[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2869":3,"related-2869":49},{"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,"search_phrases":25,"slug":28,"view_count":15,"doi":29,"paper":30,"created_at":48},2869,"Evidence for the impact of fire activity on daily variations of IASI mid-tropospheric CO 2 anomalies at 8–11 km over South America: a pyroconvective fingerprint","https:\u002F\u002Fdoi.org\u002F10.5194\u002Facp-26-13103-2026","Biomass burning is a major, highly variable source of atmospheric CO 2 , but its impact on the free troposphere remains difficult to quantify because of uncertainties in injection heights and transport. In the tropics, intense fires can trigger pyroconvective plumes that loft combustion products to the mid- and upper troposphere. However, most fire emission inventories and global CO 2 inversions still assume simplified vertical distributions of CO 2 emitted by fires. Weighted columns of CO 2 retrieved from remote sensing instruments that are sensitive to such high-altitude enhancements can inform of such dynamics. Here we combine mid-tropospheric CO 2 (MT-CO 2 ) retrievals from three IASI instruments with GOES-16 observations of Fire Radiative Energy (FRE) to link daily MT-CO 2 anomalies observed by IASI at 8–11 km altitude to South American fire activity during the 2020 burning season, while accounting for long-range horizontal transport of anomalies. From August–October 2020, about 66 % of the detected anomalies originate from long-range or unknown sources and are discarded. For the remaining anomalies attributed to local fires, 72 h back trajectories do intersect with at least one active fire detection for 75 % of them. Their daily sum co-varies strongly with FRE, with the ratio between the two depending on the dominant horizontal transport regime. A comparison with the CAMS inversion-optimised CO 2 product (v23r1), sampled with the IASI vertical weighting, shows that the model fails to reproduce both the amplitude and structure of the observed anomalies. Overall, our results demonstrate that IASI MT-CO 2 anomalies carry an observational fingerprint of tropical fire activity.","生物质燃烧是大气CO₂的一个主要且高度可变的来源，但由于注入高度和输送过程的不确定性，其对自由对流层的影响仍难以量化。在热带地区，强烈的火灾可引发火积云羽流，将燃烧产物抬升至对流层中层和上层。然而，大多数火灾排放清单和全球CO₂反演仍假设火灾排放的CO₂具有简化的垂直分布。对这类高海拔增强敏感、由遥感仪器反演的CO₂加权柱浓度可为理解此类动力过程提供信息。本研究将三台IASI仪器的对流层中层CO₂（MT-CO₂）反演与GOES-16的火辐射能量（FRE）观测相结合，在考虑异常长距离水平输送的同时，将IASI在8–11 km高度观测到的每日MT-CO₂异常与2020年燃烧季南美火灾活动联系起来。2020年8月至10月，约66%的检测异常源自长距离或未知来源，被予以剔除。对于归因于本地火灾的其余异常，其中75%的72小时后向轨迹确实与至少一个活跃火点检测相交。其每日总和与FRE呈强烈协变，两者之比取决于主导的水平输送 regime。与CAMS反演优化CO₂产品（v23r1）按IASI垂直权重采样后的比较表明，该模型未能再现观测异常的振幅和结构。总体而言，我们的结果表明，IASI MT-CO₂异常携带了热带火灾活动的观测指纹。",null,"Atmospheric chemistry and physics","2026-09-17T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该论文研究南美生物质燃烧对中层大气CO2的影响，属大气科学领域，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","生物质燃烧","大气二氧化碳","卫星观测",[26,27],"IASI 中高层二氧化碳 南美火灾","GOES-16 火辐射能量 对流层","IASI中高层二氧化碳南美火灾-2869","10.5194\u002Facp-26-13103-2026",{"doi":29,"openalex_id":31,"authors":32,"venue":10,"cited_by_count":15,"oa_url":40,"card":41,"direction":45,"ingested_from":47},"W7213452355",[33,35,37],{"name":34,"orcid":9},"Victor Bon",{"name":36,"orcid":9},"Cyril Crevoisier",{"name":38,"orcid":39},"Virginie Capelle","https:\u002F\u002Forcid.org\u002F0000-0002-9912-3742","https:\u002F\u002Facp.copernicus.org\u002Farticles\u002F26\u002F13103\u002F2026\u002Facp-26-13103-2026.pdf",{"tldr":42,"method":43,"finding":44,"direction":45,"opportunity":46},"结合IASI中高层CO2与GOES-16火辐射能，证实南美火活动在8–11 km留下可观测指纹。","IASI三仪器MT-CO2反演、GOES-16 FRE、72h后向轨迹与CAMS","75%局地异常轨迹遇火点，日总量与FRE强共变，CAMS无法重现其幅度与结构。","农业遥感与作物表型","可发展火排放注入高度参数化，改进CO2反演与生物质燃烧碳核算的垂直约束。","openalex","2026-09-18T23:30:29.021060Z",{"total":50,"page":51,"page_size":50,"items":52},6,1,[53,82,131,178,229,270],{"id":54,"title":55,"url":56,"summary":57,"summary_zh":9,"content":58,"source_name":59,"source_url":9,"published_at":60,"category":61,"cover_url":9,"hotness":13,"is_selected":14,"score":62,"score_detail":63,"sources":69,"tags":71,"search_phrases":77,"slug":80,"view_count":15,"doi":9,"paper":9,"created_at":81},2898,"苏垦农发神农慧种农业AI大模型规模化落地:天空地一体化闭环,百万亩自有农田实景数据","https:\u002F\u002Fcaifuhao.eastmoney.com\u002Fnews\u002F20260918101757264727920","苏垦农发9月18日发文,公司依托百万亩自有连片高标准农田,持续产出真实大田数据训练神农慧种农业AI智能体,实现天空地一体化数据闭环:空中多光谱无人机集群常态化农田巡测;地面全域四情监测传感器、北斗智能农机、智能灌溉终端;云端苏垦智云平台+神农慧种AI模型,形成采集数据→AI分析研判→输出水肥植保方案→农机落地执行完整闭环。苏垦智云是全国农林牧渔领域唯一入选工信部信创典型案例的农业数字化平台。","[在东方财富看资讯行情，选东方财富证券一站式开户交易>>](https:\u002F\u002Facttg.eastmoney.com\u002Fpub\u002Fwebtg_hskh_act_zixun_01_01_01_0)\n\n（国内A股找不到第二家，像苏垦农发依托百万亩自有连片高标准农田，持续产出真实大田数据训练神农慧种农业AI智能体；苏垦实现天空地一体化数据闭环，苏垦智云是农林牧渔唯一工信部信创典型案例，智慧农业+低空经济双主线落地。）\n\n- 空中：多光谱无人机集群开展农田巡测；\n\n![Image 1](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002FB9B393E75E9CCC55E11A2686662765C9_w1080h720.jpg)\n\n- 地面：农田四情监测传感器、北斗智能农机；\n\n![Image 2](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002F4D780511671CDAB50B4A1CCFE057703D_w1339h892.jpg)\n\n- 云端：苏垦智云平台与神农慧种AI模型，形成「采集数据→AI分析研判→输出水肥植保方案→农机落地执行」完整闭环。\n\n![Image 3](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002F1059027DFA2A605F6861D7C69D6D4309_w1440h1080.jpg)\n\n百万亩自有连片农田源源不断产出真实田间数据，持续迭代优化AI模型。国内很多农业AI企业仅拥有小片试验田，唯有苏垦农发拥有大规模现代农业实景数据用于农业模型训练。\n\n苏垦智云平台，也是全国农林牧渔领域唯一入选工信部信创典型案例的农业数字化平台。苏垦农发一一智慧农业与低空经济的天空地一体化闭环落地，AI大田规模化实体应用！\n\n苏垦农发打造天空地一体化智慧农业完整体系，AI大田并非实验室试验，而是在百万亩自有连片农田实现规模化落地运行。\n\n1、空中：多光谱无人机集群常态化农田巡测，低空遥感采集作物长势、病虫害、墒情数据；依托农业农村部低空技术创新重点实验室，主攻低空多模态农情感知。\n\n2、地面：全域农田“四情”监测传感器、北斗智能农机、智能灌溉终端，实时采集土壤、苗情、虫情、气象数据。\n\n3、云端：苏垦智云平台 神农慧种农业AI智能体，形成完整闭环：采集田间数据→AI模型分析研判→输出水肥、植保作业处方→下发农机执行落地。\n\n4、核心稀缺壁垒：手握百万亩自有连片高标准农田，源源不断产出真实大田实景数据，持续迭代训练神农慧种AI模型。\n\n国内绝大多数农业AI企业，仅拥有小片试验田做演示；苏垦是少数拥有大规模真实农业场景用于模型训练与生产验证的实体龙头。\n\n5、苏垦智云一体化平台，也是全国农林牧渔领域唯一入选工信部信创典型案例的农业数字化平台，国产化底层架构，是农业数字化可复制的标杆样板。配套全国首个农业农村部农业低空技术创新重点实验室（苏垦为依托单位、河海大学共建），同步布局低空经济与智慧农业新质生产力。\n\n2026-09-18 11:16:07 作者更新了以下内容\n\n全球领先的风险咨询公司Verisk Maplecroft 在周四（9月17日）发布的一份报告中表示，极端天气灾害将加剧亚洲的粮食安全风险，并可能在印度、印尼和菲律宾等脆弱的国家引发动荡。\n\n![Image 4](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002F9ACF9C92E7B35A242970CDC4D55B8DA9_w1080h15645.jpg)\n\n![Image 5](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002FFE1BF075831D4DAEB94ADAE924123EE9_w1080h2400.jpg)\n\n2026-09-18 21:02:06 作者更新了以下内容\n\n苏垦农发一一 AI赋能农业真实落地案例：临海农场——国内首个10万亩级无人值守巡田农场（核心标杆）\n\n地点：江苏盐城临海农场，苏垦智慧农业科技园\n\n1. 空中低空遥感AI巡田\n\n![Image 6](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002FE4E4DDE3ED1EEF061A2D631622A46F73_w1424h800.jpg)\n\n多光谱无人机集群常态化巡航，采集苗情、墒情、病虫害影像数据，AI自动识别长势差异、病斑，生成热力图；替代人工徒步巡田，十几分钟就能完成万亩农田普查。依托农业农村部低空技术创新重点实验室，开展低空多模态农情感知研究。\n\n2. AI智能光伏远程灌溉系统\n\n![Image 7](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002F9FF2DAAEBED53D7BB8C825B388C102BD_w1424h800.jpg)\n\n万亩稻田布设太阳能智能闸门，通过土壤墒情传感器采集数据，AI分析土壤缺水程度，手机APP一键远程开关水渠闸门。\n\n量化效果：过去管500亩农田，人工开关闸门半天；现在2分钟完成全部闸门调控，灌溉效率提升20倍，每亩节约管水人工成本约30元。\n\n3. AR眼镜AI虫害识别\n\n![Image 8](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002F9C7DFBF3F14BDE7E73309CA953C0C8E7_w1424h800.jpg)\n\n农技人员佩戴AR眼镜在田间巡查，拍摄虫体，AI毫秒级识别稻飞虱等害虫种类、统计虫口密度，识别准确率＞95%，自动推送防治方案，新手农技员也能快速判别田间虫害。\n\n4. AI变量施肥无人机作业\n\n![Image 9](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002FC257CCA1B1E8AE1621C403E96DF222EE_w1424h800.jpg)\n\nAI读取水稻营养、长势数据，为每一块条田生成独立追肥处方，无人机分区精准施肥，一地一策，实现肥药双减，农药化肥年均用量下降约3%。\n\n5. 北斗智能农机 AI收割决策\n\n![Image 10](https:\u002F\u002Fgbres.dfcfw.com\u002FFiles\u002Fpicture\u002F20260918\u002F00A2AE87C3E170BFBFE610FABE2556A5_w1424h800.jpg)\n\n北斗导航插秧机、无人收割机，AI根据成熟度、含水率数据，指导分块错峰收割，减少粮食收割损耗。\n\n[恭喜解锁12个月手机L2专属领取资格，立即领取>>](https:\u002F\u002Facttg.eastmoney.com\u002Fpub\u002Fwebtg_hskh_act_zixun_01_01_01_0)\n\n暗盘资金榜已更新!这些个股\u002F板块可以关注>\n\n追加内容\n\n本文作者可以追加内容哦 !\n\n**郑重声明：**用户在社区发表的所有信息将由本网站记录保存，仅代表作者个人观点，与本网站立场无关，不对您构成任何投资建议，据此操作风险自担。**请勿相信代客理财、免费荐股和炒股培训等宣传内容，远离非法证券活动。请勿添加发言用户的手机号码、公众号、微博、微信及QQ等信息，谨防上当受骗！**\n\n[![Image 11](https:\u002F\u002Favator.eastmoney.com\u002Fqface\u002F9825094237066000\u002F360)](https:\u002F\u002Fi.eastmoney.com\u002F9825094237066000)\n\n总收益 20日收益 日收益\n------\n\n历史收益率走势(%)\n\nChart\n\n代码 名称 最新价 涨跌幅\n[查看更多](http:\u002F\u002Figuba.eastmoney.com\u002F9825094237066000)\n\n浪客视频\n\n![Image 12](https:\u002F\u002Fnp-newspic.dfcfw.com\u002Fdownload\u002FD25261481966621695940_w340h340.jpg)\n\n![Image 13](https:\u002F\u002Fgbapi.eastmoney.com\u002Fshareopt\u002Fweb\u002Fweb_click.gif?id=20260918101757264727920&type=20&version=200&product=EastMoney&plat=Web&deviceid=caifuhao)\n\n郑重声明：东方财富网发布此信息的目的在于传播更多信息，与本站立场无关。东方财富网不保证该信息（包括但不限于文字、视频、音频、数据及图表）全部或者部分内容的准确性、真实性、完整性、有效性、及时性、原创性等。相关信息并未经过本网站证实，不对您构成任何投资建议，据此操作，风险自担。","东方财富财富号\u002F苏垦农发","2026-09-18T00:00:00Z","报道",69,{"impact":64,"substance":65,"depth":66,"authority":67,"freshness":13,"relevant":51,"comment":68},22,18,14,5,"苏垦农发百万亩自有农田上实现天空地一体化AI闭环，含临海农场10万亩无人巡田等量化案例，产业参考价值较高，但来源为财富号自媒体、宣传色彩浓，权威性偏弱。",[70],{"name":59,"url":56},[72,73,74,75,21,76],"智慧农业","低空经济","农业人工智能","智能农机","数字农田",[78,79],"苏垦农发 神农慧种 AI大模型","临海农场 无人值守巡田","苏垦农发神农慧种AI大模型-2898","2026-09-19T00:06:07.612319Z",{"id":83,"title":84,"url":85,"summary":86,"summary_zh":87,"content":9,"source_name":88,"source_url":85,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":89,"score_detail":90,"sources":96,"tags":98,"search_phrases":103,"slug":106,"view_count":15,"doi":107,"paper":108,"created_at":130},2871,"Spatial and Temporal characteristics and driving force analysis of vegetation cover change in Shanxi Province, China based on kNDVI and XGBoost-SHAP model","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffenvs.2026.1948848","Introduction Systematically clarifying the spatiotemporal evolution of vegetation coverage and elucidating its response mechanisms to climatic fluctuations and human activities carries substantial practical implications for advancing the Dual Carbon Strategy and optimizing the pattern of territorial spatial development and conservation across Shanxi Province, China. Methods Based on kernel Normalized Difference Vegetation Index (kNDVI) datasets, multi-source meteorological, topographic and socioeconomic datasets, this study integrated Theil-Sen slope estimation, Mann-Kendall significance test, Hurst exponent, standard deviational ellipse and gravity center migration model to systematically characterize the spatiotemporal patterns of vegetation coverage for the period 2000 to 2024 and predict the potential persistence of its future evolution. Meanwhile, an interpretable XGBoost-SHAP machine learning framework was constructed to quantify the independent contributions, nonlinear marginal responses, and temporal evolutionary characteristics of seven driving factors: elevation, slope, aspect, annual mean temperature, annual mean precipitation, population density and nighttime light intensity. Results (1) Temporally, the multi-year average kNDVI of the whole province reached approximately 0.1699, presenting an extremely significant fluctuating upward trend with an annual growth rate of 0.0041. Annual kNDVI ranges from 0.1132 recorded in 2001 to a peak value of 0.2227 in 2024. (2) Spatially, vegetation coverage exhibited a remarkable differentiation pattern of “low values in the north and high values in the south”. Higher kNDVI values were predominantly distributed within the forest-covered mountainous regions of the Taihang, Lüliang, Zhongtiao, and Taiyue Mountains, whereas relatively low values were distributed across the sandy-hilly regions of northern Shanxi and urban agglomerations along the Fen River Valley. Vegetation restoration was observed across 98.02% of the study area, among which roughly 92.18% exhibited an extremely significant improvement. (3) Over 2000–2024, the major axis of vegetation spatial distribution maintained a stable northeast-southwest orientation, accompanied by moderate outward expansion of the standard deviational ellipse, and a general northwestward shift of the vegetation gravity centre. (4) The Hurst exponent indicated that the vegetation improvement trend in 98.03% of the region would persist, while scattered patches of industrial, mining and urban land (accounting for 1.97%) faced the potential vegetation degradation risk. (5) The regional vegetation driving system experienced three evolutionary stages: a single precipitation-dominated natural driving stage (2000–2005), a climate-human coupled transitional stage (2005–2010), and a multi-factor synergistic balanced stage (2010–2020). Precipitation and temperature acted as core climatic drivers, and elevation largely determined the vertical differentiation baseline of vegetation distribution. Population density and nighttime light intensity exerted persistent suppressive effects on vegetation growth. Empirically derived tentative thresholds are identified for major predictors: elevation ∼1,200 m, slope ∼8°, annual mean temperature ∼7.5 °C, annual mean precipitation ∼500 mm, population density 500 persons\u002Fkm 2 , and nighttime light intensity ∼5. These values can serve as reference boundaries for differentiating ecological conservation zones from human-disturbed zones. Conclusion This research identified the intertemporal differentiation and nonlinear coupling laws governing vegetation dynamics in Shanxi Province, a temperate transition zone on the Loess Plateau. Differentiated ecological governance strategies targeting climate adaptation, topographic zoning, and anthropogenic-pressure regulation are proposed, which provide observational scientific support for the construction of ecological security barriers on China’s Loess Plateau.","引言 系统厘清植被覆盖的时空演变规律并阐明其对气候波动与人类活动的响应机制，对推进双碳战略、优化山西省国土空间开发保护格局具有重要现实意义。方法 基于核归一化植被指数（kNDVI）数据集及多源气象、地形和社会经济数据，本研究综合运用Theil-Sen斜率估计、Mann-Kendall显著性检验、Hurst指数、标准差椭圆和重心迁移模型，系统刻画了2000—2024年植被覆盖的时空格局，并预测其未来演变的潜在持续性。同时，构建了可解释的XGBoost-SHAP机器学习框架，量化了高程、坡度、坡向、年平均气温、年平均降水量、人口密度和夜间灯光强度7个驱动因子的独立贡献、非线性边际响应及时间演变特征。结果 （1）时间上，全省多年平均kNDVI约为0.1699，呈极显著波动上升趋势，年增长率为0.0041。年kNDVI从2001年的0.1132变化至2024年的峰值0.2227。（2）空间上，植被覆盖呈现“北低南高”的显著分异格局。较高kNDVI值主要分布于太行山、吕梁山、中条山和太岳山等森林覆盖山区，而较低值分布于晋北沙丘丘陵区和汾河谷地城市群。研究区98.02%的区域植被呈恢复态势，其中约92.18%表现为极显著改善。（3）2000—2024年间，植被空间分布的主轴保持稳定的东北—西南走向，标准差椭圆呈中度向外扩张，植被重心总体向西北方向迁移。（4）Hurst指数表明，98.03%区域的植被改善趋势将持续，而零星斑块区域的工业","Frontiers in Environmental Science",82,{"impact":65,"substance":91,"depth":92,"authority":93,"freshness":94,"relevant":51,"comment":95},23,19,13,9,"基于kNDVI与XGBoost-SHAP的山西植被时空演变与驱动力研究，方法新颖、数据扎实，对黄土高原生态治理有参考价值。",[97],{"name":88,"url":85},[99,100,21,101,102],"机器学习","生态保护","植被覆盖","黄土高原",[104,105],"山西 kNDVI 植被覆盖","XGBoost-SHAP 植被驱动","山西kNDVI植被覆盖-2871","10.3389\u002Ffenvs.2026.1948848",{"doi":107,"openalex_id":109,"authors":110,"venue":88,"cited_by_count":15,"oa_url":124,"card":125,"direction":45,"ingested_from":47},"W7213469060",[111,113,115,117,119,122],{"name":112,"orcid":9},"Jie Chen",{"name":114,"orcid":9},"Yi Hou",{"name":116,"orcid":9},"Jianhua Xue",{"name":118,"orcid":9},"Jianhua Ni",{"name":120,"orcid":121},"Hao Liu","https:\u002F\u002Forcid.org\u002F0000-0001-8903-0983",{"name":123,"orcid":9},"Pengxiang Gao","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fenvironmental-science\u002Farticles\u002F10.3389\u002Ffenvs.2026.1948848\u002Fpdf",{"tldr":126,"method":127,"finding":128,"direction":45,"opportunity":129},"基于kNDVI与XGBoost-SHAP分析山西2000-2024年植被覆盖时空变化及驱动机制。","kNDVI数据结合Theil-Sen、Mann-Kendall、Hurst指数与","山西植被呈显著上升趋势，98.02%区域改善，驱动因子具非线性与时空差异。","可引入多源遥感与作物物候数据，将kNDVI驱动分析拓展至农田尺度精准管理与碳汇评估。","2026-09-18T23:30:29.212735Z",{"id":132,"title":133,"url":134,"summary":135,"summary_zh":136,"content":9,"source_name":137,"source_url":134,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":138,"score_detail":139,"sources":143,"tags":145,"search_phrases":148,"slug":151,"view_count":15,"doi":152,"paper":153,"created_at":177},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",76,{"impact":140,"substance":141,"depth":65,"authority":93,"freshness":13,"relevant":51,"comment":142},15,20,"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。",[144],{"name":137,"url":134},[74,99,21,146,147],"秸秆焚烧","卫星数据",[149,150],"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":152,"openalex_id":154,"authors":155,"venue":137,"cited_by_count":15,"oa_url":9,"card":171,"direction":176,"ingested_from":47},"W7213455429",[156,159,161,164,166,169],{"name":157,"orcid":158},"Mohit Dua","https:\u002F\u002Forcid.org\u002F0000-0001-7071-8323",{"name":160,"orcid":9},"Oshin Rastogi",{"name":162,"orcid":163},"Ashish Saini","https:\u002F\u002Forcid.org\u002F0000-0003-3061-2342",{"name":165,"orcid":9},"Raviya",{"name":167,"orcid":168},"Nidhi Chakravarty","https:\u002F\u002Forcid.org\u002F0000-0002-5454-1561",{"name":170,"orcid":9},"Shelza Dua",{"tldr":172,"method":173,"finding":174,"direction":45,"opportunity":175},"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":179,"title":180,"url":181,"summary":182,"summary_zh":183,"content":9,"source_name":184,"source_url":181,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":185,"score_detail":186,"sources":189,"tags":191,"search_phrases":194,"slug":197,"view_count":15,"doi":198,"paper":199,"created_at":228},2801,"A comprehensive survey on machine vision applications in precision agriculture: current trends and future perspectives","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs41060-026-01278-4","A comprehensive survey on machine vision applications in precision agriculture: current trends and future perspectives。International Journal of Data Science and Analytics","精准农业中机器视觉应用的综合综述：当前趋势与未来展望。《国际数据科学与分析杂志》","International Journal of Data Science and Analytics",77,{"impact":65,"substance":141,"depth":187,"authority":93,"freshness":94,"relevant":51,"comment":188},17,"核心期刊发表的机器视觉精准农业综述，方法梳理与趋势判断具参考价值，但属综述类论文，产业影响有限。",[190],{"name":184,"url":181},[72,74,192,21,193],"精准农业","机器视觉",[195,196],"农业人工智能 智慧农业 机器视觉 精准农业","农业人工智能 智慧农业","农业人工智能智慧农业机器视觉精准农业-2801","10.1007\u002Fs41060-026-01278-4",{"doi":198,"openalex_id":200,"authors":201,"venue":184,"cited_by_count":15,"oa_url":9,"card":223,"direction":176,"ingested_from":47},"W7213471057",[202,204,206,208,211,213,215,218,221],{"name":203,"orcid":9},"Shirun Gu",{"name":205,"orcid":9},"Xinyuan Fan",{"name":207,"orcid":9},"Lihui Zhu",{"name":209,"orcid":210},"Caixia Song","https:\u002F\u002Forcid.org\u002F0000-0003-3897-7629",{"name":212,"orcid":9},"Lei Mu",{"name":214,"orcid":9},"Zichen Zhang",{"name":216,"orcid":217},"Rui Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-8634-3519",{"name":219,"orcid":220},"Tong Xu","https:\u002F\u002Forcid.org\u002F0000-0001-5564-192X",{"name":222,"orcid":9},"Zhiyuan Zhang",{"tldr":224,"method":225,"finding":226,"direction":176,"opportunity":227},"综述机器视觉在精准农业中的应用现状与未来趋势。","文献综述，梳理机器视觉在精准农业中的技术路线。","机器视觉已广泛用于作物监测、病虫害识别等，但落地仍受数据与算力限制。","可聚焦轻量化模型与边缘部署，解决田间实时性与数据稀缺问题。","2026-09-17T23:30:54.103781Z",{"id":230,"title":231,"url":232,"summary":233,"summary_zh":234,"content":9,"source_name":235,"source_url":232,"published_at":236,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":237,"score_detail":238,"sources":242,"tags":244,"search_phrases":249,"slug":252,"view_count":15,"doi":253,"paper":254,"created_at":269},2800,"Spatial Persistence and Interannual Variability of Floating Algae in Kainji Lake (Nigeria): Insights from Multi-Year Sentinel-2 Observations","https:\u002F\u002Fdoi.org\u002F10.62622\u002Fteiee.026.4.3.55-64","Background: Floating algae are increasingly recognized as indicators of ecological change in freshwater ecosystems. Although satellite remote sensing has been widely applied to monitor algal dynamics, little is known about the long-term spatial organization of floating algae in large tropical reservoirs in West Africa. Objectives: This study investigated the spatio-temporal dynamics of floating algae in Kainji Lake, Nigeria, from 2020 to 2025, with the aim of identifying recurrent hotspot zones, assessing interannual variability, and evaluating the long-term stability of floating algae distribution. Methods: Sentinel-2 Level-2A Surface Reflectance imagery was processed within the Google Earth Engine platform. Images with less than 10% cloud cover were selected and subjected to cloud masking and water extraction procedures. Floating algae were mapped using the Floating Algae Index (FAI), calculated from the red, near-infrared, and short-wave infrared spectral bands. Annual median FAI composites were generated for each year from 2020 to 2025. Descriptive statistics of positive FAI values were calculated to assess temporal variability. Multi-year persistence analysis was then performed to identify recurrent floating algae hotspot zones. Results: The results revealed pronounced spatial heterogeneity in floating algae occurrence across Kainji Lake. Floating algae were consistently concentrated within shoreline and embayment environments, whereas most open-water areas exhibited comparatively low occurrence. Mean annual positive FAI values ranged from 0.0656 to 0.0841, indicating interannual variability in floating algae intensity. Annual bloom extent varied from 139.93 to 168.64 km², representing 12 –15% of the lake surface, with the largest coverage recorded in 2022 and the smallest in 2025. Despite these temporal fluctuations, recurrent hotspot zones remained spatially consistent throughout the study period. Conclusion: Floating algae in Kainji Lake exhibit persistent spatial organization rather than random distribution. This study provides the first multi-year evidence of recurrent floating algae hotspot zones in the reservoir, establishing an important baseline for understanding floating algae dynamics, thereby addressing a significant knowledge gap in West African reservoir ecosystems and providing a foundation for future ecological assessments and monitoring programmes.","背景：浮游藻类日益被视为淡水生态系统生态变化的指示物。尽管卫星遥感已被广泛应用于监测藻类动态，但关于西非大型热带水库中浮游藻类长期空间格局的认识仍然有限。目标：本研究调查了2020年至2025年尼日利亚凯恩吉湖浮游藻类的时空动态，旨在识别反复出现的热点区域、评估年际变异性，并评价浮游藻类分布的长期稳定性。方法：在Google Earth Engine平台上处理Sentinel-2 Level-2A地表反射率影像。选取云量低于10%的影像，并进行去云和水体提取处理。利用浮游藻类指数（Floating Algae Index, FAI）对浮游藻类进行制图，该指数由红光、近红外和短波红外光谱波段计算得出。生成2020年至2025年各年的FAI年中值合成影像。计算FAI正值的描述性统计量以评估时间变异性。随后进行多年持续性分析，以识别反复出现的浮游藻类热点区域。结果：结果揭示了凯恩吉湖浮游藻类 occurrence 的显著空间异质性。浮游藻类持续集中于湖岸和湖湾环境，而大部分开阔水域的出现相对较低。年均FAI正值范围为0.0656至0.0841，表明浮游藻类强度存在年际变异性。年度藻华范围在139.93至168.64 km²之间变化，占湖泊表面积的12%–15%，其中2022年记录到最大覆盖面积，2025年最小。尽管存在这些时间波动，反复出现的热点区域在整个研究期间保持空间一致性。结论：凯恩吉湖的浮游藻类表现出持续的空间组织格局，而非随机分布。本研究首次提供了该水库浮游藻类热点区域反复出现的多年证据，为理解浮游藻类动态建立了重要基线，从而填补了西非水库生态系统中的重大知识空白，并为未来的生态评估和监测计划奠定了基础。","Trends in Ecological and Indoor Environmental Engineering","2026-09-15T00:00:00Z",66,{"impact":239,"substance":141,"depth":187,"authority":240,"freshness":94,"relevant":51,"comment":241},8,12,"基于Sentinel-2多年观测揭示尼日利亚Kainji湖浮藻热点空间稳定性，方法规范、数据扎实，但属区域生态研究，与国内三农信息化关联间接，可作遥感应用参考而非每日必读。",[243],{"name":235,"url":232},[245,21,246,247,248],"非洲农业","水质监测","卫星遥感","湖泊生态",[250,251],"卫星遥感 水质监测 湖泊生态 遥感监测","卫星遥感 水质监测","卫星遥感水质监测湖泊生态遥感监测-2800","10.62622\u002Fteiee.026.4.3.55-64",{"doi":253,"openalex_id":255,"authors":256,"venue":235,"cited_by_count":15,"oa_url":263,"card":264,"direction":45,"ingested_from":47},"W7213293788",[257,260],{"name":258,"orcid":259},"Osasere Abike Omoruyi","https:\u002F\u002Forcid.org\u002F0000-0003-4505-9551",{"name":261,"orcid":262},"Ruby Asunomeh","https:\u002F\u002Forcid.org\u002F0009-0009-7529-254X","https:\u002F\u002Fwww.teiee.net\u002Fpdf-226150-145194?filename=Spatial-Persistence-and-I.pdf",{"tldr":265,"method":266,"finding":267,"direction":45,"opportunity":268},"利用2020-2025年Sentinel-2影像分析尼日利亚凯恩吉湖浮藻时空动态与热点区。","Google Earth Engine处理Sentinel-2 L2A影像，用F","浮藻集中于湖岸与湾口，年际面积波动但热点区位置持续稳定。","可结合水质、气象与人类活动数据，探究热带水库浮藻热点持续性的驱动机制与预警模型。","2026-09-17T23:30:38.937817Z",{"id":271,"title":272,"url":273,"summary":274,"summary_zh":275,"content":9,"source_name":276,"source_url":273,"published_at":277,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":278,"score_detail":279,"sources":281,"tags":283,"search_phrases":288,"slug":291,"view_count":15,"doi":292,"paper":293,"created_at":312},2799,"Multi-Temporal Assessment of Bimodal Monsoon Flood Dynamics and Agricultural Exposure Using Integrated Sentinel-1 SAR and Sentinel-2 Optical Data in Punjab, Pakistan","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fgeohazards7040114","Floods in monsoon-dominated river basins exhibit high spatio-temporal variability, necessitating high-resolution, multi-sensor approaches for reliable monitoring and impact assessment. In flood-prone agricultural regions, continuous monitoring using optical remote sensing is frequently hindered by dense monsoon cloud cover. This study establishes a comprehensive multi-sensor framework within the Google Earth Engine (GEE) to examine the spatio-temporal dynamics and land surface impacts of the 2025 monsoon floods in Punjab, Pakistan. Flood inundation mapping was executed using a 12-day Sentinel-1 Synthetic Aperture Radar (SAR) time series via a dual-threshold change detection methodology. Concurrently, Sentinel-2 imagery facilitated the derivation of land use\u002Fland cover (LULC) changes and vegetation dynamics using a Random Forest classifier, achieving overall accuracy of 93% (pre-flood), 91% (during flood), and 94% (post-flood). These accuracy levels were consistent across all three phases despite spectral confusion between water, saturated soil, and vegetation during peak inundation, indicating consistent classification performance under monsoon conditions. The analysis revealed a distinct bimodal flooding regime, characterized by an early monsoon peak in July–August and a more severe late monsoon peak in August-September. The cumulative maximum flood extent reached 9495.33 km2, with peak single-date inundation reaching 5449 km2. Mapped cropland declined by 6.7% (8181 km2) during peak flooding, with 3.9% (4796 km2) remaining non-cropland by the end of the observation period; 5892 km2 of pre-flood cropland was identified as inundated through spatial intersection. In addition, the Normalized Difference Vegetation Index (NDVI) declined by 28.6%, from 0.28 to 0.20, indicating a substantial reduction in vegetation greenness. Spatial consistency was checked with the United Nations Satellite Centre (UNOSAT) and the Food and Agriculture Organization (FAO), independently collected data showing moderate spatial agreement. The proposed framework is highly scalable for continuous flood monitoring, offering critical insights for disaster management and climate adaptation planning in monsoon regions plagued by data scarcity and persistent cloudiness. The approach is particularly relevant for near-real-time operational monitoring, given its reliance on freely available Sentinel data and cloud-based processing that requires no specialized ground infrastructure.","在季风主导的流域，洪水表现出高度的时空变异性，因此需要高分辨率、多传感器方法来进行可靠监测和影响评估。在易受洪水影响的农业区域，利用光学遥感进行连续监测常常受到季风期密集云层的阻碍。本研究在Google Earth Engine（GEE）中建立了一个综合多传感器框架，以考察2025年巴基斯坦旁遮普省季风洪水的时空动态及其对地表的影 响。利用12天Sentinel-1合成孔径雷达（SAR）时间序列，通过双阈值变化检测方法进行洪水淹没制图。同时，利用Sentinel-2影像，通过随机森林分类器提取土地利用\u002F土地覆盖（LULC）变化和植被动态，总体精度分别达到93%（洪水前）、91%（洪水期间）和94%（洪水后）。尽管在淹没峰值期水体、饱和土壤和植被之间存在光谱混淆，这三个阶段的精度水平仍保持一致，表明在季风条件下分类性能稳定。分析揭示出明显的双峰洪水情势，其特征是7—8月季风早期峰值和8—9月更为严重的季风晚期峰值。累计最大洪水范围达到9495.33 km²，单日最大淹没面积达到5449 km²。在洪水峰值期，制图耕地减少6.7%（8181 km²），到观测期末仍有3.9%（4796 km²）为非耕地；通过空间交集识别出5892 km²的洪水前耕地被淹没。此外，归一化植被指数（NDVI）下降28.6%，从0.28降至0.20，表明植被绿度显著降低。利用联合国卫星中心（UNOSAT）和联合国粮食及农业组织（FAO）独立收集的数据进行了空间一致性检验，结果显示具有中等空间一致性。所提出的框架对于连续洪水监测具有高度可扩展性，为受数据稀缺和持续多云困扰的季风地区的灾害管理和气候适应规划提供了关键见解。鉴于该方法依赖免费可用的Sentinel数据和无需专门地面基础设施的云端处理，它尤其适用于近实时业务化监测。","GeoHazards","2026-09-16T00:00:00Z",80,{"impact":65,"substance":64,"depth":65,"authority":93,"freshness":94,"relevant":51,"comment":280},"基于Sentinel-1\u002F2多源遥感与GEE平台的洪水-农业暴露评估，方法可迁移、数据详实，对农业灾害遥感监测有参考价值，但属区域性案例研究，非国内三农政策或产业级事件。",[282],{"name":276,"url":273},[284,21,285,286,287],"农业遥感","洪涝灾害","作物受灾评估","巴基斯坦",[289,290],"作物受灾评估 农业遥感 巴基斯坦 洪涝灾害","作物受灾评估 农业遥感","作物受灾评估农业遥感巴基斯坦洪涝灾害-2799","10.3390\u002Fgeohazards7040114",{"doi":292,"openalex_id":294,"authors":295,"venue":276,"cited_by_count":15,"oa_url":273,"card":307,"direction":45,"ingested_from":47},"W7213432591",[296,298,301,304],{"name":297,"orcid":9},"Nida Khursheed",{"name":299,"orcid":300},"Asif Sajjad","https:\u002F\u002Forcid.org\u002F0000-0003-1921-8213",{"name":302,"orcid":303},"Mazhar Iqbal","https:\u002F\u002Forcid.org\u002F0000-0001-5891-9798",{"name":305,"orcid":306},"Rana Waqar Aslam","https:\u002F\u002Forcid.org\u002F0000-0002-8711-8700",{"tldr":308,"method":309,"finding":310,"direction":45,"opportunity":311},"基于GEE融合Sentinel-1\u002F2监测2025年巴基斯坦旁遮普季风洪水双峰动态及农田暴露。","GEE平台、Sentinel-1 SAR双阈值变化检测、Sentinel-2随机","洪水呈7-8月与8-9月双峰，最大淹没9495平方公里，耕地减少6.7%，NDVI降28.6%。","可延伸至多云区近实时洪涝-作物损失耦合评估与灾后恢复监测模型构建。","2026-09-17T23:30:37.558541Z"]