[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1535":3,"related-1535":69},{"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":30,"classification":31,"search_phrases":48,"slug":51,"view_count":22,"doi":52,"paper":53,"created_at":68},1535,"Field-level spectral support auditing of legacy land-use inventories using PRISMA hyperspectral data","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.rsase.2026.102232","Field-level spectral support auditing of legacy land-use inventories using PRISMA hyperspectral data。Remote Sensing Applications Society and Environment","使用PRISMA高光谱数据对历史土地利用清单进行田间尺度光谱支持审计。《遥感应用：社会与环境》",null,"Remote Sensing Applications Society and Environment","2026-09-01T00:00:00Z","论文",10,false,72,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},15,20,18,12,7,1,"利用PRISMA高光谱数据审计历史土地利用清单，方法新颖，对农业资源管理有参考价值。",[25],{"name":10,"url":6},[27,28,29],"农业遥感","高光谱","土地调查",[],{"status":32,"locked":14,"version":33,"method":34,"evidence":35},"needs_review","2026-10-22.2","rules",{"candidates":36,"agricultural_evidence":14,"reason":47},[37],{"code":38,"score":39,"supported":40,"title":41,"abstract":43,"tags":46},"I3",19,true,[42],"hyperspectral",[44,45,42],"遥感","remote sensing",[27,28],"农业相关性需要复核",[49,50],"农业遥感 土地调查 高光谱","农业遥感 土地调查","农业遥感土地调查高光谱-1535","10.1016\u002Fj.rsase.2026.102232",{"doi":52,"openalex_id":54,"authors":55,"venue":10,"cited_by_count":59,"oa_url":60,"card":61,"direction":65,"ingested_from":67},"W7164903955",[56],{"name":57,"orcid":58},"Andrea Guerriero","https:\u002F\u002Forcid.org\u002F0000-0001-6728-6988",0,"https:\u002F\u002Fdoi.org\u002F10.2139\u002Fssrn.6955539",{"tldr":62,"method":63,"finding":64,"direction":65,"opportunity":66},"利用PRISMA高光谱数据对历史土地利用清单进行田间尺度光谱支持审计。","PRISMA高光谱遥感数据与历史土地利用清单对比分析。","高光谱数据可有效审计历史土地利用清单的田间尺度准确性。","农业遥感与作物表型","可探索将高光谱审计方法应用于其他区域或历史数据，提高土地利用变化监测精度。","openalex","2026-09-03T23:30:30.357156Z",{"total":70,"page":22,"page_size":70,"items":71},6,[72,171,252,345,406,484],{"id":73,"title":74,"url":75,"summary":76,"summary_zh":77,"content":9,"source_name":78,"source_url":75,"published_at":79,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":80,"score_detail":81,"sources":86,"tags":88,"primary_topic":92,"secondary_topics":98,"classification":103,"search_phrases":142,"slug":145,"view_count":146,"doi":147,"paper":148,"created_at":170},4079,"Pixel-level high-throughput estimation of crop photosynthetic phenotyping parameters using a multi-task deep learning framework","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.aiia.2026.08.014","Hyperspectral remote sensing holds promise for estimating crop physiological and biochemical traits, yet conventional approaches are often constrained by small sample sizes, limited accuracy in multi-trait retrieval, and poor robustness. To alleviate these challenges, we present an integrated framework that combines a pixel-wise dataset construction strategy with a multi-task deep learning architecture for estimating photosynthetic phenotyping parameters (PPPs)—a suite of traits encompassing gas exchange, pigment composition, and structural properties. Specifically, under controlled experimental conditions, we developed MTI-Net, a lightweight multi-task inversion network that simultaneously estimates multiple PPPs in rice and tomato with high precision ( R 2 > 0.90). Since physiological ground-truth measurements were obtained at the leaf level, the pixel-wise predictions should be interpreted as weakly supervised, model-derived spatial estimates or relative distributions rather than directly validated pixel-level physiological measurements. This methodological framework demonstrated stable performance across data partitions and random initializations, and maintained stable performance when applied to pixel-averaged spectra. A complementary differentiable band selection module (DRS-Net) further reduced spectral dimensionality to 40 informative bands while maintaining retrieval accuracy above 0.75, suggesting potential pathways for simplified sensor design. Importantly, the estimated PPPs revealed biologically interpretable patterns in tomato, including stage-dependent dynamics, irradiance-driven shifts, and canopy-layer differentiation—where middle leaves dominated early photosynthesis and upper leaves prevailed at later stages. Collectively, this study provides a methodological foundation for proximal hyperspectral phenotyping under specific environments. While further validation in diverse field conditions is required, the framework demonstrates potential for high-throughput multi-parameter estimation and provides a methodological basis for future cost-effective hyperspectral systems.","高光谱遥感在估算作物生理生化性状方面具有潜力，但传统方法常受限于样本量小、多性状反演精度有限以及鲁棒性差等问题。为缓解这些挑战，我们提出了一种集成框架，将像元级数据集构建策略与多任务深度学习架构相结合，用于估算光合表型参数（photosynthetic phenotyping parameters, PPPs）——一组涵盖气体交换、色素组成和结构特性的性状。具体而言，在受控实验条件下，我们开发了MTI-Net，一种轻量级多任务反演网络，可同时高精度估算水稻和番茄的多个PPPs（R² > 0.90）。由于生理真值测量是在叶片尺度获得的，像元级预测应被解释为弱监督的、模型导出的空间估计或相对分布，而非直接验证的像元级生理测量。该方法框架在数据划分和随机初始化下表现出稳定性能，并在应用于像元平均光谱时保持稳定性能。一个互补的可微分波段选择模块（DRS-Net）进一步将光谱维度降至40个信息波段，同时保持反演精度高于0.75，提示了简化传感器设计的潜在路径。重要的是，估算的PPPs在番茄中揭示了具有生物学可解释性的模式，包括阶段依赖性动态、辐照度驱动的变化以及冠层分层分化——其中中层叶片在早期光合作用中占主导，而上层叶片在后期阶段占优势。总体而言，本研究为特定环境下的近地高光谱表型分析提供了方法学基础。尽管需要在多样化田间条件下进一步验证，但该框架展示了高通量多参数估算的潜力，并为未来经济高效的高光谱系统提供了方法学依据。","Artificial Intelligence in Agriculture","2026-10-01T00:00:00Z",80,{"impact":19,"substance":82,"depth":19,"authority":83,"freshness":84,"relevant":22,"comment":85},22,14,8,"方法新颖、结论可靠的高光谱作物表型多任务反演研究，对智慧农业遥感监测有参考价值。",[87],{"name":78,"url":75},[89,27,90,28,91],"智慧农业","深度学习","作物表型",{"code":93,"slug":94,"name":95,"path":96,"parent_code":97},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[99],{"code":38,"slug":100,"name":101,"path":102,"parent_code":97},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":104,"locked":14,"version":33,"method":34,"evidence":105},"classified",{"candidates":106,"agricultural_evidence":40},[107,112,116,123,130,137],{"code":93,"score":17,"supported":40,"title":108,"abstract":110,"tags":111},[109],"deep learning",[90,109],[90],{"code":38,"score":20,"supported":40,"title":113,"abstract":114,"tags":115},[],[44,45,42],[91,27,28],{"code":117,"score":70,"supported":40,"title":118,"abstract":119,"tags":122},"A1",[],[120,121],"水稻","rice",[],{"code":124,"score":70,"supported":40,"title":125,"abstract":126,"tags":129},"B1",[],[127,128],"番茄","tomato",[],{"code":131,"score":132,"supported":40,"title":133,"abstract":134,"tags":136},"I7",3,[],[135],"methodological framework",[],{"code":138,"score":22,"supported":14,"title":139,"abstract":140,"tags":141},"I1",[],[],[89],[143,144],"MTI-Net 高光谱 作物表型","水稻 番茄 光合表型 高光谱","MTI-Net高光谱作物表型-4079",5,"10.1016\u002Fj.aiia.2026.08.014",{"doi":147,"openalex_id":149,"authors":150,"venue":78,"cited_by_count":59,"oa_url":75,"card":165,"direction":65,"ingested_from":67},"W7215067733",[151,153,155,158,161,163],{"name":152,"orcid":9},"Mengqi Zhang",{"name":154,"orcid":9},"Jiaren Zhou",{"name":156,"orcid":157},"Qingfeng Song","https:\u002F\u002Forcid.org\u002F0000-0003-4314-6897",{"name":159,"orcid":160},"Man Zhang","https:\u002F\u002Forcid.org\u002F0009-0007-4481-0514",{"name":162,"orcid":9},"Xinguang Zhu",{"name":164,"orcid":9},"Minjuan Wang",{"tldr":166,"method":167,"finding":168,"direction":65,"opportunity":169},"提出像素级多任务深度学习框架，从高光谱数据高通量估算作物光合表型参数。","构建像素级数据集，设计轻量多任务网络MTI-Net及可微波段选择模块DRS-Ne","MTI-Net对水稻和番茄多个光合参数估算R²>0.90，DRS-Net降至40波段仍保持精度>0.","可探索弱监督像素级预测的田间验证，以及低成本高光谱传感器与多任务模型的联合优化。","2026-10-03T23:30:24.577846Z",{"id":172,"title":173,"url":174,"summary":175,"summary_zh":176,"content":9,"source_name":177,"source_url":174,"published_at":178,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":179,"score_detail":180,"sources":184,"tags":186,"primary_topic":190,"secondary_topics":191,"classification":192,"search_phrases":200,"slug":203,"view_count":204,"doi":205,"paper":206,"created_at":251},2797,"Generation of representative datasets of future Copernicus Sentinel Expansion Mission Data (hyperspectral, thermal and L-band) as basis for innovative agricultural products","https:\u002F\u002Fdoi.org\u002F10.62880\u002Frars26005","The Copernicus Sentinel Expansion Missions will provide new and unique remote sensing data. To enable rapid use of real data as soon as it becomes available, it is essential to generate comparable synthetic data in advance. This study proposes a novel data set for three of the upcoming sensors. CHIME hyperspectral data are generated by inverting multispectral reflectance data from Sentinel-2 time series by radiative transfer modelling to retrieve land surface parameters and subsequently forward-simulating bottom-of-atmosphere reflectance using expected CHIME sensor characteristics. Future LSTM land surface temperature data are derived from Sentinel-3 and Sentinel-2 data using the Sen-ET workflow with spatial data mining sharpening. L-band backscatter and coherence data for ROSE-L are simulated using SAOCOM-1 data, which are transformed to match the expected spatial and radiometric characteristics. The novel data set is available for three areas of interest (AOIs) defined by Sentinel-2 tiles located in Germany, Belgium, and Estonia. A validation of simulated CHIME data using existing comparable sensor data from EnMAP showed a high spectral correlation with an average RMSE of 6.154 [%] and a correlation of 0.924 for the German AOI in 2024. This publicly available, unique and well validated dataset already enables the preparation and development of future products and services across a wide range of application areas based on data from the Sentinel Expansion Mission. Due to the high data availability resulting from extensive two-year time series, as well as the various AOIs, future products can already be tested for their temporal and spatial transferability.","哥白尼哨兵扩展任务将提供新的独特遥感数据。为了在真实数据可用时尽快加以利用，必须提前生成可比的合成数据。本研究为其中三个即将发射的传感器提出了一个新的数据集。CHIME高光谱数据通过辐射传输建模对来自Sentinel-2时间序列的多光谱反射率数据进行反演，以获取地表参数，随后利用预期的CHIME传感器特征正向模拟大气底层反射率来生成。未来的LSTM地表温度数据利用Sen-ET工作流结合空间数据挖掘锐化方法，从Sentinel-3和Sentinel-2数据中导出。ROSE-L的L波段后向散射和相干性数据使用SAOCOM-1数据进行模拟，并将其转换为符合预期空间和辐射特征的形式。该新数据集可用于三个感兴趣区域（AOIs），分别位于德国、比利时和爱沙尼亚的Sentinel-2瓦片范围内。利用EnMAP现有可比传感器数据对模拟CHIME数据进行的验证表明，2024年德国AOI的光谱相关性较高，平均RMSE为6.154 [%]，相关系数为0.924。这一公开可用、独特且经过充分验证的数据集，已经能够支持基于哨兵扩展任务数据在广泛的应用领域中准备和开发未来产品与服务。由于两年广泛时间序列所带来的高数据可用性以及多个AOIs，未来产品已经可以测试其时间和空间可迁移性。","Recent advances in remote sensing.","2026-09-16T00:00:00Z",81,{"impact":19,"substance":82,"depth":39,"authority":181,"freshness":182,"relevant":22,"comment":183},13,9,"面向未来Sentinel扩展任务的高光谱、热红外与L波段合成数据集研究，方法新颖、验证充分且公开可用，对农业遥感产品预研具有实质价值，值得进入每日精选。",[185],{"name":177,"url":174},[27,28,44,187,188,189],"地表温度","哥白尼计划","合成数据",{"code":38,"slug":100,"name":101,"path":102,"parent_code":97},[],{"status":104,"locked":14,"version":33,"method":34,"evidence":193},{"candidates":194,"agricultural_evidence":40},[195],{"code":38,"score":18,"supported":40,"title":196,"abstract":197,"tags":199},[42],[44,45,198,42],"multispectral",[27,187,44,28],[201,202],"哥白尼计划 农业遥感 合成数据 地表温度","哥白尼计划 农业遥感","哥白尼计划农业遥感合成数据地表温度-2797",2,"10.62880\u002Frars26005",{"doi":205,"openalex_id":207,"authors":208,"venue":177,"cited_by_count":59,"oa_url":174,"card":246,"direction":65,"ingested_from":67},"W7213413531",[209,211,213,216,219,222,225,228,230,233,235,237,239,241,244],{"name":210,"orcid":9},"Christian Miesgang",{"name":212,"orcid":9},"Sandra Dotzler",{"name":214,"orcid":215},"Anusha Sanmathi Sathyaniranjan","https:\u002F\u002Forcid.org\u002F0009-0009-8710-4622",{"name":217,"orcid":218},"Silke Migdall","https:\u002F\u002Forcid.org\u002F0000-0001-9089-6274",{"name":220,"orcid":221},"Heike Bach","https:\u002F\u002Forcid.org\u002F0000-0001-8060-2498",{"name":223,"orcid":224},"J. A. D. L. Blommaert","https:\u002F\u002Forcid.org\u002F0000-0002-5797-2439",{"name":226,"orcid":227},"Astrid Vannoppen","https:\u002F\u002Forcid.org\u002F0000-0001-5140-832X",{"name":229,"orcid":9},"Louis Snyders",{"name":231,"orcid":232},"Mihkel Veske","https:\u002F\u002Forcid.org\u002F0000-0003-2367-9215",{"name":234,"orcid":9},"Sven Kautlenbach",{"name":236,"orcid":9},"Catherine Odera",{"name":238,"orcid":9},"Tetiana Shtym",{"name":240,"orcid":9},"Tanel Tamm",{"name":242,"orcid":243},"Anke Schickling","https:\u002F\u002Forcid.org\u002F0000-0001-7446-7752",{"name":245,"orcid":9},"Melisa Soledad Heredia",{"tldr":247,"method":248,"finding":249,"direction":65,"opportunity":250},"生成CHIME高光谱、LSTM热红外和ROSE-L L波段模拟数据集，为未来Sentinel扩展任务","辐射传输模型反演、Sen-ET时空锐化、SAOCOM-1模拟，覆盖德比爱三区两年","模拟CHIME与EnMAP光谱相关性0.924，RMSE 6.154%，数据集公开且验证良好。","可基于该模拟数据集提前开发高光谱、热红外与L波段融合的作物监测和表型反演新算法。","2026-09-17T23:30:37.404847Z",{"id":253,"title":254,"url":255,"summary":256,"summary_zh":257,"content":9,"source_name":258,"source_url":255,"published_at":259,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":80,"score_detail":260,"sources":263,"tags":265,"primary_topic":269,"secondary_topics":275,"classification":283,"search_phrases":313,"slug":316,"view_count":59,"doi":317,"paper":318,"created_at":344},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",{"impact":261,"substance":82,"depth":19,"authority":83,"freshness":13,"relevant":22,"comment":262},16,"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。",[264],{"name":258,"url":255},[89,266,27,267,268],"农业人工智能","氮素监测","紫花苜蓿",{"code":270,"slug":271,"name":272,"path":273,"parent_code":274},"A7","forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping","A",[276,277],{"code":38,"slug":100,"name":101,"path":102,"parent_code":97},{"code":278,"slug":279,"name":280,"path":281,"parent_code":282},"C4","feed","饲料与饲草","\u002Ftopic\u002Flivestock\u002Ffeed","C",{"status":104,"locked":14,"version":33,"method":34,"evidence":284},{"candidates":285,"agricultural_evidence":40},[286,290,296,302,309],{"code":38,"score":19,"supported":40,"title":287,"abstract":288,"tags":289},[42],[44,45,42],[27],{"code":270,"score":17,"supported":40,"title":291,"abstract":293,"tags":295},[292],"alfalfa",[294,292],"苜蓿",[268],{"code":278,"score":13,"supported":40,"title":297,"abstract":298,"tags":301},[],[299,294,300],"饲草","forage",[268],{"code":93,"score":13,"supported":40,"title":303,"abstract":304,"tags":308},[],[90,305,306,109,307],"机器学习","machine learning","decision support",[266],{"code":138,"score":22,"supported":14,"title":310,"abstract":311,"tags":312},[],[],[89],[314,315],"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":317,"openalex_id":319,"authors":320,"venue":258,"cited_by_count":59,"oa_url":255,"card":339,"direction":65,"ingested_from":67},"W7223154576",[321,323,325,327,329,332,334,337],{"name":322,"orcid":9},"Shuai Fu",{"name":324,"orcid":9},"Yunhao Li",{"name":326,"orcid":9},"Chunli Miao",{"name":328,"orcid":9},"Jie Liu",{"name":330,"orcid":331},"Tiangang Liang","https:\u002F\u002Forcid.org\u002F0000-0001-5599-6587",{"name":333,"orcid":9},"Yongjia Hu",{"name":335,"orcid":336},"Qisheng Feng","https:\u002F\u002Forcid.org\u002F0009-0000-1391-9591",{"name":338,"orcid":9},"吴彩霞",{"tldr":340,"method":341,"finding":342,"direction":65,"opportunity":343},"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":346,"title":347,"url":348,"summary":349,"summary_zh":9,"content":9,"source_name":350,"source_url":9,"published_at":259,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":351,"score_detail":352,"sources":354,"tags":356,"primary_topic":359,"secondary_topics":364,"classification":372,"search_phrases":393,"slug":396,"view_count":22,"doi":9,"paper":397,"created_at":405},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":19,"substance":82,"depth":19,"authority":83,"freshness":13,"relevant":22,"comment":353},"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。",[355],{"name":350,"url":348},[89,27,91,357,358],"无人机多光谱","小麦渍害",{"code":360,"slug":361,"name":362,"path":363,"parent_code":274},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat",[365,366],{"code":38,"slug":100,"name":101,"path":102,"parent_code":97},{"code":367,"slug":368,"name":369,"path":370,"parent_code":371},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":104,"locked":14,"version":33,"method":34,"evidence":373},{"candidates":374,"agricultural_evidence":40},[375,380,384,389],{"code":360,"score":376,"supported":40,"title":377,"abstract":378,"tags":379},11,[362],[362],[],{"code":38,"score":376,"supported":40,"title":381,"abstract":382,"tags":383},[357],[],[91,27,357],{"code":367,"score":84,"supported":40,"title":385,"abstract":387,"tags":388},[386],"渍害",[],[],{"code":138,"score":22,"supported":14,"title":390,"abstract":391,"tags":392},[],[],[89],[394,395],"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":398,"venue":9,"cited_by_count":59,"oa_url":9,"card":399,"direction":65,"ingested_from":404},[],{"tldr":400,"method":401,"finding":402,"direction":65,"opportunity":403},"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":407,"title":408,"url":409,"summary":410,"summary_zh":411,"content":9,"source_name":412,"source_url":409,"published_at":413,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":414,"score_detail":415,"sources":417,"tags":419,"primary_topic":423,"secondary_topics":428,"classification":429,"search_phrases":448,"slug":451,"view_count":59,"doi":452,"paper":453,"created_at":483},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":84,"substance":19,"depth":261,"authority":181,"freshness":182,"relevant":22,"comment":416},"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。",[418],{"name":412,"url":409},[27,305,420,421,422],"灌溉管理","水资源评估","气候干旱",{"code":424,"slug":425,"name":426,"path":427,"parent_code":371},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",[],{"status":104,"locked":14,"version":33,"method":34,"evidence":430},{"candidates":431,"agricultural_evidence":40},[432,440,444],{"code":424,"score":19,"supported":40,"title":433,"abstract":435,"tags":439},[434],"water resource",[436,437,425,434,438],"灌溉","水资源","evapotranspiration",[420],{"code":93,"score":22,"supported":14,"title":441,"abstract":442,"tags":443},[],[],[305],{"code":38,"score":22,"supported":14,"title":445,"abstract":446,"tags":447},[],[],[27],[449,450],"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":452,"openalex_id":454,"authors":455,"venue":412,"cited_by_count":59,"oa_url":409,"card":477,"direction":482,"ingested_from":67},"W7222973420",[456,458,461,464,467,469,471,473,475],{"name":457,"orcid":9},"Yan Li",{"name":459,"orcid":460},"Xiaohua Yang","https:\u002F\u002Forcid.org\u002F0000-0001-8387-5213",{"name":462,"orcid":463},"Dehui Bian","https:\u002F\u002Forcid.org\u002F0000-0001-6921-1875",{"name":465,"orcid":466},"Qi Wang","https:\u002F\u002Forcid.org\u002F0000-0002-3156-9246",{"name":468,"orcid":9},"Lequn Gu",{"name":470,"orcid":9},"Ailing Zhang",{"name":472,"orcid":9},"Wei Gao",{"name":474,"orcid":9},"Wenjie Qi",{"name":476,"orcid":9},"Yayun Ma",{"tldr":478,"method":479,"finding":480,"direction":65,"opportunity":481},"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":485,"title":486,"url":487,"summary":488,"summary_zh":9,"content":9,"source_name":489,"source_url":9,"published_at":490,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":491,"score_detail":492,"sources":496,"tags":498,"primary_topic":499,"secondary_topics":500,"classification":503,"search_phrases":523,"slug":526,"view_count":59,"doi":9,"paper":527,"created_at":534},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":261,"substance":493,"depth":494,"authority":83,"freshness":84,"relevant":22,"comment":495},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。",[497],{"name":489,"url":487},[89,27,91,357,358],{"code":360,"slug":361,"name":362,"path":363,"parent_code":274},[501,502],{"code":367,"slug":368,"name":369,"path":370,"parent_code":371},{"code":38,"slug":100,"name":101,"path":102,"parent_code":97},{"status":104,"locked":14,"version":33,"method":34,"evidence":504},{"candidates":505,"agricultural_evidence":40},[506,510,514,518],{"code":360,"score":376,"supported":40,"title":507,"abstract":508,"tags":509},[362],[362],[],{"code":367,"score":376,"supported":40,"title":511,"abstract":512,"tags":513},[386],[386],[],{"code":38,"score":376,"supported":40,"title":515,"abstract":516,"tags":517},[357],[],[91,27,357],{"code":138,"score":519,"supported":40,"title":520,"abstract":521,"tags":522},4,[],[89],[89],[524,525],"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":528,"venue":9,"cited_by_count":59,"oa_url":9,"card":529,"direction":65,"ingested_from":404},[],{"tldr":530,"method":531,"finding":532,"direction":65,"opportunity":533},"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"]