[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3628":3,"related-3628":76},{"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":75},3628,"Identifying ecosystem service drivers to inform land use scenario simulation: An InVEST-PLSR-PLUS framework for the Danjiangkou Reservoir Basin, China","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.ecolind.2026.115581","The Danjiangkou Reservoir Basin is the core water source of the Middle Route of China's South-to-North Water Diversion Project, yet research in this basin has largely focused on static ecosystem service (ES) assessments, leaving the drivers of ES dynamics and their future trajectories poorly understood. This study develops an integrated InVEST–PLSR–PLUS framework that quantifies the spatiotemporal patterns of water yield, soil conservation, and carbon storage, identifies their dominant drivers objectively via Partial Least Squares Regression (PLSR), and projects land-use dynamics to 2030 using the PLUS model parameterized with PLSR-derived drivers rather than empirical selection. The main findings are as follows. (1) From 2010 to 2020, water yield increased markedly (from 79.22 to 895.38 to 166.69–1000.24 mm year −1 ), soil conservation declined (maximum from 941.60 to 641.91 t ha −1 year −1 ), and carbon storage remained stable (maximum around 20.4 t ha −1 year −1 ); high soil conservation and carbon storage values were concentrated in ecological and economic forests, whereas elevated water yield occurred mainly in farmland, orchards, and built-up land. (2) Vegetation and land-use variables dominated ES dynamics: EVI (VIP = 1.609), NDVI (VIP = 1.583), ecological forest area (VIP = 1.553), economic forest area (VIP = 1.476), and farmland area (VIP = 1.445) exerted strong positive effects; among landscape-pattern metrics, the landscape shape index (LSI; VIP = 1.313) was positively associated with all three services, whereas patch-subdivision metrics (PD, ED, IJI) showed negative associations; climatic and socioeconomic variables contributed only marginally (VIP \u003C 0.8, except GDP at 0.864). (3) Under the natural evolution scenario, ecological forest and orchard areas are projected to decrease by 40.89 km 2 and 20.54 km 2 by 2030, while economic forest (+29.63 km 2 ), water area (+24.10 km 2 ), farmland (+1.68 km 2 ), and built-up land (+5.98 km 2 ) expand. These findings demonstrate that vegetation cover and landscape connectivity fundamentally regulate ES provision, and indicate that ecological forest protection, control landscape fragmentation, and regulate built-up land and orchard expansion to safeguard the ecological security of this strategic water-source region.","丹江口库区是中国南水北调中线工程的核心水源区，但该区域的研究多集中于静态生态系统服务（ES）评估，对其动态变化的驱动机制及未来演变趋势的认识仍十分有限。本研究构建了InVEST–PLSR–PLUS集成框架，量化了产水量、土壤保持和碳储量的时空格局，通过偏最小二乘回归（PLSR）客观识别其主导驱动因子，并利用以PLSR-derived驱动因子而非经验选择参数化的PLUS模型，将土地利用动态模拟至2030年。主要结论如下：（1）2010—2020年，产水量显著增加（从79.22增至895.38，再到166.69~1000.24 mm·a⁻¹），土壤保持量下降（最大值从941.60降至641.91 t·ha⁻¹·a⁻¹），碳储量保持稳定（最大值约为20.4 t·ha⁻¹·a⁻¹）；土壤保持和碳储量的高值集中于生态林和经济林，而产水量的高值主要出现在耕地、果园和建设用地。（2）植被和土地利用变量主导了ES动态变化：EVI（VIP=1.609）、NDVI（VIP=1.583）、生态林面积（VIP=1.553）、经济林面积（VIP=1.476）和耕地面积（VIP=1.445）具有较强正向影响；在景观格局指标中，景观形状指数（LSI；VIP=1.313）与三项服务均呈正相关，而斑块细分指标（PD、ED、IJI）呈负相关；气候和社会经济变量的贡献较小（VIP\u003C0.8，GDP为0.864除外）。（3）在自然演变情景下，预计到2030年生态林和果园面积将分别减少40.89 km²和20.54 km²，而经济林（+29.63 km²）、水域（+24.10 km²）、耕地（+1.68 km²）和建设用地（+5.98 km²）将扩张。研究结果表明，植被覆盖和景观连通性从根本上调控着生态系统服务的供给，并指出应保护生态林、控制景观破碎化、调控建设用地和果园扩张，以保障这一战略性水源区的生态安全。",null,"Ecological Indicators","2026-09-25T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,23,18,14,9,1,"以PLSR驱动PLUS模型预测丹江口库区生态系统服务与土地利用变化，方法有创新、数据扎实，对水源区生态安全与农业空间管控有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"遥感","生态系统服务","土地利用模拟","水源地保护","丹江口库区",[33,34],"丹江口水库 生态系统服务","InVEST PLUS 土地利用模拟","丹江口水库生态系统服务-3628",0,"10.1016\u002Fj.ecolind.2026.115581",{"doi":37,"openalex_id":39,"authors":40,"venue":10,"cited_by_count":36,"oa_url":66,"card":67,"direction":73,"ingested_from":74},"W7214192017",[41,44,47,49,52,54,56,59,62,64],{"name":42,"orcid":43},"Bojun Ma","https:\u002F\u002Forcid.org\u002F0000-0002-7867-3817",{"name":45,"orcid":46},"Kun Sun","https:\u002F\u002Forcid.org\u002F0000-0002-9460-6681",{"name":48,"orcid":9},"Jian Zhang",{"name":50,"orcid":51},"Chucai Peng","https:\u002F\u002Forcid.org\u002F0000-0003-3366-9547",{"name":53,"orcid":9},"孙宝洋",{"name":55,"orcid":9},"Xiaoshuang Wang",{"name":57,"orcid":58},"Jigen Liu","https:\u002F\u002Forcid.org\u002F0009-0006-6798-2864",{"name":60,"orcid":61},"Jinquan Huang","https:\u002F\u002Forcid.org\u002F0000-0001-7372-1915",{"name":63,"orcid":9},"Lin Li",{"name":65,"orcid":9},"Feipeng Ren","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1470160X26009830\u002Fpdf",{"tldr":68,"method":69,"finding":70,"direction":71,"opportunity":72},"构建InVEST-PLSR-PLUS框架，识别丹江口库区生态系统服务驱动因子并模拟2030年土地利用","InVEST模型、偏最小二乘回归（PLSR）与PLUS模型耦合，基于2010-2","植被与土地利用主导生态系统服务，自然演变下生态林和果园将减少，经济林与建设用地扩张。","农业绿色发展与碳","可将PLSR驱动因子筛选推广至其他水源区，并结合多情景政策模拟优化生态补偿与土地管控。","农业人工智能与决策模型","openalex","2026-09-27T23:31:29.684586Z",{"total":77,"page":22,"page_size":77,"items":78},6,[79,128,184,233,268,308],{"id":80,"title":81,"url":82,"summary":83,"summary_zh":84,"content":9,"source_name":85,"source_url":82,"published_at":86,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":87,"score_detail":88,"sources":92,"tags":94,"search_phrases":98,"slug":101,"view_count":36,"doi":102,"paper":103,"created_at":127},2424,"Nonlinear relationships between vegetation dynamics and ecosystem services in the Yangtze River Basin: insights from random forest and GTWR analyzes","https:\u002F\u002Fdoi.org\u002F10.1080\u002F17538947.2026.2722459","Understanding the nonlinear relationships between vegetation dynamics and ecosystem services (ES) is essential for sustainable ecosystem management in the Yangtze River Basin (YRB). Using the Google Earth Engine platform, we analyzed NDVI dynamics in the YRB from 2000 to 2020. The InVEST model was used to assess four key ES, including water yield (WY), carbon storage (CS), habitat quality (HQ), and soil retention (SR). Random forest and spatiotemporal geographically weighted regression models were further applied to explore the nonlinear relationships and spatial heterogeneity between NDVI and ES. The results showed that: (1) mean NDVI in the YRB was 0.692, 0.697, and 0.724 in 2000, 2010, and 2020, respectively, exhibiting clear spatiotemporal heterogeneity. (2) WY showed a non-monotonic trajectory of initial increase followed by decline, SR increased markedly, HQ remained relatively stable, and CS showed a slight decrease. (3) CS and SR showed more pronounced positive associations with NDVI, whereas WY and HQ exhibited stronger interval variability or weaker localized effects. (4) NDVI generally had positive local effects on CS, HQ, and SR, while the NDVI–WY relationship exhibited both positive associations and localized negative effects. These findings provide valuable insights for ecological restoration and ecosystem service management in the YRB.","理解植被动态与生态系统服务（ES）之间的非线性关系，对长江流域（YRB）生态系统的可持续管理至关重要。本研究基于Google Earth Engine平台，分析了2000—2020年长江流域NDVI的动态变化，并利用InVEST模型评估了四项关键生态系统服务，包括产水量（WY）、碳储量（CS）、生境质量（HQ）和土壤保持（SR）。进一步采用随机森林模型和时空地理加权回归模型，探讨NDVI与生态系统服务之间的非线性关系及空间异质性。结果表明：（1）长江流域NDVI均值在2000年、2010年和2020年分别为0.692、0.697和0.724，呈现出明显的时空异质性。（2）产水量呈先增后减的非单调变化轨迹，土壤保持显著增加，生境质量相对稳定，碳储量略有下降。（3）碳储量和土壤保持与NDVI的正相关关系更为显著，而产水量和生境质量则表现出更强的区间变异性或较弱的局部效应。（4）NDVI总体上对碳储量、生境质量和土壤保持具有正向局部效应，而NDVI与产水量之间既存在正相关关系，也存在局部负效应。这些发现可为长江流域生态修复与生态系统服务管理提供有价值的参考。","International Journal of Digital Earth","2026-09-12T00:00:00Z",77,{"impact":17,"substance":89,"depth":19,"authority":20,"freshness":90,"relevant":22,"comment":91},21,8,"基于GEE与随机森林、GTWR方法揭示长江流域植被与生态系统服务非线性关系，方法新颖、数据扎实，对流域生态修复有参考价值，但属学术论文、公共政策影响有限。",[93],{"name":85,"url":82},[27,95,28,96,97],"长江流域","植被动态","数字地球",[99,100],"生态系统服务 数字地球 植被动态 长江流域","生态系统服务 数字地球","生态系统服务数字地球植被动态长江流域-2424","10.1080\u002F17538947.2026.2722459",{"doi":102,"openalex_id":104,"authors":105,"venue":85,"cited_by_count":36,"oa_url":120,"card":121,"direction":126,"ingested_from":74},"W7212379230",[106,108,110,113,116,118],{"name":107,"orcid":9},"Jinhuang Lin",{"name":109,"orcid":9},"Jixing Huang",{"name":111,"orcid":112},"Yongwu Dai","https:\u002F\u002Forcid.org\u002F0000-0002-8562-5733",{"name":114,"orcid":115},"Guoqing Li","https:\u002F\u002Forcid.org\u002F0000-0003-1767-3052",{"name":117,"orcid":9},"Yuanrui Zang",{"name":119,"orcid":9},"Yan Huang","https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F17538947.2026.2722459?needAccess=true",{"tldr":122,"method":123,"finding":124,"direction":71,"opportunity":125},"用随机森林与GTWR揭示长江流域2000—2020年NDVI与四项生态系统服务的非线性关系。","GEE提取NDVI，InVEST评估四项ES，随机森林与GTWR分析非线性及空间","NDVI与碳储、土壤保持正相关更强，与产水呈先增后减且存在局部负效应。","可引入多源遥感与作物物候变量，探究NDVI-ES非线性阈值及跨尺度驱动机制。","农业遥感与作物表型","2026-09-14T23:30:24.653682Z",{"id":129,"title":130,"url":131,"summary":132,"summary_zh":133,"content":9,"source_name":134,"source_url":131,"published_at":135,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":136,"score_detail":137,"sources":142,"tags":144,"search_phrases":149,"slug":152,"view_count":36,"doi":153,"paper":154,"created_at":183},3620,"Assessment of corn chlorophyll content via UAV‐derived vegetative indices across growth stages","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fagg2.70437","Abstract Traditional chlorophyll (Chl) assessment methods are labor‐intensive and spatially limited. This study evaluated unmanned aerial vehicle (UAV) multispectral imagery for nondestructive field‐scale canopy Chl estimation in corn ( Zea mays L.) to support precision nutrient management. Field experiments were conducted at two Virginia Tech research farms under two nitrogen (N) rates, 112 and 224 kg N ha − 1 , and five phosphorus (P) rates, 0, 56, 112, 168, and 224 kg P 2 O 5 ha − 1 . Canopy Chl was measured using a SPAD‐502 Plus chlorophyll meter (where SPAD is soil plant analysis development). UAV imagery was collected at three sampling times using a DJI Mavic 3 M: early vegetative (Ve‐E), late vegetative (Ve‐L), and reproductive stages, corresponding to V4–V6, V11–V13, and R3–R5 growth stages, respectively. Eight vegetation indices (VIs) were derived from green, red, red‐edge, and near‐infrared bands. Relationships between SPAD and spectral predictors were evaluated using Pearson correlation, simple and multiple linear regression, Lasso, and Elastic Net. Model performance was assessed using fivefold cross‐validated (CV) estimates repeated 30 times with CV R 2 , root mean square error, and relative RMSE. Predictive accuracy was moderate and varied by site and time, with strongest relationships during Ve‐L. At Ve‐L, Green Normalized Difference Vegetation Index (GNDVI) and Chlorophyll Index Red Edge were the best predictors at Orange and Kentland, respectively. Multivariate and regularized models performed comparably but did not improve accuracy over the best single‐VI models. Overall, GNDVI and red‐edge‐based indices were the most useful indicators of canopy Chl variation. These findings indicate that UAV‐derived GNDVI and red‐edge indices can provide moderately accurate and interpretable estimates of SPAD‐based Chl status.","传统叶绿素（Chl）评估方法费时费力且空间覆盖有限。本研究评估了无人机（UAV）多光谱影像在玉米（Zea mays L.）田块尺度冠层叶绿素无损估测中的应用，以支持精准养分管理。田间试验在弗吉尼亚理工大学的两个研究农场进行，设置两个氮（N）水平，分别为112和224 kg N ha⁻¹，以及五个磷（P）水平，分别为0、56、112、168和224 kg P₂O₅ ha⁻¹。使用SPAD-502 Plus叶绿素仪（SPAD即土壤植物分析开发）测定冠层叶绿素。使用DJI Mavic 3 M在三个采样时期采集无人机影像：营养生长早期（Ve-E）、营养生长晚期（Ve-L）和生殖生长阶段，分别对应V4–V6、V11–V13和R3–R5生育时期。从绿、红、红边和近红外波段提取了8种植被指数（VIs）。采用Pearson相关、简单和多元线性回归、Lasso及弹性网（Elastic Net）评估SPAD与光谱预测变量之间的关系。模型性能通过重复30次的五折交叉验证（CV）估计进行评估，指标包括交叉验证R²、均方根误差和相对均方根误差。预测精度为中等水平，且因地点和时间而异，其中Ve-L时期关系最强。在Ve-L时期，绿归一化差异植被指数（GNDVI）和红边叶绿素指数分别在Orange和Kentland表现最佳。多变量模型和正则化模型表现相当，但未能比最佳单植被指数模型提高精度。总体而言，GNDVI和基于红边的指数是冠层叶绿素变异最有用的指标。这些结果表明，无人机获取的GNDVI和红边指数能够对基于SPAD的叶绿素状况提供中等精度且可解释的估测。","Agrosystems Geosciences & Environment","2026-09-24T00:00:00Z",69,{"impact":138,"substance":139,"depth":17,"authority":140,"freshness":90,"relevant":22,"comment":141},12,20,13,"基于无人机多光谱影像评估玉米冠层叶绿素，方法规范、结论明确，对精准养分管理有参考价值，但属细分领域研究，公共影响有限。",[143],{"name":134,"url":131},[145,146,27,147,148],"智慧农业","玉米","氮肥管理","植被指数",[150,151],"无人机 玉米 叶绿素 遥感","Virginia Tech 玉米 氮肥 SPAD","无人机玉米叶绿素遥感-3620","10.1002\u002Fagg2.70437",{"doi":153,"openalex_id":155,"authors":156,"venue":134,"cited_by_count":36,"oa_url":177,"card":178,"direction":126,"ingested_from":74},"W7214232972",[157,160,163,166,168,170,172,174],{"name":158,"orcid":159},"Aarati Khulal","https:\u002F\u002Forcid.org\u002F0000-0003-4405-1320",{"name":161,"orcid":162},"Huijie Gan","https:\u002F\u002Forcid.org\u002F0000-0001-6634-5704",{"name":164,"orcid":165},"Jitender Rathore","https:\u002F\u002Forcid.org\u002F0009-0006-5764-1652",{"name":167,"orcid":9},"Sheetal Kumari",{"name":169,"orcid":9},"Ivy Flory",{"name":171,"orcid":9},"Caleb Bishop",{"name":173,"orcid":9},"Santosh Rijal",{"name":175,"orcid":176},"Olga S. Walsh","https:\u002F\u002Forcid.org\u002F0000-0002-2958-931X","https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1002\u002Fagg2.70437",{"tldr":179,"method":180,"finding":181,"direction":126,"opportunity":182},"用无人机多光谱植被指数评估玉米不同生育期冠层叶绿素含量。","DJI Mavic 3 M多光谱影像，8个植被指数，SPAD实测，线性回归与La","GNDVI与红边指数在营养生长后期预测SPAD最准，多变量模型未优于单指数。","可探索多生育期时序融合与多源数据耦合，提升跨站点跨年份叶绿素估算泛化能力。","2026-09-27T23:30:35.468236Z",{"id":185,"title":186,"url":187,"summary":188,"summary_zh":9,"content":9,"source_name":189,"source_url":187,"published_at":135,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":190,"score_detail":191,"sources":194,"tags":196,"search_phrases":200,"slug":203,"view_count":36,"doi":204,"paper":205,"created_at":232},3619,"Comparison of NDVI obtained from an active proximal sensor and UAV multispectral imagery in coffee","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-10767247\u002Fv1","Comparison of NDVI obtained from an active proximal sensor and UAV multispectral imagery in coffee。Research Square","Research Square",51,{"impact":90,"substance":20,"depth":192,"authority":77,"freshness":90,"relevant":22,"comment":193},15,"咖啡园NDVI主动传感器与无人机多光谱对比研究，方法有参考价值但属细分作物技术验证，影响面有限。",[195],{"name":189,"url":187},[145,197,27,198,199],"无人机","NDVI","咖啡种植",[201,202],"咖啡 NDVI 无人机 多光谱","Research Square 咖啡 遥感","咖啡NDVI无人机多光谱-3619","10.21203\u002Frs.3.rs-10767247\u002Fv1",{"doi":204,"openalex_id":206,"authors":207,"venue":189,"cited_by_count":36,"oa_url":231,"card":9,"direction":126,"ingested_from":74},"W7214220732",[208,210,212,214,216,219,221,223,226,229],{"name":209,"orcid":9},"Gabriel de Morais Campos",{"name":211,"orcid":9},"Aline Bhering Silva",{"name":213,"orcid":9},"Cileimar Aparecida da Silva",{"name":215,"orcid":9},"Vanda Maria Salles Andrade",{"name":217,"orcid":218},"Thaline M. Pimenta","https:\u002F\u002Forcid.org\u002F0000-0002-5002-177X",{"name":220,"orcid":9},"Andressa Barcellos Silva",{"name":222,"orcid":9},"Marco Thúlio Gonçalves Vieira",{"name":224,"orcid":225},"Daniel Marçal de Queiroz","https:\u002F\u002Forcid.org\u002F0000-0003-0987-3855",{"name":227,"orcid":228},"Fábio Daniel Tancredi","https:\u002F\u002Forcid.org\u002F0000-0002-7619-2200",{"name":230,"orcid":9},"Flora Maria Melo Villar","https:\u002F\u002Fwww.researchsquare.com\u002Farticle\u002Frs-10767247\u002Flatest.pdf","2026-09-27T23:30:34.698079Z",{"id":234,"title":235,"url":236,"summary":237,"summary_zh":238,"content":9,"source_name":239,"source_url":236,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":240,"score_detail":241,"sources":243,"tags":245,"search_phrases":250,"slug":253,"view_count":36,"doi":254,"paper":255,"created_at":267},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",63,{"impact":138,"substance":19,"depth":192,"authority":13,"freshness":90,"relevant":22,"comment":242},"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。",[244],{"name":239,"url":236},[246,247,248,27,249],"农业人工智能","深度学习","语义分割","生态监测",[251,252],"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":254,"openalex_id":256,"authors":257,"venue":239,"cited_by_count":36,"oa_url":260,"card":261,"direction":266,"ingested_from":74},"W7214298981",[258],{"name":259,"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":262,"method":263,"finding":264,"direction":126,"opportunity":265},"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":269,"title":270,"url":271,"summary":272,"summary_zh":273,"content":9,"source_name":274,"source_url":271,"published_at":275,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":87,"score_detail":276,"sources":279,"tags":281,"search_phrases":285,"slug":288,"view_count":36,"doi":289,"paper":290,"created_at":307},3607,"Analysis of drought carry-over effect on apple trees using the Leafiness-LiDAR index","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.biosystemseng.2026.104603","Apple trees ( Malus × domestica Borkh.) are widely grown in Mediterranean regions, where drought-induced irrigation restrictions can impair canopy development and productivity. This study assessed the Leafiness-LiDAR Index (LLI), derived from terrestrial Light Detection and Ranging (LiDAR) point clouds, as a proxy for Leaf Area Index (LAI) and as a non-destructive indicator of canopy response and recovery following water deficit. An ‘Opal®’ apple orchard was monitored over three growing seasons (2023–2025), encompassing one drought year and two recovery years. In 2023, full irrigation (FI; 480 mm) was compared with deficit irrigation (DI; 300 mm; 37.5% less water) under two planting densities (0.5 and 1.0 m tree spacing). Mixed-effects models revealed significant Year × Irrigation interactions for LAI (p = 0.006) and LLI (p \u003C 0.001), but not for yield (p = 0.654), suggesting a partial decoupling between structural and productive recovery. During the drought year, DI reduced LLI by 37% and yield by 47%. Following irrigation restoration, LLI differences persisted, particularly in 2024, indicating lasting structural effects of water deficit. Higher planting density promoted canopy recovery and increased yield. LLI proved to be a valuable, non-destructive estimator of LAI for detecting drought legacy effects and tracking canopy recovery, supporting precision orchard management under Mediterranean water scarcity.","苹果树（Malus × domestica Borkh.）广泛种植于地中海地区，该地区因干旱导致的灌溉限制会损害树冠发育和生产力。本研究评估了基于地面激光雷达（LiDAR）点云衍生的叶量-激光雷达指数（Leafiness-LiDAR Index, LLI）作为叶面积指数（Leaf Area Index, LAI）的替代指标，以及作为水分亏缺后树冠响应与恢复的非破坏性指标的可行性。对一个‘Opal®’苹果园进行了三个生长季（2023—2025年）的监测，涵盖一个干旱年和两个恢复年。2023年，在两种种植密度（株距0.5 m和1.0 m）下，比较了充分灌溉（FI；480 mm）与亏缺灌溉（DI；300 mm；减少37.5%水量）。混合效应模型显示，LAI（p = 0.006）和LLI（p \u003C 0.001）存在显著的年×灌溉交互作用，而产量（p = 0.654）则无此交互作用，表明结构与产量恢复之间存在部分脱耦。在干旱年，DI使LLI降低37%，产量降低47%。灌溉恢复后，LLI差异持续存在，尤其在2024年，表明水分亏缺具有持久的结构性影响。较高的种植密度促进了树冠恢复并提高了产量。LLI被证明是一种有价值的、非破坏性的LAI估算指标，可用于检测干旱遗留效应并追踪树冠恢复，为地中海水资源短缺条件下的精准果园管理提供支持。","Biosystems Engineering","2026-09-26T00:00:00Z",{"impact":192,"substance":277,"depth":19,"authority":20,"freshness":90,"relevant":22,"comment":278},22,"地中海苹果园三年试验证实LiDAR叶量指数可无损监测干旱遗留效应与冠层恢复，方法新颖、数据扎实，对果园精准灌溉有参考价值。",[280],{"name":274,"url":271},[145,282,27,283,284],"苹果","果园管理","精准灌溉",[286,287],"LiDAR 苹果园 干旱","Leafiness-LiDAR Index 苹果","LiDAR苹果园干旱-3607","10.1016\u002Fj.biosystemseng.2026.104603",{"doi":289,"openalex_id":291,"authors":292,"venue":274,"cited_by_count":36,"oa_url":271,"card":302,"direction":126,"ingested_from":74},"W7214458453",[293,296,299],{"name":294,"orcid":295},"Leire Sandonís-Pozo","https:\u002F\u002Forcid.org\u002F0000-0003-2472-0259",{"name":297,"orcid":298},"José Antonio Martínez-Casasnovas","https:\u002F\u002Forcid.org\u002F0000-0003-1480-3632",{"name":300,"orcid":301},"Miquel Pascual","https:\u002F\u002Forcid.org\u002F0000-0002-3329-4207",{"tldr":303,"method":304,"finding":305,"direction":126,"opportunity":306},"用LiDAR叶量指数追踪苹果园干旱遗留效应与冠层恢复。","三年果园试验，地面LiDAR点云提取LLI，混合效应模型分析。","亏缺灌溉使LLI降37%、产量降47%，复水后结构差异仍持续。","可探索LLI与多源遥感融合，构建果园干旱遗留效应早期预警与精准补灌决策模型。","2026-09-27T23:30:09.907348Z",{"id":309,"title":310,"url":311,"summary":312,"summary_zh":313,"content":9,"source_name":314,"source_url":311,"published_at":275,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":315,"score_detail":316,"sources":318,"tags":320,"search_phrases":323,"slug":326,"view_count":36,"doi":327,"paper":328,"created_at":375},3603,"Uncertainty propagation and intercomparison of multi-sensor measurements of vegetation stress in sub-optimal conditions","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10455-1","Abstract Purpose This study evaluates the metrological consistency and uncertainty propagation of spectral reflectance and vegetation indices derived from multiple remote sensing sensors under sub-optimal and variable illumination conditions. While multi-sensor data fusion is increasingly common in precision farming, the extent to which sensor differences arise from biological variation versus measurement uncertainty remains poorly quantified. Methods and results Field measurements were conducted on spring wheat ( Triticum aestivum L.) using three ground-based field spectroradiometers and two uncrewed aerial vehicles (UAV)-mounted multispectral cameras. Following a standardised Guide to the Expression of Uncertainty in Measurement (GUM) framework, uncertainties from sensor noise, spatial plot heterogeneity, and transient cloud cover were propagated using Monte Carlo simulations. The results indicate that total reflectance uncertainty peaked at approximately 11% in the red-edge and near-infrared regions, primarily driven by plot-level heterogeneity and fluctuating irradiance. Among the evaluated indices, the Optimized Soil-Adjusted Vegetation Index (OSAVI) demonstrated the highest stability across platforms, whereas the Enhanced Vegetation Index (EVI) proved highly sensitive to environmental noise, leading to metrological breakdown in sensor interoperability. The findings demonstrate that under unstable atmospheric conditions, the window for reliable multi-sensor data integration is limited to synchronous acquisitions within minutes. Conclusion This research provides a rigorous statistical foundation for identifying the limits of sensor agreement, ensuring that management decisions in precision agriculture are based on true crop signals rather than measurement artifacts.","摘要 目的 本研究评估了在次优和变化光照条件下，来自多个遥感传感器的光谱反射率和植被指数的计量一致性与不确定度传播。尽管多传感器数据融合在精准农业中日益普遍，但传感器差异在多大程度上源于生物变异而非测量不确定度，仍缺乏充分的量化。方法与结果 使用三台地面野外光谱辐射计和两台无人机（UAV）搭载的多光谱相机，对春小麦（Triticum aestivum L.）进行了田间测量。依据测量不确定度表示指南（GUM）的标准框架，采用蒙特卡洛模拟对传感器噪声、空间小区异质性和瞬时云覆盖所产生的不确定度进行了传播分析。结果表明，总反射率不确定度在红边和近红外区域达到约11%的峰值，主要由小区尺度异质性和辐照度波动驱动。在评估的指数中，优化土壤调节植被指数（OSAVI）在不同平台间表现出最高的稳定性，而增强植被指数（EVI）对环境噪声高度敏感，导致传感器互操作性在计量学上失效。研究结果表明，在不稳定大气条件下，可靠的多传感器数据整合窗口仅限于数分钟内的同步采集。结论 本研究为识别传感器一致性的限度提供了严格的统计学基础，确保精准农业中的管理决策基于真实的作物信号而非测量伪影。","Precision Agriculture",75,{"impact":138,"substance":277,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":317},"该研究量化了多传感器植被胁迫测量中的不确定性传播，方法严谨、结论对精准农业多源数据融合具有实质指导价值，但属细分领域学术进展，影响范围有限。",[319],{"name":314,"url":311},[145,27,148,321,322],"多光谱遥感","作物表性",[324,325],"春小麦 多光谱 遥感 不确定性","OSAVI EVI 传感器 互操作","春小麦多光谱遥感不确定性-3603","10.1007\u002Fs11119-026-10455-1",{"doi":327,"openalex_id":329,"authors":330,"venue":314,"cited_by_count":36,"oa_url":311,"card":370,"direction":126,"ingested_from":74},"W7214488126",[331,334,337,340,343,345,347,349,352,355,358,361,364,367],{"name":332,"orcid":333},"Mike Werfeli","https:\u002F\u002Forcid.org\u002F0000-0001-5768-8887",{"name":335,"orcid":336},"Michal Antala","https:\u002F\u002Forcid.org\u002F0000-0003-1294-9507",{"name":338,"orcid":339},"Abdallah Yussuf Ali Abdelmajeed","https:\u002F\u002Forcid.org\u002F0000-0002-3662-1824",{"name":341,"orcid":342},"Álvaro Sánchez-Virosta","https:\u002F\u002Forcid.org\u002F0000-0002-3842-3792",{"name":344,"orcid":9},"Zoe Halem",{"name":346,"orcid":9},"A. Merrington",{"name":348,"orcid":9},"Yousra El‐Mejjaouy",{"name":350,"orcid":351},"Bojana Petrović","https:\u002F\u002Forcid.org\u002F0000-0002-9901-3471",{"name":353,"orcid":354},"Andreas Hueni","https:\u002F\u002Forcid.org\u002F0000-0002-4283-2484",{"name":356,"orcid":357},"El Houssaine Bouras","https:\u002F\u002Forcid.org\u002F0000-0002-6973-6644",{"name":359,"orcid":360},"Shawn C. Kefauver","https:\u002F\u002Forcid.org\u002F0000-0002-1687-1965",{"name":362,"orcid":363},"Sahameh Shafiee","https:\u002F\u002Forcid.org\u002F0000-0002-3586-2327",{"name":365,"orcid":366},"Anshu Rastogi","https:\u002F\u002Forcid.org\u002F0000-0002-0953-7045",{"name":368,"orcid":369},"Laura Mihai","https:\u002F\u002Forcid.org\u002F0000-0003-3869-1890",{"tldr":371,"method":372,"finding":373,"direction":126,"opportunity":374},"评估多传感器植被胁迫测量在非理想条件下的不确定度传播与一致性。","三种地面光谱仪与两架无人机多光谱相机，基于GUM框架和蒙特卡洛模拟传播不确定度。","红边与近红外总反射率不确定度达11%，OSAVI跨平台最稳定，EVI易受环境噪声影响。","可研究分钟级同步采集协议与不确定度感知的传感器融合算法，提升多源数据互操作性。","2026-09-27T23:30:04.847804Z"]