[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4886":3,"related-4886":94},{"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":55,"slug":58,"view_count":59,"doi":60,"paper":61,"created_at":93},4886,"Mapping Fuel Categories in Mediterranean Regions with Sentinel-2 and PlanetScope Satellite Acquisitions","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fisprs-archives-xlviii-m-13-2026-9-2026","Accurate and spatially explicit mapping of forest fuel types is essential for developing integrated wildfire risk management strategies, estimating fire danger, and modeling fire propagation in fire-prone Mediterranean ecosystems. While time series of Copernicus Sentinel-2 multispectral imagery at 10 m spatial resolution have proven effective for regional fuel mapping, severe spectral and spatial ambiguities persist in distinguishing complex canopy structures, heterogeneous shrublands, and pastures. This study explores the synergistic integration of Sentinel-2 time series and high-resolution PlanetScope satellite acquisitions (3 m spatial resolution) to produce a 10 m fuel type map (9 classes) over a pilot area in Sardinia, Italy. A total of 114 features were synthesized, combining dynamic Sentinel-2 metrics (kNDVI, FAPAR, NDMI, ReNDVI harmonics), PlanetScope seasonal kNDVI zonal statistics and Gray-Level Co-occurrence Matrix (GLCM) textural features, and static terrain\u002Fcanopy parameters. Using an XGBoost machine learning classifier trained on 245 photo-interpreted Regions of Interest (80\u002F20 spatial split), three sets of features were compared. The combined model achieved a test overall accuracy of 74.46%, compared to 74.05% for Sentinel-2 alone and 74.66% for PlanetScope alone. Notably, incorporating PlanetScope textural features significantly improved classification metrics for dense canopy classes, increasing the F1-score of broadleaf evergreen trees and needleleaf evergreen trees. The PlanetScope GLCM kNDVI autumn entropy emerged as the most important feature overall. Conversely, PlanetScope performed poorly on the grass class. These findings highlight the critical trade-offs and high complementarity between fine spatial texture and broad spectral coverage for operational wildfire management.","在易发生火灾的地中海生态系统中，准确且具有空间明确性的森林可燃物类型制图对于制定综合野火风险管理策略、估算火险以及模拟火蔓延至关重要。尽管10 m空间分辨率的哥白尼Sentinel-2多光谱影像时间序列已被证明对区域可燃物制图有效，但在区分复杂冠层结构、异质性灌丛和草地时，仍存在严重的光谱和空间模糊性。本研究探索了Sentinel-2时间序列与高分辨率PlanetScope卫星影像（3 m空间分辨率）的协同集成，以在意大利撒丁岛的一个试验区生成10 m可燃物类型图（9类）。共合成114个特征，结合了动态Sentinel-2指标（kNDVI、FAPAR、NDMI、ReNDVI谐波）、PlanetScope季节性kNDVI分区统计和灰度共生矩阵（GLCM）纹理特征，以及静态地形\u002F冠层参数。使用在245个照片判读感兴趣区（80\u002F20空间划分）上训练的XGBoost机器学习分类器，比较了三组特征。组合模型的测试总体精度为74.46%，而仅使用Sentinel-2为74.05%，仅使用PlanetScope为74.66%。值得注意的是，纳入PlanetScope纹理特征显著改善了密集冠层类的分类指标，提高了阔叶常绿乔木和针叶常绿乔木的F1分数。PlanetScope GLCM kNDVI秋季熵成为总体最重要的特征。相反，PlanetScope在草地类上表现较差。这些发现凸显了精细空间纹理与广域光谱覆盖在业务化野火管理中的关键权衡和高互补性。",null,"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences\u002FInternational archives of the photogrammetry, remote sensing and spatial information sciences","2026-10-12T00:00:00Z","论文",10,false,72,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,22,17,13,8,1,"方法新颖、数据扎实的遥感制图论文，对林火风险管理有参考价值，但属细分领域研究，公共影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"Sentinel-2","机器学习","遥感监测","地中海农业","林火风险",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":14,"reason":54},[39,47],{"code":40,"score":41,"supported":42,"title":43,"abstract":44,"tags":46},"I2",7,true,[],[28,45],"machine learning",[28],{"code":48,"score":49,"supported":42,"title":50,"abstract":51,"tags":53},"I3",5,[],[52],"multispectral",[27,29],"农业相关性需要复核",[56,57],"Sentinel-2 PlanetScope 燃料类型制图","撒丁岛 林火燃料 遥感","Sentinel-2PlanetScope燃料类型制图-4886",0,"10.5194\u002Fisprs-archives-xlviii-m-13-2026-9-2026",{"doi":60,"openalex_id":62,"authors":63,"venue":10,"cited_by_count":59,"oa_url":84,"card":85,"direction":91,"ingested_from":92},"W7221532579",[64,67,70,72,75,78,81],{"name":65,"orcid":66},"Federico Filipponi","https:\u002F\u002Forcid.org\u002F0000-0002-1419-3734",{"name":68,"orcid":69},"Daniela Stroppiana","https:\u002F\u002Forcid.org\u002F0000-0002-5619-4305",{"name":71,"orcid":9},"Forough Rajabi",{"name":73,"orcid":74},"Debora Voltolina","https:\u002F\u002Forcid.org\u002F0000-0001-9186-0644",{"name":76,"orcid":77},"Bachisio Arca","https:\u002F\u002Forcid.org\u002F0000-0002-2834-2753",{"name":79,"orcid":80},"Michele Salis","https:\u002F\u002Forcid.org\u002F0000-0002-0499-9726",{"name":82,"orcid":83},"Francesco Zucca","https:\u002F\u002Forcid.org\u002F0000-0003-4119-5484","https:\u002F\u002Fisprs-archives.copernicus.org\u002Farticles\u002FXLVIII-M-13-2026\u002F9\u002F2026\u002Fisprs-archives-XLVIII-M-13-2026-9-2026.pdf",{"tldr":86,"method":87,"finding":88,"direction":89,"opportunity":90},"融合Sentinel-2时序与PlanetScope高分影像，用XGBoost绘制地中海9类可燃物类","Sentinel-2时序谐波、PlanetScope kNDVI与GLCM纹理及","联合模型精度74.46%，PlanetScope纹理显著提升密冠层F1，但草地类表现差。","农业遥感与作物表型","可探索多源高分纹理与光谱互补机制，优化稀疏植被与草地类别的特征设计。","智慧农业 \u002F 农业物联网","openalex","2026-10-14T23:30:11.734574Z",{"total":95,"page":22,"page_size":95,"items":96},6,[97,168,259,324,391,447],{"id":98,"title":99,"url":100,"summary":101,"summary_zh":102,"content":9,"source_name":103,"source_url":100,"published_at":104,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":105,"sources":109,"tags":111,"primary_topic":114,"secondary_topics":119,"classification":120,"search_phrases":146,"slug":149,"view_count":150,"doi":151,"paper":152,"created_at":167},3568,"Interpretable multi-index framework for extracting dry-season built-up areas: benchmarking machine learning with Sentinel-2","https:\u002F\u002Fdoi.org\u002F10.4995\u002Fraet.2027.25885","Spectral similarity between built-up surfaces and exposed dry soil significantly constrains built-up area extraction during dry seasons. Although machine-learning techniques can address this challenge by modeling complex spectral relationships, they generally require sufficiently large and representative labeled training datasets. This study therefore investigates a less data-demanding, rule-based multi-index approach for dry-season built-up mapping, aiming to reduce reliance on large labeled training datasets while maintaining effective classification performance. Sentinel-2A imagery acquired in April 2024 was analyzed, and temporal validation was conducted for 2020, 2022, and 2024. Among the tested combinations, the Built-up Area Extraction Index (BAEI), Dry BareSoil Index (DBSI), and Normalized Difference Vegetation Index (NDVI) achieved the highest performance, yielding 95 % overall accuracy and a Kappa coefficient of 0.89. This represents a substantial improvement over singleindex BAEI (80 % overall accuracy; Kappa 0.55), increasing the built-up user’s accuracy from 61 % to 92 %. Multitemporal validation confirmed that the optimized BAEI–DBSI–NDVI decision rules remained stable across the 2020–2024 dry-season images without recalibration, achieving 93–96 % overall accuracy and a built-up F1-score of 84–93 %. Spatial analysis demonstrated robust performance across peripheral and agricultural zones, with moderate variability in dense urban cores due to spectral heterogeneity. To evaluate the operational robustness of the proposed framework, it was benchmarked against machine-learning classifiers, specifically Support Vector Machine and Random Forest (RF). Under the specific conditions of this study, the proposed rule-based method (95 % accuracy) marginally outperformed both SVM (93 %) and RF (92 %), while offering greater transparency and reducing dependency on large, labeled training datasets. Furthermore, feature importance analysis confirmed the critical role of these selected indices in resolving spectral confusion. These findings suggest that the proposed framework offers a transparent and computationally efficient approach for dry-season urban monitoring in tropical coastal environments similar to Visakhapatnam.","建成表面与裸露干土之间的光谱相似性显著制约了旱季建成区提取。尽管机器学习技术可通过建模复杂光谱关系来应对这一挑战，但其通常需要足够大且具有代表性的标记训练数据集。因此，本研究探讨了一种对数据需求较低、基于规则的多指数方法用于旱季建成区制图，旨在减少对大规模标记训练数据集的依赖，同时保持有效的分类性能。研究分析了2024年4月获取的Sentinel-2A影像，并对2020年、2022年和2024年进行了时间验证。在测试的组合中，建成区提取指数（BAEI）、干裸土指数（DBSI）和归一化差异植被指数（NDVI）表现最佳，总体精度达95%，Kappa系数为0.89。相较于单一指数BAEI（总体精度80%；Kappa 0.55），这一结果有显著提升，建成区用户精度从61%提高至92%。多时相验证证实，优化后的BAEI–DBSI–NDVI决策规则在2020—2024年旱季影像上无需重新校准即可保持稳定，总体精度达93%—96%，建成区F1分数为84%—93%。空间分析表明，该方法在外围和农业区域表现稳健，而在密集城市核心区由于光谱异质性存在中等程度变异。为评估所提框架的业务化稳健性，将其与机器学习分类器进行了基准比较，具体为支持向量机（SVM）和随机森林（RF）。在本研究的特定条件下，所提出的基于规则的方法（95%精度）略优于SVM（93%）和RF（92%），同时具有更高的透明性并减少了对大规模标记训练数据集的依赖。此外，特征重要性分析证实了所选指数在解决光谱混淆方面的关键作用。这些发现表明，所提框架为类似维沙卡帕特南的热带沿海环境旱季城市监测提供了一种透明且计算高效的方法。","Revista de teledetección: Revista de la Asociación Española de Teledetección","2026-09-25T00:00:00Z",{"impact":17,"substance":106,"depth":19,"authority":20,"freshness":107,"relevant":22,"comment":108},21,9,"该研究提出可解释的多指数规则框架，在旱季建成区提取上以更少标注数据达到95%精度并优于SVM\u002FRF，方法新颖、验证充分，对农业遥感与乡村土地利用监测有参考价值。",[110],{"name":103,"url":100},[27,28,112,29,113],"NDVI","建成区提取",{"code":40,"slug":115,"name":116,"path":117,"parent_code":118},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":121,"locked":14,"version":35,"method":36,"evidence":122},"classified",{"candidates":123,"agricultural_evidence":42},[124,128,135,142],{"code":40,"score":17,"supported":42,"title":125,"abstract":126,"tags":127},[45],[28],[28],{"code":129,"score":95,"supported":42,"title":130,"abstract":131,"tags":134},"I4",[],[132,133],"空间分析","spatial analysis",[],{"code":136,"score":137,"supported":42,"title":138,"abstract":139,"tags":141},"H2",3,[],[140],"soil",[],{"code":48,"score":137,"supported":14,"title":143,"abstract":144,"tags":145},[],[],[112,27,29],[147,148],"Sentinel-2 建成区提取","BAEI DBSI NDVI 旱季","Sentinel-2建成区提取-3568",2,"10.4995\u002Fraet.2027.25885",{"doi":151,"openalex_id":153,"authors":154,"venue":103,"cited_by_count":59,"oa_url":100,"card":161,"direction":166,"ingested_from":92},"W7214385607",[155,158],{"name":156,"orcid":157},"Sarah Sejari","https:\u002F\u002Forcid.org\u002F0009-0002-7300-9084",{"name":159,"orcid":160},"Vazeer Mahammood","https:\u002F\u002Forcid.org\u002F0000-0003-1667-5232",{"tldr":162,"method":163,"finding":164,"direction":89,"opportunity":165},"提出可解释多指数规则框架，用Sentinel-2提取旱季建成区，性能优于机器学习。","Sentinel-2A影像，BAEI、DBSI、NDVI组合规则，对比SVM与随","BAEI-DBSI-NDVI规则达95%精度、Kappa 0.89，优于单指数及SVM\u002FRF且无需重","可探索该规则框架向其他气候带与传感器迁移，并结合少量样本的半监督学习提升城市核心区精度。","农业人工智能与决策模型","2026-09-26T23:30:53.200163Z",{"id":169,"title":170,"url":171,"summary":172,"summary_zh":173,"content":9,"source_name":174,"source_url":171,"published_at":175,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":176,"score_detail":177,"sources":180,"tags":182,"primary_topic":186,"secondary_topics":192,"classification":198,"search_phrases":236,"slug":239,"view_count":59,"doi":240,"paper":241,"created_at":258},5558,"Estimation of Soil Water Content in Winter Wheat Field at Jointing Stage Using Multi-Source Features and Machine Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16202071","Accurate and timely monitoring of soil water content (SWC) plays an essential supporting role in crop irrigation management and smart agricultural practice. Remote sensing technology provides rapid and convenient approaches to large-scale farmland SWC estimation or inversion. However, existing mechanistic modeling methods are limited by parameter localization and land surface heterogeneity, while machine learning data-driven methods lack quite good physical interpretability. To address deficiencies in single data-driven methods and single vegetation microwave scattering models, and explore the estimation effect of different feature variables in machine learning. This study proposes an innovative estimation model that integrates machine learning strategies and multi-source features, including spectral indices and features derived from the classic water cloud model (WCM). The novelty of the proposed model lies in balancing physical interpretability and data-driven accuracy, namely extracting relevant soil backscattering parameters through WCM, which are then used as input features in the machine learning. A combination of field-measured data and unmanned aerial vehicle (UAV) multi-source remote sensing data, including multispectral and synthetic aperture radar (SAR) data, was acquired synchronously at the jointing stage of winter wheat. Through extracting variables such as the SAR microwave backscattering coefficient and the optical vegetation index, and conducting feature screening and parameter sensitivity analysis, we optimized the input variable sets of models based on back-propagation (BP) neural network, support vector regression (SVR) and random forest (RF) algorithms. The study results showed that the established estimation model had excellent estimation performance, especially for the RF algorithm, with the highest R2 of 0.855, lowest MAE of 0.0212 cm3\u002Fcm3, and lowest RMSE of 0.0253 cm3\u002Fcm3. It was also shown that different feature input schemes have obvious effects on SWC estimation performance. On the whole, RF had the best estimation accuracy, followed by SVR and then BPNN. This study demonstrated that models integrating multi-source remote sensing data and machine learning could enhance SWC estimation ability for winter wheat fields, and could also serve as a promising approach for SWC monitoring in winter wheat fields in the North China Plain.","准确、及时地监测土壤含水量（SWC）对作物灌溉管理和智慧农业实践具有重要的支撑作用。遥感技术为大规模农田土壤含水量估算或反演提供了快速便捷的手段。然而，现有机理建模方法受参数本地化和地表异质性限制，机器学习数据驱动方法则缺乏良好的物理解释性。针对单一数据驱动方法和单一植被微波散射模型的不足，并探究不同特征变量在机器学习中的估算效果，本研究提出了一种融合机器学习策略与多源特征的创新估算模型，所采用特征包括光谱指数和经典水云模型（WCM）衍生的特征。该模型的新颖性在于平衡物理解释性与数据驱动精度，即通过WCM提取相关土壤后向散射参数，再将其作为机器学习的输入特征。研究在冬小麦拔节期同步获取了田间实测数据与无人机（UAV）多源遥感数据，包括多光谱和合成孔径雷达（SAR）数据。通过提取SAR微波后向散射系数和光学植被指数等变量，并进行特征筛选和参数敏感性分析，优化了基于反向传播（BP）神经网络、支持向量回归（SVR）和随机森林（RF）算法的模型输入变量集。研究结果表明，所建立的估算模型具有优良的估算性能，尤其是RF算法，最高R²为0.855，最低MAE为0.0212 cm³\u002Fcm³，最低RMSE为0.0253 cm³\u002Fcm³。研究还表明，不同特征输入方案对土壤含水量估算性能有明显影响。总体而言，RF估算精度最佳，SVR次之，BPNN再次之。本研究证明，融合多源遥感数据与机器学习的模型能够提升冬小麦田土壤含水量估算能力，也可为华北平原冬小麦田土壤含水量监测提供一种有前景的方法。","Agronomy","2026-10-21T00:00:00Z",77,{"impact":178,"substance":106,"depth":19,"authority":20,"freshness":13,"relevant":22,"comment":179},16,"该研究融合多源遥感与机器学习估算冬小麦拔节期土壤含水量，方法有创新、结论可靠，对智慧灌溉有参考价值，但属细分领域论文，影响范围有限。",[181],{"name":174,"url":171},[183,28,29,184,185],"智慧农业","土壤墒情","冬小麦",{"code":187,"slug":188,"name":189,"path":190,"parent_code":191},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[193,197],{"code":136,"slug":140,"name":194,"path":195,"parent_code":196},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":40,"slug":115,"name":116,"path":117,"parent_code":118},{"status":121,"locked":14,"version":35,"method":36,"evidence":199},{"candidates":200,"agricultural_evidence":42},[201,207,213,217,223,230],{"code":187,"score":202,"supported":42,"title":203,"abstract":205,"tags":206},26,[204,188],"winter wheat",[189,204,188],[185],{"code":136,"score":208,"supported":42,"title":209,"abstract":210,"tags":212},15,[140],[211,140],"土壤",[184],{"code":40,"score":208,"supported":42,"title":214,"abstract":215,"tags":216},[45],[28,45],[28],{"code":48,"score":13,"supported":42,"title":218,"abstract":219,"tags":222},[],[220,221,52],"遥感","remote sensing",[29],{"code":224,"score":95,"supported":42,"title":225,"abstract":226,"tags":229},"H3",[],[227,228],"灌溉","irrigation",[],{"code":231,"score":232,"supported":42,"title":233,"abstract":234,"tags":235},"I1",4,[],[183],[183],[237,238],"冬小麦 土壤含水量 遥感","多源遥感 机器学习 墒情","冬小麦土壤含水量遥感-5558","10.3390\u002Fagronomy16202071",{"doi":240,"openalex_id":242,"authors":243,"venue":174,"cited_by_count":59,"oa_url":171,"card":253,"direction":91,"ingested_from":92},"W7223156204",[244,247,249,251],{"name":245,"orcid":246},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":248,"orcid":9},"Yi Zhang",{"name":250,"orcid":9},"WEI Lei",{"name":252,"orcid":9},"Chengcai Zhang",{"tldr":254,"method":255,"finding":256,"direction":89,"opportunity":257},"融合WCM物理特征与多源遥感，用机器学习估算冬小麦拔节期土壤含水量。","无人机多光谱与SAR数据，WCM参数+光谱指数，BP\u002FSVR\u002FRF建模。","RF最优，R²达0.855，RMSE为0.0253 cm³\u002Fcm³，特征方案影响显著。","可探索物理约束与深度学习耦合，并迁移至多生育期、多作物及不同土壤质地验证。","2026-10-22T11:20:18.887301Z",{"id":260,"title":261,"url":262,"summary":263,"summary_zh":264,"content":9,"source_name":265,"source_url":262,"published_at":266,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":59,"score_detail":267,"sources":269,"tags":271,"primary_topic":274,"secondary_topics":275,"classification":280,"search_phrases":301,"slug":304,"view_count":59,"doi":305,"paper":306,"created_at":323},5485,"Integrating machine learning and geospatial technologies for spatio-temporal analysis of urban expansion and land use transformation in Gambella Town, Ethiopia","https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pone.0360419","Rapid urbanization and the associated expansion of built-up areas are transforming land resources, ecosystem functions, and the spatial structure of cities. Distinguishing urban growth, urban expansion, and urban sprawl is essential for understanding the processes and patterns of urban transformation and supporting sustainable urban planning. This study integrates machine learning and geospatial technologies to analyze spatio-temporal land use and land cover (LULC) dynamics and urban expansion in Gambella Town, Ethiopia. Multi-temporal Landsat satellite images from 2005, 2015, and 2025 were processed using remote sensing techniques and a supervised machine learning approach based on the Random Forest (RF) classifier. Six major LULC classes were identified: Agricultural Land, Bush with Grass, Built-Up Area, Bare Land, Forest Land, and Water Bodies. The classification achieved overall accuracies of 90%, 90%, and 89% for 2005, 2015, and 2025, respectively. The results revealed substantial land transformation and rapid physical urban expansion, with the built-up area increasing from 93 ha (2.0%) in 2005–1,956 ha (41.3%) in 2025. This expansion was accompanied by major transformations of surrounding land resources, particularly the conversion of Bush with Grass and other open areas to built-up land. Agricultural land increased from 634 ha (13.4%) to 1,750 ha (36.9%), whereas Bush with Grass declined from 3,375 ha (71.1%) to 652 ha (13.7%). Socio-economic information collected from 277 respondents indicated that population growth, rural–urban migration, housing demand, employment opportunities, and infrastructure development were major drivers of urban expansion. The study distinguishes urban expansion, represented by the increase in built-up land, from urban sprawl, which refers to the spatial pattern and form of outward urban development. The integration of Random Forest classification and GIS-based spatial analysis provides valuable evidence of land transformation, vegetation loss, and increasing pressure on urban services, supporting more sustainable urban planning and land management in Gambella Town.","快速城市化及与之相关的建成区扩张正在改变土地资源、生态系统功能和城市空间结构。区分城市增长（urban growth）、城市扩张（urban expansion）和城市蔓延（urban sprawl），对于理解城市转型的过程与格局、支撑可持续城市规划至关重要。本研究融合机器学习与地理空间技术，分析埃塞俄比亚甘贝拉镇（Gambella Town）土地利用与土地覆盖（LULC）的时空动态及城市扩张。采用遥感技术和基于随机森林（RF）分类器的监督机器学习方法，处理了2005年、2015年和2025年的多时相Landsat卫星影像。研究识别出六类主要LULC类型：农业用地、灌草地、建成区、裸地、林地和水体。2005年、2015年和2025年的分类总体精度分别为90%、90%和89%。结果表明，土地发生了显著转变，城市实体空间快速扩张，建成区面积从2005年的93公顷（2.0%）增至2025年的1,956公顷（41.3%）。这一扩张伴随着周边土地资源的重大转变，尤其是灌草地及其他开阔地被转化为建成用地。农业用地从634公顷（13.4%）增至1,750公顷（36.9%），而灌草地则从3,375公顷（71.1%）降至652公顷（13.7%）。从277名受访者收集的社会经济信息表明，人口增长、城乡迁移、住房需求、就业机会和基础设施建设是城市扩张的主要驱动因素。本研究将城市扩张（以建成用地增加为表征）与城市蔓延（指城市向外发展的空间格局与形态）加以区分。随机森林分类与基于GIS的空间分析的融合，为土地转变、植被丧失及城市服务压力加剧提供了有价值的证据，有助于甘贝拉镇实现更可持续的城市规划与土地管理。","PLoS ONE","2026-10-20T00:00:00Z",{"impact":59,"substance":59,"depth":59,"authority":59,"freshness":59,"relevant":59,"comment":268},"研究聚焦埃塞俄比亚城市扩张与土地利用变化，属城市地理与遥感应用，与三农、农业信息化主题无直接关联，不建议进入每日精选。",[270],{"name":265,"url":262},[28,29,272,273],"土地利用","城市扩张",{"code":40,"slug":115,"name":116,"path":117,"parent_code":118},[276],{"code":129,"slug":277,"name":278,"path":279,"parent_code":118},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"status":121,"locked":14,"version":35,"method":36,"evidence":281},{"candidates":282,"agricultural_evidence":42},[283,287,292,296],{"code":40,"score":208,"supported":42,"title":284,"abstract":285,"tags":286},[45],[28,45],[28],{"code":129,"score":107,"supported":42,"title":288,"abstract":289,"tags":291},[],[132,290,133],"GIS",[],{"code":48,"score":41,"supported":42,"title":293,"abstract":294,"tags":295},[],[220,221],[29],{"code":297,"score":22,"supported":14,"title":298,"abstract":299,"tags":300},"H6",[],[],[272],[302,303],"Gambella Town 城市扩张","Random Forest 土地利用分类","GambellaTown城市扩张-5485","10.1371\u002Fjournal.pone.0360419",{"doi":305,"openalex_id":307,"authors":308,"venue":265,"cited_by_count":59,"oa_url":262,"card":318,"direction":89,"ingested_from":92},"W7223022155",[309,312,314,316],{"name":310,"orcid":311},"Kanenus Fufa Dararo","https:\u002F\u002Forcid.org\u002F0009-0007-5747-1558",{"name":313,"orcid":9},"Goanar Jal Dup",{"name":315,"orcid":9},"Weyesa Merga Chemeda",{"name":317,"orcid":9},"Shankar Karuppannan",{"tldr":319,"method":320,"finding":321,"direction":89,"opportunity":322},"用机器学习与遥感分析埃塞俄比亚甘贝拉镇2005-2025年城市扩张与土地利用变化。","Landsat多时相影像、随机森林分类与GIS空间分析，结合277份问卷。","建成区从93公顷增至1956公顷，灌草地大幅转为建成区和农地。","可将随机森林时序分类与驱动力调查结合，扩展到更多非洲小城镇并耦合城市增长模拟。","2026-10-21T23:30:23.271293Z",{"id":325,"title":326,"url":327,"summary":328,"summary_zh":329,"content":9,"source_name":330,"source_url":327,"published_at":331,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":59,"score_detail":332,"sources":334,"tags":336,"primary_topic":9,"secondary_topics":339,"classification":340,"search_phrases":366,"slug":369,"view_count":59,"doi":370,"paper":371,"created_at":390},5175,"Driving forces of contemporary wildfire severity in the American Southwest: a remote sensing and machine learning case study in Arizona, USA","https:\u002F\u002Fdoi.org\u002F10.1080\u002F20964471.2026.2735134","As climate change continues to intensify atmospheric moisture demand across the American Southwest, predicting where wildfires cause severe vegetation mortality remains a major challenge. New thermal and optical harmonized satellite products provide emerging opportunities for evaluating how pre-fire vegetation moisture and water stress influence fire severity compared to other variables. We modeled continuous Relativized differenced Normalized Burn Ratio (RdNBR) for 45 wildfires that burned in the 2019 and 2020 fire seasons using random forest to compare Harmonized Landsat–Sentinel-2 vegetation indices, ECOSTRESS (ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station) evapotranspiration and evaporative-stress metrics, Daymet pre-fire weather variables, LANDFIRE fuels attributes, and topographic predictors across five cumulative pre-ignition windows from 1 week to 3 months before ignition. Despite exceptional drought and anomalous fire-weather conditions, low-severity fire was common over the study period. Integrated models outperformed individual product-family models, indicating that fire severity was best explained by combined antecedent weather, terrain, fuels, vegetation type, and vegetation-moisture predictors. Temperature, elevation, solar radiation, vapor pressure, and HLS Normalized Difference Moisture Index (NDMI) were among the most consistent predictors. NDMI was the most consistent HLS plant-moisture metric, but vegetation moisture alone did not explain fire severity. Canopy cover was also important in woodlands and forests. Models using data from shorter pre-ignition windows outperformed longer pre-fire window models. They varied in performance among vegetation types, with the strongest vegetation-specific performance in forests and woodlands. These findings show that emerging high-resolution satellite products can help characterize pre-fire vegetation conditions and improve fire-risk assessments, especially when integrated with short-term antecedent weather, landscape moisture, and terrain data.","随着气候变化持续加剧美国西南部的大气水分需求，预测野火在何处导致严重植被死亡仍是一项重大挑战。新的热红外与光学协调卫星产品为评估火灾前植被水分和水分胁迫相较于其他变量如何影响火烈度提供了新的机遇。我们利用随机森林对2019年和2020年火季燃烧的45场野火构建了连续相对差分归一化燃烧比（RdNBR）模型，比较了协调陆地卫星—哨兵2号（Harmonized Landsat–Sentinel-2）植被指数、ECOSTRESS（国际空间站生态系统空间热辐射计实验）蒸散发与蒸发胁迫指标、Daymet火前气象变量、LANDFIRE燃料属性以及地形预测因子，并考察了从点火前1周至3个月的五个累积点火前时间窗口。尽管期间出现了极端干旱和异常火险天气条件，研究期内低烈度火仍较为普遍。集成模型的表現优于单一产品类别模型，表明火烈度最好由前期气象、地形、燃料、植被类型和植被水分预测因子的组合来解释。温度、海拔、太阳辐射、水汽压和HLS归一化差异水分指数（NDMI）是最为一致的预测因子。NDMI是最一致的HLS植物水分指标，但仅凭植被水分并不能解释火烈度。冠层覆盖度在疏林和林地中也很重要。使用较短点火前时间窗口数据的模型优于较长火前窗口模型。不同植被类型的模型表现存在差异，其中森林和林地的植被特异性表现最强。这些发现表明，新兴高分辨率卫星产品有助于刻画火前植被状况并改进火险评估，尤其是在与短期前期气象、景观水分和地形数据集成时。","Big Earth Data","2026-10-15T00:00:00Z",{"impact":59,"substance":59,"depth":59,"authority":59,"freshness":59,"relevant":59,"comment":333},"该研究聚焦美国西南部野火严重程度的遥感与机器学习建模，属于生态与灾害遥感领域，与三农、农业信息化、智慧农业等主题无直接关联，不建议进入每日精选。",[335],{"name":330,"url":327},[28,29,337,338],"野火风险","植被水分",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":341},{"candidates":342,"agricultural_evidence":14,"reason":54},[343,352,356,360],{"code":344,"score":13,"supported":42,"title":345,"abstract":346,"tags":351},"H7",[],[347,348,349,350],"气候变化","干旱","climate change","drought",[337],{"code":40,"score":107,"supported":42,"title":353,"abstract":354,"tags":355},[45],[],[28],{"code":48,"score":107,"supported":42,"title":357,"abstract":358,"tags":359},[221],[],[29],{"code":224,"score":95,"supported":42,"title":361,"abstract":362,"tags":365},[],[363,364],"蒸散发","evapotranspiration",[],[367,368],"美国西南部 野火 遥感","ECOSTRESS 植被水分 火险","美国西南部野火遥感-5175","10.1080\u002F20964471.2026.2735134",{"doi":370,"openalex_id":372,"authors":373,"venue":330,"cited_by_count":59,"oa_url":384,"card":385,"direction":89,"ingested_from":92},"W7222006390",[374,377,379,381],{"name":375,"orcid":376},"Helen Mills Poulos","https:\u002F\u002Forcid.org\u002F0000-0003-0686-9770",{"name":378,"orcid":9},"Sarah Hardy",{"name":380,"orcid":9},"Jenna Lynne Otaola",{"name":382,"orcid":383},"Andrew  Marder Barton","https:\u002F\u002Forcid.org\u002F0000-0001-5978-0337","https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F20964471.2026.2735134?needAccess=true",{"tldr":386,"method":387,"finding":388,"direction":89,"opportunity":389},"用遥感与随机森林分析亚利桑那45场野火，识别火前植被水分等驱动因子。","HLS植被指数、ECOSTRESS蒸散、Daymet气象与地形数据，随机森林建模","火险严重度需综合火前短期天气、地形、燃料与植被水分，单一水分指标不足。","可借鉴其多源遥感融合与短窗口建模思路，研究作物干旱胁迫与灾损风险预警。","2026-10-17T23:30:30.060308Z",{"id":392,"title":393,"url":394,"summary":395,"summary_zh":396,"content":9,"source_name":10,"source_url":394,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":397,"score_detail":398,"sources":401,"tags":403,"primary_topic":9,"secondary_topics":407,"classification":408,"search_phrases":423,"slug":426,"view_count":59,"doi":427,"paper":428,"created_at":446},4823,"Comparing Sentinel-2 Spectral Indices for Mapping Forest Fire Damage in Pollino National Park, Italy","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fisprs-archives-xlviii-m-13-2026-1-2026","In recent years, climate change has significantly increased the severity and frequency of wildfires across Europe, with the 2025 season marking an unprecedented peak of one million hectares burned. Rapid and cost-effective mapping of these damaged areas is crucial for environmental recovery, making satellite remote sensing an indispensable tool. This study evaluates and compares the performance of four Sentinel-2 spectral burn indices for mapping forest fire damage within the Pollino National Park (Italy) occurred in September 2025, an area severely affected by the devastating summer fires of 2025. The analyzed indices include the Normalized Burn Ratio (NBR), Normalized Burn Ratio Plus (NBR+), Normalized Burn Ratio Short Wave Infrared (NBR-SWIR), and Normalized Difference Short Wave Infrared (ND-SWIR). An image differencing technique was applied to pre-fire (June 7, 2025) and post-fire (November 14, 2025) datasets, followed by a supervised Maximum Likelihood classification to map burned and unburned classes. Accuracy assessment via confusion matrices showed that NBR and NBR+ delivered the highest performance, achieving an Overall Accuracy (OA) of 0.976 and 0.974, respectively. ND-SWIR performed closely at 0.966, whereas NBR-SWIR yielded a lower OA of 0.873 due to the misclassification of bare soil transitions as burned areas. These findings highlight the critical role of specific SWIR-band combinations in optimizing automated wildfire monitoring systems.","近年来，气候变化显著加剧了欧洲野火的严重程度和发生频率，2025年火灾季烧毁面积达到一百万公顷，创下历史新高。快速且经济高效地绘制这些受损区域对于环境恢复至关重要，使卫星遥感成为不可或缺的工具。本研究评估并比较了四种哨兵2号（Sentinel-2）光谱燃烧指数在绘制2025年9月发生于意大利波利诺国家公园（Pollino National Park）的森林火灾损害中的表现，该地区受2025年夏季毁灭性火灾的严重影响。所分析的指数包括归一化燃烧比率（NBR）、归一化燃烧比率增强版（NBR+）、归一化燃烧比率短波红外（NBR-SWIR）以及归一化差值短波红外（ND-SWIR）。研究对火灾前（2025年6月7日）和火灾后（2025年11月14日）数据集应用了图像差值技术，随后采用监督最大似然分类法绘制燃烧与未燃烧类别。通过混淆矩阵进行的精度评估表明，NBR和NBR+表现最佳，总体精度（OA）分别达到0.976和0.974。ND-SWIR紧随其后，为0.966，而NBR-SWIR由于将裸土转变误分类为燃烧区域，总体精度较低，为0.873。这些发现凸显了特定短波红外波段组合在优化自动化野火监测系统中的关键作用。",74,{"impact":17,"substance":18,"depth":399,"authority":20,"freshness":107,"relevant":22,"comment":400},18,"方法对比扎实、结论明确，但属区域案例研究，对农业信息化有间接参考价值，可入精选但非必选。",[402],{"name":10,"url":394},[27,29,404,405,406],"灾害评估","森林火灾","植被指数",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":409},{"candidates":410,"agricultural_evidence":14,"reason":54},[411,415,419],{"code":48,"score":107,"supported":42,"title":412,"abstract":413,"tags":414},[],[220,221],[27,406,29],{"code":344,"score":21,"supported":42,"title":416,"abstract":417,"tags":418},[],[347,349],[405,404],{"code":136,"score":137,"supported":42,"title":420,"abstract":421,"tags":422},[],[140],[],[424,425],"Sentinel-2 森林火灾 遥感","Pollino 国家公园 火烧迹地","Sentinel-2森林火灾遥感-4823","10.5194\u002Fisprs-archives-xlviii-m-13-2026-1-2026",{"doi":427,"openalex_id":429,"authors":430,"venue":10,"cited_by_count":59,"oa_url":440,"card":441,"direction":89,"ingested_from":92},"W7221528348",[431,433,435,438],{"name":432,"orcid":9},"Giuseppina Costanzo",{"name":434,"orcid":9},"Gianluigi Ferrara",{"name":436,"orcid":437},"Claudio Parente","https:\u002F\u002Forcid.org\u002F0000-0002-8328-9764",{"name":439,"orcid":9},"Vincenzo Verde","https:\u002F\u002Fisprs-archives.copernicus.org\u002Farticles\u002FXLVIII-M-13-2026\u002F1\u002F2026\u002Fisprs-archives-XLVIII-M-13-2026-1-2026.pdf",{"tldr":442,"method":443,"finding":444,"direction":89,"opportunity":445},"比较四种 Sentinel-2 光谱指数在意大利 Pollino 国家公园 2025 年林火损伤制图","Sentinel-2 火前火后影像差值 + 最大似然监督分类，用混淆矩阵评估四种","NBR 与 NBR+ 精度最高（OA 0.976、0.974），NBR-SWIR 因裸土误判仅 0.","可探索多指数融合与机器学习分类器，提升复杂地表下火烧迹地自动制图鲁棒性。","2026-10-13T23:30:23.838067Z",{"id":448,"title":449,"url":450,"summary":451,"summary_zh":452,"content":9,"source_name":453,"source_url":450,"published_at":454,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":455,"score_detail":456,"sources":458,"tags":460,"primary_topic":463,"secondary_topics":469,"classification":470,"search_phrases":493,"slug":496,"view_count":59,"doi":497,"paper":498,"created_at":513},4587,"Sentinel-2 NDVI as an indicator of apple orchard yield","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffhort.2026.1904481","Satellite data is increasingly being used as a basis for operational crop monitoring. However, satellite technologies for monitoring perennial crops remain underdeveloped. The aim of this study was to assess the relationship between apple yield and NDVI calculated using all available Sentinel-2 data for the growing seasons (March-November) from 2017 to 2024. Totally 81 cloud-free images were analyzed. The seasonal absolute maximum and area-weighted average NDVI were calculated based on analysis of all images for each growing season for each plot. Intensively irrigated, mixed-age apple orchards located in northern Azerbaijan served as the research site. Totally 30 plots with 6 apple tree varieties (Arian, Gala, Golden, Golden Parsi, Granny Smith, and Fuji) planted from 2007 to 2017 were analyzed. Apple yield for each plot for 2018–2024 were collected. The linear regression models between NDVI (the seasonal absolute maximum and area-weighted average) and apple yield was used as the relationship criterion. An analysis of the relationship was conducted for individual apple varieties, for orchard plots, and for plots of different ages. It was found that reliable relationships between apple yield and NDVI are only observed for specific plots with a single variety of the same age (R2cv higher than 0,8 with RPIQ more than 2,1). Grouping plots of the same variety or age does not demonstrate a reliable relationship between NDVI and yield. Only for groups of middle-aged orchards does such a relationship become stronger. These results should be considered when developing methods for satellite monitoring of apple orchards.","卫星数据正日益被用作业务化作物监测的基础。然而，用于监测多年生作物的卫星技术仍不发达。本研究旨在评估苹果产量与利用2017年至2024年生长季（3—11月）所有可用Sentinel-2数据计算的NDVI之间的关系。共分析了81幅无云图像。基于对每个生长季每块样地所有图像的分析，计算了季节绝对最大值NDVI和面积加权平均NDVI。研究地点位于阿塞拜疆北部集约化灌溉的混龄苹果园。共分析了30块样地，涵盖2007年至2017年种植的6个苹果品种（Arian、Gala、Golden、Golden Parsi、Granny Smith和Fuji）。收集了2018—2024年每块样地的苹果产量。以NDVI（季节绝对最大值和面积加权平均值）与苹果产量之间的线性回归模型作为关系评价标准。分别针对单个苹果品种、果园样地以及不同树龄样地进行了关系分析。研究发现，仅在特定同一品种、同树龄的样地中才能观察到苹果产量与NDVI之间可靠的关系（R2cv高于0.8，RPIQ大于2.1）。将同一品种或同一树龄的样地分组后，NDVI与产量之间并未表现出可靠的关系。仅对中年果园分组时，这种关系才变得更强。在开发苹果园卫星监测方法时，应考虑这些结果。","Frontiers in Horticulture","2026-10-09T00:00:00Z",71,{"impact":17,"substance":106,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":457},"基于2017—2024年81景Sentinel-2影像评估苹果园NDVI与产量的关系，方法扎实、结论有边界条件，对果园遥感监测有参考价值，但属细分领域研究，公共影响有限。",[459],{"name":453,"url":450},[183,461,27,29,462],"产量预测","苹果园",{"code":464,"slug":465,"name":466,"path":467,"parent_code":468},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[],{"status":121,"locked":14,"version":35,"method":36,"evidence":471},{"candidates":472,"agricultural_evidence":42},[473,481,485,489],{"code":464,"score":474,"supported":42,"title":475,"abstract":478,"tags":480},25,[476,477],"orchard","apple",[479,476,477],"苹果",[],{"code":224,"score":137,"supported":42,"title":482,"abstract":483,"tags":484},[],[227],[],{"code":48,"score":137,"supported":14,"title":486,"abstract":487,"tags":488},[],[],[27,461,29],{"code":231,"score":22,"supported":14,"title":490,"abstract":491,"tags":492},[],[],[183],[494,495],"Sentinel-2 NDVI 苹果园 产量","阿塞拜疆 苹果园 遥感监测","Sentinel-2NDVI苹果园产量-4587","10.3389\u002Ffhort.2026.1904481",{"doi":497,"openalex_id":499,"authors":500,"venue":453,"cited_by_count":59,"oa_url":507,"card":508,"direction":89,"ingested_from":92},"W7221180616",[501,503,505],{"name":502,"orcid":9},"N. Gasimzade",{"name":504,"orcid":9},"A. Radzhabov",{"name":506,"orcid":9},"I. Savin","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fhorticulture\u002Farticles\u002F10.3389\u002Ffhort.2026.1904481\u002Fpdf",{"tldr":509,"method":510,"finding":511,"direction":89,"opportunity":512},"评估Sentinel-2 NDVI与苹果园产量的关系，发现仅单一品种同龄地块存在可靠相关。","用2017-2024年81景Sentinel-2影像计算NDVI，对30个苹果园","仅单一品种同龄地块NDVI与产量可靠相关（R²cv>0.8），按品种或树龄分组则关系不可靠。","多年生果园遥感估产需按品种、树龄精细分层建模，混合地块的普适性模型仍是研究空白。","2026-10-10T23:30:28.042882Z"]