[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4748":3,"related-4748":80},{"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":32,"secondary_topics":38,"classification":39,"search_phrases":59,"slug":62,"view_count":63,"doi":64,"paper":65,"created_at":79},4748,"Study of Territories Using Geoinformatics and Remote Sensing Technologies: Spectral Band Combinations Based on Sentinel-2 Data (A Case Study of Parkent District)","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23297593","This scientific paper analyzes the combination of spectral bands based on data from the European Space Agency's (ESA) Sentinel-2 satellite, using geoinformatics and remote sensing technologies. The Parkent district of the Tashkent region, Republic of Uzbekistan, was selected as the object of study. The research primarily compares the characteristics of the NIR-RED-GREEN (Near-Infrared, Red, Green) and SWIR-NIR-GREEN (Shortwave Infrared, Near-Infrared, Green) band combinations, as well as their advantages in identifying agricultural lands, forest cover, and assessing moisture levels.","本文基于欧洲空间局（ESA）Sentinel-2卫星数据，运用地理信息学与遥感技术，对光谱波段组合进行了分析。研究选取乌兹别克斯坦共和国塔什干州帕尔肯特区作为研究对象。研究主要比较了近红外-红-绿（NIR-RED-GREEN）与短波红外-近红外-绿（SWIR-NIR-GREEN）波段组合的特征，以及二者在识别农业用地、森林覆盖和评估湿度水平方面的优势。",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-10-11T00:00:00Z","论文",10,false,62,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,18,15,12,9,1,"基于Sentinel-2多光谱波段组合的遥感应用研究，方法具体、数据来源权威，但研究对象为乌兹别克斯坦地方区域，对国内三农信息化的直接参考价值有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"农业遥感","Sentinel-2","遥感","乌兹别克斯坦","光谱波段",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,52],{"code":33,"score":46,"supported":47,"title":48,"abstract":50,"tags":51},17,true,[49],"remote sensing",[29,49],[28,27,29],{"code":53,"score":54,"supported":47,"title":55,"abstract":56,"tags":58},"I4",3,[],[57],"地理信息",[],[60,61],"Sentinel-2 光谱波段 组合","Parkent 遥感 农业用地","Sentinel-2光谱波段组合-4748",0,"10.5281\u002Fzenodo.23297593",{"doi":64,"openalex_id":66,"authors":67,"venue":10,"cited_by_count":63,"oa_url":6,"card":72,"direction":76,"ingested_from":78},"W7221467289",[68,70],{"name":69,"orcid":9},"Xalilov Doniyor",{"name":71,"orcid":9},"Allambergenova Venera",{"tldr":73,"method":74,"finding":75,"direction":76,"opportunity":77},"基于Sentinel-2数据比较NIR-RED-GREEN与SWIR-NIR-GREEN波段组合，用","使用ESA Sentinel-2影像与地理信息学、遥感技术，对比两种光谱波段组合","NIR-RED-GREEN和SWIR-NIR-GREEN组合在识别农业用地、森林覆盖和评估湿度上各有","农业遥感与作物表型","可进一步量化不同波段组合在干旱区作物分类与土壤湿度反演中的精度差异，并构建自动化优选流程。","openalex","2026-10-12T23:30:24.415736Z",{"total":81,"page":22,"page_size":81,"items":82},6,[83,157,212,265,340,415],{"id":84,"title":85,"url":86,"summary":87,"summary_zh":88,"content":9,"source_name":10,"source_url":86,"published_at":89,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":90,"score_detail":91,"sources":95,"tags":97,"primary_topic":101,"secondary_topics":102,"classification":107,"search_phrases":141,"slug":144,"view_count":63,"doi":145,"paper":146,"created_at":156},5008,"ASSESSING THE GEOECOLOGICAL IMPACT OF LARGE HYDRAULIC STRUCTURES IN ARID REGIONS USING REMOTE SENSING AND GIS TECHNOLOGIES (A CASE STUDY OF THE CENTRAL FERGANA RESERVOIR)","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23359723","This article presents a comprehensive study of the geoecological and ameliorative impacts of large hydraulic structures built under arid subtropical conditions on adjacent agricultural landscapes, using the Central Fergana Reservoir (CFR) as a case study. The main objective of the study is to assess landscape changes, the spatial dynamics of vegetation cover, and secondary soil salinization processes across 29,330 hectares within the reservoir’s impact zone between 2010 and 2025 using remote sensing and GIS technologies. The study calculates the Normalized Difference Vegetation Index (NDVI) based on Landsat satellite imagery (Landsat 5 TM, Landsat 8 OLI\u002FTIRS, and Landsat 9 OLI-2) and analyzes long-term changes. The results show that the water surface area expanded from 1% (293.3 ha) in 2010 to 15% (4,399.5 ha) by 2025. Furthermore, analyses of 27 soil profiles and laboratory measurements of electrical conductivity (\\(EC_e\\)) and moisture content confirmed the formation of localized waterlogged areas and highly saline solonchaks in the 500-meter coastal buffer zone as a result of a rise in the groundwater table to 1.0–1.5 m. Environmental stress factors identified through comparative analyses with a control (background) site were scientifically substantiated, and practical recommendations for sustainable regional management were developed.","本文以中央费尔干纳水库（Central Fergana Reservoir, CFR）为例，对干旱亚热带条件下大型水利设施对邻近农业景观的地质生态与土壤改良影响进行了综合研究。研究的主要目标在于利用遥感与GIS技术，评估2010年至2025年间水库影响区内29330公顷土地上的景观变化、植被覆盖空间动态以及次生土壤盐渍化过程。研究基于Landsat卫星影像（Landsat 5 TM、Landsat 8 OLI\u002FTIRS和Landsat 9 OLI-2）计算归一化植被指数（Normalized Difference Vegetation Index, NDVI），并分析其长期变化。结果表明，水域面积从2010年的1%（293.3公顷）扩大至2025年的15%（4399.5公顷）。此外，对27个土壤剖面的分析以及电导率（\\(EC_e\\)）和含水量的实验室测定证实，由于地下水位上升至1.0–1.5米，在500米沿岸缓冲带内形成了局部渍水区和重度盐化盐土。通过与对照（背景）样地的比较分析，科学地证实了所识别的环境胁迫因子，并提出了区域可持续管理的实践建议。","2026-10-14T00:00:00Z",74,{"impact":20,"substance":92,"depth":18,"authority":93,"freshness":21,"relevant":22,"comment":94},22,13,"基于Landsat长时序遥感与GIS评估干旱区大型水利工程对灌区植被与次生盐渍化的影响，方法规范、数据翔实，对农业遥感与灌区生态监测有参考价值，但属区域案例研究，公共影响有限。",[96],{"name":10,"url":86},[27,29,98,99,100],"水资源管理","灌溉农业","盐渍化",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[103],{"code":53,"slug":104,"name":105,"path":106,"parent_code":37},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"status":40,"locked":14,"version":41,"method":42,"evidence":108},{"candidates":109,"agricultural_evidence":47},[110,115,121,129,135],{"code":33,"score":111,"supported":47,"title":112,"abstract":113,"tags":114},16,[49],[29,49],[27,29],{"code":53,"score":116,"supported":47,"title":117,"abstract":119,"tags":120},11,[118],"GIS",[118],[],{"code":122,"score":123,"supported":47,"title":124,"abstract":125,"tags":128},"H2",7,[],[126,127],"土壤","soil",[100],{"code":130,"score":54,"supported":47,"title":131,"abstract":132,"tags":134},"H7",[],[133],"干旱",[],{"code":136,"score":137,"supported":14,"title":138,"abstract":139,"tags":140},"H3",2,[],[],[98,99],[142,143],"费尔干纳水库 遥感 盐渍化","Landsat NDVI 灌区 土壤盐渍化","费尔干纳水库遥感盐渍化-5008","10.5281\u002Fzenodo.23359723",{"doi":145,"openalex_id":147,"authors":148,"venue":10,"cited_by_count":63,"oa_url":86,"card":151,"direction":76,"ingested_from":78},"W7221819099",[149],{"name":150,"orcid":9},"Akbarov Sarvarbek",{"tldr":152,"method":153,"finding":154,"direction":76,"opportunity":155},"用遥感和GIS评估费尔干纳中部水库对周边农业景观的生态影响。","Landsat影像NDVI分析、27个土壤剖面及ECe与含水量测定。","水库水面从1%扩至15%，500米缓冲带地下水位升至1.0–1.5米，形成盐渍化。","可结合多源遥感与土壤盐分模型，构建干旱区水库周边盐渍化预警与灌溉调控方案。","2026-10-15T23:30:29.814507Z",{"id":158,"title":159,"url":160,"summary":161,"summary_zh":162,"content":9,"source_name":163,"source_url":160,"published_at":164,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":165,"score_detail":166,"sources":169,"tags":171,"primary_topic":175,"secondary_topics":176,"classification":177,"search_phrases":191,"slug":194,"view_count":22,"doi":195,"paper":196,"created_at":211},4749,"Beyond site-specific archaeological prospection: Spectral-geometry reveals cross-site cropmark signals in satellite data","https:\u002F\u002Fdoi.org\u002F10.1093\u002Fpnasnexus\u002Fpgag336","Abstract Remote sensing has long supported archaeological prospection, mainly through the detection of cropmarks, a key archaeological proxy expressed as spectral vegetation anomalies over subsurface features. Yet, cropmark detection is still assumed in the literature to be highly site-specific, governed by local agronomic and environmental conditions. Using multispectral Sentinel-2 data from three European archaeological sites, we show that models trained in one landscape can recover meaningful archaeological proxy signals in other locations. By relying strictly on satellite spectral signatures for training, our models achieve measurable cross-location transferability without incorporating ancillary environmental data. We find that cross-site performance arises not from geographic proximity but from the spectral-geometric similarity of vegetation responses, and that transferability is strongly directional. We quantify this relationship through a Directional Strength Score (DSS), derived from monthly subcluster distances under multiple norms, which captures directional geometric similarity and correlates strongly with machine-learning performance. To keep the analysis grounded in the underlying spectral signal, we relied on architecturally restrained nonlinear classifiers (SVM and constrained XGBoost) and used NDVI-identified phenophases to align training and evaluation with comparable seasonal windows. Taken together, these results demonstrate that partial, spectrally mediated cross-location cropmark detection is feasible and that the underlying vegetation responses exhibit stable spectral structure, with spectral geometry offering a clear interpretive lens on why these signals can transfer across sites. Leveraging globally harmonized satellite data, this approach offers a scalable complement to uneven historical aerial archives and supports systematic regional archaeological prioritization to be deployed within integrated archaeological workflows.","摘要 遥感长期支持考古勘探，主要通过检测作物痕迹（cropmarks）——一种关键的考古代用指标，表现为地下特征上方植被的光谱异常。然而，文献中仍普遍认为作物痕迹检测高度依赖于具体遗址，受当地农艺和环境条件制约。利用来自三个欧洲考古遗址的多光谱Sentinel-2数据，我们表明在一个景观中训练的模型可以在其他地点恢复有意义的考古代用信号。通过严格依赖卫星光谱特征进行训练，我们的模型在不纳入辅助环境数据的情况下实现了可测量的跨地点迁移能力。我们发现，跨遗址性能并非源于地理邻近性，而是源于植被响应的光谱-几何相似性，并且迁移能力具有强烈的方向性。我们通过方向强度评分（Directional Strength Score, DSS）量化这一关系，该评分基于多种范数下的逐月子簇距离推导而来，捕捉方向性几何相似性并与机器学习性能强烈相关。为使分析立足于底层光谱信号，我们采用架构受限的非线性分类器（SVM和约束XGBoost），并使用NDVI识别的物候期来对齐训练和评估，使其处于可比较的季节窗口内。综合来看，这些结果表明，部分、以光谱为中介的跨地点作物痕迹检测是可行的，且底层植被响应表现出稳定的光谱结构，光谱几何为理解这些信号为何能够跨遗址迁移提供了清晰的解释视角。利用全球协调的卫星数据，该方法为不均衡的历史航空档案提供了可扩展的补充，并支持在综合考古工作流程中部署系统性的区域考古优先级排序。","PNAS Nexus","2026-10-10T00:00:00Z",82,{"impact":18,"substance":92,"depth":167,"authority":19,"freshness":17,"relevant":22,"comment":168},19,"PNAS Nexus 论文，用 Sentinel-2 多光谱数据证明作物标记信号可跨遗址迁移，方法新颖、结论可靠，对农业遥感与考古信息化有参考价值，但属细分领域突破，未达全国性产业事件层级。",[170],{"name":163,"url":160},[28,29,172,173,174],"NDVI","考古遥感","作物标记",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":178},{"candidates":179,"agricultural_evidence":47},[180,185],{"code":33,"score":20,"supported":47,"title":181,"abstract":182,"tags":184},[],[29,49,183],"multispectral",[172,28,29],{"code":186,"score":54,"supported":47,"title":187,"abstract":188,"tags":190},"I2",[],[189],"机器学习",[],[192,193],"Sentinel-2 作物标记 考古","遥感 光谱几何 跨遗址","Sentinel-2作物标记考古-4749","10.1093\u002Fpnasnexus\u002Fpgag336",{"doi":195,"openalex_id":197,"authors":198,"venue":163,"cited_by_count":63,"oa_url":205,"card":206,"direction":76,"ingested_from":78},"W7221319130",[199,202],{"name":200,"orcid":201},"Elias Gravanis","https:\u002F\u002Forcid.org\u002F0000-0002-5331-6661",{"name":203,"orcid":204},"Άθως Αγαπίου","https:\u002F\u002Forcid.org\u002F0000-0001-9106-6766","https:\u002F\u002Facademic.oup.com\u002Fpnasnexus\u002Fadvance-article-pdf\u002Fdoi\u002F10.1093\u002Fpnasnexus\u002Fpgag336\u002F71838194\u002Fpgag336.pdf",{"tldr":207,"method":208,"finding":209,"direction":76,"opportunity":210},"用Sentinel-2多光谱数据证明作物标记模型可跨遗址迁移，且迁移性由光谱几何相似性决定。","Sentinel-2多光谱、SVM与约束XGBoost、NDVI物候对齐、方向强","跨遗址作物标记检测可行，迁移性源于植被光谱几何相似而非地理邻近，且具方向性。","可将光谱几何迁移性指标引入作物表型与病害遥感，构建跨区域可迁移的植被异常检测模型。","2026-10-12T23:30:24.489835Z",{"id":213,"title":214,"url":215,"summary":216,"summary_zh":217,"content":9,"source_name":218,"source_url":215,"published_at":219,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":220,"score_detail":221,"sources":224,"tags":226,"primary_topic":230,"secondary_topics":231,"classification":232,"search_phrases":246,"slug":249,"view_count":63,"doi":250,"paper":251,"created_at":264},4583,"An Efficient Parcel-Center Sampling Strategy for Parcel-Scale Crop Classification Using Multiresolution Remote Sensing","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18203463","Parcel-scale crop classification is fundamental to agricultural remote sensing and precision agriculture. The integration of detailed parcel information with medium-spatial-resolution remote sensing data has become an important technical approach to parcel-scale crop classification. For medium-spatial-resolution imagery, the mismatch between parcel boundaries and pixel size makes the parcel–pixel matching strategy a critical step in constructing parcel-level classification features. Existing full-pixels, pixel-center inclusion, and area-weighted strategies generally require identifying pixels associated with each parcel, determining spatial relationships, and performing statistical aggregation. When applied to thousands of parcels across multiple acquisition dates, repeated spatial overlay and multipixel aggregation can become a computational bottleneck in parcel-level feature construction. To address this issue, and assuming sufficient within-parcel feature consistency, a single pixel corresponding to an interior center point or a local window around that point is used to construct parcel-level classification features, thereby reducing the number of pixels involved in spatial matching and feature aggregation. Pringsewu Regency, Lampung Province, Indonesia, which has a highly fragmented parcel pattern, was selected as a case study. Sentinel-2, Landsat-8, Sentinel-1, and topographic data were integrated for parcel-scale crop classification. The results show that the parcel-center sampling strategy substantially reduced the cost of parcel-level feature construction under both the Sentinel-2 and Landsat-8 configurations. Compared with the three multipixel strategies, under the experimental conditions of this study, PCS achieved speedups of approximately 5.2–13.3-fold under Sentinel-2 and 7.3–17.1-fold under Landsat-8. Relative to the full-pixels strategy, OA increased by 0.55 percentage points under the Sentinel-2 configuration and decreased by 1.30 percentage points under the Landsat-8 configuration. Across parcel scales, the parcel-center sampling strategy generally maintained classification performance similar to that of the conventional multipixel strategies, and the resulting spatial patterns of the major crops were broadly consistent. Using a parcel-center pixel or local window can therefore retain sufficient classification information while substantially reducing the cost of parcel-level feature construction. This approach provides a simplified solution for efficient parcel-scale crop classification mapping in fragmented agricultural landscapes using medium-resolution imagery.","地块尺度作物分类是农业遥感和精准农业的基础。将详细的地块信息与中空间分辨率遥感数据相结合，已成为地块尺度作物分类的重要技术途径。对于中空间分辨率影像而言，地块边界与像元大小之间的不匹配使得地块—像元匹配策略成为构建地块级分类特征的关键步骤。现有的全像元策略、像元中心包含策略和面积加权策略通常需要识别与每个地块相关的像元、确定空间关系并进行统计聚合。当应用于多个获取日期下的数千个地块时，重复的空间叠加和多像元聚合可能成为地块级特征构建中的计算瓶颈。为解决这一问题，在假设地块内部特征具有足够一致性的前提下，采用与内部中心点相对应的单个像元或该点周围的局部窗口来构建地块级分类特征，从而减少参与空间匹配和特征聚合的像元数量。选取地块格局高度破碎的印度尼西亚楠榜省普林塞乌县作为案例研究区，集成Sentinel-2、Landsat-8、Sentinel-1和地形数据用于地块尺度作物分类。结果表明，在Sentinel-2和Landsat-8两种配置下，地块中心采样策略均大幅降低了地块级特征构建的成本。与三种多像元策略相比，在本研究的实验条件下，PCS在Sentinel-2下实现了约5.2至13.3倍的加速，在Landsat-8下实现了约7.3至17.1倍的加速。相对于全像元策略，总体精度（OA）在Sentinel-2配置下提高了0.55个百分点，在Landsat-8配置下降低了1.30个百分点。在不同地块尺度下，地块中心采样策略总体上保持了与传统多像元策略相似的分类性能，且主要作物的空间格局基本一致。因此，使用地块中心像元或局部窗口能够在保留足够分类信息的同时，大幅降低地块级特征构建的成本。该方法提供了一种简化的解决方案","Remote Sensing","2026-10-09T00:00:00Z",71,{"impact":20,"substance":222,"depth":46,"authority":93,"freshness":17,"relevant":22,"comment":223},21,"提出地块中心采样策略，在破碎地块区大幅提速且精度基本持平，方法新颖、结论可靠，对农业遥感工程化有参考价值。",[225],{"name":218,"url":215},[227,28,29,228,229],"智慧农业","作物分类","地块尺度",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":233},{"candidates":234,"agricultural_evidence":47},[235,239],{"code":33,"score":46,"supported":47,"title":236,"abstract":237,"tags":238},[49],[29,49],[28,228,29],{"code":240,"score":123,"supported":47,"title":241,"abstract":242,"tags":245},"I1",[],[243,244],"精准农业","precision agriculture",[227],[247,248],"Sentinel-2 地块尺度 作物分类","Pringsewu 遥感 作物制图","Sentinel-2地块尺度作物分类-4583","10.3390\u002Frs18203463",{"doi":250,"openalex_id":252,"authors":253,"venue":218,"cited_by_count":63,"oa_url":215,"card":259,"direction":76,"ingested_from":78},"W7221068088",[254,257],{"name":255,"orcid":256},"Zhihua Wang","https:\u002F\u002Forcid.org\u002F0000-0002-6776-2910",{"name":258,"orcid":9},"Qin Yang",{"tldr":260,"method":261,"finding":262,"direction":76,"opportunity":263},"提出地块中心采样策略，用单像素或局部窗口构建地块级特征，大幅加速地块尺度作物分类。","以印尼Pringsewu为案例，融合Sentinel-2、Landsat-8、S","中心采样较全像素等策略提速5.2–17.1倍，精度基本相当，主要作物空间格局一致。","可探索中心采样在破碎地块、多时相与不同分辨率下的自适应窗口选择及精度-效率权衡机制。","2026-10-10T23:30:27.757031Z",{"id":266,"title":267,"url":268,"summary":269,"summary_zh":270,"content":9,"source_name":271,"source_url":268,"published_at":272,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":273,"score_detail":274,"sources":276,"tags":278,"primary_topic":281,"secondary_topics":282,"classification":283,"search_phrases":310,"slug":313,"view_count":314,"doi":315,"paper":316,"created_at":339},4025,"Remote estimation of plastic distribution in California agriculture using Sentinel-2 imagery: a case study","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffrsen.2026.1927646","Global application of agricultural plastic mulch is increasing. While these films offer benefits for crop production, they also are a large source of plastic waste and pollution in both terrestrial and aquatic systems. The current scale of global mulch application and its deleterious impacts are unclear. Current estimates of agricultural plastic use remain limited by field-specific growing patterns and data limitations. Satellite spectroscopy may offer a means to reduce this uncertainty and model agricultural plastic use on large spatial and temporal scales, though temporally variable crop canopy growth and spectrally diverse plastic targets complicate this approach. To build toward improved plasticulture estimates, this study presents an interpretable, spectral-based classification of bulk plastic use in a California agricultural site using Sentinel-2 imagery. In situ reflectance measurements of soil and white, green, and black plastic mulch endmembers were collected using a Spectra Vista Corporation HR-1024i Spectroradiometer from 350–2500 nm. A combination of field observations and high spatial resolution NearMap imagery (n = 416 spectra) were compiled and divided into 60% training and 40% testing data using a stratified sampling approach. Informed by field reflectance data, mixture analyses, and Sentinel-2 reflectance imagery, an empirical decision tree classification was parameterized to classify dark and bright plastic debris from soil and crop (accuracy = 0.982, kappa coefficient = 0.973, macro-averaged F1-score = 0.967). Modeled agricultural plastic distributions were then retrieved through a time series of Sentinel-2 imagery over Santa Maria, CA, from April 2023 to January 2025, elucidating patterns in plastic application and crop canopy growth without the need for prior knowledge of field growing and management patterns. These results are encouraging for the utility of optical remote sensing in modeling plastic use patterns and identifying contamination hotspots throughout California and, potentially, similarly mulched agroecological systems.","全球农业塑料地膜的应用日益增加。尽管这些薄膜为作物生产带来了益处，但它们同时也是陆地和水生系统中塑料废弃物和污染的重要来源。目前全球地膜应用的规模及其有害影响尚不明确。当前对农业塑料使用量的估算仍受限于特定田块的种植模式和数据局限性。卫星光谱学可能为减少这种不确定性并在大时空尺度上模拟农业塑料使用提供一种手段，尽管作物冠层的时序变化和光谱多样的塑料目标使这一方法变得复杂。为推进对塑料农业（plasticulture）估算的改进，本研究利用Sentinel-2影像，对加利福尼亚州一个农业站点的大宗塑料使用进行了可解释的、基于光谱的分类。使用Spectra Vista Corporation HR-1024i光谱辐射仪在350–2500 nm范围内采集了土壤以及白色、绿色和黑色塑料地膜端元的原位反射率测量数据。结合野外观测和高空间分辨率NearMap影像（n = 416条光谱），采用分层抽样方法将数据分为60%训练集和40%测试集。在野外反射率数据、混合分析及Sentinel-2反射率影像的指导下，参数化了一棵经验决策树分类器，以从土壤和作物中分类暗色和亮色塑料残膜（精度 = 0.982，kappa系数 = 0.973，宏平均F1分数 = 0.967）。随后通过2023年4月至2025年1月期间覆盖加利福尼亚州圣玛丽亚的Sentinel-2影像时间序列，反演了模拟的农业塑料分布，阐明了塑料施用和作物冠层生长的模式，而无需事先了解田块种植和管理模式。这些结果令人鼓舞，表明光学遥感在模拟加利福尼亚州乃至其他类似覆膜农业生态系统的塑料使用模式和识别污染热点方面具有实用价值。","Frontiers in Remote Sensing","2026-10-01T00:00:00Z",78,{"impact":111,"substance":92,"depth":18,"authority":93,"freshness":21,"relevant":22,"comment":275},"该研究利用Sentinel-2影像实现农膜分布遥感估算，方法新颖、精度高，对农业面源污染监测有参考价值，但属区域案例，影响范围有限。",[277],{"name":271,"url":268},[227,28,29,279,280],"农膜污染","加州农业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":284},{"candidates":285,"agricultural_evidence":47},[286,290,294,300,306],{"code":33,"score":17,"supported":47,"title":287,"abstract":288,"tags":289},[],[29,49],[28,29],{"code":122,"score":81,"supported":47,"title":291,"abstract":292,"tags":293},[],[126,127],[],{"code":295,"score":54,"supported":47,"title":296,"abstract":297,"tags":299},"H6",[],[298],"农业生态",[],{"code":301,"score":54,"supported":47,"title":302,"abstract":303,"tags":305},"J3",[],[304],"全球农业",[],{"code":240,"score":22,"supported":14,"title":307,"abstract":308,"tags":309},[],[],[227],[311,312],"Sentinel-2 农膜 遥感","加州 农业塑料 遥感监测","Sentinel-2农膜遥感-4025",4,"10.3389\u002Ffrsen.2026.1927646",{"doi":315,"openalex_id":317,"authors":318,"venue":271,"cited_by_count":63,"oa_url":268,"card":334,"direction":76,"ingested_from":78},"W7215027304",[319,321,323,326,328,331],{"name":320,"orcid":9},"Eva Scrivner",{"name":322,"orcid":9},"Dexter Hamilton",{"name":324,"orcid":325},"Daniel J. Sousa","https:\u002F\u002Forcid.org\u002F0000-0002-1632-1955",{"name":327,"orcid":9},"Margot Mattson",{"name":329,"orcid":330},"Nicholas E. Williams","https:\u002F\u002Forcid.org\u002F0000-0003-4013-9296",{"name":332,"orcid":333},"Seeta A. Sistla","https:\u002F\u002Forcid.org\u002F0000-0002-6345-462X",{"tldr":335,"method":336,"finding":337,"direction":76,"opportunity":338},"利用Sentinel-2影像和实地光谱，分类并制图加州农业塑料覆盖分布。","Sentinel-2影像、实地光谱与决策树分类，结合高分辨率影像验证。","决策树分类精度达0.982，可无需先验知识反演塑料覆盖时空格局。","可扩展至全球地膜污染热点监测，并融合多源遥感提升复杂作物区塑料识别。","2026-10-02T23:30:24.457168Z",{"id":341,"title":342,"url":343,"summary":344,"summary_zh":345,"content":9,"source_name":346,"source_url":343,"published_at":347,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":90,"score_detail":348,"sources":350,"tags":352,"primary_topic":355,"secondary_topics":359,"classification":366,"search_phrases":386,"slug":389,"view_count":137,"doi":390,"paper":391,"created_at":414},3504,"WaterFormer-MS: A multi-stream deep learning framework for surface water segmentation from satellite imagery","https:\u002F\u002Fdoi.org\u002F10.24850\u002Fj-tyca-18-03-08","Accurate mapping of surface water bodies is essential for dam monitoring, irrigation planning, and sustainable water resource management. Conventional methods based on spectral indices, such as NDWI, can face challenges in complex environments due to spectral ambiguity, seasonal variability, and urban or vegetated surfaces. Deep learning approaches such as U-Net and DeepLabv3+ have improved water-body segmentation; however, accurately delineating fine boundaries and detecting small water bodies remain challenging. To address these limitations, this study proposes WaterFormer-MS, a multi-stream deep learning framework for surface water segmentation from Sentinel-2 imagery. The architecture integrates spectral, geometric, and contextual information through dedicated processing streams and combines the extracted features using a convolutional fusion module enhanced with CBAM attention. This design aims to improve feature representation and support accurate water-body delineation. The proposed model was trained and evaluated using Sentinel-2 imagery from a publicly available Kaggle dataset containing diverse land-cover conditions, including urban, vegetated, and agricultural environments. At 300 epochs, WaterFormer-MS achieved an overall accuracy of 97.4%, a precision of 93.2%, a recall of 94.2%, an F1-score of 93.7%, and an IoU of 89.7%. Within the experimental framework, WaterFormer-MS achieved higher values than the other proposed variants. Comparisons with conventional NDWI-based approaches and published deep learning methods suggest competitive performance; however, these comparisons should be interpreted cautiously because datasets, preprocessing procedures, training protocols, and evaluation settings may differ. The results support the potential of the proposed framework for surface water segmentation and environmental monitoring applications in diverse remote sensing scenarios.","准确绘制地表水体分布对于大坝监测、灌溉规划和水资源可持续管理至关重要。基于NDWI等光谱指数的传统方法在复杂环境中可能因光谱模糊性、季节性变化以及城市或植被覆盖地表而面临挑战。U-Net和DeepLabv3+等深度学习方法改善了水体分割效果，但精确描绘精细边界和检测小型水体仍然具有挑战性。为解决这些局限，本研究提出WaterFormer-MS，一种用于Sentinel-2影像地表水分割的多流深度学习框架。该架构通过专门的处理流整合光谱、几何和上下文信息，并利用经CBAM注意力增强的卷积融合模块将提取的特征进行融合。该设计旨在改善特征表征并支持精确的水体描绘。所提模型使用来自公开Kaggle数据集的Sentinel-2影像进行训练和评估，该数据集包含城市、植被和农业环境等多种土地覆盖条件。在300个训练轮次时，WaterFormer-MS取得了97.4%的总体精度、93.2%的精确率、94.2%的召回率、93.7%的F1分数和89.7%的IoU。在实验框架内，WaterFormer-MS取得了高于其他所提变体的数值。与传统基于NDWI的方法及已发表的深度学习方法进行比较，结果表明其性能具有竞争力；然而，这些比较应谨慎解读，因为数据集、预处理流程、训练方案和评估设置可能存在差异。研究结果支持所提框架在多样化遥感场景中用于地表水分割和环境监测应用的潜力。","Tecnología y Ciencias del Agua","2026-09-24T00:00:00Z",{"impact":19,"substance":92,"depth":18,"authority":13,"freshness":21,"relevant":22,"comment":349},"提出多流深度学习框架用于卫星影像地表水提取，精度指标明确，对灌溉规划与水资源监测有参考价值，但属方法类论文，产业影响有限。",[351],{"name":346,"url":343},[353,28,29,98,354],"农业人工智能","地表水体提取",{"code":186,"slug":356,"name":357,"path":358,"parent_code":37},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[360,365],{"code":136,"slug":361,"name":362,"path":363,"parent_code":364},"irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":367},{"candidates":368,"agricultural_evidence":47},[369,375,382],{"code":186,"score":19,"supported":47,"title":370,"abstract":372,"tags":374},[371],"deep learning",[373,371],"深度学习",[353],{"code":136,"score":13,"supported":47,"title":376,"abstract":377,"tags":381},[],[378,379,361,380],"灌溉","水资源","water resource",[98],{"code":33,"score":17,"supported":47,"title":383,"abstract":384,"tags":385},[],[29,49],[28,29],[387,388],"WaterFormer-MS 地表水 分割","Sentinel-2 水体提取 深度学习","WaterFormer-MS地表水分割-3504","10.24850\u002Fj-tyca-18-03-08",{"doi":390,"openalex_id":392,"authors":393,"venue":346,"cited_by_count":63,"oa_url":343,"card":409,"direction":76,"ingested_from":78},"W7214228825",[394,397,400,403,406],{"name":395,"orcid":396},"Ayoub Benali","https:\u002F\u002Forcid.org\u002F0000-0002-8989-755X",{"name":398,"orcid":399},"Yesma Bendaha","https:\u002F\u002Forcid.org\u002F0000-0002-1347-856X",{"name":401,"orcid":402},"Malika Seddik Bouchouicha","https:\u002F\u002Forcid.org\u002F0009-0001-6092-784X",{"name":404,"orcid":405},"Zakaria Bellahcene","https:\u002F\u002Forcid.org\u002F0000-0003-4353-7871",{"name":407,"orcid":408},"Rachid Rimani","https:\u002F\u002Forcid.org\u002F0000-0002-1923-4312",{"tldr":410,"method":411,"finding":412,"direction":76,"opportunity":413},"提出多流深度学习框架WaterFormer-MS，从Sentinel-2影像中精准分割地表水体。","多流架构融合光谱、几何与上下文特征，CBAM注意力卷积融合，Sentinel-2","300轮训练下总体精度97.4%、IoU 89.7%，优于NDWI及对比深度学习方法。","可探索多流框架在农业灌溉水体与小型水塘精细提取中的迁移，并引入时序Sentinel-2提升季节鲁棒性。","2026-09-25T23:30:31.338437Z",{"id":416,"title":417,"url":418,"summary":419,"summary_zh":420,"content":9,"source_name":218,"source_url":418,"published_at":421,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":422,"score_detail":423,"sources":426,"tags":428,"primary_topic":432,"secondary_topics":433,"classification":434,"search_phrases":445,"slug":448,"view_count":54,"doi":449,"paper":450,"created_at":476},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。","2026-09-19T00:00:00Z",77,{"impact":111,"substance":222,"depth":18,"authority":424,"freshness":17,"relevant":22,"comment":425},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。",[427],{"name":218,"url":418},[27,29,429,430,431],"环境监测","植被监测","土壤污染",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":435},{"candidates":436,"agricultural_evidence":47},[437,441],{"code":33,"score":46,"supported":47,"title":438,"abstract":439,"tags":440},[49],[29,49],[27,430,29],{"code":122,"score":81,"supported":47,"title":442,"abstract":443,"tags":444},[],[126,127],[],[446,447],"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":449,"openalex_id":451,"authors":452,"venue":218,"cited_by_count":63,"oa_url":418,"card":471,"direction":76,"ingested_from":78},"W7213930509",[453,456,459,462,465,468],{"name":454,"orcid":455},"Marco De Mizio","https:\u002F\u002Forcid.org\u002F0009-0009-6556-2606",{"name":457,"orcid":458},"Donato Amitrano","https:\u002F\u002Forcid.org\u002F0000-0002-2355-4503",{"name":460,"orcid":461},"Massimiliano Gargiulo","https:\u002F\u002Forcid.org\u002F0000-0002-6783-366X",{"name":463,"orcid":464},"Sara Parrilli","https:\u002F\u002Forcid.org\u002F0000-0002-7225-1269",{"name":466,"orcid":467},"Claudia Savarese","https:\u002F\u002Forcid.org\u002F0000-0003-0161-2407",{"name":469,"orcid":470},"Massimiliano Lega","https:\u002F\u002Forcid.org\u002F0000-0002-4842-6049",{"tldr":472,"method":473,"finding":474,"direction":76,"opportunity":475},"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"]