[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5100":3,"related-5100":73},{"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":50,"slug":53,"view_count":54,"doi":55,"paper":56,"created_at":72},5100,"Scale‐space image analysis for mapping spotted knapweed in grassland ecosystems","https:\u002F\u002Fdoi.org\u002F10.1002\u002Faps3.70086","Abstract Premise This study investigates the value of scale‐space representations of spatial features for the identification and mapping of invasive plants using high‐resolution multispectral imagery. Scale‐space representations combine the two‐dimensional spatial domain with a third dimension representing spatial scale, such that features can be represented across both space and image spatial resolution. Methods Gaussian pyramids were constructed to create representations of the features across five image resolution levels (between 3 and 46 cm) within the scale space. A case study was employed to evaluate the importance of features across the scale space for the identification and mapping of spotted knapweed in a grassland ecosystem using multispectral imagery acquired using a remotely piloted aircraft system (RPAS). Classification was performed using a random forest classifier that was constructed using a process that included feature and hyperparameter optimization. Results The results of this case study show that classifiers constructed with only high spatial resolution features were associated with better classification performance than those constructed with only lower spatial resolution features; however, the best classification is achieved by leveraging features from throughout the feature scale spaces. Discussion These findings suggest that optimization of the spatial scale at which features are computed can substantially improve remote sensing image analysis and potentially inform RPAS flight planning to improve mission efficiency.","摘要 前提 本研究探讨了空间特征的尺度空间表示在高分辨率多光谱影像中用于入侵植物识别与制图的价值。尺度空间表示将二维空间域与表示空间尺度的第三维相结合，从而使特征能够在空间和影像空间分辨率两个维度上得到表示。方法 构建高斯金字塔，以在尺度空间内生成跨越五个影像分辨率层级（3至46厘米）的特征表示。通过一个案例研究，评估尺度空间中的特征在使用由遥控驾驶航空器系统（RPAS）获取的多光谱影像对草地生态系统中斑点矢车菊进行识别与制图时的重要性。分类使用随机森林分类器完成，该分类器通过包含特征优化和超参数优化的流程构建。结果 本案例研究的结果表明，仅使用高空间分辨率特征构建的分类器，其分类性能优于仅使用较低空间分辨率特征构建的分类器；然而，最佳分类效果是通过综合利用整个特征尺度空间中的特征实现的。讨论 这些发现表明，优化特征计算所采用的空间尺度可以显著改进遥感影像分析，并有可能为RPAS飞行规划提供依据，以提高任务效率。",null,"Applications in Plant Sciences","2026-10-14T00:00:00Z","论文",10,false,71,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,21,17,13,8,1,"方法新颖、结论可靠，但属细分领域基础研究，产业影响有限，适合作为技术前沿收录而非头条精选。",[25],{"name":10,"url":6},[27,28,29,30,31],"无人机遥感","遥感监测","多光谱影像","入侵植物","草地生态",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":14,"reason":49},[39],{"code":40,"score":41,"supported":42,"title":43,"abstract":44,"tags":48},"I3",11,true,[],[45,46,47],"遥感","remote sensing","multispectral",[27,28],"农业相关性需要复核",[51,52],"斑点矢车菊 无人机 遥感","尺度空间 多光谱 草地","斑点矢车菊无人机遥感-5100",0,"10.1002\u002Faps3.70086",{"doi":55,"openalex_id":57,"authors":58,"venue":10,"cited_by_count":54,"oa_url":64,"card":65,"direction":69,"ingested_from":71},"W7222056923",[59,61],{"name":60,"orcid":9},"Shohreh Sahebi",{"name":62,"orcid":63},"David Hill","https:\u002F\u002Forcid.org\u002F0000-0002-4530-3131","https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1002\u002Faps3.70086",{"tldr":66,"method":67,"finding":68,"direction":69,"opportunity":70},"利用尺度空间表示多光谱影像特征，结合随机森林分类器，实现草地生态系统中斑点矢车菊的识别与制图。","构建高斯金字塔生成5个分辨率层级（3-46cm）的尺度空间特征，用随机森林分类并","仅用高分辨率特征的分类器优于仅用低分辨率特征，但融合整个尺度空间特征时分类效果最佳。","农业遥感与作物表型","可探索尺度空间特征优化在入侵植物早期监测中的应用，并指导无人机飞行规划以提升效率。","openalex","2026-10-16T23:30:31.287501Z",{"total":74,"page":22,"page_size":74,"items":75},6,[76,168,224,286,366,448],{"id":77,"title":78,"url":79,"summary":80,"summary_zh":81,"content":9,"source_name":82,"source_url":79,"published_at":83,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":84,"score_detail":85,"sources":90,"tags":92,"primary_topic":9,"secondary_topics":96,"classification":97,"search_phrases":124,"slug":127,"view_count":22,"doi":128,"paper":129,"created_at":167},4579,"Drought responses of a Norway spruce forest on drained peat soil: combining sap-flow sensors, eddy-covariance, meteorological, soil and UAV data","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fbg-23-7091-2026","Abstract. The summer of 2021 brought a severe drought to southern Finland. We explored how a drained boreal peatland forest responds to drought by combining a wide range of in situ monitoring and Unmanned Aerial Vehicle (UAV) remote sensing. A dataset combining eddy-covariance (EC) fluxes, sap-flow sensors, UAV mapping, soil and weather data was collected. Spruce stand reaction to drought is examined at sub-daily to seasonal time scales, and its temporal and spatial features are identified separately for a recently thinned Continuous Cover Forestry (CCF) block and a control block. Sap flow data showed that the CCF-harvested block shows greater tree resilience to high vapour pressure deficit (VPD) than the control block, likely due to the higher soil water availability during the rainless and hot period. At the same time, both the control and CCF harvest blocks had notably reduced net ecosystem exchange (NEE) and increased Bowen ratio, particularly on high VPD days. The UAV surveys indicated that trees in the CCF block tend to have higher canopy temperatures and lower Normalized Difference Vegetation Index (NDVI) than in the control block, implying their possible susceptibility to more extreme drought.","摘要：2021年夏季，芬兰南部遭遇严重干旱。我们结合多种原位监测与无人机（UAV）遥感手段，探讨了排水泥炭地森林对干旱的响应。研究收集了涡度协方差（EC）通量、液流传感器、无人机测绘、土壤及气象数据。我们在亚日到季节尺度上考察了云杉林分对干旱的响应，并分别针对近期疏伐的连续覆盖林业（CCF）样地和对照样地识别其时间和空间特征。液流数据显示，CCF采伐样地中的树木对高饱和水汽压差（VPD）的恢复力强于对照样地，这可能归因于无雨高温期间较高的土壤水分有效性。同时，对照样地和CCF采伐样地的净生态系统交换（NEE）均显著降低，波文比升高，尤其在高VPD日更为明显。无人机调查表明，CCF样地中的树木冠层温度较高，归一化植被指数（NDVI）较低，暗示其可能更易受到更极端干旱的影响。","Biogeosciences","2026-10-09T00:00:00Z",74,{"impact":17,"substance":86,"depth":87,"authority":88,"freshness":21,"relevant":22,"comment":89},22,18,14,"多源遥感与地面通量观测结合的干旱响应研究，方法新颖、数据扎实，对林业碳汇与智慧林业监测有参考价值。",[91],{"name":82,"url":79},[93,27,28,94,95],"气候变化","土壤水分","森林碳汇",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":98},{"candidates":99,"agricultural_evidence":14,"reason":49},[100,108,115,119],{"code":101,"score":102,"supported":42,"title":103,"abstract":105,"tags":107},"H2",15,[104],"soil",[106,104],"土壤",[94],{"code":109,"score":102,"supported":42,"title":110,"abstract":112,"tags":114},"H7",[111],"drought",[113,111],"干旱",[93],{"code":40,"score":21,"supported":42,"title":116,"abstract":117,"tags":118},[],[45,46],[27,28],{"code":120,"score":22,"supported":14,"title":121,"abstract":122,"tags":123},"H4",[],[],[95],[125,126],"挪威云杉 泥炭地 干旱","UAV 涡度相关 云杉","挪威云杉泥炭地干旱-4579","10.5194\u002Fbg-23-7091-2026",{"doi":128,"openalex_id":130,"authors":131,"venue":82,"cited_by_count":54,"oa_url":79,"card":162,"direction":69,"ingested_from":71},"W7127903348",[132,135,138,141,144,147,149,152,154,157,159],{"name":133,"orcid":134},"Pavel Alekseychik","https:\u002F\u002Forcid.org\u002F0000-0002-4081-3917",{"name":136,"orcid":137},"Mikko Peltoniemi","https:\u002F\u002Forcid.org\u002F0000-0003-2028-6969",{"name":139,"orcid":140},"Raisa Mäkipää","https:\u002F\u002Forcid.org\u002F0000-0003-3146-4425",{"name":142,"orcid":143},"Ville Tuominen","https:\u002F\u002Forcid.org\u002F0000-0002-0612-128X",{"name":145,"orcid":146},"Tuomas Laurila","https:\u002F\u002Forcid.org\u002F0000-0002-1967-0624",{"name":148,"orcid":9},"Hamlyn G. Jones",{"name":150,"orcid":151},"Mitro Müller","https:\u002F\u002Forcid.org\u002F0000-0003-0456-9432",{"name":153,"orcid":9},"Evgeny Lopatin",{"name":155,"orcid":156},"Helena Rautakoski","https:\u002F\u002Forcid.org\u002F0000-0002-5297-286X",{"name":158,"orcid":9},"Timo Vesala",{"name":160,"orcid":161},"Samuli Launiainen","https:\u002F\u002Forcid.org\u002F0000-0001-6611-6573",{"tldr":163,"method":164,"finding":165,"direction":69,"opportunity":166},"结合液流、涡度协方差与无人机数据，研究芬兰排水泥炭云杉林对2021年干旱的响应。","液流传感器、涡度协方差、气象土壤及无人机多源数据，对比间伐与对照样地。","间伐样地树木对高VPD更具恢复力，但冠层温度更高、NDVI更低，可能更易受极端干旱影响。","可探究间伐后水分可利用性改善与冠层胁迫信号矛盾背后的机制，并融合多源遥感构建干旱脆弱性预警指标。","2026-10-10T23:30:20.055428Z",{"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":179,"tags":181,"primary_topic":9,"secondary_topics":185,"classification":186,"search_phrases":201,"slug":204,"view_count":22,"doi":205,"paper":206,"created_at":223},4519,"Predicting water quality in mine-affected environments: A hyperspectral remote sensing study in the Iberian pyrite Belt","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.srs.2026.100516","Monitoring water quality in Acid Mine Drainage (AMD) environments requires high-resolution data to capture the complex interplay between geochemical and hydrological dynamics. This study integrates unmanned aerial vehicle (UAV)-based hyperspectral imaging with machine learning to estimate pH, sulfate (SO 4 2- ), and iron (Fe) concentrations across nine water bodies at the Tharsis and Lagunazo abandoned mining sites in the Iberian Pyrite Belt (SW Iberian Peninsula), which span a broad hydrochemical gradient. Using in situ measurements for training, five algorithms: Random Forest (RF), k-nearest neighbors (KNN), Partial Least Squares Regression (PLSR), XGBoost, and Multilayer Perceptron (MLP), against dimensionality reduction techniques (LASSO and PCA) were evaluated. The MLP model using the full spectral set yielded the best performance for pH (R 2 = 0.952), while RF models using LASSO-selected features performed best for Fe (R 2 = 0.861) and SO 4 2- (R 2 = 0.880). SHapley Additive exPlanations (SHAP) confirmed that model decisions align with known spectral absorption features. Building on previous hyperspectral AMD studies, this work advances the state of the art by (i) exploiting UAV-scale hyperspectral data to resolve fine-scale spatial variability within individual water bodies, (ii) explicitly distinguishing between optically active and non-optically active water quality parameters, and (iii) systematically comparing multiple machine learning regression algorithms to retrieve pH, iron, and sulfate across chemically diverse systems. By integrating high-resolution imagery with hydrochemical data, this study provides a robust framework for monitoring AMD-impacted surface waters at spatial scales relevant for environmental management.","在酸性矿山排水（AMD）环境中监测水质需要高分辨率数据，以捕捉地球化学过程与水文动态之间复杂的相互作用。本研究将基于无人机（UAV）的高光谱成像与机器学习相结合，估算了伊比利亚黄铁矿带（伊比利亚半岛西南部）Tharsis和Lagunazo废弃矿区九个水体的pH、硫酸盐（SO₄²⁻）和铁（Fe）浓度，这些水体跨越了广泛的水化学梯度。利用原位测量数据进行训练，评估了五种算法：随机森林（RF）、k近邻（KNN）、偏最小二乘回归（PLSR）、XGBoost和多层感知机（MLP），并结合降维技术（LASSO和PCA）进行比较。使用全光谱集的MLP模型在pH估算中表现最佳（R² = 0.952），而使用LASSO筛选特征的RF模型在Fe（R² = 0.861）和SO₄²⁻（R² = 0.880）估算中表现最优。SHapley加法解释（SHAP）证实模型决策与已知的光谱吸收特征相一致。在以往高光谱AMD研究的基础上，本工作通过以下方面推进了该领域的技术水平：（i）利用无人机尺度的高光谱数据解析单个水体内部的精细空间变异性，（ii）明确区分光学活性与非光学活性水质参数，以及（iii）系统比较多种机器学习回归算法以反演化学组成多样系统中的pH、铁和硫酸盐。通过将高分辨率影像与水化学数据相结合，本研究为在与环境管理相关的空间尺度上监测受AMD影响的地表水提供了一个稳健的框架。","Science of Remote Sensing","2026-10-06T00:00:00Z",78,{"impact":87,"substance":86,"depth":87,"authority":88,"freshness":74,"relevant":22,"comment":178},"该研究将无人机高光谱与机器学习结合用于酸性矿山废水水质反演，方法新颖、数据扎实，对矿区农业水环境监测具有参考价值，但属细分领域国际论文，公共影响有限。",[180],{"name":174,"url":171},[182,27,28,183,184],"机器学习","水质监测","矿山生态修复",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":187},{"candidates":188,"agricultural_evidence":14,"reason":49},[189,194],{"code":40,"score":18,"supported":42,"title":190,"abstract":192,"tags":193},[46,191],"hyperspectral",[191],[27,28],{"code":195,"score":196,"supported":42,"title":197,"abstract":198,"tags":200},"I2",7,[],[182,199],"machine learning",[182],[202,203],"伊比利亚黄铁矿带 酸性矿山废水 高光谱","UAV 高光谱 水质反演","伊比利亚黄铁矿带酸性矿山废水高光谱-4519","10.1016\u002Fj.srs.2026.100516",{"doi":205,"openalex_id":207,"authors":208,"venue":174,"cited_by_count":54,"oa_url":217,"card":218,"direction":69,"ingested_from":71},"W7220716726",[209,211,214],{"name":210,"orcid":9},"Melisa Isgró",{"name":212,"orcid":213},"Maria  Dolores Basallote","https:\u002F\u002Forcid.org\u002F0000-0003-2011-3806",{"name":215,"orcid":216},"L. Barbero","https:\u002F\u002Forcid.org\u002F0000-0002-3513-2025","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2666017226001549\u002Fpdf",{"tldr":219,"method":220,"finding":221,"direction":69,"opportunity":222},"用无人机高光谱与机器学习反演矿区酸性废水pH、硫酸盐和铁浓度。","无人机高光谱成像+5种机器学习算法，结合LASSO\u002FPCA降维与SHAP解释。","MLP对pH最佳(R²=0.952)，RF结合LASSO对Fe和SO₄²⁻最佳。","可将该高光谱-机器学习框架迁移到农业面源污染水体监测，并探索跨区域迁移学习。","2026-10-09T23:30:30.251656Z",{"id":225,"title":226,"url":227,"summary":228,"summary_zh":229,"content":9,"source_name":230,"source_url":227,"published_at":231,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":232,"score_detail":233,"sources":236,"tags":238,"primary_topic":241,"secondary_topics":246,"classification":247,"search_phrases":268,"slug":271,"view_count":272,"doi":273,"paper":274,"created_at":285},3915,"PRECISION AGRICULTURE ANALYTICS USING UAV MULTISPECTRAL IMAGING AND MACHINE LEARNING FOR CROP STRESS DETECTION AND YIELD OPTIMIZATION MODELS","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22111394","PRECISION AGRICULTURE ANALYTICS USING UAV MULTISPECTRAL IMAGING AND MACHINE LEARNING FOR CROP STRESS DETECTION AND YIELD OPTIMIZATION MODELS。International Journal of Engineering Technology Research & Management","基于无人机多光谱成像与机器学习的精准农业分析用于作物胁迫检测和产量优化模型。《国际工程技术研究与管理杂志》","International Journal of Engineering Technology Research & Management","2026-12-21T00:00:00Z",34,{"impact":21,"substance":13,"depth":17,"authority":234,"freshness":54,"relevant":22,"comment":235},4,"主题契合智慧农业，但期刊权威性低、摘要信息量不足且发布日期异常，暂不建议进入每日精选。",[237],{"name":230,"url":227},[239,182,240,27,29],"智慧农业","作物胁迫",{"code":242,"slug":243,"name":244,"path":245,"parent_code":9},"I","digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":248,"locked":14,"version":35,"method":36,"evidence":249},"partial",{"candidates":250,"agricultural_evidence":42,"reason":267},[251,258,262],{"code":252,"score":102,"supported":42,"title":253,"abstract":255,"tags":257},"I1",[254],"precision agriculture",[256,254],"精准农业",[239],{"code":195,"score":102,"supported":42,"title":259,"abstract":260,"tags":261},[199],[182,199],[182],{"code":40,"score":102,"supported":42,"title":263,"abstract":264,"tags":266},[47],[265,47],"无人机多光谱",[27],"一级领域明确，二级方向需要细分",[269,270],"无人机 多光谱 作物胁迫","机器学习 产量优化","无人机多光谱作物胁迫-3915",2,"10.5281\u002Fzenodo.22111394",{"doi":273,"openalex_id":275,"authors":276,"venue":230,"cited_by_count":54,"oa_url":227,"card":279,"direction":284,"ingested_from":71},"W7204258669",[277],{"name":278,"orcid":9},"Adedayo Oluwaseyi Alawode",{"tldr":280,"method":281,"finding":282,"direction":69,"opportunity":283},"利用无人机多光谱影像与机器学习进行作物胁迫检测和产量优化建模。","无人机多光谱成像结合机器学习算法构建检测与预测模型。","多光谱特征与机器学习可有效识别作物胁迫并优化产量预测。","可探索多时相多光谱与深度学习融合，提升胁迫早期预警和跨区域泛化能力。","农业人工智能与决策模型","2026-10-01T23:30:53.373673Z",{"id":287,"title":288,"url":289,"summary":290,"summary_zh":291,"content":9,"source_name":292,"source_url":289,"published_at":293,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":176,"score_detail":294,"sources":297,"tags":299,"primary_topic":9,"secondary_topics":304,"classification":305,"search_phrases":336,"slug":339,"view_count":54,"doi":340,"paper":341,"created_at":365},5577,"Assessing Land Use and Land Cover Change in the Jamanxim River Region (Brazilian Amazon) from 2015 to 2025 Using CBERS-4\u002FPAN Imagery","https:\u002F\u002Fdoi.org\u002F10.29150\u002Fjhrs.v16i4.269068","Land use and land cover (LULC) changes in the Amazon are among the main drivers of biodiversity loss, ecosystem degradation, and the intensification of climate change. This study aimed to analyze the multitemporal dynamics of LULC around the Jamanxim River, Pará, Brazilian Amazon, between 2015 and 2025, using high-resolution imagery from the CBERS-4\u002FPAN satellite. The study area covers 492.74 km², including part of the Jamanxim National Forest, a conservation unit under strong pressure from agricultural expansion and logging activities. The methodology included geometric correction of the images, Gram–Schmidt pansharpening, and supervised classification with the Random Forest algorithm, validated with historical imagery from Google Earth Pro. Thematic maps of land use and cover were generated, and performance metrics such as overall accuracy, omission and commission errors, and F1-scores were assessed. The results indicated a significant reduction in forest cover, from 412.28 km² in 2015 to 355.47 km² in 2025 (–13.78%). Non-vegetated areas increased by 132.47%, while agricultural areas expanded by 54.97%. The forest and water body classes showed high accuracy (F1-score > 0.95), while confusion persisted between agricultural and bare soil classes. Overall accuracy remained above 90% across all years, with the highest performance recorded in 2020 (96.31%). These results highlight the potential of CBERS-4\u002FPAN imagery for monitoring multitemporal changes in tropical forest landscapes. At the same time, they reveal the intensification of deforestation and the growing threats to conservation in the Amazon, reinforcing the importance of remote sensing for environmental management and policy support.","亚马逊地区的土地利用与土地覆盖（LULC）变化是生物多样性丧失、生态系统退化以及气候变化加剧的主要驱动因素之一。本研究旨在利用CBERS-4\u002FPAN卫星的高分辨率影像，分析2015年至2025年间巴西亚马孙州雅曼辛河（Jamanxim River）周边土地利用与土地覆盖的多时相动态。研究区面积为492.74 km²，包括雅曼辛国家森林（Jamanxim National Forest）的一部分，该保护区正面临农业扩张和采伐活动的强烈压力。研究方法包括影像几何校正、Gram–Schmidt全色锐化以及基于随机森林（Random Forest）算法的监督分类，并利用Google Earth Pro的历史影像进行验证。研究生成了土地利用与覆盖专题图，并评估了总体精度、漏分误差与错分误差以及F1分数等性能指标。结果表明，森林覆盖显著减少，从2015年的412.28 km²降至2025年的355.47 km²（–13.78%）。无植被区域增加了132.47%，农业区域扩张了54.97%。森林和水体类别表现出较高精度（F1分数 > 0.95），而农业类别与裸土类别之间仍存在混淆。各年份总体精度均保持在90%以上，其中2020年性能最高（96.31%）。这些结果凸显了CBERS-4\u002FPAN影像在监测热带森林景观多时相变化方面的潜力，同时揭示了亚马逊地区森林砍伐的加剧以及保护面临的日益增长的威胁，进一步强调了遥感在环境管理和政策支持中的重要性。","Journal of Hyperspectral Remote Sensing","2026-10-20T00:00:00Z",{"impact":87,"substance":86,"depth":19,"authority":17,"freshness":295,"relevant":22,"comment":296},9,"该研究利用CBERS-4\u002FPAN影像量化亚马逊保护区十年间森林减少13.78%，方法规范、结论可靠，对遥感监测与生态保护有参考价值，但地域性强、与国内三农直接关联有限。",[298],{"name":292,"url":289},[28,300,301,302,303],"土地利用","森林保护","亚马逊","CBERS-4",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":306},{"candidates":307,"agricultural_evidence":42,"reason":335},[308,315,319,324,329],{"code":309,"score":196,"supported":42,"title":310,"abstract":311,"tags":314},"H6",[],[312,313],"生物多样性","biodiversity",[300],{"code":40,"score":196,"supported":42,"title":316,"abstract":317,"tags":318},[],[45,46],[28],{"code":109,"score":74,"supported":42,"title":320,"abstract":321,"tags":323},[],[93,322],"climate change",[],{"code":101,"score":325,"supported":42,"title":326,"abstract":327,"tags":328},3,[],[104],[],{"code":330,"score":325,"supported":42,"title":331,"abstract":332,"tags":334},"I7",[],[333],"研究方法",[],"多个产业证据相当，需要确认主归属",[337,338],"CBERS-4 亚马逊 土地利用","Jamanxim 森林覆盖 遥感","CBERS-4亚马逊土地利用-5577","10.29150\u002Fjhrs.v16i4.269068",{"doi":340,"openalex_id":342,"authors":343,"venue":292,"cited_by_count":54,"oa_url":289,"card":360,"direction":69,"ingested_from":71},"W7223141286",[344,347,349,352,355,358],{"name":345,"orcid":346},"Ubiratan Joaquim da Silva","https:\u002F\u002Forcid.org\u002F0000-0001-7995-6416",{"name":348,"orcid":9},"Gabriel Henrique Santos Silva",{"name":350,"orcid":351},"Juarez Antonio da Silva","https:\u002F\u002Forcid.org\u002F0000-0002-2898-0309",{"name":353,"orcid":354},"Anderson Luiz Ribeiro de Paiva","https:\u002F\u002Forcid.org\u002F0000-0003-3475-1454",{"name":356,"orcid":357},"Leidjane Maria Maciel de Oliveira","https:\u002F\u002Forcid.org\u002F0000-0003-1251-6998",{"name":359,"orcid":9},"Sylvana Melo dos Santos",{"tldr":361,"method":362,"finding":363,"direction":69,"opportunity":364},"用CBERS-4\u002FPAN影像分析亚马逊Jamanxim河流域2015-2025年土地利用变化。","几何校正、Gram-Schmidt融合、随机森林监督分类，Google Eart","森林覆盖减少13.78%，农业扩张54.97%，裸地增132.47%，总体精度超90%。","可探索中巴地球资源卫星在热带森林退化监测中的业务化应用与多源数据融合。","2026-10-22T11:20:42.901761Z",{"id":367,"title":368,"url":369,"summary":370,"summary_zh":371,"content":9,"source_name":372,"source_url":369,"published_at":293,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":373,"score_detail":374,"sources":377,"tags":379,"primary_topic":384,"secondary_topics":388,"classification":389,"search_phrases":410,"slug":413,"view_count":54,"doi":414,"paper":415,"created_at":447},5576,"Land cover change and soil salinization in Mozambique: A counterfactual analysis over two decades (2001−2022)","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.catena.2026.110691","Soil salinization is a major issue that constrains the sustainable development of agriculture in arid, semi-arid, and coastal lowland areas. It is of great importance for regional land management and climate adaptation to clarify the influence of land cover change on soil salinization. This study aims to quantitatively assess the contribution of land cover change to soil salinization in Mozambique, where both rapid land cover transformation and salinization issues are evident. Firstly, we integrated soil profile electrical conductivity (EC) data, Landsat time-series remote sensing imagery, TerraClimate meteorological data, and the 30 m resolution land cover data, to construct a remote sensing-based EC inversion model for Mozambique. Secondly, we applied a spatially and environmentally constrained counterfactual matching method to quantitatively identify the contributions of different land cover changes on soil salinization trends. Our results reveal that: (1) the spatial distribution of soil salinization in Mozambique exhibits distinct a clear association with proximity to water bodies, with high EC areas primarily concentrated around Lake Malawi in the north and in the coastal regions of Greater Maputo in the south. (2) 26.98% of Mozambique's territory showed a trend of intensifying soil salinization, while 10.88% exhibited a decreasing trend. Areas with intensifying trends are primarily concentrated in the central alluvial plains, localized agricultural development zones, and coastal lowlands. (3) Urbanization was significantly associated with an increasing salinization trend after false discovery rate correction, suggesting that the expansion of impervious surfaces, changes in drainage patterns, and soil compaction may exacerbate localized salt accumulation. Agricultural expansion showed a negative trend difference, consistent with the potential influence of crop cover, tillage, and wet-season leaching, although this association was not significant after false discovery rate correction. The effects of ecological restoration and other factors were not statistically significant. In addition to elucidating the heterogeneous impacts of land cover changes in Mozambique on soil salinization trends, this study also provides methodological guidance and a scientific basis for monitoring\u002Fmanaging soil salinity risks in other coastal lowlands.","土壤盐渍化是制约干旱、半干旱及沿海低地地区农业可持续发展的主要问题。阐明土地覆盖变化对土壤盐渍化的影响，对区域土地管理和气候适应具有重要意义。本研究旨在定量评估土地覆盖变化对莫桑比克土壤盐渍化的贡献，该国土地覆盖转型迅速且盐渍化问题突出。首先，我们整合了土壤剖面电导率（EC）数据、Landsat时间序列遥感影像、TerraClimate气象数据以及30 m分辨率土地覆盖数据，构建了基于遥感的莫桑比克EC反演模型。其次，我们采用空间与环境约束的反事实匹配方法，定量识别不同土地覆盖变化对土壤盐渍化趋势的贡献。研究结果表明：（1）莫桑比克土壤盐渍化的空间分布与水体邻近性呈现明显的关联，高EC区域主要集中于北部的马拉维湖周边以及南部大马普托沿海地区。（2）莫桑比克26.98%的国土面积呈现土壤盐渍化加剧趋势，而10.88%呈现减轻趋势。加剧趋势区域主要集中于中部冲积平原、局部农业开发区及沿海低地。（3）经错误发现率校正后，城市化与盐渍化加剧趋势显著相关，表明不透水面扩张、排水格局改变及土壤压实可能加剧局部盐分累积。农业扩张呈现负向趋势差异，与作物覆盖、耕作及湿季淋洗的潜在影响一致，但该关联经错误发现率校正后不显著。生态恢复及其他因素的影响在统计上不显著。本研究不仅阐明了莫桑比克土地覆盖变化对土壤盐渍化趋势的异质性影响，还为其他沿海低地土壤盐渍化风险的监测与管理提供了方法学指导和科学依据。","CATENA",79,{"impact":375,"substance":86,"depth":87,"authority":88,"freshness":295,"relevant":22,"comment":376},16,"该研究基于遥感与反事实匹配方法，定量评估土地覆盖变化对盐渍化的贡献，方法新颖、结论可靠，对沿海低地盐渍化监测具有参考价值，但属境外区域研究，国内政策与产业影响有限。",[378],{"name":372,"url":369},[28,380,381,382,383],"莫桑比克","土壤盐渍化","土地覆盖变化","盐分反演",{"code":101,"slug":104,"name":385,"path":386,"parent_code":387},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":390,"locked":14,"version":35,"method":36,"evidence":391},"classified",{"candidates":392,"agricultural_evidence":42},[393,397,401,406],{"code":101,"score":88,"supported":42,"title":394,"abstract":395,"tags":396},[104],[106,104],[],{"code":40,"score":196,"supported":42,"title":398,"abstract":399,"tags":400},[],[45,46],[28],{"code":109,"score":74,"supported":42,"title":402,"abstract":403,"tags":405},[],[404,113],"气候适应",[],{"code":309,"score":22,"supported":14,"title":407,"abstract":408,"tags":409},[],[],[382],[411,412],"莫桑比克 土壤盐渍化 遥感","土地覆盖变化 盐渍化 反事实分析","莫桑比克土壤盐渍化遥感-5576","10.1016\u002Fj.catena.2026.110691",{"doi":414,"openalex_id":416,"authors":417,"venue":372,"cited_by_count":54,"oa_url":369,"card":442,"direction":69,"ingested_from":71},"W7223069171",[418,420,422,424,427,429,432,435,437,439],{"name":419,"orcid":9},"Yue Li",{"name":421,"orcid":9},"Mengfei Shi",{"name":423,"orcid":9},"Fei Zhang",{"name":425,"orcid":426},"Brian Alan Johnson","https:\u002F\u002Forcid.org\u002F0000-0003-1911-3585",{"name":428,"orcid":9},"Gowhar Meraj",{"name":430,"orcid":431},"Jaime Carlos Macuácua","https:\u002F\u002Forcid.org\u002F0000-0002-4822-6098",{"name":433,"orcid":434},"Pankaj Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-5264-3951",{"name":436,"orcid":9},"Yuanfang Chai",{"name":438,"orcid":9},"Hongtao Xu",{"name":440,"orcid":441},"Xu Ma","https:\u002F\u002Forcid.org\u002F0000-0003-0644-7498",{"tldr":443,"method":444,"finding":445,"direction":69,"opportunity":446},"量化莫桑比克2001-2022年土地覆盖变化对土壤盐渍化的贡献。","融合EC剖面、Landsat时序与气候数据反演EC，并用空间环境约束的反事实匹配","26.98%区域盐渍化加剧；城市化显著关联盐渍化上升，农业扩张关联不显著。","可将反事实匹配框架迁移至其他滨海低地，并引入作物类型与灌溉制度细化农业扩张的盐渍化效应。","2026-10-22T11:20:42.713789Z",{"id":449,"title":450,"url":451,"summary":452,"summary_zh":453,"content":9,"source_name":454,"source_url":451,"published_at":455,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":373,"score_detail":456,"sources":458,"tags":460,"primary_topic":464,"secondary_topics":470,"classification":471,"search_phrases":487,"slug":490,"view_count":54,"doi":491,"paper":492,"created_at":503},5565,"A Unified Survival-Based Energy Framework Linking Plant Color, Stress, and Productivity","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23540961","Plant coloration, expressed through leaf greenness, floral pigmentation, and fruit color intensity, reflects the integrated outcome of pigment biosynthesis, optical absorption, metabolic regulation, and energy–mass exchange with the environment. While the biochemical pathways governing the synthesis and degradation of major pigment classes—including chlorophylls, carotenoids, and anthocyanins—are well established, existing models largely interpret color expression as a localized biochemical phenomenon. Such approaches do not explicitly account for whole-plant energy balance, transpiration-driven losses, or thermodynamic constraints, limiting their ability to explain widely observed patterns of color saturation, stress-induced fading, and species-specific stability across environmental gradients. Here we develop a unified framework for plant color generation and stability that treats pigmentation as a survival-limited energetic process embedded within the broader dynamics of absorption, transport, metabolism, and entropy production. Central to the framework is a dimensionless energy survival factor, Ψ=AE\u002FTE+ε where AE represents absorbed and retained energetic and elemental resources relevant to pigment formation, TE denotes transpiration-linked, metabolic, and environmental dissipation losses, and ε captures irreducible entropy-producing degradation inherent to non-equilibrium biological systems. By construction, 0\u003CΨ\u003C1, ensuring full compliance with the second law of thermodynamics. When combined with pigment-specific optical absorption coefficients and physiological limitation functions, the framework yields a physically interpretable description of both pigment concentration and color stability. Empirical ranges for absorbed photosynthetically active radiation (≈45–70%), excitation survival (≈85–95%), and respiratory and transpiration losses (≈30–60% of assimilated resources) naturally constrain Ψ, predicting observed limits on sustained pigment accumulation without invoking biochemical inefficiency or ad hoc correction factors. The analysis demonstrates that increased light availability or enhanced biosynthetic capacity alone does not intensify coloration unless absorbed resources survive transport and loss processes. Stress-induced chlorosis, transient anthocyanin accumulation, and saturation of greenness under high irradiance emerge as direct consequences of reduced energy survival rather than impaired pigment synthesis. By explicitly separating survival from synthesis, the framework reconciles molecular-scale pigment biochemistry with whole-plant and ecosystem-scale color dynamics. This survival-based formulation provides a unified physical basis for interpreting plant color variation across species and environments and offers a scalable foundation for crop stress diagnostics, remote-sensing interpretation, and comparative plant phenotyping. More broadly, it extends survival-limited energy principles to visible plant traits, advancing plant science beyond isolated biochemical descriptions toward a systems-level understanding of how energy governs phenotypic expression.","植物色泽通过叶片绿色度、花色苷着色和果实颜色强度体现，反映了色素生物合成、光学吸收、代谢调控以及与环境的能量-物质交换的综合结果。尽管主要色素类别——包括叶绿素、类胡萝卜素和花青素——的合成与降解生化途径已较为明确，但现有模型大多将颜色表达解释为局部生化现象。此类方法未明确考虑整株植物能量平衡、蒸腾驱动的损耗或热力学约束，限制了其解释广泛观察到的颜色饱和度、胁迫诱导褪色以及跨环境梯度的物种特异性稳定性等模式的能力。本文构建了一个统一的植物颜色生成与稳定性框架，将色素沉着视为一种受生存限制的能量过程，并将其嵌入吸收、运输、代谢和熵产生的更广泛动力学之中。该框架的核心是一个无量纲能量生存因子Ψ=AE\u002FTE+ε，其中AE代表与色素形成相关的被吸收并保留的能量和元素资源，TE表示与蒸腾相关的、代谢的和环境的耗散损失，ε则捕捉非平衡生物系统固有的不可约熵产生降解。根据构建方式，0\u003CΨ\u003C1，确保完全符合热力学第二定律。当与色素特异性光学吸收系数和生理限制函数相结合时，该框架可对色素浓度和颜色稳定性给出具有物理可解释性的描述。吸收性光合有效辐射（约45–70%）、激发能存活率（约85–95%）以及呼吸和蒸腾损失（约占同化资源的30–60%）的经验范围自然约束了Ψ，从而预测出所观察到的持续色素积累限度，而无需引入生化低效性或特设校正因子。分析表明，仅增加光照可用性或增强生物合成能力并不会加深颜色，除非被吸收的资源能够在运输和损失过程中存活下来。胁迫诱导的黄化、瞬时花青素积累以及高辐照下绿色度饱和，均表现为能量生存率降低而非色素合成受损的直接后果。通过明确分离生存","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",{"impact":87,"substance":86,"depth":87,"authority":17,"freshness":295,"relevant":22,"comment":457},"提出以能量生存因子统一解释植物颜色与胁迫的物理框架，方法新颖且对遥感解译和作物诊断有潜在价值，但属预印本理论模型，尚缺田间验证。",[459],{"name":454,"url":451},[239,28,461,462,463],"植物表型","作物胁迫诊断","能量平衡",{"code":465,"slug":466,"name":467,"path":468,"parent_code":469},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables","B",[],{"status":390,"locked":14,"version":35,"method":36,"evidence":472},{"candidates":473,"agricultural_evidence":42},[474,479,483],{"code":465,"score":325,"supported":42,"title":475,"abstract":476,"tags":478},[],[477],"萝卜",[],{"code":252,"score":22,"supported":14,"title":480,"abstract":481,"tags":482},[],[],[239],{"code":40,"score":22,"supported":14,"title":484,"abstract":485,"tags":486},[],[],[28],[488,489],"植物颜色 能量生存因子","作物胁迫 遥感诊断","植物颜色能量生存因子-5565","10.5281\u002Fzenodo.23540961",{"doi":491,"openalex_id":493,"authors":494,"venue":454,"cited_by_count":54,"oa_url":451,"card":498,"direction":69,"ingested_from":71},"W7223098474",[495],{"name":496,"orcid":497},"Mokhdum Azam Mashrafi","https:\u002F\u002Forcid.org\u002F0009-0002-1801-1130",{"tldr":499,"method":500,"finding":501,"direction":69,"opportunity":502},"提出基于生存的能量框架，用能量存活因子解释植物颜色生成与稳定性。","构建无量纲能量存活因子Ψ=AE\u002F(TE+ε)，结合色素吸收系数与生理限制函数。","颜色深浅取决于吸收资源经运输损耗后的存活率，而非单纯合成能力。","可将Ψ因子与冠层光谱\u002F表型遥感结合，发展胁迫早期诊断与生产力预测模型。","2026-10-22T11:20:31.195819Z"]