[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4754":3,"related-4754":77},{"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":18,"tags":20,"primary_topic":9,"secondary_topics":25,"classification":26,"search_phrases":47,"slug":50,"view_count":51,"doi":52,"paper":53,"created_at":76},4754,"Development of a city level nitrogen dioxide exposure (CLeNE) intelligence platform for West Africa","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44274-026-01108-1","Abstract Nitrogen dioxide (NO 2 ) is a major urban air pollutant, yet assessment of population exposure remains challenging across West Africa due to lack of uncoordinated sparse monitoring infrastructure. We present the City-Level Nitrogen Dioxide Exposure (CLeNE) platform, an interactive exposure intelligence system that integrates Sentinel-5P TROPOMI NO 2 observations, population data, and city boundaries to quantify and visualize urban exposure burden. Population-weighted exposure (PWE) and a Normalized Population-Weighted Exposure Index (NPWEI) were developed to enable standardized comparison among cities. Analysis of 2019–2024 observations identified persistent exposure hotspots along the Gulf of Guinea corridor, particularly in southern Nigeria, Ghana, Benin, and Côte d’Ivoire. Nigeria recorded the highest annual country-level PWE (1.59 × 10 15 molecules cm⁻ 2 ), while Abuja had the highest relative exposure ranking among capital cities (NPWEI = 100). The platform transforms satellite-derived observations into accessible exposure intelligence products, providing a scalable framework for sustainable urban planning, air-quality assessment, hotspot identification, and environmental decision support in data-sparse regions.","摘要 二氧化氮（NO₂）是城市主要空气污染物，然而由于西非地区缺乏协调且稀疏的监测基础设施，人口暴露评估仍面临挑战。我们提出了城市级二氧化氮暴露（CLeNE）平台，这是一个交互式暴露情报系统，整合了Sentinel-5P TROPOMI NO₂观测数据、人口数据和城市边界，以量化和可视化城市暴露负担。我们开发了人口加权暴露（PWE）和归一化人口加权暴露指数（NPWEI），以实现城市间的标准化比较。对2019—2024年观测数据的分析发现，几内亚湾走廊沿线持续存在暴露热点，尤其是尼日利亚南部、加纳、贝宁和科特迪瓦。尼日利亚录得最高的国家层面年度PWE（1.59 × 10¹⁵ 分子 cm⁻²），而阿布贾在首都城市中相对暴露排名最高（NPWEI = 100）。该平台将卫星衍生观测数据转化为可获取的暴露情报产品，为数据稀疏地区的可持续城市规划、空气质量评估、热点识别和环境决策支持提供了一个可扩展框架。",null,"Discover Environment","2026-10-10T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该研究聚焦西非城市二氧化氮暴露评估，属城市环境遥感领域，与三农、农业信息化、智慧农业无直接关联，不建议进入每日精选。",[19],{"name":10,"url":6},[21,22,23,24],"遥感监测","城市环境","卫星数据","空气质量",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":30},"needs_review","2026-10-22.2","rules",{"candidates":31,"agricultural_evidence":14,"reason":46},[32,40],{"code":33,"score":34,"supported":35,"title":36,"abstract":37,"tags":39},"I2",3,true,[],[38],"decision support",[],{"code":41,"score":42,"supported":14,"title":43,"abstract":44,"tags":45},"I3",1,[],[],[21],"农业相关性需要复核",[48,49],"CLeNE 平台 西非","Sentinel-5P 二氧化氮 暴露","CLeNE平台西非-4754",2,"10.1007\u002Fs44274-026-01108-1",{"doi":52,"openalex_id":54,"authors":55,"venue":10,"cited_by_count":15,"oa_url":68,"card":69,"direction":73,"ingested_from":75},"W7221364661",[56,59,62,65],{"name":57,"orcid":58},"Prince Junior Asilevi","https:\u002F\u002Forcid.org\u002F0000-0001-9162-326X",{"name":60,"orcid":61},"Alex Kwao Ablerdu","https:\u002F\u002Forcid.org\u002F0009-0002-2332-1432",{"name":63,"orcid":64},"Stephen Amankwah","https:\u002F\u002Forcid.org\u002F0000-0002-0397-5810",{"name":66,"orcid":67},"Emmanuel Quansah","https:\u002F\u002Forcid.org\u002F0000-0002-3382-1775","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44274-026-01108-1.pdf",{"tldr":70,"method":71,"finding":72,"direction":73,"opportunity":74},"开发了西非城市级二氧化氮暴露智能平台，整合卫星与人口数据量化城市暴露负担。","利用Sentinel-5P TROPOMI NO2、人口和城市边界数据，构建人口","几内亚湾沿岸持续为暴露热点，尼日利亚年PWE最高，阿布贾相对暴露排名第一。","农业遥感与作物表型","可借鉴该暴露评估框架，研究农业区大气污染对作物表型与产量的遥感影响。","openalex","2026-10-12T23:30:25.925235Z",{"total":78,"page":42,"page_size":78,"items":79},6,[80,134,211,281,366,447],{"id":81,"title":82,"url":83,"summary":84,"summary_zh":85,"content":9,"source_name":86,"source_url":83,"published_at":87,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":88,"sources":90,"tags":92,"primary_topic":9,"secondary_topics":95,"classification":96,"search_phrases":115,"slug":118,"view_count":42,"doi":119,"paper":120,"created_at":133},4757,"تقييم العلاقات بين عمق المظهر الهوائي والعوامل البيئية بناء على نموذج الانحدار الموزون جغرافيا وزمنيا (دراسة حالة: محافظة السليمانية، العراق ( 2020\u002F2025)","https:\u002F\u002Fdoi.org\u002F10.66026\u002F7y6hpz09","عمق البصريات الهوائية (AOD) هو أحد المؤشرات المهمة لتقييم حمل الهباء الجوي وظروف الغبار في المناطق الجافة ونصف الجافة. وبالنظر إلى التباين الطوبوغرافي والمناخي والبيئي لمحافظة السليمانية في العراق، وتعريضها لنقل الغبار، كان هدف الدراسة الحالية تقدير AOD ودراسة التغير المكاني والزماني لتأثير العوامل البيئية باستخدام نموذج الانحدار الموزون جغرافياً وزمانياً (GTWR). في هذه الدراسة، تم نمذجة AOD باستخدام خمسة متغيرات تفسيرية تشمل رطوبة التربة وسرعة الرياح ومؤشر الغطاء النباتي المختلف المعياري (NDVI) ودرجة حرارة سطح الأرض (LST) وهطول الأمطار خلال فترة ست سنوات (2020-2025). تم تقييم أداء طريقة المربعات الصغرى العادية (OLS) والانحدار الموزون جغرافياً (GWR) والانحدار الموزون زمنياً (TWR)وتمت مقارنة نماذج GTWR باستخدام مؤشرات إحصائية. كما تم تحليل المعاملات المكانية والزمانية لنموذج GTWR لتحديد أنماط غير متجانسة لتأثيرات المتغيرات. عند مقارنة النماذج، أظهر GTWR أفضل أداء مع R² (0.912)، RMSE (0.078)، و AICc (-15376.3) وكان له قدرة أعلى على تفسير تغييرات AOD مقارنة بالنماذج الأخرى. أظهر تحليل المعاملات المكانية والزمانية أن رطوبة التربة وNDVI هما العوامل الأكثر استقرارًا في تقليل AOD، بينما كان LST هو العامل الأكثر أهمية في زيادة AOD. كما أظهر سرعة الرياح والهطول تأثيرات مكانية وزمنية متغيرة، مما يشير إلى اعتمادها على الظروف المحلية والزمنية. يمكن اعتبار هذا النمط نتيجة لتفاعل ظروف الرطوبة والنباتات السطحية، والاحتباس الحراري، ووعورة زاغروس، وعملية نقل الغبار. بشكل عام، تُظهر النتائج أن نموذج GTWR، بالإضافة إلى تحسين دقة التقدير، يوفر إمكانية تحديد العوامل المسيطرة والمناطق الحساسة للغبار، ويمكن أن يكون أساسًا علميًا لمراقبة الهباء الجوي وإدارة جودة الهواء في محافظة السليمانية والمناطق المماثلة.","气溶胶光学厚度（AOD）是评估干旱和半干旱地区气溶胶负荷与沙尘状况的重要指标之一。鉴于伊拉克苏莱曼尼亚省的地形、气候和环境差异及其受沙尘输送影响，本研究旨在利用地理与时间加权回归模型（GTWR）估算AOD，并研究环境因子影响的时空变化。本研究利用土壤湿度、风速、归一化植被指数（NDVI）、地表温度（LST）和降水五个解释变量，对六年期（2020—2025年）的AOD进行了建模。评估了普通最小二乘法（OLS）、地理加权回归（GWR）和时间加权回归（TWR）的性能，并利用统计指标对GTWR模型进行了比较。同时分析了GTWR模型的空间和时间系数，以识别变量影响的非均质模式。在模型比较中，GTWR表现出最佳性能，R²为0.912，RMSE为0.078，AICc为-15376.3，与其他模型相比具有更高的解释AOD变化的能力。空间和时间系数分析表明，土壤湿度和NDVI是降低AOD最稳定的因子，而LST是增加AOD最重要的因子。风速和降水则表现出变化的时空影响，表明其依赖于当地和时序条件。这种模式可被视为湿度条件、地表植被、热截留、扎格罗斯山脉崎岖地形以及沙尘输送过程相互作用的结果。总体而言，结果表明GTWR模型在提高估算精度的同时，能够识别主导因子和沙尘敏感区域，可为苏莱曼尼亚省及类似地区的气溶胶监测和空气质量管理提供科学依据。","Journal Of Babylon Center for Humanities Studies","2026-10-09T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":89},"该论文研究伊拉克库尔德地区气溶胶光学厚度与环境因子的时空关系，属遥感与环境科学范畴，与三农、农业信息化、智慧农业无直接关联，不建议进入每日精选。",[91],{"name":86,"url":83},[21,24,93,94],"气溶胶光学厚度","干旱区环境",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":97},{"candidates":98,"agricultural_evidence":14,"reason":46},[99,105,111],{"code":100,"score":34,"supported":35,"title":101,"abstract":102,"tags":104},"H2",[],[103],"土壤",[],{"code":106,"score":34,"supported":35,"title":107,"abstract":108,"tags":110},"H7",[],[109],"干旱",[],{"code":41,"score":42,"supported":14,"title":112,"abstract":113,"tags":114},[],[],[21],[116,117],"苏莱曼尼亚 气溶胶光学厚度","GTWR 气溶胶 环境因子","苏莱曼尼亚气溶胶光学厚度-4757","10.66026\u002F7y6hpz09",{"doi":119,"openalex_id":121,"authors":122,"venue":86,"cited_by_count":15,"oa_url":127,"card":128,"direction":73,"ingested_from":75},"W7221361568",[123,125],{"name":124,"orcid":9},"ئەژین ابراهیم سمین",{"name":126,"orcid":9},"شیرین كریم احمد","https:\u002F\u002Fbcchj.uobabylon.edu.iq\u002Findex.php\u002Fbcchj\u002Farticle\u002Fdownload\u002F4860\u002F4908",{"tldr":129,"method":130,"finding":131,"direction":73,"opportunity":132},"用GTWR模型评估伊拉克苏莱曼尼亚省2020-2025年气溶胶光学厚度与环境因子的时空关系。","基于MODIS AOD与土壤湿度、风速、NDVI、LST、降水数据，比较OLS、","GTWR表现最优（R²=0.912），土壤湿度和NDVI稳定降低AOD，LST显著增加AOD。","可将GTWR时空建模框架迁移至农业区气溶胶-作物生长耦合研究，识别尘暴敏感农区并支撑精准农业环境监测。","2026-10-12T23:30:28.066889Z",{"id":135,"title":136,"url":137,"summary":138,"summary_zh":139,"content":9,"source_name":140,"source_url":137,"published_at":141,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":142,"sources":144,"tags":146,"primary_topic":148,"secondary_topics":153,"classification":154,"search_phrases":172,"slug":175,"view_count":42,"doi":176,"paper":177,"created_at":210},4357,"Evaluating When and Where TROPOMI XCH4 Uncertainties Yield Consistent Flux Estimation over East, South, and Central Asia","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fegusphere-2026-4468","Abstract. Satellite remote sensing of methane (CH4) offers the potential to monitor emissions across large scales, but skepticism remains among regulators, policymakers, and the scientific community of both the precision and accuracy of such estimations due to unquantified observational uncertainties. Using 500 perturbation experiments with four error distributions applied to TROPOMI XCH4 over East, South, and Central Asia, we systematically assess where and when divergence‑based emission estimates are reliable. We show that the shape of the uncertainty distribution – not just its magnitude – governs error propagation and thus effects the stability of derived fluxes. Furthermore we show that uncertainty propogates over both space and time, indicating that purely local accounting may lead to an underestimate of uncertainty. Coal‑dominated industrial grids retain trustworthy signals (30% retention of valid perturbations), whereas agricultural and mixed‑use grids retain only 16%, indicating that current filtering practices are insufficient for these land types. These results provide a practical, uncertainty‑aware filter for satellite‑derived methane emissions, directly supporting the Global Methane Pledge and national greenhouse gas inventories by identifying where satellite data can be trusted (coal‑dominated regions) and where additional caution or alternative approaches are needed. The process is data-neutral and can be extended to other satellite platforms and adopted by other regions around the globe.","摘要：卫星遥感甲烷（CH₄）具有在大尺度上监测排放的潜力，但由于观测不确定性尚未量化，监管机构、政策制定者和科学界对此类估算的精度和准确度仍存疑虑。本研究利用500组扰动实验，将四种误差分布应用于东亚、南亚和中亚地区的TROPOMI XCH₄数据，系统评估了基于散度（divergence）的排放估算在何时何地是可靠的。研究表明，不确定性分布的形状——而不仅仅是其量级——决定了误差传播，进而影响所推导通量的稳定性。此外，不确定性在空间和时间上均会传播，这表明纯粹的局地核算可能导致对不确定性的低估。以煤炭为主的工业网格保留了可信信号（有效扰动的保留率为30%），而农业和混合用途网格仅保留16%，表明当前的滤波方法对这些土地类型而言尚不充分。这些结果为卫星反演的甲烷排放提供了一种实用的、考虑不确定性的滤波方案，通过识别卫星数据可信的区域（以煤炭为主的地区）以及需要额外谨慎或替代方法的区域，直接支持全球甲烷承诺（Global Methane Pledge）和国家温室气体清单的编制。该方法对数据具有中立性，可推广至其他卫星平台并被全球其他地区采用。","OpenAlex","2026-10-06T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":143},"该论文聚焦卫星甲烷排放通量估算不确定性，属环境遥感领域，与三农、农业信息化、智慧农业无直接关联，不建议进入每日精选。",[145],{"name":140,"url":137},[21,147,23],"甲烷排放",{"code":41,"slug":149,"name":150,"path":151,"parent_code":152},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":155,"locked":14,"version":28,"method":29,"evidence":156},"classified",{"candidates":157,"agricultural_evidence":35},[158,165],{"code":41,"score":159,"supported":35,"title":160,"abstract":161,"tags":164},7,[],[162,163],"遥感","remote sensing",[21],{"code":166,"score":78,"supported":35,"title":167,"abstract":168,"tags":171},"B7",[],[169,170],"温室","greenhouse",[],[173,174],"TROPOMI XCH4 甲烷","卫星遥感 甲烷排放","TROPOMIXCH4甲烷-4357","10.5194\u002Fegusphere-2026-4468",{"doi":176,"openalex_id":178,"authors":179,"venue":9,"cited_by_count":15,"oa_url":137,"card":205,"direction":73,"ingested_from":75},"W7220708326",[180,183,186,189,191,194,197,200,202],{"name":181,"orcid":182},"Wei Xin Hu","https:\u002F\u002Forcid.org\u002F0000-0002-8780-7362",{"name":184,"orcid":185},"Jason Blake Cohen","https:\u002F\u002Forcid.org\u002F0000-0002-9889-8175",{"name":187,"orcid":188},"Lingxiao Lu","https:\u002F\u002Forcid.org\u002F0000-0002-3839-9083",{"name":190,"orcid":9},"Bo Zheng",{"name":192,"orcid":193},"Pravash Tiwari","https:\u002F\u002Forcid.org\u002F0000-0003-4770-4526",{"name":195,"orcid":196},"Simone Lolli","https:\u002F\u002Forcid.org\u002F0000-0001-6111-152X",{"name":198,"orcid":199},"Andrea Garzelli","https:\u002F\u002Forcid.org\u002F0000-0003-2332-780X",{"name":201,"orcid":9},"Jun Li",{"name":203,"orcid":204},"Kai Qin","https:\u002F\u002Forcid.org\u002F0000-0002-1280-6330",{"tldr":206,"method":207,"finding":208,"direction":73,"opportunity":209},"用500组扰动实验评估TROPOMI XCH4在亚洲三区甲烷通量估算的可靠性。","对TROPOMI XCH4施加四种误差分布的500次扰动实验，分析通量反演稳定性","不确定性分布形状而非大小主导误差传播，且跨时空传播；煤炭区信号可信度30%，农业混合区仅16%。","可发展面向农业混合区的误差感知滤波与替代反演方法，并推广至其他卫星平台与区域。","2026-10-07T23:30:26.011458Z",{"id":212,"title":213,"url":214,"summary":215,"summary_zh":216,"content":9,"source_name":217,"source_url":214,"published_at":218,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":219,"score_detail":220,"sources":226,"tags":228,"primary_topic":9,"secondary_topics":232,"classification":233,"search_phrases":251,"slug":254,"view_count":51,"doi":255,"paper":256,"created_at":280},2804,"A Novel Data-Driven Framework for Stubble Burning Detection Using NASA FIRMS and Machine Learning","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12524-026-02584-x","A Novel Data-Driven Framework for Stubble Burning Detection Using NASA FIRMS and Machine Learning。Journal of the Indian Society of Remote Sensing","一种基于NASA FIRMS和机器学习的新型数据驱动秸秆焚烧检测框架。《印度遥感学会杂志》","Journal of the Indian Society of Remote Sensing","2026-09-17T00:00:00Z",76,{"impact":221,"substance":222,"depth":223,"authority":224,"freshness":13,"relevant":42,"comment":225},15,20,18,13,"该论文提出基于NASA FIRMS卫星数据与机器学习的秸秆焚烧检测新框架，方法新颖、数据规模明确，对农业遥感监测有实质参考价值，但属细分领域学术进展，影响力限于专业圈层。",[227],{"name":217,"url":214},[229,230,21,231,23],"农业人工智能","机器学习","秸秆焚烧",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":234},{"candidates":235,"agricultural_evidence":14,"reason":46},[236,242,246],{"code":33,"score":237,"supported":35,"title":238,"abstract":240,"tags":241},16,[239],"machine learning",[230,239],[229,230],{"code":41,"score":159,"supported":35,"title":243,"abstract":244,"tags":245},[],[162,163],[21],{"code":247,"score":42,"supported":14,"title":248,"abstract":249,"tags":250},"H5",[],[],[231],[252,253],"农业人工智能 卫星数据 机器学习 秸秆焚烧","农业人工智能 卫星数据","农业人工智能卫星数据机器学习秸秆焚烧-2804","10.1007\u002Fs12524-026-02584-x",{"doi":255,"openalex_id":257,"authors":258,"venue":217,"cited_by_count":15,"oa_url":9,"card":274,"direction":279,"ingested_from":75},"W7213455429",[259,262,264,267,269,272],{"name":260,"orcid":261},"Mohit Dua","https:\u002F\u002Forcid.org\u002F0000-0001-7071-8323",{"name":263,"orcid":9},"Oshin Rastogi",{"name":265,"orcid":266},"Ashish Saini","https:\u002F\u002Forcid.org\u002F0000-0003-3061-2342",{"name":268,"orcid":9},"Raviya",{"name":270,"orcid":271},"Nidhi Chakravarty","https:\u002F\u002Forcid.org\u002F0000-0002-5454-1561",{"name":273,"orcid":9},"Shelza Dua",{"tldr":275,"method":276,"finding":277,"direction":73,"opportunity":278},"提出基于NASA FIRMS与机器学习的数据驱动框架，用于检测秸秆焚烧。","使用NASA FIRMS火点数据结合机器学习分类算法。","该框架能有效识别秸秆焚烧事件，提升检测精度。","可结合多源遥感与深度学习，提升小尺度焚烧检测与实时预警能力。","农业人工智能与决策模型","2026-09-17T23:30:59.313408Z",{"id":282,"title":283,"url":284,"summary":285,"summary_zh":286,"content":9,"source_name":287,"source_url":284,"published_at":288,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":289,"score_detail":290,"sources":296,"tags":298,"primary_topic":9,"secondary_topics":303,"classification":304,"search_phrases":336,"slug":339,"view_count":15,"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",78,{"impact":223,"substance":291,"depth":292,"authority":293,"freshness":294,"relevant":42,"comment":295},22,17,12,9,"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。",[297],{"name":287,"url":284},[21,299,300,301,302],"土地利用","森林保护","亚马逊","CBERS-4",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":305},{"candidates":306,"agricultural_evidence":35,"reason":335},[307,314,318,324,329],{"code":308,"score":159,"supported":35,"title":309,"abstract":310,"tags":313},"H6",[],[311,312],"生物多样性","biodiversity",[299],{"code":41,"score":159,"supported":35,"title":315,"abstract":316,"tags":317},[],[162,163],[21],{"code":106,"score":78,"supported":35,"title":319,"abstract":320,"tags":323},[],[321,322],"气候变化","climate change",[],{"code":100,"score":34,"supported":35,"title":325,"abstract":326,"tags":328},[],[327],"soil",[],{"code":330,"score":34,"supported":35,"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":287,"cited_by_count":15,"oa_url":284,"card":360,"direction":73,"ingested_from":75},"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":73,"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":288,"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":409,"slug":412,"view_count":15,"doi":413,"paper":414,"created_at":446},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":237,"substance":291,"depth":223,"authority":375,"freshness":294,"relevant":42,"comment":376},14,"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},[21,380,381,382,383],"莫桑比克","土壤盐渍化","土地覆盖变化","盐分反演",{"code":100,"slug":327,"name":385,"path":386,"parent_code":387},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":155,"locked":14,"version":28,"method":29,"evidence":390},{"candidates":391,"agricultural_evidence":35},[392,396,400,405],{"code":100,"score":375,"supported":35,"title":393,"abstract":394,"tags":395},[327],[103,327],[],{"code":41,"score":159,"supported":35,"title":397,"abstract":398,"tags":399},[],[162,163],[21],{"code":106,"score":78,"supported":35,"title":401,"abstract":402,"tags":404},[],[403,109],"气候适应",[],{"code":308,"score":42,"supported":14,"title":406,"abstract":407,"tags":408},[],[],[382],[410,411],"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":413,"openalex_id":415,"authors":416,"venue":372,"cited_by_count":15,"oa_url":369,"card":441,"direction":73,"ingested_from":75},"W7223069171",[417,419,421,423,426,428,431,434,436,438],{"name":418,"orcid":9},"Yue Li",{"name":420,"orcid":9},"Mengfei Shi",{"name":422,"orcid":9},"Fei Zhang",{"name":424,"orcid":425},"Brian Alan Johnson","https:\u002F\u002Forcid.org\u002F0000-0003-1911-3585",{"name":427,"orcid":9},"Gowhar Meraj",{"name":429,"orcid":430},"Jaime Carlos Macuácua","https:\u002F\u002Forcid.org\u002F0000-0002-4822-6098",{"name":432,"orcid":433},"Pankaj Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-5264-3951",{"name":435,"orcid":9},"Yuanfang Chai",{"name":437,"orcid":9},"Hongtao Xu",{"name":439,"orcid":440},"Xu Ma","https:\u002F\u002Forcid.org\u002F0000-0003-0644-7498",{"tldr":442,"method":443,"finding":444,"direction":73,"opportunity":445},"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":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":373,"score_detail":455,"sources":457,"tags":459,"primary_topic":464,"secondary_topics":470,"classification":471,"search_phrases":488,"slug":491,"view_count":15,"doi":492,"paper":493,"created_at":504},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":223,"substance":291,"depth":223,"authority":293,"freshness":294,"relevant":42,"comment":456},"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。",[458],{"name":453,"url":450},[460,21,461,462,463],"智慧农业","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":155,"locked":14,"version":28,"method":29,"evidence":472},{"candidates":473,"agricultural_evidence":35},[474,479,484],{"code":465,"score":34,"supported":35,"title":475,"abstract":476,"tags":478},[],[477],"萝卜",[],{"code":480,"score":42,"supported":14,"title":481,"abstract":482,"tags":483},"I1",[],[],[460],{"code":41,"score":42,"supported":14,"title":485,"abstract":486,"tags":487},[],[],[21],[489,490],"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":492,"openalex_id":494,"authors":495,"venue":453,"cited_by_count":15,"oa_url":450,"card":499,"direction":73,"ingested_from":75},"W7223098474",[496],{"name":497,"orcid":498},"Mokhdum Azam Mashrafi","https:\u002F\u002Forcid.org\u002F0009-0002-1801-1130",{"tldr":500,"method":501,"finding":502,"direction":73,"opportunity":503},"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"]