[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3507":3,"related-3507":79},{"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,"search_phrases":31,"slug":34,"view_count":35,"doi":36,"paper":37,"created_at":78},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。",null,"Geophysical Research Letters","2026-09-23T00:00:00Z","论文",10,false,81,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,15,8,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"农业遥感","遥感","水资源利用","生态监测","碳汇",[32,33],"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",0,"10.1029\u002F2026gl124105",{"doi":36,"openalex_id":38,"authors":39,"venue":10,"cited_by_count":35,"oa_url":70,"card":71,"direction":75,"ingested_from":77},"W7214122316",[40,43,46,49,52,55,58,61,64,67],{"name":41,"orcid":42},"Zoe Pierrat","https:\u002F\u002Forcid.org\u002F0000-0002-6726-2406",{"name":44,"orcid":45},"Thomas P. Kurosu","https:\u002F\u002Forcid.org\u002F0000-0003-2555-7780",{"name":47,"orcid":48},"Abhishek Chatterjee","https:\u002F\u002Forcid.org\u002F0000-0002-3680-0160",{"name":50,"orcid":51},"Joshua B. Fisher","https:\u002F\u002Forcid.org\u002F0000-0003-4734-9085",{"name":53,"orcid":54},"Margaret C. Johnson","https:\u002F\u002Forcid.org\u002F0000-0003-1481-9706",{"name":56,"orcid":57},"Le Kuai","https:\u002F\u002Forcid.org\u002F0000-0001-6406-1150",{"name":59,"orcid":60},"Kaniska Mallick","https:\u002F\u002Forcid.org\u002F0000-0002-2735-930X",{"name":62,"orcid":63},"Nicholas Cody Parazoo","https:\u002F\u002Forcid.org\u002F0000-0002-4424-7780",{"name":65,"orcid":66},"Benjamin C. Wiebe","https:\u002F\u002Forcid.org\u002F0000-0002-9325-1540",{"name":68,"orcid":69},"Kerry A. Cawse-Nicholson","https:\u002F\u002Forcid.org\u002F0000-0002-0510-4066","https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1029\u002F2026GL124105",{"tldr":72,"method":73,"finding":74,"direction":75,"opportunity":76},"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。","农业遥感与作物表型","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。","openalex","2026-09-25T23:30:33.881463Z",{"total":80,"page":21,"page_size":80,"items":81},6,[82,126,177,212,304,346],{"id":83,"title":84,"url":85,"summary":86,"summary_zh":87,"content":9,"source_name":88,"source_url":85,"published_at":89,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":90,"score_detail":91,"sources":97,"tags":99,"search_phrases":103,"slug":106,"view_count":35,"doi":107,"paper":108,"created_at":125},3191,"Spatial prediction of soil organic carbon stocks in Sudanese clay soils using regression kriging","https:\u002F\u002Fdoi.org\u002F10.3389\u002Fsjss.2026.16733","Soil organic carbon (SOC) stocks are a critical component of terrestrial carbon pools, influencing soil quality, agricultural productivity, and climate change mitigation. This study aimed to map and improve spatial estimation of SOC stocks in Sudan’s Blue Nile clay soils using regression kriging (RK). The model integrated 554 spatially unique soil profiles with nine environmental covariates: precipitation, temperature, relative humidity, normalized difference vegetation index (NDVI), land use\u002Fcover, bare soil index (BSI), digital elevation model (DEM), LS-factor, and aspect. Spectral indices were derived from Landsat 9 imagery (April 2024), while climate and terrain data were obtained from CHIRPS\u002FWorldClim and SRTM (30 m). RK performance was robust, with spatial cross-validation R 2 = 0.72, RMSE = 8.4 Mg C ha −1 (29% of mean observed stock), and mean bias = −0.8 Mg C ha −1 . Predicted SOC stocks (0–30 cm) ranged from 12.4 to 51.2 Mg C ha −1 (mean 28.6 Mg C ha −1 ). NDVI, clay content, and topographic wetness index were the most influential predictors. Agricultural lands exhibited the highest stocks (51.2 Mg C ha −1 ), while bare lands had the lowest (14.2 Mg C ha −1 ). This study (1) applies spatially explicit validation for SOC mapping in Sudan’s Blue Nile region, (2) harmonizes legacy and contemporary soil data using equivalent soil mass correction, and (3) provides high-resolution SOC maps for climate-resilient agricultural planning. Findings support soil carbon management and climate mitigation in semi-arid regions.","土壤有机碳（SOC）储量是陆地碳库的重要组成部分，影响土壤质量、农业生产力及气候变化减缓。本研究旨在利用回归克里金（RK）方法对苏丹青尼罗河黏土区SOC储量进行制图并改进其空间估算。该模型整合了554个空间独立土壤剖面与9个环境协变量：降水、温度、相对湿度、归一化植被指数（NDVI）、土地利用\u002F覆盖、裸土指数（BSI）、数字高程模型（DEM）、LS因子和坡向。光谱指数源自Landsat 9影像（2024年4月），气候与地形数据分别来自CHIRPS\u002FWorldClim和SRTM（30 m）。RK表现稳健，空间交叉验证R²=0.72，RMSE=8.4 Mg C ha⁻¹（为实测储量均值的29%），平均偏差=−0.8 Mg C ha⁻¹。预测SOC储量（0–30 cm）范围为12.4–51.2 Mg C ha⁻¹（均值28.6 Mg C ha⁻¹）。NDVI、黏粒含量和地形湿度指数是最具影响力的预测因子。农地储量最高（51.2 Mg C ha⁻¹），裸地最低（14.2 Mg C ha⁻¹）。本研究（1）对苏丹青尼罗河地区SOC制图采用空间显式验证，（2）利用等效土壤质量校正协调历史与当代土壤数据，（3）为气候韧性农业规划提供高分辨率SOC图。研究结果支持半干旱地区的土壤碳管理与气候减缓。","Spanish Journal of Soil Science","2026-09-22T00:00:00Z",68,{"impact":20,"substance":92,"depth":93,"authority":94,"freshness":95,"relevant":21,"comment":96},21,17,13,9,"基于554个土壤剖面与多源遥感协变量的回归克里金制图研究，方法规范、验证充分，对半干旱区土壤碳管理与气候适应型农业规划有参考价值，但属区域性学术成果，公共影响有限。",[98],{"name":88,"url":85},[26,100,27,101,102],"气候变化","土壤碳汇","数字土壤制图",[104,105],"苏丹青尼罗河 土壤有机碳 回归克里金","Landsat 9 土壤有机碳 空间预测","苏丹青尼罗河土壤有机碳回归克里金-3191","10.3389\u002Fsjss.2026.16733",{"doi":107,"openalex_id":109,"authors":110,"venue":88,"cited_by_count":35,"oa_url":85,"card":119,"direction":124,"ingested_from":77},"W7213971196",[111,113,115,117],{"name":112,"orcid":9},"Faroug A.H. Jadalla",{"name":114,"orcid":9},"Kolapo O. Oluwasemire",{"name":116,"orcid":9},"Abd Elmagid A. Elmobarak",{"name":118,"orcid":9},"Mohammed A. M. Mohammed Zein",{"tldr":120,"method":121,"finding":122,"direction":75,"opportunity":123},"用回归克里金结合多源环境协变量预测苏丹青尼罗河粘土区土壤有机碳储量。","554个土壤剖面与9个环境协变量，Landsat 9、CHIRPS\u002FWorldC","模型R²=0.72，NDVI、粘土含量和地形湿度指数影响最大，农地碳储量最高。","可引入时序遥感与机器学习提升半干旱区SOC动态预测，并耦合农业管理措施评估固碳潜力。","数字乡村与农业信息化","2026-09-22T23:30:31.028178Z",{"id":127,"title":128,"url":129,"summary":130,"summary_zh":131,"content":9,"source_name":132,"source_url":129,"published_at":133,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":134,"score_detail":135,"sources":139,"tags":141,"search_phrases":145,"slug":148,"view_count":35,"doi":149,"paper":150,"created_at":176},3166,"Case Studies of Environmental Monitoring Based on the Integrated Use of Bioindicators and Remote Sensing","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183227","This narrative review examines how plant bioindication can be integrated with remote sensing to support the preliminary screening of soil contamination, framed as an environmental situational awareness problem addressed through multi-scale imagery and field validation. Seven case studies—illegal waste deposits, pipeline leaks, landfill impacts, industrial areas, agrochemical drift, oil-spills in coastal wetlands, and acid mine drainage—were selected based on joint documentation of a contamination source or exposure condition, a measurable plant response, and a remotely detected signal related to vegetation stress. This synthesis clarifies how vegetation response can serve as a proxy for large-scale contamination screening. A common contamination–vegetation–remote sensing logic emerges across the cases examined, linking contamination drivers and exposure pathways to canopy responses detectable as spectral, thermal, spatial, or temporal anomalies. Recurring spatial patterns of vegetation anomalies are identified as first-order interpretive cues rather than diagnostic signatures. Vegetation stress responses lack spectral specificity, making spectral equifinality the primary operational challenge for vegetation-based biomonitoring. Future workflows should integrate spectral, spatial, temporal, multi-sensor, and ancillary data validated against ground-truth. Future research should expand the evidence base through formal literature searches, multi-site validation, standardized protocols, and infrastructures integrating plant phenotyping and remote sensing to support quantitatively validated screening approaches.","本叙述性综述探讨了如何将植物生物指示与遥感相结合，以支持土壤污染的初步筛查，并将其构建为一个通过多尺度影像和实地验证来解决的环境态势感知问题。基于对污染源或暴露条件的联合记录、可测量的植物响应以及与植被胁迫相关的遥感信号，选取了七个案例研究——非法废物堆放、管道泄漏、垃圾填埋场影响、工业区、农用化学品飘移、沿海湿地溢油和酸性矿山排水。本综述阐明了植被响应如何能够作为大规模污染筛查的替代指标。在所考察的案例中，浮现出一种共同的污染—植被—遥感逻辑，将污染驱动因素和暴露途径与可被检测为光谱、热、空间或时间异常的冠层响应联系起来。植被异常的重复性空间格局被识别为一级解释线索，而非诊断性特征。植被胁迫响应缺乏光谱特异性，使得光谱等终性成为基于植被的生物监测面临的首要操作挑战。未来的工作流程应整合光谱、空间、时间、多传感器和辅助数据，并以地面真值进行验证。未来研究应通过正式文献检索、多站点验证、标准化协议以及整合植物表型分析与遥感的基础设施来扩展证据基础，以支持经过定量验证的筛查方法。","Remote Sensing","2026-09-19T00:00:00Z",77,{"impact":136,"substance":92,"depth":17,"authority":137,"freshness":20,"relevant":21,"comment":138},16,14,"综述性论文，系统梳理植被生物指示与遥感融合筛查土壤污染的七个案例，方法学与结论对农业环境遥感监测有参考价值，但非全国性政策或突破性成果。",[140],{"name":132,"url":129},[26,27,142,143,144],"环境监测","植被监测","土壤污染",[146,147],"植被生物指示 遥感 土壤污染","Remote Sensing 植被胁迫 遥感监测","植被生物指示遥感土壤污染-3166","10.3390\u002Frs18183227",{"doi":149,"openalex_id":151,"authors":152,"venue":132,"cited_by_count":35,"oa_url":129,"card":171,"direction":75,"ingested_from":77},"W7213930509",[153,156,159,162,165,168],{"name":154,"orcid":155},"Marco De Mizio","https:\u002F\u002Forcid.org\u002F0009-0009-6556-2606",{"name":157,"orcid":158},"Donato Amitrano","https:\u002F\u002Forcid.org\u002F0000-0002-2355-4503",{"name":160,"orcid":161},"Massimiliano Gargiulo","https:\u002F\u002Forcid.org\u002F0000-0002-6783-366X",{"name":163,"orcid":164},"Sara Parrilli","https:\u002F\u002Forcid.org\u002F0000-0002-7225-1269",{"name":166,"orcid":167},"Claudia Savarese","https:\u002F\u002Forcid.org\u002F0000-0003-0161-2407",{"name":169,"orcid":170},"Massimiliano Lega","https:\u002F\u002Forcid.org\u002F0000-0002-4842-6049",{"tldr":172,"method":173,"finding":174,"direction":75,"opportunity":175},"综述植物生物指示与遥感结合用于土壤污染初步筛查的七个案例，提出污染-植被-遥感逻辑。","叙事性综述，选取七类污染案例，整合多尺度影像与地面验证。","植被胁迫光谱缺乏特异性，光谱等终性是植被生物监测的主要操作挑战。","可构建标准化植物表型-遥感集成平台，开展多站点验证以量化污染筛查。","2026-09-22T23:30:20.917796Z",{"id":178,"title":179,"url":180,"summary":181,"summary_zh":182,"content":9,"source_name":183,"source_url":180,"published_at":184,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":185,"score_detail":186,"sources":188,"tags":190,"search_phrases":194,"slug":197,"view_count":35,"doi":198,"paper":199,"created_at":211},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","2026-09-18T00:00:00Z",64,{"impact":20,"substance":17,"depth":93,"authority":94,"freshness":20,"relevant":21,"comment":187},"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。",[189],{"name":183,"url":180},[191,27,192,29,193],"无人机","生物多样性","海岸生态",[195,196],"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":198,"openalex_id":200,"authors":201,"venue":183,"cited_by_count":35,"oa_url":205,"card":206,"direction":124,"ingested_from":77},"W7213604527",[202],{"name":203,"orcid":204},"Murat Yakar","https:\u002F\u002Forcid.org\u002F0000-0002-2664-6251","https:\u002F\u002Fwww.natureandconservation.com\u002Findex.php\u002Fnature\u002Farticle\u002Fdownload\u002F8949\u002F5099",{"tldr":207,"method":208,"finding":209,"direction":75,"opportunity":210},"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。","2026-09-21T23:30:37.840826Z",{"id":213,"title":214,"url":215,"summary":216,"summary_zh":217,"content":9,"source_name":218,"source_url":215,"published_at":184,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":219,"score_detail":220,"sources":223,"tags":225,"search_phrases":229,"slug":232,"view_count":35,"doi":233,"paper":234,"created_at":303},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。","bioRxiv (Cold Spring Harbor Laboratory)",85,{"impact":18,"substance":221,"depth":17,"authority":137,"freshness":20,"relevant":21,"comment":222},23,"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。",[224],{"name":218,"url":215},[27,226,29,227,228],"干旱胁迫","植物水分","数据库",[230,231],"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","10.64898\u002F2026.09.17.752364",{"doi":233,"openalex_id":235,"authors":236,"venue":218,"cited_by_count":35,"oa_url":297,"card":298,"direction":75,"ingested_from":77},"W7213559802",[237,240,243,246,249,252,255,258,261,264,267,270,273,276,279,282,285,288,291,294],{"name":238,"orcid":239},"Jessica Guo","https:\u002F\u002Forcid.org\u002F0000-0002-9566-9182",{"name":241,"orcid":242},"Ana Maria Restrepo Acevedo","https:\u002F\u002Forcid.org\u002F0000-0003-4861-838X",{"name":244,"orcid":245},"Marvin Browne","https:\u002F\u002Forcid.org\u002F0000-0002-9640-0759",{"name":247,"orcid":248},"Daniel M. Johnson","https:\u002F\u002Forcid.org\u002F0000-0003-1015-9560",{"name":250,"orcid":251},"Katherine A. McCulloh","https:\u002F\u002Forcid.org\u002F0000-0003-0801-3968",{"name":253,"orcid":254},"Jesse B. Nippert","https:\u002F\u002Forcid.org\u002F0000-0002-7939-342X",{"name":256,"orcid":257},"Rafael Poyatos","https:\u002F\u002Forcid.org\u002F0000-0003-0521-2523",{"name":259,"orcid":260},"Steven A. Kannenberg","https:\u002F\u002Forcid.org\u002F0000-0002-4097-9140",{"name":262,"orcid":263},"Daniel P. Beverly","https:\u002F\u002Forcid.org\u002F0000-0002-1267-4147",{"name":265,"orcid":266},"K. Arthur Endsley","https:\u002F\u002Forcid.org\u002F0000-0001-9722-8092",{"name":268,"orcid":269},"Andrew F. Feldman","https:\u002F\u002Forcid.org\u002F0000-0003-1547-6995",{"name":271,"orcid":272},"Alexandra G. Konings","https:\u002F\u002Forcid.org\u002F0000-0002-2810-1722",{"name":274,"orcid":275},"Yanlan Liu","https:\u002F\u002Forcid.org\u002F0000-0001-5129-6284",{"name":277,"orcid":278},"Jordi Martínez‐Vilalta","https:\u002F\u002Forcid.org\u002F0000-0002-2332-7298",{"name":280,"orcid":281},"William M. Hammond","https:\u002F\u002Forcid.org\u002F0000-0002-2904-810X",{"name":283,"orcid":284},"Kevin R. Hultine","https:\u002F\u002Forcid.org\u002F0000-0001-9747-6037",{"name":286,"orcid":287},"Lauren E. L. Lowman","https:\u002F\u002Forcid.org\u002F0000-0003-2960-7095",{"name":289,"orcid":290},"Jeffrey S. Dukes","https:\u002F\u002Forcid.org\u002F0000-0001-9482-7743",{"name":292,"orcid":293},"Julia K. Green","https:\u002F\u002Forcid.org\u002F0000-0002-8466-2313",{"name":295,"orcid":296},"Lawren Sack","https:\u002F\u002Forcid.org\u002F0000-0002-7009-7202","https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002Fbiorxiv\u002Fearly\u002F2026\u002F09\u002F18\u002F2026.09.17.752364.full.pdf",{"tldr":299,"method":300,"finding":301,"direction":75,"opportunity":302},"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。","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。","2026-09-21T23:30:27.557264Z",{"id":305,"title":306,"url":307,"summary":308,"summary_zh":309,"content":9,"source_name":310,"source_url":307,"published_at":133,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":311,"score_detail":312,"sources":316,"tags":318,"search_phrases":322,"slug":325,"view_count":35,"doi":326,"paper":327,"created_at":345},3060,"Geospatial Intelligence for Peri-Urban Land-Use Conflicts: Evaluating Agricultural Suitability against Rapid Urbanisation using the Analytical Hierarchy Process and Cloud Computing","https:\u002F\u002Fdoi.org\u002F10.59543\u002F6mpcwr41","This paper presents a geospatial multi-criteria evaluation of agricultural potential in the suburban region of Bapatla using eight physical and land-use characteristics: elevation, slope, road accessibility, proximity to water bodies, Land Surface Temperature (LST), Normalised Difference Vegetation Index (NDVI), Land Use\u002FLand Cover (LULC), and soil texture, processed using Google Earth Engine. The Analytic Hierarchy Process (AHP) was used to determine the relative weights of each criterion. NDVI received the highest weight (26.33%), followed by LST, slope, and proximity to water bodies (14.96% each), while elevation received the lowest weight (4.6%) due to the region's flat terrain. Weighted overlay analysis classified the 142.25 km² study area into Suitable (108.90 km²; 76.55%), Not Suitable (32.60 km²; 22.92%), and Highly Suitable (0.75 km²; 0.53%) categories. Suitable areas are mainly distributed across the southern and peripheral agricultural zones, whereas unsuitable areas are concentrated within Bapatla Urban and its surroundings. The limited extent of highly suitable land highlights the scarcity of optimal agricultural sites. The results reveal land-use conflicts driven primarily by urbanisation rather than environmental constraints. The AHP-weighted suitability map provides an evidence-based tool for agricultural land conservation, water-resource management, and sustainable urban expansion.","本文基于八项自然与土地利用特征，对巴帕特拉（Bapatla）郊区农业潜力进行了地理空间多准则评价，这些特征包括：海拔、坡度、道路可达性、距水体远近、地表温度（LST）、归一化植被指数（NDVI）、土地利用\u002F土地覆盖（LULC）以及土壤质地，并利用Google Earth Engine进行处理。采用层次分析法（AHP）确定各准则的相对权重。NDVI权重最高（26.33%），其次为LST、坡度和距水体远近（均为14.96%），而海拔因该地区地形平坦权重最低（4.6%）。加权叠加分析将142.25 km²的研究区划分为适宜（108.90 km²；76.55%）、不适宜（32.60 km²；22.92%）和高适宜（0.75 km²；0.53%）三类。适宜区主要分布于南部及外围农业区，而不适宜区集中于巴帕特拉城区及其周边。高适宜土地面积有限，凸显了优质农业用地的稀缺性。结果表明，土地利用冲突主要由城市化驱动，而非环境限制。基于AHP的适宜性地图为农业用地保护、水资源管理和可持续城市扩张提供了循证工具。","Journal of Urban Intelligence and Smart Systems",70,{"impact":313,"substance":314,"depth":93,"authority":94,"freshness":20,"relevant":21,"comment":315},12,20,"该论文利用遥感与AHP方法评估城郊农业用地冲突，方法新颖、数据详实，对农业土地保护有参考价值，但属细分领域研究，影响范围有限。",[317],{"name":310,"url":307},[319,26,320,27,321],"智慧农业","农业信息化","土地利用",[323,324],"Bapatla 农业用地 城市化","Google Earth Engine 农业适宜性","Bapatla农业用地城市化-3060","10.59543\u002F6mpcwr41",{"doi":326,"openalex_id":328,"authors":329,"venue":310,"cited_by_count":35,"oa_url":338,"card":339,"direction":344,"ingested_from":77},"W7213644047",[330,332,334,336],{"name":331,"orcid":9},"Sreerama Naik Naik S R",{"name":333,"orcid":9},"T K Prasad",{"name":335,"orcid":9},"Feba Jose Jasmine",{"name":337,"orcid":9},"Jayapal G","https:\u002F\u002Fjuiss.org\u002Findex.php\u002Fjuiss\u002Farticle\u002Fdownload\u002F366\u002F231",{"tldr":340,"method":341,"finding":342,"direction":75,"opportunity":343},"用AHP与云平台评估印度Bapatla城郊农业适宜性，揭示城市化引发的土地利用冲突。","Google Earth Engine处理8个因子，AHP加权叠加分析142.2","76.55%区域适宜农业，但高度适宜仅0.53%，冲突主因是城市化而非环境限制。","可引入时序遥感与动态城市扩张模拟，构建城郊农业保护与城市增长协同优化模型。","智慧农业 \u002F 农业物联网","2026-09-21T23:30:09.448414Z",{"id":347,"title":348,"url":349,"summary":350,"summary_zh":351,"content":9,"source_name":352,"source_url":349,"published_at":353,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":354,"sources":357,"tags":359,"search_phrases":364,"slug":367,"view_count":35,"doi":368,"paper":369,"created_at":414},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",{"impact":17,"substance":18,"depth":355,"authority":94,"freshness":95,"relevant":21,"comment":356},19,"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。",[358],{"name":352,"url":349},[26,360,27,361,362,363],"高光谱","地表温度","哥白尼计划","合成数据",[365,366],"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","10.62880\u002Frars26005",{"doi":368,"openalex_id":370,"authors":371,"venue":352,"cited_by_count":35,"oa_url":349,"card":409,"direction":75,"ingested_from":77},"W7213413531",[372,374,376,379,382,385,388,391,393,396,398,400,402,404,407],{"name":373,"orcid":9},"Christian Miesgang",{"name":375,"orcid":9},"Sandra Dotzler",{"name":377,"orcid":378},"Anusha Sanmathi Sathyaniranjan","https:\u002F\u002Forcid.org\u002F0009-0009-8710-4622",{"name":380,"orcid":381},"Silke Migdall","https:\u002F\u002Forcid.org\u002F0000-0001-9089-6274",{"name":383,"orcid":384},"Heike Bach","https:\u002F\u002Forcid.org\u002F0000-0001-8060-2498",{"name":386,"orcid":387},"J. A. D. L. Blommaert","https:\u002F\u002Forcid.org\u002F0000-0002-5797-2439",{"name":389,"orcid":390},"Astrid Vannoppen","https:\u002F\u002Forcid.org\u002F0000-0001-5140-832X",{"name":392,"orcid":9},"Louis Snyders",{"name":394,"orcid":395},"Mihkel Veske","https:\u002F\u002Forcid.org\u002F0000-0003-2367-9215",{"name":397,"orcid":9},"Sven Kautlenbach",{"name":399,"orcid":9},"Catherine Odera",{"name":401,"orcid":9},"Tetiana Shtym",{"name":403,"orcid":9},"Tanel Tamm",{"name":405,"orcid":406},"Anke Schickling","https:\u002F\u002Forcid.org\u002F0000-0001-7446-7752",{"name":408,"orcid":9},"Melisa Soledad Heredia",{"tldr":410,"method":411,"finding":412,"direction":75,"opportunity":413},"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"]