[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4032":3,"related-4032":116},{"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":69,"slug":72,"view_count":73,"doi":74,"paper":75,"created_at":115},4032,"Investigating climate-phenology relationships among the most common Italian forest species using Sentinel-2-derived vegetation phenology and productivity products","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agrformet.2026.111496","Climate change is profoundly altering forest phenology and productivity across Europe, particularly in Mediterranean regions characterized by high climatic heterogeneity. Understanding how climatic and site-specific drivers regulate the start, end, and length of the growing season, and how these phenological shifts translate into productivity responses, remains a key challenge for predicting forest carbon dynamics. In this study, we investigate phenological timing and total seasonal productivity across multiple Italian forest species spanning Mediterranean, temperate, and mountain environments, leveraging the new High-Resolution Vegetation Productivity and Phenology product from the Copernicus Land Monitoring Service, machine learning (random forests) modeling, and explainable artificial intelligence analysis (SHAP). Our results indicate a general tendency toward lengtheningof the growing season driven mainly by chilling accumulation and spring temperatures. Warmer conditions advance the start of the season by 1–10 days across species, while the combined effects of temperature, radiation, and moisture can extend the growing season by up to 20–30 days. End-of-season dynamics and season length are more strongly controlled by light and water availability than by temperature alone. In several Mediterranean species, the end of the season can advance by up to 40 days due to summer drought, high vapor pressure deficit, and site exposure. Mediterranean species often show compensatory shifts between season onset and senescence, maintaining a relatively stable length of the season, whereas mountain species exhibit a tighter coupling between delayed onset and shortened season length. Because of phenological shifts, overall season length is often decoupled from productivity, which is mainly regulated by energy and water availability, highlighting species- and site-specific responses to climate change. The findings of this study highlight the substantial advantage of combining remote sensing data with machine learning to advance understanding of forest phenology and productivity across broad spatial and climatic gradients.","气候变化正在深刻改变欧洲森林的物候与生产力，尤其是在气候高度异质的地中海地区。理解气候与立地特异性驱动因子如何调控生长季的开始、结束和长度，以及这些物候变化如何转化为生产力响应，仍是预测森林碳动态的关键挑战。本研究利用哥白尼土地监测服务新发布的高分辨率植被生产力与物候产品、机器学习（随机森林）建模以及可解释人工智能分析（SHAP），考察了意大利多个森林物种在 Mediterranean、温带和山地环境中的物候时序与总季节生产力。结果表明，生长季总体呈延长趋势，主要由冷激积累和春季温度驱动。较暖条件使各物种生长季开始提前1–10天，而温度、辐射和水分可用性的综合效应可使生长季延长达20–30天。生长季结束动态和生长季长度受光照和水分可用性的控制强于仅受温度的控制。在若干地中海物种中，由于夏季干旱、高饱和水汽压差和立地暴露，生长季结束可提前达40天。地中海物种常在生长季开始与衰老之间表现出补偿性位移，维持相对稳定的生长季长度，而山地物种则表现出延迟开始与缩短生长季长度之间更紧密的耦合。由于物候变化，总体生长季长度常与生产力脱钩，后者主要受能量和水分可用性调控，凸显了物种和立地对气候变化的特异性响应。本研究结果凸显了将遥感数据与机器学习相结合，以增进对广泛空间和气候梯度上森林物候与生产力理解的显著优势。",null,"Agricultural and Forest Meteorology","2026-09-29T00:00:00Z","论文",10,false,81,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,22,19,14,8,1,"结合Sentinel-2遥感与机器学习揭示意大利森林物候与生产力对气候的响应，方法新颖、结论可靠，对农业遥感与碳汇监测有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"机器学习","气候变化","遥感监测","碳汇","森林物候",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":14,"reason":68},[39,48,55,63],{"code":40,"score":13,"supported":41,"title":42,"abstract":43,"tags":47},"H7",true,[],[28,44,45,46],"干旱","climate change","drought",[28],{"code":49,"score":13,"supported":41,"title":50,"abstract":51,"tags":54},"I2",[],[27,52,53],"machine learning","artificial intelligence",[27],{"code":56,"score":57,"supported":41,"title":58,"abstract":59,"tags":62},"I3",7,[],[60,61],"遥感","remote sensing",[29],{"code":64,"score":22,"supported":14,"title":65,"abstract":66,"tags":67},"H4",[],[],[30],"农业相关性需要复核",[70,71],"Sentinel-2 森林物候 意大利","Copernicus 植被生产力 物候","Sentinel-2森林物候意大利-4032",3,"10.1016\u002Fj.agrformet.2026.111496",{"doi":74,"openalex_id":76,"authors":77,"venue":10,"cited_by_count":106,"oa_url":107,"card":108,"direction":112,"ingested_from":114},"W7214770281",[78,81,84,87,89,91,94,97,100,103],{"name":79,"orcid":80},"Elia Vangi","https:\u002F\u002Forcid.org\u002F0000-0002-9772-2258",{"name":82,"orcid":83},"Giovanni D’Amico","https:\u002F\u002Forcid.org\u002F0000-0002-2341-3268",{"name":85,"orcid":86},"Vincenzo Saponaro","https:\u002F\u002Forcid.org\u002F0000-0002-2082-3990",{"name":88,"orcid":9},"Mattia Niccoli",{"name":90,"orcid":9},"Gioele Tiberi",{"name":92,"orcid":93},"Saverio Francini","https:\u002F\u002Forcid.org\u002F0000-0001-6991-0289",{"name":95,"orcid":96},"Costanza Borghi","https:\u002F\u002Forcid.org\u002F0000-0002-5334-7548",{"name":98,"orcid":99},"Alessio Collalti","https:\u002F\u002Forcid.org\u002F0000-0002-4980-8487",{"name":101,"orcid":102},"Francesco Parisi","https:\u002F\u002Forcid.org\u002F0000-0002-1914-7331",{"name":104,"orcid":105},"Gherardo Chirici","https:\u002F\u002Forcid.org\u002F0000-0002-0669-5726",0,"https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0168192326004806\u002Fpdf",{"tldr":109,"method":110,"finding":111,"direction":112,"opportunity":113},"利用Sentinel-2物候与生产力产品，结合随机森林和SHAP，分析意大利主要森林物种的气候-物候","Sentinel-2高分辨率植被生产力与物候产品、随机森林建模、SHAP可解释性","生长季延长主要由冷激和春温驱动，地中海物种通过物候补偿维持稳定季长，生产力与季长脱钩。","农业遥感与作物表型","可探索将物候补偿机制纳入碳循环模型，并验证其他地中海区域森林的普适性。","openalex","2026-10-02T23:30:26.934673Z",{"total":117,"page":22,"page_size":117,"items":118},6,[119,189,237,332,400,504],{"id":120,"title":121,"url":122,"summary":123,"summary_zh":124,"content":9,"source_name":125,"source_url":122,"published_at":126,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":127,"score_detail":128,"sources":131,"tags":133,"primary_topic":136,"secondary_topics":141,"classification":142,"search_phrases":161,"slug":164,"view_count":106,"doi":165,"paper":166,"created_at":188},5172,"Forest green-up in the Pearl River Basin: trends, driving factors, and relationships with net primary productivity","https:\u002F\u002Fdoi.org\u002F10.1080\u002F17538947.2026.2738487","The spring green-up date (GUD), a critical indicator of ecosystem response to climate change, profoundly influences terrestrial productivity. However, the complex drivers of GUD remain elusive, while the contentious relationship between GUD and net primary productivity (NPP) challenges the universal validity of the hypothesis that earlier green-up enhances productivity. Using MODIS NDVI data (2000-2020), we quantified forest GUD dynamics in the Pearl River Basin and combined random forest, SHAP, and structural equation modelling to identify drivers and assess GUD-NPP relationships. GUD was advanced by approximately 5 days, with 81.8% of forestland pixels advancing and 18.2% delaying. Only 5.39% of advances showed significant trends, whereas 30.45% of delays were concentrated near croplands and developed areas. The random forest model explained 58.2% of GUD variation. Elevation, precipitation, and soil potassium and phosphorus were dominant; elevation-induced thermal gradients outweighed ambient temperature effects, with elevations above 1,200 m exhibiting markedly delayed GUD. Spatially, GUD had a positive correlation with NPP, indicating that later GUD was associated with higher forest NPP, possibly because of early-spring cold snaps and frost. Temporally, areas with stronger GUD delays tended to show greater NPP increases. This study identified key controls on subtropical forest phenology and revealed a GUD-NPP trade-off.","春季返青期（GUD）是生态系统响应气候变化的关键指标，深刻影响陆地生产力。然而，GUD的复杂驱动机制仍不明确，且GUD与净初级生产力（NPP）之间存在争议性关系，挑战了“提前返青提高生产力”这一假说的普适性。本研究利用MODIS NDVI数据（2000—2020年），量化了珠江流域森林GUD动态，并结合随机森林、SHAP和结构方程模型识别驱动因子并评估GUD-NPP关系。GUD整体提前约5天，其中81.8%的林地像元提前、18.2%推迟。仅5.39%的提前像元呈显著趋势，而30.45%的推迟像元集中在农田和建成区附近。随机森林模型解释了58.2%的GUD变异。海拔、降水以及土壤钾和磷为主导因子；海拔引起的热梯度效应超过环境温度的影响，海拔1200 m以上区域GUD显著推迟。空间上，GUD与NPP呈正相关，表明GUD越晚森林NPP越高，这可能与早春寒潮和霜冻有关。时间上，GUD推迟越强的区域往往表现为NPP增加越大。本研究识别了亚热带森林物候的关键控制因子，揭示了GUD-NPP之间的权衡关系。","International Journal of Digital Earth","2026-10-15T00:00:00Z",74,{"impact":129,"substance":18,"depth":17,"authority":20,"freshness":21,"relevant":22,"comment":130},12,"基于MODIS长时序数据的区域森林物候研究，方法组合新颖、结论有增量，但属区域基础研究，产业与政策影响有限，可作专业补充而非每日精选头条。",[132],{"name":125,"url":122},[28,29,134,31,135],"植被生产力","珠江流域",{"code":40,"slug":137,"name":138,"path":139,"parent_code":140},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",[],{"status":143,"locked":14,"version":35,"method":36,"evidence":144},"classified",{"candidates":145,"agricultural_evidence":41},[146,150,157],{"code":40,"score":57,"supported":41,"title":147,"abstract":148,"tags":149},[],[28,45],[28],{"code":151,"score":117,"supported":41,"title":152,"abstract":153,"tags":156},"H2",[],[154,155],"土壤","soil",[],{"code":56,"score":22,"supported":14,"title":158,"abstract":159,"tags":160},[],[],[29],[162,163],"珠江流域 森林 返青期","MODIS NDVI 植被生产力","珠江流域森林返青期-5172","10.1080\u002F17538947.2026.2738487",{"doi":165,"openalex_id":167,"authors":168,"venue":125,"cited_by_count":106,"oa_url":182,"card":183,"direction":112,"ingested_from":114},"W7221897424",[169,172,174,176,178,180],{"name":170,"orcid":171},"Shijie Yang","https:\u002F\u002Forcid.org\u002F0009-0001-7573-5509",{"name":173,"orcid":9},"Zhaoju Zheng",{"name":175,"orcid":9},"Xueming Zhao",{"name":177,"orcid":9},"Zhipeng Zhang",{"name":179,"orcid":9},"Xiaohui Cui",{"name":181,"orcid":9},"Yuan Zeng","https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F17538947.2026.2738487?needAccess=true",{"tldr":184,"method":185,"finding":186,"direction":112,"opportunity":187},"基于MODIS NDVI量化珠江流域森林返青期动态，并解析其驱动因子及与NPP的关系。","MODIS NDVI（2000-2020）、随机森林、SHAP与结构方程模型。","返青期整体提前约5天，但延迟区NPP更高，揭示返青期与NPP的权衡关系。","可探究早春寒潮霜冻对返青-NPP权衡的调控机制，并拓展至其他亚热带流域验证普适性。","2026-10-17T23:30:29.750744Z",{"id":190,"title":191,"url":192,"summary":193,"summary_zh":194,"content":9,"source_name":195,"source_url":192,"published_at":196,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":106,"score_detail":197,"sources":199,"tags":201,"primary_topic":9,"secondary_topics":204,"classification":205,"search_phrases":216,"slug":219,"view_count":106,"doi":220,"paper":221,"created_at":236},5560,"How do weather, topography and fuel condition drive the extreme behavior of a Mediterranean megafire? A case study of the 2025 Jarilla wildfire (western Spain)","https:\u002F\u002Fdoi.org\u002F10.3389\u002Fffgc.2026.1953360","The 2025 wildfire season was the most destructive on record in the European Union, with more than 1 million hectares burned, and the Iberian Peninsula was the hardest-hit region, accounting for 43% of that area. The Jarilla wildfire (Cáceres, Spain; 17,317 ha, august 2025) exemplifies how meteorology, topography, and fuel condition jointly controlled the behavior of these Iberian megafires. Our aim was to analyze the role of these three factors in the fire’s behavior and to evaluate their interpretation against the post-fire severity footprint, within a reproducible workflow relying entirely on open-access data. We combined ERA5 hourly reanalysis, the fire weather index (FWI), Sentinel-2 spectral indices (NDVI, NDII, dNBR), VIIRS active-fire detections, and the 5 m PNOA digital elevation model. During the active phase (august 12–17), the atmosphere was extremely desiccating—vapor pressure deficit peaking at 5.9 kPa, minimum relative humidity near 15%, and FWI in the “extreme” class—acting on a dense but already water-stressed fuel cover (mean NDVI 0.53, mean NDII 0.05). The wind regime was bimodal: a frequent but weak nocturnal north-easterly flow and a diurnal south–south-westerly (SSW) flow that drove the observed SW–NE progression. Fire spread corridors—steep, wind-aligned, fuel-laden terrain delineated with the SSW driving wind—covered 13.7% of the burned area and showed higher mean severity (dNBR 665) than their surroundings (525); however, 84% of the high-severity area lay outside them. These results indicate that, in such “hot-drought” megafires, severity is driven primarily by extreme atmospheric drying acting on continuous, dry fuel, whereas topography and wind spatially organize its peaks rather than determining them. Beyond reconstructing a single event, the study provides a transferable, open-data framework for diagnosing megafire drivers and suggests that fuel- and weather-based prevention, more than topographic targeting, is key to mitigating Mediterranean megafires.","2025年野火季是欧盟有记录以来破坏性最强的一个野火季，过火面积超过100万公顷，其中伊比利亚半岛是受灾最严重的地区，占该面积的43%。Jarilla野火（西班牙卡塞雷斯，17,317公顷，2025年8月）体现了气象、地形和可燃物条件如何共同控制这些伊比利亚特大火灾的行为。我们的目标是分析这三个因素在火灾行为中的作用，并在完全依赖开放获取数据的可重复工作流程中，评估它们与火后严重程度足迹的对应解释。我们结合了ERA5逐小时再分析数据、火险天气指数（FWI）、Sentinel-2光谱指数（NDVI、NDII、dNBR）、VIIRS活跃火点检测以及5米PNOA数字高程模型。在活跃阶段（8月12日至17日），大气极度干燥——饱和水汽压差峰值达5.9 kPa，最低相对湿度接近15%，FWI处于“极端”等级——作用于密集但已受水分胁迫的可燃物覆盖层（平均NDVI为0.53，平均NDII为0.05）。风况呈双峰分布：夜间频繁但微弱的东北气流，以及白天驱动观测到的西南—东北方向蔓延的南—西南（SSW）气流。火势蔓延走廊——陡峭、与风向一致、可燃物载量高的地形，以SSW驱动风 delineated——覆盖了过火面积的13.7%，并显示出比周围区域更高的平均严重程度（dNBR 665对525）；然而，84%的高严重程度区域位于这些走廊之外。这些结果表明，在此类“高温—干旱”特大火灾中，严重程度主要由极端大气干燥作用于连续、干燥的可燃物所驱动，而地形和风在空间上组织其峰值而非决定峰值。除重建单一事件外，该研究提供了一个可迁移的开放数据框架，用于诊断特大火灾驱动因素，并表明基于可燃物和天气的预防，而非地形靶向，是缓解地中海特大火灾的关键。","Frontiers in Forests and Global Change","2026-10-20T00:00:00Z",{"impact":106,"substance":106,"depth":106,"authority":106,"freshness":106,"relevant":106,"comment":198},"该研究聚焦西班牙地中海野火的气象与地形驱动机制，属于林火生态与遥感应用，与三农、农业信息化、智慧农业主题无直接关联，不建议进入每日精选。",[200],{"name":195,"url":192},[28,29,202,203],"野火研究","西班牙",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":206},{"candidates":207,"agricultural_evidence":14,"reason":68},[208,212],{"code":40,"score":57,"supported":41,"title":209,"abstract":210,"tags":211},[],[44,46],[28],{"code":56,"score":22,"supported":14,"title":213,"abstract":214,"tags":215},[],[],[29],[217,218],"气候变化 遥感监测 野火研究 西班牙","气候变化 遥感监测","气候变化遥感监测野火研究西班牙-5560","10.3389\u002Fffgc.2026.1953360",{"doi":220,"openalex_id":222,"authors":223,"venue":195,"cited_by_count":106,"oa_url":229,"card":230,"direction":235,"ingested_from":114},"W7223002169",[224,226],{"name":225,"orcid":9},"Luis Hernández-Sánchez",{"name":227,"orcid":228},"Marcos Francos","https:\u002F\u002Forcid.org\u002F0000-0002-3311-5686","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fforests-and-global-change\u002Farticles\u002F10.3389\u002Fffgc.2026.1953360\u002Fpdf",{"tldr":231,"method":232,"finding":233,"direction":112,"opportunity":234},"分析2025年西班牙Jarilla特大火灾，揭示气象、地形与燃料条件如何共同驱动火行为。","融合ERA5再分析、FWI、Sentinel-2指数、VIIRS火点和5米DEM","极端大气干燥叠加连续干旱燃料主导火强度，地形与风仅空间组织高严重度峰值而非决定其分布。","可延伸至多源遥感融合的野火驱动因子归因与火险预警，并检验该开源框架在其他地中海火区的可迁移性。","智慧农业 \u002F 农业物联网","2026-10-22T11:20:20.437834Z",{"id":238,"title":239,"url":240,"summary":241,"summary_zh":242,"content":9,"source_name":243,"source_url":240,"published_at":244,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":245,"score_detail":246,"sources":252,"tags":254,"primary_topic":258,"secondary_topics":264,"classification":273,"search_phrases":309,"slug":312,"view_count":106,"doi":313,"paper":314,"created_at":331},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":247,"substance":248,"depth":249,"authority":250,"freshness":13,"relevant":22,"comment":251},16,21,17,13,"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。",[253],{"name":243,"url":240},[255,27,29,256,257],"智慧农业","土壤墒情","冬小麦",{"code":259,"slug":260,"name":261,"path":262,"parent_code":263},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[265,268],{"code":151,"slug":155,"name":266,"path":267,"parent_code":140},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"code":49,"slug":269,"name":270,"path":271,"parent_code":272},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":143,"locked":14,"version":35,"method":36,"evidence":274},{"candidates":275,"agricultural_evidence":41},[276,282,287,291,296,303],{"code":259,"score":277,"supported":41,"title":278,"abstract":280,"tags":281},26,[279,260],"winter wheat",[261,279,260],[257],{"code":151,"score":283,"supported":41,"title":284,"abstract":285,"tags":286},15,[155],[154,155],[256],{"code":49,"score":283,"supported":41,"title":288,"abstract":289,"tags":290},[52],[27,52],[27],{"code":56,"score":13,"supported":41,"title":292,"abstract":293,"tags":295},[],[60,61,294],"multispectral",[29],{"code":297,"score":117,"supported":41,"title":298,"abstract":299,"tags":302},"H3",[],[300,301],"灌溉","irrigation",[],{"code":304,"score":305,"supported":41,"title":306,"abstract":307,"tags":308},"I1",4,[],[255],[255],[310,311],"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":313,"openalex_id":315,"authors":316,"venue":243,"cited_by_count":106,"oa_url":240,"card":326,"direction":235,"ingested_from":114},"W7223156204",[317,320,322,324],{"name":318,"orcid":319},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":321,"orcid":9},"Yi Zhang",{"name":323,"orcid":9},"WEI Lei",{"name":325,"orcid":9},"Chengcai Zhang",{"tldr":327,"method":328,"finding":329,"direction":112,"opportunity":330},"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":333,"title":334,"url":335,"summary":336,"summary_zh":337,"content":9,"source_name":338,"source_url":335,"published_at":196,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":106,"score_detail":339,"sources":341,"tags":343,"primary_topic":346,"secondary_topics":347,"classification":353,"search_phrases":377,"slug":380,"view_count":106,"doi":381,"paper":382,"created_at":399},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",{"impact":106,"substance":106,"depth":106,"authority":106,"freshness":106,"relevant":106,"comment":340},"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。",[342],{"name":338,"url":335},[27,29,344,345],"土地利用","城市扩张",{"code":49,"slug":269,"name":270,"path":271,"parent_code":272},[348],{"code":349,"slug":350,"name":351,"path":352,"parent_code":272},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"status":143,"locked":14,"version":35,"method":36,"evidence":354},{"candidates":355,"agricultural_evidence":41},[356,360,368,372],{"code":49,"score":283,"supported":41,"title":357,"abstract":358,"tags":359},[52],[27,52],[27],{"code":349,"score":361,"supported":41,"title":362,"abstract":363,"tags":367},9,[],[364,365,366],"空间分析","GIS","spatial analysis",[],{"code":56,"score":57,"supported":41,"title":369,"abstract":370,"tags":371},[],[60,61],[29],{"code":373,"score":22,"supported":14,"title":374,"abstract":375,"tags":376},"H6",[],[],[344],[378,379],"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":381,"openalex_id":383,"authors":384,"venue":338,"cited_by_count":106,"oa_url":335,"card":394,"direction":112,"ingested_from":114},"W7223022155",[385,388,390,392],{"name":386,"orcid":387},"Kanenus Fufa Dararo","https:\u002F\u002Forcid.org\u002F0009-0007-5747-1558",{"name":389,"orcid":9},"Goanar Jal Dup",{"name":391,"orcid":9},"Weyesa Merga Chemeda",{"name":393,"orcid":9},"Shankar Karuppannan",{"tldr":395,"method":396,"finding":397,"direction":112,"opportunity":398},"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":401,"title":402,"url":403,"summary":404,"summary_zh":405,"content":9,"source_name":406,"source_url":403,"published_at":196,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":407,"score_detail":408,"sources":410,"tags":412,"primary_topic":416,"secondary_topics":417,"classification":422,"search_phrases":475,"slug":478,"view_count":106,"doi":479,"paper":480,"created_at":503},5483,"Soil Salinity under Climate Change in Pakistan: Emerging Challenges and Sustainable Management Approaches for Agriculture","https:\u002F\u002Fdoi.org\u002F10.71317\u002Fkjard.2.10.2026.883","Soil salinity is a major constraint to agricultural productivity, soil health, and food security in Pakistan, particularly under changing climatic conditions. Rising temperatures, increased evapotranspiration, irregular precipitation, droughts, flooding, and sea-level rise can intensify salt accumulation and alter soil–water interactions. This review examines the emerging challenges associated with climate change and soil salinity in Pakistan, focusing on their impacts on soil properties, crop physiology, agricultural production, and rural livelihoods. It discusses the mechanisms of soil salinity and sodicity, their spatial distribution, and the interactions among climate variability, irrigation practices, groundwater quality, and secondary salinization. Furthermore, sustainable management approaches are evaluated, including gypsum application, organic amendments, integrated nutrient management, efficient irrigation, drainage improvement, and appropriate leaching strategies. Climate-resilient approaches, such as conventional crop breeding, modern biotechnology, halophyte utilization, plant growth-promoting microorganisms, and mycorrhizal associations, are also explored. The review highlights the potential of remote sensing, geographic information systems, precision agriculture, and predictive modelling to support salinity monitoring and site-specific management. Despite these opportunities, important challenges remain, including insufficient long-term monitoring, limited region-specific data, economic constraints, and gaps in farmer awareness and institutional coordination. Addressing these challenges requires integrated research, effective agricultural policies, improved extension services, and collaboration among researchers, policymakers, and farming communities. Prioritizing locally adapted, economically feasible, and environmentally sustainable interventions can strengthen agricultural resilience, restore salt-affected soils, and protect freshwater resources. Overall, integrating climate adaptation with sustainable soil and water management is essential for maintaining agricultural productivity and improving long-term food security in Pakistan.","土壤盐渍化是制约巴基斯坦农业生产力、土壤健康和粮食安全的主要因素，在气候变化条件下尤为突出。气温升高、蒸散加剧、降水不规律、干旱、洪水以及海平面上升可能加剧盐分积累并改变土壤—水相互作用。本文综述了巴基斯坦气候变化与土壤盐渍化相关的新兴挑战，重点关注其对土壤性质、作物生理、农业生产和农村生计的影响。文章讨论了土壤盐渍化和钠质化的机制、其空间分布，以及气候变率、灌溉实践、地下水质量和次生盐渍化之间的相互作用。此外，评估了可持续管理途径，包括施用石膏、有机改良、综合养分管理、高效灌溉、排水改良和适宜的淋洗策略。还探讨了气候韧性方法，如常规作物育种、现代生物技术、盐生植物利用、植物促生微生物和菌根共生。本文强调了遥感、地理信息系统、精准农业和预测建模在支持盐渍化监测和因地制宜管理方面的潜力。尽管存在这些机遇，重要挑战依然存在，包括长期监测不足、区域特定数据有限、经济制约以及农民意识和制度协调方面的差距。应对这些挑战需要综合研究、有效的农业政策、改进的推广服务以及研究人员、政策制定者和农业社区之间的协作。优先采取适应当地、经济可行且环境可持续的干预措施，可增强农业韧性、修复盐渍化土壤并保护淡水资源。总体而言，将气候适应与可持续水土管理相结合，对于维持巴基斯坦农业生产力和改善长期粮食安全至关重要。","Kashmir Journal of Academic Research and Development",66,{"impact":250,"substance":17,"depth":283,"authority":129,"freshness":21,"relevant":22,"comment":409},"综述性论文，系统梳理气候变化下巴基斯坦土壤盐渍化成因与可持续治理路径，兼具遥感与精准农业视角，对盐碱地治理议题有参考价值，但属区域性综述、非突破性成果。",[411],{"name":406,"url":403},[28,413,29,414,415],"盐碱地治理","智慧灌溉","土壤盐渍化",{"code":40,"slug":137,"name":138,"path":139,"parent_code":140},[418,419],{"code":151,"slug":155,"name":266,"path":267,"parent_code":140},{"code":297,"slug":301,"name":420,"path":421,"parent_code":140},"水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",{"status":143,"locked":14,"version":35,"method":36,"evidence":423},{"candidates":424,"agricultural_evidence":41},[425,430,434,440,444,451,457,463,469],{"code":40,"score":17,"supported":41,"title":426,"abstract":427,"tags":429},[45],[28,428,44,45],"气候适应",[28],{"code":151,"score":283,"supported":41,"title":431,"abstract":432,"tags":433},[155],[154,155],[413],{"code":297,"score":13,"supported":41,"title":435,"abstract":436,"tags":439},[],[300,437,301,438],"水资源","evapotranspiration",[414],{"code":56,"score":57,"supported":41,"title":441,"abstract":442,"tags":443},[],[60,61],[29],{"code":445,"score":117,"supported":41,"title":446,"abstract":447,"tags":450},"A8",[],[448,449],"粮食安全","food security",[],{"code":304,"score":117,"supported":41,"title":452,"abstract":453,"tags":456},[],[454,455],"精准农业","precision agriculture",[],{"code":349,"score":117,"supported":41,"title":458,"abstract":459,"tags":462},[],[460,461],"地理信息","geographic information",[],{"code":464,"score":73,"supported":41,"title":465,"abstract":466,"tags":468},"F3",[],[467],"养分管理",[],{"code":470,"score":73,"supported":41,"title":471,"abstract":472,"tags":474},"J1",[],[473],"农业政策",[],[476,477],"巴基斯坦 土壤盐渍化","盐碱地 遥感监测","巴基斯坦土壤盐渍化-5483","10.71317\u002Fkjard.2.10.2026.883",{"doi":479,"openalex_id":481,"authors":482,"venue":406,"cited_by_count":106,"oa_url":403,"card":498,"direction":112,"ingested_from":114},"W7222999158",[483,485,487,489,491,493,496],{"name":484,"orcid":9},"Asifa Naz",{"name":486,"orcid":9},"Muhammad Abu Baker Siddiq",{"name":488,"orcid":9},"Syed Asghar Hussain Shah",{"name":490,"orcid":9},"Shahrish Nawaz",{"name":492,"orcid":9},"Muhmmad Irfan",{"name":494,"orcid":495},"Rizwana Kausar","https:\u002F\u002Forcid.org\u002F0000-0002-1339-3312",{"name":497,"orcid":9},"Muhammad Nouman Khalid",{"tldr":499,"method":500,"finding":501,"direction":112,"opportunity":502},"综述气候变化下巴基斯坦土壤盐渍化的挑战与可持续管理途径。","文献综述，整合盐渍化机制、空间分布及遥感GIS与预测模型应用。","需将气候适应与水土管理结合，优先本地化、经济可行的干预措施。","可填补长期盐渍化监测与区域数据空白，发展遥感+预测模型的精准分区治理。","2026-10-21T23:30:23.059448Z",{"id":505,"title":506,"url":507,"summary":508,"summary_zh":509,"content":9,"source_name":510,"source_url":507,"published_at":126,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":106,"score_detail":511,"sources":513,"tags":515,"primary_topic":9,"secondary_topics":518,"classification":519,"search_phrases":539,"slug":542,"view_count":106,"doi":543,"paper":544,"created_at":563},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",{"impact":106,"substance":106,"depth":106,"authority":106,"freshness":106,"relevant":106,"comment":512},"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。",[514],{"name":510,"url":507},[27,29,516,517],"野火风险","植被水分",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":520},{"candidates":521,"agricultural_evidence":14,"reason":68},[522,526,530,534],{"code":40,"score":13,"supported":41,"title":523,"abstract":524,"tags":525},[],[28,44,45,46],[516],{"code":49,"score":361,"supported":41,"title":527,"abstract":528,"tags":529},[52],[],[27],{"code":56,"score":361,"supported":41,"title":531,"abstract":532,"tags":533},[61],[],[29],{"code":297,"score":117,"supported":41,"title":535,"abstract":536,"tags":538},[],[537,438],"蒸散发",[],[540,541],"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":543,"openalex_id":545,"authors":546,"venue":510,"cited_by_count":106,"oa_url":557,"card":558,"direction":112,"ingested_from":114},"W7222006390",[547,550,552,554],{"name":548,"orcid":549},"Helen Mills Poulos","https:\u002F\u002Forcid.org\u002F0000-0003-0686-9770",{"name":551,"orcid":9},"Sarah Hardy",{"name":553,"orcid":9},"Jenna Lynne Otaola",{"name":555,"orcid":556},"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":559,"method":560,"finding":561,"direction":112,"opportunity":562},"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"]