[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3666":3,"related-3666":105},{"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":32,"secondary_topics":38,"classification":39,"search_phrases":80,"slug":83,"view_count":22,"doi":84,"paper":85,"created_at":104},3666,"Influence of Water Borehole Development on Rangeland Ecosystem Dynamics in Amballo Location, Obbu Ward, Moyale Sub-County, Marsabit County, Kenya","https:\u002F\u002Fdoi.org\u002F10.71274\u002Fijpp.v14i6.725","Sustainable management of arid and semi-arid rangelands is essential for maintaining ecosystem health and supporting pastoral livelihoods. This study examined the influence of water infrastructure development on rangeland ecosystem dynamics in the Amballo location of Obbu Ward, Moyale Sub-County, Marsabit County, Kenya. A mixed-methods approach integrating vegetation surveys, remote sensing and household interviews was employed. Vegetation data were collected from transects established near the borehole and a control site, while Landsat imagery from 2000, 2007, and 2020 was used to assess long-term vegetation cover changes. Data were analyzed using descriptive statistics, two-sample t-tests, and the Shannon–Wiener diversity index. The findings showed significant reductions in woody plant density ( p = 0.006) and herbaceous biomass (p = 0.013) near the borehole compared with the control site. Plant diversity was also lower near the water source (H′ = 2.20) than in the control area (H′ = 2.86), indicating increased dominance of disturbance-tolerant species. Remote sensing analysis revealed a 16.97% decline in open grassland and a 17.60% increase in bare land between 2000 and 2020, suggesting progressive rangeland degradation. Household responses identified livestock concentration, overgrazing, trampling, tree harvesting, drought, and climate variability as key drivers of vegetation change. The study concludes that while water infrastructure improves water access and support pastoral livelihoods, they also contribute to localized degradation by altering livestock distribution and increasing grazing pressure around water points. Improved grazing management, strategic water point planning and restoration of degraded areas, are recommended to promote sustainable pastoralism.","干旱和半干旱牧场的可持续管理对于维持生态系统健康和支持牧民生计至关重要。本研究考察了水利基础设施发展对肯尼亚马萨比特郡莫亚莱分区奥布瓦区安巴洛地区牧场生态系统动态的影响。研究采用混合方法，整合了植被调查、遥感影像和农户访谈。植被数据采集自钻孔附近设置的样带和对照样地，同时利用2000年、2007年和2020年的Landsat影像评估长期植被覆盖变化。数据分析采用描述性统计、双样本t检验和Shannon-Wiener多样性指数。研究结果表明，与对照样地相比，钻孔附近的木本植物密度（p = 0.006）和草本生物量（p = 0.013）显著降低。水源附近的植物多样性也低于对照区域（H′ = 2.20 vs H′ = 2.86），表明耐干扰物种的优势度增加。遥感分析显示，2000年至2020年间，开阔草地减少了16.97%，裸地增加了17.60%，表明牧场退化逐步加剧。农户访谈结果指出，牲畜集中、过度放牧、践踏、树木砍伐、干旱和气候变率是植被变化的主要驱动因素。研究结论认为，水利基础设施虽然改善了水资源可及性并支持了牧民生计，但也通过改变牲畜分布和增加水源点周围的放牧压力，导致了局部退化。建议改进放牧管理、优化水源点规划并恢复退化区域，以促进可持续的畜牧业发展。",null,"International Journal of Professional Practice","2026-09-25T00:00:00Z","论文",10,false,67,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,21,17,12,9,1,"以遥感与样方调查量化水井周边牧场退化，方法扎实、数据具体，但属肯尼亚区域案例，对国内三农信息化仅有方法借鉴价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"遥感监测","草原生态","水资源开发","荒漠化治理","牧民生计",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,53,61,68,75],{"code":33,"score":46,"supported":47,"title":48,"abstract":49,"tags":52},7,true,[],[50,51],"遥感","remote sensing",[27],{"code":54,"score":55,"supported":47,"title":56,"abstract":57,"tags":60},"C",6,[],[58,59],"畜牧","livestock",[],{"code":62,"score":55,"supported":47,"title":63,"abstract":64,"tags":67},"H7",[],[65,66],"干旱","drought",[],{"code":69,"score":70,"supported":47,"title":71,"abstract":72,"tags":74},"H3",3,[],[73],"水资源",[],{"code":76,"score":22,"supported":14,"title":77,"abstract":78,"tags":79},"H6",[],[],[28],[81,82],"肯尼亚 Marsabit 牧场 水井","Landsat 植被覆盖 牧场退化","肯尼亚Marsabit牧场水井-3666","10.71274\u002Fijpp.v14i6.725",{"doi":84,"openalex_id":86,"authors":87,"venue":10,"cited_by_count":94,"oa_url":95,"card":96,"direction":102,"ingested_from":103},"W7214388681",[88,90,92],{"name":89,"orcid":9},"Adan Jarso Adi",{"name":91,"orcid":9},"Mworia Mugambi",{"name":93,"orcid":9},"David Mushimiyimana",0,"https:\u002F\u002Fijpp.kemu.ac.ke\u002Findex.php\u002Fijpp\u002Farticle\u002Fdownload\u002F725\u002F290",{"tldr":97,"method":98,"finding":99,"direction":100,"opportunity":101},"研究肯尼亚半干旱牧区水井建设对牧场生态的影响。","植被样带调查、Landsat遥感与农户访谈的混合方法。","水井周边木本密度、草本生物量和多样性显著下降，牧场退化。","农业遥感与作物表型","可结合多时相遥感与放牧模型，量化水点布局对牧场退化的阈值效应。","智慧农业 \u002F 农业物联网","openalex","2026-09-28T23:30:13.016262Z",{"total":55,"page":22,"page_size":55,"items":106},[107,188,263,342,403,472],{"id":108,"title":109,"url":110,"summary":111,"summary_zh":112,"content":9,"source_name":113,"source_url":110,"published_at":114,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":115,"score_detail":116,"sources":120,"tags":122,"primary_topic":126,"secondary_topics":127,"classification":128,"search_phrases":152,"slug":155,"view_count":156,"doi":157,"paper":158,"created_at":187},2536,"Mapping Native Grass Cover with Random Forest Models: Sentinel-2 Versus Sentinel-2 Combined with Sentinel-1 SAR-Derived GLCM Texture Metrics","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183150","Temperate native grasslands in southeastern Australia have been extensively cleared for agriculture, and the remaining patches are under growing pressure from further land use change, climate variability, and invasive species. Mapping and monitoring their distribution and the cover of native and exotic grasses are critical for their conservation and management. Field-based methods are not always scalable or time-effective, and this study aimed to develop a scalable method to map and monitor the fractional cover-class maps of native C3 and native C4 grass cover as a component of remnant native grasslands on the western outskirts of Melbourne, Victoria, Australia. Field-based reference data for training and validation of random forest machine learning models were collected across multiple sites in 2021. Sentinel-2 optical spectral bands and vegetation indices were used as the primary input data, and Sentinel-1 Synthetic Aperture Radar (SAR)-derived Grey Level Co-occurrence Matrix (GLCM) texture metrics were assessed for their capacity to improve the model. Results show that random forest models trained on Sentinel-2 data without GLCM texture information derived from Sentinel-1 SAR data provided a moderate overall accuracy (C3: 59.1%, C4: 78.1%). Class-specific metrics showed that reliability was highest for better represented lower-cover classes, particularly the 6–25% native C3 class and the 0–5% native C4 class, while higher-cover classes were less reliable because of the limited number of training and validation samples. Grass cover fractions were modelled well for sparse to moderate grass cover, but dense grass cover was not modelled accurately, probably due to limited high-cover samples in the training dataset. Model performance was not improved by the inclusion of Sentinel-1 SAR-derived GLCM texture metrics, indicating that C-band VH-polarised SAR is not sensitive to the fine-scale structural heterogeneity that characterises native grassland ecosystems. Sparse native C3 and C4 grasses could be mapped most reliably in the lower-cover classes as a component of grasslands with optical remote sensing, and the method developed here can now be applied to enable evidence-based management of grasslands, biodiversity conservation and the monitoring of grassland composition in the WGR and elsewhere. Higher-resolution structural datasets and more sophisticated machine learning approaches may be required to accurately predict native C3 and C4 grass cover fractions in denser grasslands.","澳大利亚东南部的温带原生草原已被大面积开垦用于农业，残余斑块正面临土地利用进一步变化、气候变率和入侵物种日益增大的压力。对其分布以及原生和外来草类覆盖度进行制图和监测，对于草原的保护和管理至关重要。基于实地调查的方法并非总是可扩展或省时的，本研究旨在开发一种可扩展的方法，以制图和监测作为残余原生草原组成部分的原生C3和原生C4草类覆盖度的分数覆盖等级图，研究区位于澳大利亚维多利亚州墨尔本西郊。用于训练和验证随机森林机器学习模型的实地参考数据于2021年在多个样点采集。研究以Sentinel-2光学光谱波段和植被指数作为主要输入数据，并评估了Sentinel-1合成孔径雷达（SAR）衍生的灰度共生矩阵（GLCM）纹理指标对提升模型性能的能力。结果表明，仅使用Sentinel-2数据（不含Sentinel-1 SAR衍生的GLCM纹理信息）训练的随机森林模型提供了中等的总体精度（C3：59.1%，C4：78.1%）。分类别指标显示，对于代表性较好的低覆盖度类别，可靠性最高，尤其是6–25%原生C3类别和0–5%原生C4类别，而较高覆盖度类别的可靠性较低，原因是训练和验证样本数量有限。草类覆盖度分数在稀疏至中等草类覆盖条件下建模效果良好，但茂密草类覆盖未能准确建模，可能是由于训练数据集中高覆盖度样本有限。纳入Sentinel-1 SAR衍生的GLCM纹理指标并未改善模型性能，表明C波段VH极化SAR对原生草原生态系统所特有的精细尺度结构异质性不敏感。作为草原的组成部分，稀疏的原生C3和C4草类在低覆盖度类别中利用光学遥感可最可靠地制图，本研究开发的方法现可应用于西维多利亚草原（WGR）及其他地区，以实现基于证据的草原管理、生物多样性保护和草原组成监测。准确预测原生C3和C4草类覆盖度分数可能需要更高分辨率的结构数据集和更先进的机器学习方法。","Remote Sensing","2026-09-13T00:00:00Z",71,{"impact":20,"substance":117,"depth":19,"authority":118,"freshness":17,"relevant":22,"comment":119},20,14,"方法新颖、结论明确（SAR纹理未提升精度），对草地遥感监测有参考价值，但属细分领域研究，影响范围有限。",[121],{"name":113,"url":110},[123,124,27,28,125],"智慧农业","机器学习","植被覆盖",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":129},{"candidates":130,"agricultural_evidence":47},[131,135,141,147],{"code":33,"score":17,"supported":47,"title":132,"abstract":133,"tags":134},[],[50,51],[125,27],{"code":76,"score":46,"supported":47,"title":136,"abstract":137,"tags":140},[],[138,139],"生物多样性","biodiversity",[28],{"code":142,"score":46,"supported":47,"title":143,"abstract":144,"tags":146},"I2",[],[124,145],"machine learning",[124],{"code":148,"score":22,"supported":14,"title":149,"abstract":150,"tags":151},"I1",[],[],[123],[153,154],"智慧农业 机器学习 植被覆盖 草原生态","智慧农业 机器学习","智慧农业机器学习植被覆盖草原生态-2536",2,"10.3390\u002Frs18183150",{"doi":157,"openalex_id":159,"authors":160,"venue":113,"cited_by_count":94,"oa_url":110,"card":182,"direction":100,"ingested_from":103},"W7212561645",[161,164,167,170,172,174,177,179],{"name":162,"orcid":163},"Sabah Sabaghy","https:\u002F\u002Forcid.org\u002F0000-0002-9453-8922",{"name":165,"orcid":166},"M. Abuzar","https:\u002F\u002Forcid.org\u002F0000-0002-6101-1307",{"name":168,"orcid":169},"Steve J. Sinclair","https:\u002F\u002Forcid.org\u002F0000-0002-4282-1021",{"name":171,"orcid":9},"Tony Dugdale",{"name":173,"orcid":9},"Vanessa Hutchins",{"name":175,"orcid":176},"Yogendra K. Karna","https:\u002F\u002Forcid.org\u002F0000-0002-2120-4710",{"name":178,"orcid":9},"Jonathan Wilson",{"name":180,"orcid":181},"Kathryn Sheffield","https:\u002F\u002Forcid.org\u002F0000-0003-2624-9739",{"tldr":183,"method":184,"finding":185,"direction":100,"opportunity":186},"用随机森林结合Sentinel-2与Sentinel-1纹理特征，绘制澳洲原生草地C3\u002FC4草覆盖度","2021年野外样点训练随机森林，Sentinel-2光谱与植被指数为主，Sent","仅用Sentinel-2精度中等（C3 59.1%、C4 78.1%），加入SAR纹理未提升，高覆盖","高覆盖度草地样本不足且C波段SAR不敏感，可探索高分辨率结构数据与深度模型提升密草覆盖反演。","2026-09-15T23:30:21.287053Z",{"id":189,"title":190,"url":191,"summary":192,"summary_zh":193,"content":9,"source_name":194,"source_url":191,"published_at":195,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":94,"score_detail":196,"sources":198,"tags":200,"primary_topic":204,"secondary_topics":205,"classification":206,"search_phrases":229,"slug":232,"view_count":94,"doi":233,"paper":234,"created_at":262},5390,"Land use and socio-economic changes caused by riverbank erosion - case study: Obrenovac municipality, Serbia","https:\u002F\u002Fdoi.org\u002F10.59954\u002Fstnv.797","Lateral bank erosion of meandering rivers represents a significant geomorphological process in the lower part of the Kolubara River Basin in Serbia. This process directly impacts land-use and agricultural productivity and lead to broader environmental, socioeconomic, and demographic changes. Historically, the Kolubara riverbanks have been intensively used for agriculture. Today, lateral channel migration and riverbank erosion, driven by the interaction between natural process and human activities, directly threaten traditionally active land plots along the river. This study investigates the long-term dynamics of riverbank erosion along the four active meanders of the Kolubara River in Obrenovac Municipality over a 97-year period (1930–2026). Main tasks include quantification of lateral channel migration and land loss rates, analysis of land-use changes within the endangered plots, and an investigation of socioeconomic consequences and demographic dynamics in affected settlements. The methodological framework integrates cadastral maps analysis, remote sensing techniques (UAVs), and GIS software, with analysis of demographic and land-use data. The results indicate that maximum lateral channel migration reached 259.3 m during the observed period, while the total land loss amounted to 17.6 ha, consisting mostly of arable land. This reduction has directly affected the local agricultural sector, resulting in an estimated land loss value of approximately 24,122 USD. Demographic analysis reveals population decline exceeding 40% in the analysed settlements (Draževac, Konatice and Poljane) between 1961 and 2022, from over 90% of the economically active population in 1961 to less than 2% in 2022. Ultimately, by highlighting the socioeconomic impacts of riverbank erosion such as irreversible agricultural land loss and rural livelihood disruption, these findings offer valuable insights for spatial planning and sustainable land management.","蜿蜒河流的侧向河岸侵蚀是塞尔维亚科卢巴拉河（Kolubara River）流域下游一个重要的地貌过程。该过程直接影响土地利用和农业生产力，并引发更广泛的环境、社会经济和人口变化。历史上，科卢巴拉河岸曾被密集用于农业生产。如今，在自然过程与人类活动相互作用驱动下的侧向河道迁移和河岸侵蚀，直接威胁着沿河传统上活跃的地块。本研究考察了奥布雷诺瓦茨市（Obrenovac Municipality）科卢巴拉河四个活跃曲流段在97年期间（1930—2026年）河岸侵蚀的长期动态。主要任务包括：量化侧向河道迁移和土地流失速率，分析受威胁地块内的土地利用变化，以及调查受影响居民点的社会经济后果和人口动态。方法框架整合了地籍图分析、遥感技术（无人机，UAVs）和GIS软件，并结合人口与土地利用数据分析。结果表明，在观测期内侧向河道迁移最大距离达259.3 m，土地流失总量为17.6 ha，其中大部分为耕地。这一减少直接影响了当地农业部门，估计土地流失价值约为24,122美元。人口分析显示，1961年至2022年间，所分析居民点（Draževac、Konatice和Poljane）的人口下降幅度超过40%，经济活动人口比例从1961年的超过90%降至2022年的不足2%。最终，通过突出河岸侵蚀的社会经济影响，如不可逆的农业土地流失和农村生计破坏，这些发现为空间规划和可持续土地管理提供了有价值的见解。","Stanovnistvo","2026-10-19T00:00:00Z",{"impact":94,"substance":94,"depth":94,"authority":94,"freshness":94,"relevant":94,"comment":197},"塞尔维亚河岸侵蚀案例研究，属地理与土地利用领域，与三农及农业信息化主题关联弱，不建议进入每日精选。",[199],{"name":194,"url":191},[27,201,202,203],"土地利用","土壤侵蚀","乡村人口",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":207},{"candidates":208,"agricultural_evidence":47},[209,213,219,225],{"code":33,"score":46,"supported":47,"title":210,"abstract":211,"tags":212},[],[50,51],[27],{"code":214,"score":70,"supported":47,"title":215,"abstract":216,"tags":218},"I4",[],[217],"GIS",[],{"code":220,"score":70,"supported":47,"title":221,"abstract":222,"tags":224},"I7",[],[223],"methodological framework",[],{"code":76,"score":22,"supported":14,"title":226,"abstract":227,"tags":228},[],[],[201],[230,231],"Kolubara 河岸侵蚀","Obrenovac 土地利用","Kolubara河岸侵蚀-5390","10.59954\u002Fstnv.797",{"doi":233,"openalex_id":235,"authors":236,"venue":194,"cited_by_count":94,"oa_url":191,"card":256,"direction":100,"ingested_from":103},"W7222945959",[237,240,243,245,248,251,254],{"name":238,"orcid":239},"Slavoljub Dragićević","https:\u002F\u002Forcid.org\u002F0000-0002-1417-5405",{"name":241,"orcid":242},"Marko Langović","https:\u002F\u002Forcid.org\u002F0000-0002-4614-7265",{"name":244,"orcid":9},"Милан Радовић",{"name":246,"orcid":247},"Radislav Tošić","https:\u002F\u002Forcid.org\u002F0000-0002-3994-125X",{"name":249,"orcid":250},"Milica Langović","https:\u002F\u002Forcid.org\u002F0000-0002-6979-377X",{"name":252,"orcid":253},"Novica Lovrić","https:\u002F\u002Forcid.org\u002F0009-0006-0838-4370",{"name":255,"orcid":9},"Manojlović Stefan",{"tldr":257,"method":258,"finding":259,"direction":260,"opportunity":261},"研究塞尔维亚Kolubara河岸侵蚀97年间的河道迁移、耕地损失及社会经济影响。","结合地籍图、UAV遥感、GIS与人口土地利用数据分析四条曲流。","最大侧向迁移259.3米，损失17.6公顷耕地约2.4万美元，村落人口降超40%。","其他","可将河岸侵蚀遥感监测与农户生计、土地价值评估耦合，构建侵蚀风险预警与补偿决策模型。","2026-10-20T23:30:18.982943Z",{"id":264,"title":265,"url":266,"summary":267,"summary_zh":268,"content":9,"source_name":269,"source_url":266,"published_at":270,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":271,"score_detail":272,"sources":278,"tags":280,"primary_topic":284,"secondary_topics":290,"classification":292,"search_phrases":313,"slug":316,"view_count":94,"doi":317,"paper":318,"created_at":341},5367,"Integration of multi-source UAV remote sensing features and derived features for winter wheat aboveground biomass estimation","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102656","Accurate estimation of winter wheat ( Triticum aestivum L.) aboveground biomass (AGB) is important for crop growth monitoring and field management. However, unmanned aerial vehicle (UAV)-based AGB estimation remains challenging due to variations in canopy conditions across growth stages, and different remote sensing features can capture complementary characteristics related to AGB estimation. This study integrated multi-source UAV-derived features, including vegetation indices (VIs), texture features (TFs), light detection and ranging (LiDAR)-derived features, and derived features (DFs) generated from the original feature sets, with four machine learning algorithms for winter wheat AGB estimation. Results showed that TFs provided better prediction performance than other single-source feature sets. Compared with single-source feature sets, the integration of VIs, TFs, LiDAR-derived features, and DFs further improved AGB estimation, and the random forest (RF) model achieved the best performance among the evaluated models (R² = 0.923, RMSE = 1.731 Mg ha⁻¹). Incremental R² analysis based on a stage-only baseline model showed that the RF model explained additional variation in AGB beyond growth-stage information, with an incremental R² of 0.479. Stage-specific evaluation indicated that model performance varied among growth stages, with the highest accuracy observed at the booting stage (R² = 0.632, RMSE = 1.425 Mg ha⁻¹). Feature contribution analysis based on the full-feature fusion model using all growth-stage data identified canopy coverage and several DFs as important predictors, while stage-specific analyses showed differences in dominant predictors across growth stages. The integration of multi-source UAV-derived features and DFs with machine learning provides an effective approach for winter wheat AGB estimation, while model performance remains strongly dependent on growth stage.","准确估算冬小麦（Triticum aestivum L.）地上生物量（aboveground biomass, AGB）对作物生长监测和田间管理具有重要意义。然而，由于不同生育期冠层条件存在差异，且不同遥感特征能够捕捉与AGB估算相关的互补信息，基于无人机（unmanned aerial vehicle, UAV）的AGB估算仍面临挑战。本研究将多源无人机衍生特征，包括植被指数（vegetation indices, VIs）、纹理特征（texture features, TFs）、激光雷达（light detection and ranging, LiDAR）衍生特征以及由原始特征集生成的衍生特征（derived features, DFs），与四种机器学习算法相结合，用于冬小麦AGB估算。结果表明，TFs的预测性能优于其他单一来源特征集。与单一来源特征集相比，融合VIs、TFs、LiDAR衍生特征和DFs进一步提高了AGB估算精度，其中随机森林（random forest, RF）模型在所评估的模型中表现最佳（R² = 0.923，RMSE = 1.731 Mg ha⁻¹）。基于仅含生育期信息的基线模型进行的增量R²分析表明，RF模型在生育期信息之外解释了AGB的额外变异，增量R²为0.479。分生育期评估表明，模型性能在不同生育期之间存在差异，其中在孕穗期观察到最高精度（R² = 0.632，RMSE = 1.425 Mg ha⁻¹）。基于使用全部生育期数据的全特征融合模型进行的特征贡献分析识别出冠层覆盖度和若干DFs为重要预测因子，而分生育期分析则表明不同生育期的主导预测因子存在差异。将多源无人机衍生特征和DFs与机器学习相结合，为冬小麦AGB估算提供了一种有效方法，但模型性能仍强烈依赖于生育期。","Smart Agricultural Technology","2026-10-17T00:00:00Z",77,{"impact":273,"substance":274,"depth":275,"authority":276,"freshness":17,"relevant":22,"comment":277},16,22,18,13,"该研究融合无人机多源遥感特征与衍生特征，用随机森林估算冬小麦地上生物量，方法新颖、结论可靠，对作物长势监测有参考价值。",[279],{"name":269,"url":266},[123,281,27,282,283],"无人机","冬小麦","生物量估算",{"code":285,"slug":286,"name":287,"path":288,"parent_code":289},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[291],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":293},{"candidates":294,"agricultural_evidence":47},[295,301,305,309],{"code":285,"score":296,"supported":47,"title":297,"abstract":299,"tags":300},26,[298,286],"winter wheat",[287,298,286],[282],{"code":33,"score":273,"supported":47,"title":302,"abstract":303,"tags":304},[51],[50,51],[283,27],{"code":142,"score":55,"supported":47,"title":306,"abstract":307,"tags":308},[],[124,145],[],{"code":148,"score":22,"supported":14,"title":310,"abstract":311,"tags":312},[],[],[123],[314,315],"无人机 多源遥感 冬小麦 生物量","冬小麦 地上生物量 随机森林","无人机多源遥感冬小麦生物量-5367","10.1016\u002Fj.atech.2026.102656",{"doi":317,"openalex_id":319,"authors":320,"venue":269,"cited_by_count":94,"oa_url":266,"card":336,"direction":100,"ingested_from":103},"W7222806066",[321,323,325,327,330,332,334],{"name":322,"orcid":9},"Guangtao Wang",{"name":324,"orcid":9},"Sumei Yao",{"name":326,"orcid":9},"Yanzheng Xie",{"name":328,"orcid":329},"Anzhen Qin","https:\u002F\u002Forcid.org\u002F0000-0002-2550-0015",{"name":331,"orcid":9},"Xiangdong Chen",{"name":333,"orcid":9},"Yuhao Zhang",{"name":335,"orcid":9},"Lina Jiang",{"tldr":337,"method":338,"finding":339,"direction":100,"opportunity":340},"融合无人机多源遥感特征与衍生特征，用机器学习估算冬小麦地上生物量。","无人机植被指数、纹理、LiDAR特征及衍生特征结合四种机器学习算法建模。","多源特征融合优于单一特征，随机森林最优（R²=0.923），精度随生育期变化。","可探索生育期自适应建模与特征选择，提升跨期生物量估算的稳健性与可迁移性。","2026-10-20T23:30:03.106267Z",{"id":343,"title":344,"url":345,"summary":346,"summary_zh":347,"content":9,"source_name":348,"source_url":345,"published_at":270,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":94,"score_detail":349,"sources":351,"tags":353,"primary_topic":357,"secondary_topics":358,"classification":368,"search_phrases":385,"slug":388,"view_count":94,"doi":389,"paper":390,"created_at":402},5326,"Flood Hazard and Vulnerability Assessment of Chitrakoot District, Uttar Pradesh: An Integrated Geographical, GIS and Remote Sensing Approach","https:\u002F\u002Fdoi.org\u002F10.36948\u002Fijfmr.2026.v08i05.89708","Flood is one of the most important hydro-meteorological hazards affecting human settlements, agriculture, infrastructure and regional economies. Chitrakoot district of Uttar Pradesh presents a distinctive case of hydro-climatic vulnerability because it belongs to the drought-prone Bundelkhand region but simultaneously experiences recurrent riverine and flash-flood conditions during intense monsoon rainfall. The present study assesses the spatial characteristics and determinants of flood hazard and vulnerability in Chitrakoot district using an integrated geographical, GIS and remote sensing approach. The analysis incorporates the Digital Elevation Model (DEM), drainage network, physiography, river proximity, historical flood information, satellite-based flood observations and socio-economic exposure. The district's DEM indicates a marked elevation gradient, approximately from 66 m to 451 m, with relatively higher terrain in the southern part and lower terrain towards the northern Yamuna valley. This topographic configuration facilitates rapid surface runoff from elevated southern terrain towards comparatively low-lying riverine areas. The drainage system is dominated by the Yamuna and Mandakini\u002FPaisuni systems along with Baghain, Gunta, Bardaha and smaller tributaries. The Uttar Pradesh Flood Hazard Atlas identifies 5,114 ha of flood-hazard area in Chitrakoot for 1998–2020, of which 4,129 ha (81%) falls in the very-low hazard category and 985 ha (19%) in the low-hazard category. However, the historical classification does not fully represent the potential consequences of extreme rainfall events. The August 2026 flood provides compelling evidence: the Mandakini at Chitrakoot reached a reported new high flood level of 145.60 m on 29 August 2026, approximately 2.95 m above the previous reported high level, while the water level rose by about 7.5 m within six hours. The study concludes that flood risk in Chitrakoot is spatially concentrated around river corridors, low-elevation areas, floodplains, agricultural land, settlements and tourism infrastructure. A shift from conventional relief-oriented flood management towards integrated floodplain regulation, watershed management, GIS-based risk zonation, real-time monitoring and community-based preparedness is therefore essential.","洪水是影响人类住区、农业、基础设施和区域经济的最重要的水文气象灾害之一。北方邦吉德古德（Chitrakoot）县在水文气候脆弱性方面呈现出独特案例，因为它属于易受干旱影响的本德尔坎德（Bundelkhand）地区，但同时在强季风降雨期间反复遭遇河流洪水和山洪状况。本研究采用地理学、地理信息系统（GIS）和遥感相结合的综合方法，评估吉德古德县洪水灾害与脆弱性的空间特征及其决定因素。分析纳入了数字高程模型（DEM）、河网、地貌、距河流远近、历史洪水信息、基于卫星的洪水观测以及社会经济暴露度。该县的DEM显示出明显的高程梯度，大约从66米到451米，南部地形相对较高，向北部的亚穆纳（Yamuna）河谷地势降低。这种地形格局促使地表径流从较高的南部地形快速流向相对低洼的河流区域。排水系统以亚穆纳河和曼达基尼\u002F派苏尼（Mandakini\u002FPaisuni）水系为主，另有巴盖恩（Baghain）、贡塔（Gunta）、巴尔达哈（Bardaha）及较小支流。北方邦洪水灾害图集确认，1998—2020年吉德古德县洪水灾害面积为5，114公顷，其中4，129公顷（81%）属于极低灾害类别，985公顷（19%）属于低灾害类别。然而，历史分类并不能充分反映极端降雨事件的潜在后果。2026年8月的洪水提供了有力证据：据报道，2026年8月29日吉德古德的曼达基尼河达到145.60米的新高洪水位，比此前报告的高水位高出约2.95米，而水位在六小时内上升了约7.5米。研究得出结论，吉德古德的洪水风险在空间上集中于河流廊道、低海拔地区、洪泛平原、农业用地、住区和旅游基础设施周围。因此，必须从传统的以救济为导向的洪水管理转向洪泛平原综合调控、流域管理、基于GIS的风险区划、实时监测和社区为基础的备灾。","International Journal For Multidisciplinary Research",{"impact":94,"substance":94,"depth":94,"authority":94,"freshness":94,"relevant":94,"comment":350},"该研究聚焦印度北方邦的洪水灾害评估，虽涉及GIS与遥感方法，但与三农、农业信息化、智慧农业等主题无直接关联，不建议进入每日精选。",[352],{"name":348,"url":345},[354,27,355,356],"防灾减灾","洪水风险评估","GIS应用",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[359,363],{"code":214,"slug":360,"name":361,"path":362,"parent_code":37},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"code":62,"slug":364,"name":365,"path":366,"parent_code":367},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":40,"locked":14,"version":41,"method":42,"evidence":369},{"candidates":370,"agricultural_evidence":47},[371,376,381],{"code":33,"score":372,"supported":47,"title":373,"abstract":374,"tags":375},15,[51],[50,51],[27],{"code":214,"score":118,"supported":47,"title":377,"abstract":378,"tags":380},[217],[379,217],"地理信息",[],{"code":62,"score":17,"supported":47,"title":382,"abstract":383,"tags":384},[],[65,66],[355,354],[386,387],"Chitrakoot 洪水风险 GIS","Yamuna 洪水脆弱性 遥感","Chitrakoot洪水风险GIS-5326","10.36948\u002Fijfmr.2026.v08i05.89708",{"doi":389,"openalex_id":391,"authors":392,"venue":348,"cited_by_count":94,"oa_url":396,"card":397,"direction":100,"ingested_from":103},"W7222885988",[393],{"name":394,"orcid":395},"Birendra Yadav","https:\u002F\u002Forcid.org\u002F0009-0007-0864-474X","https:\u002F\u002Fwww.ijfmr.com\u002Fpapers\u002F2026\u002F5\u002F89708.pdf",{"tldr":398,"method":399,"finding":400,"direction":100,"opportunity":401},"用GIS与遥感综合评估Chitrakoot区洪水灾害与脆弱性空间分布。","DEM、河网、历史洪水与卫星观测及社会经济数据叠加分析。","洪水风险集中于河谷低地、农田与居民点，历史分级低估极端降雨风险。","可结合时序遥感与水文模型，构建极端降雨情景下的农田洪涝风险动态制图与预警。","2026-10-19T23:30:24.356743Z",{"id":404,"title":405,"url":406,"summary":407,"summary_zh":408,"content":9,"source_name":409,"source_url":406,"published_at":195,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":410,"score_detail":411,"sources":413,"tags":415,"primary_topic":420,"secondary_topics":421,"classification":422,"search_phrases":434,"slug":437,"view_count":94,"doi":438,"paper":439,"created_at":471},5318,"Lagged Effects and Mechanisms of Central Environmental Inspection Policies on Agricultural Straw Burning: Evidence from Remote Sensing Data in China","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10668-026-08214-3","Lagged Effects and Mechanisms of Central Environmental Inspection Policies on Agricultural Straw Burning: Evidence from Remote Sensing Data in China。Environment Development and Sustainability","中央环保督察政策对农业秸秆焚烧的滞后效应与作用机制：来自中国遥感数据的证据。《环境、发展与可持续性》","Environment Development and Sustainability",84,{"impact":274,"substance":117,"depth":275,"authority":118,"freshness":13,"relevant":22,"comment":412},"基于全国遥感数据评估中央环保督察对秸秆焚烧的滞后效应，数据规模大、方法新颖，对农业环境政策有参考价值，值得进入每日精选。",[414],{"name":409,"url":406},[416,27,417,418,419],"农业政策","卫星遥感","秸秆焚烧","环保督察",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":423},{"candidates":424,"agricultural_evidence":47},[425,429],{"code":33,"score":273,"supported":47,"title":426,"abstract":427,"tags":428},[51],[50,51],[417,27],{"code":430,"score":22,"supported":14,"title":431,"abstract":432,"tags":433},"H5",[],[],[418],[435,436],"中央环保督察 秸秆焚烧","遥感数据 秸秆焚烧","中央环保督察秸秆焚烧-5318","10.1007\u002Fs10668-026-08214-3",{"doi":438,"openalex_id":440,"authors":441,"venue":409,"cited_by_count":94,"oa_url":9,"card":465,"direction":100,"ingested_from":103},"W7222877438",[442,444,446,448,450,452,454,457,459,461,463],{"name":443,"orcid":9},"Lu Dong",{"name":445,"orcid":9},"Linru Zhang",{"name":447,"orcid":9},"Na Liu",{"name":449,"orcid":9},"Xin Long",{"name":451,"orcid":9},"Ziyi Wang",{"name":453,"orcid":9},"Siyue Yang",{"name":455,"orcid":456},"Jie Tian","https:\u002F\u002Forcid.org\u002F0000-0001-6585-3643",{"name":458,"orcid":9},"Qiyuan Wang",{"name":460,"orcid":9},"Zhanfeng Dong",{"name":462,"orcid":9},"Long Wu",{"name":464,"orcid":9},"Yichen Wang",{"tldr":466,"method":467,"finding":468,"direction":469,"opportunity":470},"利用遥感数据评估中央环保督察政策对秸秆焚烧的滞后效应与作用机制。","基于中国遥感火点数据构建面板，采用滞后效应模型识别政策影响机制。","中央环保督察对秸秆焚烧具有显著但滞后的抑制效应，并存在区域异质性。","农业绿色发展与碳","可结合地块级遥感与农户调查，量化督察滞后期内秸秆离田利用与替代处置的路径。","2026-10-19T23:30:22.693590Z",{"id":473,"title":474,"url":475,"summary":476,"summary_zh":477,"content":9,"source_name":478,"source_url":475,"published_at":270,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":94,"score_detail":479,"sources":481,"tags":483,"primary_topic":487,"secondary_topics":488,"classification":489,"search_phrases":496,"slug":499,"view_count":94,"doi":500,"paper":501,"created_at":516},5244,"Diurnal and Seasonal Surface Urban Heat Island Dynamics and Thermal Field Variance in Faridabad District, India: A MODIS-Based Assessment (2001–2025)","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fajgr\u002F2026\u002Fv9i4489","Aims: To examine the spatial and temporal dynamics of land surface temperature (LST), urban–rural biophysical indices (BUI and NDVI), daily Surface Urban Heat Island Intensity (SUHII), and the Urban Thermal Field Variance Index (UTFVI) on a diurnal and seasonal basis in Faridabad District of northern India. Study Design: Multi-temporal geospatial remote sensing and statistical modelling study. Place and Duration of Study: Faridabad District (Urban Core and Rural Hinterland), Haryana, India; Summer and Winter Seasons Analysed through Six Benchmark Periods (2001, 2005, 2010, 2015, 2020, and 2025). Methodology: MODIS optical (MOD09A1) and thermal infrared (MOD11A2) composite data were analysed using Google Earth Engine at the daytime (10:30 am) and night-time (10:30 pm) overpasses. LST values were grouped into UTFVI (Urban Thermal Field Variance Index) classes. Multivariable OLS regressions and empirical biophysical sensitivities such as Vegetation Cooling Efficiency (VCE) and Impervious Heating Effect (IHE) were evaluated in Python using a stratified random grid (N = 2,500 sampling locations; 235 n 872 pixels). Results: Over the 25-year study period, the built-up gradient for winter (BUI) increased from +0.096 to +0.233 in association with agricultural changes and urban development in the region, while the vegetative deficit for winter (NDVI) increased from -0.061 to -0.136. In terms of temperature, Faridabad showed a SUHI regime during summer (ranging from to ), where the urban core nocturnal LST increased from in 2001 to in 2025, together with regional warming and compression of the urban DTR. The pre-monsoon diurnal regimes showed the SUCI phenomenon (as low as in 2005) owing to the effects of sensible heating in the desiccated fallow rural lands after harvest season. Urban Core thermal field variance showed that moderate thermal stress (UTFVI Class 3) expanded from to , while severe stress tiers (Classes 4–6) concentrated within industrial estates. Daytime vegetation provided consistent cooling (VCE up to per ), whereas nocturnal VCE exhibited temporal variability and diminished transpirational cooling following stomatal closure. Conclusion: Urban greening offers a strong mechanism for daytime heat mitigation but is insufficient to address independently nocturnal heat trapping resulting from the high thermal inertia of construction materials. The sustainable heat-mitigation strategy in industrial nodes entails a combination of urban greening and reflective cool coatings on industrial sheds and impervious surfaces.","目的：考察印度北部法里达巴德县地表温度（LST）、城乡生物物理指数（BUI和NDVI）、日间地表城市热岛强度（SUHII）以及城市热场方差指数（UTFVI）在昼夜和季节尺度上的时空动态。研究设计：多时相地理空间遥感与统计建模研究。研究地点与时长：印度哈里亚纳邦法里达巴德县（城市核心区与乡村腹地）；通过六个基准时段（2001、2005、2010、2015、2020和2025年）分析夏季与冬季。方法：利用Google Earth Engine分析MODIS光学（MOD09A1）和热红外（MOD11A2）合成数据，分别对应白天（上午10：30）和夜间（晚上10：30）过境时刻。LST值按UTFVI（城市热场方差指数）类别分组。在Python中采用分层随机网格（N=2，500个采样点；235 n 872像素）评估多变量OLS回归及经验生物物理敏感性指标，如植被冷却效率（VCE）和不透水面增温效应（IHE）。结果：在25年研究期内，冬季建成区梯度（BUI）从+0.096增至+0.233，与该地区农业变化和城市发展相关；冬季植被亏缺（NDVI）从-0.061增至-0.136。温度方面，法里达巴德在夏季呈现SUHI态势（范围为至），城市核心区夜间LST从2001年的增至2025年的，同时伴随区域增暖和城市日较差（DTR）压缩。季风前昼夜态势因收获季后干燥休耕乡村土地的感热加热效应而呈现SUCI现象（2005年低至）。城市核心区热场方差显示，中度热胁迫（UTFVI第3类）从扩大至，而严重胁迫层级（第4—6类）集中于工业园区。白天植被提供持续冷却（VCE最高达每），而夜间VCE呈现时间变异性，且气孔关闭后蒸腾冷却减弱。结论：城市绿化是白天热缓解的有力机制，但不足以独立应对建筑材料高热惯性导致的夜间热量滞留。工业节点的可持续热缓解策略需要将城市绿化与反射性","Asian Journal of Geographical Research",{"impact":94,"substance":94,"depth":94,"authority":94,"freshness":94,"relevant":94,"comment":480},"印度城市热岛遥感研究，属城市气候与环境领域，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。",[482],{"name":478,"url":475},[27,484,485,486],"地表温度","城市热岛","MODIS",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":490},{"candidates":491,"agricultural_evidence":47},[492],{"code":33,"score":17,"supported":47,"title":493,"abstract":494,"tags":495},[],[50,51],[484,27],[497,498],"Faridabad 城市热岛","MODIS 地表温度 遥感","Faridabad城市热岛-5244","10.9734\u002Fajgr\u002F2026\u002Fv9i4489",{"doi":500,"openalex_id":502,"authors":503,"venue":478,"cited_by_count":94,"oa_url":475,"card":511,"direction":100,"ingested_from":103},"W7222794736",[504,507,509],{"name":505,"orcid":506},"Abhishek Malik","https:\u002F\u002Forcid.org\u002F0009-0009-4878-0871",{"name":508,"orcid":9},"Shubham Mor",{"name":510,"orcid":9},"Gaurav Kalotra",{"tldr":512,"method":513,"finding":514,"direction":100,"opportunity":515},"基于MODIS分析印度Faridabad 2001–2025年昼夜与季节地表城市热岛及热场差异演变。","MODIS光学与热红外数据、Google Earth Engine、OLS回归及","白天绿化降温显著，但夜间建筑热惯性导致的热滞留难以单独缓解。","可结合城乡梯度与作物休耕期感热效应，研究农业用地变化对昼夜热岛的非对称贡献。","2026-10-18T23:30:31.211684Z"]