[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2795":3,"related-2795":85},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":9,"secondary_topics":32,"classification":33,"search_phrases":61,"slug":64,"view_count":65,"doi":66,"paper":67,"created_at":84},2795,"Statistical evaluation of sentinel-2 spectral indices as environmental indicators of shallow soil resistivity for soil corrosion assessment in a semi-arid region of northwestern Nigeria","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44288-026-00732-x","Abstract Soil corrosion poses a significant threat to buried engineering infrastructure, particularly in semi-arid environments where variations in soil properties and surface environmental conditions influence electrochemical processes. This study integrated Sentinel-2-derived environmental indices with shallow-layer electrical resistivity and soil pH measurements to evaluate environmental factors associated with soil corrosion susceptibility in Sabon Gida, Katsina State, northwestern Nigeria. Sentinel-2 Level-2 A imagery acquired during the 2026 dry season was processed to derive the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Bare Soil Index (BSI), and Salinity Index (SI). Spectral index values were extracted at 44 Vertical Electrical Sounding (VES) locations, where shallow-layer resistivity and soil pH data were available. Descriptive statistics, Pearson correlation analysis, and multiple linear regression were employed to investigate the relationships between the remotely sensed environmental variables and field-measured soil properties. The results indicate that the study area is characterized by sparse vegetation cover, low surface moisture, limited bare-soil exposure, and relatively higher salinity-index values that may indicate spatial variations in salinity-related environmental conditions during the dry-season image acquisition period. Strong correlations were observed among the Sentinel-2-derived indices, whereas only weak relationships were found between the spectral indices and shallow-layer resistivity. Multiple linear regression analysis yielded an R² value of 0.197, indicating that approximately 19.7% of the variation in soil resistivity was explained by the model. Although the salinity index was identified as the only statistically significant predictor, the overall regression model was not statistically significant (F = 1.867, p = 0.123). These findings suggest that the remotely sensed variables provide only limited explanatory power with respect to shallow soil resistivity. Nevertheless, the results indicate that Sentinel-2-derived environmental indices provide useful information for characterizing surface environmental conditions associated with soil corrosion but should be used as complementary indicators rather than direct substitutes for field-based geophysical investigations. The study demonstrates that integrating remote sensing with geophysical measurements provides a more robust framework for preliminary soil corrosion susceptibility assessment and offers valuable support for infrastructure planning and environmental monitoring in data-scarce semi-arid regions.","摘要 土壤腐蚀对埋地工程基础设施构成重大威胁，尤其是在半干旱环境中，土壤性质与地表环境条件的变化会影响电化学过程。本研究将Sentinel-2衍生的环境指数与浅层电阻率及土壤pH测量相结合，评估尼日利亚西北部卡齐纳州Sabon Gida地区与土壤腐蚀敏感性相关的环境因素。研究处理了2026年旱季获取的Sentinel-2 Level-2A影像，以提取归一化差异植被指数（NDVI）、归一化差异水体指数（NDWI）、裸土指数（BSI）和盐分指数（SI）。在44个垂直电测深（VES）点位提取了光谱指数值，这些点位具备浅层电阻率和土壤pH数据。研究采用描述性统计、Pearson相关分析和多元线性回归，探讨遥感环境变量与实地测量土壤性质之间的关系。结果表明，研究区植被覆盖稀疏、地表湿度较低、裸土暴露有限，且盐分指数值相对较高，这可能指示旱季影像获取期间盐分相关环境条件的空间变化。Sentinel-2衍生的各指数之间观察到较强相关性，而光谱指数与浅层电阻率之间仅存在弱关系。多元线性回归分析得出R²值为0.197，表明模型可解释约19.7%的土壤电阻率变异。尽管盐分指数被识别为唯一具有统计学意义的预测因子，但整体回归模型未达到统计学显著性（F = 1.867，p = 0.123）。这些发现表明，遥感变量对浅层土壤电阻率的解释力有限。然而，结果也显示Sentinel-2衍生的环境指数可为表征与土壤腐蚀相关的地表环境条件提供有用信息，但应作为补充性指标，而非实地地球物理调查的直接替代。本研究表明，将遥感与地球物理测量相结合可提供更稳健的框架",null,"Discover Geoscience","2026-09-16T00:00:00Z","论文",10,false,61,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,18,14,12,9,1,"该研究将Sentinel-2遥感指数与土壤电阻率结合评估土壤腐蚀风险，方法有创新但解释力有限，对农业基础设施规划有参考价值，属细分领域进展。",[25],{"name":10,"url":6},[27,28,29,30,31],"Sentinel-2","遥感","土壤监测","土壤腐蚀","半干旱农业",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":14,"reason":60},[39,47,53],{"code":40,"score":19,"supported":41,"title":42,"abstract":44,"tags":46},"H2",true,[43],"soil",[45,43],"土壤",[],{"code":48,"score":17,"supported":41,"title":49,"abstract":50,"tags":52},"I3",[],[28,51],"remote sensing",[27,28],{"code":54,"score":55,"supported":41,"title":56,"abstract":57,"tags":59},"H7",3,[],[58],"干旱",[],"农业相关性需要复核",[62,63],"半干旱农业 土壤监测 土壤腐蚀 遥感","半干旱农业 土壤监测","半干旱农业土壤监测土壤腐蚀遥感-2795",2,"10.1007\u002Fs44288-026-00732-x",{"doi":66,"openalex_id":68,"authors":69,"venue":10,"cited_by_count":75,"oa_url":76,"card":77,"direction":81,"ingested_from":83},"W7213365338",[70,73],{"name":71,"orcid":72},"Abdulhakim Ahmad","https:\u002F\u002Forcid.org\u002F0009-0004-1030-8313",{"name":74,"orcid":9},"A. F. Akpaneno",0,"https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44288-026-00732-x.pdf",{"tldr":78,"method":79,"finding":80,"direction":81,"opportunity":82},"用Sentinel-2光谱指数与电法电阻率、pH对比，评估尼日利亚半干旱区土壤腐蚀环境指示能力。","Sentinel-2 L2A影像提取NDVI、NDWI、BSI、SI，结合44个","光谱指数与浅层电阻率仅弱相关，回归R²=0.197且不显著，遥感只能作辅助指标。","农业遥感与作物表型","可引入多时相\u002F土壤水分与纹理特征，或结合机器学习提升遥感对地下土壤腐蚀属性的间接反演能力。","openalex","2026-09-17T23:30:36.379942Z",{"total":86,"page":22,"page_size":86,"items":87},6,[88,190,243,291,341,391],{"id":89,"title":90,"url":91,"summary":92,"summary_zh":93,"content":9,"source_name":94,"source_url":91,"published_at":95,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":96,"score_detail":97,"sources":103,"tags":105,"primary_topic":109,"secondary_topics":114,"classification":124,"search_phrases":175,"slug":178,"view_count":65,"doi":9,"paper":179,"created_at":189},5013,"REMOTE SENSING AND GIS-BASED ASSESSMENT OF SOIL SALINITY IN IRRIGATED LANDS: METHODS, CHALLENGES AND PROSPECTS FOR PRECISION AGRICULTURE","http:\u002F\u002Fwww.arabianjournalofscience.com\u002Findex.php\u002Findex\u002Farticle\u002Fview\u002F1223","Soil salinization is a major land degradation process affecting the sustainability of irrigated agriculture, particularly in arid and semi-arid regions. Conventional soil sampling provides essential information about salinity levels but may be insufficient for continuous monitoring of large agricultural territories because of spatial variability, sampling costs, and temporal limitations. Remote sensing (RS) and geographic information systems (GIS) offer complementary approaches for identifying spatial patterns of salinity, monitoring land conditions, and supporting agricultural management. This article examines the scientific and methodological foundations of integrating multispectral satellite imagery, spectral indices, geospatial analysis, and machine learning for soil salinity assessment in irrigated lands. Particular attention is given to Landsat and Sentinel-2 imagery, the interpretation of vegetation and soil reflectance indicators, the role of electrical conductivity measurements in model calibration, and the limitations associated with vegetation cover, soil moisture, and seasonal variability. The analysis indicates that reliable salinity assessment requires the integration of satellite observations with field measurements and environmental variables rather than the isolated application of spectral indices. The article proposes a methodological framework for developing spatially explicit salinity assessment systems applicable to irrigated agricultural landscapes, including those of Central Asia. The framework provides a basis for improving monitoring strategies and supporting evidence-based land management without assuming unverified local measurement results.","土壤盐渍化是影响灌溉农业可持续性的主要土地退化过程，在干旱和半干旱地区尤为突出。传统土壤采样可提供盐分水平的基本信息，但由于空间变异性、采样成本和时间限制，可能不足以对大面积农业区域进行持续监测。遥感（RS）和地理信息系统（GIS）为识别盐渍化空间格局、监测土地状况和支持农业管理提供了互补性方法。本文探讨了整合多光谱卫星影像、光谱指数、地理空间分析和机器学习进行灌溉土地土壤盐渍化评估的科学与方法学基础。文章特别关注Landsat和Sentinel-2影像、植被与土壤反射率指标的解译、电导率测量在模型校准中的作用，以及与植被覆盖、土壤湿度和季节变异性相关的局限性。分析表明，可靠的盐渍化评估需要将卫星观测与野外测量和环境变量相结合，而非孤立地应用光谱指数。本文提出了一个方法学框架，用于开发适用于灌溉农业景观（包括中亚地区）的空间显式盐渍化评估系统。该框架为改进监测策略和支持基于证据的土地管理提供了基础，且不以未经核实的当地测量结果为假设前提。","Arabian journal of science.","2026-10-13T00:00:00Z",74,{"impact":98,"substance":99,"depth":100,"authority":101,"freshness":17,"relevant":22,"comment":102},16,20,17,13,"系统综述遥感与GIS在灌溉农田盐渍化评估中的方法框架与挑战，对盐碱地治理和精准农业有参考价值，但属综述性论文，非突破性成果。",[104],{"name":94,"url":91},[106,107,28,108,29],"智慧农业","精准农业","盐碱地治理",{"code":48,"slug":110,"name":111,"path":112,"parent_code":113},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[115,120],{"code":116,"slug":117,"name":118,"path":119,"parent_code":113},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"code":40,"slug":43,"name":121,"path":122,"parent_code":123},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"status":125,"locked":14,"version":35,"method":36,"evidence":126},"classified",{"candidates":127,"agricultural_evidence":41},[128,133,141,146,152,159,165,169],{"code":48,"score":18,"supported":41,"title":129,"abstract":130,"tags":132},[51],[28,51,131],"multispectral",[28],{"code":116,"score":100,"supported":41,"title":134,"abstract":136,"tags":140},[135],"GIS",[137,138,135,139],"地理信息","空间分析","geographic information",[],{"code":40,"score":142,"supported":41,"title":143,"abstract":144,"tags":145},15,[43],[45,43],[108],{"code":147,"score":13,"supported":41,"title":148,"abstract":150,"tags":151},"I1",[149],"precision agriculture",[],[106,107],{"code":153,"score":86,"supported":41,"title":154,"abstract":155,"tags":158},"I2",[],[156,157],"机器学习","machine learning",[],{"code":160,"score":55,"supported":41,"title":161,"abstract":162,"tags":164},"H3",[],[163],"灌溉",[],{"code":54,"score":55,"supported":41,"title":166,"abstract":167,"tags":168},[],[58],[],{"code":170,"score":55,"supported":41,"title":171,"abstract":172,"tags":174},"I7",[],[173],"methodological framework",[],[176,177],"遥感 GIS 土壤盐渍化","Landsat Sentinel-2 盐分监测","遥感GIS土壤盐渍化-5013",{"doi":9,"openalex_id":180,"authors":181,"venue":94,"cited_by_count":75,"oa_url":91,"card":184,"direction":81,"ingested_from":83},"W7221843179",[182],{"name":183,"orcid":9},"Sadullayev Sardor Zokirjon o'g'li",{"tldr":185,"method":186,"finding":187,"direction":81,"opportunity":188},"综述遥感与GIS评估灌溉地土壤盐渍化的方法、挑战与前景，并提出空间显式评估框架。","整合多光谱卫星影像、光谱指数、GIS空间分析与机器学习，结合电导率校准。","可靠盐渍化评估需卫星观测与实地测量及环境变量融合，而非孤立使用光谱指数。","可探索多源遥感与机器学习融合的盐渍化动态监测，并验证于中亚等干旱灌区。","2026-10-15T23:30:32.401626Z",{"id":191,"title":192,"url":193,"summary":194,"summary_zh":195,"content":9,"source_name":196,"source_url":193,"published_at":197,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":198,"score_detail":199,"sources":203,"tags":205,"primary_topic":209,"secondary_topics":210,"classification":211,"search_phrases":222,"slug":225,"view_count":22,"doi":226,"paper":227,"created_at":242},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":200,"depth":201,"authority":142,"freshness":17,"relevant":22,"comment":202},22,19,"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。",[204],{"name":196,"url":193},[27,28,206,207,208],"NDVI","考古遥感","作物标记",{"code":48,"slug":110,"name":111,"path":112,"parent_code":113},[],{"status":125,"locked":14,"version":35,"method":36,"evidence":212},{"candidates":213,"agricultural_evidence":41},[214,218],{"code":48,"score":20,"supported":41,"title":215,"abstract":216,"tags":217},[],[28,51,131],[206,27,28],{"code":153,"score":55,"supported":41,"title":219,"abstract":220,"tags":221},[],[156],[],[223,224],"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":226,"openalex_id":228,"authors":229,"venue":196,"cited_by_count":75,"oa_url":236,"card":237,"direction":81,"ingested_from":83},"W7221319130",[230,233],{"name":231,"orcid":232},"Elias Gravanis","https:\u002F\u002Forcid.org\u002F0000-0002-5331-6661",{"name":234,"orcid":235},"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":238,"method":239,"finding":240,"direction":81,"opportunity":241},"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":244,"title":245,"url":246,"summary":247,"summary_zh":248,"content":9,"source_name":249,"source_url":246,"published_at":250,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":251,"score_detail":252,"sources":254,"tags":256,"primary_topic":260,"secondary_topics":261,"classification":262,"search_phrases":273,"slug":276,"view_count":75,"doi":277,"paper":278,"created_at":290},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。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-11T00:00:00Z",62,{"impact":17,"substance":18,"depth":142,"authority":20,"freshness":21,"relevant":22,"comment":253},"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。",[255],{"name":249,"url":246},[257,27,28,258,259],"农业遥感","乌兹别克斯坦","光谱波段",{"code":48,"slug":110,"name":111,"path":112,"parent_code":113},[],{"status":125,"locked":14,"version":35,"method":36,"evidence":263},{"candidates":264,"agricultural_evidence":41},[265,269],{"code":48,"score":100,"supported":41,"title":266,"abstract":267,"tags":268},[51],[28,51],[27,257,28],{"code":116,"score":55,"supported":41,"title":270,"abstract":271,"tags":272},[],[137],[],[274,275],"Sentinel-2 光谱波段 组合","Parkent 遥感 农业用地","Sentinel-2光谱波段组合-4748","10.5281\u002Fzenodo.23297593",{"doi":277,"openalex_id":279,"authors":280,"venue":249,"cited_by_count":75,"oa_url":246,"card":285,"direction":81,"ingested_from":83},"W7221467289",[281,283],{"name":282,"orcid":9},"Xalilov Doniyor",{"name":284,"orcid":9},"Allambergenova Venera",{"tldr":286,"method":287,"finding":288,"direction":81,"opportunity":289},"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","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。","2026-10-12T23:30:24.415736Z",{"id":292,"title":293,"url":294,"summary":295,"summary_zh":296,"content":9,"source_name":297,"source_url":294,"published_at":298,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":299,"score_detail":300,"sources":302,"tags":304,"primary_topic":9,"secondary_topics":308,"classification":309,"search_phrases":320,"slug":323,"view_count":22,"doi":324,"paper":325,"created_at":340},4591,"Uydu görüntüleri ile yanan orman alanının değerlendirilmesi: Çandır orman yangını örneği","https:\u002F\u002Fdoi.org\u002F10.18182\u002Ftjf.1721381","Bu çalışmada, 2021 yılında Çandır Orman İşletme Şefliğinde meydana gelen orman yangınında zarar gören orman alanlarının uzaktan algılama veri ve yöntemleriyle belirlenmesi ve değerlendirilmesi amaçlanmıştır. Bu kapsamda, yangın öncesi ve yangın sonrası Landsat-8 ve Sentinel-2 uydu görüntülerinden Normalleştirilmiş Fark Vejetasyon İndeksi (NDVI) ve Normalleştirilmiş Yanma Oranı (NBR) spektral indeksleri üretilmiştir. Yangının etkisini nicel olarak ortaya koymak amacıyla yangın öncesi ve sonrası indeks görüntüleri arasındaki fark alınarak dNDVI ve dNBR görüntüleri elde edilmiştir. Bu fark görüntülerine eşikleme yöntemi uygulanarak yanan orman alanları belirlenmiştir. Ayrıca Landsat-8 ve Sentinel-2 görüntüleri üzerinde kontrollü sınıflandırma yöntemi uygulanmış ve Maximum Likelihood algoritmasıyla tematik haritalar üretilmiştir. Elde edilen sonuçlara göre en yüksek sınıflandırma doğrulukları Sentinel-2 verilerinden üretilen dNBR ve dNDVI görüntülerinde elde edilmiş olup, genel doğruluk değerleri sırasıyla 0.92 ve 0.88, kappa değerleri ise 0.83 ve 0.77 olarak hesaplanmıştır. Landsat-8 verilerinden üretilen dNBR ve dNDVI görüntülerinde ise genel doğruluk değerleri sırasıyla 0.87 ve 0.85, kappa değerleri ise 0.73 ve 0.7 olarak belirlenmiştir. Kontrollü sınıflandırma sonucunda üretilen tematik haritalarda Sentinel-2 ve Landsat-8 için genel doğruluk değerleri sırasıyla 0.88 ve 0.85, kappa değerleri ise 0.85 ve 0.82 olarak bulunmuştur. Farklı yöntemlerden elde edilen sonuçların ortalamasına göre yanan orman alanının büyüklüğü 760 hektar hesaplanmıştır. Sonuç olarak, Sentinel-2 ve Landsat-8 uydu verilerinden elde edilen dNDVI ve dNBR görüntülerinin, yanan orman alanlarının belirlenmesi ve yangın etkisinin değerlendirilmesinde verimli, güvenilir ve uygulanabilir sonuçlar üretme potansiyeline sahip olduğu ortaya konulmuştur.","本研究旨在利用遥感数据和方法，确定并评估2021年昌德尔森林经营管理局辖区内发生的森林火灾中受损的森林面积。在此范围内，利用火灾前后的Landsat-8和Sentinel-2卫星影像生成了归一化差异植被指数（NDVI）和归一化燃烧比率（NBR）光谱指数。为了定量揭示火灾的影响，通过计算火灾前后指数影像之间的差值，获得了dNDVI和dNBR影像。对这些差值影像应用阈值法，确定了过火森林区域。此外，对Landsat-8和Sentinel-2影像应用了监督分类方法，并利用最大似然法（Maximum Likelihood）生成了专题地图。根据所得结果，最高的分类精度来自Sentinel-2数据生成的dNBR和dNDVI影像，其总体精度分别为0.92和0.88，Kappa系数分别为0.83和0.77。在Landsat-8数据生成的dNBR和dNDVI影像中，总体精度分别为0.87和0.85，Kappa系数分别为0.73和0.7。在监督分类生成的专题地图中，Sentinel-2和Landsat-8的总体精度分别为0.88和0.85，Kappa系数分别为0.85和0.82。根据不同方法所得结果的平均值，过火森林面积计算为760公顷。综上所述，由Sentinel-2和Landsat-8卫星数据获得的dNDVI和dNBR影像在确定过火森林区域和评估火灾影响方面具有产生高效、可靠且可应用结果的潜力。","Turkish Journal of Forestry | Türkiye Ormancılık Dergisi","2026-10-08T00:00:00Z",73,{"impact":20,"substance":200,"depth":18,"authority":101,"freshness":17,"relevant":22,"comment":301},"该论文利用Landsat-8与Sentinel-2遥感数据评估土耳其Çandır森林火灾过火面积，方法可靠、结论具体，对农业信息化中的遥感监测有参考价值，但属区域性案例研究，影响范围有限。",[303],{"name":297,"url":294},[27,28,305,306,307],"森林火灾","植被指数","Landsat-8",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":310},{"candidates":311,"agricultural_evidence":14,"reason":60},[312,316],{"code":48,"score":86,"supported":41,"title":313,"abstract":314,"tags":315},[],[28],[27,306,28],{"code":54,"score":22,"supported":14,"title":317,"abstract":318,"tags":319},[],[],[305],[321,322],"Çandır orman yangını","森林火灾 植被指数 遥感 Sentinel-2","Çandırormanyangını-4591","10.18182\u002Ftjf.1721381",{"doi":324,"openalex_id":326,"authors":327,"venue":297,"cited_by_count":75,"oa_url":334,"card":335,"direction":81,"ingested_from":83},"W7220904781",[328,331],{"name":329,"orcid":330},"Abdurrahman Cankut Göz","https:\u002F\u002Forcid.org\u002F0000-0003-1786-7658",{"name":332,"orcid":333},"HÜSEYİN OĞUZ ÇOBAN","https:\u002F\u002Forcid.org\u002F0000-0002-4037-4811","https:\u002F\u002Fdergipark.org.tr\u002Ftr\u002Fdownload\u002Farticle-file\u002F4966779",{"tldr":336,"method":337,"finding":338,"direction":81,"opportunity":339},"利用Landsat-8和Sentinel-2影像评估2021年Çandır森林火灾过火面积。","计算火灾前后NDVI、NBR及dNDVI、dNBR，结合阈值法和最大似然监督分类","Sentinel-2的dNBR精度最高（总体精度0.92，Kappa 0.83），过火面积约760公","可探索多源卫星时序数据融合与深度学习，提升火烧区制图精度并拓展至植被恢复监测。","2026-10-10T23:30:29.424999Z",{"id":342,"title":343,"url":344,"summary":345,"summary_zh":346,"content":9,"source_name":347,"source_url":344,"published_at":348,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":349,"score_detail":350,"sources":353,"tags":355,"primary_topic":358,"secondary_topics":359,"classification":360,"search_phrases":372,"slug":375,"view_count":75,"doi":376,"paper":377,"created_at":390},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":351,"depth":100,"authority":101,"freshness":17,"relevant":22,"comment":352},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。",[354],{"name":347,"url":344},[106,27,28,356,357],"作物分类","地块尺度",{"code":48,"slug":110,"name":111,"path":112,"parent_code":113},[],{"status":125,"locked":14,"version":35,"method":36,"evidence":361},{"candidates":362,"agricultural_evidence":41},[363,367],{"code":48,"score":100,"supported":41,"title":364,"abstract":365,"tags":366},[51],[28,51],[27,356,28],{"code":147,"score":368,"supported":41,"title":369,"abstract":370,"tags":371},7,[],[107,149],[106],[373,374],"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":376,"openalex_id":378,"authors":379,"venue":347,"cited_by_count":75,"oa_url":344,"card":385,"direction":81,"ingested_from":83},"W7221068088",[380,383],{"name":381,"orcid":382},"Zhihua Wang","https:\u002F\u002Forcid.org\u002F0000-0002-6776-2910",{"name":384,"orcid":9},"Qin Yang",{"tldr":386,"method":387,"finding":388,"direction":81,"opportunity":389},"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":392,"title":393,"url":394,"summary":395,"summary_zh":396,"content":9,"source_name":397,"source_url":394,"published_at":398,"category":12,"cover_url":9,"hotness":13,"is_selected":41,"score":399,"score_detail":400,"sources":402,"tags":404,"primary_topic":407,"secondary_topics":408,"classification":410,"search_phrases":437,"slug":440,"view_count":22,"doi":441,"paper":442,"created_at":473},4345,"HyBEAR: a hyperspectral benchmark for bare soil detection","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fessd-18-7301-2026","Detecting bare soil areas is an important step in the analysis of Earth observation data in a variety of Precision Agriculture (PA) applications focused on quantifying soil properties and assessing soil quality. In this paper, we introduce the HyBEAR benchmark – a novel large-scale collection of high-resolution hyperspectral aerial images (with 2 m ground sampling distance) accompanied by manual bare soil annotations verified by domain experts. Usually, the bare soil detection problem is tackled at the pixel level, meaning that detection methods classify all pixels as either bare soil or background. In contrast to this approach, we provide pixel-level annotations for the entire agricultural parcels (if the parcel is labeled as bare soil, then all pixels within that parcel are labeled accordingly), and aim to support the development of methods that identify entire fields with no vegetation. Commonly, such fields undergo further analysis to determine specific soil parameters and characteristics that are important when planning various PA activities, such as fertilization. The HyBEAR benchmark includes (i) the largest-to-date (108 064 591 pixels, corresponding to 43 225 ha) and most heterogeneous dataset for bare soil detection, as well as (ii) the validation procedure (training-test splits and quality metrics) and a set of baseline results, obtained for a set of machine learning bare soil detection models. From the FULL collection of 1954 images in HyBEAR, which we divided into 5 spatially-disjoint folds, we additionally selected a random, stratified subset (MINI) of the images which may be useful for designing and verifying bare soil detection algorithms. Overall, HyBEAR is a step toward standardizing the way the community builds and confronts bare soil detection algorithms in a thorough, reproducible, and unbiased way. The dataset is publicly available at Zenodo: https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.17607897 (Wijata et al., 2025).","在多种精准农业（PA）应用中，裸土区域检测是对地观测数据分析的重要步骤，其重点在于量化土壤性质和评估土壤质量。本文介绍了HyBEAR基准——一个全新的大规模高分辨率高光谱航空影像数据集（地面采样距离为2 m），并附有经领域专家验证的人工裸土标注。通常，裸土检测问题在像素层面进行处理，即检测方法将所有像素分类为裸土或背景。与此方法不同，我们为整个农业地块提供像素级标注（如果地块被标记为裸土，则该地块内的所有像素均被相应标记），旨在支持开发能够识别无植被整块田地的方法。通常，此类田地会经过进一步分析以确定特定的土壤参数和特征，这些在规划施肥等各种精准农业活动时至关重要。HyBEAR基准包含：（i）迄今为止规模最大（108 064 591个像素，对应43 225公顷）且最具异质性的裸土检测数据集；（ii）验证流程（训练-测试划分和质量指标）以及一组基线结果，这些结果是在一组机器学习裸土检测模型上获得的。从HyBEAR中全部1954幅图像（我们将其划分为5个空间不相交的折）中，我们额外选择了一个随机分层子集（MINI），该子集可能有助于设计和验证裸土检测算法。总体而言，HyBEAR朝着以全面、可复现且无偏见的方式标准化社区构建和对比裸土检测算法的方向迈出了一步。该数据集已在Zenodo上公开：https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.17607897（Wijata et al., 2025）。","Earth system science data","2026-10-06T00:00:00Z",81,{"impact":18,"substance":200,"depth":18,"authority":19,"freshness":21,"relevant":22,"comment":401},"发布迄今最大规模高光谱裸土检测基准数据集，含像素级标注与基线结果，对精准农业土壤分析有实质推动。",[403],{"name":397,"url":394},[106,405,406,28,29],"农业人工智能","高光谱",{"code":40,"slug":43,"name":121,"path":122,"parent_code":123},[409],{"code":48,"slug":110,"name":111,"path":112,"parent_code":113},{"status":125,"locked":14,"version":35,"method":36,"evidence":411},{"candidates":412,"agricultural_evidence":41},[413,417,422,426,430],{"code":40,"score":19,"supported":41,"title":414,"abstract":415,"tags":416},[43],[45,43],[],{"code":48,"score":101,"supported":41,"title":418,"abstract":420,"tags":421},[419],"hyperspectral",[419],[28,406],{"code":147,"score":368,"supported":41,"title":423,"abstract":424,"tags":425},[],[107,149],[106],{"code":153,"score":368,"supported":41,"title":427,"abstract":428,"tags":429},[],[156,157],[405],{"code":431,"score":86,"supported":41,"title":432,"abstract":433,"tags":436},"F3",[],[434,435],"施肥","fertilization",[],[438,439],"HyBEAR 裸土检测 高光谱","裸土检测 基准数据集 遥感","HyBEAR裸土检测高光谱-4345","10.5194\u002Fessd-18-7301-2026",{"doi":441,"openalex_id":443,"authors":444,"venue":397,"cited_by_count":75,"oa_url":466,"card":467,"direction":472,"ingested_from":83},"W7220738771",[445,448,451,454,457,460,463],{"name":446,"orcid":447},"Agata M. Wijata","https:\u002F\u002Forcid.org\u002F0000-0001-6180-9979",{"name":449,"orcid":450},"Bogdan Ruszczak","https:\u002F\u002Forcid.org\u002F0000-0003-1089-1778",{"name":452,"orcid":453},"Adriana Niepala","https:\u002F\u002Forcid.org\u002F0009-0005-8274-9852",{"name":455,"orcid":456},"Michał Gumiela","https:\u002F\u002Forcid.org\u002F0009-0000-2501-2786",{"name":458,"orcid":459},"Krzysztof Smykała","https:\u002F\u002Forcid.org\u002F0000-0003-1970-5388",{"name":461,"orcid":462},"Nicolas Longépé","https:\u002F\u002Forcid.org\u002F0000-0002-6832-3274",{"name":464,"orcid":465},"Jakub Nalepa","https:\u002F\u002Forcid.org\u002F0000-0002-4026-1569","https:\u002F\u002Fessd.copernicus.org\u002Farticles\u002F18\u002F7301\u002F2026\u002Fessd-18-7301-2026.pdf",{"tldr":468,"method":469,"finding":470,"direction":81,"opportunity":471},"提出HyBEAR高光谱航空影像基准数据集，用于整田块裸土检测。","2米分辨率高光谱航空影像，专家验证的田块级像素标注，含训练测试划分与基线模型。","构建了迄今最大（43225公顷）且异质的裸土检测数据集，并给出可复现的评估流程与基线结果。","可基于该基准探索田块级裸土检测的迁移学习与多光谱\u002F高光谱融合方法，并关联土壤参数反演。","智慧农业 \u002F 农业物联网","2026-10-07T23:30:09.147409Z"]