[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3081":3,"related-3081":71},{"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,"search_phrases":32,"slug":35,"view_count":36,"doi":37,"paper":38,"created_at":70},3081,"Evaluating vegetation indices for monitoring drought and post-drought declines in European forest productivity","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fbg-23-6557-2026","Drought is causing increasingly severe and widespread negative impacts on forest gross primary productivity (GPP), but modelling these impacts over large spatial scales with remote sensing data is challenging. It is especially problematic in forests that have lower spectral sensitivity to drought compared to other ecosystems and where the timing of vegetation index (VI) response may lag GPP. However, the length of time lags between drought start, GPP and VI response in forests have not been quantified or compared among VIs. We tested the ability of 12 MODIS variables (land surface temperature, leaf area index, fraction absorbed photosynthetic active radiation and nine VIs) to capture drought-induced reductions in GPP from ICOS and FLUXNET eddy covariance data at 18 forest sites across Europe. Our analysis quantified the time lags between the Standardized Precipitation Evapotranspiration Index, GPP and VI response to drought as well as legacy effects in the first year post-drought. We found that land surface temperature was the only MODIS variable that showed significant change between drought and non-drought reference periods at both deciduous broadleaf and evergreen coniferous forests. At deciduous sites, the Chlorophyll\u002FCarotenoid Index, Normalized Difference Water Index and Normalized Difference Vegetation Index (NDVI) were also significantly reduced during drought while the Near Infrared Reflectance Index (NIRv) was significantly reduced at coniferous sites. There were substantial variations in the magnitude and timing of drought response among the VIs which we relate to drought-induced changes in tree physiology and their differences between the five tree genera represented at the study sites. VIs related to canopy structure (NDVI, Plant Phenology Index and NIRv) remained low in the first year following drought at both broadleaf and coniferous sites (although not statistically significant), while GPP often recovered to long-term mean values, implying possible post-drought decoupling between GPP and these VIs.","干旱正对森林总初级生产力（GPP）造成日益严重且广泛的负面影响，但利用遥感数据在大空间尺度上模拟这些影响仍具挑战性。对于光谱对干旱敏感性低于其他生态系统的森林，以及植被指数（VI）响应时间可能滞后于GPP的森林，这一问题尤为突出。然而，森林中干旱开始、GPP与VI响应之间的时间滞后长度尚未被量化，也未在不同VI之间进行比较。我们测试了12个MODIS变量（地表温度、叶面积指数、吸收光合有效辐射分数及9个植被指数）在捕捉欧洲18个森林站点由干旱引起的GPP下降方面的能力，所用GPP数据来自ICOS和FLUXNET涡度相关观测。我们的分析量化了标准化降水蒸散指数、GPP和VI对干旱响应之间的时间滞后，以及干旱后第一年的遗留效应。我们发现，地表温度是唯一在落叶阔叶林和常绿针叶林中均显示干旱期与非干旱参考期之间存在显著差异的MODIS变量。在落叶站点，叶绿素\u002F类胡萝卜素指数、归一化差异水分指数和归一化差异植被指数（NDVI）在干旱期间也显著降低，而在针叶站点，近红外反射率指数（NIRv）显著降低。各VI在干旱响应的幅度和时间上存在显著差异，我们将其与干旱引起的树木生理变化以及研究站点所代表的五个树属之间的差异联系起来。与冠层结构相关的VI（NDVI、植物物候指数和NIRv）在干旱后第一年在阔叶和针叶站点均保持较低水平（尽管未达统计显著性），而GPP通常恢复至长期均值，暗示干旱后GPP与这些VI之间可能出现解耦。",null,"Biogeosciences","2026-09-18T00:00:00Z","论文",10,false,76,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,22,18,14,6,1,"基于18个欧洲森林站点与12种MODIS变量的对比研究，量化了干旱及灾后GPP与植被指数的时滞与解耦，方法新颖、数据扎实，对遥感监测森林生产力具有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"遥感","植被指数","森林干旱","GPP监测","欧洲森林",[33,34],"MODIS 植被指数 干旱","欧洲森林 GPP 遥感监测","MODIS植被指数干旱-3081",0,"10.5194\u002Fbg-23-6557-2026",{"doi":37,"openalex_id":39,"authors":40,"venue":10,"cited_by_count":36,"oa_url":62,"card":63,"direction":67,"ingested_from":69},"W7213558520",[41,44,47,50,53,56,59],{"name":42,"orcid":43},"Julia Kelly","https:\u002F\u002Forcid.org\u002F0000-0002-7370-1401",{"name":45,"orcid":46},"Tim Schacherl","https:\u002F\u002Forcid.org\u002F0009-0005-6778-0763",{"name":48,"orcid":49},"Lars Eklundh","https:\u002F\u002Forcid.org\u002F0000-0001-7644-6517",{"name":51,"orcid":52},"Hongxiao Jin","https:\u002F\u002Forcid.org\u002F0000-0003-3100-7814",{"name":54,"orcid":55},"Anne Klosterhalfen","https:\u002F\u002Forcid.org\u002F0000-0001-7999-8966",{"name":57,"orcid":58},"Alexander Knohl","https:\u002F\u002Forcid.org\u002F0000-0002-7615-8870",{"name":60,"orcid":61},"Natascha Kljun","https:\u002F\u002Forcid.org\u002F0000-0001-9650-2184","https:\u002F\u002Fbg.copernicus.org\u002Farticles\u002F23\u002F6557\u002F2026\u002Fbg-23-6557-2026.pdf",{"tldr":64,"method":65,"finding":66,"direction":67,"opportunity":68},"评估12种MODIS植被指数监测欧洲森林干旱及灾后生产力下降的能力。","利用18个欧洲森林站点的涡度协方差GPP与MODIS数据，量化干旱响应时滞。","地表温度是唯一在两类森林均显著变化的指标，灾后GPP恢复但结构类植被指数仍偏低。","农业遥感与作物表型","可探究灾后GPP与植被指数解耦机制，并开发融合时滞与遗留效应的干旱监测模型。","openalex","2026-09-21T23:30:27.481162Z",{"total":21,"page":22,"page_size":21,"items":72},[73,124,171,201,237,302],{"id":74,"title":75,"url":76,"summary":77,"summary_zh":78,"content":9,"source_name":79,"source_url":76,"published_at":80,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":81,"score_detail":82,"sources":84,"tags":86,"search_phrases":90,"slug":93,"view_count":36,"doi":94,"paper":95,"created_at":123},2660,"Red-Edge Information in Agricultural Remote Sensing: From Spectral Theory to Explainable Machine Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183180","The red-edge (RE) spectral region has become a central component of agricultural remote sensing because it captures physiologically meaningful changes in chlorophyll content, canopy structure and vegetation functioning. The availability of dedicated RE bands on modern multispectral satellites and advances in hyperspectral sensing have stimulated widespread applications for crop monitoring, nutrient assessment, stress detection and yield prediction. However, reported improvements over conventional visible–near-infrared (VIS–NIR) approaches remain highly variable, and the mechanisms governing when and why RE information provides additional value are often poorly synthesised. This review presents a conceptual framework that links the physical and physiological basis of RE reflectance with its condition-dependent agronomic performance and its emerging role within modern machine learning (ML) systems. We first examine how pigment absorption, canopy structure and sensor characteristics jointly determine the representation of RE information from hyperspectral measurements to operational multispectral observations. We then synthesise evidence demonstrating that the agronomic value of RE information is strongly dependent on crop characteristics, phenological stage, environmental conditions and observation geometry, explaining much of the variability reported across previous studies. Finally, we show how recent advances in ML and explainable artificial intelligence have changed the interpretation of RE information. Rather than evaluating RE-derived vegetation indices in isolation, contemporary predictive frameworks integrate RE observations with complementary spectral, climatic, structural and temporal predictors, allowing their physiological contribution to be quantified within multidimensional models. We conclude that future value of RE remote sensing will require not only continued advances in spectral measurement and vegetation index development, but also improved interpretation, transferability and operational integration of physiologically meaningful RE information within explainable, multi-source agricultural monitoring systems.","红边（RE）光谱区已成为农业遥感的核心组成部分，因为它能够捕捉叶绿素含量、冠层结构和植被功能等方面具有生理意义的变化。现代多光谱卫星上专用红边波段的可用性以及高光谱传感技术的进步，推动了其在作物监测、养分评估、胁迫检测和产量预测中的广泛应用。然而，相较于传统可见光—近红外（VIS–NIR）方法所报道的改进效果仍高度可变，而关于红边信息何时以及为何提供额外价值的机制往往缺乏系统梳理。本综述提出了一个概念框架，将红边反射率的物理与生理基础与其条件依赖的农学表现及其在现代机器学习（ML）系统中新兴的作用联系起来。我们首先探讨色素吸收、冠层结构和传感器特性如何共同决定从高光谱测量到业务化多光谱观测中红边信息的表征。随后，我们综合证据表明，红边信息的农学价值强烈依赖于作物特征、物候阶段、环境条件和观测几何，这解释了以往研究中报道的大部分变异性。最后，我们展示了机器学习和可解释人工智能的最新进展如何改变了对红边信息的解读。当代预测框架不再孤立地评估红边衍生的植被指数，而是将红边观测与互补的光谱、气候、结构和时间预测因子相结合，从而在多维模型中量化其生理贡献。我们得出结论：红边遥感的未来价值不仅需要光谱测量和植被指数开发的持续进步，还需要在可解释的多源农业监测系统中改进对具有生理意义的红边信息的解读、可迁移性和业务化整合。","Remote Sensing","2026-09-16T00:00:00Z",82,{"impact":19,"substance":18,"depth":19,"authority":20,"freshness":13,"relevant":22,"comment":83},"系统综述红边遥感从光谱机理到可解释机器学习的农学价值条件，方法框架新颖、结论可靠，对作物监测与产量预测有实质参考意义。",[85],{"name":79,"url":76},[87,88,27,89,28],"智慧农业","农业人工智能","作物监测",[91,92],"农业人工智能 作物监测 智慧农业 植被指数","农业人工智能 作物监测","农业人工智能作物监测智慧农业植被指数-2660","10.3390\u002Frs18183180",{"doi":94,"openalex_id":96,"authors":97,"venue":79,"cited_by_count":36,"oa_url":76,"card":118,"direction":67,"ingested_from":69},"W7213344870",[98,101,103,106,109,112,115],{"name":99,"orcid":100},"Ignacio Fuentes","https:\u002F\u002Forcid.org\u002F0000-0001-7066-7482",{"name":102,"orcid":9},"Nikolas Hoskin",{"name":104,"orcid":105},"Patrick Filippi","https:\u002F\u002Forcid.org\u002F0000-0003-3573-084X",{"name":107,"orcid":108},"Abhasha Joshi","https:\u002F\u002Forcid.org\u002F0000-0002-1422-465X",{"name":110,"orcid":111},"Yi Yu","https:\u002F\u002Forcid.org\u002F0000-0002-1140-2713",{"name":113,"orcid":114},"Thomas F. A. Bishop","https:\u002F\u002Forcid.org\u002F0000-0002-6723-7323",{"name":116,"orcid":117},"Dhahi Al-Shammari","https:\u002F\u002Forcid.org\u002F0000-0001-6608-8322",{"tldr":119,"method":120,"finding":121,"direction":67,"opportunity":122},"综述红边遥感在农业中的物理基础、条件依赖性与可解释机器学习中的新角色。","文献综述，整合光谱理论、多光谱\u002F高光谱数据与可解释AI方法。","红边信息的农学价值高度依赖作物、物候、环境与观测几何，需多源融合与可解释建模。","可研究红边信息在不同作物-物候-环境组合下的可迁移性，并构建可解释多源融合模型。","2026-09-16T23:30:28.719858Z",{"id":125,"title":126,"url":127,"summary":128,"summary_zh":129,"content":9,"source_name":130,"source_url":127,"published_at":131,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":132,"score_detail":133,"sources":140,"tags":142,"search_phrases":144,"slug":146,"view_count":36,"doi":147,"paper":148,"created_at":170},2169,"GeoWombat: Scalable geospatial and remote sensing analysis in Python","https:\u002F\u002Fdoi.org\u002F10.21105\u002Fjoss.10812","GeoWombat is an open-source Python library that provides an end-to-end platform for geospatial raster data processing and remote sensing analysis at scale.Built on xarray (Hoyer & Hamman, 2017), Dask (Rocklin, 2015), and rasterio (Gillies & others, 2013--), GeoWombat simplifies common but complex operations-such as mosaicking multi-tile imagery, reprojecting across coordinate reference systems, aligning rasters of varying resolutions, and performing radiometric corrections-into concise, intuitive commands.The library includes built-in sensor profiles for Landsat 1-8, Sentinel-1 and Sentinel-2, MODIS, and NAIP that automate band naming, scaling, and metadata handling.It supports workflows spanning cloud-based data access via SpatioTemporal Asset Catalogs (STAC) from multiple providers, a full radiometric processing chain (DN-to-reflectance conversion, atmospheric correction, BRDF normalization, and topographic correction), vegetation index computation, raster-vector interoperability, Cython-accelerated moving window statistics, scikit-learn-based (Pedregosa et al., 2011) machine learning classification, deep learning with PyTorch (Paszke et al., 2019), and georeferenced object detection.By leveraging Dask's lazy evaluation and task graphs, GeoWombat enables out-of-core processing of raster datasets of any size on commodity hardware.","GeoWombat是一个开源Python库，为大规模地理空间栅格数据处理和遥感分析提供端到端平台。它构建于xarray（Hoyer & Hamman, 2017）、Dask（Rocklin, 2015）和rasterio（Gillies & others, 2013--）之上，将多瓦片影像镶嵌、跨坐标参考系重投影、不同分辨率栅格对齐以及辐射校正等常见但复杂的操作简化为简洁直观的命令。该库内置了Landsat 1-8、Sentinel-1和Sentinel-2、MODIS以及NAIP的传感器配置文件，可自动完成波段命名、缩放和元数据处理。它支持的工作流涵盖通过来自多个提供商的时空资产目录（STAC）进行云端数据访问、完整的辐射处理链（DN到反射率转换、大气校正、BRDF归一化和地形校正）、植被指数计算、栅格-矢量互操作、Cython加速的移动窗口统计、基于scikit-learn（Pedregosa et al., 2011）的机器学习分类、基于PyTorch（Paszke et al., 2019）的深度学习以及地理参考目标检测。通过利用Dask的惰性求值和任务图，GeoWombat能够在普通硬件上对任意大小的栅格数据集进行外存处理。","The Journal of Open Source Software","2026-09-09T00:00:00Z",70,{"impact":134,"substance":135,"depth":136,"authority":137,"freshness":138,"relevant":22,"comment":139},12,20,17,13,8,"开源遥感分析库，集成多源卫星与深度学习能力，对农业遥感监测有实用价值，但属工具类论文，公共影响有限。",[141],{"name":130,"url":127},[88,27,89,143,28],"开源工具",[145,92],"农业人工智能 作物监测 开源工具 植被指数","农业人工智能作物监测开源工具植被指数-2169","10.21105\u002Fjoss.10812",{"doi":147,"openalex_id":149,"authors":150,"venue":130,"cited_by_count":36,"oa_url":164,"card":165,"direction":67,"ingested_from":69},"W7212022895",[151,154,157,159,162],{"name":152,"orcid":153},"Jordan Graesser","https:\u002F\u002Forcid.org\u002F0000-0002-6137-7050",{"name":155,"orcid":156},"Michael Mann","https:\u002F\u002Forcid.org\u002F0000-0002-6268-6867",{"name":158,"orcid":9},"Leonardo Hardtke",{"name":160,"orcid":161},"Robert Denham","https:\u002F\u002Forcid.org\u002F0000-0002-3342-7970",{"name":163,"orcid":9},"Sharon Xu","https:\u002F\u002Fjoss.theoj.org\u002Fpapers\u002F10.21105\u002Fjoss.10812.pdf",{"tldr":166,"method":167,"finding":168,"direction":67,"opportunity":169},"发布开源Python库GeoWombat，实现大规模地理空间栅格数据与遥感分析的一站式处理。","基于xarray、Dask、rasterio，集成STAC云数据访问、辐射校正、","可借助Dask惰性求值在普通硬件上核外处理任意大小栅格，并内置多传感器配置。","可基于该库构建作物表型参数自动化提取与深度学习分类流程，降低大规模遥感分析门槛。","2026-09-11T23:30:31.360896Z",{"id":172,"title":173,"url":174,"summary":175,"summary_zh":9,"content":9,"source_name":176,"source_url":9,"published_at":177,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":178,"score_detail":179,"sources":182,"tags":184,"search_phrases":188,"slug":191,"view_count":36,"doi":9,"paper":192,"created_at":200},3121,"Optimization of Farmland Management Zoning in the Black Soil Region: A Climate Adaptability Assessment Considering Crop Growth Response and Topographic Characteristics（黑土区农田管理分区优化：考虑作物生长响应与地形特征的气候适应性评估）","https:\u002F\u002Fwww.mdpi.com\u002F2072-4292\u002F18\u002F18\u002F3260","吉林农业大学 Han Yongqi 等联合中科院东北地理与农业生态研究所、东北农业大学在《Remote Sensing》18(18): 3260 发表论文（2026-09-21 发表）。针对精准农业管理分区对单日期影像依赖难以捕捉年际作物环境变化问题，研究评估 29 个特征组合（融合 Sentinel-2 多光谱、PCA、NDVI 和 DEM 数据）在黑土区友谊农场干旱、湿润和融合场景下的气候适应性。实施异构空间注意力网络（HSAN）和 K-means 聚类，以变异系数（CV）评估稳定性与适应性。结果显示 HSAN 在多源融合下优于 K-means，CV 分别为 11.303-14.774% 与 14.823-16.011%；多期 NDVI 数据是主导因素，相对 CV 减少 34.850-53.701%；DEM 贡献有限；PCA 增强稳定性；多期融合在极端气候年份提升分区生态一致性与适用性。","MDPI Remote Sensing","2026-09-21T00:00:00Z",79,{"impact":180,"substance":18,"depth":19,"authority":20,"freshness":13,"relevant":22,"comment":181},15,"黑土区精准农业管理分区研究，方法新颖、数据扎实，对农业遥感应用有参考价值。",[183],{"name":176,"url":174},[87,185,186,27,187],"精准农业","黑土区","管理分区",[189,190],"黑土区 管理分区 遥感","Sentinel-2 黑土区 气候适应性","黑土区管理分区遥感-3121",{"doi":9,"openalex_id":9,"authors":193,"venue":9,"cited_by_count":36,"oa_url":9,"card":194,"direction":67,"ingested_from":199},[],{"tldr":195,"method":196,"finding":197,"direction":67,"opportunity":198},"评估黑土区多源遥感特征组合在干旱湿润场景下的农田管理分区气候适应性。","融合Sentinel-2多光谱、NDVI、PCA与DEM，用HSAN和K-mea","HSAN优于K-means，多期NDVI主导稳定性提升，DEM贡献有限，PCA增强稳定性。","可探索多期时序特征与深度聚类在极端气候下的跨区域迁移及分区决策落地。","agent","2026-09-22T00:05:38.189091Z",{"id":202,"title":203,"url":204,"summary":205,"summary_zh":206,"content":9,"source_name":207,"source_url":204,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":208,"score_detail":209,"sources":211,"tags":213,"search_phrases":218,"slug":221,"view_count":36,"doi":222,"paper":223,"created_at":236},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":138,"substance":19,"depth":136,"authority":137,"freshness":138,"relevant":22,"comment":210},"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。",[212],{"name":207,"url":204},[214,27,215,216,217],"无人机","生物多样性","生态监测","海岸生态",[219,220],"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":222,"openalex_id":224,"authors":225,"venue":207,"cited_by_count":36,"oa_url":229,"card":230,"direction":235,"ingested_from":69},"W7213604527",[226],{"name":227,"orcid":228},"Murat Yakar","https:\u002F\u002Forcid.org\u002F0000-0002-2664-6251","https:\u002F\u002Fwww.natureandconservation.com\u002Findex.php\u002Fnature\u002Farticle\u002Fdownload\u002F8949\u002F5099",{"tldr":231,"method":232,"finding":233,"direction":67,"opportunity":234},"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":238,"title":239,"url":240,"summary":241,"summary_zh":242,"content":9,"source_name":243,"source_url":240,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":244,"score_detail":245,"sources":247,"tags":249,"search_phrases":254,"slug":257,"view_count":36,"doi":258,"paper":259,"created_at":301},3083,"Multi-index analysis reveals complexity of tundra greening and shrubification on Yamal Peninsula","https:\u002F\u002Fdoi.org\u002F10.1088\u002F2752-664x\u002Faea99e","Abstract Arctic vegetation cover is undergoing rapid structural and spatial change under a warming climate. Shrubification is a major component of these changes, whereby shrubs grow outwards and upwards, infilling existing patches and spreading into new areas. Satellite-derived vegetation indices (VIs), such as the Normalized Difference Vegetation Index (NDVI), show increasing trends across decades in many regions of the Arctic, a phenomenon referred to as greening, and interpreted as an indicator of compositional, structural, spatial and functional changes in vegetation. However, the direct contribution of shrubification on satellite-level greening remains poorly understood, partly due to the spectral limitations of single-index methods and the coarse spatial resolution of multi-decadal satellite records. Here we examine and compare the spatial patterns and magnitude of greening on Yamal Peninsula, Arctic Russia, measured with four commonly used vegetation indices derived from Landsat imagery (1987-2023): NDVI, Enhanced Vegetation Index 2 (EVI2), Soil-Adjusted Vegetation Index (SAVI), and kernel NDVI (kNDVI). We also examine how willow (Salix lanata) cover (%) relates to greening, using cover data derived from field observations, and Unoccupied Aerial Vehicle (UAV) and Very High Resolution WorldView 3 satellite imagery. Vegetation greened in 12–25% of the study area, driven primarily by vegetation regeneration on cryogenic landslides and possibly due to shrub expansion. However, greening magnitude was dependent on the VI selected, as they showed only a maximum of 54% similarity in spatial distribution. Uncertainty in the relationship between spectral greening and shrubification adds complexity to interpreting drivers of vegetation change, including how herbivory, for example by reindeer (Rangifer tarandusi), inhibits shrub expansion and how abiotic and other biotic influences may be detected by different or some combination of VIs. We demonstrate that decadal vegetation greenness changes have not been homogenous on Yamal Peninsula, with contrasting Salix-dominated areas responding differently to shared changes in climate, likely due to herbivore dynamics, soil effects, as well as microclimatic or topographical differences in the area.","摘要 在气候变暖背景下，北极植被覆盖正经历快速的结构和空间变化。灌木化是这些变化的主要组成部分，表现为灌木向外和向上生长，填充现有斑块并向新区域扩散。基于卫星的植被指数（VIs），如归一化差异植被指数（NDVI），在北极许多地区显示出数十年间的增长趋势，这一现象被称为绿化，并被解释为植被组成、结构、空间和功能变化的指标。然而，灌木化对卫星尺度绿化的直接贡献仍知之甚少，部分原因在于单一指数方法的光谱局限性以及多年代际卫星记录的空间分辨率较粗。本研究利用1987—2023年Landsat影像衍生的四种常用植被指数——NDVI、增强植被指数2（EVI2）、土壤调节植被指数（SAVI）和核NDVI（kNDVI）——考察并比较了俄罗斯北极亚马尔半岛绿化的空间格局和幅度。我们还利用野外观测、无人驾驶飞行器（UAV）和甚高分辨率WorldView 3卫星影像获得的覆盖度数据，研究了柳树（Salix lanata）覆盖度（%）与绿化的关系。研究区12%—25%的区域出现植被绿化，主要由低温滑坡上的植被再生驱动，也可能源于灌木扩张。然而，绿化幅度取决于所选用的植被指数，因为它们在空间分布上的相似性最高仅为54%。光谱绿化与灌木化之间关系的不确定性增加了解释植被变化驱动因素的复杂性，包括植食作用（例如驯鹿Rangifer tarandus的啃食）如何抑制灌木扩张，以及非生物和其他生物影响如何通过不同植被指数或其组合被检测到。我们证明，亚马尔半岛数十年尺度的植被绿度变化并非均一，以柳树为主的对比区域对共同的气候变化响应不同，这可能归因于植食动物动态、土壤效应以及该地区微气候或地形的差异。","Environmental Research Ecology",69,{"impact":138,"substance":18,"depth":19,"authority":137,"freshness":138,"relevant":22,"comment":246},"多指数遥感揭示北极苔原绿化与灌木扩张复杂性，方法新颖数据扎实，但属基础生态研究，与三农信息化关联间接，公共价值有限。",[248],{"name":243,"url":240},[250,251,28,252,253],"气候变化","遥感监测","北极苔原","灌木扩张",[255,256],"北极苔原 灌木扩张 遥感","北极苔原 植被指数 气候变化 灌木扩张","北极苔原灌木扩张遥感-3083","10.1088\u002F2752-664x\u002Faea99e",{"doi":258,"openalex_id":260,"authors":261,"venue":243,"cited_by_count":36,"oa_url":295,"card":296,"direction":67,"ingested_from":69},"W7213598351",[262,265,268,270,272,275,278,281,284,287,289,292],{"name":263,"orcid":264},"Elias Koivisto","https:\u002F\u002Forcid.org\u002F0009-0007-6204-0963",{"name":266,"orcid":267},"Anton Kuzmin","https:\u002F\u002Forcid.org\u002F0000-0001-5066-5535",{"name":269,"orcid":9},"Logan Berner",{"name":271,"orcid":9},"Jeff T Kerby",{"name":273,"orcid":274},"Mariana Verdonen","https:\u002F\u002Forcid.org\u002F0000-0001-9780-0052",{"name":276,"orcid":277},"Anna Skarin","https:\u002F\u002Forcid.org\u002F0000-0003-3221-1024",{"name":279,"orcid":280},"Tiina H. M. Kolari","https:\u002F\u002Forcid.org\u002F0000-0003-0955-2402",{"name":282,"orcid":283},"Teemu Tahvanainen","https:\u002F\u002Forcid.org\u002F0000-0002-7856-299X",{"name":285,"orcid":286},"Pasi Korpelainen","https:\u002F\u002Forcid.org\u002F0009-0005-9956-6016",{"name":288,"orcid":9},"Miguel Villosada",{"name":290,"orcid":291},"Bruce C. Forbes","https:\u002F\u002Forcid.org\u002F0000-0002-4593-5083",{"name":293,"orcid":294},"Timo Kumpula","https:\u002F\u002Forcid.org\u002F0000-0002-2716-7420","https:\u002F\u002Fiopscience.iop.org\u002Farticle\u002F10.1088\u002F2752-664X\u002Faea99e\u002Fpdf",{"tldr":297,"method":298,"finding":299,"direction":67,"opportunity":300},"用四种植被指数分析亚马尔半岛苔原绿化，揭示灌木扩张与光谱绿化的复杂关系。","Landsat 1987-2023年NDVI、EVI2、SAVI、kNDVI及无","绿化面积12-25%，但不同指数空间分布相似度仅54%，灌木扩张与绿化关系不确定。","多指数遥感可揭示植被变化的异质性，需结合地面与高分辨率数据解析生物与非生物驱动机制。","2026-09-21T23:30:27.685080Z",{"id":303,"title":304,"url":305,"summary":306,"summary_zh":307,"content":9,"source_name":308,"source_url":305,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":309,"score_detail":310,"sources":313,"tags":315,"search_phrases":319,"slug":322,"view_count":36,"doi":323,"paper":324,"created_at":393},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":311,"depth":19,"authority":20,"freshness":138,"relevant":22,"comment":312},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。",[314],{"name":308,"url":305},[27,316,216,317,318],"干旱胁迫","植物水分","数据库",[320,321],"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":323,"openalex_id":325,"authors":326,"venue":308,"cited_by_count":36,"oa_url":387,"card":388,"direction":67,"ingested_from":69},"W7213559802",[327,330,333,336,339,342,345,348,351,354,357,360,363,366,369,372,375,378,381,384],{"name":328,"orcid":329},"Jessica Guo","https:\u002F\u002Forcid.org\u002F0000-0002-9566-9182",{"name":331,"orcid":332},"Ana Maria Restrepo Acevedo","https:\u002F\u002Forcid.org\u002F0000-0003-4861-838X",{"name":334,"orcid":335},"Marvin Browne","https:\u002F\u002Forcid.org\u002F0000-0002-9640-0759",{"name":337,"orcid":338},"Daniel M. Johnson","https:\u002F\u002Forcid.org\u002F0000-0003-1015-9560",{"name":340,"orcid":341},"Katherine A. McCulloh","https:\u002F\u002Forcid.org\u002F0000-0003-0801-3968",{"name":343,"orcid":344},"Jesse B. Nippert","https:\u002F\u002Forcid.org\u002F0000-0002-7939-342X",{"name":346,"orcid":347},"Rafael Poyatos","https:\u002F\u002Forcid.org\u002F0000-0003-0521-2523",{"name":349,"orcid":350},"Steven A. Kannenberg","https:\u002F\u002Forcid.org\u002F0000-0002-4097-9140",{"name":352,"orcid":353},"Daniel P. Beverly","https:\u002F\u002Forcid.org\u002F0000-0002-1267-4147",{"name":355,"orcid":356},"K. Arthur Endsley","https:\u002F\u002Forcid.org\u002F0000-0001-9722-8092",{"name":358,"orcid":359},"Andrew F. Feldman","https:\u002F\u002Forcid.org\u002F0000-0003-1547-6995",{"name":361,"orcid":362},"Alexandra G. Konings","https:\u002F\u002Forcid.org\u002F0000-0002-2810-1722",{"name":364,"orcid":365},"Yanlan Liu","https:\u002F\u002Forcid.org\u002F0000-0001-5129-6284",{"name":367,"orcid":368},"Jordi Martínez‐Vilalta","https:\u002F\u002Forcid.org\u002F0000-0002-2332-7298",{"name":370,"orcid":371},"William M. Hammond","https:\u002F\u002Forcid.org\u002F0000-0002-2904-810X",{"name":373,"orcid":374},"Kevin R. Hultine","https:\u002F\u002Forcid.org\u002F0000-0001-9747-6037",{"name":376,"orcid":377},"Lauren E. L. Lowman","https:\u002F\u002Forcid.org\u002F0000-0003-2960-7095",{"name":379,"orcid":380},"Jeffrey S. Dukes","https:\u002F\u002Forcid.org\u002F0000-0001-9482-7743",{"name":382,"orcid":383},"Julia K. Green","https:\u002F\u002Forcid.org\u002F0000-0002-8466-2313",{"name":385,"orcid":386},"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":389,"method":390,"finding":391,"direction":67,"opportunity":392},"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"]