[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3521":3,"related-3521":63},{"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,"search_phrases":32,"slug":35,"view_count":36,"doi":37,"paper":38,"created_at":62},3521,"A practical model and online tool to predict cover crop nitrogen content and carbon-to-nitrogen ratio via remote sensing","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10434-6","Abstract Purpose Estimations of cover crop (CC) nitrogen (N) content and carbon-to-nitrogen (C:N) ratio are crucial for guiding N fertilizer recommendations for subsequent cash crops, yet prediction tools that can be easily adopted by practitioners are lacking. This study aims to (1) develop models to predict CC N content and C:N ratio using Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge Index (NDRE) from Uncrewed Aerial Vehicles (UAV) and satellite imagery, combined with growing degree days (GDD, base 5 °C) and CC species; and (2) integrate the models developed into an online application. Methods Over 260 CC biomass samples were collected from 10 Pennsylvania counties in the Chesapeake Bay watershed representing diverse species (triticale, cereal rye, wheat, brassicas, legumes, and mixtures). Four models were evaluated: (1) UAV NDVI + GDD + species, (2) UAV NDRE + GDD + species, (3) satellite NDVI + GDD + species, and (4) satellite NDRE + GDD + species. Results Random forest models (R² > 0.74 for N content and R² > 0.86 for C: N ratio) generally explained more variability than generalized additive models (R² > 0.70 for N content and R² > 0.85 for C: N ratio) based on 10-fold cross validation. GDD and vegetation indices were the strongest predictors for N content, while GDD and species group influenced C: N ratio the most. In the independent site-years validation, UAV-NDRE explained the most variability in CC N content (R² = 0.82), and both UAV-based models (R² = 0.52) explained more variability than the satellite models for C: N ratio prediction. Conclusion Random forest models accurately predicted CC N content and C: N ratio and were integrated into an online application to support precision agriculture practices.","摘要 目的 估算覆盖作物（cover crop，CC）氮（N）含量及碳氮比（C:N）对于指导后续经济作物的氮肥推荐至关重要，但目前缺乏便于从业者采用的预测工具。本研究旨在（1）利用无人机（Uncrewed Aerial Vehicles，UAV）和卫星影像获取的归一化差异植被指数（Normalized Difference Vegetation Index，NDVI）和归一化差异红边指数（Normalized Difference Red Edge Index，NDRE），结合生长度日（growing degree days，GDD，基准温度5 °C）和覆盖作物物种，开发预测覆盖作物氮含量和碳氮比的模型；（2）将所开发模型整合至在线应用程序中。方法 在切萨皮克湾流域宾夕法尼亚州10个县采集了260余份覆盖作物生物量样品，涵盖多种物种（小黑麦、黑麦、小麦、芸薹属、豆科及混播）。评估了四种模型：（1）无人机NDVI + GDD + 物种，（2）无人机NDRE + GDD + 物种，（3）卫星NDVI + GDD + 物种，（4）卫星NDRE + GDD + 物种。结果 基于10折交叉验证，随机森林模型（氮含量R² > 0.74，碳氮比R² > 0.86）总体上比广义加性模型（氮含量R² > 0.70，碳氮比R² > 0.85）解释了更多的变异。GDD和植被指数是氮含量的最强预测因子，而GDD和物种组对碳氮比影响最大。在独立站点-年份验证中，无人机NDRE对覆盖作物氮含量变异的解释能力最强（R² = 0.82），且两个基于无人机的模型（R² = 0.52）在碳氮比预测方面均比卫星模型解释了更多的变异。结论 随机森林模型准确预测了覆盖作物氮含量和碳氮比，并已整合至在线应用程序中以支持精准农业实践。",null,"Precision Agriculture","2026-09-25T00:00:00Z","论文",10,false,79,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,22,18,14,9,1,"基于无人机与卫星遥感的覆盖作物氮素与碳氮比预测模型并落地在线工具，方法扎实、数据规模可观，对精准施肥有直接应用价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","精准施肥","覆盖作物","遥感监测","氮素管理",[33,34],"覆盖作物 氮含量 遥感 预测","UAV NDRE 碳氮比 在线工具","覆盖作物氮含量遥感预测-3521",0,"10.1007\u002Fs11119-026-10434-6",{"doi":37,"openalex_id":39,"authors":40,"venue":10,"cited_by_count":36,"oa_url":6,"card":55,"direction":59,"ingested_from":61},"W7214325666",[41,43,45,47,49,51,53],{"name":42,"orcid":9},"N. Kaur",{"name":44,"orcid":9},"C. White",{"name":46,"orcid":9},"A. Lefever",{"name":48,"orcid":9},"A. Thieme",{"name":50,"orcid":9},"J. Jennewein",{"name":52,"orcid":9},"WD. Hively",{"name":54,"orcid":9},"D. Carrijo",{"tldr":56,"method":57,"finding":58,"direction":59,"opportunity":60},"用无人机和卫星遥感结合积温与物种，构建覆盖作物氮含量及碳氮比预测模型并开发在线工具。","260个覆盖作物样本，随机森林与广义加性模型，UAV\u002F卫星NDVI、NDRE、G","随机森林预测精度高（N含量R²>0.74，C:N比R²>0.86），UAV-NDRE对N含量解释力最","农业遥感与作物表型","可探索多源遥感融合与实时在线工具在精准氮肥推荐中的田间验证和推广。","openalex","2026-09-26T23:30:02.797286Z",{"total":64,"page":22,"page_size":64,"items":65},6,[66,108,134,175,209,263],{"id":67,"title":68,"url":69,"summary":70,"summary_zh":71,"content":9,"source_name":72,"source_url":69,"published_at":73,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":74,"score_detail":75,"sources":79,"tags":81,"search_phrases":83,"slug":86,"view_count":36,"doi":87,"paper":88,"created_at":107},2006,"Precision fertilizer management for germplasm maintenance of queen’s flower (Lagerstroemia speciosa)","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffhort.2026.1933427","Introduction Nitrogen management in container-grown ornamentals must balance plant quality against the risk of nutrient loss to surrounding water systems, a challenge that is especially pronounced in subtropical nursery environments. This study evaluated precision nitrogen strategies for Lagerstroemia speciosa ‘Big Pink,’ an increasingly popular landscaping tree, by integrating leachate monitoring with plant-based optical sensing. Methods We evaluated the effects of six Azalea fertilizer (8-4–8 N-P-K) treatments: T0 (25 g), T1 (15 g + 10 g supplemental fertilizer, SF), T2 (20 g + 10 g SF), T3 (25 g + 10 g SF), T4 (30 g + 10 g SF), and T5 (35 g + 10 g SF), each with five replications, on container-grown Lagerstroemia speciosa ‘Big Pink’ under subtropical nursery conditions in southern Florida. Plant growth (leaf count), chlorophyll-based indices (SPAD, atLEAF), canopy reflectance (NDVI), and leachate chemical properties (Ca 2+ , NO 3 − , K + , Na + , pH, salinity, and EC) were assessed monthly and analyzed across early, mid-, and late growth stages using ANOVA, Tukey’s HSD test, and Pearson correlation analysis. Results The two highest fertilizer treatments, T4 (30 g base rate plus a 10 g supplemental application) and T5 (35 g base rate plus a 10 g supplemental application), consistently produced greater NO 3 − and K + concentrations in leachate across all growth stages, indicating elevated nutrient mobility and leaching risk. Treatment (T5) further lowered substrate pH and raised salinity and EC at the late stage, reflecting salt accumulation in the root zone. Leaf production was maximized under intermediate fertilizer rates (T2-T4), while both insufficient (T0-T1) and excessive (T5) fertilization reduced growth. Chlorophyll indices (SPAD, atLEAF) reliably detected nitrogen deficiency under T0 from the mid-stage onward, whereas NDVI became a responsive indicator only at the late stage as canopy development increased. Correlation analysis showed progressively stronger associations among leachate variables over time, alongside consistent agreement between SPAD and atLEAF across all stages. Conclusion Moderate fertilizer rates (T2-T3) optimized leaf growth and plant nitrogen status in container-grown L . speciosa ‘Big Pink’ while minimizing nutrient losses, supporting their adoption as a precision nitrogen management strategy for subtropical ornamental nursery production.","引言 容器栽培观赏植物的氮素管理需在植物品质与养分流失至周边水体的风险之间取得平衡，这一挑战在亚热带苗圃环境中尤为突出。本研究通过整合淋滤液监测与基于植物的光学传感技术，评估了针对紫薇‘大粉’（一种日益流行的景观树种）的精准氮素管理策略。方法 本研究在佛罗里达州南部的亚热带苗圃条件下，评估了六种杜鹃花肥料（8-4-8 N-P-K）处理对容器栽培紫薇‘大粉’的影响：T0（25克）、T1（15克+10克补充肥料，SF）、T2（20克+10克SF）、T3（25克+10克SF）、T4（30克+10克SF）和T5（35克+10克SF），每个处理设五次重复。每月测定植物生长量（叶片数）、基于叶绿素的指数（SPAD、atLEAF）、冠层反射率（NDVI）以及淋滤液化学性质（Ca²⁺、NO₃⁻、K⁺、Na⁺、pH、盐度和电导率EC），并在早期、中期和晚期生长阶段采用方差分析（ANOVA）、Tukey HSD检验和Pearson相关分析进行评估。结果 两个最高施肥处理T4（30克基肥加10克补充肥）和T5（35克基肥加10克补充肥）在所有生长阶段均持续产生较高的淋滤液NO₃⁻和K⁺浓度，表明养分迁移性和淋失风险升高。处理T5进一步降低了基质pH，并在晚期阶段提高了盐度和EC，反映出根区盐分积累。叶片产量在中等施肥量（T2-T4）下达到最大，而施肥不足（T0-T1）和过量（T5）均减少了生长量。叶绿素指数（SPAD、atLEAF）从中期阶段起即可可靠检测T0处理下的氮素缺乏，而NDVI仅在晚期阶段随着冠层发育增加才成为响应性指标。相关分析显示，淋滤液变量之间的关联随时间逐渐增强，同时SPAD和atLEAF在所有阶段均保持一致的一致性。结论 中等施肥量（T2-T3）在容器栽培紫薇‘大粉’中优化了叶片生长和植物氮素状况，同时最大限度地减少了养分损失，支持其作为亚热带观赏植物苗圃生产中精准氮素管理策略的推广应用。","Frontiers in Horticulture","2026-09-08T00:00:00Z",60,{"impact":76,"substance":19,"depth":17,"authority":76,"freshness":77,"relevant":22,"comment":78},12,2,"针对紫薇精准氮肥管理的研究，结合光学传感与淋洗监测，为亚热带苗圃生产提供减损增效方案，但影响范围限于细分领域。",[80],{"name":72,"url":69},[27,28,30,82,31],"园艺作物",[84,85],"园艺作物 智慧农业 氮素管理 精准施肥","园艺作物 智慧农业","园艺作物智慧农业氮素管理精准施肥-2006","10.3389\u002Ffhort.2026.1933427",{"doi":87,"openalex_id":89,"authors":90,"venue":72,"cited_by_count":36,"oa_url":99,"card":100,"direction":106,"ingested_from":61},"W7211954156",[91,93,95,97],{"name":92,"orcid":9},"Sofia Lopez Farina",{"name":94,"orcid":9},"Isha Poudel",{"name":96,"orcid":9},"Madhugiri Nageswara-Rao",{"name":98,"orcid":9},"Amir Ali Khoddamzadeh","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fhorticulture\u002Farticles\u002F10.3389\u002Ffhort.2026.1933427\u002Fpdf",{"tldr":101,"method":102,"finding":103,"direction":104,"opportunity":105},"通过淋洗监测与光学传感，优化紫薇容器苗氮肥管理，减少养分流失。","设置六种施肥处理，结合SPAD、NDVI及淋洗液化学分析，进行方差分析和相关分析","中等施肥量（T2-T3）最佳，兼顾生长与环保；过量施肥增加淋失和盐分积累。","智慧农业 \u002F 农业物联网","可探索基于实时传感的闭环精准施肥系统，结合机器学习预测作物需肥量，减少环境风险。","农业人工智能与决策模型","2026-09-09T23:30:37.456859Z",{"id":109,"title":110,"url":111,"summary":112,"summary_zh":9,"content":113,"source_name":114,"source_url":9,"published_at":11,"category":115,"cover_url":9,"hotness":13,"is_selected":14,"score":116,"score_detail":117,"sources":122,"tags":124,"search_phrases":129,"slug":132,"view_count":36,"doi":9,"paper":9,"created_at":133},3586,"黑龙江省农科院科技包联现场观摩会举办——'松粳22'优质食味水稻新品种示范田实测亩产557.5公斤","https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2026-09\u002F25\u002Fcontent_588143.html","9月22日，黑龙江省农科院科技包联哈尔滨市现场观摩会暨乔府大院第十一届开镰节开幕式上，试验田新品种展示区块18个水稻品种分区规整排布，其中9个是黑龙江省农科院自主选育品种。黑龙江省农科院生物技术研究所（五常水稻研究所）副所长、研究员闫平介绍，新品种'松粳22'150亩示范田按照14.5%标准含水率折算，平均亩产达到557.5公斤，在优质食味水稻中属于上乘水平，抗倒伏能力显著优于传统优质稻。秸秆基质育苗示范点位展示秸秆基质育苗技术，今年在哈尔滨18个点位示范。","![Image 1](https:\u002F\u002Fstatics.kjrb.com.cn\u002Fresource\u002Fkjw\u002Findex\u002Fimages\u002Fshare_logo.jpg)\n\n[![Image 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[滚动](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fnode_324.html)\n\n[![Image 3](https:\u002F\u002Fstatics.kjrb.com.cn\u002Fresource\u002Fkjw\u002Fimages\u002Ffdj.png)](https:\u002F\u002Fsearch.stdaily.com:8888\u002Ffounder\u002FNewSearchServlet.do?siteID=1)[登录](https:\u002F\u002Fwww.stdaily.com\u002Fapi\u002Fwebsite\u002FpersonalCenter.html?url=https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2026-09\u002F25\u002Fcontent_588143.html)[-](https:\u002F\u002Fwww.stdaily.com\u002Fapi\u002Fwebsite\u002FpersonalCenter.html)\n\n 所在位置： [中国科技网首页](https:\u002F\u002Fwww.stdaily.com\u002Findex.html)>[滚动](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fnode_324.html)> 正文 \n\n# 乡村行 看振兴丨科技赋能丰产 定制深耕良田——黑龙江省农科院科技包联现场观摩会举办\n\n 2026-09-25 12:39:56 来源: 科技日报 点击数：\n\n![Image 4](https:\u002F\u002Fstatics.kjrb.com.cn\u002Fresource\u002Fkjw\u002Fimg\u002F20240301dzv.png)0\n\n[](javascript:; \"新浪微博\")[](javascript:; \"微信\")\n\n**科技日报记者 朱虹**\n\n金色稻海漫过黑龙江五常的黑土地，稻浪深处，开镰号令响起。随着乔府大院董事长乔文志一声“开镰”，嘉宾们手持镰刀踏入金黄稻田，割下了今秋第一束稻穗。\n\n9月22日，黑龙江省农科院科技包联哈尔滨市现场观摩会暨乔府大院第十一届开镰节开幕式上，传统农耕与现代科技撞个满怀，一场丰收开镰，也是一次看得见摸得着的农业科技大考。\n\n在试验田新品种展示区块，18个水稻品种分区规整排布，其中9个是黑龙江省农科院自主选育品种。该院生物技术研究所（五常水稻研究所）副所长、研究员闫平站在田垄之间，伸手托起沉甸甸的“松粳22”稻穗，招呼围观嘉宾观察茎秆与籽粒状态。现场发布前，实收测产数据显示，150亩示范田按照14.5%标准含水率折算，松粳22平均亩产达到557.5公斤，在优质食味水稻中属于上乘水平。\n\n“传统稻花香大米口感出众，可茎秆偏软，遇上风雨天气极易大面积倒伏。”闫平指着成片挺立的稻株讲解道，“今天展示的新品种，抗倒伏能力显著优于传统优质稻。”\n\n一句“抗倒伏”，点破了好米难产的旧账。传统优质稻往往“娇贵”——品质好却秆软易倒、产量不稳，种高端米像押宝。新品种把“好吃”和“抗倒”拧在一起：抗倒伏，意味着风雨天不趴窝、机械收割进得去、规模种植稳得住，高端米供给才算有了“压舱石”。从“靠天吃饭”到“凭种稳产”，良种攻关为高端稻米产业蹚出一条新路。\n\n米食品鉴区，新米出锅，香气四溢，米粒莹润油亮。定制客户捧着一碗米饭感慨：“一碗好米的根基就在种子，科研选育的新品种，让老百姓既吃得饱，更吃得好。”\n\n观摩队伍在试验田里走走停停。秸秆基质育苗示范点位旁，黑龙江省农科院耕作栽培研究所作物栽培与装备研究室主任董文军被团团围住，他弯腰捧起一把棕褐色基质，讲起秸秆基质育苗的门道。\n\n“原料就是经多年沤熟的秸秆有机肥，配上三成秸秆打包回收时附带的表层土，育苗完全不用再挖黑土。”董文军介绍，杀菌剂、控旺剂都已预配好，农户拿到手直接铺苗床，取土、晒土、混拌这些累活全省了。“今年在哈尔滨18个点位示范，秧苗长势不比传统育苗土差，产量持平就可以得到推广，前期试验发现不同的配方基质比当地基质和黑土能增产5%到15%。”董文军说。秸秆从田间收走、发酵成基质，秧苗育成再回归水田。这条闭环，把曾经的“田间负担”变成“黑土养料”，围观的种植大户连连点头，当场追问推广条件。\n\n智慧农业贯穿作物生长每一环。黑龙江省农科院农业遥感与信息研究所副所长张有智介绍：“无人机图像结合人工智能模型算法，实现作物的长势情况和杂草分布反演识别，自动生成施肥处方图和施药处方图，直接推送到无人机飞手账号进行作业。作业精度能到厘米级。”今年试验，这套技术实现减肥6%、减药17%左右。\n\n黑龙江省农科院耕作栽培研究所所长李柱刚告诉科技日报记者，2023年黑龙江省农科院建立科技包联服务机制以来，哈尔滨已建成粮食单产提升示范田、科技服务田28个，示范推广面积25800亩。\n\n“为龙头企业提供高端定制服务，是科技包联的重头戏。”黑龙江省农科院副院长焦少杰站在田埂上说，“从品种定向选育、栽培技术集成，到植保方案、加工品鉴，全程‘一对一’定制，让高端米从‘卖原粮’升级为‘卖标准、卖品质’。”他介绍，黑龙江省农科院已深化科技包联13个市（地）服务机制，推动“百项技术、千个基地、万名人才”增粮示范落地。\n\n责任编辑：王倩\n\n相关稿件：\n\n![Image 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观摩会","黑龙江省农科院松粳22水稻-3586","2026-09-27T00:05:16.125922Z",{"id":135,"title":136,"url":137,"summary":138,"summary_zh":139,"content":9,"source_name":140,"source_url":137,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":141,"score_detail":142,"sources":144,"tags":146,"search_phrases":150,"slug":153,"view_count":36,"doi":154,"paper":155,"created_at":174},3550,"Asymmetric Vegetation Responses to Flood Exposure in the Chi River Basin: A Multi-Temporal Remote Sensing and Machine Learning Investigation","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsym18101606","Flooding and drought alternate in many tropical floodplains, but how vegetation responds to both hazards over multiple years remains poorly quantified. We examined vegetation–flood associations in the Chi River Basin, Northeast Thailand, drawing on Sentinel-2 imagery from 2020, 2023, and 2024, flood records from 2017, 2018, 2021, and 2022, and machine learning methods across 230,911 point-based spatial units. Nine forms of asymmetry emerged from the analysis. The most striking was recovery asymmetry: areas that flooded at least twice between 2017 and 2022 gained vegetation during 2020–2023 (mean NDVI change = +0.0270), whereas areas with little or no flood exposure lost vegetation (mean change = −0.0430). This gap was statistically significant (Cohen’s d = 0.5586, p \u003C 0.001) and suggests that repeated flooding may buffer vegetation against subsequent drought. Agricultural land declined less (−0.0316) than forest (−0.0937; ANOVA F = 1717.7, p \u003C 0.001). Baseline vegetation condition, measured as NDVI in 2020, contributed 32.9% to model importance, more than twice the contribution of elevation (14.5%). Spectral indices together accounted for 76.6% of importance, compared with 23.5% for topographic variables. The 2023 El Niño year produced the largest difference between high-flood and low-flood areas (+0.0364); because only one year per ENSO phase was available, we treat this as a case-based comparison rather than a general ENSO response. Threshold analysis identified two distinct values: an operational cut-off at NDVI = 0.05 (overall accuracy 82.67%) and an ecological transition around 0.25–0.30. Spatial clustering was weak but significant (Moran’s I = 0.2179, p \u003C 0.001). Spatial block cross-validation gave lower accuracy (0.597) than random cross-validation (0.627), pointing to spatial autocorrelation in the data. High-flood areas had 1.67 times the vulnerability index of low-flood areas (0.4306 vs. 0.2573). These patterns support differentiated management: elevation-based zoning, warning systems calibrated to local flood regimes, and focused interventions at hotspots.","在许多热带洪泛平原，洪水与干旱交替发生，但植被如何在多年间同时响应这两种灾害，仍缺乏充分的定量研究。我们以泰国东北部栖河（Chi River）流域为研究区，考察植被与洪水的关联，数据来源包括2020年、2023年和2024年的Sentinel-2影像，2017年、2018年、2021年和2022年的洪水记录，并采用机器学习方法，覆盖230 911个基于点的空间单元。分析中出现了九种不对称形式。其中最显著的是恢复不对称：2017—2022年间至少发生两次洪水的区域，在2020—2023年间植被增加（NDVI平均变化=+0.0270），而洪水暴露很少或没有洪水暴露的区域则植被减少（平均变化=−0.0430）。这一差异具有统计显著性（Cohen’s d=0.5586，p\u003C0.001），表明反复洪水可能缓冲植被对随后干旱的响应。农田的下降幅度（−0.0316）小于森林（−0.0937；ANOVA F=1717.7，p\u003C0.001）。以2020年NDVI衡量的基线植被状况对模型重要性的贡献为32.9%，是海拔贡献（14.5%）的两倍多。光谱指数合计占重要性的76.6%，而地形变量占23.5%。2023年厄尔尼诺年（El Niño year）高洪水区与低洪水区之间的差异最大（+0.0364）；由于每个ENSO相位仅有1年数据，我们将其视为基于个案的比较，而非一般性的ENSO响应。阈值分析识别出两个不同的值：操作性截断值为NDVI=0.05（总体精度82.67%），生态过渡值约为0.25~0.30。空间聚类较弱但显著（Moran’s I=0.2179，p\u003C0.001）。空间分块交叉验证的精度（0.597）低于随机交叉验证（0.627），表明数据中存在空间自相关。高洪水区的脆弱性指数是低洪水区的1.67倍（0.4306对0.2573）。这些格局支持差异化治理：基于海拔的分区、根据当地洪水情势校准的预警系统，以及在热点区域的重点干预。","Symmetry",70,{"impact":120,"substance":18,"depth":19,"authority":119,"freshness":21,"relevant":22,"comment":143},"基于Sentinel-2与机器学习的多时相洪涝-植被响应研究，数据规模大、结论具体，对农业遥感与灾害风险管理有参考价值，但属区域案例研究，公共影响有限。",[145],{"name":140,"url":137},[27,147,30,148,149],"农业人工智能","洪涝灾害","植被恢复",[151,152],"湄公河支流 流域 遥感 洪水","Sentinel-2 NDVI 洪涝 植被","湄公河支流流域遥感洪水-3550","10.3390\u002Fsym18101606",{"doi":154,"openalex_id":156,"authors":157,"venue":140,"cited_by_count":36,"oa_url":137,"card":169,"direction":59,"ingested_from":61},"W7214283372",[158,160,163,166],{"name":159,"orcid":9},"Jiradech Majandang",{"name":161,"orcid":162},"Patiwat Littidej","https:\u002F\u002Forcid.org\u002F0000-0002-1024-547X",{"name":164,"orcid":165},"Benjamabhorn Pumhirunroj","https:\u002F\u002Forcid.org\u002F0009-0009-6607-5594",{"name":167,"orcid":168},"D. C. Slack","https:\u002F\u002Forcid.org\u002F0000-0003-0324-2163",{"tldr":170,"method":171,"finding":172,"direction":59,"opportunity":173},"利用多时相遥感和机器学习揭示泰国湄公河支流流域植被对洪水的非对称响应。","Sentinel-2影像、洪水记录与机器学习，分析23万个空间单元。","重复洪水区植被增加，低洪水区减少，表明洪水可缓冲干旱影响。","可探索洪水-干旱交替下植被恢复机制，并开发基于阈值的差异化预警系统。","2026-09-26T23:30:29.514361Z",{"id":176,"title":177,"url":178,"summary":179,"summary_zh":180,"content":9,"source_name":181,"source_url":178,"published_at":182,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":183,"score_detail":184,"sources":187,"tags":189,"search_phrases":192,"slug":195,"view_count":36,"doi":196,"paper":197,"created_at":208},3549,"Artificial Intelligence-Based Innovations for Environmental Management in Uganda: A Scoping Review of Current Innovations","https:\u002F\u002Fdoi.org\u002F10.64643\u002Fjatir.141025","Uganda's extraordinary biodiversity, encompassing the Albertine Rift's endangered primate populations, East Africa's most significant freshwater wetland systems, and montane forest corridors of global conservation significance, is under compound and steadily accelerating environmental stress from widespread deforestation, wetland encroachment, agricultural expansion, climate variability, and persistently inadequate environmental governance capacity.Simultaneously, artificial intelligence technologies are undergoing rapid global deployment across environmental monitoring, predictive modelling, natural resource management, and climate adaptation domains, raising the question of whether these innovations are reaching contexts such as Uganda.This scoping review systematically maps the emerging landscape of artificial intelligence applications in environmental management in Uganda, examining which specific technologies have been deployed or piloted, across which environmental domains, by which actor types, and with what documented outcomes or limitations.A systematic search of peer-reviewed literature, grey literature, policy documents, and institutional reports published between 2020 and 2026 was conducted across five databases, ultimately yielding just 18 studies and reports meeting inclusion criteria after careful screening of 247 initial records identified.Findings reveal that artificial intelligence applications in Ugandan environmental management remain genuinely promising yet geographically and thematically concentrated, with remote sensing and machine learning applications for land cover change detection and forest monitoring constituting the single dominant application cluster identified.Applications in wetland management, water quality monitoring, wildlife protection, and climate adaptation do exist but remain isolated, under-resourced, and poorly integrated into government environmental governance frameworks.Critical cross-cutting barriers, including persistent data infrastructure deficits, digital skills shortages, electricity access constraints, and an underdeveloped AI policy ecosystem, systematically limit the effective translation of global AI innovations into practical, Uganda-specific environmental management solutions.The review concludes by proposing an AI-Environmental Governance Integration Framework","乌干达拥有非凡的生物多样性，涵盖艾伯丁裂谷（Albertine Rift）的濒危灵长类动物种群、东非最重要的淡水湿地系统以及具有全球保护意义的山地森林廊道，然而这些生态系统正面临来自大面积森林砍伐、湿地侵占、农业扩张、气候变率以及长期不足的环境治理能力的复合且持续加速的环境压力。与此同时，人工智能技术正在全球范围内迅速部署于环境监测、预测建模、自然资源管理和气候适应等领域，这引发了一个问题：这些创新是否正在惠及乌干达等地区。本范围综述（scoping review）系统梳理了乌干达环境管理中人工智能应用的新兴图景，考察了哪些具体技术已被部署或试点、涉及哪些环境领域、由哪些类型的行动者实施，以及有哪些已记录的结果或局限。研究对2020年至2026年间发表的同行评审文献、灰色文献、政策文件和机构报告在五个数据库中进行了系统检索，在仔细筛选初步识别的247条记录后，最终仅有18项研究和报告符合纳入标准。研究结果表明，人工智能在乌干达环境管理中的应用确实具有前景，但在地理和主题上高度集中，其中用于土地覆盖变化检测和森林监测的遥感与机器学习应用构成了唯一占主导地位的应用集群。湿地管理、水质监测、野生动物保护和气候适应方面的应用确实存在，但仍然孤立、资源不足，且未能有效纳入政府环境治理框架。关键的跨领域障碍，包括持续存在的数据基础设施赤字、数字技能短缺、电力获取限制以及欠发达的人工智能政策生态系统，系统性地限制了全球人工智能创新向乌干达本土化环境管理解决方案的有效转化。综述最后提出了一个人工智能-环境治理整合框架（AI-Environmental Governance Integration Framework）。","OpenAlex","2026-09-24T00:00:00Z",66,{"impact":120,"substance":118,"depth":185,"authority":76,"freshness":21,"relevant":22,"comment":186},17,"系统梳理乌干达AI环境管理应用的综述论文，方法规范、结论可靠，但属区域性研究且与三农信息化关联偏间接，公共影响有限。",[188],{"name":181,"url":178},[27,147,190,30,191],"环境治理","乌干达",[193,194],"乌干达 人工智能 环境管理","AI 遥感 森林监测","乌干达人工智能环境管理-3549","10.64643\u002Fjatir.141025",{"doi":196,"openalex_id":198,"authors":199,"venue":9,"cited_by_count":36,"oa_url":202,"card":203,"direction":104,"ingested_from":61},"W7214195868",[200],{"name":201,"orcid":9},"Henry Omara","https:\u002F\u002Fjatir.org\u002Fpublishedpapers\u002F141025_PAPER.pdf",{"tldr":204,"method":205,"finding":206,"direction":59,"opportunity":207},"综述乌干达环境管理中AI应用现状，发现应用集中于遥感与森林监测，整体零散且受基础设施制约。","系统检索2020-2026年五个数据库的文献与政策报告，筛选出18项研究做范围综","AI应用有前景但地理与主题集中，湿地、水质、野生动物等领域应用孤立，数据与政策短板明显。","可研究低成本遥感与机器学习在乌干达湿地、水质及小农农业环境监测中的落地路径与治理整合。","2026-09-26T23:30:15.512560Z",{"id":210,"title":211,"url":212,"summary":213,"summary_zh":214,"content":9,"source_name":10,"source_url":212,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":215,"score":216,"score_detail":217,"sources":220,"tags":222,"search_phrases":226,"slug":229,"view_count":36,"doi":230,"paper":231,"created_at":262},3525,"Multi-source data assimilation of Sentinel-2 reflectance and SMAP soil moisture into APSIM for maize biomass estimation","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10442-6","Crop growth models (CGMs) are valuable tools for agricultural monitoring. However, the need for many input parameters, the uncertainties related to model parametrization and structure, and the lack of spatial information motivate the application of techniques such as data assimilation (DA). This paper proposes a DA framework to improve maize biomass estimation. A particle filter (PF) was used to assimilate remotely sensed reflectance and soil moisture (SM) data, both independently and simultaneously, into the Agricultural Production Systems sIMulator (APSIM) model. Reflectance observations from Sentinel-2 were assimilated through coupling APSIM with the radiative transfer model (RTM) PROSAIL, while SMAP L-band SM products were directly assimilated into APSIM. The synthetic experiment, designed to evaluate the reliability of the proposed procedure, highlighted the strength of assimilating reflectance to constrain crop traits and of SM to provide complementary information on crop water status and to contribute to more robust ensemble trajectories. Real-case results confirmed these findings. DA assimilation of SM proved valuable particularly under data gaps and drought conditions. Although it did not consistently surpass single-source assimilation strategies, the joint assimilation yielded consistent results, especially in 2023, where RMSE, nRMSE and bias were 1258.83 kg\u002Fha, 16.62%, and - 220.52 kg\u002Fha, respectively. The proposed framework demonstrates the potential of multi-source DA to enhance biomass estimation and support robust, spatially explicit crop monitoring.","作物生长模型（CGM）是农业监测的重要工具。然而，其对大量输入参数的需求、模型参数化与结构相关的不确定性以及空间信息的缺乏，推动了数据同化（DA）等技术的应用。本文提出了一种数据同化框架，以改进玉米生物量估算。采用粒子滤波（PF）将遥感反射率和土壤水分（SM）数据分别独立及同时同化进入农业生产系统模拟器（APSIM）模型。Sentinel-2反射率观测数据通过将APSIM与辐射传输模型（RTM）PROSAIL耦合进行同化，而SMAP L波段土壤水分产品则直接同化进入APSIM。为评估所提方法可靠性而设计的合成实验，凸显了同化反射率在约束作物性状方面的优势，以及同化土壤水分在提供作物水分状况补充信息和促进更稳健集合轨迹方面的作用。实际案例结果证实了这些发现。土壤水分数据同化在数据缺失和干旱条件下尤为有价值。尽管联合同化未能持续超越单源同化策略，但其结果具有一致性，尤其在2023年，RMSE、nRMSE和偏差分别为1258.83 kg\u002Fha、16.62%和-220.52 kg\u002Fha。所提出的框架展示了多源数据同化在增强生物量估算和支持稳健、空间显式作物监测方面的潜力。",true,82,{"impact":19,"substance":18,"depth":218,"authority":20,"freshness":21,"relevant":22,"comment":219},19,"将Sentinel-2反射率与SMAP土壤水分同化进APSIM模型估算玉米生物量，方法新颖、结论可靠，对作物遥感监测有参考价值。",[221],{"name":10,"url":212},[27,223,224,30,225],"玉米","作物模型","数据同化",[227,228],"Sentinel-2 SMAP APSIM 玉米生物量","多源数据同化 玉米 遥感","Sentinel-2SMAPAPSIM玉米生物量-3525","10.1007\u002Fs11119-026-10442-6",{"doi":230,"openalex_id":232,"authors":233,"venue":10,"cited_by_count":36,"oa_url":212,"card":257,"direction":59,"ingested_from":61},"W7214405444",[234,236,239,242,245,247,249,252,254],{"name":235,"orcid":9},"Manuela Montella",{"name":237,"orcid":238},"Christian Bossung","https:\u002F\u002Forcid.org\u002F0000-0003-4651-2645",{"name":240,"orcid":241},"Thanh Huy Nguyen","https:\u002F\u002Forcid.org\u002F0000-0003-2471-350X",{"name":243,"orcid":244},"Marco Chini","https:\u002F\u002Forcid.org\u002F0000-0002-9094-0367",{"name":246,"orcid":9},"Jean François Iffly",{"name":248,"orcid":9},"Thomas Udelhoven",{"name":250,"orcid":251},"Julia Kubanek","https:\u002F\u002Forcid.org\u002F0000-0001-9597-2029",{"name":253,"orcid":9},"Zoltan Szantoi",{"name":255,"orcid":256},"Miriam Machwitz","https:\u002F\u002Forcid.org\u002F0000-0002-4999-673X",{"tldr":258,"method":259,"finding":260,"direction":59,"opportunity":261},"将Sentinel-2反射率与SMAP土壤水分同化进APSIM模型，提升玉米生物量估算精度。","粒子滤波同化Sentinel-2反射率（经PROSAIL耦合）与SMAP土壤水分","联合同化结果稳健，2023年RMSE为1258.83 kg\u002Fha，nRMSE 16.62%，偏差-2","可探索多源数据同化在数据缺失与干旱条件下的自适应权重策略，并推广至其他作物与区域。","2026-09-26T23:30:03.247019Z",{"id":264,"title":265,"url":266,"summary":267,"summary_zh":268,"content":9,"source_name":269,"source_url":266,"published_at":182,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":270,"score_detail":271,"sources":274,"tags":276,"search_phrases":279,"slug":282,"view_count":36,"doi":283,"paper":284,"created_at":300},3515,"Soil mapping and fertilizer optimization for precision agriculture using artificial intelligence","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs13198-026-03435-1","Soil mapping and fertilizer optimization for precision agriculture using artificial intelligence。International Journal of Systems Assurance Engineering and Management","基于人工智能的精准农业土壤制图与肥料优化。《国际系统保障工程与管理杂志》","International Journal of Systems Assurance Engineering and Management",62,{"impact":76,"substance":20,"depth":272,"authority":119,"freshness":120,"relevant":22,"comment":273},15,"论文探讨AI用于土壤制图与施肥优化，属智慧农业细分方向，但摘要信息有限、影响面偏窄，暂不建议进入每日精选。",[275],{"name":269,"url":266},[27,147,28,277,278],"遥感","土壤制图",[280,281],"土壤制图 人工智能 精准施肥","精准农业 肥料优化 AI","土壤制图人工智能精准施肥-3515","10.1007\u002Fs13198-026-03435-1",{"doi":283,"openalex_id":285,"authors":286,"venue":269,"cited_by_count":36,"oa_url":9,"card":295,"direction":106,"ingested_from":61},"W7214144821",[287,290,292],{"name":288,"orcid":289},"Neetu Mittal","https:\u002F\u002Forcid.org\u002F0000-0002-2012-0523",{"name":288,"orcid":291},"https:\u002F\u002Forcid.org\u002F0000-0001-6923-0013",{"name":293,"orcid":294},"Pradeepta Kumar Sarangi","https:\u002F\u002Forcid.org\u002F0000-0003-3827-6208",{"tldr":296,"method":297,"finding":298,"direction":106,"opportunity":299},"利用人工智能进行土壤制图和肥料优化，以支持精准农业。","人工智能方法，用于土壤制图与肥料优化。","AI可提升土壤制图与肥料优化的精准性，促进精准农业。","可探索多源数据融合与实时决策模型，提升肥料推荐的自适应性和可解释性。","2026-09-25T23:30:49.869002Z"]