[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3082":3,"related-3082":147},{"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":68,"slug":71,"view_count":72,"doi":73,"paper":74,"created_at":146},3082,"The PSInet Plant Water Potential Database: advancing new perspectives on plant water status, traits, and hydraulic processes","https:\u002F\u002Fdoi.org\u002F10.64898\u002F2026.09.17.752364","Water potential gradients drive water flow within and between soils and plants, and the internal plant water potential controls a wide range of physiological processes including photosynthesis, growth, and mortality. Notwithstanding this clear relevance for many critical aspects of ecosystem function, water potential data have historically been relatively inaccessible and unnetworked. The absence of a centralized repository for plant water potential time series limits our ability to integrate a wealth of ecophysiological information from other networks and from remote sensing. Closing this gap is necessary to address unresolved questions about plant responses to drought and heat stress, and to make confident predictions about plant and ecosystem function in a warming world. Here, we introduce the PSInet database -- a global collection of plant water potential time series from 285 datasets representing 523 species. We present the workflow that guided database development and evaluate its key features. Through a series of preliminary analyses, we then highlight the potential of the PSInet database for applications including: a) advancing plant water use strategy frameworks; b) disentangling the impacts of soil versus atmospheric drought stress; c) assessing the long-held assumption of pre-dawn equilibration of ecosystem water potential; d) understanding the risk of drought-driven mortality; and e) benchmarking remote-sensing data products and land-surface models.","水势梯度驱动着土壤与植物内部及二者之间的水分流动，而植物内部水势调控着包括光合作用、生长和死亡在内的一系列广泛生理过程。尽管水势数据与生态系统功能的诸多关键方面明显相关，但此类数据历来相对难以获取且缺乏网络化整合。植物水势时间序列缺乏集中式存储库，这限制了我们整合来自其他网络和遥感手段的大量生态生理信息的能力。弥合这一差距对于解答有关植物对干旱和热胁迫响应的未解问题，以及在全球变暖背景下对植物和生态系统功能作出可靠预测，都是必要的。在此，我们介绍PSInet数据库——一个全球性的植物水势时间序列集合，涵盖285个数据集、523个物种。我们展示了指导数据库开发的工作流程，并评估了其关键特征。通过一系列初步分析，我们进而凸显了PSInet数据库在以下应用方面的潜力：a) 推进植物水分利用策略框架；b) 区分土壤干旱胁迫与大气干旱胁迫的影响；c) 评估长期以来的生态系统水势黎明前平衡假设；d) 理解干旱驱动死亡的风险；以及e) 为遥感数据产品和陆面模型提供基准验证。",null,"bioRxiv (Cold Spring Harbor Laboratory)","2026-09-18T00:00:00Z","论文",10,false,85,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},22,23,18,14,8,1,"全球植物水势数据库整合285个数据集、523个物种，为干旱胁迫与遥感模型验证提供关键数据基础设施，专业价值突出。",[25],{"name":10,"url":6},[27,28,29,30,31],"遥感","干旱胁迫","生态监测","植物水分","数据库",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":14,"reason":67},[39,47,55,62],{"code":40,"score":41,"supported":42,"title":43,"abstract":44,"tags":46},"I3",7,true,[],[27,45],"remote sensing",[27],{"code":48,"score":49,"supported":42,"title":50,"abstract":51,"tags":54},"H2",6,[],[52,53],"土壤","soil",[],{"code":56,"score":49,"supported":42,"title":57,"abstract":58,"tags":61},"H7",[],[59,60],"干旱","drought",[],{"code":63,"score":22,"supported":14,"title":64,"abstract":65,"tags":66},"H6",[],[],[29],"农业相关性需要复核",[69,70],"PSInet 植物水势数据库","植物水势 时间序列","PSInet植物水势数据库-3082",2,"10.64898\u002F2026.09.17.752364",{"doi":73,"openalex_id":75,"authors":76,"venue":10,"cited_by_count":137,"oa_url":138,"card":139,"direction":143,"ingested_from":145},"W7213559802",[77,80,83,86,89,92,95,98,101,104,107,110,113,116,119,122,125,128,131,134],{"name":78,"orcid":79},"Jessica Guo","https:\u002F\u002Forcid.org\u002F0000-0002-9566-9182",{"name":81,"orcid":82},"Ana Maria Restrepo Acevedo","https:\u002F\u002Forcid.org\u002F0000-0003-4861-838X",{"name":84,"orcid":85},"Marvin Browne","https:\u002F\u002Forcid.org\u002F0000-0002-9640-0759",{"name":87,"orcid":88},"Daniel M. Johnson","https:\u002F\u002Forcid.org\u002F0000-0003-1015-9560",{"name":90,"orcid":91},"Katherine A. McCulloh","https:\u002F\u002Forcid.org\u002F0000-0003-0801-3968",{"name":93,"orcid":94},"Jesse B. Nippert","https:\u002F\u002Forcid.org\u002F0000-0002-7939-342X",{"name":96,"orcid":97},"Rafael Poyatos","https:\u002F\u002Forcid.org\u002F0000-0003-0521-2523",{"name":99,"orcid":100},"Steven A. Kannenberg","https:\u002F\u002Forcid.org\u002F0000-0002-4097-9140",{"name":102,"orcid":103},"Daniel P. Beverly","https:\u002F\u002Forcid.org\u002F0000-0002-1267-4147",{"name":105,"orcid":106},"K. Arthur Endsley","https:\u002F\u002Forcid.org\u002F0000-0001-9722-8092",{"name":108,"orcid":109},"Andrew F. Feldman","https:\u002F\u002Forcid.org\u002F0000-0003-1547-6995",{"name":111,"orcid":112},"Alexandra G. Konings","https:\u002F\u002Forcid.org\u002F0000-0002-2810-1722",{"name":114,"orcid":115},"Yanlan Liu","https:\u002F\u002Forcid.org\u002F0000-0001-5129-6284",{"name":117,"orcid":118},"Jordi Martínez‐Vilalta","https:\u002F\u002Forcid.org\u002F0000-0002-2332-7298",{"name":120,"orcid":121},"William M. Hammond","https:\u002F\u002Forcid.org\u002F0000-0002-2904-810X",{"name":123,"orcid":124},"Kevin R. Hultine","https:\u002F\u002Forcid.org\u002F0000-0001-9747-6037",{"name":126,"orcid":127},"Lauren E. L. Lowman","https:\u002F\u002Forcid.org\u002F0000-0003-2960-7095",{"name":129,"orcid":130},"Jeffrey S. Dukes","https:\u002F\u002Forcid.org\u002F0000-0001-9482-7743",{"name":132,"orcid":133},"Julia K. Green","https:\u002F\u002Forcid.org\u002F0000-0002-8466-2313",{"name":135,"orcid":136},"Lawren Sack","https:\u002F\u002Forcid.org\u002F0000-0002-7009-7202",0,"https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002Fbiorxiv\u002Fearly\u002F2026\u002F09\u002F18\u002F2026.09.17.752364.full.pdf",{"tldr":140,"method":141,"finding":142,"direction":143,"opportunity":144},"构建全球植物水势时间序列数据库PSInet，整合285个数据集523个物种。","汇集全球植物水势时间序列数据，建立数据库并开展初步分析。","该数据库可支撑植物水分策略、干旱胁迫、遥感与模型基准等研究。","农业遥感与作物表型","可基于该数据库融合遥感与陆面模型，发展作物水分状态监测与干旱预警方法。","openalex","2026-09-21T23:30:27.557264Z",{"total":49,"page":22,"page_size":49,"items":148},[149,233,300,383,432,499],{"id":150,"title":151,"url":152,"summary":153,"summary_zh":154,"content":9,"source_name":155,"source_url":152,"published_at":156,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":157,"score_detail":158,"sources":164,"tags":166,"primary_topic":171,"secondary_topics":176,"classification":182,"search_phrases":200,"slug":203,"view_count":204,"doi":205,"paper":206,"created_at":232},3784,"Analysis of the Growth Responses and Multispectral Reflectance Characteristics of Rehmannia glutinosa Plug Seedlings under Drought Stress","https:\u002F\u002Fdoi.org\u002F10.7235\u002Fhort.20260033","Rehmannia glutinosa is a high-value medicinal crop with extreme sensitivity to drought stress.However, non-destructive methods by which to assess the quality of plug seedlings remain limited.This study evaluated the spectral reflectance and vegetation index responses of R. glutinosa plug seedlings to drought stress using multispectral imaging.Drought stress significantly increased the leaf temperature and decreased shoot and root growth parameters compared to the control.NIR (near-infrared) band reflectance declined initially in the early stage of drought stress, with this followed by a significant decrease in visible bands (blue, green, red) as stress intensified.Among the five vegetation indices, the NGRDI (normalized green-red difference index) responded earliest at the early stage of drought stress, while the GNDVI (green normalized difference vegetation index), PSRI (plant senescence reflectance index), and NDRE (normalized difference red edge index) showed significant changes as stress intensified.The NDVI (normalized difference vegetation index) showed a delayed response and was significantly lower in the drought-stressed seedlings than in the control only after rewatering.These results suggest that multispectral imaging is a promising nondestructive tool for early drought stress detection and seedling quality assessments in medicinal plug seedling production systems.","地黄是一种高价值药用作物，对干旱胁迫极为敏感。然而，目前用于评估穴盘苗质量的无损方法仍然有限。本研究利用多光谱成像技术，评估了地黄穴盘苗对干旱胁迫的光谱反射率和植被指数响应。与对照相比，干旱胁迫显著提高了叶片温度，并降低了地上部和根系的生长参数。近红外（NIR）波段反射率在干旱胁迫早期首先下降，随后随着胁迫加剧，可见光波段（蓝、绿、红）反射率显著降低。在5个植被指数中，归一化绿红差异指数（NGRDI）在干旱胁迫早期响应最早，而绿归一化差异植被指数（GNDVI）、植物衰老反射指数（PSRI）和归一化红边差异指数（NDRE）随着胁迫加剧表现出显著变化。归一化差异植被指数（NDVI）响应滞后，仅在复水后干旱胁迫幼苗才显著低于对照。这些结果表明，多光谱成像在药用穴盘苗生产系统中用于干旱胁迫早期检测和种苗质量评估具有良好的无损应用前景。","Horticultural Science and Technology","2026-09-28T00:00:00Z",66,{"impact":21,"substance":159,"depth":160,"authority":161,"freshness":162,"relevant":22,"comment":163},20,16,13,9,"多光谱成像用于药用作物穴盘苗干旱胁迫早期无损检测，方法新颖、结论可靠，但属细分领域研究，产业影响有限。",[165],{"name":155,"url":152},[167,27,28,168,169,170],"智慧农业","多光谱成像","地黄","种苗质量",{"code":56,"slug":172,"name":173,"path":174,"parent_code":175},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",[177],{"code":40,"slug":178,"name":179,"path":180,"parent_code":181},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":183,"locked":14,"version":35,"method":36,"evidence":184},"classified",{"candidates":185,"agricultural_evidence":42},[186,190,195],{"code":56,"score":20,"supported":42,"title":187,"abstract":188,"tags":189},[60],[59,60],[],{"code":40,"score":161,"supported":42,"title":191,"abstract":193,"tags":194},[192],"multispectral",[192],[168,27],{"code":196,"score":22,"supported":14,"title":197,"abstract":198,"tags":199},"I1",[],[],[167],[201,202],"地黄 穴盘苗 多光谱","地黄 干旱胁迫 植被指数","地黄穴盘苗多光谱-3784",5,"10.7235\u002Fhort.20260033",{"doi":205,"openalex_id":207,"authors":208,"venue":155,"cited_by_count":137,"oa_url":226,"card":227,"direction":143,"ingested_from":145},"W7214608276",[209,212,214,215,217,220,223,225],{"name":210,"orcid":211},"Ji Hye Yun","https:\u002F\u002Forcid.org\u002F0009-0004-9551-5353",{"name":213,"orcid":9},"So Yeong Hwang",{"name":213,"orcid":9},{"name":216,"orcid":9},"Jeong Kil Koo",{"name":218,"orcid":219},"Hyeong Eun Choi","https:\u002F\u002Forcid.org\u002F0009-0009-9318-9845",{"name":221,"orcid":222},"Seong Ha Park","https:\u002F\u002Forcid.org\u002F0009-0005-9189-8972",{"name":224,"orcid":9},"Seung Jae Hwang",{"name":224,"orcid":9},"https:\u002F\u002Fwww.hst-j.org\u002Farticles\u002Fpdf\u002Fbz4M\u002Fkshs-2026-044-00-15.pdf",{"tldr":228,"method":229,"finding":230,"direction":143,"opportunity":231},"用多光谱成像无损检测地黄穴盘苗干旱胁迫下的生长与光谱响应。","多光谱成像测反射率与5种植被指数，结合生长参数和叶温。","NGRDI最早响应早期干旱，GNDVI、PSRI、NDRE随胁迫加剧变化，NDVI复水后才显著。","可构建基于早期敏感植被指数的药用植物穴盘苗干旱预警与质量分级模型。","2026-09-29T23:30:21.147972Z",{"id":234,"title":235,"url":236,"summary":237,"summary_zh":238,"content":9,"source_name":239,"source_url":236,"published_at":240,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":241,"score_detail":242,"sources":246,"tags":248,"primary_topic":252,"secondary_topics":253,"classification":259,"search_phrases":282,"slug":285,"view_count":21,"doi":286,"paper":287,"created_at":299},3618,"Multi-Target Remote Sensing Segmentation with Cross-Scale Attention for Urban-Ecological Intelligence","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fijgem.vol.12.no1.2026.pg131.154","Remote sensing image segmentation is essential to extract valuable information from satellite and aerial images to achieve significant applications such as urban planning and ecological monitoring. Yet, it is hard to accurately segment diverse and complicated features because of the constraints of conventional approaches and the requirement to process variable object sizes and complicated boundaries. This research aims to address the above difficulties through the use and assessment of advanced deep learning models specifically UNet, SegNet, and TransU-Net in multi target semantic segmentation. These networks have been applied in single-object segmentation tasks with a focus on structures and roads, while the UNet architecture was additionally utilized for multi-object segmentation comprising buildings, woods, grasslands, water bodies, and cultivated land. Using appropriate remote sensing datasets, the accuracy of the models was thoroughly evaluated using common evaluation metrics, including pixel accuracy, Intersection over Union (IoU), and F1-score. The experimental outcomes illustrate the capabilities of these deep learning methods to provide accurate identification of essential features. Notably, the maximum attainable accuracy for single-object building segmentation was 96.85%, whereas the overall accuracy for multi-object segmentation with the UNet was 84.2%. The experimental results show these deep learning algorithms can effectively separate different targets from remote sensing imagery, thus making them fundamental tools for geospatial analysis and related fields.","遥感图像分割对于从卫星和航空图像中提取有价值信息，以实现城市规划和生态监测等重要应用至关重要。然而，由于传统方法的局限以及处理可变目标尺寸和复杂边界的需要，准确分割多样且复杂的地物十分困难。本研究旨在通过使用和评估先进的深度学习模型，特别是UNet、SegNet和TransU-Net在多目标语义分割中的应用，来解决上述难题。这些网络已被应用于单目标分割任务，重点关注建筑物和道路，而UNet架构还被额外用于多目标分割，包括建筑物、林地、草地、水体和耕地。利用适当的遥感数据集，通过常用评价指标对模型的精度进行了全面评估，包括像素精度、交并比（IoU）和F1分数。实验结果表明了这些深度学习方法在准确识别关键地物方面的能力。值得注意的是，单目标建筑物分割的最高可达精度为96.85%，而使用UNet进行多目标分割的总体精度为84.2%。实验结果表明，这些深度学习算法能够有效地从遥感图像中分离出不同目标，从而使其成为地理空间分析及相关领域的基础工具。","IIARD INTERNATIONAL JOURNAL OF GEOGRAPHY AND ENVIRONMENTAL MANAGEMENT","2026-09-25T00:00:00Z",63,{"impact":243,"substance":19,"depth":244,"authority":13,"freshness":21,"relevant":22,"comment":245},12,15,"将UNet、SegNet、TransU-Net用于多目标遥感语义分割，建筑单目标精度96.85%、多目标84.2%，方法常规但结论对农业与生态遥感监测有参考价值。",[247],{"name":239,"url":236},[249,250,251,27,29],"农业人工智能","深度学习","语义分割",{"code":40,"slug":178,"name":179,"path":180,"parent_code":181},[254],{"code":255,"slug":256,"name":257,"path":258,"parent_code":181},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":183,"locked":14,"version":35,"method":36,"evidence":260},{"candidates":261,"agricultural_evidence":42},[262,266,271,278],{"code":40,"score":244,"supported":42,"title":263,"abstract":264,"tags":265},[45],[27,45],[27],{"code":255,"score":162,"supported":42,"title":267,"abstract":268,"tags":270},[],[250,269],"deep learning",[249,250,251],{"code":272,"score":273,"supported":42,"title":274,"abstract":275,"tags":277},"I4",3,[],[276],"空间分析",[],{"code":63,"score":22,"supported":14,"title":279,"abstract":280,"tags":281},[],[],[29],[283,284],"UNet 遥感 多目标分割","TransU-Net 城市生态 遥感","UNet遥感多目标分割-3618","10.56201\u002Fijgem.vol.12.no1.2026.pg131.154",{"doi":286,"openalex_id":288,"authors":289,"venue":239,"cited_by_count":137,"oa_url":292,"card":293,"direction":298,"ingested_from":145},"W7214298981",[290],{"name":291,"orcid":9},"Chibueze Favour Aririguzo","https:\u002F\u002Fiiardjournals.org\u002Fget\u002FIJGEM\u002FVOL. 12 NO. 1 2026\u002FMulti-Target Remote Sensing Segmentation 131-154.pdf",{"tldr":294,"method":295,"finding":296,"direction":143,"opportunity":297},"评估UNet、SegNet和TransU-Net在多目标遥感语义分割中的性能，用于城市与生态监测。","使用UNet、SegNet、TransU-Net模型，在遥感数据集上以像素精度、","单目标建筑分割最高精度96.85%，UNet多目标分割总体精度84.2%。","可探索跨尺度注意力机制提升多目标分割精度，并迁移至耕地与作物精细分类。","智慧农业 \u002F 农业物联网","2026-09-27T23:30:28.366758Z",{"id":301,"title":302,"url":303,"summary":304,"summary_zh":305,"content":9,"source_name":306,"source_url":303,"published_at":307,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":308,"score_detail":309,"sources":311,"tags":313,"primary_topic":9,"secondary_topics":317,"classification":318,"search_phrases":338,"slug":341,"view_count":204,"doi":342,"paper":343,"created_at":382},3507,"OCO‐3 Meets ECOSTRESS: Insights Into Ecosystem Diurnal Water‐Use Efficiency From Co‐Located Solar‐Induced Fluorescence and Thermal Observations","https:\u002F\u002Fdoi.org\u002F10.1029\u002F2026gl124105","Abstract Terrestrial ecosystem regulation of carbon and water fluxes is critical for constraining climate–biosphere feedbacks but remains poorly quantified across space and time. Here, we evaluate whether co‐located observations from NASA's Orbiting Carbon Observatory‐3 (OCO‐3) and the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) can reproduce ecosystem water use efficiency (WUE) dynamics observed at FLUXNET sites across temporal scales, vegetation types, climates, and drought conditions. We created the ECOCO3 data set, which harmonizes OCO‐3 and ECOSTRESS observations in space and time. ECOCO3 captures broad seasonal and diurnal carbon and water flux patterns including midday drought responses. Sampling sensitivity analysis shows that ECOCO3 is primarily limited by available sample size for distinguishing vegetation and climate driven differences in WUE. Our findings highlight both the promise and limitations of remote sensing for resolving sub‐daily carbon–water coupling.","陆地生态系统对碳通量和水通量的调节对于约束气候–生物圈反馈至关重要，但在空间和时间尺度上仍缺乏充分的量化。在此，我们评估了来自NASA轨道碳观测站-3（OCO-3）和空间站生态系统星载热辐射计实验（ECOSTRESS）的同位观测能否在时间尺度、植被类型、气候条件和干旱状况下重现FLUXNET站点观测到的生态系统水分利用效率（WUE）动态。我们创建了ECOCO3数据集，该数据集在空间和时间上协调了OCO-3和ECOSTRESS的观测。ECOCO3能够捕捉广泛的季节性和日间碳通量与水通量模式，包括正午干旱响应。采样敏感性分析表明，ECOCO3主要受限于可用样本量，难以区分植被和气候驱动的WUE差异。我们的研究结果既凸显了遥感在解析亚日尺度碳–水耦合方面的前景，也揭示了其局限性。","Geophysical Research Letters","2026-09-23T00:00:00Z",81,{"impact":19,"substance":17,"depth":19,"authority":244,"freshness":21,"relevant":22,"comment":310},"NASA两颗卫星协同观测提升生态系统碳水耦合监测能力，方法新颖、数据可靠，对农业遥感与水资源管理有参考价值。",[312],{"name":306,"url":303},[314,27,315,29,316],"农业遥感","水资源利用","碳汇",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":319},{"candidates":320,"agricultural_evidence":14,"reason":67},[321,325,329,334],{"code":40,"score":21,"supported":42,"title":322,"abstract":323,"tags":324},[],[27,45],[314,27],{"code":56,"score":49,"supported":42,"title":326,"abstract":327,"tags":328},[],[59,60],[],{"code":330,"score":22,"supported":14,"title":331,"abstract":332,"tags":333},"H3",[],[],[315],{"code":63,"score":22,"supported":14,"title":335,"abstract":336,"tags":337},[],[],[29],[339,340],"OCO-3 ECOSTRESS 水分利用效率","ECOCO3 数据集 碳水通量","OCO-3ECOSTRESS水分利用效率-3507","10.1029\u002F2026gl124105",{"doi":342,"openalex_id":344,"authors":345,"venue":306,"cited_by_count":137,"oa_url":376,"card":377,"direction":143,"ingested_from":145},"W7214122316",[346,349,352,355,358,361,364,367,370,373],{"name":347,"orcid":348},"Zoe Pierrat","https:\u002F\u002Forcid.org\u002F0000-0002-6726-2406",{"name":350,"orcid":351},"Thomas P. Kurosu","https:\u002F\u002Forcid.org\u002F0000-0003-2555-7780",{"name":353,"orcid":354},"Abhishek Chatterjee","https:\u002F\u002Forcid.org\u002F0000-0002-3680-0160",{"name":356,"orcid":357},"Joshua B. Fisher","https:\u002F\u002Forcid.org\u002F0000-0003-4734-9085",{"name":359,"orcid":360},"Margaret C. Johnson","https:\u002F\u002Forcid.org\u002F0000-0003-1481-9706",{"name":362,"orcid":363},"Le Kuai","https:\u002F\u002Forcid.org\u002F0000-0001-6406-1150",{"name":365,"orcid":366},"Kaniska Mallick","https:\u002F\u002Forcid.org\u002F0000-0002-2735-930X",{"name":368,"orcid":369},"Nicholas Cody Parazoo","https:\u002F\u002Forcid.org\u002F0000-0002-4424-7780",{"name":371,"orcid":372},"Benjamin C. Wiebe","https:\u002F\u002Forcid.org\u002F0000-0002-9325-1540",{"name":374,"orcid":375},"Kerry A. Cawse-Nicholson","https:\u002F\u002Forcid.org\u002F0000-0002-0510-4066","https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1029\u002F2026GL124105",{"tldr":378,"method":379,"finding":380,"direction":143,"opportunity":381},"融合OCO-3与ECOSTRESS观测评估生态系统日间水分利用效率动态。","构建ECOCO3数据集，结合FLUXNET站点验证与采样敏感性分析。","ECOCO3能捕捉季节与日间碳-水通量模式，但样本量限制其区分植被与气候差异。","可探索多源遥感融合提升亚日尺度碳水耦合估算精度，并扩展至农田生态系统。","2026-09-25T23:30:33.881463Z",{"id":384,"title":385,"url":386,"summary":387,"summary_zh":388,"content":9,"source_name":389,"source_url":386,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":390,"score_detail":391,"sources":394,"tags":396,"primary_topic":9,"secondary_topics":400,"classification":401,"search_phrases":413,"slug":416,"view_count":273,"doi":417,"paper":418,"created_at":431},3086,"UAV photogrammetry and remote sensing for coastal biodiversity and habitat conservation","https:\u002F\u002Fdoi.org\u002F10.6008\u002Fcbpc2318-2881.2026.001.0002","Coastal ecosystems support high biological diversity while experiencing rapid change caused by shoreline dynamics, sea-level rise, extreme events, pollution, urban development, and intensive resource use. Conventional field surveys provide essential ecological observations but often lack the spatial extent or repetition needed to describe heterogeneous and short-lived coastal conditions. This review evaluates how uncrewed aerial vehicle photogrammetry and remote sensing can support habitat assessment, species monitoring, restoration, and conservation decisions. A structured narrative synthesis was undertaken across studies on image-based mapping, multispectral and thermal observation, laser scanning, direct georeferencing, automated classification, and environmental change detection. The evidence shows that these platforms are most useful when surveys are designed around explicit ecological variables rather than image production alone. RGB imagery and structure-from-motion models provide detailed surface geometry; multispectral, thermal, and laser sensors add information on vegetation condition, moisture, temperature, and three-dimensional structure. Repeated surveys can reveal erosion, inundation, habitat fragmentation, restoration performance, and wildlife distribution at operational scales. Major constraints include variable illumination, water reflectance, wind, tides, positional uncertainty, disturbance risk, regulation, limited training data, and inconsistent validation. Effective programs therefore require standardized timing, field calibration, uncertainty reporting, ethical flight practice, and workflows that convert mapped patterns into management indicators. Future progress depends on sensor integration, explainable automation, interoperable time series, and sustained cooperation between remote-sensing specialists, ecologists, managers, and coastal communities.","沿海生态系统维持着高度的生物多样性，同时经历着由岸线动态、海平面上升、极端事件、污染、城市发展和密集资源利用所引发的快速变化。传统野外调查提供了重要的生态观测，但往往缺乏描述异质性和短生命周期沿海状况所需的空间范围或重复频次。本文综述评估了无人驾驶航空器摄影测量与遥感如何支持栖息地评估、物种监测、恢复和保护决策。我们对基于影像的制图、多光谱与热红外观测、激光扫描、直接地理配准、自动分类和环境变化检测等研究进行了结构化叙述性综合。证据表明，当调查围绕明确的生态变量而非仅以影像生产为目的进行设计时，这些平台最为有用。RGB影像和运动恢复结构（structure-from-motion）模型可提供详细的地表几何信息；多光谱、热红外和激光传感器则补充了植被状况、湿度、温度和三维结构信息。重复调查能够在业务尺度上揭示侵蚀、淹没、栖息地破碎化、恢复成效和野生动物分布。主要制约因素包括光照变化、水体反射、风、潮汐、位置不确定性、干扰风险、法规、训练数据有限以及验证不一致。因此，有效的项目需要标准化的时间安排、野外校准、不确定性报告、符合伦理的飞行实践，以及将制图格局转化为管理指标的工作流程。未来的进展取决于传感器集成、可解释的自动化、可互操作的时间序列，以及遥感专家、生态学家、管理者和沿海社区之间的持续合作。","Nature and Conservation",64,{"impact":21,"substance":19,"depth":392,"authority":161,"freshness":21,"relevant":22,"comment":393},17,"综述系统梳理无人机摄影测量与遥感在海岸生境评估、物种监测与修复中的应用与局限，方法学价值明确，但偏生态保护领域，与农业信息化关联间接，属细分方向参考。",[395],{"name":389,"url":386},[397,27,398,29,399],"无人机","生物多样性","海岸生态",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":402},{"candidates":403,"agricultural_evidence":14,"reason":67},[404,408],{"code":40,"score":19,"supported":42,"title":405,"abstract":406,"tags":407},[45],[27,45,192],[27],{"code":63,"score":161,"supported":42,"title":409,"abstract":411,"tags":412},[410],"biodiversity",[398],[29,398],[414,415],"无人机 摄影测量 海岸生态","UAV 遥感 生物多样性监测","无人机摄影测量海岸生态-3086","10.6008\u002Fcbpc2318-2881.2026.001.0002",{"doi":417,"openalex_id":419,"authors":420,"venue":389,"cited_by_count":137,"oa_url":424,"card":425,"direction":430,"ingested_from":145},"W7213604527",[421],{"name":422,"orcid":423},"Murat Yakar","https:\u002F\u002Forcid.org\u002F0000-0002-2664-6251","https:\u002F\u002Fwww.natureandconservation.com\u002Findex.php\u002Fnature\u002Farticle\u002Fdownload\u002F8949\u002F5099",{"tldr":426,"method":427,"finding":428,"direction":143,"opportunity":429},"综述无人机摄影测量与遥感在海岸生物多样性和栖息地保护中的应用与局限。","结构化叙述性综述，涵盖RGB、多光谱、热红外、激光扫描及自动分类等研究。","围绕明确生态变量设计调查时最有效，但受光照、风潮、法规和验证不一致等制约。","可探索多传感器融合与可解释自动化，构建标准化时间序列以支撑海岸管理指标。","数字乡村与农业信息化","2026-09-21T23:30:37.840826Z",{"id":433,"title":434,"url":435,"summary":436,"summary_zh":437,"content":9,"source_name":438,"source_url":435,"published_at":439,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":390,"score_detail":440,"sources":442,"tags":444,"primary_topic":447,"secondary_topics":451,"classification":452,"search_phrases":475,"slug":478,"view_count":72,"doi":479,"paper":480,"created_at":498},1767,"Assessment of water use efficiency in arid ecosystems of the coastal land of Egypt using MODIS dataset","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-67232-3","Abstract In drylands, the limited availability of reliable measurements makes it more difficult to understand how climatic and biogeochemical factors shape ecosystem processes across spatial gradients compared to mesic or humid regions. Water use efficiency (WUE) is a key physiological indicator for assessing interactions between carbon and water cycles in any ecosystem. Yet, in dryland ecosystems with sparse vegetation where evaporation represents a large fraction of total evapotranspiration (ET), the response of WUE to land use changes needs more investigation. To address this gap, the present study examines the spatial and temporal patterns of WUE in coastal dryland ecosystems of Egypt to better understand how land use practices affect water dynamics. Using an integrated approach that combines remote sensing products, WUE trends were assessed across representative sites. The results show high spatial variation in the WUE pattern over the 25-year period, with absolute values ranging from below 1.0 × 10 −3 g C mm −1 in sparsely vegetated zones up to 2.9 × 10 −3 g C mm −1 in reclaimed lands. The highest values of WUE are for the vegetated areas of croplands and reed vegetation, where the biomass reflects the growth of developed vegetation. The natural land covers are subjected to various changes due to human activities and expansion in the reclaimed agricultural areas where the urbanization activities increase ET and reduce WUE. These insights can contribute to a better understanding of dryland ecosystem functioning and can inform sustainable water and land management strategies in arid regions facing increasing water scarcity.","在干旱地区，可靠测量数据的有限性使得相较于湿润或半湿润地区，理解气候和生物地球化学因素如何塑造生态系统过程的空间梯度变得更加困难。水分利用效率（WUE）是评估任何生态系统中碳与水循环相互作用的关键生理指标。然而，在植被稀疏、蒸发占实际蒸散发（ET）很大比例的干旱生态系统中，水分利用效率对土地利用变化的响应仍需进一步研究。为弥补这一空白，本研究考察了埃及沿海干旱生态系统水分利用效率的时空格局，以更好地理解土地利用实践如何影响水分动态。通过结合遥感产品的综合方法，评估了代表性样点的水分利用效率趋势。结果显示，25年间水分利用效率格局存在高度空间变异性，绝对值范围从稀疏植被区的低于1.0×10⁻³ g C mm⁻¹到开垦土地的2.9×10⁻³ g C mm⁻¹不等。水分利用效率最高值出现在农田和芦苇植被的植被覆盖区域，其生物量反映了发育植被的生长状况。由于人类活动和开垦农业区的扩张，自然土地覆盖类型经历了各种变化，城市化活动增加了蒸散发并降低了水分利用效率。这些见解有助于加深对干旱生态系统功能的理解，并可为面临日益严重水资源短缺的干旱地区提供可持续水资源和土地管理策略的参考。","Scientific Reports","2026-09-05T00:00:00Z",{"impact":243,"substance":19,"depth":160,"authority":161,"freshness":204,"relevant":22,"comment":441},"利用MODIS数据评估埃及干旱区水分利用效率，对农业水资源管理有参考价值，但地域较远，影响有限。",[443],{"name":438,"url":435},[445,27,446,29],"农业水资源","干旱区",{"code":330,"slug":448,"name":449,"path":450,"parent_code":175},"irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",[],{"status":183,"locked":14,"version":35,"method":36,"evidence":453},{"candidates":454,"agricultural_evidence":42},[455,462,466,471],{"code":330,"score":13,"supported":42,"title":456,"abstract":457,"tags":461},[],[458,459,460],"水资源","蒸散发","evapotranspiration",[445],{"code":40,"score":41,"supported":42,"title":463,"abstract":464,"tags":465},[],[27,45],[27],{"code":56,"score":467,"supported":42,"title":468,"abstract":469,"tags":470},4,[],[59],[446],{"code":63,"score":22,"supported":14,"title":472,"abstract":473,"tags":474},[],[],[29],[476,477],"农业水资源 生态监测 干旱区 遥感","农业水资源 生态监测","农业水资源生态监测干旱区遥感-1767","10.1038\u002Fs41598-026-67232-3",{"doi":479,"openalex_id":481,"authors":482,"venue":438,"cited_by_count":137,"oa_url":492,"card":493,"direction":143,"ingested_from":145},"W7208807648",[483,486,489],{"name":484,"orcid":485},"Amira M. Hotaiba","https:\u002F\u002Forcid.org\u002F0000-0001-5305-1555",{"name":487,"orcid":488},"Boshra Salem","https:\u002F\u002Forcid.org\u002F0000-0001-9715-6324",{"name":490,"orcid":491},"Marwa Waseem A. Halmy","https:\u002F\u002Forcid.org\u002F0000-0002-4183-973X","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-67232-3.pdf",{"tldr":494,"method":495,"finding":496,"direction":143,"opportunity":497},"利用MODIS数据评估埃及沿海干旱生态系统水分利用效率的时空格局及土地利用影响。","集成遥感产品，分析25年WUE趋势，对比不同土地覆盖类型。","WUE空间变异大，耕地和芦苇植被最高，城市化增加蒸散降低WUE。","可研究干旱区土地利用变化对WUE的驱动机制，结合高分辨率数据优化水资源管理。","2026-09-06T23:30:27.242542Z",{"id":500,"title":501,"url":502,"summary":503,"summary_zh":504,"content":9,"source_name":505,"source_url":502,"published_at":506,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":507,"score_detail":508,"sources":510,"tags":512,"primary_topic":9,"secondary_topics":514,"classification":515,"search_phrases":534,"slug":537,"view_count":72,"doi":538,"paper":539,"created_at":556},1729,"CAFF-Net for coupled eco-health prediction of urban green space and water body with multi-source remote sensing","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.asej.2026.104404","Aiming at three bottlenecks of multi-source remote sensing eco-health inversion: cross-modal feature redundancy, decoupled single-task modeling, and detail distortion of fragmented urban land covers, this paper proposes CAFF-Net for coupled eco-health prediction of urban green spaces and water bodies using Sentinel-1 SAR and Sentinel-2 optical data. A dual-branch Swin-Transformer separately extracts heterogeneous features, and serial CBAM realizes adaptive feature filtering to suppress redundant information. An improved multi-task UNet synchronously predicts green space, water body and coupled ecological health indices with joint loss to model aquatic-terrestrial ecological linkages. Experiments show CAFF-Net achieves an 𝑅 2 of 0.928, 3.3% higher than SOTA, with strong robustness under heavy cloud interference, supporting sponge city and watershed governance.","针对多源遥感生态健康反演中存在的三大瓶颈——跨模态特征冗余、单任务解耦建模以及城市破碎化地物细节失真问题，本文提出CAFF-Net，利用Sentinel-1 SAR与Sentinel-2光学数据实现城市绿地和水体耦合生态健康预测。双分支Swin-Transformer分别提取异质特征，串联CBAM实现自适应特征过滤以抑制冗余信息。改进的多任务UNet通过联合损失同步预测绿地、水体及耦合生态健康指数，以建模水陆生态关联。实验表明，CAFF-Net的R²达到0.928，较SOTA提升3.3%，且在强云干扰下具有较强鲁棒性，可为海绵城市和流域治理提供支撑。","Ain Shams Engineering Journal","2026-09-03T00:00:00Z",65,{"impact":243,"substance":159,"depth":19,"authority":243,"freshness":273,"relevant":22,"comment":509},"提出CAFF-Net模型，耦合预测城市绿地与水体生态健康，方法新颖，数据可靠，对智慧城市生态管理有参考价值。",[511],{"name":505,"url":502},[167,249,27,29,513],"城市绿地",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":516},{"candidates":517,"agricultural_evidence":14,"reason":67},[518,522,526,530],{"code":40,"score":244,"supported":42,"title":519,"abstract":520,"tags":521},[45],[27,45],[27],{"code":63,"score":22,"supported":14,"title":523,"abstract":524,"tags":525},[],[],[29],{"code":196,"score":22,"supported":14,"title":527,"abstract":528,"tags":529},[],[],[167],{"code":255,"score":22,"supported":14,"title":531,"abstract":532,"tags":533},[],[],[249],[535,536],"农业人工智能 城市绿地 智慧农业 生态监测","农业人工智能 城市绿地","农业人工智能城市绿地智慧农业生态监测-1729","10.1016\u002Fj.asej.2026.104404",{"doi":538,"openalex_id":540,"authors":541,"venue":505,"cited_by_count":137,"oa_url":550,"card":551,"direction":298,"ingested_from":145},"W7207862972",[542,545,547],{"name":543,"orcid":544},"Yifeng Shen","https:\u002F\u002Forcid.org\u002F0009-0009-3887-4938",{"name":546,"orcid":9},"Y. F. Ye",{"name":548,"orcid":549},"Shulai Wang","https:\u002F\u002Forcid.org\u002F0009-0007-7101-3449","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2090447926004351\u002Fpdf",{"tldr":552,"method":553,"finding":554,"direction":143,"opportunity":555},"提出CAFF-Net，用多源遥感数据耦合预测城市绿地和水体生态健康。","双分支Swin-Transformer提取特征，CBAM过滤冗余，改进多任务UN","R²达0.928，比SOTA高3.3%，抗云干扰强，支持海绵城市治理。","可延伸至农业生态系统，耦合预测农田植被与水体健康，探索多任务联合损失在农业遥感中的应用。","2026-09-05T23:30:13.221496Z"]