[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1441":3,"related-1441":73},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":9,"secondary_topics":31,"classification":32,"search_phrases":48,"slug":51,"view_count":52,"doi":53,"paper":54,"created_at":72},1441,"Feasibility of empirical remote sensing for estimating total nitrogen in the coral reef lagoon of Yoron Island, Japan","https:\u002F\u002Fdoi.org\u002F10.1080\u002F2150704x.2026.2725172","Land-derived nutrient enrichment is a major stressor affecting coral reef lagoon ecosystems, yet its spatial distribution remains poorly resolved. This study investigated the feasibility of using empirical remote sensing to estimate total nitrogen (T-N) concentrations in the eastern reef lagoon of Yoron Island, southwestern Japan, using Landsat imagery and limited in-situ observations. Empirical models were developed by testing multiple regression forms for individual spectral bands against five in-situ T-N measurements. Model performance was evaluated using the coefficient of determination (R2), F-test, t-test, and Root Mean Square Error (RMSE). Owing to the limited calibration dataset and the absence of an independent validation dataset, resulting model should be regarded as a preliminary, feasibility-level empirical relationship rather than a validated predictive model. Among the tested models, a quadratic polynomial based on Landsat-8 blue band reflectance showed the best relative performance and was applied to Landsat-8 imagery acquired in 2014, 2020, and 2023 to map the spatial distribution of T-N. Additional Landsat-5 TM and Landsat-7 ETM+ imagery (1984–2010) was used to provide qualitative historical context only because cross-sensor harmonization was not performed. Elevated T-N near groundwater discharge zones points to submarine groundwater discharge and runoff as key nutrient pathways into the lagoon. Though ecological impacts weren’t directly tested, the patterns flag areas of likely vulnerability, demonstrating that satellite-based modelling can map nutrient enrichment in shallow reef lagoons and setting up future integration with hydrological or ecological data.","陆源营养盐富集是影响珊瑚礁泻湖生态系统的主要压力因素之一，然而其空间分布仍未得到充分解析。本研究探讨了利用经验遥感方法估算日本西南部与论岛东部礁泻湖中总氮（T-N）浓度的可行性，所采用的数据为Landsat影像和有限的现场观测数据。通过针对单个光谱波段测试多种回归形式，并基于五次现场T-N测量数据，建立了经验模型。模型性能通过决定系数（R2）、F检验、t检验和均方根误差（RMSE）进行评估。由于校准数据集有限且缺乏独立的验证数据集，所得模型应被视为初步的、可行性层面的经验关系，而非经过验证的预测模型。在测试的模型中，基于Landsat-8蓝波段反射率的二次多项式模型表现出最佳的相对性能，并将其应用于2014年、2020年和2023年获取的Landsat-8影像，以绘制T-N的空间分布图。此外，由于未进行跨传感器协调处理，1984年至2010年的Landsat-5 TM和Landsat-7 ETM+影像仅用于提供定性的历史背景参考。地下水排放区附近T-N浓度升高表明，海底地下水排放和径流是营养盐进入泻湖的关键途径。尽管未直接测试生态影响，但这些分布模式标示了可能脆弱的区域，证明了基于卫星的建模能够绘制浅水礁泻湖中的营养盐富集情况，并为未来与水文或生态数据的整合奠定了基础。",null,"Remote Sensing Letters","2026-09-01T00:00:00Z","论文",10,false,63,{"impact":13,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},18,16,12,7,1,"研究探索利用Landsat影像估算珊瑚礁潟湖总氮浓度，方法可行但数据有限，为农业面源污染监测提供新思路。",[24],{"name":10,"url":6},[26,27,28,29,30],"农业遥感","珊瑚礁潟湖","氮营养盐","水质监测","日本",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":14,"reason":47},[38],{"code":39,"score":40,"supported":41,"title":42,"abstract":44,"tags":46},"I3",15,true,[43],"remote sensing",[45,43],"遥感",[26],"农业相关性需要复核",[49,50],"珊瑚礁潟湖 农业遥感 氮营养盐 水质监测","珊瑚礁潟湖 农业遥感","珊瑚礁潟湖农业遥感氮营养盐水质监测-1441",3,"10.1080\u002F2150704x.2026.2725172",{"doi":53,"openalex_id":55,"authors":56,"venue":10,"cited_by_count":63,"oa_url":64,"card":65,"direction":69,"ingested_from":71},"W7204879551",[57,60],{"name":58,"orcid":59},"Pingkan Mayestika Afgatiani","https:\u002F\u002Forcid.org\u002F0000-0003-2630-8137",{"name":61,"orcid":62},"Ryuichi Shinjo","https:\u002F\u002Forcid.org\u002F0000-0001-7087-8773",0,"https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F2150704X.2026.2725172?needAccess=true",{"tldr":66,"method":67,"finding":68,"direction":69,"opportunity":70},"研究利用Landsat影像和有限实测数据，评估经验遥感估算珊瑚礁泻湖总氮浓度的可行性。","使用Landsat影像和5个实测点，测试多种回归模型，基于蓝波段二次多项式建模。","蓝波段二次多项式模型表现最佳，成功绘制总氮空间分布，识别地下水排放区高值。","农业遥感与作物表型","可扩展至其他珊瑚礁区域，结合水文生态数据，或探索跨传感器校准以提高模型泛化能力。","openalex","2026-09-02T23:30:53.549888Z",{"total":74,"page":21,"page_size":74,"items":75},6,[76,182,245,325,376,446],{"id":77,"title":78,"url":79,"summary":80,"summary_zh":81,"content":9,"source_name":82,"source_url":79,"published_at":83,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":84,"score_detail":85,"sources":89,"tags":91,"primary_topic":96,"secondary_topics":102,"classification":114,"search_phrases":150,"slug":153,"view_count":63,"doi":154,"paper":155,"created_at":181},5573,"Improving alfalfa nitrogen estimation with canopy structural priors from DSSAT–PROSAIL and UAV hyperspectral data","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.eja.2026.128375","Accurate estimation of nitrogen (N) concentration in alfalfa ( Medicago sativa L .) supports forage quality assessment and harvest scheduling. Empirical remote sensing models often overfit because of confounding effects of three-dimensional canopy structure. Incorporating structural parameters can stabilize biochemical inversions, but concurrent in situ morphological measurements are difficult to acquire at scale. We developed the Morphology-Aware Latent Embedding Network (MALENet), a hybrid framework integrating physiological and morphological mechanisms with hyperspectral data. The Decision Support System for Agrotechnology Transfer (DSSAT), coupled with the PROSAIL radiative transfer model, generated a structurally rich synthetic hyperspectral prior. A convolutional neural network (CNN) with a Squeeze-and-Excitation (SE) attention mechanism, was pre-trained on these data to distill ecophysiological knowledge into a dense latent representation. This mechanistically derived embedding was then fused with empirical hyperspectral measurements collected under diverse field conditions. Evaluation using multi-year field quadrats in the Hexi Corridor showed improved predictive performance ( R² = 0.71, RMSE = 0.30%) relative to conventional machine learning and deep learning models (LASSO-ANN: R² = 0.61; CNN: R² = 0.66). Feature attribution indicated that MALENet used near-infrared multiple scattering (58.3% attribution) as a structural proxy to calibrate nitrogen concentration retrieval against the confounding effects of canopy architecture. Despite constraints imposed by sample size and PROSAIL's 1D canopy assumption, these findings demonstrate the potential of physically guided deep learning to use structural information for agricultural trait retrieval in precision forage management.","准确估算紫花苜蓿（Medicago sativa L.）的氮浓度有助于饲草品质评估和收获期安排。经验性遥感模型常因三维冠层结构的混杂效应而出现过拟合。引入结构参数可以稳定生化反演，但同步开展原位形态学测量难以大规模获取。我们开发了形态感知潜在嵌入网络（Morphology-Aware Latent Embedding Network，MALENet），这是一个将生理与形态机制同高光谱数据相结合的混合框架。农业技术转移决策支持系统（Decision Support System for Agrotechnology Transfer，DSSAT）与PROSAIL辐射传输模型耦合，生成了结构信息丰富的合成高光谱先验数据。在此基础上，预训练了一个带有挤压—激励（Squeeze-and-Excitation，SE）注意力机制的卷积神经网络（convolutional neural network，CNN），将生态生理学知识提炼为稠密潜在表征。随后，将这一机制性导出的嵌入与在不同田间条件下采集的经验性高光谱测量数据相融合。利用河西走廊多年田间样方进行的评估显示，相较于传统机器学习和深度学习模型（LASSO-ANN：R² = 0.61；CNN：R² = 0.66），预测性能有所提升（R² = 0.71，RMSE = 0.30%）。特征归因表明，MALENet利用近红外多次散射（归因占比58.3%）作为结构代理，以校准氮浓度反演，抵御冠层结构的混杂效应。尽管受样本量和PROSAIL一维冠层假设的限制，这些发现表明，物理引导的深度学习有潜力利用结构信息，在精准饲草管理中进行农业性状反演。","European Journal of Agronomy","2026-10-21T00:00:00Z",80,{"impact":18,"substance":86,"depth":17,"authority":87,"freshness":13,"relevant":21,"comment":88},22,14,"该研究将作物模型与辐射传输模型耦合生成结构先验，并结合无人机高光谱数据反演苜蓿氮素，方法新颖、田间验证扎实，对精准饲草管理有参考价值，值得进入每日精选。",[90],{"name":82,"url":79},[92,93,26,94,95],"智慧农业","农业人工智能","氮素监测","紫花苜蓿",{"code":97,"slug":98,"name":99,"path":100,"parent_code":101},"A7","forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping","A",[103,108],{"code":39,"slug":104,"name":105,"path":106,"parent_code":107},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"code":109,"slug":110,"name":111,"path":112,"parent_code":113},"C4","feed","饲料与饲草","\u002Ftopic\u002Flivestock\u002Ffeed","C",{"status":115,"locked":14,"version":34,"method":35,"evidence":116},"classified",{"candidates":117,"agricultural_evidence":41},[118,123,129,135,145],{"code":39,"score":17,"supported":41,"title":119,"abstract":121,"tags":122},[120],"hyperspectral",[45,43,120],[26],{"code":97,"score":40,"supported":41,"title":124,"abstract":126,"tags":128},[125],"alfalfa",[127,125],"苜蓿",[95],{"code":109,"score":13,"supported":41,"title":130,"abstract":131,"tags":134},[],[132,127,133],"饲草","forage",[95],{"code":136,"score":13,"supported":41,"title":137,"abstract":138,"tags":144},"I2",[],[139,140,141,142,143],"深度学习","机器学习","machine learning","deep learning","decision support",[93],{"code":146,"score":21,"supported":14,"title":147,"abstract":148,"tags":149},"I1",[],[],[92],[151,152],"DSSAT PROSAIL 苜蓿 氮素","无人机 高光谱 苜蓿","DSSATPROSAIL苜蓿氮素-5573","10.1016\u002Fj.eja.2026.128375",{"doi":154,"openalex_id":156,"authors":157,"venue":82,"cited_by_count":63,"oa_url":79,"card":176,"direction":69,"ingested_from":71},"W7223154576",[158,160,162,164,166,169,171,174],{"name":159,"orcid":9},"Shuai Fu",{"name":161,"orcid":9},"Yunhao Li",{"name":163,"orcid":9},"Chunli Miao",{"name":165,"orcid":9},"Jie Liu",{"name":167,"orcid":168},"Tiangang Liang","https:\u002F\u002Forcid.org\u002F0000-0001-5599-6587",{"name":170,"orcid":9},"Yongjia Hu",{"name":172,"orcid":173},"Qisheng Feng","https:\u002F\u002Forcid.org\u002F0009-0000-1391-9591",{"name":175,"orcid":9},"吴彩霞",{"tldr":177,"method":178,"finding":179,"direction":69,"opportunity":180},"构建MALENet融合DSSAT-PROSAIL结构先验与无人机高光谱，提升苜蓿氮浓度估算精度。","DSSAT耦合PROSAIL生成合成高光谱先验，预训练CNN-SE网络，再融合田","MALENet精度优于传统机器学习和深度学习模型，R²达0.71，近红外多次散射作为结构代理贡献58","可探索将作物模型结构先验迁移至其他牧草或作物生化参数反演，并缓解PROSAIL一维冠层假设局限。","2026-10-22T11:20:39.622844Z",{"id":183,"title":184,"url":185,"summary":186,"summary_zh":9,"content":9,"source_name":187,"source_url":9,"published_at":83,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":188,"score_detail":189,"sources":191,"tags":193,"primary_topic":197,"secondary_topics":202,"classification":210,"search_phrases":232,"slug":235,"view_count":21,"doi":9,"paper":236,"created_at":244},5535,"基于无人机多光谱图像与VGG21模型的小麦渍害调控效果识别","https:\u002F\u002Ffinance.sina.cn\u002F2026-10-16\u002Fdetail-inivmprm8363597.d.html","江苏省农科院农业信息研究所联合中国农科院环发所与湖北省农科院粮作所，在小麦拔节至抽穗、抽穗至灌浆两个阶段设置对照、渍水胁迫、硅肥与氨基酸调控试验，用大疆精灵4采集冠层多光谱影像。VGG21模型对硅肥调控识别正确率超91%，精度96.42%、召回率89.53%。","智慧农业（中英文）2026-10-21",82,{"impact":17,"substance":86,"depth":17,"authority":87,"freshness":13,"relevant":21,"comment":190},"省级科研机构联合攻关，方法新颖、数据扎实，对渍害遥感识别与调控评估有实质参考价值，值得入选。",[192],{"name":187,"url":185},[92,26,194,195,196],"作物表型","无人机多光谱","小麦渍害",{"code":198,"slug":199,"name":200,"path":201,"parent_code":101},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat",[203,204],{"code":39,"slug":104,"name":105,"path":106,"parent_code":107},{"code":205,"slug":206,"name":207,"path":208,"parent_code":209},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":115,"locked":14,"version":34,"method":35,"evidence":211},{"candidates":212,"agricultural_evidence":41},[213,218,222,228],{"code":198,"score":214,"supported":41,"title":215,"abstract":216,"tags":217},11,[200],[200],[],{"code":39,"score":214,"supported":41,"title":219,"abstract":220,"tags":221},[195],[],[194,26,195],{"code":205,"score":223,"supported":41,"title":224,"abstract":226,"tags":227},8,[225],"渍害",[],[],{"code":146,"score":21,"supported":14,"title":229,"abstract":230,"tags":231},[],[],[92],[233,234],"江苏省农科院 小麦渍害 多光谱","VGG21 小麦 硅肥调控","江苏省农科院小麦渍害多光谱-5535",{"doi":9,"openalex_id":9,"authors":237,"venue":9,"cited_by_count":63,"oa_url":9,"card":238,"direction":69,"ingested_from":243},[],{"tldr":239,"method":240,"finding":241,"direction":69,"opportunity":242},"用无人机多光谱图像和VGG21模型识别小麦渍害后硅肥与氨基酸调控效果。","大疆精灵4采集冠层多光谱影像，构建VGG21深度学习分类模型。","VGG21对硅肥调控识别正确率超91%，精度96.42%、召回率89.53%。","可扩展多生育期、多调控组合与轻量化模型，探索渍害胁迫等级与调控效果的同步识别。","agent","2026-10-22T00:07:10.070916Z",{"id":246,"title":247,"url":248,"summary":249,"summary_zh":250,"content":9,"source_name":251,"source_url":248,"published_at":252,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":253,"score_detail":254,"sources":258,"tags":260,"primary_topic":264,"secondary_topics":269,"classification":270,"search_phrases":289,"slug":292,"view_count":63,"doi":293,"paper":294,"created_at":324},5399,"Spatial Heterogeneity and Nonlinear Environmental Associations of Positive Climatic Moisture Deficit with Implications for Sustainable Water Resource Assessment: A Case Study of Anyang","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu182010595","Positive climatic moisture deficit (CMD+) characterizes the seasonal imbalance between precipitation supply and atmospheric water demand, yet its spatial heterogeneity and associations with environmental and human-related factors remain insufficiently understood. Taking Anyang, China, as a case study, we calculated annual CMD+ for 2000–2020 using 1 km monthly precipitation and potential evapotranspiration data and applied an extreme gradient boosting (XGBoost)–SHapley Additive exPlanations (SHAP) framework to the corresponding long-term mean fields to examine climatological spatial heterogeneity. Five predictors were considered, including elevation, normalized difference vegetation index (NDVI), irrigation fraction, built-up fraction, and population density. Nested spatial-block cross-validation showed good model generalization. Long-term mean CMD+ was generally lower in the western mountainous area and higher across the central and eastern plains. The digital elevation model (DEM) accounted for 66.10% of the normalized mean absolute SHAP importance, followed by NDVI (13.47%) and irrigation fraction (9.72%). A DEM-excluded sensitivity model retained a spatial cross-validated R2 of 0.781, compared with 0.898 for the full model. SHAP dependence analysis revealed pronounced nonlinear responses, with primary local model response transition points at approximately 297.1 m for DEM, 0.812 for NDVI, and 23.1% for irrigation fraction. The strongest pairwise SHAP interaction occurred between NDVI and irrigation fraction, accounting for 21.87% of total pairwise interaction strength. These results show that the spatial heterogeneity of CMD+ in Anyang is closely associated with topographic background, while vegetation status and irrigated agriculture provide additional nonlinear and interactive explanatory information. The identified spatial patterns and model-response transitions provide spatial information for understanding regional climatic moisture deficits and for subsequent sustainable water resource assessment.","正向气候水分亏缺（CMD+）刻画了降水供给与大气水分需求之间的季节性失衡，但其空间异质性及其与环境因素和人为因素的关联仍缺乏充分认识。以中国安阳为案例区，本研究利用1 km月降水量与潜在蒸散量数据计算了2000—2020年的年CMD+，并将极端梯度提升（XGBoost）—SHapley加性解释（SHAP）框架应用于相应的长期均值场，以考察气候态空间异质性。研究考虑了5个预测因子，包括高程、归一化植被指数（NDVI）、灌溉面积比例、建成区比例和人口密度。嵌套空间分块交叉验证表明模型具有良好的泛化能力。长期平均CMD+总体表现为西部山区较低、中部和东部平原较高。数字高程模型（DEM）占归一化平均绝对SHAP重要性的66.10%，其次为NDVI（13.47%）和灌溉面积比例（9.72%）。剔除DEM的敏感性模型保持了0.781的空间交叉验证R²，而完整模型为0.898。SHAP依赖分析揭示了显著的非线性响应，主要局部模型响应转变点分别约为DEM 297.1 m、NDVI 0.812和灌溉面积比例23.1%。最强的成对SHAP交互作用出现在NDVI与灌溉面积比例之间，占总成对交互强度的21.87%。结果表明，安阳CMD+的空间异质性与地形背景密切相关，而植被状况和灌溉农业提供了额外的非线性和交互解释信息。所识别的空间格局和模型响应转变点为理解区域气候水分亏缺及后续可持续水资源评估提供了空间信息。","Sustainability","2026-10-19T00:00:00Z",64,{"impact":223,"substance":17,"depth":18,"authority":255,"freshness":256,"relevant":21,"comment":257},13,9,"以安阳为案例，用遥感与XGBoost-SHAP揭示气候水分亏缺的空间异质性，方法有新意、结论可靠，但属区域案例研究，公共影响有限。",[259],{"name":251,"url":248},[26,140,261,262,263],"灌溉管理","水资源评估","气候干旱",{"code":265,"slug":266,"name":267,"path":268,"parent_code":209},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",[],{"status":115,"locked":14,"version":34,"method":35,"evidence":271},{"candidates":272,"agricultural_evidence":41},[273,281,285],{"code":265,"score":17,"supported":41,"title":274,"abstract":276,"tags":280},[275],"water resource",[277,278,266,275,279],"灌溉","水资源","evapotranspiration",[261],{"code":136,"score":21,"supported":14,"title":282,"abstract":283,"tags":284},[],[],[140],{"code":39,"score":21,"supported":14,"title":286,"abstract":287,"tags":288},[],[],[26],[290,291],"安阳 气候水分亏缺 遥感","XGBoost SHAP 水资源评估","安阳气候水分亏缺遥感-5399","10.3390\u002Fsu182010595",{"doi":293,"openalex_id":295,"authors":296,"venue":251,"cited_by_count":63,"oa_url":248,"card":318,"direction":323,"ingested_from":71},"W7222973420",[297,299,302,305,308,310,312,314,316],{"name":298,"orcid":9},"Yan Li",{"name":300,"orcid":301},"Xiaohua Yang","https:\u002F\u002Forcid.org\u002F0000-0001-8387-5213",{"name":303,"orcid":304},"Dehui Bian","https:\u002F\u002Forcid.org\u002F0000-0001-6921-1875",{"name":306,"orcid":307},"Qi Wang","https:\u002F\u002Forcid.org\u002F0000-0002-3156-9246",{"name":309,"orcid":9},"Lequn Gu",{"name":311,"orcid":9},"Ailing Zhang",{"name":313,"orcid":9},"Wei Gao",{"name":315,"orcid":9},"Wenjie Qi",{"name":317,"orcid":9},"Yayun Ma",{"tldr":319,"method":320,"finding":321,"direction":69,"opportunity":322},"以安阳为例，用XGBoost-SHAP分析2000—2020年气候水分亏缺的空间异质性及其环境关联。","1 km月降水与潜在蒸散数据计算CMD+，结合XGBoost-SHAP与嵌套空间","CMD+西部山区低、中东部平原高；高程贡献66.1%，NDVI与灌溉比例呈显著非线性交互。","可引入多源遥感与作物种植结构，探究CMD+非线性阈值对灌溉需水与水资源配置的传导机制。","数字乡村与农业信息化","2026-10-20T23:30:23.927575Z",{"id":326,"title":327,"url":328,"summary":329,"summary_zh":9,"content":9,"source_name":330,"source_url":9,"published_at":331,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":332,"score_detail":333,"sources":337,"tags":339,"primary_topic":340,"secondary_topics":341,"classification":344,"search_phrases":364,"slug":367,"view_count":63,"doi":9,"paper":368,"created_at":375},5296,"雷添杰研究员团队《基于无人机多光谱图像和VGG21模型的小麦渍害调控效果识别》","https:\u002F\u002Ffinance.sina.com.cn\u002Froll\u002F2026-10-16\u002Fdoc-inivmprm8363597.shtml","《智慧农业(中英文)》2026年第8卷第4期刊载梁万杰、刘晓君、雷添杰等论文,在小麦拔节—抽穗和抽穗—灌浆两个生长阶段进行对照、渍水胁迫、硅肥调控、氨基酸调控控制试验;利用大疆精灵4多光谱无人机采集小麦冠层多光谱图像建立数据集,基于VGG21模型构建识别模型,发现硅肥调控样本识别性能最好,识别精度最高,具良好鲁棒性和可扩展性,可为小麦渍害综合防控提供理论与技术支持。","智慧农业(中英文)(2026,8(4))","2026-10-16T00:00:00Z",76,{"impact":18,"substance":334,"depth":335,"authority":87,"freshness":223,"relevant":21,"comment":336},21,17,"核心期刊论文，方法新颖、结论可靠，对小麦渍害防控有实用价值，值得进入每日精选。",[338],{"name":330,"url":328},[92,26,194,195,196],{"code":198,"slug":199,"name":200,"path":201,"parent_code":101},[342,343],{"code":205,"slug":206,"name":207,"path":208,"parent_code":209},{"code":39,"slug":104,"name":105,"path":106,"parent_code":107},{"status":115,"locked":14,"version":34,"method":35,"evidence":345},{"candidates":346,"agricultural_evidence":41},[347,351,355,359],{"code":198,"score":214,"supported":41,"title":348,"abstract":349,"tags":350},[200],[200],[],{"code":205,"score":214,"supported":41,"title":352,"abstract":353,"tags":354},[225],[225],[],{"code":39,"score":214,"supported":41,"title":356,"abstract":357,"tags":358},[195],[],[194,26,195],{"code":146,"score":360,"supported":41,"title":361,"abstract":362,"tags":363},4,[],[92],[92],[365,366],"雷添杰 小麦渍害 无人机多光谱","VGG21 小麦渍害 识别模型","雷添杰小麦渍害无人机多光谱-5296",{"doi":9,"openalex_id":9,"authors":369,"venue":9,"cited_by_count":63,"oa_url":9,"card":370,"direction":69,"ingested_from":243},[],{"tldr":371,"method":372,"finding":373,"direction":69,"opportunity":374},"用无人机多光谱图像和VGG21模型识别小麦渍害调控效果。","大疆精灵4采集冠层多光谱图像，构建VGG21识别模型。","硅肥调控样本识别性能最好、精度最高，模型鲁棒可扩展。","可探索多生育期多光谱特征与不同调控措施的耦合机制，并扩展到其他作物渍害监测。","2026-10-19T00:07:48.071055Z",{"id":377,"title":378,"url":379,"summary":380,"summary_zh":381,"content":9,"source_name":382,"source_url":379,"published_at":383,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":84,"score_detail":384,"sources":386,"tags":388,"primary_topic":9,"secondary_topics":392,"classification":393,"search_phrases":405,"slug":408,"view_count":360,"doi":409,"paper":410,"created_at":445},5174,"Remote sensing of aquatic nutrients from proxy estimation to integration with process models","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs43247-026-04152-0","Leveraging its capability for large-scale, repeated monitoring, remote sensing has increasingly supported nitrogen and phosphorus estimation in surface waters. However, the evolution of this field from proxy-based nutrient retrieval to integration with process-based models remains insufficiently synthesized. Here we review remote sensing approaches for estimating aquatic nutrients, with emphasis on their roles in supporting process-based modeling, and compare their methodologies, applications, and limitations. At the watershed scale, remote sensing supports land use mapping and pollution load estimation, identifying nutrient sources and constraining transport simulations. At the lake scale, it provides continuous observations and water quality products that inform hydrodynamic and water quality models. At the field and interface scales, remote sensing helps identify hotspots of nutrient loss by capturing the heterogeneity of management practices. Despite these advances, nutrient estimation remains constrained by proxy relationships and spatial scale. Future research should integrate remote sensing with hydrodynamic and biogeochemical models to constrain nutrient fluxes and improve mechanistic understanding of aquatic nutrient cycling. At the watershed scale, remote sensing maps land use and estimates pollution loads; at the lake scale, it provides water quality data for models; at field and interface scales, it identifies nutrient-loss hotspots, based on a review of remote sensing to approaches aquatic nutrients.","凭借大规模、重复监测的能力，遥感日益支撑地表水中氮磷的估算。然而，该领域从基于代理指标的养分反演向与过程模型融合的演进仍缺乏充分综合。本文综述了用于估算水生养分的遥感方法，重点阐述其在支撑过程模型方面的作用，并比较其方法、应用与局限。在流域尺度，遥感支撑土地利用制图与污染负荷估算，识别养分来源并约束输移模拟。在湖泊尺度，遥感提供连续观测与水质产品，为水动力与水质模型提供输入。在田块与界面尺度，遥感通过捕捉管理措施的异质性，帮助识别养分流失热点。尽管取得上述进展，养分估算仍受代理关系与空间尺度的制约。未来研究应将遥感与水动力及生物地球化学模型相结合，以约束养分通量并提升对水生养分循环机理的认识。在流域尺度，遥感可制图土地利用并估算污染负荷；在湖泊尺度，遥感为模型提供水质数据；在田块与界面尺度，遥感可识别养分流失热点——上述结论基于对水生养分遥感方法的综述。","Communications Earth & Environment","2026-10-15T00:00:00Z",{"impact":17,"substance":86,"depth":17,"authority":87,"freshness":223,"relevant":21,"comment":385},"系统综述遥感估算水体氮磷及其与过程模型耦合的进展与局限，方法学价值明确，但属综述类论文，公共影响有限。",[387],{"name":382,"url":379},[45,29,389,390,391],"水环境","面源污染","氮磷养分",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":394},{"candidates":395,"agricultural_evidence":14,"reason":47},[396,400],{"code":39,"score":40,"supported":41,"title":397,"abstract":398,"tags":399},[43],[45,43],[45],{"code":401,"score":21,"supported":14,"title":402,"abstract":403,"tags":404},"H5",[],[],[390],[406,407],"遥感 水体营养盐 估算","氮磷 遥感 过程模型","遥感水体营养盐估算-5174","10.1038\u002Fs43247-026-04152-0",{"doi":409,"openalex_id":411,"authors":412,"venue":382,"cited_by_count":63,"oa_url":439,"card":440,"direction":69,"ingested_from":71},"W7221994887",[413,415,418,421,423,425,427,429,431,433,435,437],{"name":414,"orcid":9},"Chenyi Shi",{"name":416,"orcid":417},"Xihua Wang","https:\u002F\u002Forcid.org\u002F0000-0002-4795-304X",{"name":419,"orcid":420},"Y. Jun Xu","https:\u002F\u002Forcid.org\u002F0000-0003-3646-626X",{"name":422,"orcid":9},"Xingyu Zhu",{"name":424,"orcid":9},"Shunqing Jia",{"name":426,"orcid":9},"Zejun Liu",{"name":428,"orcid":9},"Boyang Mao",{"name":430,"orcid":9},"Yan Dai",{"name":432,"orcid":9},"Yanxin Rong",{"name":434,"orcid":9},"Qinya Lv",{"name":436,"orcid":9},"Xuming Ji",{"name":438,"orcid":9},"Chengming Luo","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs43247-026-04152-0.pdf",{"tldr":441,"method":442,"finding":443,"direction":69,"opportunity":444},"综述遥感估算水体氮磷的方法，及其与过程模型在多尺度上的集成。","文献综述，按流域、湖泊、田块界面三个尺度比较遥感方法与局限。","遥感仍受代理关系与空间尺度制约，需与水文和生物地球化学模型集成。","可研究遥感与过程模型耦合以约束营养盐通量，填补多尺度集成与机理理解的空白。","2026-10-17T23:30:29.898801Z",{"id":447,"title":448,"url":449,"summary":450,"summary_zh":451,"content":9,"source_name":452,"source_url":449,"published_at":331,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":453,"score_detail":454,"sources":457,"tags":459,"primary_topic":462,"secondary_topics":463,"classification":468,"search_phrases":491,"slug":494,"view_count":21,"doi":495,"paper":496,"created_at":518},5165,"Proximal Remote Sensing of Oil Palm Basal Stem Rot Using Deep Learning Vision Algorithms and Ground-based Images","https:\u002F\u002Fdoi.org\u002F10.22266\u002Fijies2026.1130.31","Basal Stem Rot (BSR), caused by Ganoderma boninense, threatens oil palm productivity in Southeast Asia.This study develops and benchmarks a low-cost remote sensing framework for BSR severity diagnosis from smartphone-captured RGB trunk images, evaluating two pathways for future field deployment.The first pairs YOLO detection with a Random Forest classifier: YOLO26n gave the best accuracy-to-cost trade-off among three nanovariants for localizing BSR symptoms (mushroom, rot, white-button), and the end-to-end YOLO26n+RF pipeline achieved 0.83 accuracy and macro-F1 of 0.79 across Healthy, Mild, Moderate, and Severe classes.Rot was consistently the hardest symptom to localize, owing to its diffuse, boundary-ambiguous appearance.The second pathway trained four image classifiers (EfficientNet-B0, MobileNetV2, Swin-Tiny, and ConvNeXt-V2-Tiny) directly on severity labels, with ConvNeXt-V2-Tiny performing best (0.83 accuracy, 0.83 macro-F1).Both pathways performed well at severity extremes but struggled with Moderate, reflecting visual overlap between stages.SHAP analysis identified rot-area ratio and fruiting-body count as top predictors.These offline results show accurate BSR diagnosis is potentially achievable from smartphone imagery, laying groundwork for future on-device deployment.","由灵芝（Ganoderma boninense）引起的基茎腐病（Basal Stem Rot, BSR）威胁着东南亚油棕的生产力。本研究开发并基准测试了一个低成本遥感框架，用于从智能手机拍摄的RGB树干图像中诊断基茎腐病的严重程度，并评估了未来田间部署的两条技术路径。第一条路径将YOLO检测与随机森林分类器相结合：在三种纳米变体中，YOLO26n在定位基茎腐病症状（蘑菇、腐烂、白钮）方面实现了最佳的精度-成本权衡，端到端的YOLO26n+RF流程在健康、轻度、中度和重度四个类别上达到了0.83的准确率和0.79的宏F1值。腐烂始终是最难定位的症状，原因在于其弥散且边界模糊的外观。第二条路径直接在严重程度标签上训练了四个图像分类器（EfficientNet-B0、MobileNetV2、Swin-Tiny和ConvNeXt-V2-Tiny），其中ConvNeXt-V2-Tiny表现最佳（准确率0.83，宏F1值0.83）。两条路径在严重程度极端类别上表现良好，但在中度类别上表现欠佳，反映出各阶段之间存在视觉重叠。SHAP分析确定腐烂面积比和子实体数量为最重要的预测因子。这些离线结果表明，利用智能手机图像有望实现准确的基茎腐病诊断，为未来设备端部署奠定了基础。","International journal of intelligent engineering and systems",69,{"impact":19,"substance":455,"depth":18,"authority":19,"freshness":256,"relevant":21,"comment":456},20,"该研究利用智能手机图像与深度学习实现油棕基腐病严重度诊断，方法新颖、结论可靠，但属细分作物研究，产业影响有限，适合作为技术类精选。",[458],{"name":452,"url":449},[92,26,139,460,461],"油棕","病害诊断",{"code":39,"slug":104,"name":105,"path":106,"parent_code":107},[464],{"code":136,"slug":465,"name":466,"path":467,"parent_code":107},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":115,"locked":14,"version":34,"method":35,"evidence":469},{"candidates":470,"agricultural_evidence":41},[471,475,480,487],{"code":39,"score":40,"supported":41,"title":472,"abstract":473,"tags":474},[43],[45,43],[26],{"code":136,"score":19,"supported":41,"title":476,"abstract":477,"tags":479},[142],[478],"YOLO",[139],{"code":481,"score":74,"supported":41,"title":482,"abstract":483,"tags":486},"B5",[],[484,485],"蘑菇","mushroom",[],{"code":146,"score":21,"supported":14,"title":488,"abstract":489,"tags":490},[],[],[92],[492,493],"油棕 基腐病 遥感","Ganoderma boninense 智能手机 检测","油棕基腐病遥感-5165","10.22266\u002Fijies2026.1130.31",{"doi":495,"openalex_id":497,"authors":498,"venue":452,"cited_by_count":63,"oa_url":449,"card":513,"direction":69,"ingested_from":71},"W7222626135",[499,502,505,507,510],{"name":500,"orcid":501},"Wan Siu Hong","https:\u002F\u002Forcid.org\u002F0000-0002-8254-4962",{"name":503,"orcid":504},"Jonathan Yong Chung Ee","https:\u002F\u002Forcid.org\u002F0000-0003-4079-8005",{"name":506,"orcid":9},"Jin Yuan Chan",{"name":508,"orcid":509},"Mohamad Izzuddin Anuar","https:\u002F\u002Forcid.org\u002F0000-0002-4929-5175",{"name":511,"orcid":512},"Mahmud Iwan Solihin","https:\u002F\u002Forcid.org\u002F0000-0002-5293-7466",{"tldr":514,"method":515,"finding":516,"direction":69,"opportunity":517},"用手机拍摄油棕树干图像，结合深度学习检测与分类，实现基腐病严重度诊断。","YOLO26n+随机森林与四种图像分类器，基于手机RGB图像，SHAP解释。","两条路径准确率均约0.83，中度病级最难区分，腐区比例与子实体数为关键预测因子。","可探索轻量化模型在手机端离线部署，并扩充多地区、多品种数据以提升中度病级辨识。","2026-10-17T23:30:28.307953Z"]