0009-0000-8361-6879",{"name":246,"orcid":247},"Panagiotis Kossieris","https:\u002F\u002Forcid.org\u002F0000-0002-5201-3079",{"name":249,"orcid":250},"Georgios Bariamis","https:\u002F\u002Forcid.org\u002F0000-0002-9548-3054",{"name":252,"orcid":9},"Konstantinos Peroulis",{"name":254,"orcid":255},"G. Katsouras","https:\u002F\u002Forcid.org\u002F0000-0003-3249-3113",{"name":257,"orcid":258},"Christos Makropoulos","https:\u002F\u002Forcid.org\u002F0000-0003-0308-4265",{"tldr":260,"method":261,"finding":262,"direction":111,"opportunity":263},"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":266,"title":267,"url":268,"summary":269,"summary_zh":270,"content":9,"source_name":271,"source_url":268,"published_at":272,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":273,"score_detail":274,"sources":277,"tags":279,"primary_topic":284,"secondary_topics":285,"classification":286,"search_phrases":314,"slug":317,"view_count":75,"doi":318,"paper":319,"created_at":338},4435,"Land Use Change, Flood Exposure, and Vegetation Stress in the Chi River Basin, Northeast Thailand: A Multi-Temporal Remote Sensing and Machine Learning Assessment","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu181910188","Climate change has intensified hydrological extremes in Southeast Asia, yet the relationships between land use change, flood exposure, and vegetation stress remain poorly understood in tropical floodplain environments. This study assessed land use dynamics, flood exposure, and vegetation stress in the Upper Chi River Basin, Northeast Thailand, using multi-temporal remote sensing datasets spanning 2015–2024, official land use data from the Land Development Department (LDD), and machine learning approaches, including Random Forest classification of binary flood occurrence, SHAP analysis, and spatial statistics. Agricultural land dominated the landscape (84.2% in 2023), with 11.0% land use change occurring between 2015 and 2023, primarily agricultural conversion from forest (38.6% of Forest Area converted) and water body (30.0% of Water Body converted). Flood frequency was highest in areas classified as Water Bodies (0.38 events) and Agricultural Areas (0.55 events), while Forest Areas showed the greatest resilience (0.05 events). Random Forest classification of flood occurrence achieved an overall accuracy of 0.920 and AUC-ROC of 0.905, with DEM emerging as the dominant predictor in both Random Forest importance (0.435) and mean absolute SHAP (0.308). Vegetation stress affected 21.1% of the basin, with 59.2% of points showing an NDVI decline greater than 0.02 and 33.3% falling below the VCI \u003C 0.4 drought threshold. Four sensitivity classes were identified: Low (66.4%), Moderate (19.2%), High (12.4%), and Very High (2.0%), with Very High Sensitivity concentrated along the Chi River corridor in low-lying areas (mean DEM = 146.0 m). Moran’s I confirmed strong spatial clustering of flood frequency (I = 0.787) and sensitivity class (I = 0.534), highlighting the need for spatial statistical approaches. The findings support elevation-based land use zoning, forest conservation, and climate-resilient agricultural practices to enhance flood resilience and contribute to Sustainable Development Goals 2 (Zero Hunger), 11 (Sustainable Cities), 13 (Climate Action), and 15 (Life on Land).","气候变化加剧了东南亚的水文极端事件，但在热带洪泛平原环境中，土地利用变化、洪水暴露与植被胁迫之间的关系仍缺乏深入认识。本研究利用2015—2024年多时相遥感数据集、泰国土地发展厅（LDD）的官方土地利用数据以及机器学习方法（包括对二元洪水发生事件的随机森林分类、SHAP分析和空间统计），评估了泰国东北部奇河上游流域的土地利用动态、洪水暴露和植被胁迫。农业用地在景观中占主导地位（2023年为84.2%），2015—2023年间土地利用变化率为11.0%，主要表现为农业用地扩张导致的林地转换（38.6%的林地面积被转换）和水体转换（30.0%的水体面积被转换）。洪水频率在水体分类区域（0.38次）和农业用地分类区域（0.55次）最高，而林地表现出最强的恢复力（0.05次）。洪水发生事件的随机森林分类总体精度达到0.920，AUC-ROC为0.905，其中DEM在随机森林重要性（0.435）和平均绝对SHAP值（0.308）中均为最主要的预测因子。植被胁迫影响了流域21.1%的区域，59.2%的样点NDVI下降幅度超过0.02，33.3%的样点低于VCI \u003C 0.4的干旱阈值。研究识别出四个敏感性等级：低敏感（66.4%）、中等敏感（19.2%）、高敏感（12.4%）和极高敏感（2.0%），其中极高敏感区集中在奇河沿岸低洼地带（平均DEM = 146.0 m）。Moran's I证实了洪水频率（I = 0.787）和敏感性等级（I = 0.534）具有强烈的空间聚集性，凸显了空间统计方法的必要性。研究结果支持基于海拔的土地利用分区、森林保护以及气候适应性农业实践，以增强洪水恢复力，并助力实现可持续发展目标2（零饥饿）、11（可持续城市）、13（气候行动）和15（陆地生物）。","Sustainability","2026-10-07T00:00:00Z",70,{"impact":128,"substance":18,"depth":17,"authority":205,"freshness":275,"relevant":20,"comment":276},9,"基于多时相遥感与机器学习的流域土地利用—洪涝—植被胁迫评估，方法扎实、数据翔实，对农业防灾与气候韧性有参考价值，但属区域性研究，公共影响有限。",[278],{"name":271,"url":268},[280,25,28,281,282,283],"数字乡村","土地利用","洪涝风险","植被胁迫",{"code":31,"slug":32,"name":33,"path":34,"parent_code":9},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":287},{"candidates":288,"agricultural_evidence":45,"reason":70},[289,293,297,304,309],{"code":43,"score":44,"supported":45,"title":290,"abstract":291,"tags":292},[47],[25,47],[25],{"code":51,"score":44,"supported":45,"title":294,"abstract":295,"tags":296},[53],[55,53],[28],{"code":150,"score":275,"supported":45,"title":298,"abstract":299,"tags":303},[],[153,300,301,154,302],"气候适应","干旱","drought",[],{"code":305,"score":20,"supported":14,"title":306,"abstract":307,"tags":308},"H6",[],[],[281],{"code":310,"score":20,"supported":14,"title":311,"abstract":312,"tags":313},"I6",[],[],[280],[315,316],"泰国 Chi River 流域 遥感","土地利用变化 洪涝暴露 植被胁迫","泰国ChiRiver流域遥感-4435","10.3390\u002Fsu181910188",{"doi":318,"openalex_id":320,"authors":321,"venue":271,"cited_by_count":106,"oa_url":268,"card":333,"direction":111,"ingested_from":113},"W7220850940",[322,324,327,330],{"name":323,"orcid":9},"Jiradech Majandang",{"name":325,"orcid":326},"Patiwat Littidej","https:\u002F\u002Forcid.org\u002F0000-0002-1024-547X",{"name":328,"orcid":329},"Benjamabhorn Pumhirunroj","https:\u002F\u002Forcid.org\u002F0009-0009-6607-5594",{"name":331,"orcid":332},"D. C. Slack","https:\u002F\u002Forcid.org\u002F0000-0003-0324-2163",{"tldr":334,"method":335,"finding":336,"direction":111,"opportunity":337},"用多时相遥感和机器学习评估泰国栖河流域土地利用变化、洪水暴露与植被胁迫的关系。","2015–2024年多时相遥感、LDD土地利用数据、随机森林、SHAP与空间统计","农地占84.2%，洪水频发于农地和水体，21.1%流域植被受胁迫，DEM是洪水主导因子。","可结合高分辨率时序遥感与作物模型，量化洪水胁迫对热带平原农业产量的级联影响。","2026-10-08T23:30:29.519607Z",{"id":340,"title":341,"url":342,"summary":343,"summary_zh":344,"content":9,"source_name":201,"source_url":342,"published_at":345,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":346,"score_detail":347,"sources":351,"tags":353,"primary_topic":358,"secondary_topics":359,"classification":360,"search_phrases":394,"slug":397,"view_count":106,"doi":398,"paper":399,"created_at":411},5563,"AI-Powered Early Detection of Crop Diseases and Pest Infestations: A New Paradigm for Plantation Risk Management","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531525","Abstract: Crop diseases and pest infestations remain persistent threats to plantation productivity, with pests estimated to destroy up to 40% of global crop production and plant diseases and invasive insects imposing substantial economic losses worldwide (Food and Agriculture Organization of the United Nations [FAO], 2019). Climate change is intensifying these risks by altering the geographic range, seasonality, and population dynamics of pathogens and pests (Gullino et al., 2021; Skendžić et al., 2021). This paper examines how artificial intelligence (AI) — including convolutional neural networks (CNNs), vision transformers (ViTs), unmanned aerial vehicle (UAV)-based multispectral and hyperspectral sensing, and Internet of Things (IoT)-enabled monitoring — can strengthen the early detection of biotic stress in plantation crops and thereby support plantation risk management. Drawing on recent peer-reviewed and industry literature, the paper synthesises evidence on detection performance, deployment modalities, and pathways for integration into enterprise risk-management systems. It proposes a conceptual framework in which AI-enabled early-warning systems operate as a continuous, spatially explicit risk-sensing layer linking agronomic observations with operational, financial, and insurance decision-making. AI can improve the timeliness, spatial precision, and consistency of surveillance; however, its risk-management value depends on field validation, representative training data, reliable infrastructure, affordable deployment, and appropriate governance. AI should therefore currently be positioned as decision support that augments, rather than replaces, agronomic expertise and ground-truthing. Keywords: artificial intelligence; plant disease detection; pest infestation; precision agriculture; plantation risk management; machine learning; remote sensing. Title: AI-Powered Early Detection of Crop Diseases and Pest Infestations: A New Paradigm for Plantation Risk Management Author: Dr. Geetha Govindan K Gopalakrishnan International Journal of Management and Commerce Innovations ISSN 2348-7585 (Online) Vol. 14, Issue 2, October 2026 - March 2027 Page No: 54-59 Research Publish Journals Website: www.researchpublish.com Published Date: 21-October-2026 DOI: https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531526 Paper Download Link (Source) https:\u002F\u002Fwww.researchpublish.com\u002Fpapers\u002Fai-powered-early-detection-of-crop-diseases-and-pest-infestations-a-new-paradigm-for-plantation-risk-management","摘要：作物病害与虫害侵染始终是种植园生产力的持续威胁，据估计，害虫每年可摧毁全球高达40%的作物产量，植物病害与入侵昆虫在全球范围内造成重大经济损失（联合国粮食及农业组织[FAO]，2019）。气候变化通过改变病原体与害虫的地理分布范围、季节性规律及种群动态，正在加剧这些风险（Gullino等，2021；Skendžić等，2021）。本文探讨人工智能（AI）——包括卷积神经网络（CNN）、视觉变换器（ViT）、基于无人机（UAV）的多光谱与高光谱遥感，以及物联网（IoT）赋能的监测技术——如何强化种植园作物生物胁迫的早期检测，从而支撑种植园风险管理。本文基于近期同行评审文献与行业文献，综合梳理了检测性能、部署模式及其融入企业风险管理系统的路径等方面的证据。文章提出一个概念框架，其中AI赋能的预警系统作为持续运行、空间显性的风险感知层，将农艺观测与运营、财务及保险决策相衔接。AI能够提升监测的及时性、空间精度与一致性；然而，其风险管理价值取决于田间验证、具有代表性的训练数据、可靠的基础设施、可负担的部署成本以及适当的治理。因此，当前应将AI定位为决策支持工具，用以增强而非替代农艺专业知识与实地核验。关键词：人工智能；植物病害检测；虫害侵染；精准农业；种植园风险管理；机器学习；遥感。题目：AI驱动的作物病害与虫害侵染早期检测：种植园风险管理的新范式。作者：Geetha Govindan K Gopalakrishnan博士。《国际管理与商业创新期刊》ISSN 2348-7585（在线）第14卷第2期，2026年10月—2027年3月，第54-59页。研究出版期刊网站：www.researchpublish.com。发布日期：2026年10月21日。DOI：https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531526。论文下载链接（来源）：https:\u002F\u002Fwww.researchpublish.com\u002Fpapers\u002Fai-powered-early-detection-of-crop-diseases-and-pest-infestations-a-new-paradigm-for-plantation-risk-ma","2026-10-21T00:00:00Z",56,{"impact":348,"substance":349,"depth":205,"authority":59,"freshness":275,"relevant":20,"comment":350},12,16,"综述性论文，系统梳理AI与遥感在作物病虫害早期预警中的应用并提出风险管理框架，有参考价值但方法创新与数据规模有限，信源为普通出版平台。",[352],{"name":201,"url":342},[354,355,28,356,357],"智慧农业","农业人工智能","风险管理","病虫害监测",{"code":31,"slug":32,"name":33,"path":34,"parent_code":9},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":361},{"candidates":362,"agricultural_evidence":45,"reason":70},[363,369,375,383,390],{"code":43,"score":13,"supported":45,"title":364,"abstract":365,"tags":368},[],[25,47,366,367],"artificial intelligence","decision support",[355],{"code":51,"score":13,"supported":45,"title":370,"abstract":371,"tags":374},[],[55,53,372,373],"multispectral","hyperspectral",[28],{"code":376,"score":275,"supported":45,"title":377,"abstract":378,"tags":382},"I5",[],[379,380,381],"物联网","internet of things","IoT",[],{"code":384,"score":141,"supported":45,"title":385,"abstract":386,"tags":389},"I1",[],[387,388],"精准农业","precision agriculture",[354],{"code":150,"score":59,"supported":45,"title":391,"abstract":392,"tags":393},[],[153,154],[],[395,396],"AI 作物病害 早期检测","无人机 多光谱 病虫害监测","AI作物病害早期检测-5563","10.5281\u002Fzenodo.23531525",{"doi":398,"openalex_id":400,"authors":401,"venue":201,"cited_by_count":106,"oa_url":342,"card":404,"direction":410,"ingested_from":113},"W7223085400",[402],{"name":403,"orcid":9},"Dr. Geetha Govindan K Gopalakrishnan",{"tldr":405,"method":406,"finding":407,"direction":408,"opportunity":409},"综述AI（CNN、ViT、无人机遥感、物联网）用于作物病虫害早期检测并融入种植园风险管理。","文献综述，整合CNN、ViT、无人机多\u002F高光谱与IoT监测的检测性能与部署方式。","AI可提升监测及时性与空间精度，但价值取决于田间验证、代表性数据与治理，应作决策支持而非替代专家。","农业人工智能与决策模型","可将AI预警层与保险、金融风控系统对接，研究跨域数据融合与可解释风险定价的空白。","智慧农业 \u002F 农业物联网","2026-10-22T11:20:24.428080Z",{"id":413,"title":414,"url":415,"summary":416,"summary_zh":417,"content":9,"source_name":418,"source_url":415,"published_at":419,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":420,"score_detail":421,"sources":425,"tags":427,"primary_topic":432,"secondary_topics":433,"classification":434,"search_phrases":447,"slug":450,"view_count":20,"doi":451,"paper":452,"created_at":473},4586,"Advanced Assessment of the Agro-Silvo-Pastoral Coherence in Transboundary Transitional Water Regions","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fland15101916","Transitional waters play a vital role in the ecological health and sustainability of coastal areas. According to European Union legislation, transitional waters are coastal water bodies influenced by freshwater flows but are partly saline due to their proximity to the sea. This study focuses on agricultural and forestry regions adjacent to transboundary transitional waters—i.e., areas where two or more countries share an estuary, tidal inlet, delta, or lagoon and have a contiguous border in their territorial waters. The objective of the study is to determine the patterns of riparian agroforestry around the transboundary transitional waters in Europe and the dependence of the sustainability of these agro-silvo-pastoral systems under stress on the overall cross-border coherence of the countries sharing these waters. The innovative methodology uses GIS and geostatistical interpretation to quantitatively interpret multi-layered spatial data from remote sensing and other sources. The article’s main finding is that in the direct catchment areas of 12 transboundary transitional waters in three European bioregions—the Baltic Sea, the Adriatic and Black Seas, and the Northeast Atlantic—the Dissimilarity Index values vary from 1.5% to 8.2% in different agroforestry systems.","过渡水域对沿海地区的生态健康与可持续性具有至关重要的作用。根据欧盟立法，过渡水域是受淡水径流影响但因靠近海洋而具有一定盐度的沿海水体。本研究聚焦于毗邻跨界过渡水域的农业与林业区域，即两个或多个国家共享同一河口、潮汐入口、三角洲或潟湖，且其领海边界相连的区域。本研究旨在确定欧洲跨界过渡水域周边河岸农林复合系统的格局，以及这些处于压力下的农林牧系统可持续性对共享这些水域的国家之间整体跨境协调性的依赖程度。该创新性方法采用地理信息系统（GIS）和地统计解译技术，对来自遥感及其他来源的多层空间数据进行定量解译。本文的主要发现是：在欧洲三个生物区——波罗的海、亚得里亚海与黑海以及东北大西洋——的12处跨界过渡水域的直接集水区内，不同农林复合系统的相异指数值在1.5%至8.2%之间变化。","Land","2026-10-09T00:00:00Z",72,{"impact":348,"substance":422,"depth":423,"authority":205,"freshness":275,"relevant":20,"comment":424},21,17,"方法新颖、数据扎实的欧洲跨境滨海农林牧系统遥感评估论文，对农业空间信息化有参考价值，但属区域案例研究，公共影响有限。",[426],{"name":418,"url":415},[28,428,429,430,431],"跨境流域","农林牧复合系统","GIS","滨海湿地",{"code":31,"slug":32,"name":33,"path":34,"parent_code":9},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":435},{"candidates":436,"agricultural_evidence":45,"reason":70},[437,441],{"code":51,"score":141,"supported":45,"title":438,"abstract":439,"tags":440},[],[55,53],[28],{"code":442,"score":141,"supported":45,"title":443,"abstract":444,"tags":446},"I4",[],[445,430],"地理信息",[430],[448,449],"欧洲 跨境过渡水域 农林复合","GIS 遥感 农林牧系统 一致性","欧洲跨境过渡水域农林复合-4586","10.3390\u002Fland15101916",{"doi":451,"openalex_id":453,"authors":454,"venue":418,"cited_by_count":106,"oa_url":415,"card":468,"direction":111,"ingested_from":113},"W7221179136",[455,457,459,462,465],{"name":456,"orcid":9},"Aistė Jurkienė",{"name":458,"orcid":9},"Egidijus Jurkus",{"name":460,"orcid":461},"Ramūnas Povilanskas","https:\u002F\u002Forcid.org\u002F0000-0003-0895-3917",{"name":463,"orcid":464},"Eduardas Spiriajevas","https:\u002F\u002Forcid.org\u002F0000-0002-4684-5979",{"name":466,"orcid":467},"Arvydas Urbis","https:\u002F\u002Forcid.org\u002F0000-0002-9641-9214",{"tldr":469,"method":470,"finding":471,"direction":111,"opportunity":472},"用GIS与地统计量化欧洲12处跨境过渡水域周边农林牧系统的一致性差异。","GIS与地统计解读遥感等多源多层空间数据，计算相异指数。","三大生物区12处水域集水区农林系统相异指数为1.5%至8.2%。","可拓展跨境流域农林牧系统一致性对生态可持续性的驱动机制与动态监测研究。","2026-10-10T23:30:27.974100Z",{"id":475,"title":476,"url":477,"summary":478,"summary_zh":479,"content":9,"source_name":480,"source_url":477,"published_at":481,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":482,"score_detail":483,"sources":485,"tags":487,"primary_topic":490,"secondary_topics":491,"classification":492,"search_phrases":518,"slug":521,"view_count":20,"doi":522,"paper":523,"created_at":541},4312,"Identification of Cultivable Wastelands in Meghalaya, India using Geospatial Technology for Agricultural Reclamation","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fjgeesi\u002F2026\u002Fv30i101131","Cultivable wastelands suitable for cultivation are a significant land resource that can be utilised to improve agricultural productivity and sustainable land management. The present study aimed to estimate the cultivable wastelands of Meghalaya using Remote Sensing and Geographic Information System (GIS) techniques and to assess the possibilities for reclaiming wastelands for agriculture. Multispectral satellite data from the LISS-IV sensor for the period 2025-2026 were processed to create a False Colour Composite (FCC), and Land Use\u002FLand Cover (LULC) classification was performed using visual image interpretation to distinguish the major cultivable wasteland categories: Dense Scrub Land, Sparse Scrub Land, Grasslands\u002FGrazing Lands, and Shifting Cultivation Area. About 340,673.36 ha, representing almost 15.2% of the total geographical area of Meghalaya, were identified as cultivable wastelands. Among the identified categories, Sparse Scrub Land occupied the largest area (206,595.18 ha), followed by Dense Scrub Land (99,488.89 ha), Shifting Cultivation Area (34,331.34 ha), and Grasslands\u002FGrazing Lands (257.94 ha). The distribution of these wastelands suggests considerable potential for reclamation through appropriate land restoration and sustainable management practices. The spatial database and thematic maps generated provide baseline information for land-use planning, resource management, and policy formulation aimed at enhancing agricultural productivity and the sustainable utilisation of wastelands in Meghalaya.","适宜耕作的 cultivable wasteland（可耕荒地）是一项重要的土地资源，可用于提高农业生产力和可持续土地管理。本研究旨在利用遥感（Remote Sensing）和地理信息系统（Geographic Information System, GIS）技术估算梅加拉亚邦的可耕荒地，并评估将荒地开垦用于农业的可能性。对2025—2026年LISS-IV传感器多光谱卫星数据进行处理，生成假彩色合成影像（False Colour Composite, FCC），并通过目视图像解译进行土地利用\u002F土地覆盖（Land Use\u002FLand Cover, LULC）分类，以区分主要可耕荒地类别：密灌丛地、疏灌丛地、草地\u002F放牧地以及轮垦区。约340,673.36公顷土地被识别为可耕荒地，占梅加拉亚邦总地理面积的近15.2%。在已识别类别中，疏灌丛地面积最大（206,595.18公顷），其次为密灌丛地（99,488.89公顷）、轮垦区（34,331.34公顷）和草地\u002F放牧地（257.94公顷）。这些荒地的分布表明，通过适当的土地恢复和可持续管理措施，具有相当大的开垦潜力。所生成的空间数据库和专题图件为土地利用规划、资源管理和政策制定提供了基础信息，旨在提高梅加拉亚邦的农业生产力和实现荒地的可持续利用。","Journal of Geography Environment and Earth Science International","2026-10-05T00:00:00Z",69,{"impact":348,"substance":127,"depth":349,"authority":348,"freshness":275,"relevant":20,"comment":484},"基于LISS-IV遥感与GIS的印度梅加拉亚邦可耕荒地清查，数据翔实、方法规范，对农业土地复垦与空间规划有参考价值，但属区域案例，公共影响有限。",[486],{"name":480,"url":477},[161,488,28,430,489],"耕地保护","土地整治",{"code":31,"slug":32,"name":33,"path":34,"parent_code":9},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":493},{"candidates":494,"agricultural_evidence":45,"reason":70},[495,499,504,509,514],{"code":51,"score":13,"supported":45,"title":496,"abstract":497,"tags":498},[],[55,53,372],[28],{"code":442,"score":13,"supported":45,"title":500,"abstract":501,"tags":503},[],[445,430,502],"geographic information",[430],{"code":505,"score":75,"supported":14,"title":506,"abstract":507,"tags":508},"H1",[],[],[489,488],{"code":510,"score":20,"supported":14,"title":511,"abstract":512,"tags":513},"G2",[],[],[489],{"code":157,"score":20,"supported":14,"title":515,"abstract":516,"tags":517},[],[],[161],[519,520],"Meghalaya 可耕荒地 遥感","LISS-IV 土地利用 分类","Meghalaya可耕荒地遥感-4312","10.9734\u002Fjgeesi\u002F2026\u002Fv30i101131",{"doi":522,"openalex_id":524,"authors":525,"venue":480,"cited_by_count":106,"oa_url":477,"card":536,"direction":111,"ingested_from":113},"W7220436664",[526,528,531,533],{"name":527,"orcid":9},"Ronak Meena",{"name":529,"orcid":530},"Chandan Goswami","https:\u002F\u002Forcid.org\u002F0000-0002-9487-5080",{"name":532,"orcid":9},"C. P. Suresh",{"name":534,"orcid":535},"Akmaul Hoque","https:\u002F\u002Forcid.org\u002F0009-0002-4475-566X",{"tldr":537,"method":538,"finding":539,"direction":111,"opportunity":540},"利用遥感与GIS识别印度梅加拉亚邦可耕荒地并评估其农业复垦潜力。","LISS-IV多光谱影像、假彩色合成与目视解译的土地利用分类。","识别出约34万公顷可耕荒地，占全邦15.2%，以稀疏灌木地为主。","可结合机器学习自动分类与土壤、气候数据，构建荒地复垦适宜性评价模型。","2026-10-06T23:30:20.340575Z"]